Field Of Water Resources Management Research Articles

A Study on the Driving Mechanisms of Water Supply and Demand Perception in the Digital Age

This study investigates the drivers of water supply and demand perception in the digital age, focusing on the impact of various factors including climate change awareness, socio-economic impact perception, environmental policy awareness, and water resource protection consciousness. Through questionnaire surveys conducted in Chengdu City and data analysis using the Partial Least Squares Structural Equation Modeling (PLS-SEM), this study reveals how the aforementioned factors collectively influence the perception of water supply and demand. The findings indicate that a profound understanding of climate change and socio-economic activities, along with a high awareness of environmental policies, can significantly enhance the public’s consciousness towards water resource protection, thereby affecting the accurate perception of water supply and demand conditions. This study not only offers a new perspective in the field of water resource management but also provides a theoretical basis and data support for formulating effective water resource protection strategies, emphasizing the critical role of intelligent water resource management in addressing climate change and promoting sustainable development. The limitations of the study include the restricted sample size and scope, which may affect the generalizability of the results. Future research is recommended to consider more variables, such as regional cultural and economic differences, and to conduct validations across a broader area and population.

Advanced evapotranspiration forecasting in Central Italy: Stacked MLP-RF algorithm and correlated Nystrom views with feature selection strategies

Evapotranspiration is a key parameter in hydrology, particularly in the field of water resources management. Reference evapotranspiration (ETo) stands as a crucial metric, embodying the influence of climate on water loss from both soil and plant surfaces. The accurate forecasting of future ETo values is paramount for informed decision-making in agricultural practices and water supply planning. The anticipation of evapotranspiration variations supports optimized irrigation, drought assessment, and efficient water allocation. Employing innovative algorithms, specifically the Multilayer Perceptron-Random Forest (MLP-RF) Stacked Model and the Correlated Nystrom Views (XNV), this study focuses on predicting ETo up to 60 days ahead in Agro Pontino, an area in Central Italy known for its flourishing agricultural production in the Mediterranean Europe. A Radial Basis Function (RBF) Neural Network serves as a benchmark. Taking into account firstly all available weather-climatic variables and subsequently adopting two different strategies for reducing the variables, based respectively on Principal Component Analysis and feature selection using Particle Swarm Optimization, three distinct sets of input variables were considered. The models based on the complete set of exogenous climatic variables demonstrated superior accuracy. However, even models relying on only mean temperature, maximum relative humidity, and shortwave solar radiation as inputs produced good results, also for the 60-day forecasting horizon, with Kling-Gupta Efficiency (KGE) and Mean Absolute Percentage Error (MAPE) equal to 0.98 and 8.356 %, respectively, in the case of MLP-RF Stacked Model. The latter consistently outperformed XNV and RBF across various combinations of input variables and forecasting horizons. Notably, the reduction in accuracy with extended forecasting horizons was mild, suggesting the potential for accurate results over significantly more extended horizons. These forecasting models facilitate precise irrigation scheduling, minimizing water wastage and conserving resources. This targeted approach enhances crop yields, quality, and environmental sustainability, rendering agriculture economically viable and adaptable to climate variability.

Geospatial Technology for Sustainable Agricultural Water Management in India—A Systematic Review

Effective management of water resources is crucial for sustainable development in any region. When considering computer-aided analysis for resource management, geospatial technology, i.e., the use of remote sensing (RS) combined with Geographic Information Systems (GIS) proves to be highly valuable. Geospatial technology is more cost-effective and requires less labor compared to ground-based surveys, making it highly suitable for a wide range of agricultural applications. Effectively utilizing the timely, accurate, and objective data provided by RS technologies presents a crucial challenge in the field of water resource management. Satellite-based RS measurements offer consistent information on agricultural and hydrological conditions across extensive land areas. In this study, we carried out a detailed analysis focused on addressing agricultural water management issues in India through the application of RS and GIS technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we systematically reviewed published research articles, providing a comprehensive and detailed analysis. This study aims to explore the use of RS and GIS technologies in crucial agricultural water management practices with the goal of enhancing their effectiveness and efficiency. This study primarily examines the current use of geospatial technology in Indian agricultural water management and sustainability. We revealed that considerable research has primarily used multispectral Landsat series data. Cutting-edge technologies like Sentinel, Unmanned Aerial Vehicles (UAVs), and hyperspectral technology have not been fully investigated for the assessment and monitoring of water resources. Integrating RS and GIS allows for consistent agricultural monitoring, offering valuable recommendations for effective management.

