Water Resources Management Research Articles

Sustainable water resource management and ecological restoration in the Syr Darya River basin: assessment of the impact of channel regulation near the Saryshyganak dam

ABSTRACT The purpose of this study was to analyse different scenarios of water resources use to assess the variability of water levels in the aquatic environment of the Small Aral Sea. The following methods were employed during the study: method of mathematical modelling; method of hydrological calculations; comparative analysis; analysis of hydrological data. The methods were chosen for their reliability and applicability to hydrological studies, enabling accurate water level predictions, assessment of resource management options, and analysis of hydraulic structures impact. Based on the results of the calculation of the filling of the Small Aral Sea, it was established that the time required to reach water levels of 50 and 44 metres was between 2 and 15 years. Using the method of mathematical modelling used for forecasting, it was found that the period of water level restoration in the Small Aral Sea under the application of the single-level option could be 26 years on average. The most effective approach for the Northern Aral Sea is the two-tier approach with the marks of the normal headwater level of 42 m and for the Saryshyganak Bay − 50 m, which helped to optimise the use of water resources and reduce water consumption for evaporation. The findings of this study can be applied in practice by hydrologists, hydraulic engineers, resource scientists, environmental engineers, ecologists, ecologists-hydrobiologists, specialists in water management and environmental monitoring.

Assessment of agricultural soil integrity and crop quality following multiple years of flood and modern irrigation systems

ABSTRACT Nations, particularly water-scarce regions like Egypt, are increasingly modernising irrigation systems to boost water productivity and resource management. This shift entails evaluating factors such as soil salinity, crop yield, and economics, departing from traditional flood irrigation (FI). The choice between open-field (OF) and greenhouse (GH) practices requires a thorough assessment of long-term water and crop productivity. The study was conducted over five years on a private farm in southwestern Egypt, encompassing (GH) and (OF) environments to compare the long-term effects of FI and drip irrigation (DI) systems on aging land in Nile Delta. The key findings revealed that DI in GH was highly effective, yielding a net return of 204.6 LE m−3 and a water use efficiency of 36 kg m−3. Despite its efficiency, a slight rise in the sodium adsorption ratio (SAR) was noted. Greenhouse DI yielded the highest average profitability (7.03%), contrasting with open-field FI (1.44%). After three years, DI resulted in decreased tomato yield and increased soil salinity, prompting a return to traditional-irrigation for soil-leaching. This study recommends the transition to modern irrigation for sustainable tomato production, providing a framework for assessing productivity and economic feasibility. Highlighting the role of contemporary irrigation-methods in addressing water-resource challenges.

Bioinspired wood-based wedge-shaped surface with gradient wettability for enhanced directional liquid transport and fog harvesting.

Inspired by cactus spine and desert beetle back structures, we developed a wood-based wedge-shaped surface with gradient wettability for efficient and controlled spontaneous directional liquid transport. Utilizing the natural anisotropic and porous structure of wood, the wedge-shaped surface exhibited a continuous gradient wettability after chemical treatments combined with UV-induced modifications. The resulting surface enabled highly efficient directional liquid transport with transport rates reaching up to 8.9 mm s-1 on horizontal placement and 0.64 mm s-1 on vertical surfaces against gravity. By integrating geometric curvature and surface energy gradients, the innovative design achieved synergistic Laplace pressure-driven and wettability-driven liquid motions. To further demonstrate its potential for practical application, a fog-driven power device constructed using the gradient wettability wood with cactus spines not only enhanced water harvesting and energy conversion capabilities but also offered an environmentally friendly system. This study expanded the design toolbox for bioinspired liquid management surfaces, offering promising applications in water resource management, energy harvesting, and microfluidic devices.

