Accurately quantifying the impacts of climate change and human activities on runoff is critical. However, conventional Budyko-based frameworks face challenges in characterizing glacial melt contributions and fail to resolve the cascading effect of climate change, which is transmitted through alterations in underlying surface conditions. This study proposes a new attribution framework that integrates glacier mass balance into the Budyko equation and couples it with ridge regression to achieve a complete separation of the impacts on runoff from climate change (including direct and cascading effects) and from human activities. Specifically, glacier mass balance and glacier fraction were incorporated into the Budyko framework to quantify the glacial contribution to runoff changes. The modified Budyko framework is then coupled with a multiple regression model to identify relationships between the watershed characteristic parameter (n) and both climatic factors and human activities, ultimately decomposing their contributions to runoff variation. Application in an alpine watershed in Central Asia (the Upper Tailan River Basin) demonstrates that: (1) The framework performed robustly, with ridge regression achieving significantly superior accuracy (R2 = 0.83) in simulating the parameter n compared to principal component regression (R2 = 0.48). (2) Runoff changes exhibited a distinct antagonistic effect: the direct climate effect (+80.44 mm) was the primary driver of the observed runoff increase, while the cascading effect (-21.78 mm) and human activities (-11.65 mm) collectively suppressed runoff growth. (3) Strong internal offsetting mechanisms existed among the driving factors; for instance, GDP growth pushed the parameter n upward, whereas enhanced irrigation efficiency significantly suppressed its increase, thereby modulating runoff. This study underscores that neglecting the cascading effect can distort the true response of the hydrological system to climate change. The proposed framework provides a novel methodology for in-depth analysis of hydrological change mechanisms in complex environments and offers fresh insights for scientific and sustainable water resource management.

Oases in arid regions are crucial for sustaining agricultural production and ecological stability, yet few studies have simultaneously examined the coupled dynamics of land use/cover change (LUCC), carbon emissions, and ecosystem service value (ESV) at the oasis–agricultural scale. This gap limits our understanding of how different land use trajectories shape trade-offs between carbon processes and ecosystem services in fragile arid ecosystems. This study examines the spatiotemporal interactions between land use carbon emissions and ESV from 1990 to 2020 in the Wensu Oasis, Northwest China, and predicts their future trajectories under four development scenarios. Multi-period remote sensing data, combined with the carbon emission coefficient method, modified equivalent factor method, spatial autocorrelation analysis, the coupling coordination degree model, and the PLUS model, were employed to quantify LUCC patterns, carbon emission intensity, ESV, and its coupling relationships. The results indicated that (1) cultivated land, construction land, and unused land expanded continuously (by 974.56, 66.77, and 1899.36 km2), while grassland, forests, and water bodies declined (by 1363.93, 77.92, and 1498.83 km2), with the most pronounced changes occurring between 2000 and 2010; (2) carbon emission intensity increased steadily—from 23.90 × 104 t in 1990 to 169.17 × 104 t in 2020—primarily driven by construction land expansion—whereas total ESV declined by 46.37%, with water and grassland losses contributing substantially; (3) carbon emission intensity and ESV exhibited a significant negative spatial correlation, and the coupling coordination degree remained low, following a “high in the north, low in the south” distribution; and (4) scenario simulations for 2030–2050 suggested that this negative correlation and low coordination will persist, with only the ecological protection scenario (EPS) showing potential to enhance both carbon sequestration and ESV. Based on spatial clustering patterns and scenario outcomes, we recommend spatially differentiated land use regulation and prioritizing EPS measures, including glacier and wetland conservation, adoption of water-saving irrigation technologies, development of agroforestry systems, and renewable energy utilization on unused land. By explicitly linking LUCC-driven carbon–ESV interactions with scenario-based prediction and evaluation, this study provides new insights into oasis sustainability, offers a scientific basis for balancing agricultural production with ecological protection in the oasis of the arid region, and informs China’s dual-carbon strategy, as well as the Sustainable Development Goals.

