Water shortage is one bottleneck that limits economic and social developments in arid and semi-arid areas. As the impacts of climate change and human disturbance intensify across time, uncertainties in both water resource supplies and demands increase in arid and semi-arid areas. Taking a typical arid region in China, Xinjiang Uygur Autonomous Region, as an example, water yield depth (WYD) and water utilization depth (WUD) from 2002 to 2018 were simulated using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and socioeconomic data. The supply-demand relationships of water resources were analyzed using the ecosystem service indices including water supply-demand difference (WSDD) and water supply rate (WSR). The internal factors in changes of WYD and WUD were explored using the controlled variable method. The results show that the supply- demand relationships of water resources in Xinjiang were in a slight deficit, but the deficit was alleviated due to increased precipitation and decreased WUD of irrigation. WYD generally experienced an increasing trend, and significant increase mainly occurred in the oasis areas surrounding both the Junggar Basin and Tarim Basin. WUD had a downward trend with a decline of 20.70%, especially in oasis areas. Water resources in most areas of Xinjiang were fully utilized and the utilization efficiency of water resources increased. The water yield module in the InVEST model was calibrated and validated using gauging station data in Xinjiang, and the result shows that the use of satellite-based water storage data helped to decrease the bias error of the InVEST model by 0.69×10⁸ m³. This study analyzed water resource supplies and demands from a perspective of ecosystem services, which expanded the scope of the application of ecosystem services and increased the research perspective of water resource evaluation. The results could provide guidance for water resource management such as spatial allocation and structural optimization of water resources in arid and semi-arid areas.

As important freshwater resources in alpine basins, glaciers and snow cover tend to decline due to climate warming, thus affecting the amount of water available downstream and even regional economic development. However, impact assessments of the economic losses caused by reductions in freshwater supply are quite limited. This study aims to project changes in glacier meltwater and snowmelt of the Urumqi River in the Tianshan Mountains under future climate change scenarios (RCP2.6 (RCP, Representative Concentration Pathway), RCP4.5, and RCP8.5) by applying a hydrological model and estimate the economic losses from future meltwater reduction for industrial, agricultural, service, and domestic water uses combined with the present value method for the 2030s, 2050s, 2070s, and 2090s. The results indicate that total annual glacier meltwater and snowmelt will decrease by 65.6% and 74.5% under the RCP4.5 and RCP8.5 scenarios by the 2090s relative to the baseline period (1980-2010), respectively. Compared to the RCP2.6 scenario, the projected economic loss values of total water use from reduced glacier meltwater and snowmelt under the RCP8.5 scenario will increase by 435.10×10⁶ and 537.20×10⁶ CNY in the 2050s and 2090s, respectively, and the cumulative economic loss value for 2099 is approximately 2124.00×10⁶ CNY. We also find that the industrial and agricultural sectors would likely face the largest and smallest economic losses, respectively. The economic loss value of snowmelt in different sectorial sectors is greater than that of glacier meltwater. These findings highlight the need for climate mitigation actions, industrial transformation, and rational water allocation to be considered in decision-making in the Tianshan Mountains in the future.

It is common knowledge that Yarmouk River Basin (YRB) is shared between Jordan and Syria. Management of YRB trans-boundary water resources is attracting increasing interest because it is a strategic water resource for the riparian countries. Actually, lack of sharing information regarding hydrological flows and basin's water management between partners' countries makes it difficult to distinguish between natural and man-made factors affecting the water body. Therefore, this study seeks to address and assess the main on-site changes that exert on YRB. Geospatial technique and arithmetic equations were combined to carry out an assessment of the changes on water resources in YRB. Data, information and field measurements of the basin were aggregated, compiled and presented to determine the extent of changes during the period 1980-2020. Remarkable findings showed that precipitation amount in the basin significantly declined during the period 1980-2020 in particularly after the year 1992. Pumping rate of groundwater was 550×10³ m³/a, exceeding the basin's safe yield. Draw down of static groundwater level over time approached the value of -3.2 m/a due to the over abstraction in the aquifer body. Additionally, the evaporation rate reached more than 99% in some regions in the basin. Moreover, the number of private wells has increased from 98 wells in 1980 to 126 wells in 2020, showing the excessive extraction of groundwater. These findings indicate that the study area is subjected to a considerable groundwater depletion in the near future due to extensive abstraction, continuous drilling of illegal wells and decreased annual precipitation under the shadow of the rapid population growth and continuous influx of refugees. Therefore, decision makers-informed scenarios are suggested in the development of water resource portfolios, which involves the combination of management and infrastructural actions that enhance the water productivity of the basin. Further studies are recommended to evaluate the on-site changes on water resources in YRB in collaboration with riparian countries and to establish monitoring system for continuous and accurate measurements of the basin.

The status of regional biodiversity is determined by habitat quality. The effective assessment of habitat quality can help balance the relationship between economic development and biodiversity conservation. Therefore, this study used the InVEST model to conduct a dynamic evaluation of the spatial and temporal changes in habitat quality of the Tarim River Basin in southern Xinjiang Uygur Autonomous Region of China by calculating the degradation degree levels for habitat types that were caused by threat factors from 1990 to 2018 (represented by four periods of 1990, 2000, 2010 and 2018). Specifically, we used spatial autocorrelation analysis and Getis-Ord G* i analysis to divide the study area into three heterogeneous units in terms of habitat quality: cold spot areas, hot spot areas and random areas. Hemeroby index, population density, gross domestic product (GDP), altitude and distance from water source (DWS) were then chosen as the main disturbance factors. Linear correlation and spatial regression models were subsequently used to analyze the influences of disturbance factors on habitat quality. The results demonstrated that the overall level of habitat quality in the TRB was poor, showing a continuous degradation state. The intensity of the negative correlation between habitat quality and Hemeroby index was proven to be strongest in cold spot areas, hot spot areas and random areas. The spatial lag model (SLM) was better suited to spatial regression analysis due to the spatial dependence of habitat quality and disturbance factors in heterogeneous units. By analyzing the model, Hemeroby index was found to have the greatest impact on habitat quality in the studied four periods (1990, 2000, 2010 and 2018). The research results have potential guiding significance for the formulation of reasonable management policies in the TRB as well as other river basins in arid areas.