Impact of Climate Change on Water Resources and Ecological Sustainability in Morocco: A 1990–2022 Analysis

This study comprehensively examines the multifaceted impact of climate change on Morocco’s ecological sustainability and economic development, focusing on four critical environmental stressors: water stress, deforestation, greenhouse gas emissions, and rising temperatures. These interrelated factors contribute significantly to the degradation of natural ecosystems, the decline in biodiversity, reductions in carbon sequestration, and the disruption of ecological balance. Water scarcity—exacerbated by declining precipitation, excessive groundwater extraction, and rising evapotranspiration—threatens the functionality of wetlands, agricultural productivity, and the livelihoods of rural populations. Deforestation accelerates soil erosion, alters hydrological cycles, and leads to the loss of critical habitats, while greenhouse gas emissions and temperature rise intensify climate variability and increase the frequency of extreme events such as droughts and heatwaves. Using longitudinal data from the World Bank (1990–2022) and advanced econometric modeling through EViews 12 software, this study reveals that water stress and rising temperatures have a statistically significant and negative impact on GDP, indicating that climate pressures undermine Morocco’s economic performance, particularly in climate-sensitive sectors. Conversely, the findings show that deforestation and greenhouse gas emissions are positively correlated with short-term economic growth, reflecting a development pattern heavily reliant on natural resource exploitation and carbon-intensive activities, which may offer temporary gains but pose serious long-term risks to sustainability. These results underscore the urgent need for a paradigm shift toward ecosystem-based adaptation and mitigation strategies, including afforestation, wetland restoration, integrated land and water resource management, and the incorporation of climate resilience into national development frameworks.

Delineating groundwater recharge zones: semi-arid NW Algeria case study

In arid and semi-arid regions, groundwater is a vital resource increasingly impacted by population growth and agricultural demands. This study identifies potential groundwater recharge zones in the Oued Isser-Sikkak Basin, northwestern Algeria, using the Analytic Hierarchy Process (AHP) integrated with Geographic Information Systems (GIS). Six thematic layers—land use, slope, geology, drainage density, lineament density, and rainfall—were weighted and combined to produce a recharge potential map. The basin was classified into five categories: very high (4%), high (33.7%), moderate (45.6%), low (12.5%), and very low (1.9%) recharge potential. The highest potential zones were found in the south and east, where limestone and dolomite formations prevail. Validation with field observations and previous research showed an 83.7% match, confirming the model’s reliability. The study demonstrates the effectiveness of integrating AHP and GIS in assessing groundwater potential, offering a valuable tool for sustainable water resource management in data-limited, dryland areas.

Improving seasonal precipitation forecasts in the Western United States through statistical downscaling

Abstract Seasonal precipitation forecasts in the western United States are critical resources for water resource management, especially during winter. While current seasonal forecasting systems provide monthly precipitation forecasts operationally, their coarse resolution limits their effectiveness in capturing the localized precipitation patterns and snowpack conditions essential for water resource managers in the mountainous regions. Here, analog statistical downscaling is demonstrated as an effective approach to enhance the spatial resolution of operational seasonal forecasts provided by the North American Multi-Model Ensemble. Downscaling was performed by building an analog ‘library’, in which corresponding model forecasts and observed values during the training period were stored. In the testing period, unseen model forecasts referenced the closest historical forecast from the analog library and applied the corresponding observational value for each point. This analysis indicates that downscaled products can capture localized features more accurately than the original coarse resolution forecasts, reducing forecast error across the western United States. Moreover, downscaling individual ensemble members—rather than downscaling the ensemble mean—further reduces forecasting error for their multi-model ensemble mean products. The greatest error reductions in the downscaled product, measured by root mean squared error (RMSE), were observed at low to mid-elevations (500–2000 meters), with 50%–70% improvement relative to the original forecasts. In the higher elevations (2000 meters and above), changes in RMSE relative to the original forecast were limited to 10%–30% improvements. The improvement is more substantial for forecast systems with 10 ensemble members compared to that with 4 members, but this relationship does not hold for the system with 24 ensemble members. These findings show that analog statistical downscaling can effectively address the spatial limitations of seasonal precipitation forecasts with minimal computational cost, providing a valuable framework for enhancing coarse resolution forecasting products while providing insights into the timing of ensemble mean calculations during the downscaling process.