This study used the Thornthwaite method to calculate SPEI. The characteristics of spatiotemporal variations of temperature, precipitation, SPEI, and drought variables were analyzed using methods such as climate inclination rate. The study found that: (1) Spring temperature and growing season precipitation show increase of 0.4 degrees C and 9.2 mm for every ten years, respectively. The temperature inclination rate gradually increases from the eastern to central regions, while the precipitation inclination rate gradually increases from the southern to northern regions. The SPEI inclination rate gradually increases from the southwest to the northeast. (2) There are differences in abrupt change points in temperature, precipitation, and SPEI, the years of abrupt change points in annual temperature, annual precipitation, and SPEI-12 were 2010, 2015, and 2016, respectively. (3) Light drought occurs in the northeast and western regions, moderate drought in the central and southern regions, severe drought in the eastern and central western regions, and extreme drought in the northeast regions. (4) The drought intensity increases throughout the year and in spring, with an increase of 0.04 and 0.18 for every ten years, respectively, while it decreases during summer, autumn, and growing season, with a decrease of 0.18, 0.14 and 0.03 for every ten years, respectively. The drought intensity throughout the year and in each season is mainly light. The study contributes to a deeper understanding of the spatiotemporal characteristics of drought in the Tarim River Basin.

The low water and fertiliser utilisation efficiency and soil quality degradation caused by high water and fertiliser inputs are the primary challenges facing goji berry cultivation in arid regions. A two-year field experiment was conducted from 2021 to 2022. The experiment included three irrigation rates (I1, I2, I3) of 2160, 2565, and 2970 m3·hm−2 and three nitrogen application rates (N1, N2, N3) of 165, 225, and 285 kg·hm−2 to quantify their impacts on soil nutrients, enzyme activity, and goji berry yield in the root zone. Results indicate that the indicators of soil nutrients decrease with increasing soil depth, with depths of 0–20 cm accounting for 24.80–72.48% of total content. With fertility period progression, soil organic matter at depths of 0–80 cm exhibits a “folded-line” trend, while total nitrogen, nitrate nitrogen, and available phosphorus show an “M”-type trend. At depths of 0–40 cm, the proportions of urease, sucrase, and alkaline phosphatase activities all exceeded 70%. At I1 irrigation rate, enzyme activities gradually increased with rising nitrogen application rates. At I2 and I3 irrigation rates, enzyme activities first increased, then decreased with increasing nitrogen application. The highest yields of both fresh and dried fruits were achieved at I2N2 treatment, increasing by 14.17% and 14.78%, respectively, compared to conventional management (CK). Analysis of the random forest model indicates that the soil-driven factors influencing yield formation include SA, UA, APA, HPA, SOM, NH4+-N, and TP. Analysis of SQI and yield fitted data indicates that water–nitrogen coupling significantly influences wolfberry yield by regulating soil quality. Partial least squares (PLS-PM) showed that N application and irrigation of soil nutrients did not cause a significant indirect impact on goji berry yield, but a significant positive effect on goji berry yield occurred through enzyme activity.

