Soil erosion has both on-farm and off-farm effects. On-farm, reduced soil depth can decrease land productivity, while off-farm, sediment transfer can damage streams, lakes, and estuaries. Therefore, optimal soil erosion modeling is a crucial first step in soil erosion research. One of the most important aspects of this modeling is the accuracy and applicability of the soil erosion factors used. Various methods for calculating these factors are discussed in the literature, but no single method is universally accurate. After an extensive review of the literature, we propose using the existing revised universal soil loss equation (RUSLE) factors for global application. Additionally, we conducted a grassroots-level experiment to demonstrate the effectiveness of the proposed methods. RUSLE is identified as the most suitable model for global-scale soil erosion modeling. We evaluated the potential impacts of climate and land use and land cover (LULC) by utilizing shared socio-economic pathways (SSPs) alongside projected LULC scenarios. A suitable general circulation model (GCM) was selected after comparing it with recorded data from a base period. This model was validated with experimental observations, confirming its effectiveness. This review article outlines the future direction of soil erosion modeling and provides recommendations.Graphical AbstractThe graphical abstract visually summarizes the comprehensive methodology and key findings associated with optimal soil erosion modeling and management. It highlights a structured approach, beginning with identifying optimal methods for assessing soil erosion factors: Rainfall and Runoff Erosivity (R), Soil Erodibility (K), Slope Length and Steepness (LS), Cover and Management (C), and Support Practice (P) integral components of the Revised Universal Soil Loss Equation (RUSLE). It illustrates the detailed methodological framework, emphasizing selecting suitable climate models for projecting future R factors, combined with projected land use and land cover (LULC) scenarios derived from Shared Socio-economic Pathways (SSPs). The scenarios shown range from lower emissions (SSP 126) to higher emissions (SSP 585), indicating progressive increases in future erosion risk. Moreover, it explicitly ties the research findings to policy recommendations, underscoring a holistic approach aligning soil conservation with Sustainable Development Goals (SDGs): specifically, Climate Action (SDG 13), Life on Land (SDG 15), and Zero Hunger (SDG 2). Suggested measures include integrating soil erosion control into broader policy frameworks, promoting sustainable land management practices such as agroforestry and contour plowing, and fostering policy integration and collaboration to enhance conservation effectiveness. Overall, the graphical abstract succinctly depicts how climate change, socio-economic dynamics, and LULC variations amplify future soil erosion risks, reinforcing the need for targeted, sustainable, and integrated soil conservation strategies globally.

Decline in snow mass threatens the regional economy that critically depends on meltwater. However, the economic scale of snow mass loss is hardly understood, and its role in the vulnerability of future economic development is unclear. We investigate the current reserves of snow cover and the value of its loss. The result showed that the total annual snow mass in western China declines at a rate of 3.3 x 10(9) Pg per decade (p < 0.05), which accounts for approximately 0.46% of the mean of annual snow mass (7.2 x 10(11) Pg). Snow mass loss over the past 40 years in western China turns into an average loss value of CN0.1 billion (in the present value) every year ($1 = CN7). If the trend continues at the current rate, the accumulated loss value would rise to CN63 billion by 2040. Furthermore, subject to the combinations of RCPs and SSPs scenario, the future economic value of snow mass loss in western China appears to accelerate driven by both declining snowmelt resources and socioeconomic development demand. RCP26-SSP1 is the pathway among all to have the least economic cost in replacing the snowmelt loss, and the cost would be quadrupled in RCP80-SSP3 scenario by 2100. At a basin scale, the declining snow mass would turn the regional economy to be more vulnerable except Junggar and Ili endorheic basin. The Ertis river and Qaidam endorheic basins display to be most vulnerable. It highlights that the snowvalue can be economically important in the regions ofwest China and should be considered more properly in water resources management. (C) 2020 The Author(s). Published by Elsevier B.V.

Cold regions contain a large amount of soil organic carbon, and the warming-accelerated loss of this carbon pool could cause important feedback to climatic change. The changes of carbon budgets in cold regions are poorly quantified especially for the Qinghai-Tibet Plateau (QTP) due to limited field observation data. By considering the soil freeze-thaw process and establishing new plant functional types with localized parameters, we used the Integrated Biosphere Simulator (IBIS) model to simulate the changes of carbon budget on the QTP during 1980-2016. The model was calibrated and validated using carbon flux data from eddy covariance observations at 16 sites. The results showed that the QTP has assimilated 43.16 Tg C/yr during 1980-2016, with permafrost and non-permafrost regions accounting for approximately 15% and 85% of the carbon sink, respectively. During the past four decades, the gross primary production and ecosystem respiration have increased by 1.74 and 2.04 Tg C/ yr(2), resulting in that the carbon sink on the QTP has weakened during 1980-2016. Moreover, the weakening of carbon sink is more pronounced in the non-permafrost regions. We project that the ecosystems will release 12.30 and 24.40 Tg C by 2080-2100 under the moderate and high shared socio-economic pathways (SSP 370 and SSP 585), respectively. This could largely offset the carbon sink and even shift the carbon sink to carbon source on the QTP.

积雪深度的变化对地表水热平衡起着至关重要的作用。选用了国际耦合模式比较计划第六阶段（CMIP6）中目前情景比较齐全的五个全球气候模式，通过对比新疆地区1979—2014年积雪深度长时间序列数据集，评估了气候模式在新疆地区模拟积雪深度的模拟能力，接着预估了未来不同SSPs-RCPs情景下新疆地区在2021—2040年（近期）、2041—2060年（中期）、2081—2100年（末期）相对于基准期（1995—2014年）的积雪深度变化。气温和降水对积雪深度变化有着重要的影响，因此还分析了新疆地区到21世纪末期气温和降水的变化趋势。结果表明：订正后的气候模式模拟的积雪深度数据与观测数据的相关系数均达到0.8以上，其中1月至3月与观测数据的结果更为吻合。气候模式基本上能够反映积雪深度年内变化的基本特征，气候模式模拟的积雪深度空间分布和观测数据具有相似的特征。气温和降水在未来不同情景下均会波动上升，其中气温的增幅相对比较明显，达0.43℃·（10a）-1，而降水的增幅为0.63mm·（10a）-1，新疆未来的气候总体上呈现出变暖变湿的趋势。新疆地区的平均积雪深度在未来不同时...

积雪深度的变化对地表水热平衡起着至关重要的作用。选用了国际耦合模式比较计划第六阶段（CMIP6）中目前情景比较齐全的五个全球气候模式，通过对比新疆地区1979—2014年积雪深度长时间序列数据集，评估了气候模式在新疆地区模拟积雪深度的模拟能力，接着预估了未来不同SSPs-RCPs情景下新疆地区在2021—2040年（近期）、2041—2060年（中期）、2081—2100年（末期）相对于基准期（1995—2014年）的积雪深度变化。气温和降水对积雪深度变化有着重要的影响，因此还分析了新疆地区到21世纪末期气温和降水的变化趋势。结果表明：订正后的气候模式模拟的积雪深度数据与观测数据的相关系数均达到0.8以上，其中1月至3月与观测数据的结果更为吻合。气候模式基本上能够反映积雪深度年内变化的基本特征，气候模式模拟的积雪深度空间分布和观测数据具有相似的特征。气温和降水在未来不同情景下均会波动上升，其中气温的增幅相对比较明显，达0.43℃·（10a）-1，而降水的增幅为0.63mm·（10a）-1，新疆未来的气候总体上呈现出变暖变湿的趋势。新疆地区的平均积雪深度在未来不同时...