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Soil salinization is closely related to land degradation, and it is supposed to exert a significant negative effect on soil organic carbon (SOC) stock dynamics. This effect and its mechanism have been examined at site and transect scales in previous studies while over a large spatial extent, the salinity-induced changes in SOC stock over space and time have been less quantified, especially by machine learning and remote sensing techniques. The main focus of this study is to answer the following question: to what extent can soil salinity exert an additional effect on SOC stock over time at a larger spatial scale? Thus, we employed the extreme gradient boosting models (XGBoost) combined with field site-level measurements from 433 sites and 41 static and time-varying environmental covariates to construct methods capable of quantifying the salinity-induced SOC changes in a typical inland river basin of China between the 1990s and 2020s. Results showed that the XGBoost models performed well in predicting the soil electrical conductivity (EC) and SOC stock at 0–20 cm, with the R2 value reaching 0.85 and 0.81, respectively. SOC stock was found to vary significantly with increasing soil salinity following an exponential decay function (R2 = 0.27), and salinity sensitivity analysis showed that soils in oasis were expected to experience the largest carbon loss (−137.78 g m−2), which was about 4.84, 14.37, and 25.95 times higher than that in the saline, bare, and sandy land, respectively, if the soil salinity increased by 100%. In addition, the decrease in the soil salinity (−0.32 dS m−1) from the 1990s to the 2020s was estimated to enhance the SOC stock by 0.015 kg m−2, which contributed an additional 10% increase to the total SOC stock enhancement. Overall, the proposed methods can be applied for quantification of the direction and size of the salinity effect on SOC stock changes in other salt-affected regions. Our results also suggest that the role of soil salinization should not be neglected in SOC changes projection, and soil salinization control measures should be further taken into practice to enhance soil carbon sequestration in arid inland river basins. [ABSTRACT FROM AUTHOR]

Land-use change is supposed to exert significant effects on the spatio-temporal patterns of ecosystem carbon storage in arid regions, while the relative size of land-use change effect under future environmental change conditions is still less quantified. In this study, we combined a land-use change dataset with a satellite-based high-resolution biomass and soil organic carbon dataset to determine the role of land-use change in affecting ecosystem carbon storage from 1980 to 2050 in the Gansu province of China, using the MCE-CA-Markov and InVEST models. In addition, to quantify the relative size of the land-use change effect in comparison with other environmental drivers, we also considered the effects of climate change, CO2 enrichment, and cropland and forest managements in the models. The results show that the ecosystem carbon storage in the Gansu province increased by 208.9 ± 99.85 Tg C from 1980 to 2020, 12.87% of which was caused by land-use change, and the rest was caused by climate change, CO2 enrichment, and ecosystem managements. The land-use change-induced carbon sequestration was mainly associated with the land-use category conversion from farmland to grassland as well as from saline land and desert to farmland, driven by the grain-for-green projects in the Loess Plateau and oasis cultivation in the Hexi Corridor. Furthermore, it was projected that ecosystem carbon storage in the Gansu province from 2020 to 2050 will change from −14.69 ± 12.28 Tg C to 57.83 ± 53.42 Tg C (from 105.62 ± 51.83 Tg C to 177.03 ± 94.1 Tg C) for the natural development (ecological protection) scenario. By contrast, the land-use change was supposed to individually increase the carbon storage by 56.46 ± 9.82 (165.84 ± 40.06 Tg C) under the natural development (ecological protection) scenario, respectively. Our results highlight the importance of ecological protection and restoration in enhancing ecosystem carbon storage for arid regions, especially under future climate change conditions. [ABSTRACT FROM AUTHOR]