Global challenges such as climate change, ecological imbalance, and resource scarcity are closely related with land-use change. Arid land, which is 41% of the global land area, has fragile ecology and limited water resources. To ensure food security, ecological resilience, and sustainable use of land resources, there is a need for multi-scenario analysis of land-use change in arid regions. To carry this out, multiple spatial analysis techniques and land change indicators were used to analyze spatial land-use change in a typical inland river basin in arid Northwest China—the Tailan River Basin (TRB). Then, the PLUS model was used to analyze, in a certain time period (1980–2060), land-use change in the same basin. The scenarios used included the Natural Increase Scenario (NIS), Food Security Scenario (FSS), Economic Development Scenario (EDS), Water Protection Scenario (WPS), Ecological Protection Scenario (EPS), and Balanced Eco-economy Scenario (BES). The results show that for the period of 1980–2020, land-use change in the TRB was mainly driven by changes in cultivated land, grassland, forest land, and built-up land. For this period, there was a substantial increase in cultivated land (865.56 km2) and a significant decrease in forest land (197.44 km2) and grassland (773.55 km2) in the study area. There was a notable spatial shift in land use in the period of 1990–2010. The overall accuracy (OA) of the PLUS model was more than 90%, with a Kappa value of 85% and a Figure of Merit (FOM) of 0.18. The most pronounced expansion in cultivated land area in the 2020–2060 period was for the FSS (661.49 km2). This led to an increase in grain production and agricultural productivity in the region. The most significant increase in built-up area was under the EDS (61.7 km2), contributing to economic development and population growth. While the conversion of grassland area into other forms of land use was the smallest under the BES (606.08 km2), built-up area increased by 55.82 km2. This presented an ideal scenario under which ecological conservation was in balance with economic development. This was the most sustainable land management strategy with a harmonized balance across humans and the ecology in the TRB study area. This strategy may provide policymakers with a realistic land-use option with the potential to offer an acceptable policy solution to land use.

The dynamic evolution pattern of regional water supply-demand risks under the combined effects of climate change and human activities remains unclear, particularly against the backdrop of agricultural expansion in arid regions. This study focuses on the Tailan River Basin (TRB), a typical arid watershed in China and a vital base for high-quality fruit and grain production. By integrating the PLUS (Patch-generating Land Use Simulation) and InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) models, we constructed a water supply-demand risk assessment framework encompassing 24 climate-land change scenarios to quantify their impacts on regional water resource patterns and risks. Results reveal that climate change profoundly influences water supply, while land use significantly affects water demand. Under the Balanced Economic and Ecological Development Scenario (BES), 531.2 km2 of additional cultivated land could be developed by 2050. However, this cultivated land expansion leads to a sharp increase in irrigation water demand, with the minimum demand reaching 4.87 × 108 m3, while the maximum regional water supply is only 0.16 × 108 m3, resulting in a significant supply-demand gap (> 4.71 × 108 m3). The risk assessment framework indicates that by 2050, the entire TRB will face a water supply-demand crisis, with at least 46 % of the area experiencing severe (Level III) or higher risks. The study demonstrates that continuous cultivated land expansion driven by agricultural activities – which drastically increases irrigation water demand – is the root cause of intensifying water supply-demand conflicts and high risks in the TRB. By 2050, the proportion of irrigation water to total water use will exceed 70 %, regardless of scenario. These findings underscore the necessity of deeply integrating multidisciplinary approaches within a water risk framework to elucidate land-eco-hydrological feedback mechanisms and better address water security challenges under climate change. The results provide a scientific basis for optimizing regional water-land resource allocation and promoting agro-ecological sustainable development.

Characterizing the effects of previous water and salinity stresses is critical for the evaluation of plant water status, which, in turn, is essential for understanding soil-plant water relations and optimizing irrigation schemes. Recent research has found that hysteresis of plant response following water stress alone can be described by an exponential function of the stress degree on the previous day. To explore and quantify the effects of hysteresis concerning salinity stress and combined water-salinity stress, a hydroponic experiment and a soil column experiment on winter wheat, and a field experiment on cotton were conducted. Like water stress, previous salinity stress and combined water-salinity stress also resulted in hysteretic effects on root-water-uptake. Leaf stomatal conductance and plant transpiration rate of stressed crops could only recover gradually from a previous stressed status after re-watering. When stress was mild, compensatory recovery was found, while incomplete recovery occurred when stress was severe. Although the recovery process was closely related to stress history and type, a recovery coefficient was quantified universally with an exponential function of the stress extent on the previous day (with a coefficient of determination R2 >= 0.60). Consideration of hysteresis for water and salinity stresses with a mathematical model led to significant improvement in the simulation of both relative transpiration rate (R2 = 0.94, root mean squared error RMSE = 0.04, maximal absolute error MAE = 0.12) and soil water content (R2 = 0.90, RMSE = 0.01 cm3 cm-3, MAE = 0.03 cm3 cm-3), especially during the recovery periods severely affected by historical stress. Consideration of hysteresis is expected to benefit regulation of soil water and salinity and thus enhance water use efficiency. However, the mechanisms underlying hysteresis, especially the compensatory recovery mechanisms, still need to be further investigated.