Transboundary Urban Basin Analysis Using GIS and RST for Water Sustainability in Arid Regions

Water, often described as the elixir of life, is a critical resource that sustains life on Earth. The acute water scarcity in the major basins of the Arabian Peninsula has been further aggravated by rapid population growth, urbanization, and the impacts of climate change. This situation underscores the urgent need for a comprehensive analysis of the region’s morphometric characteristics. Such an analysis is essential for informed decision-making in water resource management, infrastructure development, and conservation efforts. This study provides a foundational basis for implementing sustainable water management strategies and preserving ecological systems by deepening the understanding of the unique hydrological processes within the Arabian Peninsula. Additionally, this research offers valuable insights to policymakers for developing effective flood mitigation strategies by identifying vulnerable areas. The study focuses on an extensive investigation and assessment of morphometric parameters in the primary basins of the Arabian Peninsula, emphasizing their critical role in addressing water scarcity and promoting sustainable water management practices. The findings reveal that the Arabian Peninsula comprises 12 major basins, collectively forming a seventh-order drainage system and covering a total land area of 3.24 million km2. Statistical analysis demonstrates a strong correlation between stream order and cumulative stream length, as well as a negative correlation between stream order and stream number (R2 = 99%). Further analysis indicates that many of these basins exhibit a high bifurcation ratio, suggesting the presence of impermeable rocks and steep slopes. The hypsometric integral (HI) of the Peninsula is calculated to be 60%, with an erosion integral (EI) of 40%, indicating that the basin is in a mature stage of geomorphological development. Importantly, the region is characterized by a predominantly coarse drainage texture, limited infiltration, significant surface runoff, and steep slopes, all of which have critical implications for water resource management.

The Hydrogeochemical Characteristics and Formation Mechanisms of the High-Salinity Groundwater in Yuheng Mining Area of the Jurassic Coalfield, Northern Shaanxi, China

In the Yuheng mining area (Jurassic coalfield, northern Shaanxi, China), the Yan’an Formation groundwater is characterized by elevated salinity, posing challenges for mine water pollution control and regional water resource management. However, the spatial distribution patterns and formation mechanisms of this high-salinity groundwater remain poorly studied. This study integrates hydrogeochemical data from 18 coal mines, analyzing the spatial salinity variations, major ion compositions and isotopic signatures. Combined with the evolution characteristics of ancient sedimentary environments and the composition analysis of rock salt minerals in the coal rock interlayers, the formation mechanism of high salinity water was explored. The results indicate that the groundwater mineralization degree of the Yan’an Formation in the Jurassic strata encountered in the Yuheng mining area is the highest, showing a decreasing trend upwards. On the plane, the western and northern regions are generally higher than the eastern and southern regions. The highest mineralization level of groundwater can reach 36.25g/L, and the high mineralization hydrochemical type is mainly SO4-Na·Ca type, with occasional Cl-Na type in areas with extremely high mineralization level. The cause analysis shows that the highly mineralized groundwater in the Yuheng mining area comes from atmospheric precipitation, which infiltrates and dissolves salt rocks. In addition, the mining area is located in the arid area of northern Shaanxi, with insufficient water supply and no obvious structural faults, and has good sealing properties, thus exhibiting the characteristics of high mineralization. These mechanisms provide a formation model for the high-salinity groundwater in Jurassic coal-bearing strata, offering critical implications for predictive hydrogeochemical modeling and sustainable water management in arid mining regions.

CONCN: a high-resolution, integrated surface water–groundwater ParFlow modeling platform of continental China

Abstract. Large-scale hydrologic modeling at the national scale is an increasingly important effort worldwide to tackle ecohydrologic issues induced by global water scarcity. In this study, a surface water–groundwater integrated hydrologic modeling platform was built using ParFlow, covering the entirety of continental China with a resolution of 30 arcsec. This model, CONCN 1.0, offers a full treatment of 3D variably saturated groundwater by solving Richards' equation, along with the shallow-water equation at the ground surface. The performance of CONCN 1.0 was rigorously evaluated using both global data products and observations. RSR values (the ratio of the root mean squared error to the standard deviation of observations) show satisfying performance with regard to streamflow, yet the streamflow is lower in the endorheic, Hai, and Liao rivers due to uncertainties in potential recharge. RSR values also indicate satisfying performance in terms of the water table depth of the CONCN model. This is an intermediate performance compared to two global groundwater models, highlighting the uncertainties that persist in current large-scale groundwater modeling. Our modeling work is also a comprehensive evaluation of the current workflow for continental-scale hydrologic modeling using ParFlow and could be a good starting point for modeling in other regions worldwide, even when using different modeling systems. More specifically, the vast arid and semi-arid regions in China with substantial sinks (i.e., the endpoints of endorheic rivers) and the large uncertainties in potential recharge pose challenges for the numerical solution and model performance, respectively. Incompatibilities between data and the model, such as the mismatch of spatial resolutions between models and products and the shorter, less frequent observation records, necessitate further refinement of the workflow to enable fast modeling. This work not only establishes the first integrated hydrologic modeling platform in China for efficient water resources management but will also benefit the improvement of next-generation models worldwide.