Under the influence of climate change, the hydrological processes of glaciers have undergone significant changes, a fact which is seriously affecting agricultural production in the downstream region of the Tianshan Mountains, China. In order to explore the intrinsic relationship between climate change and hydrological elements, we proposed an "evaluation-driving-prediction" system to study it. First, we constructed a glacier-enhanced soil and water assessment tool model (GE-SWAT) and used a two-stage calibration method to optimize the model parameters. Next, a scenario analysis was used to evaluate the driving factors of historical runoff changes. Finally, we projected future runoff changes using bias-corrected regional climate model (RCM) outputs. The results of the case study on the Jinghe River Basin in the Tianshan Mountains show that from 1963 to 2016, total runoff increased by 13.3%, 17.7% of which was due to increasing precipitation and 1.8% of which was negated by rising temperatures. The glacier runoff increased by 14.5%, mainly due to the rising temperatures. A 3.4% reduction in snowmelt was caused by a lower snowfall/precipitation ratio, which significantly reduced the snowfall from June to August. The RCM projection indicated that the warming and humidification phenomenon in the study area will continue at least through to the mid-21st century. A consistent increase in glacier runoff and total runoff is projected, but the contribution rate of the glacier runoff will have little to no change under the RCP4.5 and RCP8.5 emission scenarios. Our research demonstrates the simulation performance of the GE-SWAT model in a basin with moderate glacier cover. This method is shown to be efficient in quantifying the impact of climate change on glacier hydrological processes and predicting future streamflow changes, providing a good research reference for similar regions. [ABSTRACT FROM AUTHOR]

Derived from realistic global warming scenarios, long‐term projections of spatial patterns in precipitation and temperature in hydrology and climatology can serve to evaluate climate risk, explore sources of renewable energies and allow local‐scale data to inform decisions regarding agricultural, ecosystem, social, recreational and economic activities. Under the CORDEX‐EA project, the precipitation and temperature projections (2020–2045) for the economically and socially important region of Northwestern China were derived from high‐resolution regional climate model (RCM) simulations for RCP 4.5 and 8.5 scenarios, and compared against a historical period or baseline of 1980–2005. Drawing data from four key RCMs [Weather Research and Forecasting (WRF); Regional Spectral Model (RSM); Regional Climate Model version 4.0 (RegCM4); Mesoscale Model version 5 (MM5)], reliable local‐scale projections were generated by applying suitable bias correction that accords with the multivariate bias correction (MBC) approach. To validate this approach and then evaluate climate change impacts, the adjusted precipitation and temperature estimated from bias‐corrected models for the historical period were compared to the observed data. The results showed that the simulated spatiotemporal distribution of multiyear average precipitation and temperature appear to fit relatively well with the observations, however, the wet‐cold and dry‐warm climate‐related biases were still evident for the high altitude regions. Bias‐corrected future projections of RCMs indicated spatially averaged annual precipitation is expected to rise by about 23.6 and 35.3 mm under the RCP 4.5 and 8.5 scenarios, respectively, while spatially averaged annual temperature is expected to rise by about 1.95 and 1.10°C. Precipitation is projected to increase in all seasons, albeit, more so in the cold season (i.e., boreal winter and spring) than the warm season (i.e., boreal summer and autumn). The annual increase is expected to be about 56.7% under the RCP 4.5 scenario, compared to 67.6% under the RCP 8.5 scenario. The changes of mean temperature in winter are expected to be significant, −37.8% under both RCP scenarios. For spring, the mean temperature will rise by 23.1% (28.9%) under the RCP 4.5 (8.5) scenario. A MBC approach was found to be effective in yielding reliable projected changes in precipitation and temperature variables. The proposed approach has important implications for climatological studies and evaluation of climate change impacts on localized regions, not only in China, but also in other similar areas of the world, where decisions for managing climate risk must be implemented by policy makers, government, industry and stakeholders. [ABSTRACT FROM AUTHOR]

Desertification affects about 36 × 106 km2 of land around the world, two‐thirds of the world's countries and one‐fifth of its population, but poverty prevents these people from developing effective solutions. To balance the need for sustainable development with environmental protection, China's scientists have tested a new and more environmentally friendly approach based on ecological restoration projects that simultaneously provide a sustainable livelihood for the citizens affected by the projects. This strategy for controlling desertification requires cooperation among governments, scientists, and researchers from many disciplines and the communities affected by the program. To document the effectiveness of this strategy, we monitored the implementation of a programme to combat desertification in the Alxa League of China's Inner Mongolia Autonomous Region. Our study showed that the new strategy can significantly increase vegetation cover, species richness, aboveground biomass, soil organic matter content, and soil nutrients. The successful ecological restoration provides potentially important lessons for other parts of the world that face the joint burden of poverty and environmental degradation. [ABSTRACT FROM AUTHOR]

