Water Resources Management Research Articles

Innovative solar-assisted direct contact membrane distillation system: Dynamic modeling and performance analysis

The study presents an innovative solar-assisted dual-tank direct contact membrane distillation (DCMD) system designed to enhance the operational stability and efficiency of solar-powered desalination. The proposed system integrates a dual thermal storage tank configuration, allowing for continuous operation by alternating between two tanks that store pre-heated water, thereby mitigating the impact of solar energy fluctuations. The dynamic modeling approach used in this study predicts the system's performance under varying solar conditions, focusing on key parameters such as permeate flux, evaporation efficiency, and specific thermal energy consumption. The simulation results show that the system achieves an average permeate flux of 14.4 L/h m² and a thermal efficiency of 53.3 % at a hot water temperature of 60 °C, with a corresponding average specific thermal energy consumption of 1567 kWh/m³. These findings highlight a substantial improvement in both thermal efficiency and water production compared to conventional single-tank systems.The dual-tank DCMD system is particularly suited for deployment in remote or arid regions where stable and efficient freshwater production is critical. This research provides a comprehensive analysis of a novel solar-assisted desalination technology, contributing to the advancement of sustainable water resources management by providing a reliable and scalable solution that can maintain high operational efficiency even in remote areas with variable solar conditions.

Assessment of catchment water resources allocation under climate change in Luwombwa sub-catchment, Zambia

The uncertainty in climate change and high water demand pose pressure on the natural water resources supply. Not only does this require better understanding but also a call for immediate interventions, mitigation and adaptive measures. This study evaluates catchment water resources in the Luwombwa sub-catchment in Zambia through statistical analysis in the downscaling of past, present and future climatic variables from the CMIP6 climatic model. These variables are then integrated into WEAP - a semi-distributed hydrological and water evaluation model - to perform water demand and allocation scenario modelling. Mult-site calibration and validation were conducted on five selected micro-catchments within Luwombwa sub-catchment. The model performance was assessed usng the R2, NSE and PBIAS as the objective functions. Satisfactory values of 92 % for R2, 82 % for NSE and 6.9 % for PBIAS were achieved. This allowed for scenario modelling on water demand and allocation among competing users. Three future scenarios (2022–2050) were developed from the historical to baseline (1988–2022) and included state of water resources availability under climate change, expansion of irrigation area and impact of dam construction in the sub-catchment. The study reveals a decrease of 20 % in sub-catchment's water availability resulting from 9.3 % (equivalent to 4oC) rise in maximum temperature and 4.5 % reduction in rainfall within the entire sub-catchment. This is especially under the persistence of SSP370 climate variability scenario projections downscaled from four GCM models by the year 2050. The study further revealed that the change point for anticipated future climate extremes is likely to occur between 2027 and 2030. The results are indicative of downward trends in streamflow under climate change and socioeconomic development leading to increase in water value and water scarcity. The insights from the study are critical to inform formulation of effective catchment water resources management strategies such as the development of management plans and adapation measures in the face of climate change and the needs for different stakeholders involvement.

Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India.

Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact on society and individuals. This research paper explores the integration of the Standardized Precipitation Evapotranspiration Index (SPEI) and machine learning techniques for an assessment of drought characteristics in the Middle Ganga Plain, a crucial agro-climatic region in India. The study focuses on evaluating the frequency, intensity, magnitude, and recurrence interval of drought events. Various drought models, including Random Forest (RF), Artificial Neural Networks (ANN), and an ensemble model combining ANN and RF, were employed to analyze and predict drought patterns at different temporal scales (3-month, 6-month, and 12-month). The performance of these models was rigorously validated using key metrics such as precision, accuracy, proportion incorrectly classified, over-all area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE). Furthermore, the research extends its application to delineating drought vulnerability zones by establishing demarcations for high and very high drought vulnerability areas for each model and temporal scale. Results indicate that the south-western part of the middle Ganga plain falls under the highly drought-vulnerable zone, which averagely covers 40% of the study region. The core and buffer regions of drought vulnerability have also been identified. The south-western part of the study area is identified as the core region of drought. Ground verification of the drought-vulnerable area has been done by using soil moisture meter. Validation metrics show that the ensemble model of ANN and RF exhibits the highest accuracy across all temporal scales. This research's findings can be applied to improve drought preparedness and water resource management in the Middle Ganga Plain. By identifying high-risk drought zones and utilizing accurate prediction models, policymakers and farmers can implement targeted mitigation strategies. This approach could enhance agricultural resilience, protect livelihoods, and optimize water allocation in this vital agro-climatic region.

The impact of drought and climate change on economy, environment and human health in southern Iran: A qualitative study

Drought and climate change are one of the most important environmental health challenges of the current century. It has been predicted that the occurrence of drought, its intensity, and its adverse effects on the environment, human health, societies, and economy will increase in the coming decades. This qualitative content analysis study aimed to explore the impact of drought on the lives of people living in the south of Iran. Data were collected through in-depth semi-structured interviews with farmers, urban and rural officials, and physicians. A total of 25 interviews were conducted. All interviews were recorded and then transcribed. Data were analyzed using MAXQDA version 18. The concepts raised by interviewees regarding the consequences of drought were categorized into four themes, including economic consequences, environmental hazards, health consequences, and adaptation options; and most of these consequences were inter-related. Participants believed that drought has affected the soil, environment, ecosystem, and the quality and quantity of water, and this can lead to extensive effects on humans’ physical, mental, and community wellbeing. Drought can also cause adverse economic effects, which can further intensify its adverse effects. It seems like the adverse effects of drought in Iran are severe and destructive, and this is due to the mismanagement of water and environmental resources and other socio-economic problems. Fundamental changes in the management of water and environmental resources are necessary.

Modeling the rainfall–runoff process in the Bird Creek River Basin: a comparative analysis of conceptual and artificial neural network techniques

ABSTRACT The modeling of the rainfall–runoff (RR) process is a key component for water resources projects, planning, and management for which conceptual and data-driven modeling techniques are utilized. However, these techniques in the modeling of the RR process have their own benefits, and their performance need to be explored in real basins. In this paper, five conceptual models (namely, AWBM, Sacramento, SimHyd, SMAR, and TANK) and an artificial neural network (ANN) model have been developed and their performances have been assessed using the rainfall, runoff, and other climatic data derived from the Bird Creek Basin, USA. The results obtained from the study suggest that the SimHyd performed the best among all the conceptual models during testing. The ANN model performance in simulating the RR process was found to be the best among all the models developed in this study during testing with the highest values of R = 0.941, E = 0.994 and all threshold statistics and least values of AARE = 38.9 and NRMSE = 0.031. Overall, it can be concluded that although the conceptual models are highly comprehensible, the ANN models are able to simulate the flow more accurately than any of the conceptual models developed in this study.

Climate Change Adaptation and Resource Resilience in Semi-Arid Regions: Strategies, Challenges, and Case Studies

Semi-arid regions face unique climate change challenges that threaten local ecosystems, agriculture, and water resources. This review explores climate adaptation strategies, resource resilience practices, and policy interventions tailored to semi-arid ecosystems, with a focus on Rajasthan, India, as a representative region. Key strategies include sustainable water resource management, soil conservation, and climate-smart agriculture. Through case studies and a comparative global analysis, this review underscores the importance of blending traditional and modern practices to enhance resilience and advocates for policy and technological innovation to support sustainable adaptation. Insights gained from this review may serve as a foundation for adaptation strategies applicable to other semi-arid regions globally.

