Chao Wu

Dr. Chao Wu


Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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12 July 2026 in Land
Verified
Journal Article
Open access
PDF file
佳李佳琪 李
佳琪 李
PYPeng Yu
Peng Yu
CWChao Wu
Chao Wu
Guobin FuGuobin Fu
Guobin Fu
CMChongya Ma
Chongya Ma
JLJiping Liu
Jiping Liu
Wetland area and quality have continued to decline worldwide due to the combined impacts of global climate change and human activities, so there is a need for a scientific and standardized framework to assess wetland degradation risk and support conservation and restoration efforts. In this study, a wetland degradation risk assessment framework integrating pressure level and degradation level was developed and applied to the western Songnen Plain, one of China’s major marsh wetland regions, using data from 2000, 2010, and 2020. The results showed that: (1) anthropogenic activity intensity and wetland area change were the most influential indicators. The degradation risk index ranged from 0.20 to 0.68, with mean values of 0.52, 0.46, and 0.46 in 2000, 2010, and 2020, respectively, indicating a slight overall decline. (2) The pressure level increased from 0.56 to 0.59 from 2000 to 2020, suggesting a growing influence of external pressures. In the same period, the degradation level decreased from 0.34 to 0.30, reflecting a reduced effect of internal environmental conditions. (3) Spatially, medium-risk areas have expanded from 47.5% to 64.1% and became the dominant area, while high-risk areas remained relatively stable at 14–16%, whereas relatively high-risk areas decreased sharply from 33.87% to 6.29%. These results provide useful guidance for regional land management for sustaining ecological systems, and the proposed framework provides a transferable assessment logic for wetland degradation risk, but its application to other regions require local calibration of indicators, weights, thresholds, and validation datasets.