CRWR Online Report 08-05 
 
Texas Integrated Drought Information System  
A Prototype of the Trinity River Basin  
 
by 
 
Virginia B. Smith, M.S.E. 
David R. Maidment, Ph.D. 
 
 
May 2008 
 
 
CENTER FOR RESEARCH IN WATER RESOURCES 
Bureau of Engineering Research  
The University of Texas at Austin 
J.J. Pickle Research Campus 
Austin, TX 78712-4497 
 
This document is available online via World Wide Web at 
http://www.ce.utexas.edu/centers/crwr/reports/online.html 
iii 
 
 
 
Acknowledgements 
 
The author would like to thank Dr. David Maidment for his teaching, guidance 
and support throughout this project.  Additionally, the author would like to thank faculty 
and students of the Center for Research in Water Resources for their support and 
assistance.  
The author would also like to thank the Texas Natural Resources Information 
System for sponsoring this research, and for guiding her work.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
May 2008
iv 
 
Abstract 
 
 
 
A Texas Integrated Drought Information System Prototype  
 
For the Trinity River Basin 
 
 
 
 
Virginia Burton Smith, M.S.E. 
The University of Texas at Austin, 2008 
 
 
Supervisor:  David R. Maidment 
 
 
 
 
In order to improve drought mitigation, the Texas Water Development Board 
(TWDB) initiated a research project to design a prototype of an Integrated Drought 
Information System (IDIS) for Texas.  The IDIS is a tool for drought monitoring that 
functions by incorporating several sources of disparate drought information.  The goals of 
the IDIS are to create an information system that will allow decision makers, researchers 
and the general public access and information about drought conditions.  This report 
describes the ideas, framework and process used in developing the IDIS prototype for the 
Trinity River Basin, in Texas.  
v 
 
Table of Contents 
 
List of Tables .............................................................................................................................. viii 
List of Figures .................................................................................................................... ix 
List of Abbreviations ......................................................................................................... xi 
Chapter 1  Introduction                  1 
 Definition of an Integrated Drought Information System  1.1................................ 1 
 IDIS Functions  1.2 ................................................................................................. 3 
 Types of Drought Information  1.3 ......................................................................... 6 
 Report Objectives  1.4............................................................................................. 7 
Chapter 2  Literature and Technology Review              8 
 Drought Classification  2.1 ..................................................................................... 8 
 Drought in the United States  2.2 .......................................................................... 17 
 Drought in Texas  2.3............................................................................................ 19 
 Drought Mitigation and Planning  2.4 .................................................................. 22 
 Drought Mitigation and Planning in Texas  2.5 .................................................... 28 
 Integrated Information System Technology  2.6 .................................................. 32 
Chapter 3  Methodology                34 
 IDIS Preliminary Design  3.1................................................................................ 34 
 Hydro BaseMap  3.2 ............................................................................................. 37 
 Space-Time Layers  3.3 ........................................................................................ 39 
 Types of Drought Infromation  3.4 ....................................................................... 43 
 Integrating Drought Information  3.5.................................................................... 56 
 Web Services  3.6 ................................................................................................. 61 
 Scheduled Task  3.7 .............................................................................................. 62 
 Technical Limiations  3.8 ..................................................................................... 62 
Chapter 4  Texas IDIS Prototype               64 
 Trinity River basin Prototype  4.1......................................................................... 64 
 Prototype Preliminary Design  4.2 ........................................................................ 68 
 Hydro Basemap  4.3 .............................................................................................. 73 
 Space-Time Layers  4.4 ........................................................................................ 77 
vi 
 
 Functions  4.5 ........................................................................................................ 84 
 Future Work  4.6 ................................................................................................... 89 
Chapter 5  Conclusions                91 
Appendix A ....................................................................................................................... 96 
Appendix B ....................................................................................................................... 99 
Appendix C ..................................................................................................................... 121 
Appendix D ..................................................................................................................... 127 
Appendix E ..................................................................................................................... 129 
Bibliography ................................................................................................................... 152 
Vita  ............................................................................................................................. 156 
 
 
 
 
 
 
 
 
 
vii 
 
List of Tables 
 
Table 2.1:    Types of Drought .......................................................................................... 13 
Table 2.2:    Recent Major Drought Events in Texas ........................................................ 22 
Table 2.3:    TCEQ Drought Planning Response/Advisory Levels .................................. 31 
Table 3.1:    Variable Description Table ........................................................................... 37 
Table 3.2:    Time Series Fields Table .............................................................................. 37 
Table 3.3:    USGS Monitored Reservoirs in the Trinity River Basin .............................. 49 
Table 3.4:    US Drought Monitor Classification Scheme ................................................ 52 
Table 4.1:    Trinity River basin Recreation-based Businesses......................................... 68 
Table 4.2:    IDIS Feature Layer Visibility ....................................................................... 72 
Table 4.3:    Town Feature Layer Description .................................................................. 74 
Table 4.4:    City Feature Layer Description .................................................................... 75 
Table 4.5:    Roads Feature Layer Description ................................................................. 76 
Table 4.6:    Interstate Feature Layer Description ............................................................ 76 
Table 4.7:    Texas Feature Layer Description .................................................................. 77 
Table 4.8:    Counties Feature Layer Description ............................................................. 79 
Table 4.9:    Time Series Table for Climate Variables ..................................................... 80 
Table 4.10:  Time Series Table for Stream Gauges .......................................................... 81 
Table 4.11:  Time Series Table for Reservoirs ................................................................. 81 
Table 4.12:  Climate Statistics Table ................................................................................ 82 
Table 4.13:  Stream Statistics Table ................................................................................. 83 
Table 4.14:  Dynamic Variables Table ............................................................................. 84 
viii 
 
List of Figures 
 
Figure 1.1:   Time Series Diagram ...................................................................................... 4 
Figure 1.2:    IDIS Feature Layers ...................................................................................... 5 
Figure 2.1:    The Water Cycle .......................................................................................... 10 
Figure 2.2:    Types of Drought......................................................................................... 12 
Figure 2.3:    Satellite Image of ENSO SST from NOAA ................................................ 15 
Figure 2.4:    Drought in the US During the 20th Century ................................................ 18 
Figure 2.5:    PDSI Changes in the US During the 20th Century ...................................... 19 
Figure 2.6:    Texas Gulf Basin Region Drought Events During the 20th Century ........... 21 
Figure 2.7:    Drought Analysis Diagram for Agricultural Regions ................................. 24 
Figure 2.8:    Drought Analysis Diagram for Urban Regions ........................................... 25 
Figure 2.9:    Existing Drought Plans in the US................................................................ 27 
Figure 2.10:  Climate Regions of Texas ........................................................................... 30 
Figure 3.1:    IDIS Preliminary Layout ............................................................................. 35 
Figure 3.2:    IDIS Server Data Layout ............................................................................. 36 
Figure 3.3:    Trinity River Authority NHD ...................................................................... 39 
Figure 3.4:    Lake Grapevine TIN .................................................................................... 42 
Figure 3.5:    Lake Grapevine Depth Raster ..................................................................... 42 
Figure 3.6:    NARR Domain Map .................................................................................... 44 
Figure 3.7:    Stream Gauges of Texas .............................................................................. 47 
Figure 3.8:    USGS Reservoir Elevation Gauge .............................................................. 48 
Figure 3.9:    US Drought Monitor ................................................................................... 50 
Figure 3.10:  Drought Impact Reporter ............................................................................. 54 
Figure 3.11:  Crop Moisture Index ................................................................................... 55 
Figure 3.12:  ArcGIS Multidemensional Tool .................................................................. 56 
Figure 3.13:  NetCDF Integration Tool ............................................................................ 58 
Figure 3.14:  Raster Depth Creation Tool ......................................................................... 60 
Figure 4.1:    Trinity River Basin Water Management Regions ....................................... 65 
Figure 4.2:    Trinity River Basin Aquifers ....................................................................... 66 
Figure 4.3:    Trinity River Basin Reservoirs .................................................................... 67 
ix 
 
Figure 4.4:    IDIS Prototype Preliminary ......................................................................... 69 
Figure 4.5:    IDIS Prototype Hydro Basemap Layers ...................................................... 70 
Figure 4.6:    IDIS Prototype Space-time Layers .............................................................. 71 
Figure 4.7:    IDIS Prototype Website............................................................................... 73 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 1.  Introduction 
 
 The word ?drought? can conjure up many different ideas.  Simply defined, 
drought results from a deficiency in precipitation for an extended period of time.  
Drought is a naturally reoccurring hazard, which varies by geographic region.  Droughts 
have always plagued societies.  All inhabited land is subject to drought.  Texas is far from 
an exception.  Droughts occur regularly in the state, impacting the industries, economies 
and societies of the state.  Despite advancements in technology, geosciences and 
hydrologic engineering, droughts continue to ravage communities.  However, today new 
concepts and approaches are being developed to mitigate drought.  Leading the way in 
drought mitigation is the development of integrated drought information systems.   
 Integrated information systems are useful because they are able to incorporate 
information from many resources.  Due to the complexities of drought many resources of 
climate data and hydrologic data must be combined to create a realistic and 
comprehensive drought monitor.  Scientists have known this for a long time.  To 
overcome this problem drought experts have developed an array of drought indices to 
describe droughts.  However, these indices are often complex and too scientifically 
cumbersome for non-scientist and non-engineers.  Until recently the technology has not 
existed to create a true integrated system for drought.  Advancements in data collection, 
data management, server technology and the internet have enabled the idea of a drought 
integrated information system a reality.  Not only does this technology advance drought 
research, but will also inform decision makers and the general public as to drought 
conditions. 
 
1.1 Integrated Drought Information System (IDIS) Definition  
 
An integrated drought information system is a computer based system for providing 
drought related information.  The system is hosted and served from one location; 
however it integrates information related to drought from several different networks.  By 
2 
 
integrating disparate drought information, it is able to create a more complete picture of 
drought. 
To better combat drought in Texas the Texas Department of Natural Resource 
Information Systems (TNRIS) requested the Center for Research in Water Resources 
(CRWR) at the University of Texas ? Austin (UT) to develop an Integrated Drought 
Information System prototype.  The prototype was commissioned for the Trinity River 
Basin, one of the largest and most populated basins in the state.  The software being used 
is ArcGIS, a product of the Environmental Systems Research Institute, Inc. (ESRI). 
The purpose of the prototype IDIS is to create a system to aid decision makers, the 
public and researchers.  To meet the disparate information needs of these diverse groups 
the IDIS incorporates many different types and forms of information.  Additionally, to 
truly describe drought, climate data must be combined with hydrologic data.  The 
services that the IDIS incorporates are drought indices, hydrologic data, climate related 
data, drought education and drought links.  Through the IDIS, users can obtain 
information about recreation access points, reservoir levels, stream flow information, 
drought index, crop moisture levels, historical precipitation, soil moisture, potential 
evaporation, and temperature, and reported drought impacts.  This data is made available 
through the Geographic Information System (GIS) component of the project.  In addition, 
the IDIS serves as an educational portal.  The IDIS website provides links to educational 
material and other informative drought related websites.   
The IDIS project uses an amalgamation of information resources.  The drought 
information accessed for the purpose of the IDIS comes from the United States Geologic 
Survey (USGS) National Water Information System (NWIS), National Oceanic 
Atmospheric Administration (NOAA) National Center for Environmental Prediction 
(NCEP) North American Regional Reanalysis (NARR), the NOAA Climate Prediction 
Center (CPC) Crop Moisture Index (CMI), the National Drought Mitigation Center?s 
(NDMC) U.S. Drought Monitor and Drought Impact Reporter.  The basemap for the 
project is created from the National Hydrography Dataset Plus (NHDPlus) and the 
TNRIS Strategic Mapping Program (StratMap). 
3 
 
 
1.2 Types of Drought Information  
 
The IDIS is designed in an attempt to overcome some of the ambiguities in drought 
models.  It is unique because incorporates information from so many disparate types of 
drought information resources.  The IDIS is composed of four main components:  
drought education, links to drought resources, current reservoir elevation information, 
and a geographic drought information system.  Of these four components the geographic 
information system is the most complex.  This system defines the IDIS, as it integrates 
drought and geographic data from many sources to describe drought conditions as they 
change in relation to time. 
The IDIS is composed of features that can be broken in to two groups:  static and 
space-time.  Static features do not change.  The only data they possess is data that 
describes the feature.  They make up the Hydro Basemap.  The Hydro Basemap is the 
backdrop geographic to the whole system.  It serves as a reference for users.  The features 
of the basemap don?t actually contain any drought information; however, their existence 
gives users a base to interpret drought data.  That base may be physical, political, or 
regional.  The features included in the Hydro Basemap are typically:  towns, cities, 
counties (however, counties is not one of the Hydro Basemap layers for the prototype 
described in this paper, this will be explained further in chapter 4), state boundaries, 
roads, interstates, water bodies, flowlines, drainage basins and river basins.   
 The space-time layers of the system are the dynamic layers.  These layers contain 
features that have data attached to them changes through time.  The feature geometries 
may be static through time or change with time.  They may be feature layers or a catalog 
of rasters.  The important aspect is that each is associated with time series data. The idea 
of these layers can be described in Figure 1.1.  This figure shows the relationship 
between geographic data and variable values in relation to time series.  As can be seen 
through the model in Figure 1.1, data change features are associated with variables that 
change through time.  Time series are required to create a full hydrologic model.  
4 
 
Hydrology changes with time and therefore, time must be incorporated into an accurate 
hydrologic model.  Space-time layers may include:  stream gauges, reservoirs, and 
regions (for the purpose of this paper, counties and a raster catalog of reservoir depths are 
included). [8]    
 
Figure 1.1:  Time series values are a function of features, time and variable type. [8] 
 
 Figure 1.2 describes the different types of layers used in the IDIS prototype 
project described by this paper.  The map to the absolute left shows the study region for 
the prototype.  The rows of the slanted maps show the feature layers of the project.  The 
flat layers (to the left) represent static layers of the project.  They are the Hydro Basemap.  
The feature layers that appear to have depth (to the absolute right) represent the space-
time layers of the project.  They have data attached to them that makes them dynamic 
through time. 
5 
 
 
Figure 1.2:  The IDIS prototype project, for the Trinity River Basin, uses both static feature layers and feature 
layers that are dynamic through time. 
 
6 
 
1.3 IDIS Functions 
 
The IDIS functions as a public information system for drought.  As mentioned earlier 
in this section, there are four main types of information accessible to the public:  drought 
links, drought education, reservoir elevation levels, and a geographic drought data 
information system.  The first three of these types are simple forms of information.  The 
users can obtain these automatically through accessing the IDIS website.  The drought 
education portion of the project is available as a document.  The document is designed to 
explain and define drought and its many variables to the non-scientist.  The drought links 
available on the IDIS website are links to on-going national drought information projects.  
The drought links include:  the US Drought Monitor, the Drought Impact Reporter, the 
Crop Moisture Index and the National Integrated Drought Information System (NIDIS).  
The reservoir elevation table is a table of reservoirs within the Trinity River Basin that 
are monitored by the USGS NWIS.  Using scheduled tasks, the reservoir elevations are 
updated daily with the previous day?s average elevation, as reported by NWIS.  The 
geographic drought data information component of the IDIS is more complex has several 
functions within it.  This report will delve greater into the science behind these functions.  
However, a summary of these functions are summarized here: 
 
Drought Related Indices - There are several drought indices produced in the US by 
government organizations.  Some of these include the US Drought Monitor, the 
Drought Impact Reporter and the Crop Moisture Index.  The IDIS uses Windows 
Mapping Service (WMS) files to map updated versions of these indices for users to 
view in relation to other drought related data. 
 
Hydrologic Data? Hydrologic data for surface water is obtained from the USGS 
NWIS.  The data used describes the reservoirs and stream flow of the Trinity River 
Basin.  The data is retrieved via web services, and is available for the user to 
7 
 
download as a table or view the data as a graph.  The data is attached to stream gauge 
features and reservoirs by a HydroCode, which matches the USGS identifier. 
 
Climate Data? Climate data is available for four variables:  soil moisture, 
temperature, potential evaporation, potential evaporation, subsurface runoff and non-
infiltrating surface runoff, and precipitation.  These variables were chosen because of 
their relationship to and impact on drought.  The data to support this information is 
obtained from the NARR (more information about NARR can be found in section 
3.4).  The IDIS server hosts a catalog of the NARR data, which extends from 1979 to 
the present.  The data is updated monthly, with the previous month?s average values 
using scheduled tasks.  The data is attached to the Trinity River Basin counties 
feature layer.  Users can obtain the data as a downloadable table or view the data as a 
graph for specified variables and time periods. 
 
1.4 Report Objectives 
 
The objectives of this report are to describe the format of a Texas IDIS prototype 
and the method for its creation.  This paper predominately focuses on the technical 
aspects of the creating this system.  The goals of this paper are to: 
 give a full description of drought classification and its impact on drought 
planning 
 Explain the role of IDIS in the Texas?s drought plan for drought mitigation 
 Describe the framework for an IDIS for Texas such as methods of data 
integration and available data resources 
 Detail the IDIS prototype in the Trinity River Basin 
 Show the steps taken to develop the IDIS prototype  
 Make recommendations for future efforts in developing an IDIS for the 
state of Texas 
 
8 
 
Chapter 2. Literature and Technology Review 
 
 To establish an IDIS, it is important to understand the impacts, implications and 
policies surrounding drought.  The IDIS is a tool for drought mitigation that functions as 
a public information source.  Therefore, it is important to understand drought, the drought 
cycle, drought?s relationship to the environment and droughts relationship to society.  For 
the IDIS to be a useful tool it must provide users, such as decision makers, with the 
appropriate tools to manage a drought.  Therefore the tool must be adaptive intellectually 
to an array of scientific backgrounds.  In creating a tool such as this, first the history of 
drought must be studied; as well as policy for managing and mitigating drought events.  
Finally, it is important understand the technology that has been developed for creating an 
integrated information system such as this.  This section reviews the existing literature 
and technology of these topics. 
 
2.1   Drought Classification 
 
Before understanding the ideas behind creating a drought information system it is 
import to have a clear definition of drought.  The word ?drought? has different meanings.  
Tannehill gave a good description of the word in his book, Drought and Its Causes and 
Effects (1947) [1]   
 
"We have no good definition of drought. We may say truthfully that we 
scarcely know a drought when we see one. We welcome the first clear day 
after a rainy spell. Rainless days continue for a time and we are pleased to 
have a long spell of such fine weather. It keeps on and we are a little 
worried. A few days more and we are really in trouble. The first rainless 
day in a spell of fine weather contributes as much to the drought as the 
last, but no one knows how serious it will be until the last dry day is gone 
9 
 
and the rains have come again... we are not sure about it until the crops 
have withered and died." 
 
The most basic definition of drought is a deficiency in precipitation for an extended 
period of time. [2]   It?s difficult to establish when a drought is truly beginning.  When do 
a string of sunny days turn into a drought?  The question can be answered differently 
according to the type of drought that is being analyzed. [3]    The range of definitions for 
droughts can be defined by rainfall amounts, vegetation conditions, agricultural 
productivity, soil moisture, levels in reservoirs, stream flow or economic impacts.  It 
results in water shortages for residents, farmers, industry, and environments. [1]   Drought 
is a natural reoccurring hazard of nature, which varies by region.  There are not a 
standard number of sunny days that define drought; drought must be defined relative to 
?normal? water balance for that region. [3]    
The water balance depends on the water cycle described in Figure 2.1.  The water 
cycle includes all of the hydrologic processes; such as precipitation, evaporation, 
evapotranspiration, streamflow and runoff.  The water cycle interacts with the land 
surface and to the atmospheric cycle to drive climate and hydrologic processes for a 
region.  Climate regions differ by water cycle due to geography, geology, and land cover.  
Water cycles may be more or less variable and differ by volume. 
 
