Remote Sensing, GIS and Modelling for Water Resource Management

Groundwater is an essential natural resource, which becomes even more crucial in regions with water scarcity and unpredictable weather patterns. There is a growing demand for water resources across various sectors in Ethiopia’s Wabe River catchment. “Therefore, it is essential to use remote sensing data, Geographic Information Systems (GIS), and the Analytic Hierarchy Process (AHP) to assess groundwater potentiality effectively”. These methodologies allow for prioritizing geological, hydrological, and environmental variables. By utilizing these technologies, researchers can gain valuable insights into the suitability and sustainability of groundwater resources. Thematic layers such as lithology, lineament density, elevation, rainfall, soil composition, NDVI (normalized difference vegetation index), land use/land cover, slope, and drainage density were used to identify potential groundwater zones within the study area. Aquifer permeability and storage are directly influenced by subsurface lithology. In addition to elevation, slope significantly affects surface runoff, groundwater movement, and hydraulic head and pressure fluctuations; therefore, NDVI indirectly reflects groundwater potential by indicating vegetation health. Various data sources were used to map the mentioned thematic layers, including on-site field observations, existing maps, reports, and multispectral and microwave images. Different image processing and geographic information systems techniques were utilized to derive thematic layers. A weighted linear combination analysis was applied to produce a definitive groundwater potential map based on the significance and characteristics of each layer and its sublayers. Based on the cumulative impact of all thematic layers, this map indicates areas with varying degrees of groundwater potential, with higher values indicating more significant potential. Per the investigation, 40% of the entire expanse exhibits diminished groundwater potential, while 46% manifests a moderate level, leaving merely 14% characterized by a heightened groundwater potential. The groundwater potential of areas characterized by highly weathered rocks, gentle slopes, adequate rainfall, and a high lineament density is particularly promising. Employing the area under the curve technique alongside well placements, the model underwent validation, showcasing an accuracy of 81% for both well positions and groundwater potential.

The Vizianagaram district, located in the Northeastern parts of Andhra Pradesh State, is underlain by a complex array of geological formations of Eastern Ghat Mobile Belt (EGMB), forming a typical hard rock aquifer system. The district is mainly agricultural. However, only 52.5% of 345,870 hectare (ha) of the total cropped area is under irrigation and remaining 47.5% is rain-fed. Groundwater contributes around 30% of irrigation needs despite having good groundwater potential. Thus, the district exhibits a unique combination of untapped groundwater potential of 1703 million cubic meter (MCM) for future utilization within the typical hard rock aquifer system. Scope for groundwater extraction with proper management interventions can be achieved through systematic analysis of aquifer characteristics and disposition through aquifer mapping and management. A detailed analysis of hydrogeological settings in the district and characterization of aquifers is done through Aquifer Mapping and suggesting specific management interventions for sustainable development and management of aquifers. The management approach recommends a judicious enhancement of groundwater extraction to realize increased irrigation potential, agricultural area and production in 16,385 ha within the ambit of the current stage of groundwater extraction coupled with supply and demand side interventions for sustainable groundwater development and management in the district. It recommends the Adoption of micro-irrigation in areas with lower groundwater yields, complemented by combining surface water and groundwater resources in the Uttarandhra Sujala Sravanthi project areas and consistent maintenance of 4940 existing artificial recharge structures, including check dams and percolation tanks, constructed through government initiatives over the years.

Long-term groundwater depletion is a global issue recorded worldwide due to rapid industrialization, urbanization, deforestation, immense agricultural activities, demographic rise, automobile services, and over-exploitation of groundwater. Artificial Recharge Structures (ARS) on local scales can effectively contribute to improving the groundwater levels that fulfill the daily demands of agricultural, industrial, domestic, and other usages. The present study strengthens the rise in groundwater levels by modifying surface runoff and artificial storage techniques in the temple town of Nanjangud taluk using a GIS platform. Toposheets, LISS-III, and DEM data are successfully adopted to generate the required thematic layers in ArcGIS v10. Priority-based weights are assigned for each factor considered using the Analytical Hierarchical Process (AHP) in deriving suitable sites for ARS. The integrated map was categorized into 5 zones (1–5), with 1 denoting the most suitability and 5 denoting the least suitability for ARS. This method assisted in extracting 80 possible locations for artificial recharges. The final outcomes portray a better perspective for periodic monitoring and planning strategies for achieving groundwater sustainability.

Among water-related disasters, flood is one of the most overwhelming impacts which causes loss of lives and livelihood worldwide. Risk zonation and estimation is a prolonged task which require a multiple factors analysis because always not only a single factor responsible for flood incidence, and it changes with different geographical location. Therefore, flood risk mapping is essential for pre-preparing, managing and mitigating of water-related disaster. The present study was designed to evaluate the efficiency of the geostatistical method i.e., rank sum (RS) along with geospatial techniques to identify potential flood risk areas in Sundarban Delta of India. Thus, primarily, nine possible factors such as rainfall deviation, slope, slope aspect, land use land cover, soil type, clay content in soil, distance from river, river flow direction and catchment area were considered for assessment of flood risk mapping. The task employs two stages of analyses including preparation of thematic layers in geographic information system (GIS) environment and combining with flood causative factors using RS as a geostatistical method. Finally, to fine possible flood risk areas, weighted overlay analysis was performed. The result reveals that rainfall has a great influence on flood in Sundarban with weight of 0.20, concomitantly slope, catchment area, distance from river and flow direction have also an important influence on flood risk with weight of 0.18, 0.16, 0.13 and 0.11, respectively. This analysis finds that the geostatistical analysis is capable of making a correct and consistent prediction for flood extent. So, the rank sum technique integrated with GIS are proposed for evaluating flood risk in other regions of the world.

