Explainable Machine Learning Insights into Wetland Dynamics and Carbon Storage in the Irtysh River Basin
Luo K. Samat A. Van de Voorde T. Jiang W. Abuduwaili J.
September 2025Springer Science and Business Media Deutschland GmbH
Earth Systems and Environment
2025#9Issue 31793 - 1820 pp.
Wetlands are vital for global carbon storage, yet face significant pressures. This study quantifies wetland landscape pattern changes and their impact on carbon storage in the transboundary Irtysh River Basin (IRB) from 2000 to 2020, identifies key landscape drivers, and projects future carbon storage under distinct scenarios for 2030. We utilized multi-temporal land cover data (GWL_FCS30), landscape metrics (Fragstats), the InVEST model for carbon storage estimation, interpretable machine learning (NGBoost coupled with SHAP analysis) to link landscape patterns to carbon dynamics, sensitivity analysis, and the PLUS model for scenario-based future projections (Natural Scenario - S1, Wetland Protection - S2, Wetland Degradation - S3). From 2000 to 2020, total wetland area increased by 10,417 km², primarily driven by marsh and swamp expansion, resulting in a net carbon storage increase from 2.827 × 10⁸ tC to 2.885 × 10⁸ tC (net gain: 5.8 × 10⁶ tC). Sensitivity analysis revealed high responsiveness (Sensitivity Index = 10.812) of carbon storage to wetland area change. The NGBoost model accurately predicted carbon storage based on landscape metrics (MSE = 0.682, RMSE = 0.8259, MAE = 0.7811). SHAP analysis identified the aggregation index (AI), largest patch index (LPI), and number of patches (NP) as the most critical landscape predictors influencing carbon storage. Future projections for 2030 estimate total carbon storage at 3.229 × 10⁸ tC under S1 (stabilization), increasing to 3.421 × 10⁸ tC under S2 (protection), but declining sharply to 1.871 × 10⁸ tC under S3 (degradation). Landscape structure, particularly aggregation and the extent of large patches, significantly influences wetland carbon storage in the IRB. Proactive wetland protection policies are crucial for enhancing and maintaining carbon sequestration capacity in this sensitive transboundary basin, contributing to regional climate change mitigation efforts.
InVEST model , Irtysh river basin , Landscape patterns , Machine learning (SHAP) , Transboundary wetlands , Wetland carbon storage
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State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
College of Geography and Remote Sensing Science, Xinjiang University, Urumqi, 830017, China
China-Kazakhstan Joint Laboratory for Remote Sensing Technology and Application, Al- Farabi Kazakh National University, Almaty, 050012, Kazakhstan
Department of Geography, Ghent University, Ghent, 9000, Belgium
Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
University of Chinese Academy of Sciences, Beijing, 100049, China
State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands
College of Geography and Remote Sensing Science
China-Kazakhstan Joint Laboratory for Remote Sensing Technology and Application
Department of Geography
Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities
University of Chinese Academy of Sciences
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