Terrestrial water storage in semi-arid basins is increasingly affected by the combined pressures of climate variability, land use change and intensive human activities. However, how landscape composition and configuration interact with climatic forcing to shape basin-scale terrestrial water storage anomalies (TWSA) remains insufficiently understood, particularly in rapidly urbanising tributary basins of the Yellow River. This study integrates GRACE/GRACE-FO-derived TWSA, meteorological observations and multi-period land use data to examine the coupled relationships among climate variability, landscape patterns and water storage in the human-dominated Yiluo River Basin. The results show that basin-averaged TWSA experienced a significant long-term decline during the GRACE/GRACE-FO period (−4.47 mm yr−1), with a statistically detectable transition around 2012 and stronger depletion in the northern and eastern parts of the basin. Precipitation exhibited a cumulative and delayed relationship with TWSA, with the strongest raw association occurring under a six-month accumulation and one-month lag (Pearson’s r = 0.44, p < 0.001, n = 134). After removing the seasonal cycle, the relationship remained significant but weaker (r = 0.30, p = 0.001, n = 129), indicating that precipitation explains part, but not all, of the interannual variability in water storage. Landscape composition showed stronger associations with TWSA than landscape configuration. Construction land was negatively associated with water storage, whereas cultivated land and grassland showed positive associations, suggesting that impervious surface expansion and the loss of permeable land may weaken the basin’s water retention capacity. These findings indicate that water storage change in the Yiluo River Basin is shaped by both climatic forcing and human-induced land surface transformation. Basin management should therefore prioritise the control of urban impervious surface expansion, the protection of permeable agricultural and ecological land, and the integration of land use planning with adaptive water resource regulation.

Irrigation-driven greening is essential for northwest China's dryland ecosystems, where vegetation growth depends on key hydrological factors, including precipitation (PRE), evapotranspiration (ET), soil moisture (SM), and irrigation water use (IWU), which affect water availability to a certain extent. To assess greening sustainability, a 1 km IWU dataset was created for 2001-2022 by combining remote sensing and ancillary data using machine learning, overcoming limited irrigation records. By linking IWU with the normalized difference vegetation index (NDVI) and analyzing trends in irrigated areas, we implemented a regional zonation approach to identify specific risk areas and evaluated both greening sustainability and vegetation responses using water balance (WB) and various hydrological variables. The results show that NDVI has increased widely over the past two decades, with sustained positive WB and stable irrigation, indicating improved water availability. However, spatial differences exist: 35.98% of irrigated areas have rising NDVI but falling IWU, especially in the east, where higher NDVI, IWU, WB, PRE, and Delta SM (soil moisture difference between growing season end and start) reflect favorable climate and hydrology; attention should also be directed toward potential deep percolation and saline sinks. In contrast, areas with high IWU often displayed elevated NDVI but declining water availability, suggesting unsustainable greening due to excessive irrigation. In addition, the SCDIWU-SCDNDVI class dominates among significant NDVI-IWU trends, indicating potential for sustainable irrigation under certain drought and climate conditions. Overall, the northwestern portion of the study area exhibits the lowest water availability; cities such as Urumqi warrant particular attention. These findings identify at-risk areas and those with better water resilience, supporting targeted water-vegetation management.

As a major grain-producing region in Northeast China, the Songnen Plain faces mounting challenges to groundwater sustainability due to climate change and human activities, particularly in agriculturally intensive areas. Understanding its spatiotemporal responses to these dual pressures remains critical for sustainable management. This study innovatively integrates STL-Mann-Kendall analysis with geodetector modeling to analyze 36-year records (1986-2021), revealing three distinct groundwater evolution phases: gradual decline (1980s-1990s), accelerated depletion (2000s-2010s), and partial recovery post-2010, with persistent drawdown in vulnerable aquifers. The geodetector analysis quantifies the shifting dominance from precipitation controls (22% explanatory power) to anthropogenic drivers (groundwater extraction explains 18% of post-2000 variations), with significant factor interactions (q-value >0.6) between land use and precipitation. These findings demonstrate escalating human impacts on groundwater systems and highlight the need for a zonal management framework that balances agricultural demands with aquifer resilience in water-scarce regions.