Soil salinization is one of the primary factors contributing to land degradation in arid areas, severely restricting the sustainable development of agriculture and the economy. Satellite remote sensing is essential for real-time, large-scale soil salinity content (SSC) evaluation. However, some satellite images have low temporal resolution and are affected by weather conditions, leading to the absence of satellite images synchronized with ground observations. Additionally, some high-temporal-resolution satellite images have overly coarse spatial resolution compared to ground features. Therefore, the limitations of these spatiotemporal features may affect the accuracy of SSC evaluation. This study focuses on the arable land in the Manas River Basin, located in the arid areas of northwest China, to explore the potential of integrated spatiotemporal data fusion and deep learning algorithms for evaluating SSC. We used the flexible spatiotemporal data fusion (FSDAF) model to merge Landsat and MODIS images, obtaining satellite fused images synchronized with ground sampling times. Using support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models, we evaluated the differences in SSC evaluation results between synchronized and unsynchronized satellite images with ground sampling times. The results showed that the FSDAF model’s fused image was highly similar to the original image in spectral reflectance, with a coefficient of determination (R2) exceeding 0.8 and a root mean square error (RMSE) below 0.029. This model effectively compensates for the missing fine-resolution satellite images synchronized with ground sampling times. The optimal salinity indices for evaluating the SSC of arable land in arid areas are S3, S5, SI, SI1, SI3, SI4, and Int1. These indices show a high correlation with SSC based on both synchronized and unsynchronized satellite images with ground sampling times. SSC evaluation models based on synchronized satellite images with ground sampling times were more accurate than those based on unsynchronized images. This indicates that synchronizing satellite images with ground sampling times significantly impacts SSC evaluation accuracy. Among the three models, the CNN model demonstrates the highest predictive accuracy in SSC evaluation based on synchronized and unsynchronized satellite images with ground sampling times, indicating its significant potential in image prediction. The optimal evaluation scheme is the CNN model based on satellite image synchronized with ground sampling times, with an R2 of 0.767 and an RMSE of 1.677 g·kg−1. Therefore, we proposed a framework for integrated spatiotemporal data fusion and CNN algorithms for evaluating soil salinity, which improves the accuracy of soil salinity evaluation. The results provide a valuable reference for the real-time, rapid, and accurate evaluation of soil salinity of arable land in arid areas.

Enhancing global agricultural sustainability critically requires improving the physicochemical properties of saline–alkali soil. Biochar has gained increasing attention as a strategy due to its unique properties. However, its effect on the physicochemical properties of saline–alkali soil varies significantly. This study uses psychometric meta-analysis across 137 studies to synthesize the findings from 1447 relatively independent data sets. This study investigates the effects of biochar with different characteristics on the top 20 cm of various saline–alkali soils. In addition, aggregated boosted tree (ABT) analysis was used to identify the key factors of biochar influencing the physicochemical properties of saline soils. The results showed that biochar application has a positive effect on improving soil properties by reducing the sodium adsorption ratio (SAR) and the exchangeable sodium percentage (ESP) by 30.31% and 28.88%, respectively, with a notable 48.97% enhancement in cation exchange capacity (CEC). A significant inverse relationship was found between soil salinity (SC) and ESP, while other factors were synergistic. Biochar application to mildly saline soil (<0.2%) and moderately saline soil (0.2–0.4%) demonstrated greater improvement in soil bulk density (SBD), total porosity (TP), and soil moisture content (SMC) compared to highly saline soil (>0.4%). However, the reduction in SC in highly saline soil was 4.9 times greater than in moderately saline soils. The enhancement of soil physical properties positively correlated with higher biochar application rates, largely driven by soil movements associated with the migration of soil moisture. Biochar produced at 401–500 °C was generally the most effective in improving the physicochemical properties of various saline–alkali soils. In water surplus regions, for mildly saline soil with pH < 8.5, mixed biochar (pH 6–8) at 41–80 t ha−1 was the most effective in soil improvement. Moreover, in water deficit areas with soil at pH ≥ 8.5, biochar with pH ≤ 6 applied at rates of >80 t ha−1 showed the greatest benefits. Agricultural residue biochar showed superior efficiency in ameliorating highly alkaline (pH ≥ 8.5) soil. In contrast, the use of mixed types of biochar was the most effective in the amelioration of other soil types.