Optimization Examples for Water Allocation, Energy, Carbon Emissions, and Costs

The field of Water Resources Management (WRM) is becoming increasingly interdisciplinary, realizing its direct connections with energy, food, and social and economic sciences, among others. Computationally, this leads to more complex models, wherein the achievement of multiple goals is sought. Optimization processes have found various applications in such complex WRM problems. This entry considers the main factors involved in modern WRM, and puts them in a single optimization problem, including water allocation from different sources to different uses and non-renewable and renewable energy supplies, with their associated carbon emissions and costs. The entry explores the problem mathematically by presenting different optimization approaches, such as linear, fuzzy, dynamic, goal, and non-linear programming models. Furthermore, codes for each model are provided in Python, an open-source language. This entry has an educational character, and the examples presented are easily reproducible, so this is expected to be a useful resource for students, modelers, researchers, and water managers.

Feature Extraction from Satellite-Derived Hydroclimate Data: Assessing Impacts on Various Neural Networks for Multi-Step Ahead Streamflow Prediction

Enhancing the generalization capability of time-series models for streamflow prediction using dimensionality reduction (DR) techniques remains a major challenge in water resources management (WRM). In this study, we investigated eight DR techniques and their effectiveness in mitigating the curse of dimensionality, which hinders the performance of machine learning (ML) algorithms in the field of WRM. Our study delves into the most non-linear unsupervised representative DR techniques, including principal component analysis (PCA), kernel PCA (KPCA), multi-dimensional scaling (MDS), isometric mapping (ISOMAP), locally linear embedding (LLE), t-distributed stochastic neighbor embedding (t-SNE), Laplacian eigenmaps (LE), and autoencoder (AE), examining their effectiveness in multi-step ahead (MSA) streamflow prediction. In this study, we conducted a conceptual comparison of these techniques. Subsequently, we focused on their performance in four different case studies in the USA. Moreover, we assessed the quality of the transformed feature spaces in terms of the MSA streamflow prediction improvement. Through our investigation, we gained valuable insights into the performance of different DR techniques within linear/dense/convolutional neural network (CNN)/long short-term memory neural network (LSTM) and autoregressive LSTM (AR-LSTM) architectures. This study contributes to a deeper understanding of suitable feature extraction techniques for enhancing the capabilities of the LSTM model in tackling high-dimensional datasets in the realm of WRM.

Impact of Climate Change and Human Activities to Runoff in the Du River Basin of the Qinling-Daba Mountains, China

The hydrological response to climate change and human activities plays a pivotal role in the field of water resource management within a given basin. This study was conducted with a primary focus on the Du River basin, aiming to assess and quantify the impacts of climate change and human activities on changes in runoff patterns. The study utilized the Budyko framework in conjunction with the Soil and Water Assessment Tool (SWAT) model to project future changes in runoff while also employing statistical tests like the Pettitt and Mann–Kendall tests to identify abrupt shifts and monotonic trends in the data. The results shows that (1) The analysis of runoff data spanning from 1960 to 2016 revealed a significant declining trend (p < 0.05) in annual runoff, with an abrupt change point identified in 1994. The multi-year average runoff depth was determined to be 495 mm. (2) According to the Budyko framework, human activities were found to be the dominant driver behind runoff changes, contributing significantly at 74.42%, with precipitation changes contributing 24.81%. (3) The results obtained through the SWAT model simulation indicate that human activities accounted for 61.76% of the observed runoff changes, whereas climate change played a significant but slightly smaller role, contributing 38.24% to these changes. (4) With constant climate conditions considered, the study predicted that runoff will continue to decrease from 2017 to 2030 due to the influence of ongoing and future human activities. However, this downward trend was found to be statistically insignificant (p > 0.1). These findings provide valuable insights into the quantitative contributions of climate change and human activities to runoff changes in the Du River basin. This information is crucial for decision-makers and water resource managers, as it equips them with the necessary knowledge to develop effective and sustainable strategies for water resource management within this basin.