After years of exploration and development in the Shunbei oil and gas field, Tarim Basin, a series of technologies for ultra-deep fault-controlled fractured-vuggy target prediction, evaluation, and well location design have been formed, applicable to the No.1 and No.5 fault zones. As exploration efforts shift from the main No.1 and No.5 fault zones to the northeastern and northwestern fault zones in the eastern and western regions, the underground geological conditions become more complex, and exploration costs rise significantly. Existing reservoir characterization, target selection, and well trajectory design technologies are inadequate for the precise delineation of ultra-deep fault-controlled fractured-vuggy systems and high-yield well trajectory optimization. Through comparative analysis of the internal structural characteristics and seismic response variations of different regions and different types of strike-slip fault zones, integrated with actual well seismic calibration statistics and forward modeling, this study established a robust seismic identification model for high-yield and stable production wells. This model, based on the "source-connected faults + bead-string + deep chaotic high-amplitude background", provided a systematic framework for reservoir prediction and target selection. The Q-compensation seismic data processing technology developed through research improved the imaging resolution of fault-controlled fractured-vuggy systems in low signal-to-noise ratio seismic data under desert environments. Based on this, a reservoir quantification sculpting and target spatial positioning technology, centered on "facies-constrained inversion, " was established, which improved the accuracy of fault-controlled reservoir description and the precision of target selection. In response to the complex geological conditions of the overlying strata and Ordovician target layers in the Shunbei area, as well as challenges such as loss, overflow, and wellbore collapse during drilling, a key integrated geological engineering technology process focused on drilling risk prediction was established. This process included methods for optimizing well trajectories, selecting well locations, predicting formation pressures before drilling, and predicting wellbore stability, which improved drilling safety and efficiency. Drilling results from Shunbei's No. 4 and No. 8 fault zones indicated that the target selection and design technology for fault-controlled reservoirs could accurately identify and predict ultra-deep heterogeneous fractured-vuggy body targets, guide and optimize drilling trajectory design, avoid and reduce engineering risks along the drilling path, and improve the drilling success rate and high-yield well construction rate for large-scale reservoirs.

This study analyzed daily temperature data from 114 meteorological stations in the Qinling-Daba Mountains from 1980 to 2017, focusing on core zones (CE I ≥ 2274 m, CE II 1321-2274 m) and peripheral zones (PE I 668-1321 m, PE II < 668 m). Using 15 extreme temperature indices computed with RClimDex, we assessed the spatiotemporal patterns, trends, and response to climate warming of extreme temperature events across different elevation zones. The Mann-Kendall test and Sen's slope estimator were employed to quantify trends, and the driving mechanisms of extreme temperature indices in different altitudinal zones were explored through Random Forest models and Pearson correlation analysis. Results indicate a significant warming trend in extreme temperatures in the study area from 1980 to 2017, characterized by an increase in extreme high-temperature events, a decrease in extreme low-temperature events, and an asymmetrical warming pattern. The regional sensitivity ranking is CE II ≈ CE I > PE I > PE II, suggesting that CE I and CE II are most sensitive to warming, while the lower‑elevation PE I and PE II exert a buffering effect. Extreme temperature changes are jointly influenced by latitude and topography: heat events display a "strong south, weak north" pattern, while cold events show the opposite distribution; diurnal temperature range exhibits a "large north, small south" pattern. From west to east, extreme high temperatures and growing season length increase, while extreme low temperatures and frost days decrease. Notably, the Qinling-Daba Mountainsexhibit positive elevation-dependent warming (EDW) effect, with stronger extreme warming at higher elevations than at lower elevations. Factor analysis reveals that extreme temperature indices in the all zones are mainly controlled by temperature and large‑scale atmospheric-oceanic circulation. In contrast, CE II and PE I zones are influenced by the synergistic effects of multiple factors, including temperature, moisture conditions, sunshine duration, and topography. This results highlight climatic anomalies in mountainous regions caused by complex topography and multi-factor interactions, clarifying how regional hydrothermal conditions and large-scale atmospheric circulation regulate climate change in high-altitude mountainous areas. This study deepens understanding of climatic processes in Chinese mountain systems, offering high-resolution, multi-factor case study and theoretical basis for extreme climate response, hazard risk assessment, and adaptation management in global mountainous regions.