To minimize the emission of greenhouse gases (GHGs) and save fragile forest ecosystems, it is recommended to decrease deforestation and forest degradation. This research focuses on utilizing Landsat imagery to determine Land Use Land Cover (LULC) change detection and its impact on surface water extent and carbon sequestration in the southern region of Punjab, Pakistan. In order to gather the necessary remote sensing (RS) data for the study, Landsat imagery from the years 2000, 2007, 2014, and 2021 was used. A ground-truth LULC change classification was performed using a supervised classification technique, namely maximum likelihood classification (MLC) algorithm using Geographic Information System (GIS) software (ArcMap v.10.7.1). In 2000, 2007, 2014, and 2021, overall accuracy (OA) and Kappa coefficient (K) for water, cropland, forest, settlements, and barren land classifications recorded as 88% and 85%, 86% and 82.76%, 90% and 84.7%, 90.63% and 88.24%, 88.89% and 84.38%, respectively. Upon analysis, it was revealed that 1.02% increase in water areas, 2.63% expansion in agricultural land, significant decrease of 31.03% in forest cover, a notable rise of 14.52% in settlement areas, and a substantial increase of 12.87% in barren land. In addition, a total of − 58,467,308.08 metric tons of emissions were recorded as a result of forest degradation from 2000 to 2021. The research provides crucial information that may be used by decision-makers and environmental managers to promote afforestation and preserve the country’s current forests, to mitigate climate change.

The area selected for the study, the Enmakaje Grama Panchayath, falls within the northern part of Kasaragod district, Kerala. Covering a total land area of 78.23 km2, the location benefits from excellent accessibility due to a well-connected panchayath and village roads. The objective of this study is to conduct a comprehensive analysis of the overall groundwater quality in the designated study region. The state of the water must be assessed in these impacted locations to determine the amount of pollution and avoid additional community damage. Monitoring water quality helps us find pollution causes, fix them, and safeguard inhabitants for decades. A thorough collection of 19 groundwater samples was methodically obtained and subjected to complete physical and chemical analysis. The controversy surrounding the aerial application of endosulfan, an organochlorine pesticide, in the cashew plantations of Enmakaje Grama Panchayat, located in Kasargod district, Kerala, has raised concerns about potential health impacts, including elevated mortality rates, increased occurrence of congenital abnormalities, and malignancies in the adjacent region. During the pre-monsoon season, groundwater levels exhibited a 2.4–19.8 m range, with an average depth of 9.65 m below the ground surface. During the post-monsoon months, water depths varied between 2 and 17.2 m, with an average depth of 8.4 m. The physico-chemical analysis of pH, Iron, Sodium, Total Alkalinity, and Total Hardness revealed higher values in the post-monsoon period, primarily attributed to increased rainfall. Notably, pH, TDS, E.C., and Salinity measurements remained within permissible limits. Simultaneously, the samples exhibited a deficiency in ions such as Magnesium, Calcium, Chloride, and Fluoride during the post-monsoon period. Sodium and Iron percentages generally fell within the excellent or good range, with only a few exceptions. Most drinking water and irrigation matrices in both seasons are within the prescribed limits. A negligible difference exists in the concentration of ions throughout both monsoon seasons.

Groundwater plays a crucial role as a freshwater source in the Himalayas, supporting the social and economic development of the region. However, the major Himalayan basins are facing severe threats from depletion and contamination of their groundwater resources, attributed to the combined impacts of global climatic variations and human activities. To tackle this critical issue, a comprehensive study was undertaken in the Pohru catchment area, employing a multi-influence factor (MIF) approach and remote sensing techniques to assess potential groundwater zones. This study’s thematic layers were meticulously compiled within a Geographic Information System (GIS) environment, integrating diverse satellite datasets and field observations. The study’s results unveiled that ~ 75% of the catchment area demonstrates moderate to good groundwater potential, while the remaining 25% is classified as having less potential. Notably, downstream regions of the basin, characterized by gentler slopes and unconsolidated sediments, were identified as significantly promising for shallow groundwater aquifers. The study underscores the significant roles played by geology, geomorphology, and lineaments in determining the occurrence and distribution of groundwater. These findings contribute to an improved understanding of groundwater availability and distribution and serve as a valuable resource for local and regional water management authorities. The insights gained from this research have the potential to aid in the sustainable management of groundwater resources, thereby ensuring the preservation of water quality standards throughout the Himalayan region.

