Critical zone (CZ) plays a vital role in sustaining biodiversity and humanity. However, flux quantification within CZ, particularly in terms of subsurface hydrological partitioning, remains a significant challenge. This study focused on quantifying subsurface hydrological partitioning, specifically in an alpine mountainous area, and highlighted the important role of lateral flow during this process. Precipitation was usually classified as two parts into the soil: increased soil water content (SWC) and lateral flow out of the soil pit. It was found that 65%-88% precipitation contributed to lateral flow. The second common partitioning class showed an increase in SWC caused by both precipitation and lateral flow into the soil pit. In this case, lateral flow contributed to the SWC increase ranging from 43% to 74%, which was notably larger than the SWC increase caused by precipitation. On alpine meadows, lateral flow from the soil pit occurred when the shallow soil was wetter than the field capacity. This result highlighted the need for three-dimensional simulation between soil layers in Earth system models (ESMs). During evapotranspiration process, significant differences were observed in the classification of subsurface hydrological partitioning among different vegetation types. Due to tangled and aggregated fine roots in the surface soil on alpine meadows, the majority of subsurface responses involved lateral flow, which provided 98%-100% of evapotranspiration (ET). On grassland, there was a high probability (0.87), which ET was entirely provided by lateral flow. The main reason for underestimating transpiration through soil water dynamics in previous research was the neglect of lateral root water uptake. Furthermore, there was a probability of 0.12, which ET was entirely provided by SWC decrease on grassland. In this case, there was a high probability (0.98) that soil water responses only occurred at layer 2 (10-20 cm), because grass roots mainly distributed in this soil layer, and grasses often used their deep roots for water uptake during ET. To improve the estimation of soil water dynamics and ET, we established a random forest (RF) model to simulate lateral flow and then corrected the community land model (CLM). RF model demonstrated good performance and led to significant improvements in CLM simulation. These findings enhance our understanding of subsurface hydrological partitioning and emphasize the importance of considering lateral flow in ESMs and hydrological research.

The Grain for Green project has had a substantial influence on water conservation in the Huangshui River Basin, China through afforestation and grassland restoration over the past two decades. However, a comprehensive understanding of the spatiotemporal evolution of water conservation function and its driving factors remains incomplete in this basin. In this study, we utilized the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to examine the spatiotemporal evolution of water conservation function in the Huangshui River Basin from 2000 to 2020. Additionally, we employed the random forest model, Pearson correlation analysis, and geographical detector (Geodetector) techniques to investigate the primary factors and factor interactions affecting the spatial differentiation of water conservation function. The findings revealed several key points. First, the high-latitude northern region of the study area experienced a significant increase in water conservation over the 21-a period. Second, the Grain for Green project has played a substantial role in improving water conservation function. Third, precipitation, plant available water content (PAWC), grassland, gross domestic product (GDP), and forest land were primary factors influencing the water conservation function. Finally, the spatial differentiation of water conservation function was determined by the interactions among geographical conditions, climatic factors, vegetation biophysical factors, and socio-economic factors. The findings have significant implications for advancing ecological protection and restoration initiatives, enhancing regional water supply capabilities, and safeguarding ecosystem health and stability in the Huangshui River Basin.

Against the backdrop of global warming, climate extremes and drought events have become more severe, especially in arid and semi-arid areas. This study forecasted the characteristics of climate extremes in the Xilin River Basin (a semi-arid inland river basin) of China for the period of 2021-2100 by employing a multi-model ensemble approach based on three climate Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6). Furthermore, a linear regression, a wavelet analysis, and the correlation analysis were conducted to explore the response of climate extremes to the Standardized Precipitation Evapotranspiration Index (SPEI) and Streamflow Drought Index (SDI), as well as their respective trends during the historical period from 1970 to 2020 and during the future period from 2021 to 2070. The results indicated that extreme high temperatures and extreme precipitation will further intensify under the higher forcing scenarios (SSP5-8.5>SSP2-4.5>SSP1-2.6) in the future. The SPEI trends under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios were estimated as -0.003/a, -0.004/a, and -0.008/a, respectively, indicating a drier future climate. During the historical period (1970-2020), the SPEI and SDI trends were -0.003/a and -0.016/a, respectively, with significant cycles of 15 and 22 a, and abrupt changes occurring in 1995 and 1996, respectively. The next abrupt change in the SPEI was projected to occur in the 2040s. The SPEI had a significant positive correlation with both summer days (SU) and heavy precipitation days (R10mm), while the SDI was only significantly positively correlated with R10mm. Additionally, the SPEI and SDI exhibited a strong and consistent positive correlation at a cycle of 4-6 a, indicating a robust interdependence between the two indices. These findings have important implications for policy makers, enabling them to improve water resource management of inland river basins in arid and semi-arid areas under future climate uncertainty.

