Regional Water Resources Research Articles

Environmental and Climatic Drivers of Phytoplankton Communities in Central Asia.

Artificial water bodies in Central Asia offer unique environments in which to study plankton diversity influenced by topographic barriers. However, the complexity of these ecosystems and limited comprehensive studies in the region challenge our understanding. In this study, we systematically investigated the water environment parameters and phytoplankton community structure by surveying 14 artificial waters on the southern side of the Altai Mountains and the northern and southern sides of the Tianshan Mountains in the Xinjiang region. The survey covered physical and nutrient indicators, and the results showed noticeable spatial differences between waters in different regions. The temperature, dissolved oxygen, total nitrogen, and total phosphorus of artificial water in the southern Altai Mountains vary greatly. In contrast, the waters in the northern Tianshan Mountains have more consistent physical indicators. The results of phytoplankton identification showed that the phytoplankton communities in different regions are somewhat different, with diatom species being the dominant taxon. The cluster analysis and the non-metric multidimensional scaling (NMDS) results also confirmed the variability of the phytoplankton communities in the areas. The variance partitioning analysis (VPA) results showed that climatic and environmental factors can explain some of the variability of the observed data. Nevertheless, the residual values indicated the presence of other unmeasured factors or the influence of stochasticity. This study provides a scientific basis for regional water resource management and environmental protection.

Evolution Characteristics of Heilongtan Spring Discharge and Its Response Law to Precipitation in Lijiang City, China

The contradiction between water supply and spring preservation issues is becoming increasingly apparent as Lijiang City develops. An investigation into the dynamic variations in the discharge rate of Heilongtan Spring in Lijiang City and the response law between the water level of the spring and precipitation is crucial for safeguarding the landscape water of Heilongtan Spring. This study employed linear regression analysis, Mann–Kendall (MK) mutation test, wavelet analysis, and vector autoregression (VAR) to examine the fluctuating pattern of the Heilongtan Spring discharge and the response of the Heilongtan Spring water level to precipitation in Lijiang City. Furthermore, the study discussed the influence of human activities on the alteration of Heilongtan Spring. The results indicate that the mean discharge rate of Heilongtan Spring is 0.94 m3/s, with an annual variation of 0.05 m3/s. The time series analysis reveals that the variation pattern of Heilongtan Spring discharge aligns with the precipitation trend in Lijiang City. Nevertheless, there is a distinction between the timing of the Heilongtan Spring discharge station point and the precipitation mutation point in Lijiang City. The significant primary cycle of spring discharge change occurs every 18 months, with a cycle length of 12 months. The vector autoregression (VAR) model demonstrates a lagged relationship between the water level of Heilongtan Spring and the precipitation in Lijiang City. Specifically, the water level of Heilongtan Spring has a four-month lag response to precipitation variability in Lijiang City. The results can provide a beneficial reference for preserving spring water and managing regional water resources.

Evaluation of hydrological models for streamflow prediction: a case study of the Mille River, Lower Awash Basin, Ethiopia

ABSTRACT Accurate prediction of stream flow is essential for effective water resource management, particularly in regions with complex hydrological dynamics. The Mille River, which flows through this basin and has a watershed area of 4,862.3 km², plays a crucial role in the region's water resources. However, selecting the most suitable hydrological model for this task remains a challenge due to varying model performances. This study addresses the problem of identifying the most effective hydrological model for predicting stream flow in the Mille River by evaluating three models: Soil and Water Assessment Tool (SWAT), Hydrologiska Byråns Vattenbalansavdelning (HBV-IHMS), and Artificial Neural Network (ANN). The objectives are to compare these models’ abilities to simulate daily stream flow and to determine their accuracy. The significance of this evaluation lies to guide water management decisions by identifying which model provides the most reliable predictions. The methodology involves using performance metrics, Nash-Sutcliffe Efficiency (NSE) values, to assess the accuracy of each model. The results show that SWAT achieved the highest NSE values of 0.81 and 0.82, indicating the best performance in simulating stream flow. ANN followed with NSE values of 0.79 and 0.81, while HBV-IHMS had lower NSE values of 0.73 and 0.74. Although all models demonstrated potential, SWAT and ANN outperformed HBV-IHMS in capturing the complex hydrological processes of the Mille River. The findings from this study indicate that the choice of model significantly impacts the accuracy and reliability of streamflow predictions. The SWAT and ANN models show strong potential for use in operational streamflow forecasting in the Mille River and similar basins. In conclusion, further research is recommended to refine model calibration and validation processes. Incorporating additional data sources, such as remote sensing and climate projections, could further enhance model accuracy and provide a more comprehensive understanding of the region's hydrological dynamics.