Analysis of Water and Sediment Changes at Different Spatial Scales and Their Attribution in the Huangfuchuan River Basin

Water–sediment evolution and attribution analysis in watersheds is one of the research focuses of hydrogeology. An in-depth investigation into the spatiotemporal variation of water and sediment at multiple spatial scales within the basin, along with a systematic assessment of the respective impacts of climate change and human activities, provides a scientific foundation for formulating effective soil and water conservation practices and integrated water resource management strategies. This research holds significant implications for the sustainable development and ecological management of the basin. In this study, the Mann–Kendall nonparametric test method, double cumulative curve method, cumulative anomaly method, and cumulative slope change rate analysis method were used to quantitatively study the effects of climate change and human activities on runoff and sediment load changes at different spatial scales in the Huangfuchuan River basin. The results show that (1) from 1966 to 2020, the annual runoff and annual sediment load discharge in the Huangfuchuan River basin showed a significant decreasing trend. Among them, the reduction in runoff and sediment in the control sub-basin of Shagedu Station in the upper reaches was more obvious than that in the whole basin. The mutation points of runoff and sediment load in the two basins were 1979 and 1998. The water–sediment relationship exhibits a power function pattern. (2) After the abrupt change, in the change period B (1980–1997), the contribution rates of climate change and human activities to runoff and sediment load reduction in the Huangfuchuan River basin were 24.12%, 75.88% and 20.05%, 79.95%, respectively. In the change period C (1998–2020), the contribution rates of the two factors to the runoff and sediment load reduction in the Huangfuchuan River basin were 18.91%, 81.09% and 15.61%, 84.39%, respectively. Among them, the influence of precipitation in the upper reaches of the Huangfuchuan River basin on the change in runoff and sediment load is higher than that of the whole basin, and the influence on the decrease of sediment load discharge is more significant before 1998. There are certain stage differences and spatial scale effects. (3) Human activities such as large-scale vegetation restoration and construction of silt dam engineering measures are the main reasons for the reduction in runoff and sediment load in the Huangfuchuan River basin and have played a greater role after 1998.

Data-Driven Environmental Risk Management and Sustainability Analytics (Second Edition)

Environmental risk management (ERM) and sustainability analytics have undergone a paradigm shift from reactive, compliance-based frameworks to advanced, predictive, and data-driven methodologies. This second edition of "Data-Driven Environmental Risk Management and Sustainability Analytics" critically explores the integration of contemporary technologies such as machine learning (ML), artificial intelligence (AI), blockchain, Internet of Things (IoT), quantum computing, and cloud computing within ERM frameworks. The manuscript reviews the evolution of ERM strategies, emphasizing the transformative role of predictive analytics, real-time monitoring, and multi-stakeholder collaboration in addressing global environmental challenges including climate change, biodiversity loss, and resource depletion. Through empirical case studies on coastal flooding and urban water resource management, the research demonstrates the practical effectiveness of advanced analytics in mitigating environmental risks and enhancing resilience. Furthermore, the manuscript highlights key policy frameworks and governance models promoting transparency, data security, and sustainable development practices globally. The study concludes with actionable recommendations and identifies research gaps concerning data integration, quantum computing applications, and the ethical dimensions of emerging technologies in sustainability analytics. This edition aims to provide policymakers, researchers, practitioners, and industry professionals with actionable insights into designing and implementing robust, data-driven environmental risk management strategies aligned with sustainable development objectives.