10 
 
 
Figure 2.1: The water cycle is the cycle of water through the environment. [4]    
 
There are three main types of drought:  meteorological, agricultural, and hydrological.  
The first of these to occur is the meteorological drought.  This occurs directly from 
climate variation, a decrease in precipitation.  The results of the meteorological drought 
lead to an agricultural drought.  A decrease in the availability of water causes soil 
moisture to decrease and vegetation stress to increase.  This in turn leads to a 
hydrological drought.  During a hydrological drought the effects of drought can be seen 
on the surface water.  These three types of drought are discussed further below. [3]    
1. Meteorological drought is brought about when there is a prolonged period with 
less than average precipitation. Meteorological drought usually precedes the other 
kinds of drought.  
11 
 
2. Agricultural droughts are droughts that affect crop production or the ecology of 
the range. This condition can also arise independently from any change in 
precipitation levels when soil conditions and erosion triggered by poorly planned 
agricultural endeavors cause a shortfall in water available to the crops. However, 
in a traditional drought, it is caused by an extended period of below average 
precipitation.  
3. Hydrological drought is brought about when the water reserves available in 
sources such as aquifers, lakes and reservoirs falls below the statistical average. 
Like an agricultural drought, this can be triggered by more than just a loss of 
rainfall.  
The way these types of droughts occur can be seen below.  Figure 2.2 shows the 
process in which the types of droughts occur and what occurs during these droughts in 
relation to time.  It is important to note that meteorological, agricultural and hydrological 
droughts all have economic, social, and industrial impacts. [3]    
 
12 
 
 
Figure 2.2: There are three types of drought:  meteorological, agricultural, and hydrological. [3]    
 
Droughts are very costly disasters.  The drought of 1988 is the most costly 
weather event in the history of the United States.  The 1988 drought cost approximately 
$40 billion, predominately in losses in the corn and soybean agriculture industries. [5]    In 
a 1995 report issued by the US Federal Emergency Management Agency (FEMA) the 
annual loss in the US due to drought is $6-8 billion.   Drought occurs somewhere in the 
US every year, and approximately twelve percent of the US experiences severe drought at 
any given time (excluding Hawaii and Alaska).  The state of Texas has been subject to 
several major droughts through the history of the United States.  In both 1996 and 1998 
Texas lost nearly $5 billion to drought.  These losses were mainly due to fires and 
agricultural failures. [6]     
13 
 
However, the effects of a drought are more than economic.  The impacts of 
drought can be divided in to three main groups:  economic, social and environmental.  
Some examples of these types of impacts are listed in Table 2.1. [3]    
 
Table 2.1:  Types of Drought Impacts [3]    
Type of Impact Examples of Impacts 
 
 
 
Economic 
Costs and losses to agricultural and livestock producers, loss of 
fishery productions, decrease in land prices, loss in 
industries/manufacturers, unemployment, strain on financial 
institutions (foreclosures, credit risk and capital shortfalls), 
government revenue loss, decrease in recreational businesses and 
tourism, increase in energy demand, reduction in energy supply, 
cost of water transport/transfer and supplemental water resources, 
increase in food prices, and increase in food importation 
 
Social 
Rural population loss, health, increased conflicts, reduced quality of 
life, public dissatisfaction with government drought response 
increased data/information needs, coordination of dissemination 
activities, recognition of institutional restraints on water use 
 
 
Environmental 
Damage to animal species and plant communities, increase in the 
number of wild fires, wind/water erosion of soils, poor air quality, 
decrease in visual and landscape quality, lower water body levels, 
reduced flow from springs, reduced stream flow, loss of wetlands, 
salinity fluctuations in bays and estuaries, groundwater depletion, 
and negative effects on water quality.  
 
At this time droughts cannot be fully predicted.  In fact, it is difficult to tell when 
a drought is beginning (or if it is a spell of sunny weather).  In some regions drought can 
be defined by few days without rain (rain forest areas); while, other locations can go 
months without rain and still not be in true drought (dessert areas). [1]   It is also difficult 
to know when a drought is ending.  Some droughts (like the 1950s drought of Texas) are 
14 
 
broken up by a wet spell. [5]   Wet spells sometimes occur during droughts, but do not last 
long enough for the area to recover from drought.  Although scientists cannot fully 
predict droughts, they have identified several variables that affect drought to get an idea 
of when and where droughts will occur. [7]   The variables that are studied to predict 
drought are global weather patterns, high pressure, the tropical outlook, and the temperate 
zone outlook. [3]    
There is still much debate over why droughts occur.  Droughts are reoccurring 
events that are largely the result of fluctuations in climate.  They are caused by a decrease 
in the amount of water in a water balance for a given region.  There are so many variables 
that are believed to contribute to the onset of drought that science has yet to isolate the 
exact causes of drought.  However, science has been able to identify several factors that 
induce drought.  Some of the factors National Drought Mitigation Center (NDMC) 
scientists study are listed below. [3]    
Global Weather Patterns 
A great deal of research has been conducted in recent years on the role of 
interacting systems, or teleconnections, in explaining regional and even global 
patterns of climatic variability. These patterns tend to recur periodically with 
enough frequency and with similar characteristics over a sufficient length of time 
that they offer opportunities to improve our ability for long-range climate 
prediction, particularly in the tropics. One such teleconnection is the El 
Ni?o/Southern Oscillation (ENSO).  Figure 1.3 shows an ENSO as it moves 
across the Pacific Ocean.  This image is based on satellite data from the NOAA 
National Environmental Satellite, Data Information Service (NESDIS).  The 
colors reflect sea surface temperature.  The warmer area (the redder area) is the El 
Nino. [3]    
15 
 
 
Figure 2.3:  A satellite image produced by NOAA to show the impacts of El Nino on 2002 on global sea 
surface temperature. [6] 
High Pressure 
One immediate cause of drought is the predominant sinking motion of air 
(subsidence) that results in compressional warming or high pressure, which 
inhibits cloud formation and results in lower relative humidity and less 
precipitation. Regions under the influence of semi-permanent high pressure 
during all or a major portion of the year are usually deserts, such as the Sahara 
and Kalahari deserts of Africa and the Gobi Desert of Asia. Most climatic regions 
experience varying degrees of dominance by high pressure, often depending on 
the season. Prolonged droughts occur when large-scale anomalies in atmospheric 
circulation patterns persist for months or seasons (or longer). The extreme drought 
that affected the United States and Canada during 1988 resulted from the 
persistence of a large-scale atmospheric circulation anomaly. [3]    
The Tropical Outlook 
In the tropics, for example, meteorologists have made significant advances in 
understanding the climate system. Specifically, it is now known that a major 
portion of the atmospheric variability that occurs on time scales of months to 
several years is associated with variations in tropical sea surface temperatures. 
16 
 
The Tropical Ocean Global Atmosphere (TOGA) project has produced results that 
suggest that it may now be possible to predict certain climatic conditions 
associated with ENSO events more than a year in advance. For those regions 
whose climate is greatly influenced by ENSO events, TOGA project results may 
help produce more reliable meteorological forecasts that can reduce risks in those 
economic sectors most sensitive to climate variability and, particularly, extreme 
events such as drought. [3]    
The Temperate Zone Outlook 
In the extratropical regions, current long-range forecasts are of very limited 
reliability. The ability that does exist is primarily the result of empirical and 
statistical relationships. In the tropics, empirical relationships have been 
demonstrated to exist between precipitation and ENSO events, but few such 
relationships have been confirmed above 30 degrees north latitude. 
Meteorologists do not believe that reliable forecasts are attainable for all regions a 
season or more in advance.  One reason for this is the presence of additional 
oceanic atmospheric oscillations, such as the North Atlantic Oscillation (NAO). [3]    
Too Many Variables 
Scientists don?t know how to predict drought a month or more in advance for 
most locations. Predicting drought depends on the ability to forecast two 
fundamental meteorological surface parameters, precipitation and temperature. 
From the historical record we know that climate is inherently variable. We also 
know that anomalies of precipitation and temperature may last from several 
months to several decades. How long they last depends on air?sea interactions, 
soil moisture and land surface processes, topography, internal dynamics, and the 
accumulated influence of dynamically unstable synoptic weather systems at the 
global scale. [3]    
17 
 
The potential for improved drought predictions in the near future differs by 
region, season, and climatic regime. 
 The multiple dimensions and unpredictability of drought makes it necessary to 
incorporate several types of disparate data to draw an accurate picture of drought.  This 
project does not be using all of the resources mentioned above, because the goal of this 
project is not to predict drought, but to model drought as it has occurred and is currently 
occurring.   
2.2 Drought in the US 
 
Drought is a naturally occurring aspect in all regions of the world, although a 
drought may be defined differently region to region.  For many regions in the world 
climate change is altering the nature of drought.  Some of these changes can be seen in 
the US.  According to the Intergovernmental Panel on Climate Change?s (IPCC) Fourth 
Assessment Report, changes in temperature, precipitation and humidity have been seen 
across the US.  According to the report, like much of the rest of the high latitudes, the 
United States has experienced a general increase in precipitation over the past century.  
As the temperature increases the air is able to hold more moisture and evaporation 
increases, leading to more precipitation.  This has been the case for the eastern United 
States, but not the western.  The western United States has actually experienced a 
decrease in the amount of annual precipitation.  Not only has the amount of precipitation 
changed in the United States, but the characteristics have also changed.  Major 
precipitation events, such as floods, are more common.  And, rainfall has shown more 
seasonal trends.  These attributes balance each other out, making it difficult to assess the 
drying/wetting condition of the United States as a whole. [9] This can be seen in the 
number of drought events that has occurred over the past century.   The percent of the US 
in serve drought for a given year through the 20th century can be seen in Figure 2.4. [6]    
18 
 
 
Figure 2.4:  This graph shows the percent of the US in severe drought since 1895 to present.[6] 
 Because of the vast size of the US, the intensity of drought varies greatly across 
the country.  The Palmer Drought Severity Index (PDSI) is a useful tool for this type of 
analysis.  Figure 2.5 shows how long regions with in the United States have been 
experiencing drought through analyzing the PDSI.  In the eastern and far west states have 
had a relatively low amount of drought in the past century.  However, the Midwest and 
southwest have experienced more intense drought.  There are several theories concerning 
the influences for the dryness in the central states.  One impact is the ENSO cycle.  As 
temperature rises, weather events, such as El Ninos, become more extreme due to a rise 
in sea surface temperature.  The ENSO cycle has clear impacts on the precipitation with 
in the US, particularly in the south central and southwest states.  Therefore, ENSO may 
be a contributor to the extended dry periods.  Another aggravator of the dry periods is the 
increase in evaporation.  This rise has a more prominent and lasting impact in regions of 
the US already subject to water scarcity. [6]    
 
19 
 
 
Figure 2.5:  This map shows breaks the US down by climate region.  The regions are color coded by the percent 
of time they have experienced drought events.  The time scale for this map is 1895 to 1995. [3] 
 
2.3 Drought in Texas 
 
Agriculture has played a large role in Texas since its establishment as a state.  
Water scarcity and a water dependent economy have made Texas particularly vulnerable 
to drought.  The most expensive weather disaster in the history of the United States is the 
drought of 1988.  Damages from this event are estimated at $40 billion, predominately in 
the corn and soybean agricultural industry.  The damages due to this event spread 
throughout several states in the mid-west and western United States.  While drastic, this 
event is not isolated. [5]   Less than a decade later in 1996 another drought hit the mid-
west, causing nearly $5 billion of loss in Texas alone. [6]   Two years later the drought, 
although brief, caused an additional $6 billion in total loss to the state of Texas.  The 
following year action was taking, as House Bill 2660 was signed to legislation.  House 
20 
 
Bill 2660 established minimum levels of water conservation in water conservation plans 
with in Texas. [10]    
Drought events have occurred in Texas during every decade in this century.  The 
map in Figure 2.6 shows the major climate regions of the United States according to the 
National Climate Data Center (NCDC).   Region seven is the Texas Gulf Basin Region, 
this contains east Texas (it should also be noted that this region contains the Trinity River 
Basin).  The graph below this map should the drought events of the past century.  The 
height of the columns within the graph represent the percentage of region seven that were 
experiencing extreme drought at the time. [11]    
21 
 
 
 
 
 
Figure 2.6:  (Top) This map shows the NOAA climate regions of the US.  Region 7 is the Texas Gulf Basin 
Region. (Bottom) The graph depicts the percent of the Texas Gulf Basin Region experiencing extreme drought 
conditions at a given time. [11]    
  
22 
 
Table 2.2 shows major recent droughts of Texas.  Although not the most 
expensive, the largest Texas drought in recent history occurred in the middle of the 
twentieth century.  The drought began in the 1947 and lasted a full decade until 1957.  
This drought had severe social and economic repercussions throughout the entire Great 
Plains, as it devastated the agricultural industry.  Crop yields dropped as much as 50%.  
Meanwhile, ranchers suffered due to scorched grass and astronomical hay prices.  By the 
time the drought subsided 244 of the 254 counties of Texas were declared to be in a state 
of federal drought disaster. [12]     
  
Table 2.2: Major Recent Drought Events in Texas[13]  
Major Recent Droughts of Texas 
Time Period 
1999-2000 
1983-1984 
1963-1964 
1947-1957 
 
 
2.4 Drought Mitigation and Planning 
 
At this time, technology does not allow for the reliable prediction of drought 
events.  However, monitoring drought helps to illustrate drought vulnerability and aid 
drought mitigation.  By using drought-related information to aid decision makers, better 
plans and policies can be created to aid in the actions surrounding drought events.  The 
most accepted tool in drought mitigation is drought preparedness planning.  As outlined 
by Whilite and Svoboda, the three basic components of a drought plan are monitoring 
and early warning, risk assessment, and mitigation and response.  [6]    
During the past few decades the importance of drought preparedness planning has 
been realized by policy makers.  In 1982, only three states of the United States had 
23 
 
drought plans (Colorado, New York and South Dakota).  However, today this trend has 
extended to most the states and to many countries around the world.  [6] An outline for 
drought planning was developed in 1991 by Whilhite.  This generic ten step process was 
developed and disseminated throughout the United States and internationally.  Many 
regions have adopted this process in the creation of their drought preparedness plan, 
including Texas.  The steps are as follows:  [14]    
1. Appoint a Drought Task Force 
2. State the Purpose and Objectives of the Drought Plan 
3. Seek Stakeholder Participation and Resolve Conflicts 
4. Inventory Resources and Indentify Groups at Risk 
5. Develop Organizational Structure and Prepare Drought Plan 
6. Integrate Science and Policy, Close Institutional Gaps 
7. Publicize the Proposed Plan, Solicit Reaction 
8. Implement the Plan 
9. Develop Education Programs 
10. Post-Drought Evaluation 
 
Although these ten steps may not seem that complicated, drought planning is a 
multifaceted task.  Certain regions are more susceptible to drought impacts.  
Additionally, droughts propagate at different rates depending on the region.  One way to 
address this problem is using an analytical tool such as a drought vulnerability tree 
diagram.  Tree diagrams analyze drought vulnerability by significant drought impacts for 
a region.  These show how susceptible a region is to specific drought risks.  Two 
examples of a tree diagram are shown below.  Figure 2.7 shows a tree diagram of 
agricultural impact analysis.  Figure 2.8 is an example of tree diagram for urban drought 
impact analysis.  The diagrams require many types of inputs due to the complex nature of 
drought analysis. [3]    
24 
 
 
Figure 2.7: An example of a simplified agricultural impact tree diagram. (Notice the boldface represents the basal causes of the listed impact.  Although these items may 
be broken down further, this example illustrates the vulnerability assessment process.)  (From the National Drought Mitigation Center) [3] 
25 
 
 
Figure 2.8:  An example of a simplified urban impact tree diagram. (Notice the boldface represents the basal causes of the listed impact [in this case, the loss of tourism 
revenue].  Although these items may be broken down further, this example illustrates the vulnerability assessment process.)  (From the National Drought Mitigation 
Center) [3] 
26 
 
 
In the United States, legislation to combat drought was introduced in the late 
nineteenth century.  The settlement of the west had been hit hard by a drought that began 
in 1887 and ran into the 1890?s.  The legislation that followed focused predominantly on 
water management in the western United States.  However, it wasn?t until the late 1990s 
the United States began to address drought management as a nation.  In 1998 the US 
Congress passed the National Drought Policy Act (Public Law 105-199).  This Act 
created the base for establishing a national drought policy in order to improve drought 
preparedness and mitigation. Through this Act, the National Drought Policy Commission 
was created.  [15]    
In May of 2000, the US National Drought Policy Commission (NDPC) submitted 
a report to the Congress and the president concerning the state of drought preparedness in 
the US.  The findings of this report showed that drought preparedness may have a strong 
impact on the reduction of social, economic and environmental impacts of drought.  At 
the time of the report, 30 of the 50 US States had drought plans.  However, much of the 
drought planning and federal funding for drought related projects that was in existence at 
the time was dispersed between various levels of groups.  Through the 1990s the federal 
government funded 88 drought-related programs.  Today all but five states have drought 
plans, as can been seen in Figure 2.9.  After reviewing the state of drought programs the 
NDPC decided that to succeed in the development of national drought policy the guiding 
principles should be [6]: 
1. Favor preparedness over insurance, insurance over relief and incentives over 
regulation. 
2. Set research priorities based on the potential of the research results to reduce 
drought impacts. 
3. Coordinate the delivery of federal services through collaboration with nonfederal 
entities. 
 
27 
 
 
Figure 2.9:  Drought plans in the United States. [3] 
 
In meeting these goals the NDPC report stressed the importance of observation 
networks, monitoring, predication, information gateways and delivery, and research for 
effective drought preparedness.  In relation to this, the NDPC found that federal 
monitoring programs joined with universities, private institutions and nonfederal entities 
provide the information required to form an effective drought preparedness and 
mitigation plan.  To establish an effective drought mitigation system, a coordinated effort 
is needed to bring to information from different levels of the government and private 
sector. [15]    
A current trend in developing drought mitigation schemes is to create an IDIS.  
IDIS compiles disparate data from multiple data systems and presents the data to be 
analyzed and used by decision makers.  The systems aim to provide a tool that can help 
aid in the decision making process and serve as an early warning system.  Currently, the 
creation of the NIDIS is underway under the direction of the NOAA and the NDMC.  [6]   
If managed correctly, using reliable information an IDIS can be used as a drought early 
28 
 
warning system for government agencies and the general public.  Whilhite suggest that 
this can be obtained through pursuing the following objectives: 
1. Adopt a viable definition of drought.  By defining drought it will 
be possible to establish when a region is in or out of a drought 
phase.  Often this is based on a climate variable index or a drought 
severity index. 
2. Establish climatic regions for drought.  Drought varies 
geographically.  Therefore, areas of like geography should be 
managed in unison separate from other regions.  For example, 
Texas has been divided into seven climate regions for managing 
drought.  These regions were established by NOAA and adopted 
by NDMC and the state of Texas. 
3. Developing key networks for monitoring drought.  This should 
involved collecting data from climate monitoring systems and 
water supplies.  In addition to monitoring climate parameters. 
4. Establish the quality of the data and integrate the data. 
5. Establish the needs of the stakeholders. 
6. Develop a system to analyze drought information and disseminate 
to the necessary parties. 
  Possibly the most abstract of the IDIS project is the early warning system. [6]    
 
2.5 Drought Mitigation and Planning in Texas 
 
Following extensive and costly drought events in the mid-1990s the state of Texas 
began a movement towards drought preparedness.  In 1998 action was taken, as House 
Bill 2660 was signed to legislation.  This bill created the Drought Preparedness Council 
of Texas (DPC).  As outlined by the bill, member agencies of the DPC are to support 
drought management and monitoring, assessment, preparedness, mitigation, and 
assistance efforts.  The DPC was charged with the task of developing a comprehensive 
state-wide Drought Preparedness Plan.  The bill outlines that the Plan should involve the 
following [10]   : 
29 
 
1. Systematic data collection, analysis and dissemination of drought-related 
information; 
2. An organizational structure that defines the duties and responsibilities and 
assures information flow among all levels of government; 
3. An inventory of state and federal programs related to drought 
emergencies; 
4. A mechanism to improve the timely and accurate assessment of drought 
impact; 
5. Provision of accurate and timely information to the media.   
To best manage this plan the state of Texas has been divided into seven climate 
regions (see Figure 2.10).  Each region is issued a drought assessment level (1-advisory 
to 5-disaster).  The assessment value is based on the climatological index, agriculture 
index and water availability index.  A description of these indexes and their contributing 
data can be seen below. [10]    
a. Climatological Assessment Index ? Texas Water Development Board (TWDB) 
 (1) Standard Precipitation Index (SPI) ? TWDB 
(2) Keetch-Byram Drought Index (KBDI) ? Texas Forestry Service (TFS) 
(3) Vegetation and Temperature Condition index (VT) ? TWDB 
(4) Crop Moisture Index (CMI) ? TWDB 
(5) Palmer Drought Severity index (PDSI) ? TWDB 
b. Agriculture Assessment Index ? Texas Agricultural Extension Service (TAEX) 
(1) Soil Moisture Index ? TAEX 
(2) Crop Condition Index ? TAEX 
(3) Pasture and Range Condition Index ? TAEX 
(4) Livestock Sales Index ? Texas Department of Agriculture (TDA) 
(5) U.S. Department of Agriculture (USDA) Drought Declarations ? TDA 
c. Water Availability Assessment Index ? TWDB 
(1) Reservoir Levels ? TWDB 
(2) Stream flow Data ? TWDB 
 
 
30 
 
 
Figure 2.10:  There are seven climate regions in Texas.  The Trinity River Basin lies mainly in the Northeast 
Region.  Its outlet is in the Coastal Region. [16]    
 
One of Texas?s involved agencies has been Texas Commission on Environmental 
Quality (TCEQ).  In one of TCEQ?s related documents, ?A Drought Planning Guide for 
Public Water Systems in Texas? details that all water services must have a drought 
contingency plan.  This plan lays out operations in case of extreme drought, periods of 
abnormally high water usage, supply contamination, or extended reduction in ability to 
supply water due to equipment failure.  It is strongly suggested that the public be 
involved in the planning process.  Typically water providers involve the public by 
forming citizen?s advisory committees or task forces, holding public meetings, 
31 
 
conducting surveys, seeking input directly from large-volume water customers 
(contractors, golf course operators, owners of car washes), or distributing a draft plan for 
public review and comment prior to adoption.  Taking part in these events allows local 
residents (who will be affected by the drought contingency plan) an input to the plan and 
a familiarization with the plan. [17]    
TCEQ?s ?A Drought Planning Guide for Public Water Systems in Texas? 
establishes five levels of drought, based on the ?The Texas Drought Preparedness Plan?, 
that can be seen below in Table 2.3. [10]     Information about the counties impacted by a 
level of drought response can be found on the TCEQ website 
(http://www.tceq.state.tx.us/permitting/water_supply/pdw/trot/droughtw.html).   
 
