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[Display omitted] • The changes in nineteen secondary LU types and their ESVs were predicted under the BAU and EDP scenarios by 2030 and 2050. • The LU pattern under EDP scenario is more conducive to the increase of ESV, with the increment by 2050 is 13.6 times that under BAU scenario. • Under the EDP scenario, the growth of ESV mainly comes from the positive contributions of the expansion of Forest (55.4%), Water (48.6%), and Grassland (19.1%). • Under the EDP scenario, the negative contributions of the decreases in Farmland and Unused land to the increment of ESV are −16.2% and −10.8%, respectively. The effects of land use/cover change (LUCC) on the spatial distribution and change of ecosystem service value (ESV) are still ambiguous, and cannot effectively guide the formulation of land use (LU) management policies based on the concept of harmonious development. Therefore, based on LUCC data from 1980 to 2020, this study adopted the MCE-CA-Markov model to simulate and predict the LUCC under the Business As Usual (BAU) and Ecological Development Priority (EDP) scenarios in 2030 and 2050. The historical and future ESVs as well as the effects of LUCC on ESV change were calculated in Gansu province, Northwest China. The results showed that the LU and ESV of Gansu province changed greatly between 1980 and 2020, with an increase in Forest (568.62 km2), medium- and high-coverage grassland (1517.58 km2), and Construction land (2264.58 km2), and a decrease in Farmland (-730.17 km2) and Unused land (-2858.58 km2), resulting in an increase of 8.162 billion CNY in total ESV. From 2020 to 2050, the LUCC under the EDP scenario will be more conducive to the increase in ESV, with the increment (52.765 billion CNY) being much higher than that under the BAU scenario (3.885 billion CNY). The growth of ESV will mainly result from the expansion of Forest, Water, and Grassland, accounting for 55.4%, 48.6%, and 19.1% of the total ESV increment, respectively, while the decrease in ESV will be attributed to the changes in Farmland and Unused land, accounting for −16.2% and −10.8% of the total ESV increment, respectively. The results have a certain reference for the future LU structure optimization and ecological benefit growth in northwest China. [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]

Accurate and reliable river flow forecasts attained with data-intelligent models can provide significant information about future water resources management. In this study we employed a 50-model ensemble of three data-driven predictive models, namely the support vector regression (SVR), multivariate adaptive regression spline (MARS) and M5 model tree (M5Tree) to forecast river flow data in a semiarid and ecologically significant mountainous region of Pailugou catchment in northwestern China. To attain stable and accurate forecast results, 50 different models were trained by randomly sampling the entire river flow data into 80% for training and 20% for testing subsets. To attain a complete evaluation of the ensemble-model based results, the global mean of six quantitative statistical performance evaluation measures: the coefficient of correlation (R), mean absolute relative error (MAE), root mean squared error (RMSE), Nash-Sutcliffe efficiency coefficient (NS), relative RMSE, and the Willmott’s Index (WI), and Taylor diagrams, including skill scores relative to a persistence model, were selected to assess the performances of the developed predictive models. The results indicated that all of the averaged R value attained was higher than 0.900 and all of the averaged NS values were higher than 0.800, representing good performance of the SVR, MARS and M5Tree models applied in the 1-, 2- and 3-day ahead modeling horizon, and this also accorded with the deductions made through an assessment of the Willmott’s Index. However, the M5Tree model outperformed both the SVR and MARS models (with NS = 0.917 vs. 0.904 and 0.901 for 1-day, 0.893 vs. 0.854 and 0.845 for 2-day, and 0.850 vs. 0.828 and 0.810 for 3-day forecasting horizons, respectively), which was in concurrence with the high value of WI. Therefore, based on the ensemble of 50 models, the performance of the M5Tree can be considered as superior to the SVR and MARS models when applied in a problem of river flow forecasting at multiple forecast horizon. A detailed comparison of the overall performance of all three models evaluated through Taylor diagrams and boxplots indicated that the 1-day ahead forecasting results were more accurate for all of the predictive models compared to the 2- and 3-day ahead forecasting horizons. Data-intelligent models designed in this study indicate that the M5Tree method could successfully be explored for short-term river flow forecasting in semiarid mountainous regions, which may have useful implications in water resources management, ecological sustainability and assessment of river systems. [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]