Bibliometric Analysis of Research Trends in Water Management Aimed at Increasing the Sustainability of the Socio-Economic Development of a Region

The growing negative anthropogenic impact on the environment causes scientific interest in the problems of water management. The increasing number of publications in this scientific field requires their intellectual systematization. The purpose of this study is to conduct a bibliometric review of scientific publications related to water management issues in the context of solving the problems of increasing the sustainability of the socio-economic development of a region for better understanding of current research trends. To achieve this goal, bibliometric analysis using the VOSviewer software product (Manual for VOSviewer version 1.6.17) was used. The international database Scopus was taken as the source of information. This study examined 10,208 articles on water management issues from 2012 to 2022. The basic criterion for including a publication in the selection was that the topic of the work belongs to the subject areas of economics, econometrics and finance and business, management and accounting. As a result of the analysis, it was determined that the problems of water resources management have not lost their popularity in the global research community and the research methodology is evolving towards the concept of “water–energy–food”. The centers of knowledge forming the vector of scientific research are the USA and the Netherlands; however, in recent years, the research of Chinese scientists has become increasingly important. It is concluded that the potential for the development of research in the field of water resources management in the context of solving the problems of the sustainable development of regions is associated with the search for opportunities for revealing the synergy of intersectoral interactions while taking into account their sectoral and regional specifics.

Systems engineering and water resources management: A closer relationship is needed

AbstractSystems engineering can be applied in a broad spectrum of sectors, but only its analysis tool has been applied in the field of water resources management. Because systems engineering has a separate community of practice from water resources, there is little crosstalk between the two fields. As a result, the systems engineering functions that support planning, design, production, procurement, and customer support are not being applied to water systems. Meanwhile, water systems exhibit complexities that have generated a separate field named Integrated Water Resources Management that continues to confuse its followers after several decades. Its methods are applied to a broad spectrum of water issues that affect multiple stakeholders with conflicting interests and involve distinct subsystems, such as water supply or hydropower, as well as combinations of them. Use of systems analysis for such water issues began six decades ago, but it is still a work in progress. Evolving methods of systems engineering offer new possibilities to address problems of water resources management, but they must extend beyond systems analysis, which belongs to multiple disciplines. Examples show possibilities to apply systems engineering methods when water issues exhibit attributes of engineered systems and do not involve social and environmental complexities that cannot be included in system boundaries. Collaboration among systems engineering and water resources management would offer a fertile test bed to advance both fields.

Investigation of the Technological Advancements and Future Prospects of Atmospheric Water Generator Systems

Atmospheric Water Generator (AWG) systems have emerged as a promising technology for producing potable water in water-scarce regions. In recent years, significant progress has been made in the development of AWG systems, with new technologies and designs being introduced to improve their efficiency and reliability. This research paper investigates the technological advancements and prospects of AWG systems by reviewing the existing literature and analyzing the latest trends in research and development. The paper discusses the various types of AWG systems, including those based on refrigeration, desiccants, and membranes, and evaluates their respective advantages and limitations. The study also highlights the key challenges facing AWG systems, such as energy consumption, maintenance, and cost-effectiveness, and examines the strategies being employed to overcome these challenges. Finally, the paper discusses a new unpatented manner for achieving sustainable development goals and promoting climate resilience. The findings of this research provide insights into the current state and future directions of AWG systems and can inform policymakers, researchers, and practitioners in the field of water resource management. 

Multivariate Multi-Step Long Short-Term Memory Neural Network for Simultaneous Stream-Water Variable Prediction

Implementing multivariate predictive analysis to ascertain stream-water (SW) parameters including dissolved oxygen, specific conductance, discharge, water level, temperature, pH, and turbidity is crucial in the field of water resource management. This is especially important during a time of rapid climate change, where weather patterns are constantly changing, making it difficult to forecast these SW variables accurately for different water-related problems. Various numerical models based on physics are utilized to forecast the variables associated with surface water (SW). These models rely on numerous hydrologic parameters and require extensive laboratory investigation and calibration to minimize uncertainty. However, with the emergence of data-driven analysis and prediction methods, deep-learning algorithms have demonstrated satisfactory performance in handling sequential data. In this study, a comprehensive Exploratory Data Analysis (EDA) and feature engineering were conducted to prepare the dataset, ensuring optimal performance of the predictive model. A neural network regression model known as Long Short-Term Memory (LSTM) was trained using several years of daily data, enabling the prediction of SW variables up to one week in advance (referred to as lead time) with satisfactory accuracy. The model’s performance was evaluated by comparing the predicted data with observed data, analyzing the error distribution, and utilizing error matrices. Improved performance was achieved by increasing the number of epochs and fine-tuning hyperparameters. By applying proper feature engineering and optimization, this model can be adapted to other locations to facilitate univariate predictive analysis and potentially support the real-time prediction of SW variables.

Analysis of Mosul and Haditha Dam Flow Data

The expansion in water projects implementations in Turkey and Syria becomes of great concern to the workers in the field of water resources management in Iraq. Such expansion with the absence of bi-lateral agreement between the three riparian countries of Tigris and Euphrates Rivers; Turkey, Syria and Iraq, is expected to lead to a substantially reduction of water inflow to the territories of Iraq. Accordingly, this study consists of two parts: first part is aiming to study the changes of the water inflow to the territory of Iraq, at Turkey and Syria borders, from 1953 to 2009; the results indicated that the annual mean inflow in Tigris River was decreased from 677 m3/sec to 526 m3/sec, after operating Turkey reservoirs, while in the Euphrates River the annual mean inflow was decreased from 1006 m3/sec to 627m3/sec after operating Syria and Turkey reservoirs. Second part is forecasting the monthly inflow and the water demand under the reduced inflow data. The results show that the future inflow of the Tigris River is expected to decrease to 57%, and reaches 301m3/sec. The Mosul reservoir will be able to supply 64% only of the water requirements to the downstream. The share of Iraq from the inflow of the Euphrates River is expected to be 58%, therefore the future inflow will reach 290 m3/sec. The Haditha reservoir will be able to supply 46% only of the water requirements to the downstream, due to reduced inflow at Iraqi border in the future.

The Determination of Development Priorities Road Infrastructure at Dinas Pekerjaaan Umum dan Penataan Ruang Kabupaten Balangan Using AHP and Bayes Methods

Dinas Pekerjaan Umum dan Penataan Ruang (PUPR) Kabupaten Balangan is a Regional Government Organization Unit (SOPD) that has the task of assisting the Bupati in administering government affairs in public works, infrastructure, and housing development. In addition, it also formulates, determines, and implements policies in the field of water resources management, road management, housing provision and development of residential areas, infrastructure financing, structuring of buildings, drinking water supply systems, wastewater management systems, and environmental drainage and waste, and construction services construction. Difficulty in determining priorities for infrastructure development on the Dinas Pekerjaan Umum dan Penataan Ruang Kabupaten Balangan, then an Infrastructure Development Priority Analysis system was created to support the construction of roads and bridges on Kabupaten Balangan. To determine the priority development weights using the AHP method and the order of priorities using the Bayes method because it is one of the techniques used to analyze the best decision-making from several alternatives to produce optimal gains. The results of the completed questionnaire get an average yield of 21 (twenty-one) or 87.5% (eighty-seven point five percent). If the average is included in the rating scale, get a VS rating or Very Satisfied. Decision support systems using AHP and Bayes methods can determine the priority of road infrastructure development at Dinas Pekerjaan Umum dan Penataan Ruang Kabupaten Balangan.

Baseline occurrence of organochlorine and organophosphate pesticides in water, sediment, and fish in the Miankaleh wetland, Iran

Micropollutants such as pesticides and the prediction of water quality in aquatic environments have been known as a serious risk to the environment and human health. The pollution level of six pesticides—three organochlorines (OCPs: aldrin, dieldrin, and endrin) and three organophosphates (OPPs: diazinon, malathion, and azinphosmethyl)— in water, sediment, and fish samples was examined in the Miankaleh wetland, Iran. Water quality, including dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), salinity, electrical conductivity (EC), turbidity, total dissolved solids (TDS), pH, temperature, and physicochemical properties of sediments, was analyzed. Low concentrations of OCPs (0.70 ± 0.01 μg/L) and OPPs (1.31 ± 0.1 μg/L) were observed in water. In contrast, OCPs and OPPs were not detected in sediment and fish samples in the Miankaleh wetland. Low concentrations of OCPs and OPPs in water and no pesticide concentrations in sediment and fish samples indicate low contamination of the aquatic environment in Miankaleh. The results of this study could be used as an effective reference for policy makers in the field of water resource management.

Ant-Inspired Metaheuristic Algorithms for Combinatorial Optimization Problems in Water Resources Management

Ant-inspired metaheuristic algorithms known as ant colony optimization (ACO) offer an approach that has the ability to solve complex problems in both discrete and continuous domains. ACOs have gained significant attention in the field of water resources management, since many problems in this domain are non-linear, complex, challenging and also demand reliable solutions. The aim of this study is to critically review the applications of ACO algorithms specifically in the field of hydrology and hydrogeology, which include areas such as reservoir operations, water distribution systems, coastal aquifer management, long-term groundwater monitoring, hydraulic parameter estimation, and urban drainage and storm network design. Research articles, peer-reviewed journal papers and conference papers on ACO were critically analyzed to identify the arguments and research findings to delineate the scope for future research and to identify the drawbacks of ACO. Implementation of ACO variants is also discussed, as hybrid and modified ACO techniques prove to be more efficient over traditional ACO algorithms. These algorithms facilitate formulation of near-optimal solutions, and they also help improve cost efficiency. Although many studies are attempting to overcome the difficulties faced in the application of ACO, some parts of the mathematical analysis remain unsolved. It is also observed that despite its popularity, studies have not been successful in incorporating the uncertainty in ACOs and the problems of dimensionality, convergence and stability are yet to be resolved. Nevertheless, ACO is a potential area for further research as the studies on the applications of these techniques are few.

A Review on Remote Sensing as a Tool for Irrigation Monitoring and Management

Remote sensing has played a vital role in advancement of agriculture and is effective technical method for agriculture crop management. It is a technology which acquisite information regarding objects on earth surface as well as atmosphere from a distance without being in contact with the object. Researchers have proved its high potential with accuracy in the field of agriculture. After various experiments, the qualitative and quantitative assessment of soil, crop and atmosphere demonstrated the better understanding between the crop and its management practices. The collected spatial and temporal data via various passive and active sensors has been utilized not only for morphological study but also for monitoring the vegetation moisture content. The paper reviews about the potential studies carried out to investigate the water content in plant to make use in irrigation management. Diverse spectral reflectance indices have been mentioned from which special emphasis on NDWI has been given. It is an index which is used in remote sensing to assess the crop water status and can be utilized in efficient operation of irrigation to improve water use efficiency (WUE) in agriculture. In order to make irrigation practices more efficient by making the lab restricted irrigation scheduling methods like IW:CPE method applicable in regular practice by using remote sensing. This paper firstly identifies areas where researches and techniques have real-world application. Next, it identifies actual issues that remote sensing could address and solve with further research and its related development. All opportunities for managing agricultural water resources effectively to be explored and made successful through precision agriculture. Using the fast, impartial and reliable information offered by remote sensing is a significant difficulty in the field of water resource management.