Table 2.3:  The TCEQ?s Drought Planning Guide for Public Water Systems in Texas Examples of Drought 
Response[18] 
Level of Drought 
Response 
Type of Advisory Typical Response to Advisory Level 
 
1 
 
Advisory 
Raise public awareness of the supply 
situation and request voluntary 
reductions in nonessential water use 
2  Watch Implement mandatory restrictions on certain non essential water uses 
 
3 
 
Severe conditions 
Implement ban on certain non-essential 
water uses and water rate surcharge for 
excessive use 
 
4 
 
 
Critical conditions 
Continue ban on nonessential water 
uses, increase water rate surcharge, 
activate backup wells 
5 Emergency conditions Initiate emergency response procedures 
 
 
32 
 
The IDIS website is one source of drought information.  The idea of the IDIS for 
Texas is part of a greater drought information network called the NIDIS currently being 
developed by the NOAA.  U.S. Public Law 109-430, the NIDIS Act of 2006, established 
the framework for this interagency approach to drought preparedness.  The NIDIS Act 
aims to integrate data concerning physical/hydrological and socio-economic impacts.  In 
order to better support decision making the NIDIS will provide a suite of tools for 
analyzing observational data.  The NIDIS idea stemmed from the NDPC (2000).  The 
ultimate goal of NIDIS is to mitigate the impacts of droughts. [19]    
 
2.6  Integrated Information System Technology 
 
There is currently little existing technology for integrated drought information 
systems.  Developing these systems is a relatively new approach to mitigating drought.  
Because drought is an abstract and dynamic concept it is difficult to encapsulate it with 
policy or tools.  To create a useful monitoring system, climate and hydrologic data must 
both be considered.   Until recently, limitations in data management have limited drought 
monitoring.  The World Meteorological Organization (WMO) reported a shift in drought 
mitigation in their 2006 publication, ?Drought Monitoring and Early Warning:  Concepts, 
Progress and Future Challenges.?  In this document, the WMO showed that several 
nations were attempting to develop drought monitoring systems to mitigate drought 
impact.  Drought monitoring programs are being developed in China, the Great Horn of 
Africa, South Africa, Portugal, Australia, and North America (in particular the US).  As  
monitoring, data management, data collection and the internet continue to develop, 
drought data integration services are becoming more apt to monitor and describe drought. 
[20]    
The US began to develop an integrated drought information system in 1999.  In 
1999 NOAA, the USDA, and National Drought Mitigation Center formed an alliance to 
create the US Drought Monitor.  The US Drought Monitor integrates information from 
climate indices to develop a unique drought index spatially for the entire US.  This is 
discussed in further technical detail in section 3.4.  Today, the US Drought Monitor has 
been expanded to include all of North America, the North American Drought Monitor 
33 
 
(NADM).  However, the only data that the Drought Monitor provides is in the form of an 
index. [21]    
The costly impacts of drought have forced states to make efforts towards drought 
mitigation.  Every year the US battles drought.  Texas has spent billions of dollars in 
drought recovery.  The multi-dimensional nature of drought and its diverse impacts make 
it a difficult problem for planners and decision makers.  IDIS incorporates the many 
facets of drought to present a more accurate picture of drought conditions and previous 
drought events.  Through combining climate data, hydrologic data, and drought indices 
the IDIS offers a full scale image of how drought changes through time.  This 
information will help decision makers, planners, researchers and the general public to 
better understand drought and aid in drought mitigation. 
34 
 
Chapter 3. Methodology 
 
The Trinity River Basin represents a prototype for the Texas IDIS, as created for 
the Department of Texas Natural Resource Information Systems.  The details of the 
prototype are discussed in chapter 4.  This chapter presents over-arching ideas for 
creating an integrated information system for analyzing drought. 
 
3.1 IDIS Preliminary Design 
 
In its simplest form the IDIS has three components:  a client, a web server and 
data servers.  The client accesses the IDIS server as a website through the internet.  The 
web server provides information automatically to the user, and also offers the user the 
option to retrieve data from additional data servers where data is stored.  Upon receiving 
the data request from the client the IDIS is able to access data servers (such as NWIS) to 
retrieve the desired data.  This is done through webservices.  For certain types of data the 
IDIS automatically downloads databases and stores the data on the IDIS.  This is done as 
a scheduled task in order to improve efficiency.  These three components comprise the 
foundation of the IDIS.  The basic idea of the structure is relatively simple.  This allows 
the system to be more flexible.  The structure of system dictates the types of data the 
system can support and the functions that the system can process.  Figure 3.1 shows a 
schematic of the IDIS in relation to the client and data servers. 
 
35 
 
 
Figure 3.1:  The IDIS links to the client and to national data centers. 
 
The IDIS machine serves two types of data:  static data and dynamic data.  In 
Figure 3.2 the data below the server, labeled ?A?, or Hydro Basemap, is the static data.  
The Hydro Basemap is the static backdrop to the geographic information component of 
the IDIS.  The features of the Hydro Basemap are contained within a geodatabase on the 
server, and work as a geographic reference for the dynamic IDIS data.  The only data 
attached to the features are descriptors (ie, name or size).  Although some of the features 
in the basemap may have aspects that change through time, they remain static for the 
hydro basemap and the IDIS.   The features included in a hydro basemap are:  flowlines, 
water bodies, roads, political boundaries, cities and towns, drainage basins and river 
basins.  Also, located on the IDIS server is a document for drought education and internet 
links to websites that provide drought related information. 
 The data above the server is the dynamic data, labeled ?B? in Figure 3.2.   The 
data centers being accessed are national organization located in around the United States. 
The details of the data access processes and data centers are explained in greater detail in 
the following sections of this chapter.  The dynamic data, also known as space-time data 
36 
 
layers, are features, tables or rasters attached to data that change through time.  These 
files are saved with another geodatabase, the Space_time geodatabase.  Within this 
database are the features, rasters, raster catalog and tables.  The raster catalog serves as an 
archive for raster images through time.  The tables serve to format the feature classes and 
to link the individual features of a feature class to time series data.   
 
 
Figure 3.2: Circle A shows the static data and circle B shows the space-time data. 
 
The items within the Space_time geodatabase share the fact that they are 
dynamic.  However, they vary greatly in their nature.  The first division within space-
time data is how the data is stored.  Some of the data is stored on the IDIS server and 
updated regularly downloads that are programmed scheduled tasks.  Scheduled task use 
web services, but the user does not have input into this process.  Other data is stored 
temporarily on the IDIS server and is retrieved only at the client?s request, through web 
services.  This explained in greater detail in sections 3.6 and 3.7. 
Space-time data of the variables retrieved is attached to the dynamic features 
based on HydroIDs.  Every feature in the Space_time geodatabase possesses a HydroID 
that allows it to be linked with the FeatureID of a piece of data.  The data of the features 
37 
 
and the variable data are managed in tables contained by the Space_time geodatabase.  
Table 3.1 shows a representation of the fields that are present in a Variable tables.  This 
table serves as a catalog for all of the dynamic variables of the project.   Table 3.2 shows 
a generic example of the fields in a time series table.  The space-time data is formatted 
similarly to the Table 3.2.   Table 3.1 provides details about the time series values 
(TSValues) given.  More about this can be found in section 4.4. 
 
Table 3.1: Variable Table Description 
Field Name Field Description 
VariableID Unique Numerical Identifier of the 
Variable 
VarName Variable Name 
VarCode Variable Code, Identifier for TStime Tables 
VarUnits Units of Measurement 
VarDesc Variable Description 
 
Table 3.2:  Time Series Data Description Table 
Field Name Field Description 
FeatureID Links to HydroID 
TSTime Date (Monthly Time Stamp) 
VariableID Variable TSValue 
 
 
3.2 Hydro Basemap 
 
The hydro basemap is the backdrop for the IDIS.  The basemap serves as a 
reference for all of the features attached to data.  The features of the basemap describe the 
geography of information system.  For the purpose of the IDIS all of the features within 
the basemap are static.  All of these features are stored as layers within a similar 
geodatabase.  The data attached to features of the basemap is for the purpose of 
38 
 
description.  The features included within the basemap geodatabase are roads, towns, 
political boundaries, flowlines, water bodies, and drainage basins.   
 
Political boundaries, roads, and town features- All of the non-hydrologic features of the 
IDIS are products of the TNRIS Strategic Mapping Program (StratMap).  The StratMap is 
housed at TNRIS.  It produces, maintains and enhances Texas digital geographic data 
themes. (TNRIS website)  The StratMap is a comprehensive resource for geographic 
information for the state of Texas.  The data in the StratMap is continuously refined and 
updated at TNRIS through partnerships with public and private organizations.  Using this 
data for the Texas IDIS insures that the basemap data is of a statewide standard and of 
high quality.  The political boundaries, roads and towns are important geographic 
references for the hydrologic features and drought data.  This data is necessary to achieve 
the IDIS goals of public information and aiding decision makers. 
 
Hydrologic features- Because drought is a function of the water cycle, the hydrologic 
features of the IDIS are imperative.  Much of the data in the IDIS is linked to the 
hydrologic features (such as reservoirs and stream gauges).  Even the hydrologic features 
that are static for the purpose of this project play important roles as references (such as 
the river basin boundary, flowlines and drainage basins).  The hydrologic data features 
used for the IDIS come from the National Hydrography Dataset Plus (NHDPlus).  
NHDPlus is based on the National Hydrography Dataset (NHD).  The NHD was 
developed by the USGS working with the Environmental Protection Agency (EPA).  The 
USGS and EPA worked together using data from the National Elevation Dataset (NED) 
and the National Land Cover Dataset (NLCD) to produce a more exact hydrography for 
the nation.   More information about this can be found at the Horizon Systems website: 
http://www.horizon-systems.com/nhdplus/.   Figure 3.3 shows the NHDPlus features for 
the Trinity River Basin. [22]    
 
39 
 
 
Figure 3.3:  This figure shows the NHDPlus features for the Trinity River Basin.  These features include 
flowlines, water bodies, drainage basins, and the river basin. 
 
3.3 Space-Time Layers 
 
 The space-time layers of the IDIS are layers of features that are attached to data 
that is dynamic through space and time.  These layers are critical to the IDIS project.  
They allow for depiction of the drought related variables through time.  By analyzing 
these values the users are able to study drought better.  Space-time layers offer a greater 
understanding to the relationship between meteorological, agricultural and hydrological 
droughts.  Also, analyzing the time change functions of space-time values is enlightening 
towards comprehending the true state of drought that exists through time. 
 
Counties- For the purpose of this project the entire NARR climate data has been linked 
with the counties layer (NARR data is described in section 3.4).  This data entails model 
40 
 
data for many climate variables dating back to 1979.  This project only uses a selection of 
these variables: soil moisture; subsurface runoff; non-infiltrating surface runoff; 
evaporation; potential evaporation; temperature; and precipitation.  These variables were 
chosen because they describe the different stages of drought.  The counties feature was 
used to support this data because counties are a relatively small area and they are a 
political boundary.  Because counties represent smaller portions of the entire river basin 
they can be used to show climate variations within the river basin over time.  Due to the 
size of the river basin, climate is able to vary within the basin.  However, climate varies 
little within a county.  Additionally, because a county is a political boundary this serves 
to help decision makers.  Decisions typically based on political boundaries, not drainage 
basin boundaries.  Other projects may put the counties layer in the basemap.  However, 
for the objectives of the IDIS it is more useful to make counties a Space-time layer for 
climate data. 
 
Stream Gauges- The stream gauge feature layer in the IDIS project is made of USGS 
gauges.  These gauges represent the stream gauge network attached to the USGS NWIS 
data server.  The NWIS data server contains real time stream flow data for all of the 
gauge features within the feature layer.  The gauges can be linked to their data on the 
NWIS website through their HydroID.  The HydroID of each gauge feature matches the 
USGS gauge number identifier.  To obtain the data, the IDIS client selects a gauge and 
input a specific date.  IDIS inputs this information into a web service, which then extracts 
this data for NWIS.  This process is described in greater detail in section 3.6.   
 
Reservoirs- The IDIS links reservoirs with data from the USGS NWIS website also.  The 
NWIS website offers daily values for reservoirs across the US.  More about the reservoir 
data provided by NWIS can be found in section 3.4.  However, it is important to address 
the difference between the water bodies feature layer and the gauged reservoirs feature 
layer.  The water bodies layer is created from the NHDPlus, whereas the gauged 
reservoirs feature layer comprises only reservoirs that are monitored by the USGS.  The 
features of the gauged reservoir layer are all identified with HydroCodes that match the 
USGS numerical identifiers for the reservoirs.  IDIS clients select a reservoir and input a 
41 
 
date range.  This information is input to a web service (similar to that of the stream 
gauges) and returns the requested data.  More about this process can be found in section 
3.6. 
 
Reservoir Depth- In 1991 the Texas Legislature authorized the TWDB to begin doing 
volumetric surveys of the state?s reservoirs.  Since 1993 the Surface Water Section of the 
TWDB has done surveys on 52 of the 77 major reservoirs of Texas.  Additionally, 
surveys have been on many of the smaller reservoirs of Texas.  For many of the reservoir 
of Texas storage volumes were calculated using topographic maps upon their creation.  
However, due to constant sediment deposition over time these volumes have changed.  
New bathymetric surveys provide a better analysis of the water volume available in a 
reservoir. [23]    
Bathymetric surveys are created using satellite technology from Global 
Positioning Systems (GPS), Differential Global Positioning System (DGPS), acoustic 
depth sounder for data-collection, and GIS software.  Reservoirs are surveyed by boat.  
The boat is equipped with the sounding devise and positioning systems.  By transecting 
the reservoir (in 500ft intervals) an estimate of lake bathymetry is collected.  This data is 
then processed using GIS to create an estimated surface of the lake floor.  GIS uses 
Delaunay Triangulation to create this lake floor surface, called a TIN (Triangulated 
Irregular Network).  Ongoing studies show that error generated by using Delaunay 
Triangulation can be minimized by interpolating between the sounding points.[23]   Figure 
3.4 shows an example of the a TIN for Lake Grapevine, in the Trinity River Basin.  
Figure 3.5 shows an image of a depth raster of the Lake Grapevine.  This image was 
created by using raster math to subtract a given reservoir elevation from a rasterized 
version of the TIN.  More about this process can be found in section 3.4.     
 
42 
 
 
Figure 3.4:  This image shows a TIN of Lake Grapevine, in the Trinity River Basin. 
 
Figure 3.5:  This image shows a raster of the depth of Lake Grapevine, based on lake elevation and the lake TIN. 
 
In the IDIS project depth rasters are created each day based on the previous day?s 
reservoir elevation.  This is accomplished using a programmed scheduled task coupled 
with web services.  More about this process can be found in section 4.5.  These rasters are 
stored and archived in a raster catalog. 
 
Drought Indices- The drought indices differ from the other space-time layers because 
they are geographically dynamic feature layers.  Thus far, all of the space-time layers 
described have been static features attached to dynamic time series data.  The drought 
43 
 
indices are raster images that change on a regular basis (for the drought indices used in 
the project the change in monthly).  The drought indices used in this project are all 
obtained using weekly scheduled tasks.  The images themselves may change from week 
to week, but they do not possess any data that describes their action through time.  The 
indices featured in this project are the US Drought Monitor, the Drought Impact Reporter 
and the Corp Moisture Index.  More about these indices can be found in the next section. 
 
3.4 Sources of Drought Information 
 
To get a full understanding of drought at all of its stages through the varying 
climatic regions of Texas a spectrum of information is needed.  Different sources of 
information are described below: 
 
North American Regional Reanalysis (NARR) Data- The NCEP completed the NARR 
project in 2004.  The NARR defined as a ?long-term, dynamically consistent, high-
resolution, high-frequency, atmospheric and land surface hydrology dataset for the North 
American domain.?  The NARR represents a period from 1979 to the present.  The 
components used to create the NARR are the NCEP Eta Model and its Data Assimilation 
System, a version of the NOAA land surface model and many additional data sets.  The 
additional data sets are used as input values to the Eta Model and NOAA land surface 
model.  Due to the assimilation of information used to create the NARR, a better 
representation of land-atmosphere interaction has been created.  Some of the produced 
variables (such as precipitation and evaporation) have been improved by the Regional 
Assimilation System (RDAS).  The models are run on a three hour integration analysis.  
The results are available on a 32 km grid resolution, similar to the Eta model before 2000.  
The area covered by NARR can be seen in Figure 3.6.  [24]    
 
44 
 
 
Figure 3.6:  the NCEP regional Reanalysis domain and its 32 km topography.  Terrain elevation (m) is indicated 
by the color scale at the right. [24] 
 The NARR data is available for download online at the NOAA National 
Operational Model Archive and Distribution System (NOMADS) from the Thematic 
Real-time Environmental Distribution Data Service (THREDDS) Data Server (TDS).  
The data is available as NetCDF files.  The catalog of NARR data exists in two timestep 
formats:  three hour intervals and monthly averages of the three hour time steps.  The 
three hour time steps are based on Coordinated Universal Time(UTC), starting at 0-3 
hours and going until 21 to 24 hours.  The NARR values are cumulative for each 
timestep.  This project uses the monthly average timestep data.  IDIS obtains NARR data 
through scheduled task downloads using web services. [24]   The IDIS project uses seven 
variables from NARR:  temperature, potential evaporation, evaporation, soil moisture, 
subsurface runoff, non-infiltrating surface runoff and precipitation.  Appendix B shows 
tables and graphs detailing the data. 
45 
 
Temperature- The temperature is given in degrees Kelvin.  The time step value is the 
average for the 3 hour period.  The monthly mean value is the average of all of the 3 hour 
time steps in the month. 
Potential Evaporation- The potential evaporation represents the potential water vapor 
capacity of the air.  The values are given in mm or kg/sq meter over 3 hours (cumulative 
for the 3 hour period).  The monthly average is the average of the 3 hour time steps over 
all days of that month.  To find the total potential evaporation or evaporation  for a given 
month the NARR value would need to be multiplied by the total number of 3 hour 
timesteps that occurred during that month (eight periods a day for 28 to 31 days). 
Evaporation- The evaporation variable is a measurement of the actual evaporation that 
occurs.  Evaporation is limited by the amount of water in the soil available to be 
evaporated.  The evaporation demand of the air (potential evaporation) is typically less 
than the actual evaporation.  The difference between potential evaporation and 
evaporation is greater in more arid areas or drier times of the year.  The values are given 
in mm or kg/sq meter over 3 hours (cumulative for the 3 hour period).  The monthly 
average is the average of all the 3 hour time steps for that month.  To find the total 
potential evaporation for a given month the NARR value would need to be multiplied by 
the total number of 3 hour timesteps that occurred during that month.  
Precipitation- The precipitation represents the potential water vapor capacity of the air.  
The values are given in mm or kg/sq meter over 3 hours (cumulative for the 3 hour 
period).  The monthly average is the average of all the 3 hour time steps for that month.  
To find the total precipitation for a given month the NARR value would need to be 
multiplied by the total number of 3 hour timesteps that occurred during that month. 
Soil Moisture- The soil moisture is a measure of the volume present in the top 100 cm of 
the soil.  The values are given in mm or kg/sq meters present on average over a 3 hour 
period.  The monthly average is the average of all the 3 hour time steps for that month. 
Subsurface Runoff Baseflow- The subsurface runoff baseflow is the precipitation runoff 
that infiltrates the surface.  The values are given in mm or kg/sq meter over 3 hours 
(cumulative for the 3 hour period).  The monthly average is the average of all the 3 hour 
time steps for that month.  To find the total precipitation for a given month the NARR 
46 
 
value would need to be multiplied by the total number of 3 hour timesteps that occurred 
during that month. 
Surface Runoff Non-infiltrating- The surface runoff non-infiltrating is the precipitation 
runoff that does not infiltrate the surface.  The values are given in mm or kg/sq meter 
over 3 hours (cumulative for the 3 hour period).  The monthly average is the average of 
all the 3 hour time steps for that month.  To find the total precipitation for a given month 
the NARR value would need to be multiplied by the total number of 3 hour timesteps that 
occurred during that month. 
 