A Study on the Evolution and Interrelation of China’s Reservoir Resettlement Policies over 75 Years

As a pivotal force in the development of hydropower and water conservancy, the evolution of China’s reservoir resettlement policies has garnered significant attention. Over the past seven decades, the nation has made remarkable strides in implementing resettlement initiatives, effectively contributing to poverty alleviation and water resource management. However, emerging challenges, including diminishing opportunities for new reservoir construction, the expiration of post-relocation support policies, and the current emphasis on high-quality development, reveal critical gaps in the existing research. Specifically, macro-level analyses of policy evolution remain scarce, particularly concerning the interrelation between two cornerstone components: land acquisition compensation policies and post-relocation support policies. To address this gap, this paper adopts a holistic historical perspective to analyze the evolution of China’s reservoir resettlement policies across four distinct stages, focusing on the development of two key policies and their interrelations. The findings reveal that each stage of China’s reservoir resettlement policies is characterized by unique thematic priorities, with their interrelations gradually evolving toward greater synergy. Nevertheless, challenges persist, including insufficient per capita farmland allocation and industrial decline in resettlement areas. Accordingly, this paper proposes optimization strategies that encompass policy innovation, multi-stakeholder participation, digital management, and the enhancement of resettlement agencies. China’s experience in fostering policy synergy offers critical insights into institutional evolution while providing valuable references for other countries seeking to refine their reservoir resettlement frameworks.

Spatio-temporal patterns of evapotranspiration in the temperate Eastern German lowlands and its response to climate and land use change

Evapotranspiration (ET) is a key factor in the water and energy cycle, playing a critical role, especially in agricultural regions. The eastern German lowlands, with their continental climate, are one of the driest regions in Germany, yet agriculture is the dominant land use. This study investigates the spatio-temporal variability of ET and its response to climate and land use/land cover change (LUCC) using MODIS remote sensing products and in-situ measurements over the period 2000-2020. The results show a slight increase in the mean annual ET, with local increases of up to 7.2% in the southern and southeastern parts of Brandenburg. LUCC revealed a 22.2% decrease in grassland to cropland conversion, leading to a decline in ET of 21%, while conversion from grassland to cropland increased by 14.8%, resulting in a 10% increase in ET. The strongest relationship with ET was found for vapor pressure deficit (VPD), temperature (Temp), and relative humidity (RH), which contributed 25.2%, 30.9%, and 23.2%, respectively, to its total variability. In addition to atmospheric factors soil moisture (SM) also contributed 17.7% to ET, but only in grassland. LUCC played a minor role as 22% of the study area was under conversion. Consequently, climate change, represented by the temporal change of the climatic factors, was identified as the dominant driver of ET in the study area, accounting for 97% of its variability. Accordingly, these findings underscore the importance of Temp, RH, and SM in agricultural and water resource management.

The Key Role of Mixed‐Phase and Ice‐Phase Processes on the Seasonal Shifts in Drop Size Distribution on the Southeastern Tibetan Plateau

AbstractThis study explores the microphysical characteristics of precipitation on the southeastern Tibetan Plateau (SETP), with a focus on the seasonal variations in drop size distribution (DSD) during distinct monsoon phases. By analyzing long‐term observations from a high‐altitude region, we uncover a significant differentiation in raindrop concentrations: small raindrops peak during the monsoon phase due to enhanced warm‐cloud processes, minimal evaporation rates, and vigorous moisture deposition from sustained humid airflow. Conversely, the premonsoon phase is marked by a higher concentration of large raindrops, primarily driven by strong aggregation and vigorous convective activity. Our results reveal that mixed‐phase processes dominate the precipitation microphysics in this region with substantial implications for understanding the underlying mechanisms that govern precipitation variability in high‐altitude environments. The interplay between atmospheric dynamics and microphysical processes is crucial in shaping the DSD, highlighting the importance of considering both factors in precipitation modeling. This research not only provides novel insights into the complex interactions between microphysical processes and meteorological conditions but also emphasizes the necessity for enhanced precipitation forecasting models, particularly in regions characterized by complex terrain. These findings offer a foundation for future studies aimed at addressing the impacts of climate change on precipitation patterns and water resource management in the Tibetan Plateau and similar high‐altitude regions.

