Pulse Brain · Growing Health Evidence Index
Tier 3 — Observational / field trialPeer-reviewed

Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset

Yufei Liu, Yiwen Fang, S. A. Margulis

˜The œcryosphere · 2021

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Summary

This study presents the HMA Snow Reanalysis (HMASR), an 18-year dataset combining Landsat and MODIS satellite observations to map seasonal snow water equivalent across High Mountain Asia at 500 m resolution. The reanalysis reveals that northwestern basins (Indus, Syr Darya, Amu Darya) account for approximately two-thirds of seasonal snowpack volume, with peak storage at mid-elevations (~3500 m) and clear sensitivity to precipitation and air temperature variability. The dataset provides previously unavailable spatiotemporal detail on snow dynamics in this water-critical region with sparse ground observations.

Regional applicability

This study addresses snow dynamics in High Mountain Asia, a region distant from the United Kingdom. However, the Bayesian reanalysis methodology for assimilating satellite snow-cover data and the insights on snow–precipitation–temperature relationships may inform snow monitoring approaches in UK upland regions, particularly Scotland and the Lake District where seasonal snowpack and water resource management are relevant policy concerns.

Key measures

Snow water equivalent (SWE) volume (km³), fractional snow-covered area (fSCA), spatial resolution 500 m, daily temporal resolution, elevational distribution, seasonal accumulation and depletion timing

Outcomes reported

The study assessed spatiotemporal distribution of seasonal snow water equivalent (SWE) in High Mountain Asia using an 18-year reanalysis dataset (2000–2017), quantifying HMA-wide average SWE volume and its variation across basins and elevations. Results show mean total SWE of 163 km³ with seasonal patterns peaking in April and sensitivity to precipitation and temperature changes.

Theme
Climate & resilience
Subject
Climate & greenhouse gas mitigation
Study type
Research
Study design
Reanalysis using Bayesian data assimilation
Source type
Peer-reviewed study
Status
Published
Geography
International
System type
Other
DOI
10.5194/tc-15-5261-2021
Catalogue ID
SNmqopeyqr-kle50x

Funding & declared interests

Funding: NASA

Extracted verbatim from the paper’s funding, acknowledgements and conflict-of-interest sections — shown as neutral provenance, not a judgement on the research.

Topic tags

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