Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Browser

The Next Generation on Water Resource Management Using Computer Aid Models

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 23139

Share This Special Issue

Special Issue Editor

Dr. Zaher Mundher Yaseen

E-Mail
Guest Editor

Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Interests: hydrology; water resources engineering; environmental engineering; civil engineering; machine learning

Special Issue Information

Dear Colleagues,

Water resources operation and management have received a great deal of attention over the past two decades. This is due to several influential facts, such as climate change, water scarcity, population massive boosted, and several other human misleading activities. Hence, we have recently entered an era in which water sustainability is to be the main focus. Proper water management requires insightful understanding of the whole hydrological cycle.

Hydrological processes are highly complex and essential to be understood in proper manners. Providing accurate and reliable methodologies for such highly important environmental/climate engineering problems can contribute remarkably to the proper water management.

Of recent developments, the applications of machine learning and their advanced versions have been noticed in the domain of water science. Their merit promotes valid and reasonable solutions for planning and management of water resources, assessment of hydro-climatic hazard risk, evaluation of agricultural potential, understanding ecological distribution, etc. It is worthwhile to highlight that the main challenges of the hydrological processes are always associated with non-linear, non-stationary, and stochastic processes, which require highly complex engineering problems to be resolved. Here, where the applications of machine learning take place, the machine learning models have been evidenced to demonstrate excellent advanced computer aid models in solving such a kind of issues related to water management. Hence, the motivation of this Special Issue to propose and investigate the feasibility of advanced technologies of machine learning for decision support in water resources management, hydrological hazard risk reduction, and environmental management.

Dr. Zaher Mundher Yaseen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

climate change
watershed sustainability
uncertainty analysis
environmental engineering
federated learning
advanced machine learning
deep learning
water management
hydrological process
decision-making

Published Papers (13 papers)

Download All Papers

Research

18 pages, 15458 KiB

Open AccessArticle

Integration of HEC-RAS and HEC-HMS with GIS in Flood Modeling and Flood Hazard Mapping

by İsmail Bilal Peker, Sezar Gülbaz, Vahdettin Demir, Osman Orhan and Neslihan Beden

Sustainability 2024, 16(3), 1226; https://0-doi-org.brum.beds.ac.uk/10.3390/su16031226 - 1 Feb 2024

Cited by 4 | Viewed by 2170

Abstract

Floods are among the most devastating disasters in terms of socio-economics and casualties. However, these natural disasters can be managed and their effects can be minimized by flood modeling performed before the occurrence of a flood. In this study, flood modeling was developed for the Göksu River Basin, Mersin, Türkiye. Flood hazard and risk maps were prepared by using GIS, HEC-RAS, and HEC-HMS. In hydraulic modeling, Manning’s n values were obtained from 2018 CORINE data, return period flow rates (Q25, Q50, Q100, Q500) were obtained from HEC-HMS, and the application was carried out on a 5 m resolution digital surface model. In the study area, the water depths could reach up to 10 m, and water speeds were approximately 0.7 m/s. Considering these values and the fact that the study area is an urban area, hazard maps were obtained according to the UK Department for Environment, Food and Rural Affairs (DEFRA) method. The results indicated that possible flood flow rates from Q25 to Q500, from 1191.7 m³/s to 1888.3 m³/s, were detected in the study area with HEC-HMS. Flooding also occurred under conditions of the Q25 flow rate (from 4288 km² to 5767 km²), and the impacted areas were classified as extremely risky by the DEFRA method. Full article

(This article belongs to the Special Issue The Next Generation on Water Resource Management Using Computer Aid Models)

► Show Figures

Figure 1

32 pages, 11686 KiB

Open AccessArticle

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

by Fatemeh Ghobadi, Amir Saman Tayerani Charmchi and Doosun Kang

Sustainability 2023, 15(22), 15761; https://0-doi-org.brum.beds.ac.uk/10.3390/su152215761 - 9 Nov 2023

Cited by 2 | Viewed by 1006

Abstract

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. Full article

(This article belongs to the Special Issue The Next Generation on Water Resource Management Using Computer Aid Models)

