Seasonal Variability in the Impacts of Climatic and Landscape Dynamics on Soil Conservation Services: A Case Study from Upper and Middle Yellow River Basin, China

Abstract

A comprehensive understanding of the spatial and temporal dynamics of soil conservation (SC) and its driving mechanisms is vital for mitigating land degradation and developing erosion-control strategies. However, the influence of driving factors is time-scale dependent and spatially heterogeneous, which remains insufficiently investigated. This study employed the RUSLE to quantify SC across the upper and middle Yellow River Basin from 2000 to 2020 at seasonal and annual scales. Stepwise regression was used for predictor selection, and geographically weighted regression (GWR) was subsequently applied to evaluate the spatial non-stationarity in the relationships between SC and its driving factors. The results revealed that SC exhibited pronounced seasonal variability, with the strongest capacity occurring in summer, followed by autumn and spring, while winter demonstrated the weakest SC capacity. Except in autumn, SC showed an overall increasing trend over the examined time scales. The magnitude and direction of the impacts exerted by climatic and landscape pattern factors varied under different landscape contexts and time scales. Climatic factors had a stronger influence than landscape metrics, with precipitation and NDVI emerging as the two dominant factors driving changes in SC. SC can be improved by increasing landscape diversity and the spatial variability of landscape patches, as well as by expanding grassland cover. This study integrated stepwise regression with GWR to analyze spatial non-stationarity in SC–driver relationships across multiple time scales. This methodological framework offers a theoretical foundation for developing region- and season-specific soil and water conservation strategies in erosion-prone watersheds with marked seasonal climatic variability.

Affiliated Institutions

• Chinese Academy of Sciences CN
• National Science Library CN
• Beijing Normal University CN
• Shandong Jiaotong University CN

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