The interface between soil and water is pivotal for groundwater recharge, particularly in our study area comprising recharge and discharge zones within the piedmont Shiwalik region and the alluvial area. With an annual average rainfall of 880 mm, major soil types include sandy soils in recent floodplains, newer alluvial regions, and loamy soils in the Shiwalik Piedmont zone. Infiltration studies at 12 locations revealed varying capacities, with infiltration rates exceeding 50 mm/h in sandy soils of newer alluvial areas, ranging from 10 to 50 mm/h in the Shiwalik piedmont zone, and approximately 10 mm/h in older alluvial areas with loamy soils. Analysis of soil samples collected from 14 locations within 1 m below ground level (m bgl) indicated a dominant presence of sand, accounting for approximately 71% of soil composition, along with clay mixed with silt (28%) and a small fraction of slime (1%). These soil deposits resulted from paleo-tractive currents, displaying graded suspension characteristics in the study area (i.e parts of Yamuna Nagar district, Haryana state, India). The lithological data analysis revealed an unconfined aquifer extending up to 140 m bgl, with isopach (granular zone thickness) ranging widely from less than 40 m to over 100 m. Groundwater utilization through tube wells was extensive, with 95% of 8736 tube wells in operation, predominantly distributed in the southwest or southern parts of the study area. Isopach conditions, variations in hydraulic conductivity, and higher yields influenced this distribution pattern. Tube wells in this area exhibited high discharge rates (> 1000 liters per minute (lpm)) compared to those in the Shiwalik Piedmont zone (< 1000 lpm yields). Annual groundwater recharge was estimated at 274 mm/year in the Shiwalik piedmont zone and 326 mm/year in the alluvial plains. Groundwater levels, both shallow and deep, were influenced by hydrological recharge and discharge conditions, with groundwater flow observed in a northeast-to-southwest direction, driven by variations in hydraulic gradients. Therefore, the interaction between soils and groundwater is intricately linked, with each influencing the other and playing a crucial role in groundwater recharge.

Various assessment schemes prioritize the identification of groundwater potential using spatial and remote sensing data, with the aim of improving people's lives. International agencies are exploring efficient locations for various agricultural sectors and domestic communities. The collection of data from various sources serves as an assessment parameter. In this observation framework, the integration of topography, geology, climatology, land use, and hydro-morphology data plays an essential role in simplifying groundwater modeling, saving time, and ensuring data accuracy by adopting the Analytic Hierarchy Process (AHP) assessment. In this chapter, our research aims to integrate multi-source spatial data and Sentinel-2 remote sensing data in 2023 at 10-m resolution to model groundwater in relation to the forest area status in the Tropical Antokan Watershed, Indonesia. Ten thematic layers of influencing factors will be applied, such as geology, geomorphology, soil type, soil texture, drainage density, slope, rainfall, flow direction, topographic wetness index, and land cover. The investigation results indicate that groundwater potential is divided into four categories: excellent (14.85%), good (27.15%), moderate (43.62%), and poor (14.39%). On the other hand, optimizing land use arrangements that represent the status of forest areas can cover 74.48% of the watershed, which can be converted into cultivation areas and play a significant role in local water control. Additionally, it is important to integrate remote sensing datasets into the area requirements of the investigated zone to reduce expenditure costs. Models derived from these observations can be used for a more optimal regulatory direction for sustainable groundwater management.

This study delves into the intricate interplay between lithological characteristics and the morphometric attributes of 45 river sub-basins in Peninsular India. These sub-basins are geologically categorized into three distinct rock types: granitic gneiss, basalt, and limestone shale. The morphometric parameters scrutinized encompass infiltration number, ruggedness number, drainage texture, elongation ratio, circulatory ratio, and drainage frequency. Analysis of infiltration numbers reveals a substantial elevation, approximately 42%, in basaltic river sub-basins compared to their granitic gneiss and limestone counterparts. This disparity underscores heightened infiltration rates within basaltic terrains. Ruggedness numbers elucidate a remarkable 66% increase in the ruggedness of river sub-basins situated in granitic gneiss in contrast to those in basalt and limestone, highlighting the profound influence of lithological resistance on the topographical features. Circulatory ratios exhibit a 9% augmentation in river sub-basins entrenched within limestone shale rocks, while elongation ratios manifest a 3.5% advantage for basaltic river sub-basins. Drainage texture values indicate a substantial 50% rise in basaltic river sub-basins when juxtaposed with granitic gneiss and limestone. Furthermore, drainage frequency values elucidate a 3% ascent in basalt and limestone river sub-basins compared to granitic gneiss. This comprehensive analysis furnishes invaluable insights into lithology’s pivotal role in sculpting river basins’ morphometry. Basaltic terrains exhibit notably higher infiltration rates, reduced ruggedness, and heightened drainage texture values, while limestone shale regions demonstrate advantages regarding circulatory and elongation ratios. Understanding these intricate relationships is paramount for fostering sustainable water resource management and informed land use planning across Peninsular India.

The morphometric analysis of watersheds serves as a foundational step in comprehending and strategizing water resource management, particularly in regions characterized by arid and semi-arid climates. In this work, the Buddayapalli watershed in the YSR District of Andhra Pradesh, India, is the subject of morphometric investigation. Geologically, this area is underlain by rocks from the Cuddapah Supergroup and Kurnool group of the Proterozoic age. Due to the limited and irregular rainfall patterns in the region, drought occurrences are prevalent, especially during the summer season (March–May). Employing Strahler’s principle, the drainage system within the watershed was carried out. The analysis revealed a dendritic pattern and sub-dendritic pattern, indicating the hierarchical arrangement of streams. This specific watershed is categorized as a Vth order, where lower-order streams predominantly shape the basin. The drainage density on average, a key morphometric parameter, is measured at 2.249 km/km2 within the watershed, spanning an area of approximately 28.075 km2. The topography and slopes of the area are primarily influenced by various physiographic conditions and geological erosion cycles. Notably, the elongation ratio, quantified at 0.764, signifies a pronounced elongation of the watershed. This elongation is attributed to the region’s high to moderate elevations and steep slopes, a consequence of structural disturbances in the geological formations. Utilizing toposheets, high-resolution advanced satellite imagery (LISS IV) and google earth satellite data in the Arc GIS and QGIS environment, this study delves into the the linear, areal and relief, features of the watershed. By employing advanced spatial analysis techniques, a comprehensive understanding of the watershed’s morphometric characteristics is achieved. This knowledge is essential for informed decision-making in water resource planning, particularly in areas prone to water scarcity and environmental challenges.