► Show Figures

Figure 1

21 pages, 5319 KiB

Open AccessArticle

Drinking Water Resources Suitability Assessment Based on Pollution Index of Groundwater Using Improved Explainable Artificial Intelligence

by Sani I. Abba, Mohamed A. Yassin, Auwalu Saleh Mubarak, Syed Muzzamil Hussain Shah, Jamilu Usman, Atheer Y. Oudah, Sujay Raghavendra Naganna and Isam H. Aljundi

Sustainability 2023, 15(21), 15655; https://0-doi-org.brum.beds.ac.uk/10.3390/su152115655 - 6 Nov 2023

Cited by 3 | Viewed by 1185

Abstract

The global significance of fluoride and nitrate contamination in coastal areas cannot be overstated, as these contaminants pose critical environmental and public health challenges across the world. Water quality is an essential component in sustaining environmental health. This integrated study aimed to assess indexical and spatial water quality, potential contamination sources, and health risks associated with groundwater resources in Al-Hassa, Saudi Arabia. Groundwater samples were tested using standard methods. The physiochemical results indicated overall groundwater pollution. This study addresses the critical issue of drinking water resource suitability assessment by introducing an innovative approach based on the pollution index of groundwater (PIG). Focusing on the eastern region of Saudi Arabia, where water resource management is of paramount importance, we employed advanced machine learning (ML) models to forecast groundwater suitability using several combinations (C1 = EC + Na + Mg + Cl, C2 = TDS + TA + HCO₃ + K + Ca, and C3 = SO₄ + pH + NO₃ + F + Turb). Six ML models, including random forest (RF), decision trees (DT), XgBoost, CatBoost, linear regression, and support vector machines (SVM), were utilized to predict groundwater quality. These models, based on several performance criteria (MAPE, MAE, MSE, and DC), offer valuable insights into the complex relationships governing groundwater pollution with an accuracy of more than 90%. To enhance the transparency and interpretability of the ML models, we incorporated the local interpretable model-agnostic explanation method, SHapley Additive exPlanations (SHAP). SHAP allows us to interpret the prediction-making process of otherwise opaque black-box models. We believe that the integration of ML models and SHAP-based explainability offers a promising avenue for sustainable water resource management in Saudi Arabia and can serve as a model for addressing similar challenges worldwide. By bridging the gap between complex data-driven predictions and actionable insights, this study contributes to the advancement of environmental stewardship and water security in the region. Full article

(This article belongs to the Special Issue The Next Generation on Water Resource Management Using Computer Aid Models)

► Show Figures

Figure 1

21 pages, 13948 KiB

Open AccessArticle

Mapping Groundwater Prospective Zones Using Remote Sensing and Geographical Information System Techniques in Wadi Fatima, Western Saudi Arabia

by Mohamed Abdelkareem, Fathy Abdalla, Fahad Alshehri and Chaitanya B. Pande

Sustainability 2023, 15(21), 15629; https://0-doi-org.brum.beds.ac.uk/10.3390/su152115629 - 5 Nov 2023

Viewed by 1380

Abstract

Integration of remote sensing (RS) and GIS methods has allowed for the identification of potential water resource zones. Here, climatic, ecological, hydrologic, and topographic data have been integrated with microwave and multispectral data. Sentinel-2, SRTM, and TRMM data were developed to characterize the climatic, hydrologic, and topographic landscapes of Wadi Fatima, a portion of western Saudi Arabia that drains to the Red Sea. The physical characteristics of Wadi Fatima’s catchment area that are essential for mapping groundwater potential zones were derived from topographic data, rainfall zones, lineaments, and soil maps through RS data and GIS techniques. Twelve thematic factors were merged with a GIS-based knowledge-driven approach after providing a weight for every factor. Processing of recent Sentinel-2 data acquired on 4 August 2023 verified the existence of a zone of vegetation belonging to promising areas of groundwater potential zones (GPZs). The output map is categorized into six zones: excellent (10.98%), very high (21.98%), high (24.99%), moderate (21.44%), low (14.70%), and very low (5.91%). SAR CCD derived from Sentinel-1 from 2022 to 2023 showed that the parts of no unity are in high-activity areas in agricultural and anthropogenic activities. The model predictions were proven with the ROC curves with ground data, existing wells’ locations, and the water-bearing formations’ thickness inferred from geophysical data. Their performance was accepted (AUC: 0.73). The outcomes of the applied methodologies were excellent and important for exploring, planning, managing, and sustainable development of resources of water in desert areas. The present study successfully provided insights into the watershed’s hydrologic, climatic, vegetated variation, and terrain database information using radar, optical, and multi-temporal InSAR data. Furthermore, the applied multi-criteria overlay technique revealed promising areas for groundwater abstraction, which can be applied elsewhere in various environmental situations. Full article

