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Remotely Sensed Changes in Qinghai–Tibet Plateau Wetland Ecosystems and Their Response to Drought

Author

Listed:
  • Aodi Fu

    (School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Wenzheng Yu

    (School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Bashar Bashir

    (Department of Civil Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia)

  • Xin Yao

    (School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Yawen Zhou

    (School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Jiwei Sun

    (School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Abdullah Alsalman

    (Department of Civil Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia)

  • Karam Alsafadi

    (Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of Environment and Ecology, Xiamen University, Xiamen 361102, China)

Abstract

Wetland ecosystems in the Qinghai–Tibet Plateau are pivotal for global ecology and regional sustainability. This study investigates the dynamic changes in wetland ecosystems within the Chaidamu Basin and their response to drought, aiming to foster sustainable wetland utilization in the Qinghai–Tibet Plateau. Using Landsat TM/ETM/OLI data on the Google Earth Engine platform, we employed a random forest (RF) method for annual long-term land cover classification. Standardized precipitation evapotranspiration indices (SPEI3, SPEI6, SPEI9, and SPEI12) on different time scales were used to assess meteorological drought conditions. We employed a Pearson correlation analysis to examine the relationship between wetland changes and various SPEI scales. The BFASAT method was used to evaluate the impact of SPEI12 trends on the wetlands, while a cross-wavelet analysis explored teleconnections between SPEI12 and atmospheric circulation factors. Our conclusion is as follows: The wetlands, including lake, glacier, and marsh wetlands, exhibited a noticeable increasing trend. Wetland expansion occurred during specific periods (1990–1997, 1998–2007, and 2008–2020), featuring extensive conversions between wetlands and other types, notably the conversion from other types to wetlands. Spatially, lake and marsh wetlands predominated in the low-latitude basin, while glacier wetlands were situated at higher altitudes. There were significant negative correlations between the SPEI at various scales and the total wetland area and types. SPEI12 displayed a decreasing trend with non-stationarity and distinct breakpoints in 1996, 2002, and 2011, indicating heightened drought severity. Atmospheric circulation indices (ENSO, NAO, PDO, AO, and WP) exhibited varying degrees of resonance with SPEI12, with NAO, PDO, AO, and WP demonstrating longer resonance times and pronounced responses. These findings underscore the significance of comprehending wetland changes and drought dynamics for effective ecological management in the Chaidamu Basin of the Qinghai–Tibet Plateau.

Suggested Citation

  • Aodi Fu & Wenzheng Yu & Bashar Bashir & Xin Yao & Yawen Zhou & Jiwei Sun & Abdullah Alsalman & Karam Alsafadi, 2024. "Remotely Sensed Changes in Qinghai–Tibet Plateau Wetland Ecosystems and Their Response to Drought," Sustainability, MDPI, vol. 16(11), pages 1-25, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:11:p:4738-:d:1407423
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    References listed on IDEAS

    as
    1. Yi Liu & Xiaoli Yang & Liliang Ren & Fei Yuan & Shanhu Jiang & Mingwei Ma, 2015. "A New Physically Based Self-Calibrating Palmer Drought Severity Index and its Performance Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4833-4847, October.
    2. Yiming Wang & Yunfeng Hu & Xiaoyu Niu & Huimin Yan & Lin Zhen, 2023. "Myanmar’s Land Cover Change and Its Driving Factors during 2000–2020," IJERPH, MDPI, vol. 20(3), pages 1-22, January.
    3. Bakhtiar Feizizadeh & Davoud Omarzadeh & Mohammad Kazemi Garajeh & Tobia Lakes & Thomas Blaschke, 2023. "Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 66(3), pages 665-697, February.
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