Attribution of hydrological droughts in large river-connected lakes: Insights from an explainable machine learning model

Large lakes play an important role in water resource supply, regional climate regulation, and ecosystem support, but they face threats from frequent extreme drought events, necessitating an understanding of the mechanisms behind these events. In this study, we developed an explainable machine learning (ML) model that combines the Bayesian optimized (BO) long short-term memory (LSTM) model and the integrated gradients (IG) interpretation method to simulate and explain lake water level variations. In addition, the hydrological drought trends and extreme drought events in Poyang Lake from 1960 to 2022 were identified using the standardized water level index (SWI) and run theory. The analysis revealed that the frequency of hydrological droughts in Poyang Lake increased from 1960 to 2022, especially in the autumn after 2003. By selecting the flows of the catchment and the Yangtze River as the input features, the BO-LSTM model accurately predicted the water level of Poyang Lake. The IG method was then used to interpret the prediction results from three aspects: the importance ranking of the input features, their roles in the seasonal drought trends, and their roles in extreme drought events. The results indicate that (1) the most influential factor affecting the water level of Poyang Lake was the inflow of the Ganjiang River in the catchment. (2) The increase in the lake outflow caused by the Yangtze River's draining effect was the reason for the intensification of the autumn drought in Poyang Lake. (3) The extreme hydrological drought events were primarily caused by low catchment inflows. Overall, this research provides a new approach that balances prediction accuracy with interpretability for predicting and understanding the hydrological processes in large river-connected lakes. Moreover, this method was also applied to the attribution analysis of hydrological drought in Poyang Lake, providing theoretical support for regional water resource management.

Spatial correlations and risk transmission of virtual water flow at city scale: A case study of the Yellow River Basin

By introducing virtual water (VW) flow correlation coefficients and risk indicators, this study examines the VW transmission relationship between urban agglomerations and cities in the Yellow River Basin (YRB) and its impact on regional water resources pressure. The results show that: except for the Shandong Peninsula Urban Agglomeration (SPUA) and Central Plains Urban Agglomeration (CPUA), the other urban agglomerations primarily act as VW exporting regions, while virtual water-importing cities are concentrated in the eastern regions. Notably, the Ningxia Urban Agglomeration (NUA) demonstrates significantly higher values in VW impact and sensitivity coefficients than the remaining six urban agglomerations. First-tier cities generally display lower virtual water impact and sensitivity coefficients, whereas emerging cities exhibit the opposite trend. Additionally, we observe uneven risk variations between VW importing and exporting regions. Taking NUA as an example, the risk increase resulting from VW exports significantly exceeds the risk reduction associated with VW imports in the corresponding regions. It's important to highlight that first-tier cities consistently decrease water resource risk through VW imports in both study years. However, among second and third-tier cities, only 38.9% experience reduced water resource risk through VW imports. Therefore, we recommend a focused examination of VW imports and exports in western region urban agglomerations, cities, and second and third-tier cities within the watershed. Leveraging virtual water's asymmetric and high-value transfer can alleviate water resource pressure and scarcity risks in high-pressure regions by shifting them to lower-pressure regions, thus mitigating water resource stress across regions.

Multi-objective optimal allocation of water resources in Shule River Basin of Northwest China based on climate change scenarios