(This article belongs to the Special Issue The Next Generation on Water Resource Management Using Computer Aid Models)

► Show Figures

Figure 1

28 pages, 6360 KiB

Open AccessArticle

Quantifying the Impacts of Climate and Land Cover Changes on the Hydrological Regime of a Complex Dam Catchment Area

by Muhammad Umer Masood, Saif Haider, Muhammad Rashid, Mohammed Suleman Aldlemy, Chaitanya B. Pande, Bojan Đurin, Raad Z. Homod, Fahad Alshehri and Ismail Elkhrachy

Sustainability 2023, 15(21), 15223; https://0-doi-org.brum.beds.ac.uk/10.3390/su152115223 - 24 Oct 2023

Cited by 9 | Viewed by 1439

Abstract

In this study, hydrological modeling at the watershed level is used to assess the impacts of climate and land use changes on the catchment area of the Khanpur Dam, which is an important water source for Rawalpindi and Islamabad. The hydrological impact of past and anticipated precipitation in the Khanpur Dam watershed was forecast by using a HEC-HMS model. After calibration, the framework was employed to analyze the effects of changes in land cover and climate on the hydrological regime. The model used information from three climatic gauge stations (Murree, Islamabad Zero Point, and Khanpur Dam) to split the Khanpur Dam catchment area into five sub-basins that encompass the entire watershed region, each with distinctive characteristics. The model was evaluated and checked for 2016–2018 and 2019–2020, and it produced an excellent match with the actual and anticipated flows. After statistical downscaling with the CMhyd model, the most effective performing GCM (MPI-ESM1-2-HR) among the four GCMs was chosen and used to forecast projections of temperature and precipitation within two shared socioeconomic pathways (SSP2 and SSP5). The predictions and anticipated changes in land cover were incorporated into the calibrated HEC-HMS model to evaluate the potential impact of climate change and land cover change at the Khanpur Dam. The starting point era (1990–2015) and the projected period (2016–2100), which encompassed the basis in the present century, were analyzed annually. The results indicated a spike in precipitation for the two SSPs, which was predicted to boost inflows all year. Until the end of the twenty-first century, SSP2 predicted a 21 percent rise in precipitation in the Khanpur Dam catchment area, while SSP5 predicted a 28% rise in precipitation. Increased flows were found to be projected in the future. It was found that the calibrated model could also be used effectively for upcoming studies on hydrological effects on inflows of the Khanpur Dam basin. Full article

(This article belongs to the Special Issue The Next Generation on Water Resource Management Using Computer Aid Models)

► Show Figures

Figure 1

22 pages, 4853 KiB

Open AccessArticle

Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating

by Hadeel E. Khairan, Salah L. Zubaidi, Syed Fawad Raza, Maysoun Hameed, Nadhir Al-Ansari and Hussein Mohammed Ridha

Sustainability 2023, 15(19), 14222; https://0-doi-org.brum.beds.ac.uk/10.3390/su151914222 - 26 Sep 2023

Viewed by 761

Abstract

Hydrological resource management, including crop watering and irrigation scheduling, relies on reliable estimates of reference evapotranspiration (ETo). However, previous studies of forecasting ETo have not dealt with comparing single and hybrid metaheuristic algorithms in much detail. This study aims to assess the efficiency of a novel methodology to simulate univariate monthly ETo estimates using an artificial neural network (ANN) integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Several state-of-the-art algorithms, including constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA), the slime mould algorithm (SMA), the marine predators algorithm (MPA) and the modified PSO algorithm were used to evaluate PSOGWO’s prediction accuracy. Monthly meteorological data were collected in Al-Kut City (1990 to 2020) and used for model training, testing and validation. The results indicate that pre-processing techniques can improve raw data quality and may also suggest the best predictors scenario. That said, all models can be considered efficient with acceptable simulation levels. However, the PSOGWO-ANN model slightly outperformed the other techniques based on several statistical tests (e.g., a coefficient of determination of 0.99). The findings can contribute to better management of water resources in Al-Kut City, an agricultural region that produces wheat in Iraq and is under the stress of climate change. Full article

