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A 1985–2023 time series dataset of absolute reservoir storage in Mainland Southeast Asia (MSEA-Res).

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Titel: A 1985–2023 time series dataset of absolute reservoir storage in Mainland Southeast Asia (MSEA-Res).
Autoren: Mahto, Shanti Shwarup, Fatichi, Simone, Galelli, Stefano
Quelle: Earth System Science Data; 2025, Vol. 17 Issue 6, p2693-2712, 20p
Schlagwörter: WATER demand management, WATER levels, TIME series analysis, WATER supply, WATERSHEDS
Abstract: The recent surge in reservoir construction has increased global surface water storage, with Mainland Southeast Asia (MSEA) being a significant hotspot. Such infrastructural evolution demands updates in water management strategies and hydrological models. However, information on actual reservoir storage is hard to acquire, especially for transboundary river basins. To date, no high-resolution spatiotemporal dataset on absolute storage time series is available for reservoirs in MSEA. To address this gap, we present (1) a comprehensive open-access database of absolute storage time series (sub-monthly) for 186 reservoirs (larger than 0.1 km3) in MSEA spanning the period 1985–2023 and (2) an analysis of the reservoir storage dynamics. This dataset is derived from remote sensing observations, integrating satellite-based water surface area extraction from high-resolution (30 m) images and area–elevation–storage (A–E–S) relationships to estimate reservoir level and storage dynamics. The MSEA database includes static (area–elevation–storage curves, water frequency, and reservoir extent) and dynamic (area, water level, and absolute storage time series) components for each reservoir. The 186 reservoirs collectively store around 175 km3 of water, with a minimum of 140 km3 and a maximum of 210 km3. They cover an average area of 8700 km2, ranging from a minimum of 6500 km2 to a maximum of 10 000 km2. We show that the combined average reservoir storage increased from 70 to 160 km3 (+ 130 %) from 2008 to 2017, primarily contributed by reservoirs in the Irrawaddy, Red, Upper Mekong, and Lower Mekong basins. Our in situ validation provides a good match between estimated storage and in situ observations, with 50 % of the validation sites (10 out of 20) showing an R2>0.7 and an average nRMSE < 14 %. The indirect validation (based on altimetry-converted storage) shows even better results, with an R2>0.7 and an average nRMSE < 12 % for 70 % (14 out of 20) of the reservoirs. Furthermore, the analysis of the 2019–2020 drought event in the MSEA region reveals that nearly 30 %–40 % of the region experienced more than 5 months of drought, with the most significant impact on reservoirs in Cambodia and Thailand. As a result, storage departures ranged by up to -40 % in some reservoirs, highlighting significant impacts on water availability. Overall, this analysis demonstrates the potential of the inferred storage time series for assessing real-life water-related problems in Mainland Southeast Asia, with the possibility of applying the method to estimate reservoir storage time series in other parts of the world. The reservoir storage database in Mainland Southeast Asia (MSEA-Res database) and the associated Python code are publicly available on Zenodo at https://doi.org/10.5281/zenodo.14844580 (Mahto et al., 2025). [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:The recent surge in reservoir construction has increased global surface water storage, with Mainland Southeast Asia (MSEA) being a significant hotspot. Such infrastructural evolution demands updates in water management strategies and hydrological models. However, information on actual reservoir storage is hard to acquire, especially for transboundary river basins. To date, no high-resolution spatiotemporal dataset on absolute storage time series is available for reservoirs in MSEA. To address this gap, we present (1) a comprehensive open-access database of absolute storage time series (sub-monthly) for 186 reservoirs (larger than 0.1 km<sup>3</sup>) in MSEA spanning the period 1985–2023 and (2) an analysis of the reservoir storage dynamics. This dataset is derived from remote sensing observations, integrating satellite-based water surface area extraction from high-resolution (30 m) images and area–elevation–storage (A–E–S) relationships to estimate reservoir level and storage dynamics. The MSEA database includes static (area–elevation–storage curves, water frequency, and reservoir extent) and dynamic (area, water level, and absolute storage time series) components for each reservoir. The 186 reservoirs collectively store around 175 km<sup>3</sup> of water, with a minimum of 140 km<sup>3</sup> and a maximum of 210 km<sup>3</sup>. They cover an average area of 8700 km<sup>2</sup>, ranging from a minimum of 6500 km<sup>2</sup> to a maximum of 10 000 km<sup>2</sup>. We show that the combined average reservoir storage increased from 70 to 160 km<sup>3</sup> (+ 130 %) from 2008 to 2017, primarily contributed by reservoirs in the Irrawaddy, Red, Upper Mekong, and Lower Mekong basins. Our in situ validation provides a good match between estimated storage and in situ observations, with 50 % of the validation sites (10 out of 20) showing an R2>0.7 and an average nRMSE < 14 %. The indirect validation (based on altimetry-converted storage) shows even better results, with an R2>0.7 and an average nRMSE < 12 % for 70 % (14 out of 20) of the reservoirs. Furthermore, the analysis of the 2019–2020 drought event in the MSEA region reveals that nearly 30 %–40 % of the region experienced more than 5 months of drought, with the most significant impact on reservoirs in Cambodia and Thailand. As a result, storage departures ranged by up to -40 % in some reservoirs, highlighting significant impacts on water availability. Overall, this analysis demonstrates the potential of the inferred storage time series for assessing real-life water-related problems in Mainland Southeast Asia, with the possibility of applying the method to estimate reservoir storage time series in other parts of the world. The reservoir storage database in Mainland Southeast Asia (MSEA-Res database) and the associated Python code are publicly available on Zenodo at https://doi.org/10.5281/zenodo.14844580 (Mahto et al., 2025). [ABSTRACT FROM AUTHOR]
ISSN:18663508
DOI:10.5194/essd-17-2693-2025