Stream gauge data- USGS monitors data from approximately 1.5 million sites across the 
U.S.  All of the USGS data, from 1857 when monitoring began to the present, is stored 
on National Water Information System Web Site (NWISWeb).  The streamflow network 
was established to meet various needs for streamflow information.  The information is 
used to for water supply management, flood monitoring and environmental observation.  
The objective of each gauge station is to produce a continuous record of stage and 
discharge at that specific location.  Streamflow is measured instantaneously and averaged 
over time.  The stream data that is collected by the gauges is recorded at fifteen to sixty 
minute intervals onsite, and then transmitted to a USGS office every one to four hours.  
Each site is selected based on the needs of water management or by the requirements of 
the hydrologic network.  Due to the restrictions on gauge locations, sometimes artificial 
control structures (such as low dams, broad-crested weirs or flumes) must be built in a 
stream channel to stabilize the stage and discharge data.   [25]    
USGS presents the national streamflow network data in time series available on 
the NWISWeb.  Data is typically recorded as gauge height (in feet) and discharge (in 
cubic feet per second).  Figure 3.7 shows USGS stream gauges for the state of Texas.  In 
addition to stage and streamflow information, the NWISWeb also offers information 
about water quality and well level.  The NWISWeb organizes the data on the website 
through watershed address. This data can then be access by users through web services.  
[25]    
 
 
47 
 
 
 
 
 
 
Explanation 
High 
> 90th percentile 
75th - 89th percentile 
25th - 74th percentile 
10th - 24th percentile 
< 10th percentile 
Low 
Not ranked 
 
Figure 3.7:  This map shows the stream gauges in Texas.  The map depicts the stage of flow in the streams being 
measured on March 31, 2008. [26]    
 
 
Reservoir Gauge Data- In addition to stream gauges the NWIS system also supports data 
on reservoirs.  Unlike streamflow data this data is collected daily.  Because reservoir 
elevation does not change as frequently as stream gauge data it is not necessary to 
represent data instantaneously.  Stream gauges obtain water surface height (in feet).  This 
is accomplished through a variety of methods.  For example, Figure 3.8 shows a 
schematic of a stilling well and shelter gauge station. 
(http://nationalatlas.gov/articles/water/a_streamflow.html )  Elevation may also be sensed 
using bubble gauge which uses a gas-purge system. [25]    
 
48 
 
 
Figure 3.8:  A schematic of a stilling well and shelter gauge station for measuring water surface elevation. [25] 
 
 Not all of the reservoirs within the Trinity River basin are monitored by NWIS.  
Table 3.3 shows a list of the reservoirs that are monitored by NWIS in the Trinity River 
Basin. 
49 
 
 
 
Table 3.3:  This table is a list of the reservoirs in the Trinity River Basin that are monitored by the USGS. [27]    
USGS Reservoirs of the Trinity River Basin 
Gauge 
Number Reservoir name 
8042820  Lost Ck Res nr Jacksboro, TX  
8043000  Bridgeport Res abv Bridgeport, TX  
8043700  Lk Amon G Carter nr Bowie, TX  
8045000  Eagle Mtn Res abv Ft Worth, TX  
8045400  Lk Worth abv Ft Worth, TX  
8045800  Lk Weatherford nr Weatherford, TX  
8046500  Benbrook Lk nr Benbrook, TX  
8049200  Lk Arlington at Arlington, TX  
8049800  Joe Pool Lk nr Duncanville, TX  
8050050  Mountain Ck Lk nr Grand Prairie, TX  
8051100  Ray Roberts Lk nr Pilot Point, TX  
8052800  Lewisville Lk nr Lewisville, TX  
8054500  Grapevine Lk nr Grapevine, TX  
8060500  Lavon Lk nr Lavon, TX  
8061550  Lk Ray Hubbard nr Forney, TX  
8062730  New Terrell City Lk nr Terrell, TX  
8063010  Cedar Ck Res nr Trinidad, TX  
8063050  Navarro Mills Lk nr Dawson, TX  
8063600  Lk Waxahachie nr Waxahachie, TX  
8063700  Bardwell Lk nr Ennis, TX  
8064510  Halbert Lk nr Corsicana, TX  
8064550  Richland-Chambers Res nr Kerens, TX  
8065330  Houston County Lk nr Crockett, TX  
8066190  Livingston Res nr Goodrich, TX  
 
 
US. Drought Monitor- The US Drought Monitor produces weekly images of drought 
severity for the United States, as can be seen in Figure 3.9.  The US Drought Monitor 
bases its drought severity on a blend of indices, including the PDSI, the CPC Soil 
Moisture Model, Standardized Precipitation Index (SPI), USGS weekly streamflow 
percentiles, and objective short and long-term drought indicators.  The maps are produced 
50 
 
by the NDMC and the University of Nebraska-Lincoln in collaboration with several 
federal, state, academic and independent organizations.  Links to this data are provided 
on the US Drought Monitor website, as well as, archived data. [3]    
 
 
Figure 3.9:  The US Drought Monitor for the week of August 28th, 2007. [3] 
The US Drought Monitor is largely based on the PDSI, which is the most 
commonly used index for assess drought.  The PDSI calculation was created in 1965 by 
W.C. Palmer as a means for evaluating soil moisture.  However, the method he developed 
has been used widely to establish drought severity.  Palmer defined drought as ?an 
interval of time, generally of the order of months or years in duration, during which the 
actual moisture supply at a given place rather consistently falls short of the climatically 
expected or climatically appropriate moisture supply.?[18]   The PDSI is calculated using 
monthly values for precipitation, potential evapotranspiration, and available moisture 
stored in both the surface and underlying soil layer.  Although the PDSI is the most 
widely accepted method for defining drought severity, it is an imperfect method.  The 
PDSI is lacking in three major issues.  First, the PDSI fails to take into account 
51 
 
agricultural irrigation.  Second, it does not take into account for water stored in snow or 
in man-made reservoirs.  And third, it cannot take into account water conservation 
measures.  Table 3.4 shows the classification system that the NDMC uses to establish 
drought level.  [28]    
52 
 
Table 3.4: This table shows drought classification for the US Drought Monitor. [3]    
Drought Severity Classification 
 RANGES 
Category Description Possible Impacts Palmer 
Drought 
Index 
CPC Soil  
Moisture 
Model  
(Percentiles) 
USGS Weekly 
Streamflow 
(Percentiles) 
Standardized 
Precipitation 
Index (SPI) 
Objective Short and 
Long-term Drought 
Indicator Blends 
(Percentiles) 
D0 Abnormally Dry Going into drought: short-term dryness 
slowing planting, growth of crops or 
pastures. Coming out of drought: some 
lingering water deficits;  pastures or 
crops not fully recovered 
-1.0 to -
1.9 
21-30 21-30 -0.5 to -0.7 21-30 
D1 Moderate Drought Some damage to crops, pastures; 
streams, reservoirs, or wells low, some 
water shortages developing or imminent; 
voluntary water-use restrictions 
requested 
-2.0 to -
2.9 
11-20 11-20 -0.8 to -1.2 11-20 
D2 Severe Drought Crop or pasture losses likely;  water 
shortages common; water restrictions 
imposed 
-3.0 to -
3.9 
6-10 6-10 -1.3 to -1.5 6-10 
D3 Extreme Drought Major crop/pasture losses;  widespread 
water shortages or restrictions 
-4.0 to -
4.9 
3-5 3-5 -1.6 to -1.9 3-5 
D4 Exceptional 
Drought 
Exceptional and widespread crop/pasture 
losses; shortages of water in reservoirs, 
streams, and wells creating water 
emergencies 
-5.0 or 
less 
0-2 0-2 -2.0 or less 0-2 
 
53 
 
Drought Impact Reporter-The NDMC created the Drought Impact Reporter to catalog 
drought impacts through the United States.  Because drought is difficult to quantify, the 
Drought Impact Reporter was established to help measure the effects of drought through 
time.  Understanding impacts helps to establish the risks associated with drought.  The 
impacts are linked spatially by county.  On the Drought Impact Reporter map states and 
counties are color-coded to represent the number of drought-related events have occurred.  
Maps can be created by time period of events ranging from the past week to the past year. 
The impacts are recorded from over 5,000 online news sources and reports submitted by 
individuals (private citizens and government officials) online.  Additionally, the impacts 
are divided into six types of drought related events:  agriculture, water/energy, 
environment, fire, social and other.  Figure 3.10 shows the Drought Impact Report for 
Texas March 31st, 2008. [29]    
 
54 
 
 
Figure 3.10:  This shows the drought impacts reported in Texas the week of March 31st, 2008. [29]    
 
Crop Moisture Index- The Crop Moisture Index (CMI) is produced by NOAA?s Climate 
Prediction Center (CPC).  The CMI uses meteorological data to monitor crop conditions.  
The index is produced weekly for the each of the NOAA climate regions.  Figure 3.11 
shows the CMI for the week of April 1st, 2008.  The climate regions used in this project 
are 113, 114, 116, and 118.  The CMI was developed by Palmer in 1968 as part of the 
PDSI.  The scale is based on the mean temperature and precipitation from the past week.  
A negative three indicates the driest conditions possible, a positive three indicates the 
wettest conditions possible, and a zero indicates that the precipitation and temperature 
have been average for the region for that time of the year. [28]    
 The CMI is an excellent measure of short term drought conditions.  This was 
developed as a tool for the agriculture industry in planning for crop planting.  However, 
55 
 
the results of the CMI may be misleading concerning the long term outlook for climate 
conditions.  If a weather event lasts longer than a year, it begins to affect the mean annual 
temperature and mean annual precipitation.  Therefore, if a drought lasts for several 
years, the CMI becomes less useful. [30]      
 
 
Figure 3.11:  This image shows the CMI for the United States March 29th, 2008.  The image was created the 
following week. [30]    
56 
 
3.5  Integrating Information 
 
 As mentioned in section 3.3, the data used in IDIS comes in several different 
formats.  GIS is a sophisticated tool, which allows for the incorporation of several types 
of data.  However, for every type of data that is input into the IDIS, a system must be 
developed integrate this data.  Often, the GIS software provided tools to enable a simple 
integration.  However, sometimes the integration programs had to be written.  The most 
of the codes where developed using Python script. 
 
Network Common Data Form (NetCDF) File- NetCDF files are in a binary data format.  
They are a standard form exchanging scientific information. NetCDF was developed by 
University Corporation for Atmospheric Research (UCAR). [31]   They?re commonly used 
for remote sensing data products such as NEXRAD, and in the case of this project, 
NARR.  ArcGIS tools have been developed for converting this format into GIS friendly 
forms.  The tools developed can be found in the Multidimensional toolbox of Arc 9.2.  
The toolbox is shown in Figure 3.12.  These tools were used to integrate the NetCDF files 
into the IDIS project, in conjunction with several other ArcGIS tools.  The tools were 
strung together using Model Builder to process the NetCDF files into a form usable for 
the IDIS system. 
 
 
Figure 3.12:  This is the ArcGIS Multidimension toolbox.  Its tools are useful for working with NetCDF files in 
GIS.  
 
57 
 
There are several steps and tools needed to integrate the NetCDF files.  NARR 
provides NetCDF files for every month.  As mentioned in previous sections the NARR 
data contains monthly averages of climate data for North America.  The NARR climate 
variables used in this IDIS project are precipitation, temperature, potential evaporation, 
evaporation, subsurface runoff, non-infiltrating surface runoff and soil moisture.  These 
variables were chosen because of their pertinence to drought conditions.  A catalog of 
NetCDF files for these variables from 1979 to the previous month has been stored on the 
IDIS server.  Every month this catalog is updated with the previous month?s data.  This is 
done using schedule tasks described in the next section. 
 To convert the NetCDF files, the ArcGIS model created was exported into python 
code, and incorporated into a code that was made into an additional scheduled task to 
take place every month after the new data has been loaded to the server.  Figure 3.13 
shows an image of the ArcGIS model used for this task in model builder.  The model 
created in model builder converts the NetCDF file into a feature layer.  The feature layer 
created is a grid of points.  The model then saves the feature as an actual layer so that it 
can be used in the next step, the spatial join.  The grid points of the NARR data are then 
joined to the counties layer using a spatial join.  In this step in the process it is important 
to select the one-to-many option.  This aggregates values for grid points contained within 
the same county.  At this point a county file containing climate data has been created for 
every date.  The next step of the model merges all of these county layer files.  Finally, the 
attribute table of the final merged file is cleaned up.  All of the unnecessary fields in the 
table are deleted, and necessary fields are renamed to fit within the naming convention 
specific to the IDIS.  More about the python code for this integration task can be found in 
Appendix C. 
 
58 
 
 
Figure 3.13: This tool was created using ArcGIS to integrate NetCDF files from NARR. 
59 
 
 
Comma-Separated Values (CSV) table with feature- This process for the IDIS is still 
being developed.  However, the idea of linking CSV files with features is described here.  
Data downloaded from USGS NWIS is returned in a CSV table.  This data is obtained 
using web services and must be integrated quickly (on-the-fly).  Web services are 
described in more detail in the next section. This table is relatively simple to process, and 
easily links to GIS features.  In the case IDIS, NWIS data is used to obtain information 
about reservoirs and stream gauges.  Data from NWIS is linked to hydrologic features of 
reservoirs and stream gauges through a join of unique identifiers.  NWIS has assigned 
each reservoir and stream gauge a unique identifier from USGS.  IDIS has assigned all 
features to be attached to data with a unique identifier, called a HydroID.  In the case of 
the reservoirs and stream gauges, the HydroIDs match the NWIS identifiers.  There for 
the CSV data table can be quickly linked to the hydrologic features as the data is obtained 
from web services. 
 
Comma-Separated Values (CSV) table with raster- IDIS uses a schedule task to 
download reservoir elevation daily from the USGS NWIS website.  More can be learned 
about this process in the next chapter.  This data is retrieved in a CSV table format.  After 
retrieving this data daily it must be integrating into the IDIS system so that it is 
obtainable to users.  One of the methods for data display is to use the reservoir elevation 
level information to create showing reservoir depth based on the current reservoir 
elevation.  Like the data retrieved, this is accomplished through a scheduled task.  Similar 
to the NetCDF integration described above, this process was developed using ArcGIS 
Model Builder and python code.   
 First a model was created in ArcGIS Model Builder.  Then the model was 
exported to python code.  The code was then developed further to incorporate the data 
from the CSV file.  The model used can be seen in Figure 3.14.  The input to the model is 
the elevation data from the CSV file and rasters of the lake bathymetry.  The rasters have 
been pre-created and are stored on the IDIS server.  These rasters were created using 
60 
 
TINs from sounding points as described in the previous section.  The python code and 
additional information about this process can be found in Appendix D. 
 
 
Figure 3.14:  This is the tool created in Model Builder for creating rasters of reservoir depth.  
 
Comma-Separated Values (CSV) table to a displayed table- Reservoir elevation data is 
downloaded daily from the NWIS website.  Every morning the NWIS website posts the 
elevations from the previous day.  The IDIS server then downloads these files in an 
Extensible Markup Language (XML) format (as well as a CSV) using web services.  The 
IDIS server, through ArcGIS server manager, uses an XSLT (XML style sheet) to 
incorporate these files as an html table.  ArcGIS then allows the new html table to be 
posted directly to the website application. 
 
Web Map Service (WMS) files- WMS files are maps of spatially referenced data of 
geographic information.  The NDMC produces files of the US Drought Monitor on a 
WMS file.  These files can be loaded into an ArcMap document and ArcGIS Server.  
Without any conversion the files are loaded and projected by the server. 
 
61 
 
3.6  Web services 
 
 Once the IDIS is fully developed, it will provide data for USGS stream gauges 
and reservoirs monitored by the USGS and climate data from NARR.  The client will be 
able to request data from a selected gauge or reservoir for a specified data.  The data is 
returned using web services.  Web services are also used in the scheduled task.  IDIS 
automatically downloads data (monthly climate data and daily reservoir daily).  This is 
accomplished using the same type of web services.  The web services being developed 
for the IDIS are based on those developed for the Hydrologic Information System (HIS).  
The HIS was developed for the Consortium of Universities of the Advancement of 
Hydrologic Science, Inc. (CUASHI) at CRWR at UT.   
 These web services are designed to obtain the client requested data from the 
USGS NWIS website.  Web services work similar to a language.  Web services allow 
computers to access data being severed on another computer, and return data requests.  A 
technical definition of web services is [32]   : 
a software system designed to support interoperable machine-to-machine 
interaction over a network. It has an interface described in a machine-processable 
format (specifically WSDL). Other systems interact with the Web service in a 
manner prescribed by its description using SOAP messages, typically conveyed 
using HTTP with an XML serialization in conjunction with other Web-related 
standards.  [17]    
Web services are the manner in which data is access from one computer (the client) 
accesses data from another computer (a server).  The standard protocol for this the 
Simple Objects Access Protocol (SOAP).  SOAP can be described as being similar to a 
language that computers are able to communicate with each other.   In developing HIS, 
the CUASHI team created a series of standard web services, called WaterOneFlow.  
WaterOneFlow can obtain data from several data servers, including NWIS.  The services 
created to obtain data are GetSiteInfo, GetVariableInfo, and GetValues.  More 
information about this can be found at http://water.sdsc.edu/WaterOneFlow/. [33]    
62 
 
 
3.7 Scheduled Tasks 
  
 In the Windows operating system programs can be scheduled to run regularly on 
the computer.  This operation is called a scheduled task.  Tasks are typically programs 
that need no user input.  Once the task is scheduled it runs independently at its allotted 
time.  Tasks can be scheduled daily, weekly, monthly, when the computer starts or when 
a specific user logs on. 
 For the IDIS data requirements, there are several cases in which it is more 
efficient or even imperative for data to be downloaded as a scheduled task instead of 
through user inter-active web services.  Events of major downloads require a lot of 
computer usage and can slowdown the server.  Scheduled tasks allow downloads to occur 
at times when relatively few people are accessing the IDIS server.  For example, 
refreshing the NARR data every month is a long and consuming task.  Running this task 
causes the server to respond more slowly.  The reservoir elevations are also downloaded 
as a scheduled task.  This takes place early, every morning after USGS has posted the 
previous day?s elevations.  The server then automatically recreates rasters showing the 
depths of the reservoirs and a new reservoir elevation table is posted on the IDIS website. 
 
3.8 Technical Limitations 
 
The integrated drought information systems are a relatively new concept.  As 
mentioned in the literature review, to date there has been relatively little work done in 
developing an integrated system of data for monitoring drought.  Most of the variables of 
drought indicators are housed by different state and national organizations.  The lack of 
development in this area has caused obvious technical limitations.  As the prototype of 
the Texas IDIS is being developed, the NIDIS is also being developed.  So, most of the 
technical concepts that the IDIS is incorporating are on the cutting edge of technology.  
Many components of the Texas IDIS prototype are being developed uniquely for this 
63 
 
project.  Of course, the technology being developed for this project is designed with the 
goal to be generic enough to reproduce for future systems of a similar nature.  The IDIS 
is still a developing tool.  As its technology continues to develop, the IDIS will become 
more advance and efficient.   
64 
 
Chapter 4. Texas IDIS for the Trinity River Basin 
 
The test area for the Texas IDIS is the Trinity River Basin.  The Trinity River 
watershed contains 17,965 square miles (about 6% of Texas?s surface area).  The Trinity 
River itself is 715 miles long.  Because the basin is so large, land types, soil type and 
climate vary greatly across the state.  The northern portion of the watershed is covered by 
central Texas prairies.  The dry climate of the northern portion receives only 29 inches of 
precipitation annually.  As the river runs southeast the landscape changes from central 
Texas prairies, to piney woods, to Gulf Coastal prairies (which receive 53 inches of 
precipitation a year).[34]    
 
4.1 The Trinity River Basin as a Prototype 
TheTrinityRiver is one of the most developed watersheds in the state.  The 
reservoirs serve the Dallas/Fortworth Metroplex and parts of Houston.  The reservoirs of 
the basin hold more than 5000 acre-feet of water in storage.  In 1955, the Trinity River 
Authority (TRA) was created.  The TRA includes all or part of 17 counties (containing 
twenty percent of the state?s population) along the river basin.  Although the watershed 
falls into water management regions C  and H (as well as small portions of D, G and I), 
the TRA has been allotted much jurisdiction over the Trinity River Basin through House 
Bill 20 by the 1939 Texas Legislature.  The basin as well as the water management 
districts can be seen below in Figure 4.1. [34]    
65 
 
 
Figure 4.1:  The Trinity River Basin lies within several of Texas?s water management regions.  As can be seen, 
the basin falls primarily with in region C and H.  However, the Basin also contains counties that are part of 
region B, D, G and I. [34]    
 
Additionally the Trinity River Basin spans over three major aquifers (Carrizo, 
Gulf Coast, and Trinity) and five minor aquifers (Nacatoch, Queen City, Sparta, 
Woodbine, Yegua Jackson).  These aquifers in relation to the basin can be seen in Figure 
4.2. [34]    
66 
 
 
Figure 4.2:  The Trinity River Basin lies over three major aquifers and five minor aquifers. [34]    
 
 The Trinity River Basin contains 30 reservoirs that serve as the water supply for 
the basin.  The reservoirs of the Trinity River Basin supply water for approximately ten 
million people residing within and near the Basin.  A map of existing reservoirs can be 
seen in Figure 4.3. In addition to the 30 current reservoirs, there are plans for an 
additional thirteen reservoirs to be built within the basin. [34]    
67 
 
 
Figure 4.3:  The Trinity River Basin currently contains 30 reservoirs. [34]    
 
 The surface water is important to the region.  The river and its tributaries provide 
water for the population, industry and agriculture of the region.    The region also has 
developed a thriving recreation industry centered around the Trinity River and its 
reservoirs.  Table 4.1 shows the economic contributions from specific lakes.  Currently 
68 
 
Dallas and Fort Worth are both in the stages of planning for large scale projects for 
developing the riverfront area of the Trinity that runs through both cities. [34]    
Table 4.1:  Several of the reservoirs of the Trinity River Basin have resorts or parks that make large economic 
contributions to the region. [34]    
Reservoir Park / Resort Annual Economic 
Contribution to 
the Area 
Jobs In Sales Tax to 
Local Economies 
Lake Grapevine Gaylord Texas 
Resort Hotel 
$450,000,000 1,700  
Lake Joe Pool Cedar Hill 
State Park 
$6,400,000 114 $32,000 
Fairfield Lake Fairfield State 
Park 
$870,000 18 $4,300 
Lake Livingston Lake 
Livingston 
State Park 
$5,100,000 108 $25,700 
 
 
4.2 Prototype Preliminary Design 
 
Section 3.1 described the basic design for IDIS.  This section discusses the design 
of the IDIS prototype in the Trinity River Basin.  Figure 4.4 shows the preliminary layout 
for the Trinity River Basin IDIS.  The client accesses the server via a website.  The web 
server stores a Hydro Basemap and drought education documents, the static data of the 
project.  This is labeled ?D? in Figure 4.4.   
The web server also stores data from NARR and USGS NWIS (see labels ?A? 
and ?B?).  Data from these resources is downloaded regularly using scheduled task.  
However, there is also another component of the IDIS that does not store data on the 
server, this is the data services.  Data services are used to access stream gauge data and 
reservoir data from the USGS NWIS website.  However, unlike the scheduled task this 
69 
 
downloads data on demand for a user specified time period. ?A?, ?B? and ?C? make up 
the data retrieval mechanisms for the space-time data of the IDIS. 
 
 
Figure 4.4:  This layout represents the basic format of the IDIS prototype. 
  
The Hydro Basemap represents features of the area that are used as a geographic 
reference for the space-time IDIS data.  Most of the data used for the static layers is from 
the TNRIS StratMap.  The prototype Hydro Basemap includes the feature layers:  towns, 
cities, flowlines, roads, interstates, all water bodies, drainage basins, the Trinity River 
basin, and the Texas state boundaries.  Figure 4.5 show the study area for the prototype 
and the individual feature layers that compose the Hydro Basemap. 
70 
 
 
Figure 4.5:  This image shows the study area and the layers that make the Hydro Basemap. 
 
The space-time layers contain features that are linked to time series data.  Figure 
4.6 shows these features.  The first four diagrams in the Figure show climate data linked 
to counties.  The climate data is from NARR.  More about linking this data to the 
counties layer can be read in section 4.4.  The fifth and sixth images show stream gages 
and reservoirs within the Trinity River Basin.  These layers are both linked to NWIS data 
from the USGS.  The stream gauge data shows real-time stream flow data.  The last layer, 
71 
 
the raster lake catalog, is also linked to the USGS NWIS daily reservoir elevations.  
However, it is not linked in the same method as the rest of the space-time layers.  The 
raster lake catalog is updated daily with raster images of reservoir depth.  Details about 
that process can be found in sections 4.4 and 4.5. 
 
 
 
Figure 4.6:  This image shows the feature layers that are associated with space-time data. 
 
 Because there are so many layers it is not feasible to view them all at once.  To 
overcome this problem some of the layers will have limited visibility.  The visibility is 
dictated by the scale of the map.  Feature layers that more complex and detailed are not 
visible beyond a 1: 200,000 scale.  Features that are vast are only visible above a 
72 
 
1:200,000.  This allows the map to display all of the necessary data without becoming too 
cluttered.  Table 4.2 describes the visibility of each feature layer. 
 
Table 4.2:  IDIS Feature Layer Visibility 
Layer Name Visibility 
Towns Below 1:200,000 
Cities Above 1:200,000 
Stream Gages Always 
Lake Rasters Below 1:200,000 
All Flowlines Variable Code, Identifier for TStime 
Tables 
Rivers Above 1:200,000 
Roads Below 1:200,000 
Interstates Above 1:200,000 
Gauged Lakes Always 
All Water bodies Below 1:200,000 
Drainage Basin Always 
Trinity River Basin Always 
Counties Always 
Drought Monitor (WMS) Above 1:200,000 
Texas Always 
 
The IDIS website is still in development.  The final version of the website will 
most likely resemble the image in Figure 4.7.  The drought links and drought education 
document can be accessed through the links posted towards the top of the webpage.  
Tools at the top left of the screen allow the client to zoom in and out of the map and 
request data.  The top box on the left of the screen allows the users to view the contents 
of the map and turn on and off layers.  The next box shows a legend for the reservoir 
depth rasters.  The following boxes, task and results, are for accomplishing the IDIS 
73 
 
functions.  The client can select a task from the task box.  The results appear in the results 
box.  The overview box provides a smaller version of the IDIS map at full extent.  This 
tool provided the user with their location, in relation to the entire basin, when zoomed 
into a lake or gauge.  The final box on the left side of the screen shows the reservoir 
levels for reservoirs within the Trinity River Basin.   
 
 
Figure 4.7: The IDIS will be accessable through a website similar to this image. 
 
4.3 Hydrobasemap 
 
Basemap data provides the contextual backdrop for IDIS, and includes layers such as 
major roads and political boundaries.  This content is static, in that it is not updated over 
74 
 
time.  These layers are stored as layers in the IDIS\GISData\TXIDIS.gdb folder.  These 
layers include: 
 Towns 
 Cities 
 Rivers and Streams 
 All Flowlines  
 Roads 
 Interstates 
 All Water bodies 
 Drainage Basins 
 Trinity River Basin 
 Texas 
Towns (towns): 
This layer shows all of the towns in the Trinity River Basin.  It is only visible when 
zoomed in to a 1:200,000 scale.  This is described in table 4.3. 
 
Table 4.3:  Towns Feature Layer Description 
Field Name Field Description 
Local_ID State Town Identifier 
Name Town Name 
Population Estimate 2007 Estimated Population in 2007 
 
Cities (cities): 
This layer shows major cities in Texas.  It is only visible when zoomed out beyond a 
1:200,000 scale.  This is described in table 4.4. 
 
75 
 
Table 4.4:  City Feature Layer Description 
Field Name Field Description 
Local_ID State Town Identifier 
Name City Name 
Population Estimate 2007 Estimated Population in 2007 
 
All Flowlines (TRB_flowlines): 
This layer shows all of the flowlines in the Trinity River Basin.  It is only visible when 
zoomed in to a 1:200,000 scale. The feature class is created from the NHDPlus dataset. 
More information about the fields of this layer can be found at:  http://www.horizon-
systems.com/nhdplus/  
 
Rivers (TRB_big_flowlines): 
 This layer shows the Rivers of the Trinity River Basin.  It is only visible when zoomed 
out beyond a 1:200,000 scale.  The feature class is created from the NHDPlus dataset. 
More information about the fields of this layer can be found at:  http://www.horizon-
systems.com/nhdplus/  
 
Roads (TRB_roads): 
This layer shows all roads with in the Trinity River Basin.  It is only visible when 
zoomed in to a 1:200,000 scale.  This is described in table 4.5. 
 
76 
 
Table 4.5:  Roads Feature Layer Description 
Field Name Field Description 
StratMapID TNRIS Identifier 
Shield The Highway Shield Number for 
Interstates 
HWY Number The Highway Number for given to all 
Highways 
HWY Name Full Street Name 
 
Interstates (interstates): 
This layer shows all interstates in Texas.  It is only visible when zoomed out beyond a 
1:200,000 scale. This is described in table 4.6. 
 
Table 4.6: Interstate Feature Layer Description 
Field Name Field Description 
SRATMAPID TNRIS Identifier 
Prefix The Highway Prefix 
HWY Name The Highway Number for given to all 
Highways 
Suffix The Highway Suffix 
Direction Direction of Interstate 
Description Description of the Segment 
 
All Waterbodies (TRB_waterbodies): 
This layer shows all of the waterbodies in the Trinity River Basin.  It is only visible when 
zoomed in to a 1:200,000 scale.  The feature class is created from the NHDPlus dataset. 
More information about the fields of this layer can be found at:  http://www.horizon-
systems.com/nhdplus/  
77 
 
 
Drainage Basins (TRB_hucs): 
This layer shows the boundary of the Trinity River Basin.  The feature class is created 
from the NHDPlus dataset. More information about the fields of this layer can be found 
at:  http://www.horizon-systems.com/nhdplus/  
 
Trinity River Basin (Riverbasin_boundary): 
This layer shows the boundary of the Trinity River Basin.  The feature class is created 
from the NHDPlus dataset. More information about the fields of this layer can be found 
at:  http://www.horizon-systems.com/nhdplus/  
 
Texas Boundary (Texas): 
This layer shows the boundary of the state of Texas. This is described in table 4.7. 
 
Table 4.7:  Texas Feature Layer Description 
Field Name Field Description 
Shape Length Perimeter of Texas 
Shape Area Area of Texas 
4.4 Space-Time Layers 
 
Space-time data layers either change over time, or are associated with temporal data.  
In addition to depicting the current drought conditions in the region, some of these layers 
also provide a higher degree of user interaction to support further investigation into 
drought.  The variables chosen to be represented in these layers represent the 
environmental shift from a meteorological drought, to an agricultural drought, to a 
hydrologic drought.  These layers are stored in a file geodatabase named Space-time 
Layers in the IDIS\GISData\Space-time.gdb folder.  These layers include: 
 Counties 
78 
 
 Stream Gages 
 Gaged Lakes 
There are also tables in this geodatabase that describe the dynamic aspects of the Space-
time layers.  These tables include:  
 TSTable Climate 
 TSTable Streams 
 TSTable Lakes 
There are also tables in this geodatabase that provide statistics for the Counties and 
Stream Gages layers.  The statistics for climates are based on NARR 30 year average, 
and are determined for each county. The statistics for the streams are from the National 
Hydrography Dataset (NHDPlus).  These statistics are useful for identifying anomalies in 
current conditions by comparing current time series values with historical ones. These 
tables include:  
 Climate Statistics  
 Stream Statistics 
Additionally, another aspect of the geodatabase is the data catalogs.  There are two 
types of data catalogs: raster catalogs and dynamic variable tables.   
The Space-time datasets are described below. 
 
Counties (NARR_counties): 
This layer shows counties.  Counties are associated with climate time series data retrieved 
from NARR.  By clicking on a county in the map, the user can bring up a time series 
graph for a given climate variable, spatially averaged over the county.  Each county is 
identified with a HydroID.  The HydroID is used to associate the counties with NARR 
climate data stored in TSTable Climate.  The FeatureID from TSTable Climate matches 
the HydroID in the Counties layer.  For anomaly calculations the Climate Statistics table 
is used.  The link between this table and the Counties layer is established in the same 
method as the TSTable Climate link.  This is described in table 4.8. 
79 
 
 
Table 4.8:  Counties Feature Layer Description. 
Field Name Field Description 
HydroID Unique numerical identifier for the county 
NAME The name of the county 
Climate Region The NOAA Climate Region Identifier 
 
Stream Gauges (Stream_gages): 
This layer shows USGS stream gages within the Trinity River Basin.  Gauges are 
associated with USGS time series stream gauge data.  Each gage is identified with a 
HydroID that is equal to the USGS gauge ID number.  The HydroID is used to associate 
the gage with the stream flow time series data stored in TSTable Streams.  The FeatureID 
from TS Streams matches the HydroID in the Stream Gages layer.  For anomaly 
calculations the Stream Statistics table is used.  The link between this table and the 
Stream layer is established in the same method as the TSTable Stream link.  The feature 
class is created from the NHDPlus dataset. More information about the fields of this layer 
can be found at:  http://www.horizon-systems.com/nhdplus/. 
 
Gauged Lakes(Gaged_lakes): 
This layer shows lakes that are gauged by the USGS within the Trinity River Basin.  
Lakes are associated with USGS time series reservoir gage data.  By clicking on a lake in 
the map, the user can bring up a time series graph for daily reservoir elevation at a given 
lake.  Each lake is identified with a HydroID that is equal to the USGS gage ID number.  
The HydroID is used to associate the lake with the reservoir elevation time series data 
stored in TSTable Lakes.  The FeatureID from TS Lakes matches the HydroID in the 
Gauged Lakes layer.  For anomaly calculations the Lake Statistics table is used.  The link 
between this table and the Gauged Lakes layer is established in the same manner as the 
TSTable Stream link.  The feature class is created from the NHDPlus dataset.  More 
information about the fields of this layer can be found at:  http://www.horizon-
80 
 
systems.com/nhdplus/.  One of the fields contained in the attribute table is the 
conservation pool elevation. This field is used to calculate reservoir fullness.   These 
values are used similar to the statistics tables for Stream Statistics and Climate Statistics.    
 
TSTable Climate (TSTable_climate): 
This table shows data from the North American Regional Reanalysis (NARR).  This table 
is reloaded once a month, and is populated by a time series of climate data, in monthly 
time steps.  The climate data is divided into the seven climate variables that have been 
downloaded.  Every month the data is refreshed with additional data information from the 
most recent month.  NARR data is downloaded in NetCDF form and then converted to a 
table using a variety of GIS processing tools.  This data can then be associated with the 
Counties layer and Climate Statistics table through the HydroID/FeatureID link.  This is 
described in table 4.9. 
 
Table 4.9: Time Series Fields Table for Climate Variables 
Field Name Field Description 
FeatureID Links to HydroID 
TSTime Date (Monthly Time Stamp) 
VID30 Potential Evaporation TSValue 
VID40 Precipitation TSValue 
VID50 Soil Moisture TSValue 
VID60 Temperature TSValue 
VID70 Subsurface Runoff TSValue 
VID80 Non-infiltrating Surface Runoff TSValue 
VID90 Evaporation TSValue 
 
TSTable Streams (TSTable_stream): 
This table shows time series data downloaded from USGS NWIS website for stream 
gages.  The data provided is real time data.  This table is populated using web services to 
81 
 
harvest data from the NWIS website.  The user provides the service with the desired 
gages and time span.  The time series table will contain FeatureID (which links to the 
HydroID of the Stream Gage layer), TSTime, and a time series value (this is defined by 
the Variable Code).  This is described in table 4.10. 
 
Table 4.10:  Time Series Fields Table for Stream Gauges 
Field Name Field Description 
FeatureID Links to HydroID 
TSTime Date (Realtime) 
VID20 Stream Gage TSValue (from NWIS) 
 
TSTable Lakes (TSTable_lakes): 
This table shows time series data downloaded from USGS NWIS website for reservoirs.  
The data provided is daily average data.  This table is populated using web services to 
harvest data from the NWIS website.  The user provides the service with the desired lake  
and time span.  The time series table contains FeatureID (which links to the HydroID of 
the Gaged Lakes layer), TSTime, and a time series value (this is defined by the Variable 
Code).  This is described in table 4.11. 
 
Table 4.11: Time Series Fields Table for Reservoirs 
Field Name Field Description 
FeatureID Links to HydroID 
TSTime Date (Daily Time Stamp) 
VID10 Reservoir Elevation TSValue (from NWIS) 
 
Climate Statistics (ClimateStat): 
This table stores the average values for each county by month of year for the four climate 
variables used in this project:  soil moisture, precipitation, temperature and potential 
evaporation.  For each month, the average value is derived from 30 years of the NARR 
82 
 
values.  For example, 30 years of data for the month of January are used to compute the 
average value that one expects to see in January.  This table is used for presenting current 
data as an anomaly value.  This is described in table 4.12. 
 
Table 4.12: Climate Statistics Table 
Field Name Field Description 
NAME County Name 
(3 character month abbreviation)_VID30 NARR Monthly 30 Year Average for Potential Evaporation 
(3 character month abbreviation)_VID40 NARR Monthly 30 Year Average for Total Precipitation 
(3 character month abbreviation)_VID50 NARR Monthly 30 Year Average for Soil Moisture 
(3 character month abbreviation)_VID60 NARR Monthly 30 Year Average for Temperature 
(3 character month abbreviation)_VID70 NARR Monthly 30 Year Average for Subsurface Runoff 
(3 character month abbreviation)_VID80 NARR Monthly 30 Year Average for Non-infiltrating Surface Runoff 
(3 character month abbreviation)_VID90 NARR Monthly 30 Year Average for Evaporation 
FeatureID Unique Identifier for the Feature 
 
Stream Statistics (StreamStat): 
This table shows the statistics for each stream gage.  These values are established from 
the National Hydrography Dataset.  This table is used for presenting current data as an 
anomaly value.  This is described in table 4.13. 
 
83 
 
Table 4.13: Stream Statistics Table 
Field Name Field Description 
Station_NM Gage Station Name 
Min Minimum Flow 
P1, P5, P10, P20, P25, P30, P40, P50, P60, 
P70, P80, P90, P95, P99 Percentile of flow 
Max_ Maximum Value 
AVE Average 
STD Standard Deviation 
FeatureID Unique Identifier for the Feature 
 
*A lake average table does not exist because lake elevation statistics are based on the 
conservation pool elevation. 
 
Lake Rasters (raster_lakes); 
The raster catalog is used to index the depth raster of the lakes of the Trinity River Basin.  
New rasters displaying the depth of reservoirs are created daily through scheduled tasks.  
The raster catalog is used as an archive. 
 
Dynamic Variables (Dynamic_variables); 
This table describes variables that change through time and is used in Time Series tables.  
This is described in table 4.14. 
84 
 
 
Table 4.14: Dynamic Variables Table 
Field Name Field Description 
VariableID Unique Numerical Identifier of the 
Variable 
VarName Variable Name 
VarCode Variable Code, Identifier for TStime Tables 
VarUnits Units of Measurement 
VarDesc Variable Description 
 
 
4.5 Functions  
 
 The functions of IDIS bring the system full-circle. Through the functions users 
are able to access the drought data they need.  The design goals of these tasks is to 
develop systems by which users can easily access drought related information, make 
comparisons between drought variables and look at anomalies in the data over a period of 
time.  By enabling these tasks for users, users are able to gain a better, more complex 
comprehension of actual drought events in the Trinity River Basin.  Currently, the IDIS is 
still in development, therefore not all of the functions have been completely programmed.  
However, the following describes the tasks that the IDIS can currently achieve, and the 
tasks that the IDIS will be able to achieve upon its completion.   
 
Functions Developed 
Reservoir Table- Everyday the reservoir elevations for the previous day are downloaded 
from NWIS using web services and scheduled tasks described in sections 3.6 and 3.7.  
The web service returns an XML file that is then converted to an html file.  The 
conversion is managed by an XLST style sheet through ArcGIS server manager.  Once 
the reservoir table is in html format it can be viewed on the IDIS webpage. 
85 
 
 
Reservoir Depth Rasters- Every day the reservoir elevations for the previous day are 
downloaded from NWIS using web services and scheduled tasks described in sections 3.6 
and 3.7.  The web service returns a CSV file of the reservoir elevations.  IDIS contains a 
scheduled task python code that uses the CSV file elevation values in a raster math tool to 
create rasters of the current reservoir depth.  The elevations are subtracted from a raster 
displaying the bathymetry of the lake.  The reservoir bathymetry file is created by making 
a raster of the reservoir TIN.  More detailed information about this can be found in 
sections 3.3. 
 