(This article belongs to the Special Issue The Next Generation on Water Resource Management Using Computer Aid Models)

► Show Figures

Figure 1

27 pages, 10326 KiB

Open AccessArticle

A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities

by Vijendra Kumar, Naresh Kedam, Kul Vaibhav Sharma, Khaled Mohamed Khedher and Ayed Eid Alluqmani

Sustainability 2023, 15(18), 13724; https://0-doi-org.brum.beds.ac.uk/10.3390/su151813724 - 14 Sep 2023

Cited by 9 | Viewed by 2072

Abstract

Current research studies offer an investigation of machine learning methods used for forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their temporal complexities, are exploited using a unique data segmentation approach, providing discrete training, validation, and testing sets. Two unique models are created: Model-1, which is based on daily data, and Model-2, which is based on weekly data. A variety of performance criteria are used to rigorously analyze these models. CatBoost, XGBoost, Lasso, Ridge, Linear Regression, and LGBM are among the algorithms under consideration. This research study provides insights into their predictive abilities, revealing significant trends across the training, validation, and testing phases. The results show that ensemble-based algorithms, particularly CatBoost and XGBoost, outperform in both models. CatBoost emerged as the model of choice throughout all assessment stages, including training, validation, and testing. The MAE was 0.00077, the RMSE was 0.0010, the RMSPE was 0.49, and the R² was 0.99, confirming CatBoost’s unrivaled ability to identify deep temporal intricacies within daily rainfall patterns. Both models had an R² of 0.99, indicating their remarkable ability to predict weekly rainfall trends. Significant results for XGBoost included an MAE of 0.02 and an RMSE of 0.10, indicating their ability to handle longer time intervals. The predictive performance of Lasso, Ridge, and Linear Regression varies. Scatter plots demonstrate the robustness of CatBoost and XGBoost by demonstrating their capacity to sustain consistently low prediction errors across the dataset. This study emphasizes the potential to transform urban meteorology and planning, improve decision-making through precise rainfall forecasts, and contribute to disaster preparedness measures. Full article

(This article belongs to the Special Issue The Next Generation on Water Resource Management Using Computer Aid Models)

► Show Figures

Figure 1

17 pages, 41715 KiB

Open AccessArticle

Large-Scale Flood Hazard Monitoring and Impact Assessment on Landscape: Representative Case Study in India

by Bijay Halder, Subhadip Barman, Papiya Banik, Puja Das, Jatisankar Bandyopadhyay, Fredolin Tangang, Shamsuddin Shahid, Chaitanya B. Pande, Baqer Al-Ramadan and Zaher Mundher Yaseen

Sustainability 2023, 15(14), 11413; https://0-doi-org.brum.beds.ac.uk/10.3390/su151411413 - 23 Jul 2023

Cited by 4 | Viewed by 2230

Abstract

Currently, natural hazards are a significant concern as they contribute to increased vulnerability, environmental degradation, and loss of life, among other consequences. Climate change and human activities are key factors that contribute to various natural hazards such as floods, landslides, droughts, and deforestation. Assam state in India experiences annual floods that significantly impact the local environment. In 2022, the flooding affected approximately 1.9 million people and 2930 villages, resulting in the loss of 54 lives. This study utilized the Google Earth Engine (GEE) cloud-computing platform to investigate the extent of flood inundation and deforestation, analyzing pre-flood and post-flood C band Sentinel-1 GRD datasets. Identifying pre- and post-flood areas was conducted using Landsat 8–9 OLI/TIRS datasets and the modified and normalized difference water index (MNDWI). The districts of Cachar, Kokrajhar, Jorhat, Kamrup, and Dhubri were the most affected by floods and deforestation. The 2022 Assam flood encompassed approximately 24,507.27 km² of vegetation loss and 33,902.49 km² of flood inundation out of a total area of 78,438 km². The most affected areas were the riverine regions, the capital city Dispur, Guwahati, southern parts of Assam, and certain eastern regions. Flood hazards exacerbate environmental degradation and deforestation, making satellite-based information crucial for hazard and disaster management solutions. The findings of this research can contribute to raising awareness, planning, and implementing future disaster management strategies to protect both the environment and human life. Full article

