The potential of unconventional water in limiting water scarcity

Abstract In regions with water scarcity, utilizing Unconventional Water Resources (UWRs) is an option to meet the growing demand of water. This doctoral thesis aims to improve the understanding and insights on UWRs distribution globally, with a particular focus on benefits in different climates. Twelve UWRs were studied at the global scale for better understanding state of the art of UWRs. Among them i) fog water harvesting, ii) artificial recharge of groundwater resources, and iii) iceberg water harvesting was included for more detailed analyzing in the specific case studies. As a literature review, the global distribution of twelve types of UWRs was prepared and the results showed besides geographic, economic, and political constraints, climatic conditions are the main drivers on UWRs utilization. Also, results indicated that illustrating of opportunities and challenges in UWRs utilizations can potentially help water resources managers to better planning and policymaking. The next phase of the thesis was three case studies where different environmental variables were considered for developing Fog-water harvesting Capability Index (FCI) in the Vazroud watershed, Iran (semi-humid region) using Artificial Intelligence (AI) algorithms. The results showed all AI algorithms (Generalized Dissimilarity Model: GDM, Generalized Boosted Model: GBM, Linear Additive Model: GLM, and Generalized Additive Model: GAM) had high accuracy in FCI mapping. The highest values of importance were obtained for sky view factor and the lowest for slope curvature in FCI mapping. In the second case study, changes in groundwater levels were detected by comparing data for the periods before (1985–1996) and after (1997–2018) Managed Aquifer Recharge Structure (MARS) construction. Results showed that the rate of groundwater depletion decreased after MARS construction. Also, the permeability of the MARSs have been decreased due to sedimentation and led to reducing the efficiency of the MARSs in groundwater recharge. In the third case study, opportunities and challenges in iceberg utilization were investigated across Arctic and Antarctic areas. Economic considerations and risks associated with iceberg towing were identified as the main limitations in iceberg harvesting, while environmental impacts were identified as the main challenge to exploiting this resource. Statistical analysis of ice sheets as the main sources of icebergs showed a significant decreasing trend for all months and seasons during 2005–2019. This study demonstrated that assessing the potential of unconventional water for closing the water gap is currently difficult to be quantified globally, as data only exists in the form of singular case studies. Therefore, for the direction of future studies, providing methods to prepare quantitative information about a different type of UWRs utilization can help mitigate water deficiencies.Tiivistelmä Alueilla, joita vaivaa vesipula, voivat vaihtoetoiset vesilähteet (VVL) tarjota mahdollisuuden lisääntyvään vesivarojen kysyntään. Tämän väitöstyön tavoitteen on lisätä tietoa vaihtoehtoisten vesilähteiden jakautumisesta maailmanlaajuisesti huomioiden erityisesti eri ilmastojen erityiskysymykset. Työssä tarkasteltiin kirjallisuuteen perustuen kaksitoista erilaista vesilähdettä kokonaiskuvan hahmottamiseksi. Tapauskohtaisesti tarkasteltiin erikseen kolmea erilaista vesilähdettä tarkemmin eli i) sumun hyödyntäminen, ii) tekopohjaveden muodostamista sekä iii) jäätikkövesien käyttöä. Kirjallisuuskatsauksessa, 12 vesilähteen käyttöä tarkasteltiin maailmanlaajuisesti, mikä osoitti maantieteellisten, taloudellisten ja poliittisten sekä ilmastollisten tekijöiden vaikuttavan eniten eri vaihtoehtoisten vesilähteiden käyttöön. Tarkastelu osoitti myös että vaihtoehtoisten vesivarojen rajoitteiden ja mahdollisuuksien tarkastelu voi edesauttaa vesialan päättäjien parempaan suunnitteluun ja politiikan tekoon. Työn seuraavassa vaiheessa tarkasteltiin kolmea luonnonoloiltaan erilaista aluetta sumuveden talteenottoon Vazroudin valuma-alueella Iranissa tavoitteena kehittää sumuveden talteenotto indeksi (FCI) käyttäen tekoälyä. Tulokset osoittivat, että kaikilla tekoälyn algoritmeilla (GDM, GBM, GLM ja GAM) oli hyvä tarkkuus FCI kartoituksessa. Merkittävin tekijä oli taivas näkymällä ja huonoin selittäjä maaston kaltevuudella FCI kartoituksessa. Toisessa tapaustutkimuksessa, pohjaveden pinnan muutoksia havainnoitiin vertaamalla ennen (1985–1996) ja jälkeen (1997–2018) tekopohjavettä muodostavien rakenteiden rakentamista. Tulokset osoittivat tekopohjaveden lisäävän pohjaveden alenemaa. Sedimentaatio vähensi pohjaveden muodostusta heikentäen suodatavan rakenteen toimintaa. Kolmannessa tapaustutkimuksessa tarkasteltiin Etelä- ja Pohjoisnapojen jäätiköiden käyttöä vesilähteenä. Taloudelliset lähtökohdat ja riskit kuljetuksessa todettiin keskeisemmäksi rajoitteeksi jäätikköveden käytölle ja ympäristövaikutukset nähtiin keskeisenä haasteena tämän resurssin hyödyntämisessä. Jäätiköiden tarkastelu vesilähteenä osoittaa tilastollisesti merkittävän vähenemän vuosina 2005–2019. Tämä tutkimus osoittaa, että vaihtoehtoisten vesilähteiden tarkastelu on hankalaa, koska aineistoa on saatavilla vain yksittäisistä tapaustutkimuksista. Tämä tulisi huomioida tutkimustarpeiden määrittelyssä jatkossa, jotta erilaisia vaihtoehtoisista vesilähteistä voitaisiin paremmin hyödyntää torjumaan vesipulaa