This study area presents the geophysical and hydrogeomorphological research done to explore for groundwater in certain regions of the Brahmanavellamla watershed in Telangana State, India’s Nalgonda Mandal. The study’s goal was to evaluate the subsurface features and locate prospective groundwater resources in order to alleviate the region’s problems with water scarcity. Hydrogeological structures and underlying lithological differences were identified by means of geophysical surveys, which included Vertical Electrical Sounding (VES). In order to interpret lithological boundaries, define aquifer zones, and evaluate groundwater potential, the resistivity measurements that were acquired were evaluated. Hydrogeomorphological investigation was carried out concurrently to comprehend the impact of surface features on the occurrence and transport of groundwater. Insights into the subsurface hydrogeological environment and the identification of suitable areas for groundwater research were made possible by the integration of geophysical and hydrogeomorphological data. The results of this study provide important direction for water resource planning and development projects in the area, assisting in the sustainable management and usage of groundwater resources in the Brahmanavellamla watershed. The study of subsurface formations in order to get a better understanding of the hydrologic cycle, groundwater quality, and type of aquifers was important for groundwater exploration. There are several methods for exploring groundwater. The surface geophysical technique, namely electrical resistivity is one of the widely used techniques for groundwater exploration. Vertical Electrical Sounding (VES) is a technique that can provide valuable information about the vertical successions of subsurface geo-substances in terms of individual thicknesses and resistivity values. It is a helpful instrument for groundwater examination since it determines aquifer thickness in a particular area quickly and accurately. The purpose of this inquiry was to locate twenty well-site locations for water delivery functions utilizing surface geophysical techniques. However, hydrogeological and geological investigations were also included in addition to geophysical surveying efforts to improve the research. The resistivity values were compared with the hydrogeology and geomorphology of the area and suggested a few locations for drilling of bore wells in the area. This comparative study will be helpful to the local authorities for drilling of more bore wells to mitigate the water demand in the area.

In Thailand’s Songkhla Province, the densely populated and economically vital Hat Yai basin (HYB) experiences high water demand from agriculture, industry, and domestic use. Consequently, effective water resource management is essential for the future development of the area, especially concerning groundwater use and recharge. This study investigates the impact of land use changes (LUC) on groundwater recharge potential in the HYB using the Soil and Water Assessment Tool (SWAT) model. Utilizing land use data from 2000, 2011, and 2018, the model was calibrated with runoff data from three stations, achieving an R2 of 0.70. The model revealed that the groundwater recharge potential in HYB in 2000, 2011, and 2018 was approximately 283, 244, and 240 mm, respectively. These figures coincide with a decrease in forest area and an increase in residential areas. Notably, a 10% loss of forest between 2000–2011 contributed to an 8% reduction in recharge, highlighting the significant impact of urbanization on this vital resource. Urbanization, which involves replacing forests with residential areas, diminishes water infiltration by disrupting vegetation and root systems, thereby increasing runoff and reducing groundwater recharge potential. Therefore, the study emphasizes the need for comprehensive land use planning and water management strategies in the HYB, prioritizing the protection of natural ecosystems and promoting sustainable development practices.

In this study, a combination of geostatistical methods and water quality indices were used to analyze long-term data on the water quality of Sumendu Lake, also known as Mirik Lake, a major tourist attraction in the Darjeeling Himalaya of West Bengal, India. The emphasis was on using descriptive statistics to analyze water quality, as well as indexing approaches to classify water quality and organic pollution levels of Mirik lake over an extended period. Multivariate statistical analyses were used to identify the influencing factors. According to the results of the WQI analysis, the lake’s water quality is rated as “good” to “moderate” using the weighted arithmetic water quality index technique, which ranges from 36.38 to 68.99. The calculated organic pollution index has shown a Class II to Class III level of contamination (i.e. beginning of contamination to lightly polluted) during the observation period except 2017 (OPI = 5.68, heavily polluted). The findings of the research illustrate the beginning of organic pollution and a eutrophic condition in the monitored lake water, which may result in the deterioration of water quality and the death of the lake in the near future. Therefore, these challenges must be addressed immediately, and effective preventive measures must be implemented to protect this water body and the nearby community that relies on it. Evaluation and interpretation of long-term data on the water quality of Mirik Lake may produce vital information that is critical for water management planning, enabling more comprehensible water quality projections and conservation measures to protect this water resource.

Marine litter, comprising manufactured solid waste, predominantly plastic, enters the marine environment, posing ecological and human health risks. Efforts to address this issue involve estimating debris quantity, categorizing sources, and implementing cleanup initiatives in Kovalam coastal village, this place is famous for fish landing center and Muttukadu boating. In this study, cleanup spanning 20,879 square meters by 300 volunteers, 1560 items weighing 352 kg were collected. Plastic constituted a significant portion (118 kg), aligning with other beach litter studies. Notable items included polythene bags, syringes, and plastic fragments, underscoring plastic pollution concerns. Glass, paper, textiles, wood, rubber, and diverse items were also retrieved. Medical waste such as syringes, tonic bottles etc. is very harmful to the environment when it has been disposed without treatment. The data highlights the urgency for strategies to reduce plastic usage, promote recycling, and enhance public awareness for responsible waste disposal.