Soil salinization is a major problem affecting soils and threatening agricultural sustainability in arid and semi-arid regions, which makes it necessary to establish an efficient strategy to manage soil salinity and confront economic challenges that arise from it. Saline soil recovery involving drainage of shallow saline groundwater and the removal of soil salts by natural rainfall or by irrigation are good strategies for the reclamation of salty soil. To develop suitable management strategies for salty soil reclamation, it is essential to improve soil salinity assessment process/mechanism and to adopt new approaches and techniques. This study mapped a recovered area of 7200 m²to assess and verify variations in soil salinity in space and time in Kairouan region in Central Tunisia, taking into account the thickness of soil materials. Two electromagnetic conductivity meters (EM38 and EM31) were used to measure the electrical conductivity of saturated soil-paste extract (ECe) and apparent electrical conductivity (ECa). Multiple linear regression was established between ECe and ECa, and it was revealed that ECa-_EM38 is optimal for ECe prediction in the surface soils. Salinity maps demonstrated that the spatial structure of soil salinity in the region of interest was relatively unchanged but varied temporally. Variation in salinity at the soil surface was greater than that at a depth. These findings can not only be used to track soil salinity variations and their significance in the field but also help to identify the spatial and temporal features of soil salinity, thus improving the efficiency of soil management.

Invasive species have been the focus of ecologists due to their undesired impacts on the environment. The extent and rapid increase in invasive plant species is recognized as a natural cause of global-biodiversity loss and degrading ecosystem services. Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions. Understanding the impact of climate change on species invasion is crucial for sustainable biodiversity conservation. In this study, the possibility of mapping the distribution of invasive Prosopis juliflora (Swartz) DC. was shown using present background data in Khuzestan Province, Iran. After removing the spatial bias of background data by creating weighted sampling bias grids for the occurrence dataset, we applied six modelling algorithms (generalized additive model (GAM), classification tree analysis (CTA), random forest (RF), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt) and ensemble model) to predict invasion distribution of the species under current and future climate conditions for both optimistic (RCP2.6) and pessimistic (RCP8.5) scenarios for the years 2050 and 2070, respectively. Predictor variables including weighted mean of CHELSA (climatologies at high resolution for the Earth's land surface areas)-bioclimatic variables and geostatistical-based bioclimatic variables (1979-2020), physiographic variables extracted from shuttle radar topography mission (SRTM) and some human factors were used in modelling process. To avoid causing a biased selection of predictors or model coefficients, we resolved the spatial autocorrelation of presence points and multi-collinearity of the predictors. As in a conventional receiver operating characteristic (ROC), the area under curve (AUC) is calculated using presence and absence observations to measure the probability and the two error components are weighted equally. All models were evaluated using partial ROC at different thresholds and other statistical indices derived from confusion matrix. Sensitivity analysis showed that mean diurnal range (Bio2) and annual precipitation (Bio12) explained more than 50%of the changes in the invasion distribution and played a pivotal role in mapping habitat suitability of P. juliflora. At all thresholds, the ensemble model showed a significant difference in comparison with single model. However, MaxEnt and RF outperformed the others models. Under climate change scenarios, it is predicted that suitable areas for this invasive species will increase in Khuzestan Province, and increasing climatically suitable areas for the species in future will facilitate its future distribution. These findings can support the conservation planning and management efforts in ecological engineering and be used in formulating preventive measures.

The relationships between different aspects of diversity (taxonomic, structural and functional) and the aboveground biomass (AGB) as a major component of global carbon balance have been studied extensively but rarely under the simultaneous influence of forest dieback and management. In this study, we investigate the relationships between taxonomic, functional and structural diversity of woody species (trees and shrubs) and AGB along a gradient of dieback intensity (low, moderate, high and no dieback as control) under two contrasted management conditions (protection by central government vs. traditional management by natives) in a semi-arid oak (Quercus brantii Lindl.) forest ecosystem. AGB was estimated and taxonomic diversity, community weighted average (CWM) and functional divergence indices were produced. We found that the aerial biomass was significantly higher in the intensively used area (14.57 (±1.60) t/hm²) than in the protected area (8.70 (±1.05) t/hm²) due to persistence of some large trees but with decreasing values along the dieback intensity gradient in both areas. CWM of height (H), leaf nitrogen content (LNC) and leaf dry matter content (LDMC) were also higher in the traditional managed area than in the protected area. In contrast, in the protected area, the woody species diversity was higher and the inter-specific competition was more intense, explaining a reduced H, biomass and LDMC. Contrary to the results of CWM, none of the functional diversity traits (FDvar) was affected by dieback intensity and only FDvar values of LNC, leaf phosphorus content (LPC) and LDMC were influenced by management. We also found significantly positive linear relationships of AGB with CWM and FDvar indices in the protected area, and with taxonomic and structural diversity indices in the traditional managed area. These results emphasize that along a dieback intensity gradient, the leaf functional traits are efficient predictors in estimating the AGB in protected forests, while taxonomic and structural indices provide better results in forests under a high human pressure. Finally, species identity of the dominant species (i.e., Brant's oak) proves to be the main driver of AGB, supporting the selection effect hypothesis.