(This article belongs to the Special Issue The Next Generation on Water Resource Management Using Computer Aid Models)

► Show Figures

Figure 1

19 pages, 3154 KiB

Open AccessArticle

Mapping Groundwater Potential Zones in the Habawnah Basin of Southern Saudi Arabia: An AHP- and GIS-based Approach

by Abdulnoor A. J. Ghanim, Ahmed M. Al-Areeq, Mohammed Benaafi, Mohammed S. Al-Suwaiyan, Amran A. Al Aghbari and Mana Alyami

Sustainability 2023, 15(13), 10075; https://0-doi-org.brum.beds.ac.uk/10.3390/su151310075 - 26 Jun 2023

Cited by 4 | Viewed by 1645

Abstract

The excessive depletion of groundwater resources and significant climate change have exerted immense pressure on global groundwater reserves. Owing to the rising global demand for drinking water, as well as its use in agriculture and industry, there is an increasing need to evaluate the capacity and effectiveness of underground water reservoirs (aquifers). Recently, GIS has gained significant attention for groundwater exploration because of its ability to provide rapid and comprehensive information about resources for further development. This study aims to assess and map the groundwater potential of a large basin located in the southern region of Saudi Arabia. Techniques such as GIS and AHP were employed in this study. To perform the delineation for the groundwater potential zones (GWPZ), seven thematic layers were prepared and analyzed. These layers include geology, slope, land use, lineament densities, soil characteristics, drainage density, and rainfall. These variables were carefully considered and examined to identify and categorize areas based on their respective groundwater potentials. The assigned weights to each class in the thematic maps were determined using the well-known analytic hierarchy process (AHP) method. This methodology considered the characteristics of each class and their capacity to influence water potential. The results’ precision was verified by cross-referencing it with existing information about the area’s potential for groundwater. The resulting GWPZ map was classified into the following five categories: very low, low, moderate, high, and very high. The study revealed that approximately 42.56% of the basin is classified as having a high GWPZ. The low and moderate potential zones cover 36.12% and 19.55% of the area, respectively. Very low and very high potential zones were found only in a limited number of areas within the basin. This study holds global importance as it addresses the pressing challenge of depleting groundwater resources. With rising demands for drinking water, agriculture, and industry worldwide, the effective evaluation and management of underground water reservoirs are crucial. By utilizing GIS and AHP techniques, this study provides a valuable assessment and the mapping of groundwater potential in a large basin in southern Saudi Arabia. Its findings and methodology can serve as a model for other regions, supporting sustainable water resource management globally. Full article

(This article belongs to the Special Issue The Next Generation on Water Resource Management Using Computer Aid Models)

► Show Figures

Figure 1

28 pages, 6505 KiB

Open AccessArticle

An Enhanced Multioperator Runge–Kutta Algorithm for Optimizing Complex Water Engineering Problems

by Iman Ahmadianfar, Bijay Halder, Salim Heddam, Leonardo Goliatt, Mou Leong Tan, Zulfaqar Sa’adi, Zainab Al-Khafaji, Raad Z. Homod, Tarik A. Rashid and Zaher Mundher Yaseen

Sustainability 2023, 15(3), 1825; https://0-doi-org.brum.beds.ac.uk/10.3390/su15031825 - 18 Jan 2023

