A 10-Year Dataset of Land Surface Observations for the Semi-Humid Alpine Grassland in the Source Region of the Yellow River

Abstract The source region of the Yellow River, accounting for over 38% of its total runoff, is a critical catchment area, primarily characterized by alpine grasslands. In 2005, the Maqu land surface processes observational site was established to monitor climate, land surface dynamics, and hydrological variability in this region. Over a 10-year period (2010–19), an extensive observational dataset was compiled, now available to the scientific community. This dataset includes comprehensive details on site characteristics, instrumentation, and data processing methods, covering meteorological and radiative fluxes, energy exchanges, soil moisture dynamics, and heat transfer properties. The dataset is particularly valuable for researchers studying land surface processes, land–atmosphere interactions, and climate modeling, and may also benefit ecological, hydrological, and water resource studies. The report ends with a discussion on perspectives and challenges of continued observational monitoring in this region, focusing on issues such as cryosphere influences, complex topography, and ecological changes like the encroachment of weeds and scrubland.

Authors

Institutions

Publication Details

Journal
Advances in Atmospheric Sciences
Published
2025-03-18
DOI
https://doi.org/10.1007/s00376-024-4054-5
Citations
4
Primary Topic
Climate change and permafrost
Type
article
Field-Weighted Citation Impact
5.20
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A 10-Year Dataset of Land Surface Observations for the Semi-Humid Alpine Grassland in the Source Region of the Yellow River

Zhaoguo Li, Siqiong Luo, Mingshan Deng, Lijuan Wen et al.
4 citations
Advances in Atmospheric Sciences
Climate change and permafrost
5.20
article

A 10-Year Dataset of Land Surface Observations for the Semi-Humid Alpine Grassland in the Source Region of the Yellow River

Zhaoguo Li, Siqiong Luo, Mingshan Deng, Lijuan Wen, Suosuo Li, Danrui Sheng, Lele Shu, Xianhong Meng, Lunyu Shang, Hao Chen, Shihua Lyu, Yu Zhang, Lin Zhao, Di Ma, Shaoying Wang, Yingying An, Yinhuan Ao, Hanlin Niu
article en
4 citations

Abstract

Abstract The source region of the Yellow River, accounting for over 38% of its total runoff, is a critical catchment area, primarily characterized by alpine grasslands. In 2005, the Maqu land surface processes observational site was established to monitor climate, land surface dynamics, and hydrological variability in this region. Over a 10-year period (2010–19), an extensive observational dataset was compiled, now available to the scientific community. This dataset includes comprehensive details on site characteristics, instrumentation, and data processing methods, covering meteorological and radiative fluxes, energy exchanges, soil moisture dynamics, and heat transfer properties. The dataset is particularly valuable for researchers studying land surface processes, land–atmosphere interactions, and climate modeling, and may also benefit ecological, hydrological, and water resource studies. The report ends with a discussion on perspectives and challenges of continued observational monitoring in this region, focusing on issues such as cryosphere influences, complex topography, and ecological changes like the encroachment of weeds and scrubland.

Advances in Atmospheric SciencesVol. 42(6)
Chinese Academy of Sciences (CN), Chengdu University of Information Technology (CN), Northwest Institute of Eco-Environment and Resources (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 5%
Climate change and permafrost
5.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.