Functions in Development but Not Yet Complete 
Climate Data Graphs and Tables- One of the functions currently in development for the 
IDIS is the climate variable data retrieval.  Through this tool the user will be able to 
download climate data as a table or view graphs of the data in relation to time.  The 
following steps describe the process this tool will go through to return climate data to the 
client. 
1. User clicks on the county> county is selected 
o The county file contains county name, Hydro ID, average soil moisture, 
average precipitation, average temperature and average potential 
evaporation (area, and x and y centroids are also fields for the counties 
files, but they don?t play a role in this procedure) 
2. User is prompted for parameters and date  
o The parameters are soil moisture, precipitation, temperature, and potential 
evaporation 
o User chooses desired format for data: 
? download files (showing date and climate variables values)  
? graph showing values over time compared to normal 
? graph showing percent from normal 
3. Parameters and date range are inputted for desired parameters and date range 
86 
 
4. The server accesses tables of data containing the climate variable data and/or the 
anomaly data (the development of the climate data tables is discussed in section 
3.5 and 4.4). 
5. Depending on the user?s choice: 
o A table is can be downloaded by the user (this is accomplished by using 
caching) 
o Table is made to a graph 
? The code for creating the graphs is similar to the code using in the 
CUASHI HIS. 
? Graphs are returned 
 percent normal vs. time 
 actual value vs. time 
 
 
Stream Flow Graphs and Tables- The IDIS also allows users to access stream data as a 
downloadable table or as a graphs.  The user can zoom into (at least 1:200000) an area on 
the map to see all of the flowlines in the Trinity River Basin.  Once zoomed in the user 
can select a section of stream gauge.  Once the stream is selected the user will be 
prompted for a time span.  Data will be collected for that stream section from the USGS 
NWIS and returned to the user as a downloadable table, a viewable graph of actual 
values, or a viewable graph of values as a percent of major stage flow (or typical flow).  
The data will be downloaded from:  
http://waterdata.usgs.gov/tx/nwis/current?type=flow&group_key=basin_cd&search_site_
no_station_nm=Trinity#top_of_page.  The framework for accomplishing this task is as 
follows: 
 Files used for the stream gauges:  points based on USGS latitude and longitude, 
appended to include USGS #.  This file also includes average flow values for each 
particular gauge. 
 User selects a stream gauge 
87 
 
o User is prompted for a time span 
o User choose to download a table or view a graph 
 Selected stream gauge field is exported with the time span to a new table 
 Web services uses new table to obtain data from USGS NWIS 
 Data is returned and appended to the table 
o Field Calculator is used to find the % of stage flow for a stream 
 User can be returned a table if chosen initially 
 Table is transformed to XY data  
 Event data is exported to create a feature 
 User is returned a graph if chosen initially 
o percent of stage vs. time 
o Actual stream flow values vs. time 
 
Reservoir Data- Reservoir* data can also be accessed by zooming in to the map (at least 
1:200000).  Once the user has zoomed in they will be able to view lake access points and 
lake depth (more about this can be read in the first portion of this section, under depth 
raster).  Additionally, the user can retrieve lake elevation data over time by selecting the 
reservoir.  Using web services, the server will obtain lake elevation from USGS NWIS 
and report it back to the user as a downloadable table, a viewable graph of actual values 
or a viewable graph of the reservoir percent full (as compared to the conservation pool).  
This data will be downloaded from:  
http://waterdata.usgs.gov/tx/nwis/current/?type=lake&group_key=basin_cd.  The steps to 
accomplish this task are as follows: 
88 
 
 
 Files used for the Reservoir:  NHD appended to include USGS #, latitude and 
longitude values for a point within each reservoir, and conservation pool elevation 
 User selects a reservoir and zooms to reservoir 
o User is prompted for a time span 
o User choose to download a table or view a graph 
 Selected reservoir field is exported with the time span to a new table 
 Web services uses new table to obtain data from USGS NWIS 
 Data is returned and appended to the table 
o Field Calculator is used to find the % full of the reservoir 
 User can be returned a table if chosen initially 
 Table is transformed to XY data  
 Event data is exported to create a feature 
 User is returned a graph if chosen initially 
o % of stage vs. time 
o Actual stream flow values vs. time 
 
*Reservoirs with Data to be used: 
Lake Arlington 
Trigg Lake 
Joe Pool Lake 
Mountain Creek Lake 
Ray Roberts Lake 
Lewisville Lake 
Grapevine Lake 
Lavon Lake 
Lake Ray Hubbard 
New Terrell City Lake 
Cedar Creek Reservoir 
89 
 
Navarro Mills Lake 
Lake Waxahachie 
Bardwell Lake 
Halbert Lake 
Lost Creek Reservoir 
Bridgeport Reservoir 
Lake Worth 
Eagle Mountain Reservoir 
Lake Weatherford 
Benbrook Lake 
Richland-Chambers Reservoir 
Houston County Lake 
Livingston Reservoir 
 
4.6 Future Work 
 
The IDIS is still in development, and will mostly likely to continue to be 
developed over a long period of time.  As mentioned in the literature review (section 2.5), 
the technology for systems like the IDIS is relatively new.  As the Trinity River Basin 
IDIS continues to be developed other systems continue to be developed (such as the 
NIDIS), the technology will increase giving way to a more dynamic and useful IDIS.  
The previous section mentioned task for this prototype still being constructed.  These are 
a priority for the prototype system.  However, there are additional secondary tasks that 
would propel the IDIS further in its goals.  The additional future work is listed below. 
 
Incorporation of Additional Data Sources- More and more data resources are being 
developed and are being made available online.  As internet services and web services 
continue to develop incorporating these features will become a possibility.  Some of the 
additional data sources that could included in the IDIS are vegetation stress related 
90 
 
remote sensing data, improved precipitation data, atmospheric cycle data (from ENSO 
and TOA) land cover and land change data.  As more and more data becomes accessible 
to IDIS it should be incorporated based on its usefulness to the system as a whole. 
 
Bathymetry Interpolation- The TWDB is in the process of developing a method for 
improving lake bathymetry.  This process is being developed by Dr. Jordan Furnans.  
When the TIN of a reservoir is generated it creates a contour bubbling affect around the 
boundaries of the reservoir.  This is due to the process used in TIN development, which is 
based on the points closest to a sounding point.  By interpolating between the sounding 
points of the reservoir survey a better estimate of the lake bathymetry can be provided.  
This gives an improved bathymetry for the reservoir, particularly near the shoreline.  It 
also allows for a better estimate of the amount of water in a reservoir.  This was tested 
successfully on Grapevine.  As this process is developed it would be useful to apply it to 
lakes featured in the IDIS. 
 
Animation of Rasters- The IDIS is currently designed to display only rasters that are 
current.  In the future, as ArcGIS is developed further it would be useful to incorporate 
the ability to animate raster through time.  With this capability the IDIS could show the 
fluctuations in the US Drought Monitor, Corp Moisture Index and Drought Impact report 
(as well as any raster data that might be incorporated to IDIS in the future).  Viewing the 
fluctuations of this data through time will allow users to compare drought indices with 
climate and hydrologic data overtime. 
 
Animation of Features- Animating the features of IDIS could also be a useful tool for 
drought visualization. 
 
91 
 
Chapter 5. Conclusions 
 
Drought is a persistent and costly problem for all communities.  Texas is no 
exception.  Droughts in Texas have costly impacts environmentally, socially and 
economically.  However, the complex definition of drought and its dynamic nature makes 
it a difficult problem to mitigate.  There is no clear definition of when a drought begins or 
when a drought ends, Characteristics of drought vary from region to region.  Droughts 
occur in three consecutive stages.  First a meteorological drought occurs, in which there 
is a decrease in precipitation.  This propagates to an agricultural drought, in which soil 
moisture decreases.  Finally, the drought progresses to a hydrologic drought, in which 
surface water availability is impacted by the drought conditions.  The multiple 
dimensions of drought make it difficult to characterize, and even harder to mitigate.  The 
IDIS aims to serve as a tool for decision makers, researchers and the general public.  Its 
primary focus is to improve the understanding of drought events and drought conditions 
by allowing integrated several dimensions of drought related observational data. 
Ultimately, the IDIS will be a tool for public information.  The IDIS is a server 
that provides drought education information, hosts drought related data, hosts geographic 
data (the Hydro Basemap), and uses web services to obtain data from other sources.  The 
drought education portion of the IDIS provides links to educational drought documents 
and websites that relate drought information.  The main aspect of the IDIS is the 
geographic information component.  The server uses ArcGIS as a tool for visualizing the 
data and data integration.   
The geographic information system portion of the IDIS can be divided into two 
major portions:  static and dynamic.  The static information is contained within a Hydro 
Basemap.  The Hydro Basemap serves as a reference for drought information.  The 
features class layers of the Hydro Basemap include political boundaries, roads and 
hydrologic features.  The hydrologic features come from the NHDPlus.   The purpose of 
the Hydro Basemap is to create a backdrop to the dynamic drought information.  The 
Basemap itself contains no drought related information.   
92 
 
The dynamic features, or space-time layers, are joined to drought data.  The 
space-time layers are contained within a single geodatabase comprised of feature layer 
classes, time series data tables, and variable information tables.  The information 
contained within the layers of the space-time geodatabase allows the layers to be 
associated with temporal drought information.  The data joined to the space-time layers 
depicts current drought conditions for the region.  Some of the layers allow for user 
interactive investigation into drought.  The data sources and data variables were chosen to 
represent the environmental shift from a meteorological drought, to an agricultural 
drought, to a hydrologic drought.  Accessing data from disparate sources gives a full view 
of the current stage of drought, and how it propagates through time.   
The drought information is obtained from data servers around the country.  The 
datasets accessed by the IDIS include hydrologic data from NWIS, climate data from 
NARR and drought indices from the Drought Monitor.  These datasets where chosen 
because they describe the different stages of drought intensity.  The information may 
come in as either an xml table or a raster image.  The IDIS intakes these forms of data 
and integrates them into a similar format so that they may be attached to a geographic 
feature and accessed by a user.  This is accomplished through a system of unique 
identifiers that link data to features.  All space-time features have a HydroID that allows 
them to be linked to data sources by their unique identifiers, FeatureIDs.  For instance, 
NWIS uses unique identification number for all gauges.  IDIS refers to these numbers as 
the Feature IDs.  The gauges within the gauge space-time layer are also given an 
identifier, a HydroCode.  The HydroCode of a gauge matches the FeatureID with the 
NWIS data, allowing the NWIS gauge data to be matched with its corresponding feature 
on the map. The IDIS integrates data from several formats.  These formats include WMS 
files, NetCDF and XML tables.  
The IDIS server uses web services to obtain data from other data servers around 
the country.  The two methods employed by the IDIS web services are:  as prompted 
from the user and as a regularly scheduled task.  User prompted web services have the 
ability to retrieve data quickly.  The user is asked to input data to specify their request-- 
93 
 
such as time period and variable.  User prompted web services are compatible for 
accessing NWIS data.  Access to NARR data and US Drought Monitor data is more 
complex.  This data is automatically and regularly, downloaded by a program.  The 
program functions as a scheduled task.  Through this task the data is obtained and 
integrated without user interaction.  These tasks typically take place during the early 
morning, when user activity is at the lowest.   
The Texas IDIS prototype for the Trinity River Basin is still in development.  
There are programming obstacles to yet to be overcome.  Additionally, improvement to 
the IDIS will be an on-going effort as the technology evolves and more data is available.  
However, the work that has been accomplished to this point has created a structure on 
which future integrated drought information systems can be based.  The current prototype 
establishes a format for which users can access data from several resources to analyze 
drought conditions over time.  In time the IDIS has the potential to be a useful tool for 
decision makers, the public and researchers. 
As the IDIS continues to develop, it will become progressively more useful.  Its 
next stage of development will likely involve the completion of the functions mentioned 
in section 4.5.  Once the graphing tasks are able to be included in the IDIS, the user?s 
ability to analyze drought status and drought propagation will be greatly increased.  The 
work outlined in section 4.6 will also increase the usefulness of IDIS as a tool.  Adding 
additional data sources will allow the user to obtain a thorough comprehension of drought 
impacts.  The bathymetry interpolation techniques will be an aid to residences, boats, and 
waterside business owners, since the improved techniques will allow users to view more 
accurate estimates of reservoir depth.   
Finally, the animation of rasters and features is integral to the IDIS long-term 
development.  The graphing feature is a useful tool.  However, graphs can sometimes be 
deceiving or difficult to comprehend, particular for someone not working in a science- 
related field.  Animations offer an effective visual display, which will help IDIS clients to 
gain a full understanding of the drought situation.  
94 
 
Provided data maintenance and effective program updates, the IDIS will be a 
useful tool for the state of Texas.  It will be applicable for decision makers, researchers 
and the public at large.  Today, with the allowances of technology, the IDIS is able to 
integrate disparate data from data servers around the US.  This creates a more accurate 
image of drought through combining hydrologic data and climate data, as well as drought 
indices.  With a tool like this decisions makers, planners and researchers may be able to 
make further headway in the battle to mitigate drought. 
95 
 
Appendices
96 
 
Appendix A 
 
List of Abbreviations 
 
IDIS  Integrated Drought Information System 
TNRIS  Texas Natural Resource Information System 
CRWR Center of Research in Water Resources 
UT  University of Texas - Austin 
TWDB Texas Water Development Board 
ESRI  Environmental Systems Research Institute, Inc. 
GIS  Geographic Information System 
USGS  United States Geographic Survey 
NWIS  National Water Information System 
NOAA  National Oceanic Atmospheric Administration 
NCEP  National Centers for Environmental Prediction 
NARR  North American Regional Reanalysis 
CPC  Climate Prediction Center 
CMI  Crop Moisture Index 
NDMC National Drought Mitigation Center 
NHDPlus National Hydrography Dataset Plus 
StratMap Strategic Mapping Program 
WMS  Windows Mapping Service 
NIDIS  National Integrated Drought Information System 
FEMA  Federal Emergency Mapping Agency 
ENSO  El Nino / Southern Oscillation 
NESDIS National Environmental Satellite, Data Information Service 
TOGA  Tropical Ocean Global Atmosphere 
NAO  North Atlantic Oscillation 
IPCC  Intergovernmental Panel on Climate Change 
97 
 
PDSI  Palmer Drought Severity Index 
NCDC  National Climate Data Center 
NDPC  National Drought Policy Commission 
DPC  Drought Preparedness Council 
SPI  Standard Precipitation Index 
KBDI  Keetch-Byram Drought Index 
TFS  Texas Forestry Service 
VT  Vegetation and Temperature Condition Index 
TAEX  Texas Agricultural Extension Service 
TDA  Texas Department of Agriculture 
USDA  U.S. Department of Agriculture  
TCEQ  Texas Commission on Environmental Quality 
WMO  World Meteorological Organization 
NADM North American Drought Monitor 
NHD  National Hydrography Dataset 
NHDPlus National Hydrography Dataset Plus 
EPA  Environmental Protection Agency 
NED  National Elevation Dataset 
NLCD  National Land Cover Dataset 
GPS  Global Positioning System 
DGPS  Differential Global Positioning System 
TIN  Triangulated Irregular Network 
NOMADS National Operational Model Archive and Distribution System 
THREDDS Thematic Real-time Environmental Distribution Data Service 
TDS  THREDDS Data Server 
UTC  Coordinated Universal Time 
NWISW National Water Information System Website 
NetCDF Network Common Data Form 
UCAR  University Corporation for Atmospheric Research 
98 
 
CSV  Comma Separated Values 
XML  Extensible Markup Language  
XSLT  XML Style Sheet 
HIS  Hydrologic Information System 
CUASHI Consortium of Universities of the Advancement of Hydrologic Science, 
Inc. 
SOAP  Simple Object Access Protocol 
TRA  Trinity River Authority 
99 
 
Appendix B 
 
NARR Data 
 
Potential 
Evaporation 
(mm/3hrs) 
Evaporation 
(mm/3hrs) 
Precipitation 
(mm/3hrs) 
Soil 
Moisture 
(mm in the 
top 100cm) 
Subsurface 
Runoff 
(mm/3hrs) 
Surface Runoff 
(mm/3hrs) 
Temperature 
K 
Average 0.756829117 0.30921763 0.354108022 506.4666351 0.001709882 0.019119473 293.468674 
Standard 
Deviation 0.325762707 0.177333186 0.234286005 104.3723073 0.012816353 0.02443026 7.265588735 
Minimum -0.00718313 
-
0.01615720056 4.86409E-05 242.5140076 0.00000000000 0.00000000000 274.4129944 
Median 0.759895504 0.26535549760 0.312595502 497.769989 0.00000000000 0.00781250000 294.1730042 
Max 1.718330026 1.14924001694 1.929720044 1000.01001 0.35937500000 0.25000000000 307.2850037 
1st Quartile 0.463270999 0.162100002 0.180040002 435.6715012 0 0 287.1640015 
2nd Quartile 0.759895504 0.265355498 0.312595502 497.769989 0 0.0078125 294.1730042 
3rd Quartile 0.985358 0.45617874 0.48489251 575.1767426 0 0.0234375 300.1262589 
4th Quartile 1.718330026 1.149240017 1.929720044 1000.01001 0.359375 0.25 307.2850037 
 
100 
 
 
 
NARR by Month 
Month 
Potential 
Evaporation 
(mm/3hrs) 
Evaporation 
(mm/3hrs) 
Precipitation 
(mm/3hrs) 
Soil Moisture 
(mm in the top 
100cm) 
Subsurface 
Runoff 
(mm/3hrs) 
Surface 
Runoff 
(mm/3hrs) 
Temperature 
K 
1 0.35 0.24128836 0.31 549.41 0.000242305 0.012342869 283.00 
2 0.45 0.229924291 0.38 575.23 0.000587406 0.019443143 284.94 
3 0.63 0.234356521 0.36 583.53 0.000815026 0.017901202 289.05 
4 0.85 0.233075547 0.32 551.23 0.001269 0.013250057 293.22 
5 0.98 0.26347091 0.49 519.76 0.001651827 0.022147237 297.54 
6 1.07 0.300639653 0.46 500.04 0.00382827 0.022608049 300.67 
7 1.19 0.330810872 0.27 462.84 0.007493485 0.019155519 302.42 
8 1.14 0.409654071 0.24 437.64 0.002254424 0.018871944 302.74 
9 0.91 0.435647649 0.31 440.07 0.001481682 0.02686168 299.61 
10 0.69 0.425748504 0.39 453.15 0.000404095 0.029449313 294.63 
11 0.47 0.330584375 0.37 487.69 0.000248129 0.013455649 288.73 
12 0.32 0.262569093 0.34 524.12 6.60832E-05 0.013275378 283.93 
 
101 
 
 
Month 
Potential 
Evaporation 
Monthly 
Total 
Evaporation 
Monthly 
total 
Precipitation 
Monthly 
Total 
1 86.62618 59.83951 77.86298 
2 102.5576 51.96289 85.77361 
3 155.8799 58.12042 88.20458 
4 203.8854 55.93813 76.60705 
5 241.9746 65.34079 121.8302 
6 255.8272 72.15352 111.5201 
7 294.6579 82.0411 67.12485 
8 282.2831 101.5942 60.7364 
9 219.4779 104.5554 73.54609 
10 170.1182 105.5856 97.92048 
11 111.6082 79.34025 88.88029 
12 80.20371 65.11713 83.52337 
102 
 
 
 
103 
 
 
104 
 
 
 
105 
 
 
106 
 
 
 
 
107 
 
 
108 
 
NARR by Year 
 
Potential 
Evaporation 
(mm/3hrs) 
Evaporation 
(mm/3hrs) 
Precipitation 
(mm/3hrs) 
Soil Moisture 
(mm in the top 
100cm) 
Subsurface 
Runoff 
(mm/3hrs) 
Surface 
Runoff 
(mm/3hrs) 
Temperature 
K Temperature F 
1979 0.70 0.347336824 0.40 536.10 0.002124453 0.020062365 291.95 67.64 
1980 0.85 0.259884744 0.27 424.10 0.000565378 0.011701617 293.57 70.55 
1981 0.75 0.313374996 0.34 438.05 0 0.013620478 293.35 70.16 
1982 0.75 0.288320279 0.36 455.70 0 0.013586212 293.38 70.21 
1983 0.74 0.315172755 0.32 486.35 0.000616776 0.014391452 292.22 68.12 
1984 0.78 0.283902099 0.32 472.77 0.000633909 0.013620481 292.82 69.21 
1985 0.75 0.308159542 0.33 526.69 0.000171327 0.016738624 292.97 69.48 
1986 0.73 0.329142265 0.37 499.26 0.000119929 0.017269739 293.60 70.61 
1987 0.77 0.334254617 0.33 504.10 0.001327004 0.015195128 293.93 71.21 
1988 0.90 0.271305509 0.26 417.63 0.000776681 0.010942069 296.44 75.73 
1989 0.71 0.328986774 0.36 498.27 0.000496849 0.020473555 292.39 68.43 
1990 0.75 0.30972751 0.42 509.23 0.001576206 0.018229169 293.99 71.31 
1991 0.70 0.328412032 0.47 555.63 0.004146113 0.023745898 293.31 70.09 
1992 0.69 0.377762249 0.40 582.61 0.011958622 0.029331152 293.04 69.61 
1993 0.74 0.319660216 0.35 552.32 0.000856634 0.02134732 292.51 68.65 
1994 0.71 0.316410723 0.41 529.33 0.000239857 0.016224644 293.37 70.19 
1995 0.71 0.373454555 0.34 568.64 0.003083883 0.027069635 293.36 70.18 
1996 0.80 0.241523688 0.32 472.95 0 0.011119106 293.46 70.35 
1997 0.69 0.369894577 0.42 574.61 0.003255212 0.029262622 292.55 68.71 
1998 0.80 0.266330123 0.35 524.32 0.001524808 0.013654742 294.47 72.17 
1999 0.80 0.320863069 0.26 500.68 0.000685307 0.020662014 294.33 71.92 
2000 0.83 0.247328819 0.38 452.43 0 0.012712446 293.78 70.94 
2001 0.72 0.29948453 0.40 560.57 0.008566342 0.020353625 293.09 69.69 
2002 0.73 0.325738403 0.37 523.65 0.000753838 0.01632744 293.09 69.69 
2003 0.77 0.305503456 0.29 516.89 0.003169548 0.01644737 293.67 70.73 
2004 0.67 0.316075168 0.42 518.36 5.1398E-05 0.030907362 293.61 70.63 
2005 0.82 0.305756487 0.24 493.28 0.001970259 0.024928052 294.28 71.84 
2006 0.89 0.219034495 0.30 440.30 0 0.016995615 294.80 72.76 
2007 0.76 0.307399639 0.35 505.15 0.001754056 0.018550499 293.50 70.42 
109 
 
 
NARR Annual Values 
Year Potential Evaporation Total Annual Values (mm) Evaporation Total Annual Values (mm) Precipitation Total Annual Values (mm) 
1979 2058.024 1014.918 1174.335 
1980 2477.759 759.3832 797.8771 
1981 2188.982 915.6817 1000.149 
1982 2202.918 842.4719 1044.643 
1983 2169.887 920.9348 945.9108 
1984 2269.437 829.5619 939.9948 
1985 2186.584 900.4422 956.9046 
1986 2144.782 961.7537 1081.971 
1987 2255.507 976.692 978.6484 
1988 2633.582 792.7547 754.5631 
1989 2076.443 961.2994 1047.942 
1990 2185.336 905.0238 1219.159 
1991 2045.995 959.62 1373.228 
1992 2022.06 1103.821 1157.23 
1993 2163.269 934.0472 1035.536 
1994 2077.44 924.5521 1200.774 
1995 2077.494 1091.234 990.786 
1996 2329.11 705.7322 939.0192 
1997 2002.831 1080.832 1230.767 
1998 2340.686 778.2166 1036.098 
1999 2352.14 937.5619 764.8313 
2000 2412.083 722.6948 1112.627 
2001 2096.268 875.0938 1175.574 
2002 2133.587 951.8076 1073.11 
2003 2245.208 892.6811 849.155 
2004 1946.15 923.5716 1235.382 
2005 2407.74 893.4205 701.9404 
2006 2594.39 640.0188 881.1687 
2007 2221.129 898.2217 1027.005 
 
110 
 
 
 
 
111 
 
 
 
 
112 
 
 
113 
 
 
  
 
114 
 
 
115 
 
 
116 
 
 
117 
 
 
 
 
118 
 
 
 
 
119 
 
 
120 
 
 
 
 
121 
 
Appendix C 
 
Python North American Regional Reanalysis: 
 
Python code is used to create the time series data files for the North American Regional 
Reanalysis (NARR).  The NARR provides climate data for North America from 1978 to 
the present.  The climate variables used in the IDIS are temperature, potential 
evaporation, soil moisture and precipitation.  These values are given as monthly averages.  
A monthly scheduled task retrieves the previous monthly data.  NARR data is available 
in NetCDF format.  The follow script converts the NetCDF files to a table format and 
averages the climate variables by location.  The python code is explained in each step. 
 
In the box below the basics of the program are established.  The established code 
modules are loaded, the ArcGIS tools are loaded, and the program is told to overwrite the 
current files in the folder. 
 
# --------------------------------------------------------------------------- 
# NARR Conversion 
# Created on: Fri Feb 24 2008 
#   (generated by ArcGIS/ModelBuilder and Virginia Smith) 
# --------------------------------------------------------------------------- 
 
# Import system modules 
import sys, string, os, arcgisscripting, glob, time 
 
# Create the Geoprocessor object 
gp = arcgisscripting.create() 
 
#Set the input workspace 
122 
 
root = 'O:\\GISDATA\\codes\\NARR\\NetCDF\\' # one specific folder 
outputFolder = 'O:\\GISDATA\\codes\\NARR\\Outputs\\'  
 
#Load required toolboxes... 
gp.AddToolbox("C:/Program Files/ArcGIS/ArcToolbox/Toolboxes/Multidimension 
Tools.tbx") 
gp.AddToolbox("C:/Program Files/ArcGIS/ArcToolbox/Toolboxes/Data Management 
Tools.tbx") 
gp.AddToolbox("C:/Program Files/ArcGIS/ArcToolbox/Toolboxes/Analysis Tools.tbx") 
 
gp.overwriteoutput=1 
 
In this modules the NetCDF files downloaded from NARR are looped through.  This 
module of code also renames each NetCDF file to be named by the year and month that it 
represents. 
 
for folder in glob.glob(root): 
    layer_file_list = "" 
    #Loop through the list of netcdf files 
    for file in glob.glob(root + 'narrmon-a_221_*.nc'): 
        folder, file_name = os.path.split(file) 
        file_name = file_name.split("\\")[-1].split("_")[2] 
 
        # Local variables... 
        file_name = file_name+".lyr" 
 
The module of code below converts each NetCDF file to a feature layer.  The feature 
layer created is a grid of point data representing the four climate variables used.  A 
feature is created for every month of every year from 1978 to the previous month. 
123 
 
 
        # Process: Make NetCDF Feature Layer... 
        gp.MakeNetCDFFeatureLayer_md(file, 
"Potential_evaporation;Soil_moisture_content;Temperature;Total_precipitation;time", 
"x", "y", outputFolder + "\\" + file_name, "y;x", "", "", "", "BY_VALUE") 
         
        # Process: Save To Layer File... 
        gp.SaveToLayerFile_management(outputFolder + "\\" + file_name, outputFolder + 
"\\" + file_name) 
 
The module of code below links each new feature layer with the layer counties from the 
dynamic geodatabase.  The new feature layers are linked to the counties layer through a 
spatial join.  The nature of this spatial join aggregates the NARR grid points contained 
within each county. 
124 
 
 
        # Local variables... 
        Narr_counties_shp = outputFolder + "\\" + file_name.replace("-
","_").replace(".","_")+".shp" # Remove hyphens and periods from the file name.  These 
are invalid characters for the upcoming spatial join function when creating our output 
shapefile. 
        NARR = outputFolder + "\\" + file_name 
        Counties = 
"O:\\GISDATA\\mxds\\SpatialTimeData\\SpatialTimeDataLayers.gdb\\counties" 
 
        # Process: Spatial Join... 
        gp.SpatialJoin_analysis(Counties, NARR, Narr_counties_shp, 
"JOIN_ONE_TO_ONE", "KEEP_ALL", "NAME NAME true true false 50 Text 0 0 
,First,#,O:\\GISDATA\\mxds\\SpatialTimeData\\SpatialTimeDataLayers.gdb\\counties,N
AME,-1,-1;climate_re climate_region true true false 2 Short 0 0 
,First,#,O:\\GISDATA\\mxds\\SpatialTimeData\\SpatialTimeDataLayers.gdb\\counties,n,
-1,-1;FeatureID FeatureID true true false 4 Long 0 0 
,First,#,O:\\GISDATA\\mxds\\SpatialTimeData\\SpatialTimeDataLayers.gdb\\counties,F
eatureID,-1,-1;", "INTERSECTS", "0 Unknown") 
 
As the spatial join files are created they are add to a file list.  Then, the files from the 
layer list are merged into one feature file. 
125 
 
     
    layer_file_list = layer_file_list + "'" + Narr_counties_shp + "';" 
         
    # Local variables... 
    Output_Dataset = 
"O:\\GISDATA\\mxds\\SpatialTimeData\\SpatialTimeDataLayers.gdb\\NARR_counties" 
    layer_file_list = layer_file_list.rstrip(";") 
    Input_Datasets = layer_file_list 
        
    # Process: Merge... 
    gp.Merge_management(Input_Datasets, Output_Dataset, layer_file_list) 
 
  The files are then tailored to a format compatible for supporting time series.  The 
climate variable fields are renamed by their VarID, and unnecessary fields are deleted. 
 
# Local variables...   
  NARR_counties = 
"O:\\GISDATA\\mxds\\SpatialTimeData\\SpatialTimeDataLayers.gdb\\NARR_counties" 
 
    # Process: Add Field... 
    gp.AddField_management(NARR_counties, "FeatureID", "LONG", "", "", "", 
"FeatureID", "NULLABLE", "NON_REQUIRED", "") 
 
    # Process: Calculate Field... 
    gp.CalculateField_management(NARR_counties, "FeatureID", "[HydroID]", "VB", "") 
 
    # Process: Add Field (2)... 
    gp.AddField_management(NARR_counties, "VID30", "DOUBLE", "", "", "", 
"VID30", "NULLABLE", "NON_REQUIRED", "") 
126 
 
 
    # Process: Calculate Field (2)... 
    gp.CalculateField_management(NARR_counties, "VID30", "[Potential_]", "VB", "") 
 
    # Process: Add Field (3)... 
    gp.AddField_management(NARR_counties, "VID50", "DOUBLE", "", "", "", 
"VID50", "NULLABLE", "NON_REQUIRED", "") 
 
    # Process: Calculate Field (3)... 
    gp.CalculateField_management(NARR_counties, "VID50", "[Soil_moist]", "VB", "") 
 
    # Process: Add Field (4)... 
    gp.AddField_management(NARR_counties, "VID60", "DOUBLE", "", "", "", 
"VID60", "NULLABLE", "NON_REQUIRED", "") 
 
    # Process: Calculate Field (4)... 
    gp.CalculateField_management(NARR_counties, "VID60", "[Temperatur]", "VB", "") 
 
    # Process: Delete Field (2)... 
    gp.DeleteField_management(NARR_counties, 
"Join_Count;OBJECTID_1;NAME;climate_re;Shape_Leng;Shape_Area;HydroID;Potent
ial_;Soil_moist;Temperatur") 
 
 
 
127 
 
Appendix D 
 
Python Code for Creating Depth Rasters Daily 
 
# --------------------------------------------------------------------------- 
# Depth.py 
# Created on: Mon Feb 04 2008 10:55:11 AM 
#   (generated by ArcGIS/ModelBuilder and Virginia Smith) 
# --------------------------------------------------------------------------- 
 
# Import system modules 
import sys, string, os, arcgisscripting, csv 
 
# Create the Geoprocessor object 
gp = arcgisscripting.create() 
 
# Check out any necessary licenses 
gp.CheckOutExtension("3D") 
 
# Load required toolboxes... 
gp.AddToolbox("C:/Program Files/ArcGIS/ArcToolbox/Toolboxes/3D Analyst 
Tools.tbx") 
 
gp.overwriteoutput = 1 
128 
 
 
import glob 
for file in glob.glob("O:\\GISData\\bathy\\trial\\b*.aux"): 
    folder, file_name = os.path.split(file) 
    file_name = file_name.strip('b') 
    file_name=file_name.strip('.aux') 
    reader=csv.reader(open("reservoir_eleveation.csv","rb")) 
    for row in csv.reader (['id_num,name, id_name, elev']): 
        for id_num, name, id_name, elev in reader: 
            
 
 if id_num == file_name: 
                # Local variables... 
                Output_raster = ?O:\\GISData\bathy\\trial\\output\\depth'+file_name 
                Input_raster = ?O:\\GISData\\bathy\\trial\\b'+file_name 
                Constant_value_2 = elev 
 
                # Process: Minus... 
                gp.Minus_3d(Input_raster, Constant_value_2, Output_raster) 
129 
 
Appendix E 
 
Drought Education 
 
Table of Contents 
 
What is Drought? 
Why do Droughts Occur? 
How are Droughts Predicted? 
What are the Impacts of Drought? 
How is Drought Severity Defined? 
What Can be done to Combat Drought? 
Drought Links 
References 
 
 
What is Drought? 
 
"We have no good definition of drought. We may say truthfully that we 
scarcely know a drought when we see one. We welcome the first clear day 
after a rainy spell. Rainless days continue for a time and we are pleased to 
have a long spell of such fine weather. It keeps on and we are a little 
worried. A few days more and we are really in trouble. The first rainless 
day in a spell of fine weather contributes as much to the drought as the 
last, but no one knows how serious it will be until the last dry day is gone 
and the rains have come again... we are not sure about it until the crops 
have withered and died." 
Drought and Its Causes and Effects, Tannehill (1947) [1]   
 
The word ?drought? can imply several things.  However, the most basic definition of 
drought is a deficiency in precipitation for an extended period of time. [2]   It?s difficult to 
establish when a drought is truly on setting.  When do a string of sunny days turn into a 
drought?  The question can be answered differently according to the type of drought that 
is being analyzed (see the Types of Drought Link below). [3]    The range of definitions 
for droughts can be defined by rainfall amounts, vegetation conditions, agricultural 
productivity, soil moisture, levels in reservoirs, stream flow or economic impacts.  It 
results in water shortages for residents, farmers, industry, and environments. [1]   Drought 
is a natural reoccurring hazard of nature, which varies by region.  There isn?t a standard 
number of sunny days that define drought; drought must be defined relative to ?normal? 
water balance for that region.[3]    
 
Types of drought 
130 
 
 
Back to the top 
 
Why Do Droughts Occur? 
 
There is still much debate over why droughts occur.  Droughts are reoccurring events that 
are largely the result of fluctuations in climate.  They are caused by a decrease in the 
amount of water in a water balance for a given region.  There are so many variables that 
are believed to contribute to the onset of drought that science has yet to isolate the exact 
causes of drought.  However, science has been able to identify several factors that induce 
drought.  Some of the factors scientists study are listed below (from the National Drought 
Mitigation Center). [3]    
Global Weather Patterns 
A great deal of research has been conducted in recent years on the role of 
interacting systems, or teleconnections, in explaining regional and even global 
patterns of climatic variability. These patterns tend to recur periodically with 
enough frequency and with similar characteristics over a sufficient length of time 
that they offer opportunities to improve our ability for long-range climate 
prediction, particularly in the tropics. One such teleconnection is the El 
Ni?o/Southern Oscillation (ENSO). [3]    
 
Figure 1:  A satellite image produced by NOAA to show the impacts of El Nino on 2002 on global 
weather systems. [4]    
High Pressure 
The immediate cause of drought is the predominant sinking motion of air 
(subsidence) that results in compressional warming or high pressure, which 
inhibits cloud formation and results in lower relative humidity and less 
131 
 
precipitation. Regions under the influence of semipermanent high pressure during 
all or a major portion of the year are usually deserts, such as the Sahara and 
Kalahari deserts of Africa and the Gobi Desert of Asia. Most climatic regions 
experience varying degrees of dominance by high pressure, often depending on 
the season. Prolonged droughts occur when large-scale anomalies in atmospheric 
circulation patterns persist for months or seasons (or longer). The extreme drought 
that affected the United States and Canada during 1988 resulted from the 
persistence of a large-scale atmospheric circulation anomaly. [3]    
Too Many Variables 
Scientists don?t know how to predict drought a month or more in advance for 
most locations. Predicting drought depends on the ability to forecast two 
fundamental meteorological surface parameters, precipitation and temperature. 
From the historical record we know that climate is inherently variable. We also 
know that anomalies of precipitation and temperature may last from several 
months to several decades. How long they last depends on air?sea interactions, 
soil moisture and land surface processes, topography, internal dynamics, and the 
accumulated influence of dynamically unstable synoptic weather systems at the 
global scale. [3]    
The potential for improved drought predictions in the near future differs by 
region, season, and climatic regime. 
The Tropical Outlook 
In the tropics, for example, meteorologists have made significant advances in 
understanding the climate system. Specifically, it is now known that a major 
portion of the atmospheric variability that occurs on time scales of months to 
several years is associated with variations in tropical sea surface temperatures. 
The Tropical Ocean Global Atmosphere (TOGA) project has produced results that 
suggest that it may now be possible to predict certain climatic conditions 
associated with ENSO events more than a year in advance. For those regions 
whose climate is greatly influenced by ENSO events, TOGA project results may 
help produce more reliable meteorological forecasts that can reduce risks in those 
economic sectors most sensitive to climate variability and, particularly, extreme 
events such as drought. [3]    
The Temperate Zone Outlook 
In the extratropical regions, current long-range forecasts are of very limited 
reliability. The ability that does exist is primarily the result of empirical and 
statistical relationships. In the tropics, empirical relationships have been 
demonstrated to exist between precipitation and ENSO events, but few such 
relationships have been confirmed above 30 north latitude. Meteorologists do not 
believe that reliable forecasts are attainable for all regions a season or more in 
advance. [3]    
132 
 
Additional Information About the Water Balance and the Water Cycle 
Back to the top 
 
 
How are Droughts Predicted? 
 
At this time droughts can not be fully predicted.  In fact, it is difficult to tell when a 
drought is beginning (or if it is a spell of sunny weather).  In some regions drought can be 
defined by few days without rain (rain forest areas); while, other locations can go months 
without rain and still not be in true drought (dessert areas). [1]   It is also difficult to know 
when a drought is ending.  Some droughts (like the 1950s drought of Texas) are broken 
up by a wet spell. [5]   Wet spells sometimes occur during droughts, but do not last long 
enough for the area to recover from drought.  Although scientists cannot fully predict 
droughts, they have identified several variables that affect drought to get an idea of when 
and where droughts will occur. [6]   The variables that are studied to predict drought are 
global weather patterns, high pressure, the tropical outlook, and the temperate zone 
outlook (see the previous section, ?Why do Droughts Occur??). [3]    
 
Back to the top 
 
 
What Are the Impacts of Drought? 
 
The impacts of drought can be divided in to three main groups:  economic, social and 
environmental.  Some examples of these types of impacts are listed below in Table 1. [3]    
133 
 
 
Type of Impact Examples of Impacts 
 
 
 
Economic 
Costs and losses to agricultural and livestock producers, loss of 
fishery productions, decrease in land prices, loss in 
industries/manufacturers, unemployment, strain on financial 
institutions (foreclosures, credit risk and capital shortfalls), 
government revenue loss, decrease in recreational businesses and 
tourism, increase in energy demand, reduction in energy supply, 
cost of water transport/transfer and supplemental water resources, 
increase in food prices, and increase in food importation 
 
Social 
Rural population loss, health, increased conflicts, reduced quality of 
life, public dissatisfaction with government drought response 
increased data/information needs, coordination of dissemination 
activities, recognition of institutional restraints on water use 
 
 
Environmental 
Damage to animal species and plant communities, increase in the 
number of wild fires, wind/water erosion of soils, poor air quality, 
decrease in visual and landscape quality, lower water body levels, 
reduced flow from springs, reduced stream flow, loss of wetlands, 
salinity fluctuations in bays and estuaries, groundwater depletion, 
and negative effects on water quality.  
Table 1:  Types of drought impacts and their implications. [3]    
Certain regions are more susceptible to drought impacts.  This can be analyzed using a 
drought vulnerability tree diagram.  Tree diagrams analyze drought vulnerability by 
significant drought impacts for a region.  These show how susceptible a region is to 
specific drought risks.  Two examples of a tree diagram are shown below.  Figure 2 
shows a tree diagram of agricultural impact analysis.  Figure 3 is an example of tree 
diagram for urban drought impact analysis. [3]    
 
134 
 
Figure 2:  An example of a simplified agricultural impact tree diagram. (Notice the boldface represents the 
basal causes of the listed impact. Although these items may be broken down further, this example 
illustrates the vulnerability assessment process.)  (From the National Drought Mitigation Center) [3]    
 
Figure 3:  An example of a simplified urban impact tree diagram. (Notice the boldface items represent the 
basal causes of the listed impact [in this case, the loss of tourism revenue]. Although these items may be 
broken down further, this example illustrates the vulnerability assessment process.)  (From the National 
Drought Mitigation Center) [3]    
Assistance to Drought-Impacted Areas 
Back to the top 
 
135 
 
How is Drought Severity Defined? 
 