Salinization is considered a significant threat to soil ecosystem and plant growth in arid and semiarid regions. Soil microorganisms are supposed to be essential in relieving soil salinization and enhancing plant salt tolerance. However, although the responses of soil fungal diversity and community structure to salt stress have been investigated, major knowledge gaps remain in the interactions and community assembly of abundant and rare fungal taxa, and fungal functional characteristics under different salt intensities. Here, next-generation sequencing technology were used to investigate the response of all, abundant and rare fungal taxa to salt stress in arid and semiarid regions. The results demonstrated that high salt stress reduced the complexity and stability of fungal networks and increased fungal collaborations. Rare taxa held a key position in the co-occurrence network and exhibited greater proficiency in collaborating with intermediate and rare taxa, thereby augmenting the resistance of the community and maintaining its stability. Notably, with increasing salt stress, the importance of rare taxa gradually enhanced, while the importance of abundant taxa decreased. Moreover, fungal communities were manipulated by stochastic processes under salt stress, while were also influenced by deterministic processes (heterogeneous selection), especially for rare taxa. Furthermore, low salinity conditions promoted the colonization of saprophytic and symbiotic fungi and inhibited the growth of pathogenic fungi. Functional groups associated with fungal parasite-soil saprotroph-undefined saprotroph function were extremely sensitive to soil salinity and exerted a pivotal influence in sustaining community stability. Our findings may broaden our understanding of how abundant and rare fungi react to salt stress, making it easier to forecast the possible loss of biodiversity due to soil salinization, and offering microbial references for the development of saline-alkaline soil. [Display omitted] • Fungal networks were less stable but more collaborative under salt stress. • Rare taxa were more robust in sustaining community stability than abundant taxa. • Rich and rare taxa's importance gap in sustaining network increased as salinity rose. • Stochastic processes gradually dominated community assembly as salinity increased. • Heterogeneous selection contributed more to rare taxa assembly than to abundant taxa. [ABSTRACT FROM AUTHOR]

Accurate projection of future precipitation is a major challenge due to the uncertainties arising from the atmospheric predictors and the inherent biases that exist in the global circulation models. In this study, we employed multivariate recursive nesting bias correction (MRNBC) and multiscale wavelet entropy (MWE) to reduce the bias and improve the projection of future (i.e., 2006–2100) precipitation with artificial intelligence (AI)-based data-driven models. Application of the developed method and the subsequent analyses are performed based on representative concentration pathway (RCP) scenarios: RCP4.5 and RCP8.5 of eight Coupled Model Intercomparison Project Phase-5 (CMIP5) Earth system models for the upstream of the Heihe River. The results confirmed the MRNBC and MWE were important statistical approaches prudent in simulation performance improvement and projection uncertainty reduction. The AI-based methods were superior to linear regression method in precipitation projection. The selected CMIP5 outputs showed agreement in the projection of future precipitation under two scenarios. The future precipitation under RCP8.5 exhibited a significantly increasing trend in relative to RCP4.5. In the future, the precipitation will experience an increase by 15–19% from 2020 to 2050 and by 21–33% from 2060 to 2090. [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]

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]

This paper presents a detailed analysis of how future climate change may affect water availability in a typical arid endorheic river basin, the Heihe River basin (HRB), in northwest China. The analysis is based on the improved Soil Water Assessment Tool (SWAT), which is calibrated and validated with historical streamflow data from the upper HRB and is used to predict future hydrological responses. Six general circulation models (GCMs), under two emission scenarios (RCP4.5 and RCP8.5), are downscaled to construct future climate change scenarios. The results suggest that the climate of the upper HRB will likely become warmer and wetter in the near future (2021-50), with the largest increase in precipitation occurring in the summer. Correspondingly, the basinwide evapotranspiration, snowmelt, and runoff are projected to increase over the same period. The mean temperature in the near future is projected to rise, relative to the recent 30 years (1981-2010), by 1.2°-1.7°C under scenario RCP4.5 and by 1.4°-2.1°C under scenario RCP8.5. The mean precipitation is projected to increase by 10.0%-16.6% under scenario RCP4.5, and by 10.5%-22.0% under scenario RCP8.5. The mean values of evapotranspiration, snowmelt, and runoff are expected to increase by 14.2%, 4.3%, and 11.4%, respectively, under scenario RCP4.5 and to increase by 18.7%, 5.8%, and 12.8%, respectively, under scenario RCP8.5. Though the model simulations forecast an increase in streamflows in the headwater region of the HRB, future water availability varies significantly over space and time. The findings of this study will help to frame more effective water management strategies for the HRB under changing climatic 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]

Reference crop evapotranspiration (ETo) is an integral hydrological factor in soil–plant-atmospheric water balance studies and the management of drought events. This paper proposes a new hybrid-deep learning approach, combining convolutional neural network (CNN) and gated recurrent unit (GRU) along with Ant Colony Optimization (ACO), for a multi-step (week 1 to 4) daily-ETo forecast. The method also assimilates a comprehensive dataset with 52 diverse predictors, i.e., satellite-derived moderate resolution imaging spectroradiometer, ground-based datasets from scientific information for landowners and synoptic-scale climate indices. To develop a vigorous CNN-GRU model, a feature selection stage entails the ant colony optimization method implemented to improve the ETo forecast model for the three selected sites in Australian Murray Darling Basin. The results demonstrate excellent forecasting capability of the hybrid CNN-GRU model against the counterpart benchmark models, evidenced by a relatively small mean absolute error and high efficiency. Overall, this study shows that the proposed hybrid CNN-GRU model successfully apprehends the complex and non-linear relationships between predictor variables and the daily ETo. [ABSTRACT FROM AUTHOR]

Future soil moisture (SM) estimation is a practically useful task for eco-hydrologists, agriculturists, and stakeholders in environment health monitoring to generate comprehensive understanding of hydro-physical and soil dynamic system. This paper demonstrates the capability of a hybridised long short-term memory (LSTM) predictive framework to emulate SM under global warming scenarios. The proposed model is developed by integrating Boruta-random forest (BRF) feature selection and capturing significant antecedent memory of SM behaviour were applied to estimate the future SM using Coupled Model Intercomparison Phase-5 (CMIP5) repository. The BRF is adapted to extract pertinent features in hydro-meteorological variables intrinsically related to SM, and therefore, is used to construct a hybridised deep learning (i.e., BRF-LSTM) model. To establish the viability of deep learning model for SM estimation until 2100, five stations closely matched to the global climate model grid are selected in Australia's Murray Darling Basin. The performance skill of BRF-LSTM model is compared against standalone models (i.e., LSTM, SVR, and MARS). The results showed that the hybrid deep learning model (i.e., BRF-LSTM) with a feature selection capability could significantly outperform the standalone models for both warming simulations. The proposed hybrid model also demonstrated superiority in SM estimation with over 95% of all predictive errors lying below 0.02 mm, and low relative root means square error (≈ 1.06% for RCP4.5 and ≈ 1.888% for RCP8.5) to outperform all the benchmark models. This study demonstrates the capability of LSTM algorithm coupled with BRF feature selection to simulate future soil moisture under climate change, and so, can be successfully implemented in hydrology, agriculture, soil use management and environmental management. [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]

Highlight • Two-phase data-intelligent model is designed for multiple-horizon runoff prediction. • Integration of ELM with CEEMDAN and VMD yields hybrid (CVEE-ELM) model. • Single-phase CEE-ELM (minus VMD) and ELM model used as benchmark. • Model evaluated at 1-, 3- and 6-month lead prediction. • Hybrid two-phase model outperforms single-phase and ELM models. Abstract Expert systems adopted in real-time multi-scale runoff prediction are useful decision-making tools for hydrologists but the stochastic nature of any hydrological variable can pose significant challenges in attaining a reliable predictive model. This paper advocates a data-driven approach used to design two-phase hybrid model (i.e., CVEE-ELM). The model utilizes complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) coupled with the variational mode decomposition (VMD) algorithms for better frequency resolution of the input datasets and the extreme learning machine (ELM) algorithm as the objective predictive model. In the first stage of the presented model design, notable frequencies in the predictor-target data series are uncovered, utilizing the CEEMDAN algorithm where the model's inputs are decomposed into their respective Intrinsic Mode Functions (IMFs) and the Residual (Res) series. The second stage entails a VMD approach, used to decompose the yet-unresolved high frequencies (i.e., IMF 1) into their variational modes, further discerning and establishing the data attributes to be incorporated into the ELM model to simulate the respective IMFs, Res and VM data series, aggregated as an integrative tool for multiscale runoff prediction. In the model evaluative phase, the hybrid CVEE-ELM is cross-validated with a single-phase hybrid ELM and an autoregressive integrated moving average (ARIMA) model to benchmark its accuracy for predicting 1-, 3- and 6-month ahead runoff in Yingluoxia watershed, Northwestern China. Two-phase hybrid model exhibits superior accuracy at all lead times, to accord with high degree of correlations between the observed and the forecasted runoff, a relatively large Nash-Sutcliffe and the Legate-McCabe Index. Taylor diagrams depict the two-phase hybrid CVEE-ELM model generated forecasts located close to a reference (i.e., a perfect) model, with a lower root-mean square centered difference, and a correspondingly large correlation for all forecast horizons, although the model's accuracy for shorter lead times (1-month), as expected, are better than the 3- and 6-month horizon. The study shows that the two-phase hybrid CVEE-ELM model where an integration of two frequency resolution algorithms are made, is a preferred data-driven tool that can be explored for real-life decision-system design, particularly for hydrological forecasting problems that have significantly stochastic data features, and thus, will require reliable forecasts to be generated at multi-step horizons. [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]

Investigation of the evolution of the groundwater system and its mechanisms is critical to the sustainable management of water in river basins. Temporal and spatial distributions and characteristics of groundwater have undergone a tremendous change with the intensity of human activities in the middle reaches of the Heihe River Basin (HRB), the second largest arid inland river basin in northwestern China. Based on groundwater observation data, hydrogeological data, meteorological data and irrigation statistical data, combined with geostatistical analyses and groundwater storage estimation, the basin-scaled evolution of the groundwater levels and storage (from 1985 to 2013) were investigated. The results showed that the unbalanced allocation of water sources and expanded cropland by policy-based human activities resulted in the over-abstraction of groundwater, which induced a general decrease in the water table and groundwater storage. The groundwater level has generally fallen from 4.92 to 11.49 m from 1985 to 2013, especially in the upper and middle parts of the alluvial fan (zone I), and reached a maximum depth of 17.41 m. The total groundwater storage decreased by 177.52 × 10 m; zone I accounted for about 94.7 % of the total decrease. The groundwater balance was disrupted and the groundwater system was in a severe negative balance; it was noted that the groundwater/surface-water interaction was also deeply affected. It is essential to develop a rational plan for integration and management of surface water and groundwater resources in the HRB. [ABSTRACT FROM AUTHOR]

The glacier is an important and stable water supply in Central Asia. Monitoring the change of glacier and understanding the impacts of glacier change on river discharge are critical to predict the downstream water availability change in future. Glacier changes were discussed and their impacts on river discharge were evaluated by hydrological modeling with a distributed hydrological model SWAT under two land use and land cover scenarios (1970 and 2007) in Tekes watershed, the most important source of water discharge to the Ili River. Compared to the glacier area of 1511 km 2 in 1970s it decreased by 332 km 2 in 2007, which resulted in the contribution the discharge from precipitation in the glacier area to the average annual discharge of the watershed changing from 9.8% in the period 1966–1975 to 7.8% in the period 2000–2008. In the month scale, with the decrease of glacier area, the distribution of the contribution of monthly discharge from precipitation in the glacier area to the total of the watershed changed from bimodal pattern to unimodal pattern. By linking a hydrological model to remote sensing image analysis and Chinese glacier inventories to determine glacier area change our approach in quantifying the impacts of glacier changes on hydrology at different scales, will provide quantitative information for stakeholders in making decisions for water resource management. [ABSTRACT FROM AUTHOR]

