Use of satellite remote sensing to validate reservoir operations in global hydrological models: a case study from the CONUS

Although river discharge simulations from global hydrological models have undergone extensive validation, there has been less validation of reservoir operations, primarily because of limited observational data. However, recent advancements in satellite remote sensing technology have facilitated the collection of valuable data regarding water surface area and elevation, thereby providing the ability to validate reservoir storage. In this study, we sought to establish a methodology for validation and intercomparison of reservoir storage within global hydrological model simulations using satellite-derived data. Accordingly, we chose two satellite-derived reservoir operation products, DAHITI and GRSAD, to create monthly time series storage data for seven reservoirs in the contiguous United States (CONUS) , with access to long-term ground truth data (the total catchment area accounts for about 9 % of CONUS). We assessed two global hydrological models that participated in the Inter Sectoral Model Intercomparison Project (ISIMIP) Phase 3 project, H08 and WaterGAP2, with three distinct forcing datasets: GSWP3-W5E5 (GW), CR20v3-W5E5 (CW), and CR20v3-ERA5 (CE). The results indicated that WaterGAP2 generally outperforms H08; the CW forcing dataset demonstrated superior results compared with GW and CE; the DAHITI showed better consistency with ground observations than GRSAD if temporal coverage is sufficient. Overall, our study emphasizes the potential uses of satellite remote sensing data in reservoir operations validation and underscores the importance of normalization and decomposition techniques for improved validation efficacy. The results highlight the relative performances of different hydrological models and forcing datasets, yielding insights concerning future advancements in reservoir simulation and operational studies

Scale Effect on Hydrologic Complexity: How Drainage Area Determines Stream Flashiness

The flow in a river basin is dynamic in nature, and depends on the climatologic variations and its geomorphologic makeover. River flow variability thus depends on these factors. In this study, we consider rainfall and topographic features of the catchment as driving forces for the flow variability. We have used Richard Bakers Flashiness Index as a measure of flow variability. Also, we have developed a new Relative Flashiness Index (RFI), considering the effect that, variation in rainfall leads to variation in stream flow. The new RFI developed shows promising results, as a good measure for streamflow variability, especially for the modelled discharge, which was obtained by using an instantaneous dryness-index based zero parameter model. The model is a lumped calibration-free model, and works well for small catchments. In this study, we have expanded the model for grid-based data, by incorporating a new routing module and hence making it possible to use the model even for large catchments. The results of the new combined model show encouraging results for its applicability, especially for large catchments in ungauged basin

Global hydrological models continue to overestimate river discharge

Global hydrological models (GHMs) are widely used to assess the impact of climate change on streamflow, floods, and hydrological droughts. For the 'model evaluation and impact attribution' part of the current round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a), modelling teams generated historical simulations based on observed climate and direct human forcings with updated model versions. Here we provide a comprehensive evaluation of daily and maximum annual discharge based on ISIMIP3a simulations from nine GHMs by comparing the simulations to observational data from 644 river gauge stations. We also assess low flows and the effects of different river routing schemes. We find that models can reproduce variability in daily and maximum annual discharge, but tend to overestimate both quantities, as well as low flows. Models perform better at stations in wetter areas and at lower elevations. Discharge routed with the river routing model CaMa-Flood can improve the performance of some models, but for others, variability is overestimated, leading to reduced model performance. This study indicates that areas for future model development include improving the simulation of processes in arid regions and cold dynamics at high elevations. We further suggest that studies attributing observed changes in discharge to historical climate change using the current model ensemble will be most meaningful in humid areas, at low elevations, and in places with a regular seasonal discharge as these are the regions where the underlying dynamics seem to be best represented