Assessment of the effects of climate change and land use/cover change (LUCC) on the flow regimes in watershed regions is a fundamental research need in terms of the sustainable water resources management and ecosocial developments. In this study, a statistical and modeling integrated method utilizing the Soil and Water Assessment Tool (SWAT) has been adopted in two watersheds of northeastern Tibetan Plateau to separate the individual impacts of climate and LUCC on the flow regime metrics. The integrated effects of both LUCC and climate change have led to an increase in the annual streamflow in the Yingluoxia catchment (YLC) region and a decline in the Minxian catchment (MXC) region by 3.2% and 4.3% of their total streamflow, respectively. Climate change has shown an increase in streamflow in YLC and a decline in MXC region, occupying 107.3% and 93.75% of the total streamflow changes, respectively, a reflection of climatic latitude effect on streamflow. It is thus construed that the climatic factors contribute to more significant influence than LUCC on the magnitude, variability, duration, and component of the flow regimes, implying that the climate certainly dominates the flow regime changes in northeastern Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Climate change and land use/cover change (LUCC) are two factors that produce major impacts on hydrological processes. Understanding and quantifying their respective influence is of great importance for water resources management and socioeconomic activities as well as policy and planning for sustainable development. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated and validated in upper stream of the Heihe River in Northwest China. The reliability of the SWAT model was corroborated in terms of the Nash-Sutcliffe efficiency (NSE), the correlation coefficient (R), and the relative bias error (BIAS). The findings proposed a new method employing statistical separation procedures using a physically based modeling system for identifying the individual impacts of climate change and LUCC on hydrology processes, in particular on the aspects of runoff and evapotranspiration (ET). The results confirmed that SWAT was a powerful and accurate model for diagnosis of a key challenge facing the Heihe River Basin. The model assessment metrics, NSE, R, and BIAS, in the data were 0.91%, 0.95%, and 1.14%, respectively, for the calibration period and 0.90%, 0.96%, and −0.15%, respectively, for the validation period. An assessment of climate change possibility showed that precipitation, runoff, and air temperature exhibited upward trends with a rate of 15.7 mm, 6.1 mm, and 0.38 °C per decade for the 1980 to 2010 period, respectively. Evaluation of LUCC showed that the changes in growth of vegetation, including forestland, grassland, and the shrub area have increased gradually while the barren area has decreased. The integrated effects of LUCC and climate change increased runoff and ET values by 3.2% and 6.6% of the total runoff and ET, respectively. Climate change outweighed the impact of LUCC, thus showing respective increases in runoff and ET of about 107.3% and 81.2% of the total changes. The LUCC influence appeared to be modest by comparison and showed about −7.3% and 18.8% changes relative to the totals, respectively. The increase in runoff caused by climate change factors is more than the offsetting decreases resulting from LUCC. The outcomes of this study show that the climate factors accounted for the notable effects more significantly than LUCC on hydrological processes in the upper stream of the Heihe River. [ABSTRACT FROM AUTHOR]

