---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '22568'
abstract:
- lang: eng
  text: "The recent surge in reservoir construction has increased global surface water
    storage, with Mainland\r\nSoutheast Asia (MSEA) being a significant hotspot. Such
    infrastructural evolution demands updates in water\r\nmanagement strategies and
    hydrological models. However, information on actual reservoir storage is hard
    to\r\nacquire, especially for transboundary river basins. To date, no high-resolution
    spatiotemporal dataset on absolute\r\nstorage time series is available for reservoirs
    in MSEA. To address this gap, we present (1) a comprehensive openaccess database
    of absolute storage time series (sub-monthly) for 186 reservoirs (larger than
    0.1 km3) in MSEA\r\nspanning the period 1985–2023 and (2) an analysis of the reservoir
    storage dynamics. This dataset is derived\r\nfrom remote sensing observations,
    integrating satellite-based water surface area extraction from high-resolution\r\n(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)\r\nand 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\r\ncover 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)."
article_processing_charge: No
article_type: original
author:
- first_name: Shanti Shwarup
  full_name: Mahto, Shanti Shwarup
  last_name: Mahto
- first_name: Simone
  full_name: Fatichi, Simone
  id: cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6
  last_name: Fatichi
- first_name: Stefano
  full_name: Galelli, Stefano
  last_name: Galelli
citation:
  ama: Mahto SS, Fatichi S, Galelli S. A 1985–2023 time series dataset of absolute
    reservoir storage in Mainland Southeast Asia (MSEA-Res). <i>Earth System Science
    Data</i>. 2025;17(6):2693-2712. doi:<a href="https://doi.org/10.5194/essd-17-2693-2025">10.5194/essd-17-2693-2025</a>
  apa: Mahto, S. S., Fatichi, S., &#38; Galelli, S. (2025). A 1985–2023 time series
    dataset of absolute reservoir storage in Mainland Southeast Asia (MSEA-Res). <i>Earth
    System Science Data</i>. Copernicus Publications. <a href="https://doi.org/10.5194/essd-17-2693-2025">https://doi.org/10.5194/essd-17-2693-2025</a>
  chicago: Mahto, Shanti Shwarup, Simone Fatichi, and Stefano Galelli. “A 1985–2023
    Time Series Dataset of Absolute Reservoir Storage in Mainland Southeast Asia (MSEA-Res).”
    <i>Earth System Science Data</i>. Copernicus Publications, 2025. <a href="https://doi.org/10.5194/essd-17-2693-2025">https://doi.org/10.5194/essd-17-2693-2025</a>.
  ieee: S. S. Mahto, S. Fatichi, and S. Galelli, “A 1985–2023 time series dataset
    of absolute reservoir storage in Mainland Southeast Asia (MSEA-Res),” <i>Earth
    System Science Data</i>, vol. 17, no. 6. Copernicus Publications, pp. 2693–2712,
    2025.
  ista: Mahto SS, Fatichi S, Galelli S. 2025. A 1985–2023 time series dataset of absolute
    reservoir storage in Mainland Southeast Asia (MSEA-Res). Earth System Science
    Data. 17(6), 2693–2712.
  mla: Mahto, Shanti Shwarup, et al. “A 1985–2023 Time Series Dataset of Absolute
    Reservoir Storage in Mainland Southeast Asia (MSEA-Res).” <i>Earth System Science
    Data</i>, vol. 17, no. 6, Copernicus Publications, 2025, pp. 2693–712, doi:<a
    href="https://doi.org/10.5194/essd-17-2693-2025">10.5194/essd-17-2693-2025</a>.
  short: S.S. Mahto, S. Fatichi, S. Galelli, Earth System Science Data 17 (2025) 2693–2712.
das_tickbox: '1'
date_created: 2026-07-27T12:30:24Z
date_published: 2025-06-17T00:00:00Z
date_updated: 2026-08-07T10:54:05Z
day: '17'
doi: 10.5194/essd-17-2693-2025
extern: '1'
intvolume: '        17'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.5194/essd-17-2693-2025
month: '06'
oa: 1
oa_version: Published Version
page: 2693-2712
publication: Earth System Science Data
publication_identifier:
  eissn:
  - 1866-3516
  issn:
  - 1866-3508
publication_status: published
publisher: Copernicus Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: A 1985–2023 time series dataset of absolute reservoir storage in Mainland Southeast
  Asia (MSEA-Res)
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
volume: 17
year: '2025'
...