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The accurate knowledge of the groundwater storage variation (ΔGWS) is essential for reliable water resource assessment, particularly in arid and semi-arid environments (e.g., Australia, the North China Plain (NCP)) where water storage is significantly affected by human activities and spatiotemporal climate variations. The large-scale ΔGWS can be simulated from a land surface model (LSM), but the high model uncertainty is a major drawback that reduces the reliability of the estimates. The evaluation of the model estimate is then very important to assess its accuracy. To improve the model performance, the terrestrial water storage variation derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission is commonly assimilated into LSMs to enhance the accuracy of the ΔGWS estimate. This study assimilates GRACE data into the PCRaster Global Water Balance (PCR-GLOBWB) model. The GRACE data assimilation (DA) is developed based on the three-dimensional ensemble Kalman smoother (EnKS 3D), which considers the statistical correlation of all extents (spatial, temporal, vertical) in the DA process. The ΔGWS estimates from GRACE DA and four LSM simulations (PCR-GLOBWB, the Community Atmosphere Biosphere Land Exchange (CABLE), the Water Global Assessment and Prognosis Global Hydrology Model (WGHM), and World-Wide Water (W3)) are validated against the in situ groundwater data. The evaluation is conducted in terms of temporal correlation, seasonality, long-term trend, and detection of groundwater depletion. The GRACE DA estimate shows a significant improvement in all measures, notably the correlation coefficients (respect to the in situ data) are always higher than the values obtained from model simulations alone (e.g., ~0.15 greater in Australia, and ~0.1 greater in the NCP). GRACE DA also improves the estimation of groundwater depletion that the models cannot accurately capture due to the incorrect information of the groundwater demand (in, e.g., PCR-GLOBWB, WGHM) or the unavailability of a groundwater consumption routine (in, e.g., CABLE, W3). In addition, this study conducts the inter-comparison between four model simulations and reveals that PCR-GLOBWB and CABLE provide a more accurate ΔGWS estimate in Australia (subject to the calibrated parameter) while PCR-GLOBWB and WGHM are more accurate in the NCP (subject to the inclusion of anthropogenic factors). The analysis can be used to declare the status of the ΔGWS estimate, as well as itemize the possible improvements of the future model development.
Floodplains and other wetlands depend on seasonal river flooding and play an important role in the terrestrial water cycle. They influence evapotranspiration, water storage and river discharge dynamics, and they are the habitat of a large number of animals and plants. Thus, to assess the Earth’s system and its changes, a robust understanding of the dynamics of floodplain wetlands including inundated areas, water storages, and water flows is required.
This PhD thesis aims at improving the modeling of large floodplains and wetlands within the global-scale hydrological model WaterGAP, in order to better estimate water flows and water storage variations in different storage compartments. Within the scope of this thesis, I have developed a new approach to simulate dynamic floodplain inundation on a global-scale. This approach introduces an algorithm into WaterGAP, which has a spatial resolution of 0.5 degree (longitude and latitude) globally. The new approach uses subgrid-scale topography, based on high-resolution digital elevation models, to describe the floodplain elevation profile within each grid cell by applying a hypsographic curve. The approach comprises the modeling of a two-way river-floodplain interaction, the separate downstream water transport within the river and the floodplain – both with temporally and spatially different variable flow velocities – and the floodplain-groundwater interactions. The WaterGAP version that includes the floodplain algorithm, WaterGAP 2.2b_fpl, estimates floodplain and river water storage, inundated area and water table elevation, and also simulates backwater effects.
WaterGAP 2.2b_fpl was applied to model river discharge, river flow velocity, water storages, water heights and surface water extent on a global-scale. Model results were comprehensively validated against ground observations and remote sensing data. Overall, the modeled and observed data are in agreement. In comparison to the former version WaterGAP 2.2b, the model performance has improved significantly. The improvements are most remarkable in the Amazon River basin. However, the seasonal variation of surface water extent and total water storage anomalies are still too low in many regions on the globe when compared to observations. A detailed analysis of the simulated results suggests that in the Amazon River basin the introduction of backwater effects is important for realistically simulating water storages and surface water extent. Future efforts should focus on the simulation of water levels in order to better model the flow routing according to water slope. To further improve the model performance in specific regions, I recommend that the globally constant model parameters that affect inundation initiation, river-floodplain interaction, DEM correction for vegetation, and backwater amount at basin or subbasin-scale be adjusted.