Cited by 7 | Viewed by 1731

Abstract

Water engineering problems are typically nonlinear, multivariable, and multimodal optimization problems. Accurate water engineering problem optimization helps predict these systems’ performance. This paper proposes a novel optimization algorithm named enhanced multioperator Runge–Kutta optimization (EMRUN) to accurately solve different types of water engineering problems. The EMRUN’s novelty is focused mainly on enhancing the exploration stage, utilizing the Runge–Kutta search mechanism (RK-SM), the covariance matrix adaptation evolution strategy (CMA-ES) techniques, and improving the exploitation stage by using the enhanced solution quality (IESQ) and sequential quadratic programming (SQP) methods. In addition to that, adaptive parameters were included to improve the stability of these two stages. The superior performance of EMRUN is initially tested against a set of CEC-17 benchmark functions. Afterward, the proposed algorithm extracts parameters from an eight-parameter Muskingum model. Finally, the EMRUM is applied to a practical hydropower multireservoir system. The experimental findings show that EMRUN performs much better than advanced optimization approaches. Furthermore, the EMRUN has demonstrated the ability to converge up to 99.99% of the global solution. According to the findings, the suggested method is a competitive algorithm that should be considered in optimizing water engineering problems. Full article

(This article belongs to the Special Issue The Next Generation on Water Resource Management Using Computer Aid Models)

► Show Figures

Figure 1

24 pages, 5077 KiB

Open AccessArticle

Spatiotemporal Analysis of Future Trends in Terrestrial Water Storage Anomalies at Different Climatic Zones of India Using GRACE/GRACE-FO

by Mohd Sayeed Ul Hasan, Mufti Mohammad Saif, Nehal Ahmad, Abhishek Kumar Rai, Mohammad Amir Khan, Ali Aldrees, Wahaj Ahmad Khan, Mustafa K. A. Mohammed and Zaher Mundher Yaseen

Sustainability 2023, 15(2), 1572; https://0-doi-org.brum.beds.ac.uk/10.3390/su15021572 - 13 Jan 2023

Viewed by 2062

Abstract

This work is a climatological evaluation of terrestrial water storage anomalies (TWSAs), which act as driving forces for sustainable development, in one of the most populous countries of the world. The objective of this work is to evaluate RL06 mascon data from the GRACE and GRACE-FO satellite missions over India to explore seasonal and interannual changes in terrestrial water storage, encompassing an area of ~3.29 million km² with 285 grid points, from 2002 through to 2020. Several statistical tests are performed to check the homogeneity (i.e., Pettitt’s test, the BRT, the SNHT, and the VNRT). Most of the homogeneous data are found in winter, pre-monsoon, and post-monsoon, approximately above 42% to 47%, and the least are found in monsoons and annual with only 33%, at a 95% significance level. According to Pettitt’s test, the majority of the breakpoints are present in 2014 for winter, 2012 for pre-monsoon, 2011 for monsoons and post-monsoon, and 2008 as well as 2011 for annual. Furthermore, to detect trends and magnitudes we employed the nonparametric MK test, the MMK test, Sen’s slope estimator, and the parametric SLR test. According to the MK and MMK tests, the most significant negative and positive trends indicate the chances of droughts and floods, respectively. The Indo–Gangetic region shows the highest declination. According to Sen’s slope and the SLR test, the most declining magnitude is found in Delhi, Panjab, Uttrakhand, the northern part of Rajasthan, and Uttar Pradesh. Based on our findings, the average declining rate of yearly terrestrial water storage data from the MK, MMK, and SLR tests is −0.0075 m (−0.75 cm/year) from 2002 to 2020. Koppen–Geiger climate zones are also used to depict the seasonal and interannual descriptive statistics of TWSA trends. Interestingly, the annual means of arid desert cold (−0.1788 cm/year) and tropical savanna (−0.1936 cm/year) have the smallest declining trends when compared to other climatic zones. Northern Indian regions’ temperate dry winter, hot/warm summer, and dry arid steppe hot regions show the maximum declining future trend. This study could be useful in planning and managing water resources, agriculture, and the long-term growth of the country by using an intelligent water delivery system. Full article

(This article belongs to the Special Issue The Next Generation on Water Resource Management Using Computer Aid Models)

► Show Figures

Figure 1

18 pages, 20272 KiB

Open AccessArticle

An Investigation of Recharging Groundwater Levels through River Ponding: New Strategy for Water Management in Sutlej River

by Fahad Mushtaq, Habibur Rehman, Umair Ali, Muhammad Salman Babar, Mohammad Saleh Al-Suwaiyan and Zaher Mundher Yaseen