Integrating multi-source remote sensing and machine learning for root-zone soil moisture and yield prediction of winter oilseed rape (Brassica napus L.): A new perspective from the temperature-vegetation index feature space

Accurately assessing root-zone soil moisture is crucial for precision irrigation, as it directly influences crop yield. The Temperature-Vegetation Index (Ts-VI) Feature Space, which combines land surface temperature (Ts) and vegetation index (VI), is widely used to evaluate root-zone soil moisture in vegetated areas. However, its effectiveness in estimating crop yield remains unclear. Therefore, the objectives of this study are: (1) to collect multispectral and thermal infrared remote sensing data from a two-year (2021–2023) field experiment on winter oilseed rape (Brassica napus L.), and to optimize and evaluate the fitting methods of the dry and wet edges of the Ts-VI feature space based on the selected vegetation indices; (2) to analyze the spatiotemporal patterns of the Temperature Vegetation Dryness Index (TVDI) derived from the optimized Ts-VI feature space and estimate root-zone soil moisture (SM) and crop yield; and (3) to precisely invert the SM and yield of winter oilseed rape in the 0–60 cm root-zone using three machine learning algorithms—Support Vector Regression (SVR), Extreme Gradient Boosting Regression (XGBR), and Random Forest Regression (RFR)—based on the optimized TVDI. Results indicate that, among the various fitting methods, the polynomial fitting method shows the best performance. The performance of the root-zone soil moisture prediction models across different growth stages follows the order of budding stage > seedling stage > flowering stage, and with the increase of soil depth, the performance of the model gradually deteriorates.In the yield inversion of winter oilseed rape, TVDI effectively predicts yield, with the coefficient of determination (R2) ranging from 0.430 to 0.480 and RMSE ranging from 213.399 to 267.212 kg ha−1 during the seedling stage, R2 ranging from 0.640 to 0.747 and RMSE ranging from 110.712 to 178.133 kg ha−1 during the budding stage, and R2 ranging from 0.680 to 0.773 and RMSE ranging from 83.815 to 147.301 kg ha−1 during the flowering stage. The flowering stage effectively reflects crop yield trends and allows for accurate yield prediction of winter oilseed rape up to two months in advance. A comparison of the modeling results from XGBR, SVR, and RFR shows that XGBR provides the best fit for both root-zone soil moisture and yield predictions. Compared to linear regression models, the three machine learning models significantly improve accuracy and fit, providing more precise evaluations of root-zone soil moisture and yield. In addition, through the comparison and verification of this method in other regions, it shows that the results also have certain reference value. The combination of the Ts-VI feature space and machine learning algorithms not only enables precise monitoring of root-zone soil moisture conditions but also predicts future crop yield trends, offering valuable insights for water resource management and irrigation decision-making in precision agriculture.

Impact of Rivers on Seasonal Changes in Water Level

This study was conducted regarding the impact of rivers on Darbandikhan Lake's seasonal water level change. The aim of this study is to illustrate the impact of rivers on the lake through seasonal water level change from its tributaries. To achieve the research objectives, descriptive and quantitative methods were used. The findings showed that the highest water level of the lake was 467.72 m (a.s.l.) in Spring and that its storage capacity and area were 1.2554 billion m3 and 46.26 km2, respectively. In contrast, the lowest water level of the lake was 458.62 m (a.s.l.) in Autumn, with storage capacity and area of 908.2 million m3 and 32.38 km2, respectively. The river's share of discharge into the lake varied annually. The Tanjaro River averaged 6 m3/s (19.6%), the Chaqan River 0.47 m3/s (1.52%), the Zalm River 1.17 m3/s (3.77%), the Sirwan River 20.55 m3/s (66.28%), and the Zmkan River 2.74 m3/s (8.83%), all of which contributed to the lake annually. River discharge was influenced by physical factors such as topography, slope, and climatic elements, in addition to human factors such as water resource management policies, population and land use/land cover in the study area.