• WUE showed a significant increasing trend with a break point in 2002 in NWC. • A robust coupled attribution analysis with elasticity method was employed. • GPP contributed 88%, while ET a contributed 12%, to variation in WUE. • Precipitation was the primary factor driving the 66% increase in WUE. • A structure diagram was developed to present the driving mechanism of WUE variation. Besides contributing to a broader understanding of ecosystem-hydrology interactions, ecosystem water use efficiency (WUE) is a key factor operating in an ecosystem's balance between carbon assimilation and water transpiration. However, the effects of climate, elevated atmospheric CO 2 concentration and land surface changes on variation in regional WUE are sparsely documented. To elucidate the driving mechanisms of spatiotemporal variation in WUE in northwest China (NWC) between 1982 and 2015, the contribution of a changing environment — represented by the WUE's sensitivity to Gross Primary Productivity (GPP) and evapotranspiration (ET a) — was examined under different landscape types. Annual WUE series for NWC showed a significant increasing trend and a break point in 2002 with an increasing rate (7.40 mg C kg−1 H 2 O yr−1) prior to the break, and a slightly increasing rate (1.1 mg C kg−1 H 2 O yr−1) thereafter in NWC. A robust (Bias = 2.6%) attribution analysis revealed that GPP was the dominant factor contributing 88% variation in WUE, while ET a contributed 12%. Precipitation was the principal indirect driving factor that affected WUE variation in NWC, while the secondary factor was different among different landscapes. Regionally, precipitation drove 66% of the increase in WUE, wind speed contributed −15% of variation in WUE, and temperature contributed 12% of increase in WUE, while the remaining four factors contributed <3%. Compared to energy-related factors, water-related factors were the more sensitive drivers to WUE variation. The results can guide agricultural irrigation, integrated water resources management and ecological restoration under the changes in climate coming to Northwest China. [ABSTRACT FROM AUTHOR]

• A significant rise in ET a occurred over 75% of the vegetated area in NWC. • Results of ET a variation attribution by different methods were compared. • Partial correlation coefficient-based elasticity method was robust in capturing ET a variation. • Precipitation was the dominant variable driving the variation in ET a in NWC. Actual evapotranspiration ( ET a) has significantly increased under recent climate and vegetation changes. Discriminating the primary driver of ET a variation would improve our knowledge of the interaction between regional hydrological and ecological systems. In this study, a reliable ET a dataset was obtained by validating three ET a products from different sources with in-situ observations. We modified the elasticity-coefficient method using partial correlation, and compared it with the traditional regression-based method in estimating ET a variation in northwest China (NWC). On the basis of the magnitude and the sensitivity of ET a variation, results revealed that annual ET a significantly increased (2.32 mm/yr, p ≤ 0.05) in NWC over 1982–2015 with over 75% of the vegetated area showing an increasing trend. Robust in capturing spatial variation patterns in ET a , the partial correlation coefficient-based elasticity method was able to explain 83% of the annual variation in ET a. Precipitation, temperature, and NDVI were the most important factors controlling the increase in annual ET a from 1982 to 2015. Regionally, precipitation was the dominant factor and contributed 73% to the variation of annual ET a in the vegetated NWC. However, the contribution rates of precipitation varied across the different land cover types and ranged from 31% in irrigated cropland to 81% in the steppe. Potential uncertainty in the attribution of causative factors could arise in selecting the separation method and potential driving factors. Establishing a reliable relationship between ET a and potential driving factors should be confirmed with observations. The present study's results can guide sustainable water resources management and ecological restoration in water limited regions. [ABSTRACT FROM AUTHOR]

Model calibration is important for streamflow simulations using distributed hydrological models, especially in highland and cold areas of northwest China with scarce data. Quantitative analysis of water balance based on the accurate simulation is also essential for reasonably planning and managing water resources in these river basins facing a severe water shortage. In this study, a comprehensive method was proposed to calibrate the Soil and Water Assessment Tool (SWAT) model in the Yingluoxia watershed, upstream area of the Heihe River basin; it was based on multi-temporal, multi-variable and multi-site integrated drainage characteristics. Meanwhile a fresh approach of the parameter transferability and model validation was used by applying the set of calibrated parameters in its tributary to other area of the watershed. The results indicated that the method was effective and feasible; the values of Nash–Sutcliffe Efficiency ( NSE ) and Coefficient of Determination ( r 2 ) were greater than 0.81 and as high as 0.94 and the absolute values of the Percent Bias ( PBIAS ) were less than 2. Based the output of model the water balance in the Yingluoxia watershed was analyzed, that the mean annual precipitation, evapotranspiration, and discharge of the watershed from 1990 to 2000 were 491.8 mm, 334 mm, and 157.8 mm, respectively. The comprehensive calibration method based on multi-temporal, multi-variable and multi-site integrated drainage characteristics can better portray the hydrological processes of watershed and improve the model simulation; and the output of the model then provide a reliable reference for assessing and managing water resource of the watershed. [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]