Sustainability 2023, 15(2), 1047; https://0-doi-org.brum.beds.ac.uk/10.3390/su15021047 - 6 Jan 2023

Cited by 4 | Viewed by 1795

Abstract

Groundwater is an essential water resource in the current era, and studying its sustainability and management is highly necessary nowadays. In the current area of research interest, the reduced mean annual Sutlej River flow, the increase in the population/built-up areas, and enhanced groundwater abstractions have reduced groundwater recharge. To address this issue, groundwater recharge modeling through ponding of the Sutlej River was carried out using a modular three-dimensional finite-difference groundwater flow model (MODFLOW) in a 400 km² area adjacent to Sutlej River. The mean historical water table decline rate in the study area is 139 mm/year. The population and urbanization rates have increased by 2.23 and 1.62% per year in the last 8 years. Domestic and agricultural groundwater abstraction are increasing by 1.15–1.30% per year. Abstraction from wells and recharge from the river, the Fordwah Canal, and rainfall were modeled in MODFLOW, which was calibrated and validated using observed data for 3 years. The model results show that the study area’s average water table depletion rate will be 201 mm/year for 20 years. The model was re-run for this scenario, providing river ponding levels of 148–151 m. The model results depict that the water table adjacent to the river will rise by 3–5 m, and average water table depletion is expected to be reduced to 151 to 95 mm/year. The model results reveal that for ponding levels of 148–151 m, storage capacity varies from 26.5–153 Mm³, contributing a recharge of 7.91–12.50 million gallons per day (MGD), and benefiting a 27,650–32,100-acre area; this means that for areas benefitted by dam recharge, the groundwater abstraction rate will remain sustainable for more than 50 years, and for the overall study area, it will remain sustainable for 7–12.3 years. Considering the current water balance, a recharging mechanism, i.e., ponding in the river through the dam, is recommended for sustainable groundwater abstraction. Full article

(This article belongs to the Special Issue The Next Generation on Water Resource Management Using Computer Aid Models)

► Show Figures

Figure 1

23 pages, 3923 KiB

Open AccessArticle

Flood Susceptibility Mapping Using Watershed Geomorphic Data in the Onkaparinga Basin, South Australia

by Alaa Ahmed, Abdullah Alrajhi, Abdulaziz Alquwaizany, Ali Al Maliki and Guna Hewa

Sustainability 2022, 14(23), 16270; https://0-doi-org.brum.beds.ac.uk/10.3390/su142316270 - 6 Dec 2022

Cited by 3 | Viewed by 1663

Abstract

In the near future, natural disasters and associated risks are expected to increase, mainly because of the impact of climate change. Australia is considered one of the most vulnerable areas for natural disasters, including flooding. Therefore, an evaluation of the morphometric characteristics of the Onkaparinga basin in South Australia was undertaken using the integration of remote sensing and geospatial techniques to identify its impact on flash floods. The Shuttle Radar Topography Mission (SRTM) and Landsat images with other available geologic, topographic, and secondary data were analysed in geographic information system (GIS) to outline the drainage basins, estimate the morphometric parameters, and rank the parameters to demarcate the flash flood susceptibility zones of the basin. The main goal was to develop a flash flood susceptibility map showing the different hazard zones within the study areas. The results showed that 10.87%, 24.27%, and 64.85% are classified as low, moderate, and highly susceptible for flooding, respectively. These findings were then verified against secondary data relating to the historic flood events of the area. About 30.77% of the historical floods are found located within the high to extremely susceptible zones. Moreover, a significant correlation has been found between the high precipitation concentration index (PCI) and the irregular rainfall and high potential for flooding. Finally, the social and economic vulnerability was applied to determine the impact of the flood hazards. The result indicates a widespread threat to the economy, environment, and community in the study area. This study can be utilized to support and assist decision makers with planning and the devotion of alleviation measures to reducing and avoiding catastrophic flooding events, especially in highly susceptible areas in the world, such as South Australian basins. Full article

(This article belongs to the Special Issue The Next Generation on Water Resource Management Using Computer Aid Models)

► Show Figures

Figure 1

Journal Menu

Journal Browser

The Next Generation on Water Resource Management Using Computer Aid Models

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (13 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI