Towards actionable climate and flood prediction : understanding and advancing land surface modeling with enriched geospatial information
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Land surface models (LSMs) are central to our understanding and prediction of the terrestrial hydrological cycle. This dissertation focuses on using enriched geospatial information from remote sensing (RS) and geographic information system (GIS) to advance the snow and river routing component of state-of-the-art LSMs, and assessing their roles in predicting temperature, precipitation, and streamflow. In Chapters 2 and 3, the first systematic studies are conducted to quantify the role of land snow data assimilation (DA) in seasonal climate forecast. Using 7-yr DA products that assimilated the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) and the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS), I find a local improvement of 5%–25% in the temperature forecast, where the delayed improvement at higher latitudes is explained by incoming solar radiation that is key to the snow–atmosphere coupling. Focusing on the Asia monsoon, I detect an improvement in the precipitation forecast, which is more robust over central north India with sensor-dependent behaviors in different seasons. The results clarify that to successfully translate DA to useful atmospheric prediction skill, the regional snow–atmosphere coupling, the DA uncertainties, and the monsoon sensitivity to thermal forcing over land need to be jointly considered. In Chapters 4 and 5, I introduce a vector-based river routing model to be coupled with traditional grid-based LSMs. By conducting comprehensive model evaluations in the Texas “Flash Flood Alley” in high-impact historical floods, I identify the model strengths and weaknesses in simulating flood discharges. The best modeling results are then used to reveal the hydrometeorological factors responsible for a record-breaking local flood, which includes the rainfall location and basin physiographic features, the initial wetness in the deeper soil layer, and the flow velocity in the river network. The assessed modeling advancements have actionable societal implications because they apply to the Community Land Model 4 (CLM4) and the Noah model with multi-parameterizations (Noah-MP), both LSMs are adopted by major operational forecasting centers. They may also inform future LSM developments that aim to unify the “top-down” atmospheric modeling and the “bottom-up” hydrological modeling approaches in a generic framework.