Analysis of the Spatiotemporal Distribution Patterns and Heterogeneity of Soil Moisture Based on Automatic Measuring Station in China
DOI:
10.20944/preprints202505.0642.v1
Publication Date:
2025-05-12T02:26:36Z
AUTHORS (7)
ABSTRACT
The spatial-temporal distribution and variability of soil moisture (SM) constitutes a critical foundation for coordinating agricultural productivity, ecological preservation, mountain flood disaster mitigation. Based on observed SM data from 27 automated monitoring stations established by the Aerospace Information Research Institute during 2022-2024 across different climatic zones in China, temporal change patterns, spatial heterogeneity characteristics, dominant meteorological influencing factors respective quantitative contributions, as well time-lagged responses to variables were analyzed systematically applying integrated methodologies combining regional analysis, principal component random forest algorithm cross-correlation techniques. Temporally, monthly all exhibited consistent seasonal patterns characterized higher values spring-summer lower levels autumn-winter. Notably, tropical monsoon subtropical climate displayed fluctuations, whereas temperate monsoon, continental, alpine plateau manifested various divergence. Spatially, annual declined characteristic southeastern northwestern regions. Conversely, coefficient variation presented an inverse pattern with enhanced along same direction. Generally, relationship between depth was optimally modeled closely aligned quadratic regression, demonstrating both initially increasing then slightly decreasing trend monotonically depth. ranked descending order significance mean temperature, maximum minimum precipitation, net solar radiation intensity, wind speed. Simultaneously, temperature emerged highest contribution layers. forcing contributions significant affected texture, vegetation type topography. Meanwhile, hydroclimatic response mechanisms found that prolonged effects ranging 21 101 days, precipitation rapid coupling merely 2 6 days.
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