Study region This study focuses on the Songnen Plain, located in Jilin Province, China, a representative large plain area. Groundwater resources in this region play a critical role in the hydrological cycle and are significantly influenced by climate change. Study focus Climate change significantly impacts the development and evolution of water resources. This research examines the effects of climate change on groundwater levels in the Songnen Plain, Jilin Province, utilizing temperature and precipitation data since 1960, alongside groundwater level data from 1982 onward. The analysis employs univariate linear trend analysis, the Mann-Kendall test, Morlet wavelet analysis, and cross-wavelet analysis. New hydrological insights for the region The findings indicate that the groundwater level cycle is approximately 28 years, which corresponds with the atmospheric precipitation cycle. This implies that, over long-time scales, groundwater levels are primarily influenced by natural factors such as precipitation. The abrupt change in groundwater levels observed in 1991 is largely attributed to extensive groundwater extraction for large-scale rice cultivation in Jilin Province, underscoring significant human interference with groundwater resources. A notable correlation exists between the decline in groundwater levels and rising air temperatures, while the correlation with changes in precipitation is relatively weak. This suggests that air temperature indirectly influences groundwater levels by increasing evapotranspiration and amplifying human water demand.

The rapid expansion of photovoltaic (PV) installations under global energy transition intensifies grid frequency regulation challenges due to solar power's intermittency and volatility. Traditional forecasting methods face limitations in accuracy and efficiency under complex weather conditions. To address this, we introduce the Frequency-domain MLPs for Time Series (FreTS), a novel frequency-domain learning model with a three-stage architecture for multi-step PV output prediction. FreTS employs an adaptive weight matrix for enhanced feature representation, a dual-path frequency-domain learning mechanism capturing both spectral correlations and temporal dynamics, and an efficient decoder for multi-step forecasting. Experimental results on an Australian PV dataset show that FreTS reduces prediction error by 18.4% and model parameters by 62% compared to LSTM and MLP, achieving RMSE of 0.0068 and MAE of 0.0035. With integrated energy compression, the model offers high accuracy, low computational cost, and strong robustness, making it a promisin solution for real-time forecasting in distributed PV systems.

A reliable parameter identification approach in hydrogeological models can reduce prediction uncertainty while enhancing model robustness and reliability. Parameter ranges could directly influence uncertainty, and a reasonable reduction of the parameter space could reduce the number of local optima and avoid unnecessary computational effort. In this paper, we introduced a strategy that employed the Self-Organizing Map (SOM) to intelligently navigate the parameter space of the MODFLOW model. Our approach constructed a hypothetical model in the Taoerhe area, Jilin Province, China. First, we defined a set of hydrogeological parameters across six zones and conducted the forward run of the model to obtain the observed head within nine wells. Then, the SOM method was employed for range optimization over two rounds, constructing Particle Swarm Optimization (PSO) and SOM-PSO schemes for comparison. Finally, we evaluated the parameter estimates, groundwater head simulations, and uncertainty. Our results showed that the average relative error of the best estimates decreased from 24.27

Amidst growing concerns over climate-induced extreme weather events, precise flood forecasting becomes imperative, especially in regions like the Chaersen Basin where data scarcity compounds the challenge. Traditional hydrologic models, while reliable, often fall short in areas with insufficient observational data. This study introduces a hybrid modeling approach that combines the deep learning capabilities of the Informer model with the robust hydrological simulation by the WRF-Hydro model to enhance runoff predictions in such data-sparse regions. Trained initially on the diverse and extensive CAMELS dataset in the United States, the Informer model successfully applied its learned insights to predict runoff in the Chaersen Basin, leveraging transfer learning to bridge data gaps. Concurrently, the WRF-Hydro model, when integrated with The Global Forecast System (GFS) data, provided a basis for comparison and further refinement of flood prediction accuracy. The integration of these models resulted in a significant improvement in prediction precision. The synergy between the Informer's advanced pattern recognition and the physical modeling strength of the WRF-Hydro significantly enhanced the prediction accuracy. The final predictions for the years 2015 and 2016 demonstrated notable increases in the Nash-Sutcliffe Efficiency (NSE) and the Index of Agreement (IOA) metrics, confirming the effectiveness of the hybrid model in capturing complex hydrological dynamics during runoff predictions. Specifically, in 2015, the NSE improved from 0.5 with WRF-Hydro and 0.63 with the Informer model to 0.66 using the hybrid model, while in 2016, the NSE increased from 0.42 to 0.76. Similarly, the IOA in 2015 rose from 0.83 with WRF-Hydro and 0.84 with the Informer model to 0.87 using the hybrid approach, and in 2016, it increased from 0.78 to 0.92. Further investigation into the respective contributions of the WRF-Hydro and the Informer models revealed that the hybrid model achieved the optimal performance when the contribution of the Informer model was maintained between 60%-80%.

Hydrological models are vital tools in environmental management. Weaknesses in model robustness for hydrological parameters transfer uncertainties to the model outputs. For streamflow, the optimized parameters are the primary source of uncertainty. A reliable calibration approach that reduces prediction uncertainty in model simulations is crucial for enhancing model robustness and reliability. The optimization of parameter ranges is a key aspect of parameter calibration, yet there is a lack of literature addressing the optimization of parameter ranges in hydrological models. In this paper, we introduce a parameter calibration strategy that applies a clustering technique, specifically the Self-Organizing Map (SM), to intelligently navigate the parameter space during the calibration of the Soil and Water Assessment Tool (SWAT) model for monthly streamflow simulation in the Baishan Basin, Jilin Province, China. We selected the representative algorithm, the Sequential Uncertainty Fitting version 2 (SUFI-2), from the commonly used SWAT Calibration and Uncertainty Programs for comparison. We developed three schemes: SUFI-2, SUFI-2-Narrowing Down (SUFI-2-ND), and SM. Multiple diagnostic error metrics were used to compare simulation accuracy and prediction uncertainty. Among all schemes, SM outperformed the others in describing watershed streamflow, particularly excelling in the simulation of spring snowmelt runoff (baseflow period). Additionally, the prediction uncertainty was effectively controlled, demonstrating the SM's adaptability and reliability in the interval optimization process. This provides managers with more credible prediction results, highlighting its potential as a valuable calibration tool in hydrological modeling.

Evaluation metrics play a pivotal role in the calibration process of hydrological models, serving as objective functions that directly influence the final values of model parameters and significantly affect users' perceptions of model performance. However, the choice and interpretation of evaluation metrics are subjective; therefore, this study provides a more objective framework for assessing model performance. This paper initially explored the applicability of various commonly used evaluation metrics, providing an overview of their limitations. Following this, we decomposed errors by analysing their physical significance and geometric representation in scatter plots, categorizing them into systematic and unsystematic errors. Through the decomposition and derivation of the Nash-Sutcliffe efficiency (NSE) formula, we established the quantitative relationship among various evaluation metrics. The soil and water assessment tool (SWAT) model was utilized to simulate monthly runoff in the Baishan basin (China), for the period 1994-2017, with NSE serving as the objective function for calibration. Our findings are consistent with previous studies, indicating that the model tends to slightly underestimate high flows while significantly overestimating low flows. Further analysis through error decomposition and the examination of relationships among various evaluation metrics revealed that unsystematic errors were dominant during the spring snowmelt runoff period, while systematic errors prevailed in the dry season. By evaluating the runoff series based on the magnitude of runoff or by categorizing it according to seasons and months, a more stringent assessment of the model's performance was achieved. These findings not only highlight the necessity for careful selection of evaluation metrics but also underscore the significance of our methodological advancements in enhancing hydrological model precision and reliability. The mean square error is decomposed into systematic and unsystematic errors, further quantitatively describing two scenarios of relationships between different types of errors and evaluation metrics. image

Evapotranspiration is a crucial component of the water cycle and is significantly influenced by climate change and human activities. Agricultural expansion, as a major aspect of human activity, together with climate change, profoundly affects regional ET variations. This study proposes a quantification framework to assess the impacts of climate change (ETm) and agricultural development (ETh) on regional ET variations based on the Random Forest algorithm. The framework was applied in a large-scale agricultural expansion area in China, specifically, the Songhua River Basin. Meteorological, topographic, and ET remote sensing data for the years of 1980 and 2015 were selected. The Random Forest model effectively simulates ET in the natural areas (i.e., forest, grassland, marshland, and saline-alkali land) in the Songhua River Basin, with R2 values of around 0.99. The quantification results showed that climate change has altered ET by −8.9 to 24.9 mm and −3.4 to 29.7 mm, respectively, in the natural areas converted to irrigated and rainfed agricultural areas. Deducting the impact of climate change on the ET variation, the development of irrigated and rainfed agriculture resulted in increases of 2.9 mm to 55.9 mm and 0.9 mm to 53.4 mm in ET, respectively, compared to natural vegetation types. Finally, the Self-Organizing Map method was employed to explore the spatial heterogeneity of ETh and ETm. In the natural–agriculture areas, ETm is primarily influenced by moisture conditions. When moisture levels are adequate, energy conditions become the predominant factor. ETh is intricately linked not only to meteorological conditions but also to the types of original vegetation. This study provides theoretical support for quantifying the effects of climate change and farmland development on ET, and the findings have important implications for water resource management, productivity enhancement, and environmental protection as climate change and agricultural expansion persist.

Accurate hydrological predictions are often hindered by the lack of stream gauges in data-scarce regions, where traditional transfer learning (TL) models like Long Short-Term Memory (LSTM) networks often face limitations due to reduced accuracy and adaptability. To enhance runoff prediction in such regions, we developed DAformer, a novel TL approach that integrates domain adversarial neural networks with the Informer model. Trained on comprehensive runoff data from U.S. basins, DAformer was applied to three basins in Chile and the Chaersen basin in China, demonstrating an effective transfer from data-rich to data-scarce environments. Results show that DAformer significantly outperforms LSTM-based models, improving forecast accuracy by 16.1% for 1-day lead time and by 100.5% for 5-day lead time. These improvements indicate that the DAformer model not only enhances prediction accuracy but also holds substantial practical implications for flood risk management and water resource planning in regions with limited data availability. By clustering basins based on Shuttle Radar Topography Mission (SRTM) and other geographical data, we found that relying on multiple source basins further enhances the performance. DAformer, therefore, serves as a robust and scalable method for enhancing runoff prediction for regions with limited data.

With the intensification of climate change and global warming, the risk of extreme rain events is rising, underscoring the importance of the accurate flood forecasting especially for the data-sparse regions like the Northeast China. Presently, physics-based, distributed hydrologic models were extensively utilized for flood simulations. However, the Global Forecasting System (GFS) forcing, as one of the most common sources of global rain forecasts, still had considerable room for improvement in local accuracy. In response to this challenge, the Attention-Enhanced Meteorological Prediction Generative Adversarial Network (AEMP-GAN) model was developed based on deep learning to enhance the local rainfall predictions. WRF-Hydro was adopted to test the impact of the corrected rain forecasts on flood simulations. The results indicate that after applying the AEMP-GAN model to enhance the 24-hour GFS forecasts, the average root mean square error (RMSE) of the rain forecasts during the flood season was reduced by 28.7%, and the index of agreement (IOA) was raised by 11.3%. When this improved rain forecasts were used to drive the WRF-Hydro model to simulate runoff for the three typical rain events during August-September 2021, the Nash-Sutcliffe Efficiency (NSE) coefficients of the simulated runoff at the outlet of the studied watershed for the three events increased by 8.42%, 4.98%, and 1.01%, respectively, while the RMSE decreased by 4.53%, 3.54%, and 0.55%, respectively. At a different stream gauge near the middle of the watershed, the NSE improved for two events by 23.96% and 3.90%, respectively, owing to the corrected rain forecasts alone, while the RMSE dropped by 20.1% and 7.86%, respectively. These findings demonstrate the significant potential of the proposed method on tuning the global rain forecasts for improving the accuracy of the local flood forecasting.

