Abstract
Water resources management (WRM) is essential to sustainably improve prosperity in developing nations. WRM requires reliable estimates of key hydrometeorological variables to monitor changes in water availability. Thus, one of the biggest challenges in WRM at the national scale is accurate and timely observations of these variables from the ground networks (referred to as local datasets) that usually have low density. Without boundary restriction and global coverage, satellite-based and outputs from land surface models datasets (referred to as global datasets) are promising and can estimate these variables. However, there are several barriers to use global datasets in local WRM: (i) global datasets usually have large sizes and are not easy to handle, (ii) they often have coarse resolutions that are not suitable to local-scale WRM applications, and (iii) they have heterogeneous quality depending on climatic and geographic conditions. Therefore, large-scale validation of a global dataset is an area of research that requires more attention to provide practical insights about the usefulness of these assets over different regions. This dissertation aims to better understand the capacity of global datasets to support WRM in Vietnam–a tropical country that faces many water stresses in a warming climate and does not have a good observational network to monitor key hydrometeorological variables. Specifically, we examine satellite- and re-analysis- based precipitation products and satellite-based soil moisture products in hydrologic impact studies over a large number of catchments.
There are four key findings yielded from the four independent large-sample studies conducted within this dissertation. First, satellite-based precipitation products (e.g., TMPA; Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis) accurately estimated rainfall in wet season compared to the dry season (rain gauge as a base reference) in the Red-Thai Binh River basin, the second largest river basin in Vietnam. The quality of TMPA datasets could be improved based on a climatology-topography-based linear-scaling approach, especially to reduce their bias. Second, high-spatial resolutions (at 1-km) of re-analysis dataset (e.g., MERRA-2; Modern-Era Retrospective analysis for Research and Applications version 2) could be useful to detect percentage drought areas as well as to quantify drought trends across Vietnam. This finding reveals the feasibility of using a model-based drought index in data-sparse areas to assess drought conditions, and for practical applications of advanced re-analysis products in WRM. Third, the Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) final run version 6 (GPM IMERGv6) could be the input precipitation for a hydrological model (SWAT; Soil and Water Assessment Tool) to simulate streamflow. Also, the Climate Hazards group Infrared Precipitation with stations (CHIRPS) dataset demonstrates a relatively low bias and could benefit long-term water resources planning for droughts. These conclusions were based on a comprehensive hydrologic model study (a total of 54 simulated scenarios) across Vietnam basins. Fourth, remotely sensed soil moisture data assimilation in a hydrologic model streamflow simulation could increase the accuracy of streamflow simulation. The benefits of high-spatial resolution soil moisture (e.g., SMAP, Soil Moisture Active and Passive), at a spatial resolution of 1 km in the data assimilation framework, is outperformed by that data assimilation using SMAP at a spatial resolution of 9 km. This finding is based on an experiment using eight catchments with varying sizes and runoff patterns across contrasting climate zones in Vietnam. Overall, this dissertation is beneficial to water practitioners in developing nations, as a guide to decide whether publicly available global datasets are useful for local applications, and if so, which data sources would be the most suitable to consider.