Current State of Russian Research in the Field of Water Resources Management

Current State of Russian Research in the Field of Water Resources Management

MULTI-CRITERIA DECISION MAKING FOR WATER RESOURCE MANAGEMENT: A CASE STUDY OF ANCHAR LAKE, J&K, INDIA

Multi criteria decision making (MCDM) is a framework for ranking or scoring the overall performance of decision options against multiple objectives. The approach has widespread and growing application in the field of water resource management. The Union Territory of Jammu & Kashmir is enriched with number of lakes of different origin. Over a period of time most of these lakes are being over exploited resulting in changing their trophic status. Anchar Lake, one of the lakes needs dire attention for its restoration. In order to formulate the final decision, a minimum of 100 respondents were surveyed including local households, shikara walas, lotus stem cultivators and harvesters residing in the vicinity of this lake. Different criteria’s were put forward and were ranked accordingly as per the responses recorded from the respondents.

Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks

Energy limitation is a major issue in wireless sensor networks where a high volume of redundant data is collected periodically and transmitted through the network. Therefore, efficient energy consumption is the key solution to maximize the network lifetime. This paper proposes an adaptive sampling approach based on spatio-temporal correlation of collected data and on nodes residual energy. This approach aims to optimize sampling rates of sensor nodes while ensuring a high quality of the collected data. In addition, a data reconstruction method based on linear regression is adopted in the sink to reconstruct the missing samples due to the sampling rate reduction and adaptation compared to the case of a constant maximal sampling rate. We compared our approach with recently proposed adaptive sampling benchmark methods in different scenarios of data temporal correlation. Simulation results demonstrate the effectiveness of our proposed method in optimizing energy consumption by reducing the sampling rate while maintaining data quality. Our contribution can be applied to several fields, particularly, the field of water resources management.

Rainfall-flow Modeling Using a Global Conceptual Model: Case of the Beni Bahdel Watershed (Northwest of Algeria)

Rainfall-flow modeling remains necessary, even essential, to understand the dynamics of a watershed and to solve problems related to the disruption of hydrological regimes. It has been proven effective by providing solutions to many water-related problems, such as sizing and management of structures, and flood forecasting. Global hydrological models can simulate the transformation of rainfall data into flows on natural basins for many practical applications in the field of water resource management. Our study aims to evaluate the reliability of one of these models, that of Rural Engineering 'GR' at three time steps: annual (GR1A), monthly (GR2M), and daily (GR4J), which will be applied to the Beni Bahdel watershed with an area of 1040 km², one of the sub-basins of Northwestern Algeria. The input parameters are precipitation and potential evapotranspiration (PET), and the output parameters are flows. The results obtained, both in calibration and validations, are encouraging, where the evaluation criteria taken into consideration, namely the Nash criterion and the correlation coefficient, exceeded 70% and 0.80 respectively. The study could be a decision-making tool for the simulation of flows, and be very useful for future hydraulic developments in the study area.

Extreme precipitations and their influence on the River flood Hazards: A case study of the Sana River Basin in Bosnia and Herzegovina

The subject of the research paper is the use of remote sensing in monitoring and analyzing the impact of climate change on the occurrence of extreme precipitation, and the cause-and-effect occurrence of floods in the area of the Sana River Basin in Bosnia and Herzegovina. The goal is to process the "product" of remote sensing to identify the time intervals of occurrence of extreme precipitation, to assess their impact on water levels, and to map potential floods in space. Spatial identification of zones that are at risk of flooding is an integral part of the aforementioned goal. Precipitation monitoring was performed by processing Climate Hazards Group InfraRed Precipitation with Station Data through the Google Earth Engine platform. The observed 30-year period (1992-2022) was compared with the average precipitation for 2017, 2018 and 2019. The impact of extreme precipitation on the water level of the Sana River was analyzed. Flooding periods have been identified: February and December 2017, March 2018 and May 2019. Mapping of flooded areas was carried out by pre-processing and post-processing of Sentinel-1 radar satellite images. The total flooded area is: 710.38 ha (February 2017), 496.79 ha (December 2017), 417.86 ha (March 2018) and 422.42 ha (May 2019). Based on the identified flooded areas, a flood risk map was created on the main course of the Sana River. The research contributes to a better understanding of the changes that occur in the area under the influence of climate change, and the data presented are important for numerous practical issues in the field of water resource management and flood protection.