The importance of assessing groundwater quality is unassailable in our day and age. This assessment determines the classification of water quality for different purposes. Recent advancements in Geographic Information System techniques have allowed hydrologists to apply various approaches for more effective groundwater quality prediction and management. In this chapter, our case study was groundwater in Kanchanaburi Province, Thailand. Twelve groundwater parameters were estimated for mapping groundwater quality based on the entropy water quality index. Inverse Distance Weighting and Kriging were employed to prepare layers of groundwater parameters based on the normal distribution test. The results indicated that approximately 97.3% of the area was classified as good and very good groundwater for agricultural and drinking purposes. The coefficient of determination, root mean square error, and mean absolute error of the groundwater quality map were 0.82, 0.07, and 0.05, respectively. Polluted groundwater locations (groundwater quality from very poor to moderate levels) were identified in some agricultural areas. The findings of this study can serve as a reference for global groundwater quality assessments using geospatial techniques.

The land cover and change detection analysis are critical factors for sustainable forest management because of the increasing fragmentation of forests. With the development of remote sensing and GIS applications in forestry in recent years, significant deforestation has been measured. Maps of deforestation are created using satellite photos and ground-based data for monitoring forest degradation and identifying changes in the forest cover based on multispectral satellite data have been shown to be capable of doing so. The current work concentrated on applying cutting-edge RS and GIS technologies for LULC (Land use and Land cover), change analysis and comparative assessment. Chikmagalur taluk’s forest vegetation across two decadal periods, from 2000 to 2022. The methodologies of image differencing, reclassification comparison change detection, and normalized difference vegetation index (NDVI) were used. The results demonstrate that the study area experienced a rapid decrease in forest cover, conversion of rural to urban in towns and municipalities, forest fire, and illegal logging and the main reason is due to coffee and other plantation crops in private holdings. Accordingly, the study suggests creating and executing a guided forest conservation programme that progressively implements appropriate sensitization, regular eco-guard visits, reduced intimidation, participatory forest mapping and public awareness.

The rational utilization of groundwater is one of the pivotal factors, especially in all agricultural regions, to strengthen agricultural sustainability and support the regions’ sustainable socio-economic development. In general, groundwater resources are globally used for purposes such as supplying drinking water, facilitating irrigation, and supporting industrial activities. Increasing population, higher food demands, and rapid growth of unplanned urbanization have frequently led to the overexploitation of groundwater resources in various parts of the world. Alluvial terrains are favorable for intense agricultural activities. Consequently, these aquifers are especially vulnerable to changes in water levels and groundwater contaminations. Globally, several regions like the North China Plain, Sahelian Sudanian areas of West Africa, Sistan and Balouchestan provinces of Iran, regions of Punjab in India, and the Indo-Gangetic plains are experiencing fluctuations in alluvial aquifers due to extensive agricultural practices and over-extraction due to irrigation. This has raised concerns about the sustainability of groundwater resources. The use of remote sensing data, coupled with the integration of various hydrogeological models, has emerged as an essential tool for monitoring and analyzing the fluctuations of groundwater and associated issues within alluvial aquifers. In a specific study conducted in Dakshin Dinajpur district, West Bengal, India, Arc GIS and remote sensing data were utilized to analyze the spatiotemporal variation of depth to water level over three years for different seasons in alluvial aquifers. The findings indicated that the spatio-temporal pattern remained relatively consistent over the three years; the depth to water level was highest during the lean season compared to pre and post-monsoons. The study identified higher fluctuations in the depth to water level in the southwestern and southern parts of the district. The likely cause for the declining groundwater pattern in the district was the over-exploitation of groundwater for year-round irrigation of rice cultivation and improper utilization of groundwater resources. Implementing scientific cropping techniques and irrigation systems to conserve water resources is necessary. Managing and monitoring these fluctuations is essential for sustainable groundwater use, as excessive exploitation or depletion of alluvial aquifers can harm water availability, ecosystem health, and the overall sustainability of water resources in a region.

Smart unmanned aerial systems integrated with accurate Global Navigation Satellite System (GNSS) are now an important approach for acquiring spatial data and have the potential for a wide range of engineering applications. This study was endeavoured to determine the feasibility of Unmanned Aerial Vehicle (UAV) or Drone-based technology to monitor the status of water conservation structure and to examine their suitability by GIS-based Multi Criteria Decision Making (MCDM) inputs derived from High Resolution 3D Imagery (HRI). For the Pauha-Kurmigundra Narwa (PKN) Restoration, updated thematic data is used based on High-resolution Aerial orthomosaic images and Digital Surface/Terrain Model obtained through UAV survey. To compensate for the shift from the real world due to positional errors in UAV, six reference points were surveyed by the static mode of Differential Global Positioning System (DGPS) to find the correct relative location with respect to earth coordinates. The MCDM process was used in conjunction with overlay weighted analysis based on the Analytical Hierarchy Process (AHP) technique to determine possible zones for acceptable constructions. To store more water in the agricultural watershed, a total of 19 new recharge structures—including check dams, stop dams, and storage dams—were suggested. Based on the physical observations, recommendations were also given regarding the state of repair needed for 41 structures that were already in the research area. Of the 41 structures, 8 required restoration and repair. The study’s findings showed that since UAVs and drones meet specific requirements, like precise ground control points (GCPs), high-quality photography, and scientific planning, they might be employed as the main source of topographic data. This research also corroborates that with the advancement of technology, UAV-driven digital elevation models have largely supplanted conventional topographic map-based approaches and has become more useful in landform analysis.