Temporal-spatial characteristics of climate drought and its effects on grain yield in Hunan Province, China

The examination of the spatiotemporal characteristics and developmental trends of drought is crucial for enhancing water resource management, bolstering drought resistance, and improving disaster prevention capabilities. This study employs the Standardized Precipitation Evapotranspiration Index (SPEI) and grain yield data across various time scales, in conjunction with methodologies such as Run Theory, Mann-Kendall, and Standardized Yield Residual Series, to conduct an in-depth investigation into the spatiotemporal variation characteristics of meteorological drought in Hunan Province and its impact on grain yield. The findings suggest that: (1) Since 1960, the likelihood of seasonal drought occurrence in Hunan Province has been ranked as autumn > winter > spring > summer, with mild drought occurring most frequently, followed by moderate drought, while the frequency of severe and extreme drought remains low. (2) Meteorological drought in Hunan Province exhibits spatial differences at the seasonal scale, with the overall drought changes in spring and summer displaying a non-significant upward trend; the western and southern regions exhibit a trend of aridification in autumn; and in winter, the Zhangjiajie and Xiangxi regions show an insignificant downward trend. (3) From 1960 to 2022, grain production in Hunan Province has demonstrated a pattern of fluctuation and increase. The meteorological yield of grain crops displays a high-low-high spatial distribution from south to north. Concurrently, there is a positive correlation between short-term climate change and meteorological output, while long-term climate change is not evident. (4) El Niño Southern Oscillation (ENSO) is a significant circulation factor affecting meteorological drought in Hunan Province, and the meteorological drought in autumn and winter in Hunan Province is significantly influenced by ENSO. The research findings can provide reference significance and a scientific basis for drought research and comprehensive management in Hunan Province, and offer data and theoretical support for promoting economic development.

Variation Characteristics of Actual Evapotranspiration and Uncertainty Analysis of Its Response to Local Climate Change in Arid Inland Region of China

Exploring the variation characteristics of actual evapotranspiration (ETa) and its response to climate change in the arid inland region of China is of great significance for strengthening regional water resources management and maintaining ecological environment security and stability. Taking the Dulan River Basin as the research area, based on the meteorological data from the Wulan Station and hydrological data from the Shanggaba Station from 1981 to 2020, the variation characteristics of ETa at the annual scale were analyzed. The ETa estimation model and joint distribution model of P and potential evapotranspiration (ET0) was constructed based on climate factors, and the uncertainty of ETa response to climate change was explored with the water balance method, multiple linear regression, marginal distribution function, Copula function, and Monte Carlo algorithm. The results showed that the multi-year mean value of ETa in the study area was 261.6 mm, and the interannual process showed an insignificant upward trend, and had no abrupt change during the period. There were two obvious main cycles, which were 19-year periodic changes on the 30-year time scale and 6-year periodic changes on the 9-year time scale. The ETa estimation model based on precipitation (P) and ET0 had good simulation accuracy. The optimal marginal distributions of P and ET0 were Pearson-III (P-III) distribution and Generalized Extreme Value (GEV) distribution, respectively. The Copula joint distribution probability density of P and ET0 was a symmetric saddle-shaped distribution. ETa showed an inverted ‘S’ distribution with the change in joint guarantee rate of P and ET0, ranging from 116.9 mm to 498.6 mm. ETa was an interval range under a certain joint guarantee rate. The research results can provide support for the assessment of ETa, and help to further understand the driving mechanism of climate change on ETa in the arid inland region of China.

Generation of spatial grids for hydrometeorological data in hydrogeological investigations for Colombia

Abstract. In Colombia, water resources management is carried out with insufficient hydrometeorological information in most of the territory. Natural and social conditions make it complex and difficult to densify the monitoring of primary variables for analysis. As a result, decisions often have to be made based on national scale studies, extrapolating information from areas with somewhat better monitoring coverage. The objective of this study is to estimate hydrometeorological data with a spatial resolution that allows decision making on water resources management from the national government level to the local level. For this purpose, multivariate regression models are used where data are fitted from meteorological station measurements taken for the time period 1981–2016.

Indigenous Inclusion in Water Resource Governance and Legislation of the Upper Colorado River Basin

Indigenous tribes and governments within the Upper Colorado River Basin have been left to battle failing infrastructure, underrepresentation, outdated legislation, inadequate water allocations, and ongoing disenfranchisement that has limited equitable access to water. On a wider scale, the current state of Western legislation is not inclusive of Indigenous parties. These factors are compounded by a 23-year long drought in the western United States that has placed drought management and climate change at the forefront of critical need for policy makers and state governments. This article highlights some of the current challenges relevant to inclusive Indigenous governance, with the specific context of drought and water resource management. The authors offer recommendations to the Bureau of Reclamation and state governments of the Upper CRB for more encompassing policy practices in Western water law that provide for a just inclusion of Indigenous knowledge and tribal sovereignty within the larger discourse of basin-wide droughts and water scarcity.

EVALUACIÓN DEL IMPACTO DE LOS CAMBIOS EN EL USO Y COBERTURA DE LA TIERRA EN EL ESCURRIMIENTO SUPERFICIAL

The aim of this study was to analyze the behavior of surface runoff as a result of changes in land use and land cover (LULC), using the Rio Novo basin (Brazil) as the analysis site. To estimate surface runoff, the curve number (NRCS-CN) method was used; as input data, the LULC classifications provided by the MapBiomas project and the pedological map of the state of São Paulo (1:250,000) were used. The Rio Novo basin was discretized into ten sub-basins. The results showed major changes in LULC between 1992 and 2022, with a reduction in pasture areas and an increase in annual crops and sugarcane fields, as well as in urban areas. Such alterations are conditioning factors of changes in Curve Number (CN) values, which imply changes in surface runoff values. For a rainfall of 130 mm (roughly 10-year return period), the surface runoff depth values ranged from 3.21 mm (areas with forest cover on soils in hydrological group A) to 101.35 mm (for urban areas on soils in hydrological group C). The urbanization process that occurred in sub-basins 1, 6, and 9 led to an increase in areas with the highest CN values (CN equal to 85 and 90). Locations with increased surface runoff values should be considered as critical areas, where soil and water losses may occur, thereby compromising water security in the basin. Keywords: NRSC-CN method, Geographic Information System, Water Resources Management, MapBiomas.

Recommendations for Better Collaborative Groundwater Monitoring for the US High Plains Aquifer

Groundwater is a critical resource for the Great Plains region of the United States, providing drinking water for over 2 million people. However, the High Plains Aquifer (HPA) is under significant threat from over-extraction—defined as the excessive withdrawal of groundwater beyond its natural replenishment rate. This overuse jeopardizes not only water availability but also equitable access and allocation. Effective groundwater monitoring is essential to track trends in water availability, assess the impacts of extraction, and develop strategies to ensure long-term sustainability. Without comprehensive monitoring, it is difficult to address key issues like contamination, depletion, and groundwater quality degradation. While several state-level frameworks exist to enhance groundwater monitoring, they operate independently, leading to gaps in data sharing and collaboration, especially for a transboundary resource like the HPA. The Environmental Protection Agency (EPA) and U.S. Geological Survey (USGS) are well-positioned to play a more central role in this process. The EPA, with its mandate to protect water quality, and the USGS, with its expertise in nationwide data collection, are critical to supporting a collaborative and comprehensive groundwater monitoring system. By facilitating the integration of state-level efforts, these federal agencies can help ensure that groundwater monitoring is both consistent and accurate, enabling effective decision-making at regional and national levels. This policy memo provides a detailed analysis of current state-level efforts, highlights the role of the EPA and USGS in addressing governance challenges, and proposes a transboundary governance mechanism to enhance collaborative groundwater monitoring. The intended audience includes policymakers at the EPA and USGS, as well as water resource managers who are key to implementing these changes.

Optimizing surface water quality parameters in monitoring networks in a developing sub-tropical region with high anthropogenic pressure (São Paulo State Brazil).

Efficient water quality monitoring is a central aspect of water resources management, especially in developing countries, where water quality is under high anthropogenic pressure and resources for monitoring are usually limited. Here, we evaluated an alternative to optimize water quality parameters (WQPs) in the water quality monitoring network (WQMN) of the most populous state in Brazil (São Paulo State). We focused on the monitoring goal of identifying water quality temporal trends, selecting WQPs with high statistical explanatory power and those that were particularly sensitive to natural and anthropogenic perturbations. We considered 12 initial WQPs (dissolved copper, total zinc, total lead, total chromium, total mercury, total nickel, total cadmium, total iron, total manganese, total aluminum, total copper, and surfactant) with data from 2004 to 2018 for 56 monitoring sites distributed across four major watersheds with contrasting land uses in the state. We performed principal component analysis, followed by objective criteria to refine WQPs recommendation for the WQMN. Our results indicated the opportunity of reducing at least one parameter from the initial set of WQPs in all watersheds. Total iron, total manganese, and total aluminum were the most relevant initial WQPs, since their maintenance in monitoring were indicated in all the analyzed cases. Natural watershed conditions (e.g., geomorphology and water geochemistry) potentially governed their concentrations in surface water. On the other hand, total mercury, total chromium, and dissolved copper had the maintenance indicated in only one watershed, especially due to concentrations consistently below the respective limits of quantification (LoQs). Future investigations can complement our recommendations for these parameters, since changes in LoQs could throw another light on water quality spatial and temporal variations and the need for reference areas for assessing baseline conditions can also be relevant. Moreover, we argue that depending on the monitoring goals of the WQMN, additional sampling of biota and sediments could be useful as many of the studied WQPs' bioconcentrate. Our results illustrated an alternative approach towards adaptive monitoring in São Paulo state in accordance with the intended monitoring goal (i.e., water quality temporal trends), converging with the more flexible monitoring adopted in well-structured networks worldwide. While we did not cover other monitoring goals in our study (as the control of illegal discharge of effluents or industrial spills, for example), we expect our methodology can contribute to establishing technical guidelines for reviewing the existing WQMNs in Brazil and other developing countries with similar challenges.

AI-driven optimization of agricultural water management for enhanced sustainability

Optimizing agricultural water resource management is crucial for food production, as effective water management can significantly improve irrigation efficiency and crop yields. Currently, precise agricultural water demand forecasting and management have become key research focuses; however, existing methods often fail to capture complex spatial and temporal dependencies. To address this, we propose a novel deep learning framework that combines remote sensing technology with the UNet-ConvLSTM (UCL) model to effectively integrate spatial and temporal features from MODIS and GLDAS datasets. Our model leverages the high-resolution spatial data from UNet and the temporal dependencies captured by ConvLSTM to significantly improve prediction accuracy. Experimental results demonstrate that our UCL model achieves the best R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} compared to existing methods, reaching 0.927 on the MODIS dataset and 0.935 on the GLDAS dataset. This approach highlights the potential of AI and remote sensing technologies in addressing critical challenges in agricultural water management, contributing to more sustainable and efficient food production systems.

Enhancing streamflow prediction in the Wujiang River basin: a two-stage decomposition approach with deep learning integration

ABSTRACT Global climate change and human activities have profoundly impacted the geological and hydrological processes in watersheds, increasing the challenges in streamflow prediction. In this study, we propose a streamflow prediction model based on deep learning and dual-mode decomposition specifically tailored for the Wujiang River basin, a significant tributary on the southern bank of the upper Yangtze River. This model effectively addresses the prediction error issue caused by high-frequency components through dual-mode decomposition of time-series data. The results demonstrate that employing the coupled complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational mode decomposition (VMD)–temporal convolutional network (TCN)–long short-term memory network (LSTM) model reduces prediction errors by at least 35% compared to single decomposition models. Furthermore, compared to individual TCN or LSTM models, the TCN–LSTM coupled model exhibits greater stability and higher prediction accuracy during training, with reductions in mean absolute error and root mean square error by 43.13 and 24.57%, respectively. This model holds promising prospects for application and can provide crucial insights for water resource management.

The Spatiotemporal Dynamics of Temperature Variability Across Mts. Qinling: A Comparative Study from 1971 to 2022

Analyzing the spatiotemporal patterns of atmospheric temperature in sensitive areas is critically important for understanding the broader implications of global climate change, which remains a prominent topic in geosciences. It also plays a crucial role in advancing sustainable development. This study utilized daily minimum, maximum, and mean temperature data from twelve meteorological stations across the South and North Mts. Qinling (Qinling Mountains). Employing trend analysis, the Mann–Kendall mutation test, and Morlet wavelet analysis, we explored the predominant temperature trends and characteristics from 1971 to 2022. Our findings revealed consistent inter-annual warming trends in both regions, with more rapid temperature increases in the North compared to the South. Notably, significant shifts occurred in 2003 for both mean and minimum temperatures in the North, while the maximum and minimum temperature values were recorded in the 2010s and 1980s, respectively. Both regions exhibited a primary temperature fluctuation cycle of 28 years. Seasonally, the strongest warming effects appeared in spring, with the weakest in autumn, and moderate effects in winter and summer, indicating that spring contributes most significantly to regional warming. Monthly analysis showed positive temperature trends across all months, with higher rates in the North. The weakening temperature boundary effect of the Mts. Qinling suggested a weakening North–South division, particularly highlighted by the northward shift of the 1 °C isotherm curve for the coldest month, moving away from the previously observed 0 °C isotherm. This northward shift highlights the differential warming rates between the northern and southern regions. Overall, the analysis confirms a robust warming trend, with notable fluctuations in January’s temperatures since 1998, suggesting the Mts. Qinling’s emerging role as a climatic divider in the Chinese Mainland. This introduces new challenges for regional ecosystems, agricultural production, and water resource management, highlighting the pressing need to advance regional sustainable development in the face of climate change.

A “WEST” Theory in the East: Decoupling and synergistic evolution of water utilization, economy, and society in China

AbstractTo attain a thorough examination of the dynamic interaction mechanism between China's utilization of water resources and its economic and social development, this paper innovatively introduces a Western theoretical framework within an Eastern context. The aim is to quantify the process of decoupling and synergistic evolution from 2006 to 2020. Decoupling signifies achieving economic growth while reducing resource use or mitigating environmental impacts, whereas synergy denotes enhancing economic efficiency while contributing to environmental protection. This paper employs the Tapio elastic coefficient method to construct a decoupling model. A collaborative model, based on the entropy weight Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and weighted average methods, also assesses the organization and coordination of subsystems within the complex water‐economic‐social system. The findings reveal a strong decoupling between China's water resource management and economic and social systems, as indicated by a consistently negative decoupling index. Similarly, the economic system exhibits a degree of inefficiency influenced by factors like economic cyclicality and resource allocation. In contrast, the social system experienced a decline, particularly during the pandemic (2019–2020), leading to a social instability. This study provides valuable policy formulation and sustainable development insights, contributing to a comprehensive understanding of the intricate dynamics between water utilization, economy, and society.

Quantitative Assessment of Ecological Flow in the Yellow River Under Changing Environments

ABSTRACTStudying the streamflow characteristics of the Yellow River mainstem under changing environmental conditions is crucial for the management and sustainable development of water resources within its basin. This research employs a long short‐term memory (LSTM) model to restore the flow characteristics of the Yellow River's mainstream under natural conditions. Additionally, the range of variation approach (RVA) and nonparametric kernel density estimation (KDE) method are integrated to quantitatively assess the impact of environmental changes on streamflow. The findings indicate that: (1) Hydrological variability in the Yellow River was observed in 1985, with a degree of variability ranging from 26% to 58%, classified as moderate. (2) The annual ecological flow value of the Yellow River is 560–1001 m3/s, and the average annual ecological flow assurance is 43%. (3) Based on LSTM simulation results (NSE > 0.7, R2 > 0.8), it is concluded that the ecological flow assurance under natural conditions in the Yellow River exceeds the measured values, primarily due to human activities, which contribute over 52% to this discrepancy. These results suggest that the river ecosystem of the Yellow River's mainstem is relatively unstable and requires further management.