Drought severity is measured according to the type of drought that is being analyzed.  All 
measurements of drought are based on indices of drought severity.  Drought indices take 
several variables into account; such as, precipitation, soil moisture, snow pack, and other 
water supply indicators.  Indices differ by the variable implemented and the weight that is 
given to each variable.  Some examples of popular indices are:  the Standardized 
Precipitation Index, Palmer Drought Severity Index, Crop Moisture Index, Surface Water 
Supply Index, and Reclamation Drought Index.  Also, drought severity can be studied by 
analyzing climate and water availability (reservoir capacity, soil moisture, or stream 
flow) percent of normal.  More can be read about these types of rating systems at the 
National Drought Mitigation Center website (see the link below in the ?Links? section of 
the report). [3]    
 
For the Republic of Texas, the method of measuring drought severity is decided by 
climate region.  Each region decides the point at which drought regulations should come 
into effect. [7]   The Trinity River Basin is located mainly in Water Management Region C 
and H (with small areas also belonging to Regions D, G, I).  The river basin is mainly 
located in the Northeast region of the Climate Regions for Texas. A map of these regions 
can be seen in figure 4. [8]   However, in the case of the Trinity River Basin the Climate 
Regions have agreed to give water available jurisdiction to the Trinity River Authority 
(TRA).  The TRA has established general drought contingency triggers on the State 
Drought Preparedness Plan.  However, for the Basin the functional drought contingency 
triggers are established on the water users group.  Each water users group has its own 
triggers and set of responses.  A list of drought contingency triggers and responses can be 
found in the Trinity River Basin Master Plan ? 2001. [9]    
 
136 
 
 
Figure 4: The Climate Regions of Texas. [8]    
 
The Trinity River Basin 
 
Back to the top 
 
 
What Can You Do to Combat Drought? 
 
For communities and individuals to actively combat drought it often takes more than 
conserving water.  Water planning is required too.  For the best possible water planning 
water users should partake in the planning process, stay aware of drought conditions, 
partake in voluntary water conservation when needed, and inform water suppliers when 
there are water losses in the water system. [10]    
  
Drought Plan-Community Involvement 
According to the TCEQ?s ?A Drought Planning Guide for Public Water Systems in 
Texas? all water services must have a drought contingency plan.  This plan lays out 
operations in case of extreme drought, periods of abnormally high water usage, supply 
contamination, or extended reduction in ability to supply water due to equipment failure.  
It is strongly suggested that the public be involved in the planning process.  Typically 
water providers involve the public by forming citizen?s advisory committees or task 
forces, holding public meetings, conducting surveys, seeking input directly from large-
volume water customers (contractors, golf course operators, owners of car washes), or 
137 
 
distributing a draft plan for public review and comment prior to adoption.  Taking part in 
these events allows local residents (who will be affected by the drought contingency 
plan) an input to the plan and a familiarization with the plan. [11]   Figure 5 is a cartoon 
displaying the importance of being aware and prepared for drought conditions. 
 
 
Figure 5:  A cartoon from the Drought Mitigation Center to display the importance of being prepared for 
drought. [3]    
 
Stay Informed About the State of Water Conditions 
In the Trinity River Basin the Trinity River Basin Authority has jurisdiction over the 
definition of drought level.  According to the Trinity River Basin Master Plan, each 
individual water supplier has a drought plan (including drought contingency triggers) for 
their system.  It is the water suppliers? responsibility to inform customers of the level of 
drought.  However, it is the public?s social responsibility to ensure that they are informed 
and aware of the state of drought in their region. [9]    
 
The IDIS website is one source of drought information.  The IDIS allows users to sample 
climate data, hydrologic data, and drought indices to obtain a better understanding of 
drought.  However, there are many sources of information available.  The National 
Oceanic and Atmospheric Administration?s Climate Prediction Center provides a ?US 
Drought Assessment?. This website provides seasonal drought predictions, drought data, 
experimental drought indicator blends, soil moisture data and precipitation and 
temperature data.  The data available includes current data, as well as achieved historical 
138 
 
data. [10]   One of the links available on the ?Drought Assessment? website is to the 
National Drought Monitor.  The National Drought Monitor produces weekly images of 
drought severity for the United States, as can be seen in Figure 6. [13]     
 
 
Figure 6:  The US Drought Monitor for the week of August 28th, 2007[13]    
 
The US Drought Monitor basis its drought severity on a blend of science.  The maps are 
produced by the University of Nebraska-Lincoln in collaboration with several federal, 
state, academic and independent organizations.  Links to this data are provided on the US 
Drought Monitor website, as well as, archived data. [13]    
 
There are many drought resources available online.  Some of the links can be found 
below in the Drought Links section of this report. 
 
Voluntary Water Conservation  
Water conservation is always important for a community to practice.  Water is a valuable 
natural resource that should not be wasted.  There are many things that can be done to 
conserve water. [10]   Useful websites for tips and ideas about water conservation are:   
 
Water Conservation Tips- http://www.monolake.org/socalwater/wctips.htm 
The Water Conservation Portal- http://www.waterconserve.info/ 
Earth 911, Water Conservation- http://earth911.org/water/water-conservation/ 
139 
 
 
Voluntary water conservation is typically part of a drought preparedness plan.  For many 
drought plans, the early stages of drought call for voluntary conservation.  TCEQ?s ?A 
Drought Planning Guide for Public Water Systems in Texas? establishes five levels of 
drought, based on the ?The Texas Drought Preparedness Plan?, that can be seen below in 
Table 2. [7]    
 
Level of Drought 
Response 
Type of Advisory Typical Response to Advisory Level 
 
1 
 
Advisory 
Raise public awareness of the supply 
situation and request voluntary 
reductions in nonessential water use 
2  Watch Implement mandatory restrictions on certain non essential water uses 
 
3 
 
Severe conditions 
Implement ban on certain non-essential 
water uses and water rate surcharge for 
excessive use 
 
4 
 
 
Critical conditions 
Continue ban on nonessential water 
uses, increase water rate surcharge, 
activate backup wells 
5 Emergency conditions Initiate emergency response procedures 
Table 2:  The TCEQ?s Drought Planning Guide for Public Water Systems in Texas examples of drought 
response. [11] 
 
For every water authority the definitions of these levels vary.  For the Trinity River 
Authority (TRA) the level of drought is established by each individual water supplier.  It 
is the water suppliers? responsibility to inform their water users when water restrictions 
are being enforced.  Most of these conditions are based on percentage water availability 
for a given water system (see the Trinity River Basin Master Plan?2001, Drought 
Contingency Triggers).  Responding to voluntary conservation can help mitigate the 
impacts of drought. [14]    
 
Inform Water Supplier of Water Loss 
One way to reduce water loss and improve water conservation is to report water loss to 
the water provider. [10]   Some examples of water loss to report to the water provider are: 
Line leaks or breaks  
Leaky valves 
Water meters that don?t work correctly 
Leaky fire hydrants 
 
Back to the top 
140 
 
 
 
Drought Links 
 
National Drought Mitigation Center:  www.drougth.unl.edu 
NOAA Drought Information Center:  http://www.drought.noaa.gov/  
US Drought Monitor:  http://drought.unl.edu/dm/monitor.html  
Climate Prediction Center (CPC) US Drought Assessment:  
http://www.cpc.ncep.noaa.gov/products/expert_assessment/drought_assessment.shtml 
NOAA Climate Program Office-Intergovernmental Panel on Climate Change:  
http://www.cpo.noaa.gov/ipcc/water_drought.html  
 
Drought Education for Kids:  www.drought.unl.edu/kids  
 
Back to the top 
 
 
References  
 
1. "North American Drought: a Paleo Perspective." 28 Aug. 2007. National Oceanic 
and Atmospheric Administration. 28 Aug. 2007 
<http://www.ncdc.noaa.gov/paleo/drought/drght_what.html>. 
 
2. "Drought." Wikipedia. 27 Aug. 2007 <http://en.wikipedia.org/wiki/Drought>. 
 
3.  "The National Drought Mitigation Center." 2007. The University of Nebraska - 
Lincoln. 27 Aug. 2007 <www.drought.unl.edu>. 
 
4. "EL NI?O EXPECTED TO INFLUENCE CENTRAL U.S. WINTER 
WEATHER." National Oceanic and Atmospheric Administration. 31 Oct. 2002. 
30 Aug. 2007 <http://www.noaanews.noaa.gov/stories/s1059.htm>. 
 
5. Byrns, Steve. "Is the Drought Over?" Ag News: News and Public Affairs 07 July 
2005. 28 Aug. 2007 
<http://agnews.tamu.edu/dailynews/stories/DRGHT/Jul0705a.htm>. 
 
6. "Drought Information Center.? 08 June-July 2007.  National Oceanic and 
Atmospheric Administration.  27 Aug. 2007 http://www.drought.noaa.gov/.  
 
7. State Drought Preparedness Plan. Drought Preparedness Council. Austin, TX: 
Governor's Division of Emergency Management, Texas Department of Public 
Safety, 2005. 1-42. 
 
141 
 
8. "Texas Renewable Energy Resources." Texas State Energy Conservation Office. 
10 Sept. 2007 <www.infinitepower.org/resbuilding.htm>. 
 
9. Trinity River Basin Master Plan. Trinity River Authority of Texas. 2007. 1-48. 
 
10. "Drought and Public Water." Texas Commission on Environmental Quality. 27 
Aug. 2007. 27 Aug. 2007 
<http://www.tceq.state.tx.us/nav/util_water/drought.html>. 
 
11. A Drought Planning Guide for Public Water Systems in Texas. Water Permits and 
Resource Management Div., Texas Commission on Environmental Equality. 
Austin, TX: Texas Natural Resource Conservation Commission, 2001. 
 
12. "US Drought Assessment." Climate Prediction Center (CPC) Drought. 2007. 
Climate Prediction Center (CPC). 27 Aug. 2007 
<http://www.cpc.ncep.noaa.gov/products/expert_assessment/drought_assessment.
shtml>.  
 
13. "US Drought Monitor." 22 Aug.-Sept. 2007. University of Nebraska - Lincoln. 27 
Aug. 2007 <http://drought.unl.edu/dm/monitor.html>.  
 
14. Trinity River Basin Master Plan. Trinity River Authority of Texas. 2001. 1-35.  
 
15. "Water Balance." Wikipedia. Apr. 2007. 27 Aug. 2007 
<http://en.wikipedia.org/wiki/Water_balance>. 
 
16. "Water Science for Schools: the Water Cycle." USGS. 17 Aug. 2007. 27 Aug. 
2007 <http://ga.water.usgs.gov/edu/watercycle.html>. 
 
17. Drought Assistance Reference Guide for State Agencies. Dept. of Public Safety, 
the State of Texas. Austin, TX: Division of Emergency Management. 1-27. 
 
142 
 
 
What Are the Different Types of Drought? 
 
There are three main types of drought:  meteorological, agricultural, and hydrological.  
The first of these to occur is the meteorological drought.  This occurs directly from 
climate variation.  The results of the meteorological drought lead to an agricultural 
drought.  A decrease in the availability of water causes soil moisture to decrease and 
vegetation stress to increase.  This in turn leads to a hydrological drought.  During a 
hydrological drought the effects of drought can be seen on the surface water.  These three 
types of drought are discussed in further detail below. [3]    
1. Meteorological drought is brought about when there is a prolonged period with 
less than average precipitation. Meteorological drought usually precedes the other 
kinds of drought.  
2. Agricultural droughts are droughts that affect crop production or the ecology of 
the range. This condition can also arise independently from any change in 
precipitation levels when soil conditions and erosion triggered by poorly planned 
agricultural endeavors cause a shortfall in water available to the crops. However, 
in a traditional drought, it is caused by an extended period of below average 
precipitation.  
3. Hydrological drought is brought about when the water reserves available in 
sources such as aquifers, lakes and reservoirs falls below the statistical average. 
Like an agricultural drought, this can be triggered by more than just a loss of 
rainfall. For instance, Kazakhstan was recently awarded a large amount of money 
by the World Bank to restore water that had been diverted to other nations from 
the Aral Sea under Soviet rule. Similar circumstances also place their largest lake, 
Balkhash, at risk of completely drying out.  
143 
 
The way these types of droughts occur can be seen below.  Figure 7 shows the process in 
which the types of droughts occur and what occurs during these droughts. [3]    
 
 
Figure 7: Types of drought through time. [3]   
144 
 
 
What is the Water Balance? 
 
The water balance is an equation that accounts for the inputs and outputs of a water 
system.  The water balance can describe a water system that ranges in size from an 
agricultural field or forest, to a river basin, to the entire earth?s surface.  Typical inputs to 
a water balance are precipitation and inflow.  Typical outputs for a water balance are 
evaporation, evaportranspiration, soil infiltration and outflow.  An additional variable 
factoring into a water balance is the change in storage within the water system.  Water 
balances help to manage water supply and predict where water shortages/surpluses will 
be. [15]    
 
 
How Does Water Cycle? 
 
The earth?s water is always in action.  The relationship between the ocean, atmosphere 
and climate drives the water cycle.  Starting with water being stored in the ocean, water is 
evaporated and enters the atmosphere.  Once in the atmosphere the water vapor goes 
through condensation.  The water is stored in the atmosphere until it is released to the 
earth in the form of precipitation.  Some of the precipitation will be stored as ice and 
snow.  Rainwater and snowmelt runs off the slopes to infiltrate groundwater or streams.  
Some of the water on the surface re-enters the atmosphere through evapotranspiration and 
evaporation.  Water in the streams may be stored in reservoirs or lakes until re-entering 
the ocean.  Water in the groundwater system is stored in aquifers until it enters the 
surface water system through springs or discharges directly into the ocean.  A visual 
image of this can been seen in Figure 8. [16]    
145 
 
 
Figure 8:  The water cycle. [16]    
146 
 
The Trinity River Basin 
The Trinity River is a vast and expansive river.  The Trinity River watershed contains 
17965 square miles (about 6% of Texas?s surface area).  The Trinity River itself is 715 
miles long.  Because the basin is so large land types, soil type and climate vary greatly 
across the state.  The northern portion of the watershed is covered by central Texas 
prairies.  The dry climate of the northern portion receives only 29 inches of precipitation 
annually.  As the river runs southeast the landscape changes from central Texas prairies, 
to piney woods, to Gulf Coastal prairies (which receive 53 inches of precipitation a year).  
Figure 9 shows the Trinity River Basin with existing reservoirs. [9]    
 
 
Figure 9:  The Trinity River Basin with existing reservoirs. [9]    
147 
 
 
The river is the most developed watershed in the state.  It contains more than 29 
reservoirs, and is a primary source of water for around ten million people.  The reservoir 
serves the Dallas/Fortworth Metroplex and parts of Houston.  The reservoirs of the basin 
hold more than 5000 acre-feet of water in storage.  In 1955 the Trinity River Authority 
(TRA) was created.  The TRA includes all or part of 17 counties (containing twenty 
percent of the state?s population) along the river basin.  Although the watershed falls into 
water management regions C and H (as well as small portions of D, G and I), the TRA 
has been allotted much jurisdiction over the Trinity River Basin by House Bill 20.  The 
basin as well as the water management districts can be seen below in Figure 10. [9]    
 
 
Figure 10:  The Trinity River Basin and Texas Water Management Districts. [7]    
 
Additionally the Trinity River Basin spans over three major aquifers (Carrizo, Gulf 
Coast, and Trinity) and five minor aquifers (Nacatoch, Queen City, Sparta, Woodbine, 
Yegua Jackson).  These aquifers in relation to the basin can be seen below in Figure 11. 
[9]    
 
148 
 
 
Figure 11:  Aquifers of the Trinity River Basin. [9]    
149 
 
Assistance to Drought-Impacted Areas 
Texas Water Development Board Drought Resource Contacts and Summary  
The Texas Water Development Board (TWDB) is the state agency charged with 
statewide water resources planning and administration of low-cost financial assistance 
programs for the planning, design, and construction of water supply, wastewater 
treatment, flood control, and agricultural water conservation projects. TWDB has a 
variety of programs, services, and capabilities for assisting communities in developing 
and implementing local responses to drought-induced water supply problems. This 
includes: drought related information on drought indices, water supply reservoir contents, 
groundwater levels, etc.; technical assistance with local drought management planning 
and water conservation practices; provision of information and technical assistance for 
the identification of alternative water sources; and providing low-cost financial assistance 
for the development of alternative water sources. [17]    
150 
 
Agency Contacts for Assistance 
Type of 
Assistance Name  Telephone E-mail 
Media Contact Carla Daws 512-463-7956 Carla.Daws@twdb.state.tx.us 
Municipal & 
Industrial Drought 
Management and 
Conservation 
John Sutton 512-463-7988 John.Sutton@twdb.state.tx.us 
Agricultural 
Drought 
Management and 
Conservation: 
Mark Michon 512-463-7984 Mark.Michon@twdb.state.tx.us 
Drought Indices 
and Water 
Resources Data: 
Robert Bradley 512-936-0870  Robert.Bradley@twdb.state.tx.us 
Water Audit and 
Leak Detection: Mark Mathis 512-463-0987 mark.mathis@twdb.state.tx.us 
Water 
Conservation 
Public 
Information-
English & Spanish: 
Patsy Waters 512-463-7955 Patsy.Waters@twdb.state.tx.us 
Planning Data 
request for the state 
of Texas: 
Wendy Barron  512-936-0886 wendy.barron@twdb.state.tx.us 
Financial 
Assistance for 
Water Supply 
Improvements for 
Border and Non-
Border Areas: 
Jeff Walker 512-463-7779 jeff.walker@twdb.state.tx.us 
 
Weblink:  http://www.twdb.state.tx.us/data/drought/Contacts.asp
151 
 
Bibliography 
 
 
1. "North American Drought: a Paleo Perspective." 28 Aug. 2007. National Oceanic 
and Atmospheric Administration. 28 Aug. 2007 
<http://www.ncdc.noaa.gov/paleo/drought/drght_what.html>. 
 
2. "Drought." Wikipedia. 27 Aug. 2007 <http://en.wikipedia.org/wiki/Drought>. 
 
3.  "The National Drought Mitigation Center." 2007. The University of Nebraska - 
Lincoln. 27 Aug. 2007 <www.drought.unl.edu>. 
 
4. "Water Balance." Wikipedia. Apr. 2007. 27 Aug. 2007 
<http://en.wikipedia.org/wiki/Water_balance>. 
 
5. Wu, Hong, and Donald Whilhite. "An Operational Agricultural Drought Risk 
Assessment Model for Nebraska, USA." Natural Hazards 33 (2004): 1-21. 
 
6. Whilhite, Donald A., and Mark D. Svoboda. "Understanding the Complex 
Impacts of Drought: a Key to Enhancing Drought Mitigation and Preparedness." 
Water Resource Management 21 (2007): 763-774. 
 
7. "Water Science for Schools: the Water Cycle." USGS. 17 Aug. 2007. 27 Aug. 
2007 <http://ga.water.usgs.gov/edu/watercycle.html>. 
 
8. Maidment, David R. Arc Hydro: GIS for Water Resources. 1st ed. Redlands, CA: 
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152 
 
9. IPCC 4th Assessment Report: Working Group I Report "Physical Science Basis" 
Intergovernmental Panel on Climate Change. New York, NY: Cambridge UP, 
2007. 254-265. 
 
 
10. State Drought Preparedness Plan. Drought Preparedness Council. Austin, TX: 
Governor's Division of Emergency Management, Texas Department of Public 
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<http://www.drought.unl.edu/whatis/palmer/txgulf.gif>. 
 
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<http://www.ncdc.noaa.gov/paleo/drought/drght_history.html>. 
 
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153 
 
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154 
 
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<http://www.cpc.noaa.gov/products/monitoring_and_data/drought.shtml>. 
 
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<http://en.wikipedia.org/wiki/NetCDF>. 
 
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System.? Report, The University of Texas at Austin. 
 
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<http://www.cuahsi.org/his/docs/HIS-workbook-20061130.pdf>  
 
34. Trinity River Basin Master Plan. Trinity River Authority of Texas. 2007. 1-48 
155 
 
Vita 
 
 Virginia Burton Smith is the daughter of Patricia and Roger Smith.  She was born 
in Keflavik, Iceland October 8th, 1981.  Virginia attended primary school in Germany, 
Alabama, Arizona, Oklahoma, Japan, Virginia, Greece and Georgia.  She graduated from 
Brookwood High School in Lawrenceville, Georgia in 1999.  In the fall of 1999 Virginia 
began attending the Georgia Institute of Technology (GA Tech), in Atlanta, Georgia.  In 
2003 she graduated with a Bachelors of Science in Civil and Environmental Engineering. 
While attending GA Tech Virginia lettered in track and cross country, was an active 
member of the American Society of Civil Engineering, served as an officer in Alpha Xi 
Delta sorority, and volunteered in community building groups team buzz and the Atlanta 
Children?s Shelter.  She also worked as a co-op for Lowe Engineers, in Atlanta, Georgia.  
Upon graduating from GA Tech Virginia briefly took a job as an EIT at Lowe Engineers 
before joining the Peace Corps.  In the spring of 2004 Virginia moved to Samoa to serve 
as a Peace Corps volunteer.  She worked for the Peace for two years on the Samoan 
National Water Project and various small scale development projects.  In the summer of 
2006 Virginia returned to the United States and began graduate school at the University 
of Texas in Austin. 
 
 
 
 
 
 
 
 
Permanent address:  1979 Emerald Pointe Dr., Soddy Daisy, TN 37379 
This report was typed by the author.