There are many viewpoints about the sources of groundwater in the Badain Jaran Desert (BJD), such as precipitation and snowmelt from the Qilian Mountains (the upper reaches [UR] of the Heihe River Basin [HRB]) and precipitation from the BJD and the Yabulai Mountains. To understand the source of the groundwater of the BJD and their possible associations with nearby bodies of water, we analyzed variations of stable isotope ratios ( δD and δ18O) and the deuterium excess (d-excess) of groundwater and precipitation in the BJD, of groundwater, precipitation, river and spring water in the UR, and of groundwater and river water in the middle and lower reaches (MR and LR) of the HRB. In addition, the climatic condition under which the groundwater was formed in the BJD was also discussed. We found obvious differences in δD, δ18O, and d-excess among groundwater in the BJD, nearby water bodies and the HRB. The groundwater δD- δ18O equation for the BJD was δD = 4.509 δ18O-30.620, with a slope and intercept similar to that of nearby areas (4.856 and −29.574), indicating a strong evaporation effect in the BJD and its surrounding areas. The equation's slope of the BJD was significantly lower than those of HRB groundwater (6.634), HRB river water (6.202), precipitation in the BJD and Youqi (7.841), and the UR of the HRB (7.839). The d-excess (−17.5‰) of the BJD was significantly lower than those of nearby groundwater (−7.4‰), HRB groundwater (12.1‰), precipitation in the BJD (5.7‰) and in the UR of the HRB (15.2‰), and HRB river water (14.4‰). The spatial patterns of δ18O and d-excess values in the BJD suggest mixing and exchange of groundwater between the BJD and neighboring regions, but no hydraulic relationship between the BJD groundwater and water from more distant regions except Outer Mongolia, which is north of the BJD. Moreover, we conclude that there is little precipitation recharge to groundwater because of the obvious d-excess difference between groundwater and local precipitation, low precipitation, and high evaporation rates. The abnormally negative d-excess values in groundwater of the BJD indicate that this water was formed in the past under higher relative humidity and lower temperatures than modern values. [ABSTRACT FROM AUTHOR]

Studies on hydrological processes are often emphasized in resource and environmental studies. This paper identifies the hydrological processes in different landscape zones during the wet season based on the isotopic and hydrochemical analysis of glacier, snow, frozen soil, groundwater and other water sources in the headwater catchment of alpine cold regions. Hydrochemical tracers indicated that the chemical compositions of the water are typically characterized by: (1) CaHCO3 type in glacier snow zone, (2) MgCaSO4 type for surface runoff and CaMgHCO3 type for groundwater in alpine desert zone, (3) CaMgSO4 type for surface water and CaMgHCO3 type for groundwater in alpine shrub zone, and (4) CaNaSO4 type in surface runoff in the alpine grassland zone. The End-Members Mixing Analysis (EMMA) was employed for hydrograph separation. The results showed that the Mafengou River in the wet season was mainly recharged by groundwater in alpine cold desert zones and shrub zones (52%), which came from the infiltration and transformation of precipitation, thawed frozen soil water and glacier-snow meltwater. Surface runoff in the glacier-snow zone accounted for 11%, surface runoff in alpine cold desert zones and alpine shrub meadow zones accounted for 20%, thawed frozen soil water in alpine grassland zones accounted for 9% of recharge and precipitation directly into the river channel (8%). This study suggested that the whole catchment precipitation did not produce significant surface runoff directly, but mostly transformed into groundwater or interflow, and finally arrived in the river channel. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An accurate groundwater level (GWL) forecast at multi timescales is vital for agricultural management and water resource scheduling in arid irrigated areas such as the Hexi Corridor, China. However, the forecast of GWL in these areas remains a challenging task owing to the deficient hydrogeological data and the highly nonlinear, non-stationary and complex groundwater system. The development of reliable groundwater level simulation models is necessary and profound. In this study, a novel ensemble deep learning GWL predictive framework integrating data pro-processing, feature selection, deep learning and uncertainty analysis was constructed. Under this framework, a hybrid model equipped with currently the most effective algorithms, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for data decomposition, the genetic algorithm (GA) for feature selection, the deep belief network (DBN) model, and the quantile regression (QR) for uncertainty evaluation, denoted as CEEMDAN-GA-DBN, was proposed for the 1-, 2-, and 3-month ahead GWL forecast at three GWL observation wells in the Jiuquan basin, northwest China. The capability of the CEEMDAN-GA-DBN model was compared with the hybrid CEEMDAN-DBN and the standalone DBN model in terms of the performance metrics including R, MAE, RMSE, NSE, RSR, AIC and the Legates and McCabe's Index as well as the uncertainty criterion including MPI and PICP. The results demonstrated the higher degree of accuracy and better performance of the objective CEEMDAN-GA-DBN model than the CEEMDAN-DBN and DBN models at all lead times and all the wells. Overall, the CEEMDAN-GA-DBN reduced the RMSE of the CEEMDAN-DBN and DBN models in the testing period by about 9.16 and 17.63%, while it improved their NSE by about 6.38 and 15.32%, respectively. The uncertainty analysis results also affirmed the slightly better reliability of the CEEMDAN-GA-DBN method than the CEEMDAN-DBN and DBN models at the 1-, 2- and 3-month forecast horizons. The derived results proved the ability of the proposed ensemble deep learning model in multi time steps ahead of GWL forecasting, and thus, can be used as an effective tool for GWL forecasting in arid irrigated areas. [ABSTRACT FROM AUTHOR]

