Reservoirs play an important role in relation to water security, flood risk, hydropower and natural flow regime. This study derives a novel dataset with a long-term daily water-balance (reservoir volume, inflow, outflow, evaporation and precipitation) of headwater reservoirs and storage dynamics across the globe. The data is generated using cloud computing infrastructure and a high resolution distributed hydrological model wflow_sbm. Model results are validated against earth observed surface water area and in-situ measured reservoir volume and show an overall good model performance. Simulated headwater reservoir storage indicate that 19.4-24.4 % of the reservoirs had a significant decrease in storage. This change is mainly driven by a decrease in reservoir inflow and increase in evaporation. Deployment on a kubernetes cloud environment and using reproducible workflows shows that these kind of simulations and analyses can be conducted in less than a day.
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http://dx.doi.org/10.1016/j.scitotenv.2024.172678 | DOI Listing |
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