Data · dataset · 2025
Trends and Significance of Composite Extreme Events in the Huangshui River Basin, 1960–2022: A Dataset
Listed in ScienceDB
As the volatility and extremity of global climate change continue to intensify, the frequency, intensity, and disaster risks associated with compound extreme events are escalating, posing severe challenges to regional ecological environments and socio-economic development.
Description
To precisely investigate the patterns of change in compound extreme events and their potential impacts, this study focuses on the Huangshui River Basin in the northeastern Qinghai-Tibet Plateau.
Utilising daily mean temperature and precipitation observations from eight meteorological stations within the basin spanning January 1960 to December 2022, the cumulative distribution function method was employed to systematically identify and analyse the spatiotemporal trends and intensity characteristics of four composite extreme event types: warm-wet, warm-dry, cold-wet, and cold-dry.The core content of this dataset comprises tif-format data on the trend and significance of composite extreme events in the Huangshui River Basin.
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With its extensive temporal coverage and strong spatial representativeness, this dataset directly supports in-depth research on composite extreme events in this region. Given the Huangshui River Basin's location at the northeastern edge of the Qinghai-Tibet Plateau, where it serves both as an ecological barrier and exhibits vulnerability, deepening the analysis of composite extreme events in this region not only provides a robust theoretical foundation and data support for establishing future meteorological disaster early warning and response systems.
It also holds significant practical guidance for local management authorities in scientifically formulating meteorological disaster response strategies, optimising emergency management processes, and enhancing disaster risk prevention and control capabilities.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.28716 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Physical geography and environmental geoscience 73%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.28716 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:3709 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (73%) |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |