Snow avalanches are a major component of the mountain cryosphere that frequently create a menace for the road network. Deposit characteristics determine the extent of damage and disruption to communication networks, but the factors controlling snow-deposit volumes remain largely unknown. This study investigates the influence of meteorological and snowpack conditions on snow-avalanche deposits and road-network vulnerability based on 1986 deposit volumes from 182 paths located in two regions of the French Alps between 2003 and 2017: Guil and Haute-Maurienne valleys. During the period, 195 avalanches impacted the road network in these areas, leading to major disruptions. In Haute-Maurienne, correlations between deposit volumes and meteorological and snowpack conditions are high in winter. However, the relationships differ with path elevation and orientation. Results do not show any significant relationship between volumes and meteorological or snowpack conditions for the spring season. Focusing on deposits that disturbed the road network, winter and spring reveal a distinct influence of meteorological and snow variables compared to the overall data set, with snowfall intensity as the predominant control variable of deposit volumes leading to road cuts. When the same analysis is conducted by considering Guil valley separately or by aggregating Haute-Maurienne with Guil valley area data, results do not show any significant relationship, highlighting the specific regional nature of relations between deposit volumes and meteorological and snowpack conditions.

The limited land is under unprecedented pressure from production, living and ecology. In order to evaluate the land pressure in the Yangtze River Delta region in 1995, 2000, 2005, 2010, 2015, and 2020 from the perspective of production, living, and ecology. This study builds a land pressure evaluation index system based on a fuzzy comprehensive evaluation model using multi-source and multi-scale data. In order to investigate trade-offs and synergies among production, living, and ecology pressures, we use the mechanical equilibrium model in physics. We then analyze land pressure model reliability and uncertainty using Monte Carlo simulations. The results show that (1) Our model can effectively reveal the level of land pressure and reflect the land pressure geographical pattern of “high in the east and low in the west, high in the south and low in the north” that characterizes the Yangtze River Delta region. (2) While living and ecology pressures are tending to rise, production pressures are tending to decrease. (3) Except for Shanghai, the trade-off areas are primarily concentrated in economically successful regions with high production and living pressure and low ecology pressure. The coordinated areas are primarily found in northern Jiangsu Province and northern Anhui Province.

Abstract Qinghai-Tibetan Plateau is an important ecological security barrier in China, and alpine ecosystem presents a trend of overall improvement under the influence of climate change and human activities, yet there are localized deteriorations. In order to improve the ecological function of the Tibetan Plateau, large-scale ecological restoration projects (ERPs) have been carried out in Tibetan Plateau over the past 30 years. From following the footstep of national ecological constructions to the implementation of projects specific to the plateau characteristics, the ERP of the plateau can be divided into three stages. Major ERPs focusing on four types of projects, i.e. forest protection and construction, grassland protection and construction, water and soil erosion control and desertification land management, with a total area of 850,000km 2 across the Qinghai-Tibetan Plateau. The positive effect at sampling quadrat scale has been widely verified, and the responses of productivity and species diversity were inconsistent. The positive effect at the regional scale gradually emerges, yet the spatial difference is significant, and the quantification of driving forces is an important prerequisite. The positive and negative effects of existing ERP on biodiversity conservation are revealed, for instance, there is a significant increase of rare wild animals in natural reserves and in artificially planted areas it failed to effectively curb the loss of animal and plant diversity. The long-term effects of various ERPs on the biodiversity should be taken into account so as to optimize the ecological measures and build a sustainable management model after project restoration. By summarizing the achievements and problems of ERP across the Qinghai-Tibetan Plateau in the past 30 years, we proposed to set stepwise ecological restoration and management scheme by different restoration goals and reference modes in three stages, i.e. “Environmental treatment—Ecological restoration projects—Adaptive management”.

Although it is well known that groundwater significantly influences plant communities, there have been few studies on how the soil and plant communities respond to groundwater level decline in a short time affected by coal mining. This paper examined the changes in groundwater depth before and after coal mining and the soil-vegetation response in a typical semi-arid grassland coal mine area of Hulunbuir Steppe, Northeastern China. The IsoSource model, based on the dual stable isotopes of δ D and δ 18O, was employed to estimate groundwater contributions to shallow soil (0-100cm) water under different groundwater depths. The results revealed that groundwater was the dominant water source (75.7±17.1%) for shallow soil water when the groundwater depth is less than 4 meters, indicating that 4m is a threshold in groundwater depth, separating groundwater-dependent, and precipitation-driven vegetation system in the study area. Secondly, a strong non-linear response between vegetation species, height, coverage, and groundwater decline was identified in the coal mine area. The vegetation properties were found to be lowest in the areas where groundwater depth increased from 1.5~4 m to 4~28 m before and after coal mining. Finally, the groundwater level decline in the mining area significantly influenced the groundwater-dependent vegetation ecosystem, with the soil CEC and organic matter reduced, and the plant community degraded, succeeding from mesophytic to xerophytic. Whereas, the soil-vegetation system in the non-groundwater-dependent area has no obvious response to the groundwater decline. These results suggest that caution should be exercised when mining in groundwater-dependent ecosystem regions.

Informed, wise land management is a challenge since long, but has gained extra importance through the need for responding to the climate change effects. The European Green Deal is one example of a concertation effort in a world where data availability, sharing options, and just-in-time exploitation are not entirely solved. The LandSupport service responds to this challenge through a combination of a Spatial Decision Support System on top of a datacube management and analytics engine. In this paper we specifically focus on this datacube engine and how it can contribute to a better decision making through an ordered multi-dimensional data man­agement paired with strictly standards-compliant inter­faces. Further, the approach described shows a remedy against the current fragmentation of services: through a location-transparent federation a single common pool of information is established, including distributed fusion allowing an unlimited mix-and-match of data regardless of their location.

Root detritusphere, i.e., the soil in vicinity of decomposing root residues, plays an important role in soil microbial activity and C sequestration. Pore structure (size distributions and connectivity of soil pores) in the detritusphere serves as a major driver for these processes and, in turn, is influenced by physical characteristics of both soil and roots. This study compared pore structure characteristics in root detritusphere of soils of contrasting texture and mineralogy subjected to >6 years of contrasting vegetation: monoculture switchgrass and polyculture prairie systems. Soil samples were collected from five experimental sites in the US Midwest representing three soil types. Soil texture and mineralogy were measured using hydrometer and X-ray powder diffraction, respectively. The intact cores were scanned with X-ray computed micro-tomography to identify visible soil pores, biopores, and particulate organic matter (POM). We specifically focused on pore structure within the detritusphere around the POM of root origin. Results showed that detritusphere of coarser-textured soils, characterized by high sand and quartz contents, had lower porosity in the vicinity of POM compared to finer-textured soils. POM vicinities in finer soils had high proportions of large (>300 μm Ø) pores, and their pores were better connected than in coarser soils. Lower porosity in outer (>1 mm) parts of detritusphere of switchgrass than of prairie suggested soil compaction by roots, and the effect especially pronounced in coarser soils. The results demonstrated that soil texture and mineralogy played a major, while vegetation a more modest, role in defining the pore structure in root detritusphere.

Adjusting the planting structure, as a key intervention in agricultural ecosystems, plays a vital role in enhancing ecosystem service value (ESV) and improving system functionality. This study evaluated the farmland ecosystem service value (FESV) of Chinese counties using the equivalent factor method, and analyzed the spatio-temporal differentiation of planting structure and FESV. Spatial correlation tests confirmed the spatial dependence and heterogeneity between the two. The single-region and two-region spatial models were employed to examine the direct effects of planting structure on FESV, as well as its spillover gradients and underlying mechanisms. The results showed that: (1) The planting structure of China’s three major grains generally exhibited a pattern “higher in the east and lower in the west”, while FESV distribution aligned with the “Heihe-Tengchong Line”, with the highest contribution from regulating functions, followed by provisioning, supporting, and cultural functions. (2) Both planting structure and FESV displayed significant spatial agglomeration. Adjusting the planting structure not only improved local FESV but also affected neighboring regions through spillover effects, primarily manifesting as negative competitive effects. (3) The two-region model revealed consistent positive FESV spillover effects across various regions, including “National Key Ecological Function Area”, “China’s Major Grain Producing Area”, and “National Urban Agglomerations”, though with notable asymmetry between regions. (4) The spillover effects on FESV were especially pronounced in areas with lower grain-planting proportions. Therefore, we should pay more attention to the ecological benefits of planting structure adjustments and its asymmetric spillover effects, to ensure the health and sustainability of agricultural ecosystems, to promote mutually beneficial economic and environmental cooperation between regions.

：The Huaihe River Eco-economic Belt (HREB) is a vital agricultural hub in China, encompassing 11% of the nation’s cultivated land and producing one-sixth of its total grain. Soil organic carbon (SOC), a key indicator of soil fertility, plays a vital role in maintaining soil quality and crop productivity. However, climate change and unresonable land management practices have significantly impacted soil carbon stocks in agricultural ecosystems, leading to soil degradation and reduced crop productivity. The study analyzed the spatial distribution and temporal dynamics of SOC stocks in the HREB from 1980 to 2020, identified the primairy drivers of SOC sequestration, and proposed targeted strategies to enhance carbon storage for sustainable agricultural development. The results showed that soil organic carbon stocks (SOCS) increased by 227.73 Tg over four decades in HREB, with an average sequestration rate of 220.30 kg C ha -1 yr -1, highlighting the significant carbon sink function. Regional SOCS exhibited a ”higher-south, lower-north” spatial pattern, with south-north soil organic carbon density (SOCD) differences of 0.70, 0.57, and 1.04 kg m -2 in 1980, 2010, and 2020, respectively. Soil pH, nitrogen fertilizer input, average annual temperature, and average annual rainfall are the primary explanatory variables influencing the annual variation of SOCD in the regions, with relative importance values of 18.46%, 10.67%, 10.37%, and 8.05%, respectively. Soil pH was the most critical determinant, affecting SOC stabilization through mineral interactions, microbial communities, and enzyme activities. Despite excessive nitrogen application accelerating SOC decomposition, it also enhanced carbon inputs via crop root exudates. Additionally, rising temperatures (+0.39°C per decade) and humid conditions promoted plant and microbial activity, enhancing SOC accumulation. Optimizing soil pH regulation and nitrogen management while adjusting to climate change is crucial for enhancing soil carbon sink capacity and guaranteeing sustainable agricultural production.

Soil erosion-induced solute loss contributes to non-point source pollution (NPS). The extent and depth of soil solute involvement in runoff exchange processes are determined by soil disturbed depth ( Ds), encompassing runoff depth ( Dr), and effective mixing depth ( De). This study aimed to investigate the impacts of Ds on soil erosion and solute loss. The varying Dr (0.04 ~ 0.59 cm) and De (0.08 ~ 10.35 cm) were generated through rainfall (60, 90, and 120 mm h −1) and overland flow (0, 1, and 2 L min −1). Dr and De were quantified by the KMnO 4 and Br tracing methods, respectively. Additionally, runoff coefficient ( Rc), sediment concentration ( Cs), sediment yield rate ( Sy), Br concentration in runoff ( CBr), Dr, and De were determined at 2 or 3-minute intervals. Significant differences were observed in runoff initiation time ( Tr) (15 ~ 187 s), Rc (0.49 ~ 0.99), Sy (4.04 ~ 242.89 g m 2 min -1), Cs (2.88 ~ 48.66 g L -1), and CBr (0.70 ~15 601.26 mg L -1) across different Ds (F>3, P<0.01). A power function relationship was observed between Tr, Rc, and Ds ( R2adj> 0.89). Sy, Cs, and CBr demonstrated increasing trends with rising Ds, though the magnitude of these increases varied across different Ds ( R2adj> 0.55). Furthermore, a notable linear correlation was identified between cumulative runoff generation, sediment yield, Br loss, and mean Dr, De ( R2adj> 0.75). Collectively, Ds accounted for 64.9% and 46.2% of the variation in soil erosion and Br loss, respectively. These results would facilitate an improvement of NPS models.

Agricultural land plays a pivotal role in safeguarding food security and improving farmers’ living standards. However, its utilization faces sustainability challenges due to greenhouse gas emissions and associated environmental impacts. Improving agricultural land use benefits (ALUB) has become imperative for achieving sustainable agricultural development. This study combines the SDGs with agricultural land systems to construct a theoretical framework and evaluation index system for ALUB based on the ”element-structure-function-benefit-interaction” model. Using county-level data (2000-2020) from Hubei Province, we quantified ALUB indices for 75 counties, analyzed spatiotemporal patterns via a Mechanical Equilibrium Model, and identified key influencing factors using Optimal Parameters Geographical Detector. Results show: (1) Economic, social, and comprehensive benefits grew fluctuantly, while ecological benefits remained stable; (2) The 2020 average coordination level (0.378) indicated ”basic coordination,” with stable non-high-coordination spatial patterns; (3) Socioeconomic factors (e.g., GDP density, rural labor force) predominantly influenced trade-off/synergy relationships. Through deviation distribution analysis, the counties were categorized into three distinct typologies: (Quadrant I) excellent ecological benefits but weak economic and poor social benefits, (Quadrant II) excellent economic benefits but weak ecological and poor social benefits, and (Quadrant VI) excellent ecological benefits but weak social and poor economic benefits. Based on these classifications, tailored optimization strategies were proposed to enhance coordination among ALUB dimensions. This study not only enriches the conceptual framework of ALUB formation and its interrelationships but also provides practical guidance for sustainable agricultural land management.

As an important component of landscape design, the impact of planted landscape space on human health has received considerable attention in modern urban landscape gardening research. However, most existing studies focus on the overall benefits of urban green space and lack quantitative analyses of planted landscape spatial features (e.g., spatial scale, slope, and plant community structure level) in relation to psychological recovery and physiological regulation. This study aimed to investigate the effects of different scale groups, slope groups, and plant composition groups on stress reduction and attention recovery. Thirteen spatial models of planted landscape space were constructed using virtual reality technology, covering five scale groups, five slope groups, and three plant composition groups. Fifty-nine subjects were randomly assigned to three experimental groups, and their stress, attention, and subjective preferences were assessed. The results showed that: the largest scale space (12,000 m 2) was the most effective for stress reduction; the smallest scale (400 m 2) was more beneficial for attention restoration; and the moderate scale (6,000 m 2) received the highest subjective preference. Gently sloped space (5%) demonstrated the best performance across stress reduction, attention restoration, and subjective preference. Mid-level planting was most significant for attention restoration and preference, while lower-level planting was more conducive to stress regulation. This study provides a quantitative basis for the spatial design of planted landscape space, elucidates the mechanisms through which different spatial features influence psychological and physiological health, and offers theoretical support for urban green space planning.

Integrating process-oriented (PO) and machine learning (ML) models is effective for obtaining dynamic spatial information on soil organic carbon (SOC) stocks. However, PO-ML integration, particularly at large scales, has received insufficient attention. This gap limits our understanding of and predictive capabilities regarding SOC dynamics. To explore the adaptability and effectiveness of PO-ML integration on a large scale, we constructed a national-scale PO-ML hybrid model. Due to the extensive research on cropland, the inapplicability of wetlands, and the uncertainty in estimating other natural land cover types, we used the PO model to expand the time series of nationwide non-waterlogged mineral soil natural land cover SOC density data (excluding wetland and croplands) for the period 2000–2014 to enhance the ML model training data and predict the spatial distribution of the average SOC content during this period. The results indicated that the ML model’s accuracy ( R 2 = 0.57) aligned with the average level reported by other digital soil mapping (DSM) studies, whereas the accuracy of the PO-ML hybrid model was approximately 17% above the upper limit reported in other DSM studies. This improvement highlights the advancement our research contributes to the field. Furthermore, the study demonstrates the important role of dynamic environmental covariates in predicting SOC density by showing that they significantly enhanced the model’s ability to capture the spatiotemporal dynamics of SOC. Moreover, in the absence of Rothamsted carbon model simulation data, the ML model exhibited higher uncertainty in the sample-scarce western regions and around the latitudes of 30°N–40°N, whereas the PO-ML model effectively reduced this uncertainty. These findings indicate that the hybrid model strategy offers significant advantages in SOC simulation and provides important insights into the nationwide spatiotemporal distribution of SOC in non-waterlogged mineral soils’ natural land cover.

