Streamflow predictions remain a challenge for poorly gauged and ungauged catchments. Recent research has shown that deep learning methods based on Long Short-Term Memory (LSTM) cells outperform process-based hydrological models for rainfall-runoff modeling, opening new possibilities for prediction in ungauged basins (PUB). These studies usually feature local datasets for model development, while predictions in ungauged basins at a global scale require training on global datasets. In this study, we develop LSTM models for over 500 catchments from the CAMELS-US data base using global ERA5 meteorological forcing and global catchment characteristics retrieved with the HydroMT tool. Comparison against an LSTM trained with local datasets shows that, while the latter generally yields superior performances due to the higher spatial resolution meteorological forcing (overall median daily NSE 0.54 vs. 0.71), training with ERA5 results in higher NSE in most catchments of Western and North-Western US (median daily NSE of 0.83 vs. 0.78). No significant changes in performance occur when substituting local with global data sources for deriving the catchment characteristics. These results encourage further research to develop LSTM models for worldwide predictions of streamflow in ungauged basins using available global datasets. Promising directions include training the models with streamflow data from different regions of the world and with higher quality meteorological forcing.@en

To prevent floods from becoming disasters, social vulnerability must be integrated into flood risk management. We advocate that the welfare of different societal groups should be included by adding recovery capacity, impacts of beyond-design events, and distributional impacts.@en

The original version of this Article contained an error in the Data Availability section where “All new data and code generated in this research are available under the terms of Creative Commons BY 4.0 (for the data) and Apache 2.0 (for the code) licenses. Datasets and supplementary materials generated during this study are accessible from the TODO: upload and add Zenodo link here. The source code used to produce datasets is accessible from: https://github.com/global-water-watch/research-reservoir-water-dynamics. For more information about this research and to access the demo app visit: https://globalwaterwatch.earth.” now reads: “All new data and code generated in this research are available under the terms of Creative Commons BY 4.0 (for the data) and Apache 2.0 (for the code) licenses. Datasets and supplementary materials generated during this study are accessible from the supplementary materials document below. The source code used to produce datasets is accessible from: https://github.com/global-water-watch/research-reservoir-water-dynamics. For more information about this research and to access the demo app visit: https://globalwaterwatch.earth.” The original Article has been corrected.

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Small and medium-sized reservoirs play an important role in water systems that need to cope with climate variability and various other man-made and natural challenges. Although reservoirs and dams are criticized for their negative social and environmental impacts by reducing natural flow variability and obstructing river connections, they are also recognized as important for social and economic development and climate change adaptation. Multiple studies map large dams and analyze the dynamics of water stored in the reservoirs behind these dams, but very few studies focus on small and medium-sized reservoirs on a global scale. In this research, we use multi-annual multi-sensor satellite data, combined with cloud analytics, to monitor the state of small (10–100 ha) to medium-sized (> 100 ha, excluding 479 large ones) artificial water reservoirs globally for the first time. These reservoirs are of crucial importance to the well-being of many societies, but regular monitoring records of their water dynamics are mostly missing. We combine the results of multiple studies to identify 71,208 small to medium-sized reservoirs, followed by reconstructing surface water area changes from satellite data using a novel method introduced in this study. The dataset is validated using 768 daily in-situ water level and storage measurements (r2 > 0.7 for 67% of the reservoirs used for the validation) demonstrating that the surface water area dynamics can be used as a proxy for water storage dynamics in many cases. Our analysis shows that for small reservoirs, the inter-annual and intra-annual variability is much higher than for medium-sized reservoirs worldwide. This implies that the communities reliant on small reservoirs are more vulnerable to climate extremes, both short-term (within seasons) and longer-term (across seasons). Our findings show that the long-term inter-annual and intra-annual changes in these reservoirs are not equally distributed geographically. Through several cases, we demonstrate that this technology can help monitor water scarcity conditions and emerging food insecurity, and facilitate transboundary cooperation. It has the potential to provide operational information on conditions in ungauged or upstream riparian countries that do not share such data with neighboring countries. This may help to create a more level playing field in water resource information globally.

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The concept of resilience is used by many in different ways: as a scientific concept, as a guiding principle, as inspirational ‘buzzword’, or as a means to become more sustainable. Next to the academic debate on meaning and notions of resilience, the concept has been widely adopted and interpreted in policy contexts, particularly related to climate change and extreme weather events. In addition to having a positive connotation, resilience may cover aspects that are missed in common disaster risk management approaches. Although the precise definition of resilience may remain subject of discussion, the views on what is important to consider in the management of extreme weather events do not differ significantly. Therefore, this paper identifies the key implications of resilience thinking for the management of extreme weather events and translates these into five practical principles for policy making.

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