Polar ice as an unconventional water resource:opportunities and challenges

Abstract Global water resources are under pressure due to increasing population and diminishing conventional water resources caused by global warming. Water scarcity is a daunting global problem which has prompted efforts to find unconventional resources as an appealing substitute for conventional water, particularly in arid and semiarid regions. Ice is one such unconventional water resource, which is available mainly in the Arctic and Antarctic. In this study, opportunities and challenges in iceberg utilization as a source of freshwater were investigated on the basis of a systematic literature review (SLR). A search in three databases (Scopus, Web of Science, and ProQuest) yielded 47 separate studies from 1974 to 2019. The SLR indicated that harvesting iceberg water, one of the purest sources of water, offers benefits ranging from supplying freshwater and creating new jobs to avoiding iceberg damage to offshore structures. Economic considerations and risks associated with iceberg towing were identified as the main limitations to iceberg harvesting, while environmental impacts were identified as the main challenge to exploiting this resource. Assessment of trends in ice sheets in Arctic and Antarctic across different spatiotemporal scales indicated that the main sources of icebergs showed a statistically significant (p < 0.01) decreasing trend for all months and seasons during 2005–2019

Assessing morphological changes in a human-impacted alluvial system using hydro-sediment modeling and remote sensing

Abstract Construction of managed aquifer recharge structures (MARS) to store floodwater is a common strategy for storing depleted groundwater resources in arid and semi-arid regions, as part of integrated water resources management (IWRM). MARS divert surface water to groundwater, but this can affect downstream fluvial processes. The impact of MARS on fluvial processes was investigated in this study by combining remote sensing techniques with hydro-sediment modeling for the case of the Kaboutar-Ali-Chay aquifer, northwestern Iran. The impact of MARS on groundwater dynamics was assessed, sedimentation across the MARS was modeled using a 2D hydrodynamic model, and morphological changes were quantified in the human-impacted alluvial fan using Landsat time series data and statistical methods. Changes were detected by comparing data for the periods before (1985–1996) and after (1997–2018) MARS construction. The results showed that the rate of groundwater depletion decreased from 2.14 m/yr before to 0.86 m/yr after MARS construction. Hydro-sediment modeling revealed that MARS ponds slowed water outflow, resulting in a severe decrease in sediment load which lead to a change from sediment deposition to sediment erosion in the alluvial fan. Morphometric analyses revealed decreasing alluvial fan area and indicated significant differences (p < 0.01) between pre- and post-impact periods for different morphometric parameters analyzed. The rate of change in area of the Kaboutar-Ali-Chay alluvial fan changed from −0.228 to −0.115 km²/year between pre- and post-impact periods

Unconventional water resources:global opportunities and challenges

Abstract Water is of central importance for reaching the Sustainable Development Goals (SDGs) of the United Nations. With predictions of dire global water scarcity, attention is turning to resources that are considered to be unconventional, and hence called Unconventional Water Resources (UWRs). These are considered as supplementary water resources that need specialized processes to be used as water supply. The literature encompasses a vast number of studies on various UWRs and their usefulness in certain environmental and/or socio-economic contexts. However, a recent, all-encompassing article that brings the collective knowledge on UWRs together is missing. Considering the increasing importance of UWRs in the global push for water security, the current study intends to offer a nuanced understanding of the existing research on UWRs by summarizing the key concepts in the literature. The number of articles published on UWRs have increased significantly over time, particularly in the past ten years. And while most publications were authored from researchers based in the USA or China, other countries such as India, Iran, Australia, and Spain have also featured prominently. Here, twelve general types of UWRs were used to assess their global distribution, showing that climatic conditions are the main driver for the application of certain UWRs. For example, the use of iceberg water obviously necessitates access to icebergs, which are taken largely from arctic regions. Overall, the literature review demonstrated that, even though UWRs provide promising possibilities for overcoming water scarcity, current knowledge is patchy and points towards UWRs being, for the most part, limited in scope and applicability due to geographic, climatic, economic, and political constraints. Future studies focusing on improved documentation and demonstration of the quantitative and socio-economic potential of various UWRs could help in strengthening the case for some, if not all, UWRs as avenues for the sustainable provision of water

Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models

Abstract Accurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine–cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combinations for the models was based on principal component analysis (PCA). Uncertainty in sequential uncertainty fitting (SUFI-2) and performance indicators were used to assess the potential of models. Taylor diagrams were used to visualize the match between model output and observed values. Assessment of daily SSL predictions for Talar station revealed that ANFIS-SCA yielded the best results (RMSE (root mean square error): 934.2 ton/day, MAE (mean absolute error): 912.2 ton/day, NSE (Nash–Sutcliffe efficiency): 0.93, PBIAS: 0.12). ANFIS-SCA also yielded the best results for Kasilian station (RMSE: 1412.10 ton/day, MAE: 1403.4 ton/day, NSE: 0.92, PBIAS: 0.14). The Taylor diagram confirmed that ANFIS-SCA achieved the best match between observed and predicted values for various hydraulic and hydrological parameters at both Talar and Kasilian stations. Further, the models were tested in Eagel Creek Basin, Indiana state, USA. The results indicated that the ANFIS-SCA model reduced RMSE by 15% and 21% compared to the MLP-SCA and RBFNN-SCA models in the training phase. Comparing models performance indicated that the ANFIS-SCA model could decrease MAE error compared to ANFIS-BA, ANFIS-PSO, ANFIS-FFA, and ANFIS models by 18%, 32%, 37%, and 49% in the training phase, respectively. The results indicated that the integration of optimization algorithms and soft computing models can improve the ability of models for predicting SSL. Additionally, the hybridization of soft computing models with optimization algorithms can decrease the uncertainty of models

Fog-water harvesting Capability Index (FCI) mapping for a semi-humid catchment based on socio-environmental variables and using artificial intelligence algorithms

Abstract Fog is an important component of the water cycle in northern coastal regions of Iran. Having accurate tools for mapping the precise spatial distribution of fog is vital for water harvesting within integrated water resources management in this semi-humid region. In this study, environmental variables were considered in prediction mapping of areas with high concentrations of fog in the Vazroud watershed, Iran. Fog probability maps were derived from four artificial intelligence algorithms (Generalized Linear Model, Generalized Additive Model, Generalized Boosted Model, and Generalized Dissimilarity Model). Models accuracy were assessed using Receiver Operating characteristic Curve (ROC). Three social variables were also selected according to their relevance for fog suitability mapping. Finally, Fog-water harvesting Capability Index (FCI) maps were produced by multiplying fog probability by fog suitability maps. The results showed high accuracy in fog probability mapping for the study area, with all models proving capable of identifying areas with high fog concentrations in the south and southeast. For all models, the highest values of importance were obtained for sky view factor and the lowest for slope curvature. Analytic Hierarchy Process results showed the relative importance of social conditioning factors in fog suitability mapping, with the highest weight given to distance to residential area, followed by distance to livestock buildings and distance to road. Based on the fog suitability map, southeast and southern parts of the study area are most suitable for fog water harvesting. The fog spatial distribution maps obtained can increase fog water harvesting efficiency. They also indicate areas for future study with regions where fog is a critical component in the water cycle

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

Abstract Land degradation (LD) is a complex process affected by both anthropogenic and natural driving variables, and its prevention has become an essential task globally. The aim of the present study was to develop a new quantitative LD mapping approach using machine learning techniques, benchmark models, and human-induced and socio-environmental variables. We employed four machine learning algorithms [Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Model (GLM), and Dragonfly Algorithm (DA)] for LD risk mapping, based on topographic (n = 7), human-induced (n = 5), and geo-environmental (n = 6) variables, and field measurements of degradation in the Pole-Doab watershed, Iran. We assessed the performance of different algorithms using receiver operating characteristic, Kappa index, and Taylor diagram. The results revealed that the main topographic, geoenvironmental, and human-induced variable was slope, geology, and land use change, respectively. Assessments of model performance indicated that DA had the highest accuracy and efficiency, with the greatest learning and prediction power in LD risk mapping. In LD risk maps produced using SVM, GLM, MARS, and DA, 19.16%, 19.29%, 21.76%, and 22.40%, respectively, of total area in the Pole-Doab watershed had a very high degradation risk. The results of this study demonstrate that in LD risk mapping for a region, topographic, and geological factors (static conditions) and human activities (dynamic conditions, e.g., residential and industrial area expansion) should be considered together, for best protection at watershed scale. These findings can help policymakers prioritize land and water conservation efforts

Development of novel hybridized models for urban flood susceptibility mapping

Abstract Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services