The optimal allocation of water resources is crucial for addressing regional water scarcity, adapting to climate change, and promoting sustainable development. This study focused on the Shule River Basin and considered three climate scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5), which is combinations of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). A water resources optimal allocation model including water supply and demand prediction, multi-objective optimization, decision-making and analysis on optimal water allocation schemes was constructed. This model aims to analyze the optimal allocation of water resources in the Shule River Basin over three planning years (2025, 2030, 2035), considering guarantee rates of 50 % and 75 % under various climate scenarios. The obtained results indicate that: (1) An increasing trend in water demand in the planning year. For instance, under the SSP1–2.6 scenario, the basin's water demand is projected to reach 1.920×10^9 m3, 2.037×10^9 m3, and 2.103×10^9 m3 in 2025, 2030, and 2035, respectively. (2) The total water supply with a guarantee rate of 50 % (75 %) in the planning years is projected to be 1.939 (1.483)×10^9, 1.957(1.502) ×10^9, and 1.974 (1.519) ×10^9 m3, respectively. (3) In the three scenarios of SSP1–2.6, SSP2–4.5, and SSP5–8.5, the water demand for domestic and ecology is met in each planning year. There is a slight water shortage in the industrial sector, with a shortage rate ranging from 0 % to 13.41 %. The agricultural sector experiences the highest water shortage, with a rate of 1.93–44.77 %. (4) The water supply structure of each optimal allocation scheme of water resources was optimized. The economic benefits mainly depend on the industrial and ecological sectors. These findings offer valuable insights for optimizing the distribution of regional water resources in response to changing climatic conditions.

ANALISIS HUKUM PEMANFAATAN SUMBER DAYA AIR UNTUK KEBUTUHAN RUMAH PENDUDUK DALAM PERSPEKTIF KESEJAHTERAAN

Water is a natural resource that is very important in everyday life. Some of the benefits of water resources in daily life include irrigation in agriculture, electricity generation, and household activities. The problem that arises is whether the regulation and use of water resources is in accordance with the needs of residents' homes, and how are sanctions applied for misuse of water resources that are not in accordance with the intended needs of residents' homes? The aim of this research is to find out and analyze the use of water resources for residents' housing needs, and to provide alternative solutions to the application of sanctions for misuse of water resources that are not in accordance with the intended use of residents' housing needs. The research method used is normative juridical, with a legislative approach as primary legal material combined with observation and observation as secondary legal material, especially a welfare perspective. results: That the use of water resources for daily needs is not yet optimal, even though there have been several statutory regulations, therefore the implementation of water quality protection and management should be carried out through a process of planning, utilization, control and maintenance, so that the need for water resources for people's homes are filled. That the alternative solution is to enforce the law firmly and clearly and strictly supervise perpetrators who commit violations against the use of water resources for the needs of residents' homes.

Understanding the response of tropical overturning circulations to greenhouse gas and aerosol forcing

Abstract While observational records provide evidence for strengthening of the Pacific Walker circulation and the boreal winter Hadley cell (HC) over the last few decades, the underlying causes are not well understood. This study investigates the radiative effects of CO2 and anthropogenic aerosols on regional variations in the intensity of tropical meridional (Hadley-like) and zonal (Walker-like) overturning circulations, based on a suite of experiments using the IITM Earth System Model (IITM-ESM) along with supplementary analysis of observed and reanalysis datasets. Two key findings emerge from our study (i) strengthening of the Pacific Walker circulation with enhanced zonal sea surface temperature (SST) gradients and precipitation increase over the tropical Indo-Pacific, in response to global warming via ocean-atmosphere coupled feedbacks (ii) aerosol-induced intensification of regional meridional overturning circulation over the (0 − 120◦E) longitudinal domain resulting from inter hemispheric energy imbalance and southward shift of the southern hemispheric (SH) rainfall belt extending eastward from Africa across the Indian Ocean. Our results suggest that the combined radiative influence of increased CO2 and northern hemispheric (NH) anthropogenic aerosols reinforces the regional meridional overturning circulation, by enhancing convection over the SH and promoting widespread descent over the NH subtropics and mid-latitudes covering northern Africa, Mediterranean, parts of middle-East, West and South Asia. The present findings have important implications for the regional water resources, agriculture and environment.

Changes in evaporation patterns and their impact on Climatic Water Balance and river discharges in central Poland, 1961–2020

This study investigates the changes in precipitation and evaporation patterns and their impact on Climatic Water Balance and river discharges in central Poland from 1961 to 2020. The analysis focuses on two “normal” periods, 1961–1990 and 1991–2020 (according to the World Meteorological Organization). Bartlett’s test and the Kruskal–Wallis rank sum test were used to assess the homogeneity of variances and compare distributions of analyzed variables over two “normal” periods. The probability density functions were estimated using a kernel density estimator with a Gaussian kernel function. Significant findings indicate alterations in evaporation rates and shifts in water balance dynamics. Mean evaporation increased from 530.8 to 637.9 mm, leading to a notable decrease in the mean Climatic Water Balance from 1.1 to − 107.5 mm (in the periods 1961–1990 and 1991–2020, respectively). Additionally, rivers showed reduced mean annual discharges (from 4.28 to 3.01 m3·s−1 and 1.25 to 0.87 m3·s−1, for the Zgłowiączka and Skrwa Lewa rivers, respectively). These climatic changes in central Poland have substantial implications for regional water resources, especially in spring and summer and particularly in agricultural areas, potentially exacerbating drought conditions and impacting agricultural productivity mainly in the warm half-year.