The paper explores the profound influence of technological advancements on contemporary groundwater management strategies. As technology continues to evolve, innovations such as the Internet of Things (IoT), Artificial Intelligence (AI), Remote Sensing, and Blockchain are reshaping the landscape of groundwater monitoring, prediction, and decision-making. This paper examines the integration of these cutting-edge technologies and their implications for sustainable groundwater resource management. Beginning with an overview of the historical context of groundwater management, the chapter progresses to highlight the transformative role of IoT in real-time monitoring, providing insights into well conditions and groundwater quality. The application of AI and Machine Learning in predictive modeling is discussed, emphasizing adaptive approaches to groundwater prediction. Remote sensing technologies, including high-resolution satellite imagery and GIS innovations, are explored for their contributions to comprehensive groundwater mapping and analysis. The paper also delves into sensor technologies and their role in real-time groundwater quality assessment, addressing challenges and opportunities in their deployment. Blockchain technology is examined for its potential in securing groundwater data, ensuring transparency, and maintaining data integrity. Autonomous vehicles and drones are showcased for their applications in efficient groundwater exploration, offering rapid data acquisition in diverse terrains. Smart infrastructure and the incorporation of automation in pumping systems are discussed as contributors to resource optimization. Ethical and social considerations related to the use of advanced technologies in groundwater management are explored, emphasizing the need for responsible and equitable implementation. The paper concludes with reflections on anticipated future advancements in technology for groundwater management and provides recommendations for researchers, policymakers, and practitioners. By examining the ethical, social, and practical dimensions of technological innovations, this chapter contributes to the understanding of the transformative potential of technology in sustaining groundwater resources for future generations.

Each model relies on parameters determined through calibration, a process involving adjusting these values to match the model’s behaviour with real-world systems, such as in hydrologic modelling, where simulated discharge aligns with observed data from a river basin. Manual calibration, known for its laborious nature, has led to the development of automatic procedures, leveraging computer power for efficiency. However, both manual and automatic methods face challenges in finding the optimal parameter values, often encountering multiple local optimal sets through optimization algorithms. As a result, identifying a definitive “best” parameter set remains a complex task, as no single set consistently outperforms others. At the core of Monte Carlo methods, utilized for numerical computations, lies the repeated random sampling process. This method involves conducting multiple simulations to acquire distribution of a stochastic variable, when obtaining a closed-form expression is challenging or impossible in various physical and mathematical challenges. Monte Carlo methods prove invaluable in scenarios where deterministic algorithms are impractical. They find application in three primary problem categories: generating samples from probability distributions, numerical integration and optimization. In hydrology, Monte Carlo simulations’ results hold crucial significance, contributing significantly to tasks like parameter optimization, conducting sensitivity analysis and estimating uncertainty. The structured method of incorporating uncertainties related to model inputs using probability distributions and propagating these uncertainties through the models of the system to obtain model outputs is termed uncertainty analysis. In hydrologic modelling, uncertainty analysis closely mirrors the principles of Monte Carlo simulation. This method involves defining probability distributions for model parameters and running the model iteratively for multiple scenarios to establish probability distributions for model outcomes. Meanwhile, sensitivity analysis evaluates how changing individual parameters, one at a time from a starting point, affects the output of a model. It calculates sensitivity coefficients by comparing output changes to input changes, thereby revealing the relative sensitivities of input parameters. This chapter provides a thorough overview of performing Monte Carlo simulations and subsequently leveraging the outcomes of these simulations to refine uncertainty estimations. This refinement is accomplished by constraining the range of model parameters through sensitivity analysis.

Digital India needs a monetized hydroinfrastructure along with data centers to store geospatial data of both hydro and energy flow in a networked infrastructure with suitable control mechanisms. Sustainable development has to be through an integrated water resources management of surface, ground, soil water, green/blue water, and energy entities available in the river basins. The geospatial knowledge infrastructure data, which is collected in large geographically spread river basins/watersheds, should be continuously stored and shared through data centers in a real-time basis. Industry 5.0 enables the use of cyber-physical systems, cloud service models, and continuum computing required for sustainable management of water. The system of systems models at the watershed scale are easy to develop using existing geospatial data and additional attribute data, for use in assessing environmental health and sustainability aspects. Machine learning-based models in surface water, groundwater, soil water, integrated water vapor, etc. are to be tuned to climate change-based operations of ICT-enabled hydroinfrastructure. The good ground and surface water governance mechanisms can be simulated in computers, and all water related operations can be automated to bring social inclusiveness and equity. Various models using machine learning methods and satellite based spatial data collections available through water geoportals in India are discussed. The necessary risk reduction measures using cybersecurity are discussed as the Geospatial Data Centers are to be secured against cyber threats and intrusions of deep fake geography.