Cotton is one of the world’s most economically significant crops. Evaluating and monitoring cotton crop growth play vital roles in precision agriculture. Unmanned aerial vehicle (UAV) based remote sensing, when integrated with machine learning technologies, exhibits considerable promise for crop growth management. Despite these technologies’ substantial impact on cotton production, there exists a scarcity of consolidated information regarding various methods used. This paper offers a comprehensive review and analysis focused on methods for monitoring and evaluating cotton growth using UAV-based imagery combined with machine learning techniques. We synthesize the existing research from the past decade within this context, particularly discussing data acquisition strategies, preprocessing methods necessary for handling UAV-acquired images effectively, and a range of machine learning models applied. This investigation offers a comprehensive outlook that could guide future research efforts towards more efficient and sustainable agricultural practices in cotton production, leveraging state-of-the-art technology.

Delineating root-water-uptake (RWU) under conditions with augmented CO2 concentrations is very important for scheduling irrigation to contend with climate change. Responses of plant growth to elevated CO(2)concentration (e[CO2]) have been widely reported, while the effects of e[CO2] on RWU has hardly been studied. A hydroponic experiment of wheat (Triticum aestivum L.) with five NO -3-N concentrations (Exp. 1) was conducted to investigate and quantify the effects of e[CO2] on RWU activity. Another experiment growing wheat in soil columns with four combinations of water and N supply levels (Exp. 2) was conducted to validate the results obtained in Exp. 1, establishing a macroscopic RWU model to simulate soil water dynamics under e[CO2]. Although CO2 acclimation was observed in both experiments, plant canopy and root growth were generally stimulated under e[CO2], while transpiration consumption was not synchronously enhanced due to decreased stomatal conductance, indicating an increase in water use efficiency while a decrease in RWU activity. Potential transpiration was found more linearly related to root nitrogen mass (RNM) than root length under various CO2 concentrations, regardless of wheat growth stage, water and N supply level. Consequently, RNM density was used to drive the RWU model. The results from Exp. 1 indicated that the effects of e[CO2] on water uptake coefficient per RNM could be quantified by a recently proposed nonlinear stomatal conductance response model (R-2 = 0.84, RMSE = 0.55 cm(3 )mg(-1) d(-1)). The RWU model reliably simulated the dynamics of soil water transport and wheat transpiration under e[CO2] in Exp. 2 with the RMSE and relative errors mostly less than 0.03 cm(3) cm(-3) and 10 %, respectively. Practical application of the established RWU model for any other specific conditions is expected to benefit from optimization of parameters following choice of most appropriate stomatal conductance response model.