An Analysis of the Spatiotemporal Variability of Key Water Quality Parameters in China

Intensifying anthropogenic disturbances have caused water pollution in China in recent decades. China has a vast territory with diverse climate conditions, land use types, and human activities, leading to significant water quality variability. However, few studies have investigated nationwide spatiotemporal patterns of key water quality parameters. In this study, we analyze monthly water quality observations from 3647 gauge stations to understand how water quality changes over time and space in China. We group the stations by water resource regions and adopt Python and SPSS to analyze the spatiotemporal variability and intercorrelations of eight water quality parameters. Results indicate that the concentrations of biochemical oxygen demand of 5 days (BOD5), chemical oxygen demand (COD), dissolved oxygen (DO), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP) show similar spatial patterns, with higher concentrations in the northern parts than the southern regions of China. The concentrations of COD and TP are higher in the rainy season than in the dry season, while DO, NH3-N, and TN show the opposite seasonal patterns. Strong positive correlations were found between BOD and COD, NH3-N and TP. The annual cumulative distribution figures demonstrate that all parameters showed slightly lower concentrations in 2022 and 2023 than in 2021, except for DO and TN. The TN/TP ratios across different water resource regions in China are significantly higher than 16, indicating that phosphorus is the limiting factor of eutrophication. This investigation provides a comprehensive understanding of the spatiotemporal variability of water quality parameters across China. The results of this study are highly valuable for investigating mechanisms regulating water quality across large spatial scales, thus providing valuable implications for improving water quality and mitigating water pollution.

Trade-Off and Synergy Mechanism of Agricultural Water Resource Spatial Allocation in Monsoon Climate Areas Based on Machine Learning: A Case Study of Reservoir Layout Optimization in Shandong Province, China

Influenced by increasing global extreme weather and the uneven spatiotemporal distribution of water resources in monsoon climate areas, the balance of agricultural water resources supply and demand currently faces significant challenges. Conducting research on the spatial allocation trade-offs and synergistic mechanisms of agricultural water resources in monsoon climate areas is extremely important. This study takes the spatial layout of reservoir site selection in water conservancy projects as an example, focusing on Shandong Province as the research area. During the site selection process, the concept of water resource demand is introduced, and the suitability of reservoir siting is integrated. It clarifies ten influencing factors for suitability degree and five influencing factors for demand. A bi-objective optimization model that includes suitability degree and demand degree is established. Utilizing machine learning methods such as the GA_BP neural network model and the GA-bi-objective optimization model to balance and coordinate the supply and demand relationship of agricultural water resources in the monsoon region. The study found that: (1) in the prediction of suitability degree, the influencing factors are most strongly correlated with the regulatory storage capacity (regulatory storage capacity > total storage capacity > regulating storage coefficient); (2) compared with single-objective optimization of suitability degree, the difference between water supply and demand can be reduced by 74.3% after bi-objective optimization; (3) according to the spatial layout optimization analysis, the utilization of water resources in the central and western parts of Shandong Province is not sufficient, and the construction of agricultural reservoirs should be carried out in a targeted manner. This study provides new ideas for promoting the efficient use of water resources in monsoon climate zones and the coordinated development of humans and nature, reflecting the importance of supply and demand balance in the spatial allocation of agricultural water resources, reducing the risk of agricultural production being affected by droughts and floods.