Through its effects on water and energy cycles, elevation plays an important role in modulating the spatial distribution of climatic changes in mountainous regions. A key hydrological indicator, reference evapotranspiration (ET0) reflects the maximum amount of water transferred to the atmosphere from the land surface. The current scarcity of information regarding elevation's impact on variation in ET0 under climate change limits our understanding of the extent to which elevation modulates interactions between ET0 and climate change and of the attendant processes involved. Drawing upon long-term (1960–2017) meteorological observations from 84 stations in Northwest China (NWC), we examined (i) spatial and temporal variations in ET0; (ii) the sensitivity and contribution of air temperature (T), sunshine duration (SD), relative humidity (RH), and wind speed (WS) to ET0; (iii) the existence of a relationship between elevation and ET0 trends; and (iv) the major factor in controlling this relationship by using attribution analysis. Overall, annual ET0 in NWC showed a declining trend between 1960 and 2017, though at a change point in 1993, the trend shifted from a decline to a rise. A significant correlation between temporal change in ET0 and elevation confirmed the existence of a relationship between elevation and ET0 variation. The effect of elevation on changes in ET0 depended mainly on the elevation-based tradeoff between the contributions of T and WS: WS was the primary factor contributing to the decrease in ET0 below 2000 m, and T was the dominant factor contributing to the increase of ET0 above 2000 m. The rate of reduction in WS declined as elevation increased, thereby diminishing its contribution to variation in ET0. The present study's results can serve to guide agricultural irrigation in different elevation zones under NWC's evolving climatic conditions. [ABSTRACT FROM AUTHOR]

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model's viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model's utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management. [ABSTRACT FROM AUTHOR]

Precise multi-time scales prediction of groundwater level is essential for water resources planning and management. However, credible and reliable predicting results are hard to achieve even to extensively applied artificial intelligence (AI) models considering the uncontrollable error, indefinite inputs and unneglectable uncertainty during the modelling process. The AI model ensembled with the data pretreatment technique, the input selection method, or uncertainty analysis has been successfully used to tackle this issue, whereas studies about the comprehensive deterministic and uncertainty analysis of hybrid models in groundwater level forecast are rarely reported. In this study, a novel hybrid predictive model combining the variational mode decomposition (VMD) data pretreatment technique, Boruta input selection method, bootstrap based uncertainty analysis, and the extreme learning machine (ELM) model named VBELM was developed for 1-, 2- and 3-month ahead groundwater level prediction in a typical arid oasis area of northwestern China. The historical observed monthly groundwater level, precipitation and temperature data were used as inputs to train and test the model. Specifically, the VMD was used to decompose all the input-outputs into a set of intrinsic mode functions (IMFs), the Boruta method was applied to determine input variables, and the ELM was employed to forecast the value of each IMF. In order to ascertain the efficiency of the proposed VBELM model, the performance of the coupled model (VELM) hybridizing VMD with ELM algorithm and the single ELM model were estimated in comparison. The results indicate that the VBELM performed best, while the single ELM model performed the worst among the three models. Furthermore, the VBELM model presented lower uncertainty than the VELM model with more observed groundwater level values falling inside the confidence interval. In summary, the VBELM model demonstrated an excellent performance for both certainty and uncertainty analyses, and can serve as an effective tool for multi-scale groundwater level forecasting. [ABSTRACT FROM AUTHOR]