As one of typical areas in the world, northern Chinese Loess Plateau experiences serious wind-water erosion, which leads to widespread land degradation. During the past decades, an ecological engineering was implemented to reduce soil erosion and improve soil protection in this area. Thus, it is necessary to recognize the basic characteristics of soil protection for sustainable prevention and wind-water erosion control in the later stage. In this study, national wind erosion survey model and revised universal soil loss equation were used to analyze the spatiotemporal evolution and driving forces of soil protection in the wind-water erosion area of Chinese Loess Plateau during 2000-2020. Results revealed that: (1) during 2000-2020, total amount of soil protection reached up to 15.47×10⁸ t, which was realized mainly through water and soil conservation, accounting for 63.20% of the total; (2) soil protection was improved, with increases in both soil protection amount and soil retention rate. The amounts of wind erosion reduction showed a decrease trend, whereas the retention rate of wind erosion reduction showed an increase trend. Both water erosion reduction amount and retention rate showed increasing trends; and (3) the combined effects of climate change and human activities were responsible for the improvement of soil protection in the wind-water erosion area of Chinese Loess Plateau. The findings revealed the spatiotemporal patterns and driving forces of soil protection, and proposed strategies for future soil protection planning in Chinese Loess Plateau, which might provide valuable references for soil erosion control in other wind-water erosion areas of the world.

Tumbleweeds participate in a common seasonal biological process in temperate grasslands, creating hanging grass fences during the grass-withering season that result in distinct ecological phenomena. In this study, we addressed the urgent need to understand and restore the degraded desert steppe in Central Mongolia, particularly considering the observed vegetation edge effects around hanging grass fences. Using field surveys conducted in 2019 and 2021 in the severely degraded desert steppe of Central Mongolia, we assessed vegetation parameters and soil physical and chemical properties influenced by hanging grass fences and identified the key environmental factors affecting vegetation changes. The results indicate that the edge effects of hanging grass fences led to changes in species distributions, resulting in significant differences in species composition between the desert steppe's interior and edge areas. Vegetation parameters and soil physical and chemical properties exhibited nonlinear responses to the edge effects of hanging grass fences, with changes in vegetation coverage, aboveground biomass, and soil sand content peaking at 26.5, 16.5, and 6.5 m on the leeward side of hanging grass fences, respectively. In the absence of sand dune formation, the accumulation of soil organic carbon and available potassium were identified as crucial factors driving species composition and increasing vegetation coverage. Changes in species composition and plant density were primarily influenced by soil sand content, electrical conductivity, and sand accumulation thickness. These findings suggest that hanging grass fences have the potential to alter vegetation habitats, promote vegetation growth, and control soil erosion in the degraded desert steppe of Central Mongolia. Therefore, in the degraded desert steppe, the restoration potential of hanging grass fences during the enclosure process should be fully considered.

Permafrost in Northeast China is undergoing extensive and rapid degradation, and it is of great importance to understand the dynamics of vegetation response to permafrost degradation during different periods in this region. Based on the meteorological station data and MODIS land surface temperature data, we mapped the distribution of permafrost using the surface frost number (SFN) model to analyze the permafrost degradation processes in Northeast China from 1981 to 2020. We investigated the spatiotemporal variation characteristics of vegetation and its response to permafrost degradation during different periods from 1982 to 2020 using the normalized difference vegetation index (NDVI). We further discussed the dominant factors influencing the vegetation dynamics in the permafrost degradation processes. Results indicated that the permafrost area in Northeast China decreased significantly by 1.01×10⁵ km² in the past 40 a. The permafrost stability continued to weaken, with large areas of stable permafrost (SP) converted to semi-stable permafrost (SSP) and unstable permafrost (UP) after 2000. From 1982 to 2020, NDVI exhibited a significant decreasing trend in the seasonal frost (SF) region, while it exhibited an increasing trend in the permafrost region. NDVI in the UP and SSP regions changed from a significant increasing trend before 2000 to a nonsignificant decreasing trend after 2000. In 78.63% of the permafrost region, there was a negative correlation between the SFN and NDVI from 1982 to 2020. In the SP and SSP regions, the correlation between the SFN and NDVI was predominantly negative, while in the UP region, it was predominantly positive. Temperature was the dominant factor influencing the NDVI variations in the permafrost region from 1982 to 2020, and the impact of precipitation on NDVI variations increased after 2000. The findings elucidate the complex dynamics of vegetation in the permafrost region of Northeast China and provide deeper insights into the response mechanisms of vegetation in cold regions to permafrost degradation induced by climate change.