Spatiotemporal patterns and driving factors of urban-rural water use from the production and domestic perspectives: A case study of Beijing-Tianjin-Hebei urban agglomeration, China

Water scarcity is becoming serious with economic growth, causing water competition across various sectors. Previous studies have mostly explored water use in specific sectors, yet little is known about the water reallocation between urban and rural areas. Here, we investigate urban-rural water use from the production (agriculture and industry) and domestic (urban and rural household) perspectives during 2000–2022 in the Beijing-Tianjin-Hebei urban agglomeration, and identify their potential drivers. We find that urban water use changes little due to the offset of industrial and urban domestic use, while rural water use decreases significantly with the trend of 0.387 ± 0.026 billion m3/yr. Water use changes derive from the joint effects of accelerated human activities and decelerated water use intensity. Urbanization explains more variability in water use changes than water resource endowment. Population urbanization, accompanied with rural-to-urban water reallocation, is a primary cause for enlarged urban-rural gap in water use. Urban-rural gap in water use intensity is narrowing, mainly due to the greater decline in agriculture. This study concludes that urban system often withdraws the neighbor agricultural water when the local water availability cannot meet its growing demand, and our findings offer references for regional water resource management and urban-rural environmental justice.

Surface water monitoring from 1984 to 2021 based on Landsat time-series images and Google Earth Engine

Dynamic monitoring of surface water bodies is essential for understanding global climate change and the impact of human activities on water resources. Satellite remote sensing is characterized by large-scale monitoring, timely updates, and simplicity, and it has become an important means of obtaining the distribution of surface water bodies. This study is based on a long time-series Landsat satellite images and the Google Earth Engine (GEE) platform, focusing on Anhui Province in China, and proposes a method for extracting surface water that combines water indices, Bias-Corrected Fuzzy Clustering Method (BCFCM), and OTSU threshold segmentation. The spatial distribution of surface water in Anhui Province was obtained from 1984 to 2021, and further analysis was conducted on the spatiotemporal characteristics of surface water in each city and three major river basins within the province. The results indicated that the overall accuracy of water extraction in this study was 94.06 %. Surface water in Anhui was most abundant in 1998 and least in 2001, with more distribution in the south than in the north. Northern Anhui is dominated by rivers, while southern Anhui has more lakes. Permanent surface water with an inundation frequency of above 75 % covered approximately 4341 km2, accounting for 32.03 % of the total water, while seasonal water with an inundation frequency between 5 % and 75 % covered about 6661 km2, accounting for 49.15 % of the total water, others were considered temporary surface water. By comparing our results with the global annual surface water released by the Joint Research Centre (JRC), we found that our study performed better in extracting lakes and rivers in terms of completeness, but the extraction results for aquaculture areas were slightly less than the JRC dataset. Overall, the long-term surface water dataset established in this study can effectively supplement the existing datasets and provide important references for regional water resource investigation, management, as well as flood monitoring.

Regional water cycle health evaluation and obstacle factor analysis: A case study of the Yellow River basin in Henan Province, China

Ecological and socioeconomic systems are interconnected through water cycles. Due to intensive anthropogenic activities and global climate change, the water cycle has undergone profound changes over the last few decades. This study aimed to evaluate the health status of the regional water cycle, explore the etiology of water cycle disorders, and provide suggestions for improving the health of the water cycle. Based on the Driving force-Pressure-State-Impact-Response (DPSIR) model, an index system was constructed to evaluate regional water cycle health. The water cycle was evaluated using a random forest (RF) model, which can achieve desirable fitting and generalization properties. The Yellow River Basin in Henan Province (YRBHN), China, was used as an example to demonstrate the application of this system. An Extended Fourier Amplitude Sensitivity Test (EFAST)-Cloud model was used to validate the evaluation results of the RF model. The main influencing factors in the water cycle process were then identified using the obstacle degree model, and ensuing policy recommendations were presented. The results demonstrated that the health state of the water cycle in the YRBHN shifted from unhealthy to sub-healthy during 2011–2020, with the response layer showing the most evident positive change. The health states of Zhengzhou and Luoyang were superior to those of the other cities, with average health index numbers of 3.1006 and 3.0078, respectively. Although the evaluation index numbers of the EFAST-Cloud and RF models were slightly different, their evaluation results were similar, indicating that the RF model was applicable for water cycle health evaluation. The proportion of ecological water use and the response (R) layer provided the greatest obstacle degree in the index and criterion layers, respectively. Based on the current state of the water cycle in the YRBHN, some policy implications were proposed. This study proposed a new method for regional water cycle health evaluation and decision making that provides theoretical support for regional water resource management and is of considerable practical significance.

Seeking a pathway towards a more sustainable human-water relationship by coupled model – From a perspective of socio-hydrology

It is essential to systematically consider social, economic, and natural endowments in managing and allocating water resources. However, few studies have comprehensively quantitatively evaluated the allocation of regional water resources from a socio-hydrology perspective and provided recommendations. To explore this research gap, we have constructed a tightly coupled framework that integrates system dynamics models and optimization algorithms to carry out an innovative redistribution of water resources in Shaanxi Province. The system dynamics model simulation results showed that the error was almost always within 10% over the research period, indicating robust simulation capability and laying a solid foundation for subsequent model coupling. The coupled model achieves convergence in approximately 30 generations by formulating the optimization problem with four individual objectives. Optimizing four objectives concurrently results in convergence around the 150th generation. The optimized Pareto solution sets visually demonstrate the trade-offs between different objectives. In the optimized water allocation schedule, the water consumption in Yulin exhibits a change of 1.22 ×108m3, reflecting the most significant optimization effects on agricultural and domestic water allocation. The results indicated that the comprehensive Gini coefficient typically ranged between 0.2 and 0.3. Over the period from the year 2010–2021, the Gini coefficient exhibited a declining trend, signifying a positive trajectory in water resource allocation throughout the research period and a high level of fairness. The annual total green WF of grain in Weinan was the highest at 14.26 ×108m3, followed by Xianyang at 9.52 ×108m3, and the lowest in Tongchuan at 0.54 ×108m3. The annual average amount of blue WF of grain is the highest in Hanzhong, at 11.33 ×108m3, followed by Weinan at 9.60 ×108m3, and the lowest in Tongchuan at 0.14 ×108m3. The coupled framework proposed in this study exhibits significant innovation, scalability, and practical efficiency. It can inspire future research and decision-making and holds the potential for application in other regions.

Utilizing Artificial Intelligence Techniques for a Long–Term Water Resource Assessment in the ShihMen Reservoir for Water Resource Allocation

Accurate long–term water resource supply simulation and demand estimation are crucial for effective water resource allocation. This study proposes advanced artificial intelligence (AI)–based models for both long–term water resource supply simulation and demand estimation, specifically focusing on the ShihMen Reservoir in Taiwan. A Long Short–Term Memory (LSTM) network model was developed to simulate daily reservoir inflow. The climate factors from the Taiwan Central Weather Bureau’s one–tiered atmosphere–ocean coupled climate forecast system (TCWB1T1) were downscaled using the K–Nearest Neighbors (KNN) method and integrated with the reservoir inflow model to forecast inflow six months ahead. Additionally, Multilayer Perceptron (MLP) and Gated Recurrent Unit (GRU) were employed to estimate agricultural and public water demand, integrating both hydrological and socio–economic factors. The models were trained and validated using historical data, with the LSTM model demonstrating a strong ability to capture seasonal variations in inflow patterns and the MLP and GRU models effectively estimating water demand. The results highlight the models’ high accuracy and robustness, offering valuable insights into regional water resource allocation. This research provides a framework for integrating AI–driven models with Decision Support Systems (DSSs) to enhance water resource management, especially in regions vulnerable to climatic variability.

Application of Artificial Intelligence in Glacier Studies: A State-of-the-Art Review

Assessing glaciers using recent and historical data and predicting the future impacts on them due to climate change are crucial for understanding global glacier mass balance, regional water resources, and downstream hydrology. Computational methods are crucial for analyzing current conditions and forecasting glacier changes using remote sensing and other data sources. Due to the complexity and large data volumes, there is a strong demand for accelerated computing. AI-based approaches are increasingly being adopted for their efficiency and accuracy in these tasks. Thus, in the current state-of-the-art review work, available research results on the application of AI methods for glacier studies are addressed. Using selected search terms, AI-based publications are collected from research databases. They are further classified in terms of their geographical locations and glacier-related research purposes. It was found that the majority of AI-based glacier studies focused on inventorying and mapping glaciers worldwide. AI techniques like U-Net, Random forest, CNN, and DeepLab are mostly utilized in glacier mapping, demonstrating their adaptability and scalability. Other AI-based glacier studies such as glacier evolution, snow/ice differentiation, and ice dynamic modeling are reviewed and classified, Overall, AI methods are predominantly based on supervised learning and deep learning approaches, and these methods have been used almost evenly in glacier publications over the years since the beginning of this research area. Thus, the integration of AI in glacier research is advancing, promising to enhance our comprehension of glaciers amid climate change and aiding environmental conservation and resource management.

Comprehensive assessment of sustainable development of water resources in the Longdong Loess Plateau

Assessment of water resources carrying capacity (WRCC) is of great significance for understanding the status of regional water resources, promoting the coordinated development of water resources with environmental, social and economic development, and promoting sustainable development. This study focuses on the Longdong Loess Plateau region and utilized panel data spanning from 2010 to 2020, established a three-dimensional evaluation index system encompassing water resources, economic, and ecological dimensions, uses the entropy-weighted TOPSIS model coupled with global spatial autocorrelation analysis (Global Moran’s I) and the hot spot analysis (Getis-Ord Gi* index) method to comprehensively evaluate the spatial distribution of the WRCC in the study region. It can provide scientific basis and theoretical support for decision-making on sustainable development strategies in the Longdong Loess Plateau region and other regions of the world.From 2010 to 2020, the overall WRCC of the Longdong Loess Plateau area show some fluctuations but maintained overall growth. The WRCC in each county and district predominantly fell within level III (normal) and level IV (good). The spatial distribution of the WRCC in each county and district is featured by clustering pattern, with neighboring counties displaying similar values, resulting in a spatial distribution pattern characterized by high carrying capacity in the south and low carrying capacity in the north. Based on these findings, our study puts forth several recommendations for enhancing the WRCC in the Longdong Loess Plateau area.

Soil water dynamics and plant water uptake: Primeval mature vs. debris flow-developed half-mature subalpine fir stands in the eastern Tibetan Plateau

Natural disaster can disrupt soil structure and replace established vegetation with younger plants, altering the local hydrological processes. We used hydrogen and oxygen stable isotopes to examine soil water dynamics and plant water uptake patterns in two adjacent fir stands in the eastern Qinghai-Tibet Plateau: a primeval mature stand (MF, finer- textured soil) and a debris flow-developed half-mature stand (HMF, coarser-textured soil). Our results showed that the isotopic composition and soil gravimetric water content (SWC) in deep soil water in MF exhibited a more pronounced hysteresis pattern in response to precipitation compared to HMF, indicating lower turnover rate of soil water in MF. This was also confirmed by a smaller contribution of preferential flow to deep soil water in MF compared to HMF. The higher water storage (higher SWC values) and lower turnover rate of soil water suggest a higher soil water buffer capacity in MF. Additionally, both stands showed no significant difference in plant water sources, but plants in MF used more winter precipitation due to the lower soil water turnover rate. These differences suggest MF may be more vulnerable to water disasters, while HMF may be more susceptible to seasonal droughts under climate change. Our insights enhance understanding of hydrological processes linked to changing surface conditions and offer valuable information for managing forest water resources in mountainous regions.

Effect of water Resource utilization in Poyang lake area on carbon emissions based on decoupling theory

The utilization of regional water resources has the potential to impact carbon emissions. Maintaining a decoupled relationship between water resources and carbon emissions facilitates harmonious regional development. Understanding the mechanism of their coordination is conducive to achieving the "Double Carbon" goal and control of regional carbon emissions and water resource consumption. This study examines the decoupling relationship between water resource utilization and carbon emissions in Poyang Lake area, China, employing the Tapio decoupling model and the LMDI(logarithmic mean divisia index) decomposition model. The results indicate that carbon emissions in Poyang Lake area exhibited a gradual increase, accompanied by an annual growth rate of 5.99 %. The water supply exhibited a slow expansion. They have exhibited state of affairs strong negative decoupling and expansive negative decoupling over the past 15 years. Moreover, this situation is most acute and worsening in the secondary industry. The water use structure effect and water economic benefit effect are the primary factors affecting carbon emission increases, contributing 57.93 % and 65.66 %, respectively. Carbon emissions intensity is the largest inhibiting factor, which accounts for a maximum contribution of 42.96 %. The order of potency of the driving factors is as follows: water economic benefit > carbon emission intensity > water use structure > water use efficiency. In summary, this research recognised the enhancement of the water economic efficiency index not only facilitates the decoupling phenomenon but also improves the water-carbon relationship, especially in the secondary industry. It serves as a compelling illustration of the significance of elucidating the interrelationship between regional water and carbon dynamics, and charting the course for the formulation of regional policies that would facilitate the advancement of environmentally conscious and carbon-neutral development, as well as water conservation.