• The hydrology changes is sensitive to energy in high-altitude. • The hydrology changes is controlled by water limitation in low-altitude area. • The water-energy coupling relation highly relies on altitudinal gradient. • Climate change dominate the hydrological shifts in alpine region. Hydrology heterogeneity refers to the different water regimes or hydrological processes in response to heterogeneous in topography, landscapes, land cover, soil properties, geology and meteorology reflecting as the spatial variation of precipitation, evapotranspiration and water yield along with landscape, elevation, terrain and climate variation. However, the hydrology heterogeneity in the alpine region of inland river basin has remained unclear to date, which limits the understanding of the hydrology and landscape interaction mechanism and affects the rational utilization of regional water resources. In this study, we combined the soil and water assessment tool (SWAT) and water-energy coupling framework to detect the hydrology heterogeneity in the arid alpine region of Heihe River Basin (HRB). We applied SWAT model to present the hydrological processes and regimes at different landscapes and elevation bands by using field measured parameters, and investigate the hydrology heterogeneity as well as the response to the changes in recent climate and land surface in the perspective of water-energy coupling framework. The results indicate that SWAT is indeed a robust tool in representing the streamflow process in alpine region with accurate performance. The distribution of landscapes reflect the comprehensive effect of regional water-energy coupling, which highly rely on the altitudinal gradient. The hydrology heterogeneity in high-altitude area is sensitive to energy and in low-altitude area is sensitive to water limitation (drought stress). Climate change is the dominate factor that driving the hydrology heterogeneity shifts in alpine region of HRB, while, the effect of land surface change is gradually strengthening over the past 50 years, reflecting with the contribution of land surface change on hydrology shifts is gradually increasing. [ABSTRACT FROM AUTHOR]

China's rapid population growth has inevitably led to a need for cropland expansion to meet food security challenges. In arid regions such an expansion is restricted by limited water resources, and an increase in productivity per cropland area must be achieved while still limiting water use. However, the response of agroecosystem Water Use Efficiency (WUE a. e.) to alterations in cropland use in Northwest China's Hexi Corridor has not been documented in any great detail. This has limited our understanding of the region's water-carbon interactions and, in particular, how effective cropland management and efficient water utilization protocols might be applied there. The response of the region's WUE a. e. to different potential shifts in cropland was evaluated by drawing on growing season (2003–2020), Gross Primary Productivity (GPP) and actual evapotranspiration (ET c act) data. Over this period net cropland area increased, with 12.3 % of total cropland transferred in, but only 2.5 % of cropland transferred out. The trend in growing season WUE a. e. was greatest in cropland regions transferred in (p < 0.05), and lowest in the cropland transferred out, indicating irrigation and field management in cropland could ensure that WUE a. e. could be maintained at a higher level. Among the different cropland transformation types, the impact of cropland change on WUE a. e. was essentially determined by shifting correlations between GPP and ET c act. This correlation was weakened when the water supply increased, heightening the negative impact of ET c act on WUE a. e. , while weakening the positive impact of GPP on WUE a. e.. There was a tradeoff point for the level of irrigation between improving WUE a. e. and crop yield. Suitable water-saving irrigation is crucial to maintaining a relatively high level of WUE a. e. , yet not decreasing crop yield. Water-saving irrigation in the Shiyang River basin was deemed to have limited potential, whereas in the Shule River and Heihe River basins there remains some potential for such irrigation. The present study's results can potentially inform agricultural water and land management in the region, and provide the basis for efficient water resources utilization. • Agroecosystem Water Use Efficiency (WUE a. e.) was evaluated considering cropland transformations. • The cropland transferred in (∆ CL ←) and out (∆ CL →) in Hexi Corridor was investigated. • The trend in WUE a. e. was the highest in the ∆ CL ← area, and lowest in ∆ CL → area. • The response of WUE a. e. to cropland change was regulated by the correlation of GPP and ET c act. • The correlation of GPP and ET c act is weakened along with an increase of water supply. [ABSTRACT FROM AUTHOR]

The nitrate pollution of groundwater in arid areas subject to human activity, and especially agricultural activity, is of immense environmental concern. Groundwater is the main water source in arid areas, once an aquifer is polluted, the impact upon the security of the water supply becomes all the more serious. The process of nitrate transformation is also different from that of humid areas, therefore, it is very important to identify and quantify the nitrate sources in aquifers. Using the hydrochemical, stable isotope tracers of 194 groundwater samples, and combining these with the MixSIAR model, the spatial distribution of groundwater nitrates in the Shiyang River Basin (SRB), China, was mapped. The relative contributions made by nitrate sources to different aquifers, and their key transformation processes, were identified. It was found that, shallow groundwater is seriously polluted, with NO 3 − concentrations ranging from 1.1 to 438.7 mg L−1. In the SRB, NO 3 − concentrations of 23% and 73% in shallow groundwater samples exceed the NO 3 − concentration limits found in natural sources (13.3 mg L−1), and the World Health Organization (WHO) nitrate limits for drinking water (50 mg L−1). Furthermore, the nitrates found in confined aquifers indicate that these are also affected by anthropogenic sources, with concentrations ranging from 1.6 to 44.7 mg L−1. Soil organic nitrogen (51.9%) and NH 4 + fertilizer (20.8%), wastewater and manure (20.5%) are the main sources of groundwater nitrates; NO 3 − fertilizer (4.4%) and precipitation (2.3%) have a relatively insignificant effect on groundwater nitrates. The process of nitrification is the main factor affecting changes in nitrate concentrations in aquifers. Other than the denitrification observed in the shallow aquifers of the Minqin Basin (MQB), there was no significant denitrification in the other sub-basins of the SRB. Anthropogenic sources, especially agricultural sources, are now major contributors to the increases in groundwater nitrate concentrations observed in the SRB. This study provides data that can be used to help prevent groundwater pollution and enhance water resources management in arid areas. [Display omitted] • Except Jinchang sub-basin, nitrate pollution in groundwater is obvious in other sub-basins of SRB. • Nitrate concentrations in confined aquifer have also been affected by anthropogenic sources. • The process of nitrification is the main factor affecting changes in nitrate concentrations in aquifers. • A denitrification process that would alleviate the nitrate pollution of groundwater bodies is relatively lacking in SRB. [ABSTRACT FROM AUTHOR]

Terrestrial evapotranspiration (ET a) reflects the complex interactions of climate, vegetation, soil and terrain and is a critical component in water and energy cycles. However, the manner in which climate change and vegetation greening influence ET a remains poorly understood, especially in alpine regions. Drawing on the Global Land Evaporation Amsterdam Model (GLEAM) ET a data, the interannual variability of ET a and its ties to precipitation (P), potential evaporation (ET p) and vegetation (NDVI) were analysed. The Budyko framework was implemented over the period of 1982 to 2015 to quantify the response of ET a to climate change's direct (P and ET p) and indirect (NDVI) impacts. The ET a , P, ET p and NDVI all showed significant increasing trends from 1981 to 2015 with rates of 1.52 mm yr−1, 3.18 mm yr−1, 0.89 mm yr−1 and 4.0 × 10−4 yr−1, respectively. At the regional level, the positive contribution of increases in P and NDVI offset the negative contribution of ET p to the change in ET a (∆ET a). The positive ∆ET a between 1982 and 2001 was strongly linked with the concomitant increase in NDVI. Increases in vegetation contributing to a positive ∆ET a differed among landscape types: for shrub, meadow and steppe they occurred during both periods, for alpine vegetation between 1982 and 2001, and for desert between 2002 and 2015. Climate change directly contributed to a rise in ET a, with P as the dominant factor affecting forested lands during both periods, and alpine vegetation between 2002 and 2015. Moreover, ET p was a dominant factor for the desert between 1982 and 2001, where the variation of P was not significant. The contributions of factors having an impact on ∆ET a are modulated by both the sensitivity of impact factors acting on ET a as well as the magnitudes of factor changes. The greening of vegetation can influence ET a by increasing vegetation transpiration and rainfall interception in forest, brush and meadow landscapes. These findings can help in developing a better understanding of the interaction of ecosystems and hydrology in alpine regions. Unlabelled Image • ET a , P, ET p and NDVI showed significant increasing trends from 1981 to 2015 in QLM. • The positive contribution of increases in P and NDVI offset the negative contribution of ET p to the ∆ET a • Climate change contributed to a rise in ET a , with P as a dominant factor for forested lands. • Increase in vegetation dominated the positive ETa trends for shrub, meadow and steppe. • ET a was controlled by the sensitivity of factors to ET a as well as their change magnitudes. [ABSTRACT FROM AUTHOR]