The incorporation of Remote Sensing (RS) and Geographic Information System (GIS) technologies for the precise assessment of groundwater potential in the Himalayan region represents a significant leap in water resource management. This study aims to provide significant insights into the evolving field of hydrology through a review by bridging the gap between old methods and contemporary technology, offering a model for areas with comparable water resource complexity. The methodology used in the study comprises the amalgamation of RS and GIS technologies to evaluate groundwater potential in the Himalayan region. These technologies are used to gather and process data on variables like temperature, soil moisture, and land use/ land cover dynamics. This paper identifies some of the existing issues that need to be addressed by the integration of modern RS and GIS technologies. The key findings include the identification of the difficulties such as topography, geological heterogeneity, rainfall that is unpredictable due to climate change, and human factors like urbanization, deforestation, and changing land use that affect groundwater supplies. The study contributes both scientifically and practically to sustainable water resources management. Through this study, we not only deciphered the current state of water resources but also forged a path toward sustainable and data-driven water resource managing practices in one of the world’s most critical and challenging ecosystems.

This study was conducted in Faisalabad, Punjab, Pakistan, a region where groundwater plays an important role in agriculture and human consumption. This chapter begins by emphasizing the importance of water for all living things. It highlights the increasing pressure on water resources due to industrialization, changing climate conditions, population growth, and, leading to freshwater crises in many regions. This is followed by a discussion of the dynamics of groundwater quality, and various factors affecting it. This chapter explores various modeling methods used to analyze these dynamics, including GIS-based machine learning techniques, GIS-based hydrological models, and GIS-based water quality parameters, and Integrated methods of remote sensing. An integrated approach to remote sensing and GIS for the specific context of Faisalabad is presented as well as each approach is evaluated based on its strengths and limitations. Groundwater levels in the Faisalabad had been declining at an average rate of approximately 0.11 m/year. Salinity is the simplest most important hazard element toward bad conditionality degree being registered throughout approximately 45 percentage of all lands below consideration. Due to commercial sports in the Faisalabad, Punjab, Pakistan result in degradation of groundwater quality. This chapter also explores future directions for the field and highlighting emerging technologies like LIDAR data and hyperspectral, agricultural drones, and deep learning and AI for improved groundwater quality monitoring and modeling in Faisalabad. Finally, the chapter illustrates the importance of using advanced modeling methods and emerging technologies to analyze land quality dynamics. It provides a roadmap for future research in this area, to enhance forecasting accuracy, enable real-time analysis, and sustainable groundwater management in Faisalabad, Punjab, Pakistan. It helpes the insights and recommendations presented in this chapter will guide future efforts in this important area of research. This comprehensive review serves as a valuable resource for researchers, policy makers and practitioners working in the field of groundwater management.

Rapid groundwater depletion is one of India’s most pressing sustainability issues today. Groundwater depletion caused by irresponsible groundwater pumping has major regional food and water security implications. The present study has reviewed the groundwater potential, causes and effects of groundwater depletion, issues and challenges, initiatives taken for managing the groundwater, and the policies and regulatory framework of the Indian government. Annual extractable groundwater resource in India ranges from 393 to 411 billion cubic meters (bcm). Whereas the annual groundwater extraction for Irrigation, Domestic and Industrial uses in India ranges from 231 to 253 bcm. The percentage of the extraction of groundwater for irrigation, domestic, and industrial use is 87, 11, and 2%, respectively. Of the total assessment units in India, 11.23% of units in different States and Union Territories have been classified as Over-exploited. Delhi, Haryana, Himachal Pradesh, Punjab, and Rajasthan are the five states with the highest proportion of over-exploited units. The subsidy in electricity for farmers, dependency on groundwater for irrigation, uncontrolled extraction of groundwater by industrial and commercial sectors, lack of implementation of the policy and guidelines of the government, and lack of public involvement and awareness in groundwater management are the factors responsible for the depletion of the groundwater resources in India. The learning and outcomes of the study would be helpful for the researcher and the policymakers.

The Yamuna River holds significant importance for environmental scientists and indigenous communities due to its social, ethical, and geographical significance. The city of Delhi is marked by ongoing anthropogenic development and various festivals that have a significant detrimental impact on the quality of surface waters. This study specifically examined the period following festivals from September to November spanning from 2012 to 2021, including Ganesh Chaturthi, Durga Puja, and Chhath Puja, during which non-biodegradable idols are immersed in the river. Three distinct sampling locations were selected to analyze the variations in Electrical Conductivity (EC), pH, Dissolved Oxygen (DO), and Lead (Pb) levels in the waters of the Yamuna. The presence of lead, a key indicator of contamination from paints, was observed to range from 0 to 1.80 mg/L throughout the study period, significantly exceeding the acceptable value of 10 µg/L. The data has been analyzed within a GIS environment, using Kriging for spatial distribution mapping alongside the Classified Land Use Land Cover map employing the Support Vector Machine Algorithm. R programming has been utilized to conduct the Mann–Kendall test, the Sequential Mann–Kendall test, and Sen’s slope analysis. The findings indicate a significant increase in overall concentrations of the parameters, largely attributed to waste fluids flowing through numerous drains. Dissolved oxygen (DO) values suggest limited support for aquatic life. However, the electrical conductivity (EC) concentration decreased, while the pH and DO marginally increased, implying an improvement in Yamuna water quality during the lockdown period. This study aims to lay the groundwork for more effective water management strategies in the region. We propose stringent regulations for monitoring the release of chemically reactive compounds into surface waters resulting from industrial and other human activities to facilitate the restoration of the river to its natural environmental state. Additionally, we recommend the use of natural colors derived from flowers, leaves, seeds, bark, wood, and roots of plants to prevent contamination. As current Machine Learning Technologies advance, water quality studies are expected to improve.

