Yuanhong You

Understanding the elevation-dependent response of mountain snow cover to climate change requires transcending statistical correlations to reveal the underlying physical mechanisms. This study investigates these mechanisms in the Upper Yarkand River Basin (U-YRB, located on the northwestern edge of the Qinghai–Tibet Plateau) from 2002 to 2020 by integrating a Gradient-Boosted Decision Tree (GBDT) model, a process-based degree-day model, and Structural Equation Modeling (SEM). Our analysis reveals a significant overall decline in Snow Cover Area (SCA) at a rate of −0.25%·a⁻¹, with the rate of decrease accelerating below 4000 m but slowing above this threshold. Snow Depth (SD) exhibited a distinct elevation-dependent trend, decreasing at elevations below 3500 m while increasing above 4000 m. GBDT analysis quantified the shifting dominance of climatic drivers: temperature was the primary factor reducing SCA across all elevations, though its contribution diminished with increasing elevation. Precipitation played a critical yet contrasting role, emerging as the key positive driver for SD accumulation at high elevations (>4500 m). A comparative analysis of snowfall and snowmelt processes identified snowmelt as the key process governing elevation-dependent patterns, peaking around 4000 m. Crucially, SEM elucidated a mechanistic shift across the 4000 m threshold: below 4000 m, snow cover loss was primarily driven by temperature via its strong positive effect on snowmelt. Above 4000 m, while the influence of temperature persisted, the dominant positive effect of precipitation on snowfall became the key driver of the observed SD increase. This shift signals a fundamental transition from melt-dominated dynamics at lower elevations to accumulation-influenced dynamics at higher elevations. Our findings clarify the physical processes behind elevation-dependent snow cover changes and underscore the necessity of elevation-stratified frameworks for hydrological prediction and water resource management in alpine basins.

Surface soil moisture (SSM) plays a critical role in climate change, hydrological processes, and agricultural production. Decision trees and deep learning are widely applied to SSM retrieval. The former excels in interpretability while the latter outperforms in generalization, neither, however, integrates both. To address this issue, an attention decision forest (ADF) was developed, comprising feature extractor, soft decision tree, and tree-attention modules. The feature extractor projects raw inputs into a high-dimensional space to reveal nonlinear relationships. The soft decision tree preserves the advantages of tree models in nonlinear partitioning and local feature interaction. The tree-attention module integrates outputs from the soft tree’s subtrees to enhance overall fitting and generalization. Experiments on conterminous United States (CONUS) watershed dataset demonstrate that, upon sample-based validation, ADF outperforms traditional models with an R² of 0.868 and a ubRMSE of 0.041 m³/m³. Further spatiotemporal independent testing demonstrated the robust performance of this method, with R² of 0.643 and0.673, and ubRMSE of 0.062 and 0.065 m³/m³. Furthermore, an evaluation of the interpretability of the ADF using the Shapley Additive Interpretative Model (SHAP) revealed that the ADF was more stable than deep learning methods (e.g., DNN) and comparable to tree-based ensemble learning methods (e.g., RF and XGBoost). Both the ADF and ensemble learning methods demonstrated that, at large scales, spatiotemporal variation had the greatest impact on the SSM, followed by environmental conditions and soil properties. Moreover, the superior spatial SSM maps produced by ADF, compared with GSSM, SMAP L4 and ERA5-Land, further demonstrate ADF’s capability for large-scale mapping. ADF thus offers a novel architecture capable of integrating prediction accuracy, generalization, and interpretability.

The sustained stability of the arable land replanting index is crucial to the national food security strategy. The exploration of the temporal and spatial changes in the arable land replanting index is of great significance for agricultural development and food security evaluation. This study investigates China’s arable land replanting index from 2010 to 2019 using MODIS NDVI image data, S-G filtering, and quadratic differentiation methods. The results show that there are significant spatial differences in China’s arable land replanting index, with the Huang-Huai-Hai region mainly producing double-cropping crops, the Northeast Plain and Loess Plateau mainly producing single-cropping crops, and the area south of the Yangtze River mainly producing multiple-cropping crops. Overall, China’s food production is mainly based on single-season crops. There is a gradual shift towards double-season crops from north to south, with lower replanting indices in the northwest and higher indices in the eastern provinces. The south has significantly higher indices than the north. During the study period, the arable land replanting index showed an overall upward trend. There were significant increases in the replanting index in the northeast, the Loess Plateau, and the northern Huang-Huai-Hai region. However, there was a downward trend in the southern Huang-Huai-Hai region and the middle and lower reaches of the Yangtze River. It is crucial to maintain the effective planting area of arable land in the Loess Plateau and the double-season planting area of arable land in the Huang-Huai-Hai region while also addressing the downward trend of the arable land replanting index in the middle and lower reaches of the Yangtze River to ensure food security by stabilizing the index.

Climate change has a significant impact on forest ecosystems worldwide, but it is unclear whether forest responses to climate fluctuations are homogeneous across regions. In this study, we investigated the impact of climatic fluctuations on forest growth in a complex terrain, in Anhui Province of China, using Enhanced Vegetation Index (EVI) data from the Moderate-Resolution Imaging Spectroradiometer (MODIS), while considering the impact of terrain characteristics and forest types. Our regional-scale analysis found that the forest response to climatic drivers in Anhui Province is not homogeneous, with only 69% of the forest area driven by temperature (TEM), while 11% is precipitation (PRE) driven and 20% is solar radiation (SWD) driven. We also found with random forest models that terrain traits (elevation and slope) contributed significantly (29.47% and 27.96%) to the spatial heterogeneity of forest response to climatic drivers, with higher elevation associated with a stronger positive correlation between the EVI and temperature (p < 0.001), a weaker positive correlation between the EVI with precipitation (p < 0.001), and a stronger negative correlation between the EVI with solar radiation (p < 0.01), while forest type contributed the least (4.21%). Our results also imply that in a warmer and dryer climate, some forest patches may switch from TEM driven to PRE driven, which could lead to a decrease in forest productivity, instead of an increase as predicted by existing climate models. These results highlight the heterogeneous response of forests within close vicinity to climate fluctuations in a complex terrain, which has important implications for climate-related risk assessments and local forest management.

Studying the dynamics of snowline altitude at the end of the melting season (SLA-EMS) is beneficial in predicting future trends of glaciers and non-seasonal snow cover and in comprehending regional and global climate change. This study investigates the spatiotemporal variation characteristics of SLA-EMS in nine glacier areas of the Himalayas, utilizing Landsat images from 1991 to 2022. The potential correlations between SLA-EMS, alterations in temperature, and variations in precipitation across the Himalayas region glacier are also being analyzed. The results obtained are summarized below: (1) the Landsat-extracted SLA-EMS exhibits a strong agreement with the minimum snow coverage at the end of the melting season derived from Sentinel-2, achieving an overall accuracy (OA) of 92.6% and a kappa coefficient of 0.85. The SLA-EMS can be accurately obtained by using this model. (2) In the last 30 years, the SLA-EMS in the study areas showed an upward trend, with the rising rate ranging from 0.4 m·a⁻¹ to 9.4 m·a⁻¹. Among them, the SLA-EMS of Longbasaba rose fastest, and that of Namunani rose slowest. (3) The SLA-EMS in different regions of the Himalayas in a W-E direction have different sensitivity to precipitation and temperature. However, almost all of them show a positive correlation with temperature and a negative correlation with precipitation.