High-resolution surface water dynamics in Earth’s small and medium-sized reservoirs

Abstract

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.

Res-CN: hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs

“…The derived reservoir water area estimations are limited by the spatial coverage and accuracy restrictions of the initial dataset. As such, three available global water area products are developed by using algorithms that reclassify contaminated pixels as water, i.e., the GRSAD (Zhao & Gao, 2018), the RealSAT (Khandelwal et al, 2022), and areas of medium-small reservoirs by Donchyts et al (2022). These products cover only a portion of the reservoirs we studied (e.g., 908 overlapping reservoirs between GRSAD and our product) and use different algorithms and source datasets (e.g., RealSAT and GRSAD use only Landsat).…”

“…Hydroweb (Crétaux et al, 2011), G-REALM (Global reservoirs and lakes monitor, Birkett et al, 2011), DAHITI (Database for hydrological time series of inland waters, Schwatke et al, 2015), AltEx (Markert et al, 2019), HydroSat (Tourian et al, 2022), Water level On VITO, and several studies (e.g., Gao et al, 2012;Tortini et al, 2020;Shen et al, 2022b) offer time series of altimetry-derived water level for inland waters by incorporating multiple laser or radar altimeters such as Jason-1/2/3, CryoSat-2, Sentinel-3A/B, and ICESat-1/2. Imagery-based water area estimates can be extracted from GSW (Global surface water, Pekel et al, 2016), DAHITI, Hydroweb, HydroSat, Bluedot Observatory, GRSAD (Global reservoir surface area dataset, Zhao & Gao, 2018), RealSAT (Khandelwal et al, 2022), and relevant studies (e.g., Busker et al, 2019;Liu et al, 2021;Donchyts et al, 2022). Storage anomalies are available in DAHITI, HydroSat, and several studies (e.g., Gao et al, 2012;Hou et al, 2022) using water level and area from satellite altimeters and images, and/or from imagery-based water area and the area-storage model constructed by digital elevation model (DEM, Vu et al, 2022).…”

High-resolution water level and storage variation datasets for 338 reservoirs in China during 2010–2021

“…In this study, we applied the new algorithm developed by Donchyts et al (2022a) to leverage freely accessible Landsat and Sentinel-2 images to generate reservoir water area time series. The Google Earth Engine (GEE) code for this water mapping algorithm is available at https://github.com/ global-water-watch/research-reservoir-water-dynamics (last access: 15 October 2022) and was applied individually to each reservoir and every satellite image intersecting a given reservoir to map accurate reservoir water.…”

“…This algorithm can efficiently address several challenges associated with optical Landsat satellites, such as contamination from clouds and limitations of previous algorithms that reclassify contaminated pixels as water. Donchyts et al (2022a) demonstrated the algorithm's good performance in mapping reservoir water areas by comparing the areas with in situ water level/storage in 768 reservoirs of varying size and geographic regions. Here, we detail how this algorithm addresses the challenges from optical images and generates a water area time series.…”

“…Last, reservoir water area time series from the final gap-filled water mask are filtered with a quantile-based temporal outlier filtering algorithm to remove the remaining errors. Detailed procedures and flowcharts can be found in Donchyts et al (2022a).…”