Nitrogen deposition and water tables are important factors to control soil microbial community structure. However, the specific effects and mechanisms of nitrogen deposition and water tables coupling on bacterial diversity, abundance, and community structure in arid alpine wetlands remain unclear. The nitrogen deposition (0, 10, and 20 kg N/(hm²•a)) experiments were conducted in the Bayinbulak alpine wetland with different water tables (perennial flooding, seasonal waterlogging, and perennial drying). The 16S rRNA (ribosomal ribonucleic acid) gene sequencing technology was employed to analyze the changes in bacterial community diversity, network structure, and function in the soil. Results indicated that bacterial diversity was the highest under seasonal waterlogging condition. However, nitrogen deposition only affected the bacterial Chao1 and beta diversity indices under seasonal waterlogging condition. The abundance of bacterial communities under different water tables showed significant differences at the phylum and genus levels. The dominant phylum, Proteobacteria, was sensitive to soil moisture and its abundance decreased with decreasing water tables. Although nitrogen deposition led to changes in bacterial abundance, such changes were small compared with the effects of water tables. Nitrogen deposition with 10 kg N/(hm²•a) decreased bacterial edge number, average path length, and robustness. However, perennial flooding and drying conditions could simply resist environmental changes caused by 20 kg N/(hm²•a) nitrogen deposition and their network structure remain unchanged. The sulfur cycle function was dominant under perennial flooding condition, and carbon and nitrogen cycle functions were dominant under seasonal waterlogging and perennial drying conditions. Nitrogen application increased the potential function of part of nitrogen cycle and decreased the potential function of sulfur cycle in bacterial community. In summary, composition of bacterial community in the arid alpine wetland was determined by water tables, and diversity of bacterial community was inhibited by a lower water table. Effect of nitrogen deposition on bacterial community structure and function depended on water tables.

Assessing and managing ecological risks in ecologically fragile areas remain challenging at present. To get to know the ecological risk situation in Turpan City, China, this study constructed an ecological risk evaluation system to obtain the ecological risk level (ERL) and ecological risk index (ERI) based on the multi-objective linear programming-patch generation land use simulation (MOP-PLUS) model, analyzed the changes in land use and ecological risk in Turpan City from 2000 to 2020, and predicted the land use and ecological risk in 2030 under four different scenarios (business as usual (BAU), rapid economic development (RED), ecological protection priority (EPP), and eco-economic equilibrium, (EEB)). The results showed that the conversion of land use from 2000 to 2030 was mainly between unused land and the other land use types. The ERL of unused land was the highest among all the land use types. The ecological risk increased sharply from 2000 to 2010 and then decreased from 2010 to 2020. According to the value of ERI, we divided the ecological risk into seven levels by natural breakpoint method; the higher the level, the higher the ecological risk. For the four scenarios in 2030, under the EPP scenario, the area at VII level was zero, while the area at VII level reached the largest under the RED scenario. Comparing with 2020, the areas at I and II levels increased under the BAU, EPP, and EEB scenarios, while decreased under the RED scenario. The spatial distributions of ecological risk of BAU and EEB scenarios were similar, but the areas at I and II levels were larger and the areas at V and VI levels were smaller under the EEB scenario than under the BAU scenario. Therefore, the EEB scenario was the optimal development route for Turpan City. In addition, the results of spatial autocorrelation showed that the large area of unused land was the main reason affecting the spatial pattern of ecological risk under different scenarios. According to Geodetector, the dominant driving factors of ecological risk were gross domestic product rating (GDPR), soil type, population, temperature, and distance from riverbed (DFRD). The interaction between driving factor pairs amplified their influence on ecological risk. This research would help explore the low ecological risk development path for urban construction in the future.