Lei Bamboo ( Phyllostachys violascens), a prevalent economic bamboo species in subtropical China, is subjected to periodic mulching practices (hereinafter ”mulching”) to increase the soil temperature and increase bamboo shoot production. While increasing yield, these practices may contribute to bamboo forest degradation in soil quality and substantial greenhouse gas emissions from the soil. Research on the temperature sensitivity of soil CO 2 emissions from bamboo forests subjected to various mulching treatments remains scarce. This study focused on four bamboo forest stands under different mulching histories in Nanjing China: S1 (abandoned after 8 years of winter rice husk mulching and 2 years without), S2 (10 years of continuous mulching), S3 (5 years of mulching followed by 5 years without), and CK (no mulching). Soil samples from various depths(0-10cm,10-20cm,20-30cm) were incubated at different temperatures(5-35℃) to assess the effects of mulching on soil CO 2 emissions, temperature sensitivity ( Q 10), and soil properties, including organic carbon (SOC), nitrogen (N), phosphorus (P), potassium (K), and water content (SWC).. The findings revealed the following: Mulching notably increased the soil temperature, fertility, and CO 2 emissions, with S3 resulting in the greatest improvement in fertility and S1 resulting in the highest CO 2 emission flux and soil CO 2 production response ( Q 10); Q 10 decreased with increasing soil depth, with S1’s Q 10 in the 0–10 cm layer significantly elevated by 72.91%, 66.41%, and 82.56% compared to S2, S3, and CK, respectively, and mulch treatment interacting with soil depth to cause greater Q 10 increases in shallower layers; Mulching altered the Q 10 by modifying the annual average soil temperature, SOC, TN, and SWC, thereby increasing CO 2 emissions. Among the treatments, S2 minimized CO 2 emissions, whereas S3 maximized them. In conclusion, relative short periodic mulching in bamboo forests significantly increased more soil CO 2 emissions and temperature sensitivity than long-term mulching by altering the soil temperature, SOC, TN, and SWC. This study provides new references and data support for the sustainable and environmentally friendly management of bamboo forests in the context of future global warming and anthropogenic N loads

Ganzhou City in Jiangxi Province, China, serves as a critical source and supplier of rare earth ions. However, rampant mining activities have led to severe soil erosion, persistent pollution, and considerable land degradation within the mining areas. Grassed waterways (GWWs) offer substantial advantages in drainage, erosion resistance, and promoting sediment deposition. This study investigated the utilization of the GWW for soil conservation in rare earth tailings in southern Jiangxi Province, focusing on the dynamic effects of flow and slope on runoff processes, sediment transport, and particle sorting during multiple scouring events. Results demonstrate that the GWW effectively reduced runoff rates and significantly controlled sediment yield. Both flow and slope influence sediment yield rates, with slope having a more pronounced impact. Particle size distribution in GWWs, under varying flow rates, slopes, and field conditions, typically exhibits bimodal patterns, with particles in the 0–0.068 mm and 0.094–0.171 mm ranges dominating the eroded sediment. Transport mechanisms, primarily suspension/saltation (>50%) and rolling, affect these particle sizes respectively, while coarse particles (>0.955 mm) are predominantly deposited. The GWW enhanced particle separation, minimizing the loss of large particles, including agglomerates and individual particles, thereby reducing overall sediment generation. This study provides valuable insights into the potential of GWW for mitigating runoff and sediment production and optimizing deposition processes, providing practical implications for engineering applications.

Green manure-tobacco rotation system has proven to be an effective strategy for improving soil nutrients and alleviating soil-borne fungal diseases. However, the differential efficacy of various green manures against tobacco root rot and their underlying microbial regulatory mechanisms remains unclear. Through pot-based experiments, this study systematically evaluates the disease-suppressive effects of two green manure, smooth vetch ( Vicia villosa) and rape ( Brassica campestris), applied at two incorporation rates on tobacco root rot (caused by Fusarium solani). Results indicated that green manure incorporation significantly reduced pathogen abundance by 36.1-64.7%, decreased root rot disease incidence by 10-20%, with smooth vetch exhibiting superior disease suppression compared to rape. Smooth vetch incorporation enriched a higher abundance of Bacillus spp. (Bacillus niacini and Bacillus megaterium) in rhizosphere soil, increasing by more than 2 times compared to no incorporation . Co-occurrence network analysis identified four microbial modules, among which Module 0 exhibited a significant negative correlation with pathogen related abundance. Within Module 0, bacterial taxa, particularly Bacillus spp., occupied central positions with extensive node interactions, while fungi maintained higher relative abundance. This module also contained other disease-resistant communities, including Paenibacillus, Lysobacter soli, Chaetomium sphaerale and Rhizopus arrhizus. Notably, smooth vetch treatment enhanced soil available nutrients, (especially alkaline nitrogen content) more effectively than rape treatment, favoring the increase of these disease-resistant communities. Collectively, smooth vetch demonstrates superior capacity in enhancing resistance to tobacco root rot and reducing disease incidence, offering an effective solution for tobacco soil-borne disease prevention and control.

Due to the combined effects of human activities and climate change, freshwater wetlands, especially in agricultural watersheds, face severe degradation threats. Therefore, it is necessary to explore in depth the changes in plant communities within these wetlands. This study investigates changes in wetland plant communities within these watersheds and assesses the feasibility of the Breaks for Additive Season and Trend (BFAST) method for detecting abrupt shifts in vegetation over long time series. Using long-term Landsat imagery (1984-2016), annual maximum NDVI values were calculated for the Naolihe Basin Nature Reserve in Northeast China. The BFAST algorithm was then applied to detect NDVI changes in wetland plant communities, with results validated through field surveys. The results revealed four distinct NDVI change trends: no significant change, high-to-low shift, low-to-high shift, and continuous decline. NDVI deviations ranged from -0.85 to 0.94, with 1 to 5 abrupt changes mainly occurring between 1993 and 2006. The study confirms BFAST’s effectiveness in detecting changes in wetland plant communities and, combined with field data, proposes a conceptual model to explain the degradation processes in freshwater wetlands. The model reveals the degradation process of different vegetation types under the influence of water competition and other factors, which contributes to a more accurate understanding of vegetation change in freshwater wetlands and provides strong support for its sustainable conservation and management.

In order to study the reinforcement effect of plant roots on soil in open-pit mine waste dump slopes, typical slope protection plants such as Alfalfa (Medicago sativa), Cogonggrass (Imperata cylindrica), and Goosegrass (Eleusine indica) were selected. The tensile mechanical properties of their roots and the shear performance of the root-soil composite were investigated. Additionally, the WWM model was used to calculate the shear strength of the soil samples, and model parameters were adjusted to quantify the additional cohesion provided by each plant’s roots at different soil depths. The results showed that the Root Area Ratio (RAR) of all three herbaceous plants decreased with increasing soil depth. Among them, Alfalfa had the highest RAR, which was 3.33 and 2.68 times that of Cogonggrass and Goosegrass, respectively. The Root Length Density (RLD) of Alfalfa initially increased and then decreased with soil depth, while the RLD of Cogonggrass and Goosegrass decreased consistently with increasing soil depth. The RLD values of the three herbaceous plants at different soil depths ranged from 0.07 to 0.4 cm/cm 3 for Alfalfa, 0.06 to 0.67 cm/cm 3 for Cogonggrass, and 0.13 to 1.3 cm/cm 3 for Goosegrass. The tensile properties of the roots of all three herbaceous plants exhibited a power-law relationship with root diameter. The tensile force increased with increasing root diameter, while the tensile strength decreased as the diameter increased. Roots can alter the mechanical characteristics of the shear behavior in the root-soil composite, increasing the shear peak strength, extending the peak shear strain, and enhancing the shear strength of the soil. When the water content is 10.5%, the cohesion and internal friction angle of the rootless soil and root-containing soil reach their maximum values, but decrease as the water content increases. Soil depth and plant roots have minimal impact on the internal friction angle. The shear strength of the root-soil composite was greater than that of rootless soil, with the increment in additional cohesion being highest for Goosegrass, followed by Cogonggrass, and Alfalfa. The correction factor k of the WWM model was optimized, and a more accurate method for quantifying the soil reinforcement effect of plant roots was established. The results of this study provide a scientific basis for the ecological restoration of slopes in open-pit mine waste dumps, guiding the selection and configuration of suitable protective plants to enhance the stability of these slopes.

The balance between supply and demand of ecosystem services (ESs) is the cornerstone for maintaining regional socio-ecological system stability and preventing land degradation. However, current research on the driving mechanisms of ESs mostly focuses on the supply side, and the driving mechanisms of supply-demand balance have not been sufficiently emphasised. Taking the Hotan Oasis as the research region, we measured the ecosystem services supply-demand ratios (ESDR) of four key services, including sand fixation (SF), water yield (WY), soil retention (SR), and food production (FP) in the Hotan Oasis from 2005 to 2020. We utilised the optimal parameter-based geographic detector (OPGD) model to explore the driving mechanisms for the four ESDRs. The results show that: (1) the supply and demand of ESs showed significant spatial and temporal variations; SF and SR were generally in deficit, with SF deficit areas mainly distributed in the northern desert zone and SR deficit concentrated in the southern mountainous areas. WY and FP had quantitative surpluses, but spatial mismatches were prominent; (2) Natural and human activities jointly drove the supply and demand of ESs. Animal husbandry had a higher driving effect on SF, and its interaction with natural factors was significant; Natural factors had the strongest driving effect on ESDRs in WY and SR; the planting industry had the strongest driving effect on FP, and its interaction with population factors was prominent. (3) Aiming at the spatial heterogeneity of the supply and demand of ESs and the driving mechanism, this study proposed a zonal management framework of “source sand control - pathway blocking - system synergy”.