One of the major components of the hydrological cycle, reference evapotranspiration (ET 0) represents the maximum amount of water transferred from the land surface to the atmosphere. Vital to quantifying crop water needs, accurate predictions of ET 0 are particularly critical in arid regions, where they allow for informed water resources management adjustments through changes to agricultural irrigation rates and scheduling. Drawing upon 84 meteorological stations in northwest China, spatiotemporal variations in present-day ET 0 were investigated. Support vector regression (SVR), Extreme learning machine (ELM), and Multivariate adaptive regression spline (MARS) — three machine learning (ML) techniques — served to establish relationships between historical ET 0 and the Coordinated Regional Climate Downscaling Experiment – East Asia (CORDEX-EA), drawn from the output data sets of each of three regional climate models (RCM): Weather Research and Forecasting (WRF), Regional Climate Model version 4.0 (RegCM4) and the Mesoscale Model version 5 (MM5). The ML-RCM combinations were calibrated and validated with separate batches (66:34, respectively) of historical ET 0 data, and their respective performance and level of uncertainty were assessed statistically. In the historical period (1960–2017) ET 0 declined by −0.15, −0.75, and − 0.42 mm y−1 in north Xinjiang, south Xinjiang, and Qinghai region, respectively, and increased in the Hexi Corridor by 0.5 mm y−1. For all four regions, the MARS-WRF and MARS-MM5 combinations performed well and showed greater predictive accuracy than either ELM-WRF or ELM-MM5 combinations. Performances in predicting future (2035–2050) ET 0 from CORDEX-EA outputs based on regional climate predictions RCP 4.5 and RCP 8.5 scenarios, depended to a greater extent on the RCM outputs that were selected, rather than the modeling methods. Future ET 0 predicted from RCMs generally exhibit increasing trends, and more significantly under the RCP 8.5 scenario. The representation and characterization ability of RCMs to future climate change is crucial for future ET 0 projection. Uncertainty analysis, achieved by employing multiple RCMs to predict future ET 0 , is highly recommended. Knowledge of trends in future ET 0 can help guide the management of agricultural irrigation in oases and support decision-makers engaged in water resources management in the future. • Historical ET 0 declined in Xinjiang, and Qinghai, and increased in Hexi Corridor, China. • The combination of MARS-WRF had the best predictive accuracy. • Prediction of ET 0 depends more on the RCM chosen rather than modeling methods. • Future ET 0 had an increasing trend, especially under RCP 8.5. [ABSTRACT FROM AUTHOR]

The relationship between climate and human society has frequently been investigated to ascertain whether climate variability can trigger social crises (e.g., migration and armed conflicts). In the current study, statistical methods (e.g., correlation analysis and Granger Causality Analysis) are used in a systematic analysis of the potential causality of climate variability on migration and armed conflicts. Specifically, the statistical methods are applied to determine the relationships between long-term fine-grained temperature and precipitation data and contemporary social conditions, gleaned from historical documents covering the last two millennia in China's Hexi Corridor. Results found the region's reconstructed temperature to be strongly coupled with precipitation dynamics, i.e., a warming climate was associated with a greater supply of moisture, whereas a cooling period was associated with more frequent drought. A prolonged cold period tended to coincide with societal instability, such as a shift from unification towards fragmentation. In contrast, a prolonged warm period coincided with rapid development, i.e., a shift from separation to unification. The statistical significance of the causality linkages between climate variability, bio-productivity, grain yield, migration and conflict suggests that climate variability is not the direct causative agent of these phenomena, but that climate reduced food production which gradually lead to migration and conflicts. A conceptual causal model developed through this study describes the causative pathway of climate variability impacts on migration and conflicts in the Hexi Corridor. Applied to current conditions, the model suggests that steady and proactive promotion of the nation's economic buffering capacity might best address the uncertainty brought on by a range of potential future climate scenarios and their potential impacts. Unlabelled Image • Cold periods coincided with social instability and the downfall of dynasties. • Causality linkages of climate-food-conflict were quantitatively justified. • Model for the pathway of climate change impacts on social systems was described. • Potential human integrated coping strategies with climate change are proposed. [ABSTRACT FROM AUTHOR]