Recent Advances in Remote Sensing and Artificial Intelligence for River Water Quality Forecasting: A Review

Rapid population growth and climate change have created challenges for managing water quality. Protecting water sources and devising practical solutions are essential for restoring impaired inland rivers. Traditional water quality monitoring and forecasting methods rely on labor-intensive sampling and analysis, which are often costly. In recent years, real-time monitoring, remote sensing, and machine learning have significantly improved the accuracy of water quality forecasting. This paper categorizes machine learning approaches into traditional, deep learning, and hybrid models, evaluating their performance in forecasting water quality parameters. In recent years, the long short-term memory (LSTMs), gated recurrent units (GRUs) and LSTM- and GRU-based hybrid models have been widely used in forecasting inland river water quality. Combining remote sensing with a real-time water quality monitoring network has enhanced data collection efficiency by capturing spatial variability within the river network, complementing the high temporal resolution of in situ measurements, and improving the overall robustness of predictive deep learning models. Additionally, leveraging weather prediction models can further enhance the accuracy of water quality forecasting and better decision-making for water resource management.

Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing

Reliable forecasts of large-scale chlorophyll-a (Chl-a) levels one week ahead in the Murray–Darling Basin are essential for water resources management, as increasing Chl-a levels in water bodies indicate possible harmful algal blooms, a serious threat for freshwater security. A lack of high-resolution data in space and time is a major constraint for delivering early warnings. To address data scarcity, we developed a forecasting model integrating remote sensing data and time-series modelling. Using in situ Chl-a measurements from Murray–Darling Basin water bodies, we locally recalibrated a two-band ratio algorithm, namely the Normalized Difference Chlorophyll Index (NDCI), from Sentinel-2 data to derive Chl-a levels. The recalibrated model significantly improved the accuracy of high Chl-a estimates in our dataset after mitigating data heteroscedasticity. Building on these improved satellite-derived Chl-a estimates, we developed a time-series model for forecasting weekly Chl-a levels including quantification of forecast uncertainty through prediction intervals. The developed model, validated at eight sites for 2021–2022 data, performed well at shorter lead times, showing R2 = 0.41 and RMSE = 8.1 μg/L for overall performance at a one-week lead time. The prediction intervals generally aligned well with nominal levels, demonstrating their reliability. This study provides a valuable tool for the water managers/decision-makers to issue early warnings of algal blooms in the Murray–Darling Basin.

Turbidity and suspended sediment relationship based on sediment composition and particle size distribution

High turbidity in rivers, intensified by extreme rainfall associated with climate change, poses a great challenge to water resource management globally. To manage and control turbidity events, prediction models have been developed utilizing suspended sediment (SS) data. However, traditional methods for measuring SS data, such as water sampling and laboratory analysis, are time-consuming and impractical for real-time applications. Although the turbidity–SS relationship is widely used, its accuracy depends on sediment particle size distribution. To address this limitation, we developed turbidity–SS equations adaptive to various SS fractions, using data from controlled circulating flume experiments designed to reflect sediment characteristics of a natural river. The results showed significant improvements in the linear turbidity–SS relationship, with R2 values ranging from 0.60 to 0.99, depending on sediment fractions. These equations were applied to field data from the area upstream of Soyanggang Dam in South Korea, yielding error rates of 1–18%. This study highlights the importance of incorporating sediment fraction variability into turbidity–SS models, which significantly improves their accuracy and reliability. The proposed approach offers a practical and scalable solution for real-time and large-scale SS monitoring, contributing to improved water quality management in various riverine conditions.

