Dominant drivers of spatiotemporal variations in carbon and water use efficiency across the Yellow River Basin revealed by interpretable machine learning

Precisely quantifying the spatiotemporal variation patterns of ecosystem water use efficiency (WUE) (i.e., WUE_NPP and WUE_GPP) and carbon use efficiency (CUE) across diverse regions, as well as identifying the spatial heterogeneity of their principal influencing factors, are crucial for elucidating the complex underlying mechanisms governing carbon and water cycles in the Yellow River Basin (YRB). In this study, we utilized multi-source remote sensing data, and employed Ensemble Empirical Mode Decomposition (EEMD) to explore the nonlinear spatiotemporal trends and patterns of WUE_NPP, WUE_GPP, and CUE within the YRB ecosystem. Additionally, we applied the optimally parameterized XGBoost and SHAP models to discern the spatial heterogeneity of the key factors driving their spatiotemporal variations. The results showed that: (1) The WUE_NPP, WUE_GPP, and CUE of the YRB ecosystem exhibited a spatial distribution pattern characterized by higher values in the southeast and lower values in the northwest, with these metrics were predominantly concentrated at elevations ranging from 1000 to 1500 meters. (2) The interannual change rates of the yearly average values of WUE_NPP, WUE_GPP and CUE in the YRB ecosystem were 0.008 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup><mml:mrow><mml:mtext>g C m</mml:mtext></mml:mrow> <mml:mrow><mml:mo>-</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msup> <mml:msup><mml:mrow><mml:mtext> mm</mml:mtext></mml:mrow> <mml:mrow><mml:mo>-</mml:mo> <mml:mn>1</mml:mn></mml:mrow> </mml:msup> <mml:msup><mml:mtext>y</mml:mtext> <mml:mrow><mml:mo>-</mml:mo> <mml:mn>1</mml:mn></mml:mrow> </mml:msup> <mml:msup><mml:mrow><mml:mtext> a</mml:mtext></mml:mrow> <mml:mrow><mml:mo>-</mml:mo> <mml:mn>1</mml:mn></mml:mrow> </mml:msup> </mml:mrow> </mml:math> , 0.005 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup><mml:mrow><mml:mtext>g C m</mml:mtext></mml:mrow> <mml:mrow><mml:mo>-</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msup> <mml:msup><mml:mrow><mml:mtext> mm</mml:mtext></mml:mrow> <mml:mrow><mml:mo>-</mml:mo> <mml:mn>1</mml:mn></mml:mrow> </mml:msup> <mml:msup><mml:mtext>y</mml:mtext> <mml:mrow><mml:mo>-</mml:mo> <mml:mn>1</mml:mn></mml:mrow> </mml:msup> <mml:msup><mml:mrow><mml:mtext> a</mml:mtext></mml:mrow> <mml:mrow><mml:mo>-</mml:mo> <mml:mn>1</mml:mn></mml:mrow> </mml:msup> </mml:mrow> </mml:math> , and 0.001, respectively. The predominant change patterns for WUE_NPP and WUE_GPP were monotonic increases, covering approximately 42.44% and 41.97% of the watershed area, respectively. In contrast, the change pattern for CUE was primarily a decrease followed by an increase, observed across 42.51% of the watershed area. (3) In the YRB ecosystem, the leaf area index (LAI) emerged as the primary determinant of WUE_NPP and WUE_GPP. Specifically, WUE_NPP and WUE_GPP both showed an upward trend in tandem with the increase in LAI. Furthermore, temperature was identified as the key driving factor for CUE within the YRB ecosystem. (4) In the YRB ecosystem, LAI exhibited the highest importance index for both WUE_NPP and WUE_GPP. It played a dominant role in approximately 42.80% and 45.35% of the study areas for WUE_NPP and WUE_GPP, respectively. Conversely, temperature was a crucial factor influencing the spatial variability of CUE in the YRB ecosystem, exerting a predominant influence in 38.88% of the study areas.