The safe utilization and risk assessment of produced water (PW) from oil and gas fields for desert irrigation have received increasing attention in recent years. In this context, this study aimed to analyze structural changes in soil bacterial community, and assess the environmental impact of PW discharge and irrigation over time. High-throughput sequencing technology was employed to examine the structure of the soil bacterial community in the constructed wetland and its surrounding desert vegetation irrigation region where PW was released for a considerable amount of time (30 years). The results revealed that long-term discharge of PW and irrigation significantly reduced the abundance of the soil bacterial community but did not significantly alter the richness and diversity of the soil bacterial community. Proteobacteria was the dominant bacterial phyla in soil, but in irrigated and drained areas, the dominant bacterial phyla changed from Alphaproteobacteria to Gammaproteobacteria, the Firmicutes abundance was significantly reduced.

为了探究有机肥高效利用和化肥减量技术的有效途径,本研究以新疆阿克苏地区沙雅县春玉米为研究对象,开展田间试验,在分别施用氮肥0、150、225、300、375 kg/hm2基础上,设置配施牛粪3 000 kg/hm2和不施牛粪两组共10个处理,研究无机氮肥配施有机肥对土壤养分、土壤酶活性和玉米产量的影响.结果表明,在玉米成熟期,随施氮量增加土壤有机质、全氮和铵态氮含量无显著差异(P＞0.05),而碱解氮和硝态氮含量总体呈现上升趋势,且配施牛粪处理的含量整体高于不施牛粪处理.在玉米不同生育期,土壤蔗糖酶和过氧化氢酶活性表现出随施氮量增加而增加的趋势,脲酶活性随施氮量增加呈现出先增加后略有降低的趋势,且配施牛粪处理的土壤蔗糖酶、脲酶和过氧化氢酶活性比不施牛粪处理分别提高了8.40％、44.31％和7.02％(P＜0.05).籽粒产量随施氮量增加呈现出先增加后降低的趋势,配施牛粪处理的产量总体高于不施牛粪处理.施氮量225 kg/hm2配施牛粪处理籽粒产量达到15 787.41 kg/hm2,比不施肥处理增产50.57％,较不施牛粪施氮225 kg/hm2处理提高13.18％.氮肥与牛粪配施后可通过提高土壤酶活性和土壤养分含量来影响玉米产量,在综合考虑土壤养分、土壤酶活性和玉米产量的基础上,建议沙雅县玉米氮肥施用量为225 kg/hm2,并配施牛粪3 000 kg/hm2.

Cotton harvest can be increased by having real-time information on the state of cotton aphid populations. However, traditional cotton aphid monitoring relies on ground sample methods supported by models such as linear regression, resulting in low forecast accuracy. Therefore, this paper purposes to enhance the precision of the remote sensing prediction model by investigating the cotton aphid prediction model construction approach. We explored the effectiveness of the XGBoost algorithm combined with the GWO algorithm and SVR method for cotton aphid prediction relying on vegetation indices derived from UAV multispectral photography. Originally, 12 indices related to cotton aphids were calculated by UAV multispectral reflectance. Additionally, the optimal index combination for pest prediction was determined utilizing analysis of correction and two-way ANOVA, combined with the XGBoost algorithm. Furthermore, a pest prevalence prediction model for cotton aphids was constructed via the SVR methodology associated with the optimal catalog combination, and the model was optimized using the GWO algorithm. Compared with the seven algorithms, experimental results demonstrate that the MSE and MAE of the XGBoost-GWO-SVR model are reduced by 90.20% and 70.36% (SVR), 90.14% and 70.26% (XGBoost-SVR), 7.47% and 0.14% (XGBoost-GA-SVR), 5.80% and 0.11% (XGBoost-PSO-SVR), 12.06% and 58.95% (LR), and 84.77% and 89.22% (BPNN), whereas the $R^{2}$ is increased by 22.5% (SVR and XGBoost-SVR), 0.3% (LR), and 12.51% (BPNN). The $R^{2}$ of the prediction model of XGBoost-SVR combined with GWO, PSO, and GA is not significantly different. Among these models, the XGBoost-GWO-SVR obtained the highest $R^{2}$ of 0.980 and the lowest MAE of 2.838.