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In Northeast China, snowmelt is an important source of spring runoff, and the reliable simulation and forecasting of snowmelt runoff can provide a reference for reasonable management and scheduling of water resources during the spring flood season. There are two runoff-generating models of snowmelt runoff and rainfall runoff in the cold Northeast region during the year. The differences in the physical and spatial properties of the basin have a significant impact on the snowmelt runoff generation. To solve the problem of spatial heterogeneity of snowmelt runoff, and to improve the simulation and forecasting accuracy of the SWAT model, we propose a single-site and multi-site combined parameter calibration for the SWAT model. Firstly, the initial parameter calibration of single station was carried out by using the inflow of Baishan Reservoir, and then the snowmelt runoff parameters were calibrated by multi-station, and the parameters were transplanted to the initial parameter set of single station. The correlation coefficients（R~2） of monthly average flow in validation periods of the annual（January-December）, flood season（June-September）, and spring season（March-May） were 0.76, 0.74, and 0.58, the daily average flow R~2 were 0.71, 0.75 and 0.51, after the initial parameter rate determination at a single station. The correlation coefficients R~2 in validation periods of the annual（January-December）, flood season（June-September）, and spring season（March-May） were 0.80, 0.74 and 0.79, the daily average flow R~2 were 0.74, 0.78 and 0.61, after using a combined single-site and multi-site parameter calibration strategy. The results showed that the simulation accuracy of snowmelt runoff was improved by 20% and 10% respectively.

Snowmelt runoff is a vital source of fresh water in cold regions.Accurate snowmelt runoff forecasting is crucial in supporting the integrated management of water resources in these regions.However,the performances of such forecasts are often very low as they involve many meteorological factors and complex physical processes.Aiming to improve the understanding of these influencing factors on snowmelt runoff forecast,this study investi-gated the time lag of various meteorological factors before identifying the key factor in snowmelt processes.The results show that solar radiation,followed by temperature,are the two critical influencing factors with time lags being 0 and 2 days,respectively.This study further quantifies the effect of the two factors in terms of their contribution rate using a set of empirical equations developed.Their contribution rates as to yearly snowmelt runoff are found to be 56％and 44％,respectively.A mid-long term snowmelt forecasting model is de-veloped using machine learning techniques and the identified most critical influencing factor with the biggest contribution rate.It is shown that forecasting based on Supporting Vector Regression(SVR)method can meet the requirements of forecast standards.

In the era of the digital economy, the deep integration of new technologies and cross-border e-commerce is forcing the redefinition of talent. Especially, data literacy has become an important indicator for measuring the capacity of cross-border e-commerce talents. Therefore, the problem in the traditional teaching mode, “stressing process and neglecting analysis,” needs to be solved urgently. To this end, we integrate big data technology into the practical teaching process of cross-border e-commerce by applying Python tools that are suitable for teachers and students with liberal arts backgrounds. Using web crawler technology in teaching design to improve the availability of business data, data analysis technology is used to enhance the scientific nature of management decision-making, and data visualization technology is used to ensure the timeliness of business decision-making. The teaching demonstration results show that the teaching design scheme has strong operability. The application of big data technology is an effective means to help the practical teaching of cross-border e-commerce and realize the organic integration of students' business thinking and data literacy. It also positively promotes the reform of e-commerce teaching in applied universities, interdisciplinary learning, and the integration of arts and sciences, and meets the needs of industry development talents.

Groundwater (GW) and surface water (SW) are important components of water resources and play key roles in social and economic development and regional ecological security. There are currently several stresses placing immense pressure on the GW resources of the Baiyangdian Lake Basin (BLB) in China, including climate change. A series of ecological and environmental challenges have manifested in the plain area of the BLB due to long-term over-exploitation of GW, including regional declines in GW level, aquifer drainage, land subsidence, and soil secondary salinization. Climate change may aggravate environmental challenges by altering GW recharge rates and availability of GW. This study applied the fully processed and physically-based numerical models, MOD FLOW and the Soil & Water Assessment Tool (SWAT) in a semi-coupled modeling framework. The aim of the study was to quantitatively analyze changes to shallow GW levels and reserves in the plain area of BLB over the next 15 years (2021-2035) under climate change and different artificial recharge schemes. The results indicated that GW storage and levels are rising under the different GW recharge schemes. The maximum variation in the GW level was 20-30 m under a rainfall assurance rate of 50% and water level in the depression cone increased 14.20-14.98 m. This study can act as a theoretical basis for the development of a more sustainable GW management scheme in the plain area of the BLB and for the management and protection of aquifers in other areas with serious GW overdraft.