Spillover Effects of Environmental Regulations on Water Use Efficiency: A Case Study of the Yellow River Basin in China

Water scarcity and low utilization efficiency are key constraints to the sustainable development of the Yellow River Basin, and environmental regulation can address these challenges by improving water use efficiency. Furthermore, given its cross-regional heterogeneity and water resource interdependencies, it is essential to investigate the spatial spillover effects of environmental regulation on water use efficiency to support integrated water resource management in the basin. This paper analyzes the spatiotemporal evolution characteristics of water resource utilization efficiency based on panel data from 78 prefecture-level cities in the Yellow River Basin from 2009 to 2021. Using the Spatial Durbin Model, this study examines the direct impact of environmental regulations on water resource utilization efficiency, along with its spatial spillover effects and heterogeneity. The results reveal that water use efficiency in the Yellow River Basin exhibits a fluctuating upward trend, with an average annual growth rate of 5.15%. Spatially, the efficiency in the middle and lower reaches is significantly higher than in the upper reaches. Additionally, the center of gravity of efficiency has shifted southwest, with a migration distance of 138.978[Formula: see text]km. Water use efficiency follows a high-to-low pattern from east to west, with the gap widening each year. In the north–south direction, the pattern has shifted from higher efficiency in the north and lower in the south to the opposite, with higher efficiency now in the south and lower in the north. The Spatial Durbin Model analysis shows that increasing environmental regulation levels can improve local water use efficiency as well as enhance efficiency in neighboring areas. The impact coefficients are 0.041 and 0.121, respectively, both of which are statistically significant at the 1% level. The analysis of regional heterogeneity indicates that environmental regulations in the midstream areas significantly improve local water resource utilization efficiency, with an impact coefficient of 0.139. These regulations also generate notable positive spatial spillover effects in the lower midstream and downstream areas, with impact coefficients of 0.239 and 0.106, respectively. The analysis of city-scale heterogeneity shows that in small cities, enhancing environmental regulation levels can effectively improve local efficiency with an impact coefficient of 0.066. In contrast, in large cities, environmental regulation policies are more likely to boost efficiency in surrounding areas, with an impact coefficient of 0.087.

Eco-profile approach in watershed and river basin management for addressing water rights and conflicts at micro scale

Eco-profile studies help in continuous assessment and monitoring of river basin functions; the planners, and decision-makers deploy study findings to address problems associated within river basins. In this paper, we reviewed the study covering the eco-profile based watershed and river basin management practices and the benefits of eco-profile in understanding and addressing water-related issues at the micro-level. The study uses a systematic literature review approach called the PRISMA framework (Preferred Reporting Items for Systematic reviews and Meta-Analyses) to collect and process the literature. Our study found that eco-profile based implementations improves the river basin functions and addresses the micro-level issues related to water rights and conflicts that are usually not addressed during water resources management. Through eco-profile study, the key ecological indicators at micro and macro scales can be identified that help to predict the continuous changes of biotic and abiotic conditions within the watershed and river basin regions. The present study discusses the advantage of eco-profile in the watershed, measures to comprehend river basin function, community role, and approach to solve the water rights and conflicts at the micro scale. The study also recommends the inclusion of eco-profile framework and eco-profile policy in integrated water resources management programs specific to river basin/watershed management activities.

An Application of Natural Cubic Spline for the Prediction and Forecasting of Rainfall in Uttar Pradesh, India

Weather and climate have a significant impact on our daily lives. The amount of data available is vast and in order to draw meaningful conclusions from the data one needs to develop appropriate models that can fit the data properly. Several models are available for analyzing and predicting the time series data. The spline-based technique produces an acceptable pattern of time series data that can be used for prediction and modelling. The data for the study is obtained from the Government of India website for the meteorological subdivision of East and West Uttar Pradesh for the past 120 years from 1901 to 2020 on a monthly basis. This study discusses the application of Autoregressive Integrated Moving Average (ARIMA) model and Natural Cubic Spline for the development of a more appropriate model for predicting and forecasting rainfall in Uttar Pradesh. The study focuses on two different approaches of applying the cubic spline. The fitted models are compared by plotting the curves and various matrices like Root Mean Square Error (RMSE), R square, adjusted R square, AIC and BIC. The analysis was performed using R software. From the comparison, the model fitted through Natural Cubic Spline is obtained as more suitable for the prediction and forecasting of Rainfall in Uttar Pradesh. With a better model for forecasting of rainfall, the study will contribute to better decision-making for policymakers, thus eventually contributing to better crop management in the state. The study will also contribute to better water resource management and enhance disaster preparedness. The study can also serve as a foundation for future research on rainfall prediction in UP and other regions and encourage further exploration of different statistical techniques and data sources.