Summary: Hydrologic processes of glacier, snow, frozen soil, groundwater, surface water and precipitation have always been the common concern of hydrologists. The purpose of this study is to identify the hydrologic processes in different landscape zones, based on the isotopic and hydrochemical analysis of glacier, snow, frozen soil and groundwater. The chemical compositions were characterized by Ca–HCO3 in the glacier snow zone, Mg–Ca–SO4–HCO3 in the alpine cold desert zone, Ca–Mg–HCO3 in the marsh meadow zone, Ca–Mg–HCO3 in the alpine shrub zone, and Na–Ca–SO4 in the mountain grassland zone. The end-member mixing model was employed for hydrograph separation. The results showed that during the melting period Mafengou River water was recharged by glacier–snow melt water about 34%, thawed frozen soil water in each of the landscape zones about 12%, and groundwater about 54%. This study suggested that Mafengou River water during the melting period was mainly recharged by glacier–snow melt water, groundwater and thawed frozen soil water. [Copyright &y& Elsevier]

Modelling workflow for BRF-LSTM and BRF-GRU predictive models. [Display omitted] • Deep learning hybrid predictive model (BRF-LSTM) is built for streamflow forecasting. • Climate indices, rainfall and periodicity are factored for accurate predictions. • Adopts feature algorithm (i.e. Boruta) with long short-term memory and gated recurrent unit. • Six gauging sites tested; BRF-LSTM yields > 98% errors in ± 0.015 m, ~1.30% relative error. • The proposed model is useful for hydrological and strategic water resources planning. Long-term forecasting of any hydrologic phenomena is essential for strategic environmental planning, hydrologic and other forms of structural design, agriculture, and water resources management. Climate mode indices, utilising machine learning methods, are frequently considered as predictor variables in order to forecast several different hydrological variables. In this study, a feature selection algorithm based on two different deep learning models, i.e., long short-term memory and a gated recurrent unit, is applied to improve the forecasting capability of streamflow water levels at six gauging stations in the Murray Darling Basin of Australia. This paper therefore aggregates the significant antecedent lag memory of climate mode indices, rainfall, and the monthly factor based on the periodicity as the predictor variables to attain significantly accurate stream water level forecasts. This novel method identifies an improved relationship between the stream water level and climate mode indices through the aggregation of the significant lagged datasets capturing the historical features to predict the future streamflow water level. The boruta feature selection algorithm (BRF) was then applied in a two phase process before and after attaining the significant lagged inputs to screen the optimum predictor variables. The merits of the forecast models were evaluated through different performance evaluation criteria. The results show that the accumulated significant lagged inputs based on climate mode indices, along with the rainfall and periodicity factors are seen to provide improved forecasting of the SWL over the non-BRF deep learning approaches where no prior feature selection was applied. The hybrid LSTM method (i.e., BRF-LSTM model) achieved a unique advantage in terms of SWL forecasting, particularly attaining over 98% of the predictive errors lying within a band of +/-0.015 m with relatively low relative errors (RRMSE ≈1.30% and RMAE ≈ 0.882%), outperforming all of the benchmark models. It is also found that the periodicity factor has a potential influence on the accuracy of the forecast models for the four monitored study stations. This study concludes that the newly developed hybrid deep learning approaches, coupled with the BRF feature selection, provide improved forecasting performance. The hybrid approach developed in this paper can therefore be used to provide a strong provide predictive response algorithm for the hydrological variables that were influenced by the low-frequency variability of the climate model indices in respect to streamflow water level. [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]

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]

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]