Management of water quality is critical issue in modern times. Water quality determination is highly sensitive. The term quality ascribed to water is fuzzy as it is only a relative term. To evaluate water quality, the goal is to establish a link between the metrics. It is vital to have a reliable model to forecast the state of water quality. A broader description would be that rising cations and anions are the source of rising Total Dissolved Solids (TDS). To precisely forecast numerical values of TDS that match the values of cations and anions, a mathematical model cannot be created. The inorganic components of TDS include dissolved gases, a small amount of organic stuff, and carbonates, bicarbonates, sulphates, chlorides, and phosphates of calcium, magnesium, potassium, and iron, among other elements. Hence, it can act as the best consequent to assess water quality since there is positive correlation between the ions present and TDS. Determining of the water quality due to contamination is highly unpredictable in exact numerical terms. With the aid of previous experience, it may be necessary to generate additional linguistic expressions to define related water quality parameters. The capability of fuzzy logic to develop qualitative model to characterise by appropriate reasoning dwells here. It facilitates influential tool to model uncertainty analogous with imprecision and vagueness. Fifteen parametric variables that include Total Dissolved Solids, Chloride, Conductivity, Fluoride, Hardness, pH, Sodium, Potassium, Iron, Calcium, Magnesium, Bicarbonate, Carbonate, Nitrates and Sulphates are dealt in the present study. Experimental output is modelled in seven sharp stages. One antecedent and one consequent model is developed to bring about a relationship in the first five steps. Two antecedents and one consequent model is developed in the final two steps. In total, seven Fuzzy Inference Systems are built. An attempt is also made to establish sensitivity of parameters. Cause-effect relationship is also dealt in the current work. Comparing the results, TDS and Total Hardness turn to be very good parameters to determine water quality. It is concluded that, fuzzy inference system furnishes a sagacious way to apprehend uncertainty in relationships amid parameters that assess water Quality giving lesser error. Based on the water quality values, the authorities/ policy makers should frame the regulations in accord so as to monitor the health and wealth of the community in an economic and efficient way.

Groundwater serves as a critical resource for drinking, irrigation and industrial purposes within the Sarada River basin, Andhra Pradesh, India. Sarada basin is currently witnessing pressures stemming from unsustainable practices, climate fluctuations and population growth. An essential step toward achieving sustainable groundwater management within this specific context is the delineation of groundwater potential zones. These zones can be effectively mapped through the integration of Remote sensing (RS) and Geographic Information System (GIS) technologies and by amalgamating diverse thematic layers encompassing geology, geomorphology, drainage, slope and land use tailored to the unique conditions of the Sarada Basin. Additionally, a machine learning approach was employed by integrating algorithms such as support vector machines (SVM) and AHP multi-criteria into the methodology to enhance the accuracy of groundwater potential zone delineation by learning various diverse datasets and the spatial relationships between various environmental factors within the Sarada River basin.

Management of the resources of groundwater has become increasingly significant as a consequence of the complex interplay of climatological, geological, and anthropogenic factors, necessitating cutting-edge methods like Geographic Information Systems (GIS) and RS (Remote Sensing). This study integrates methodologies from RS, GIS, Multi-Influence Factor (MIF), and Analytic Hierarchy Process (AHP) to identify groundwater potential zones (GWPZs). Eight thematic layers-soil, slope, rainfall, lineament density (LD), drainage density (DD), geology, geomorphology (GM), and land use and land cover (LULC)-were utilized in this analysis. Each thematic layer was assigned weights based on its relevance to groundwater occurrence. Both MIF and AHP techniques employed quantile categorization to classify GWPZs into five categories: very poor, poor, moderate, good, and very good. The analysis revealed that approximately 42.47% of the study area exhibits good GWPZs, while 45.93% shows very good GWPZs. Groundwater levels were used to validate the maps through the Receiver Operating Characteristic (ROC) curve using machine learning techniques. The MIF and AHP approaches demonstrated accuracies of 77% and 86%, respectively, validating their effectiveness in delineating GWPZs. This study establishes MIF and AHP as reliable models for groundwater potential zoning, with potential applications across other regions of Andhra Pradesh.

All living things depend on water, a precious natural resource, to survive. One of the biggest problems facing the energy, industrial, and agricultural sectors is the timely availability of water. Population development, industrialization, and urbanization all contribute to an increase in the demand for water, necessitating a deep comprehension of hydrologic processes and the spatiotemporal variability of water resources. Studies on hydrologic and water resource management have shown great potential in this respect for the application of geospatial methods in conjunction with hydrological models. Hydrologic models developed to model the behavior of watersheds are classified into eight categories: lumped, distributed, semi-distributed, conceptual, physical, empirical, deterministic, and stochastic. These categories are based on the degree of sophistication and complexity of the models. To enhance performance, geospatial techniques are also used into semi-distributed and distributed models. In this chapter, the application of geospatial techniques to hydrological modeling is covered in detail.

Groundwater is a valuable resource supporting various sectors worldwide, but it is being overexploited and its quality is deteriorating due to population growth, agriculture expansion, and increasing food demands. Sustainable development of groundwater is a global challenge due to limited freshwater resources. Water quality datasets are often imbalanced across large areas, making field-based measurements expensive and time-consuming for accurate predictions. In this chapter firstly describe about some traditional methods like geomorphological method, geological method, well-inventory, geophysical method, and electrical resistivity method, and photogeology. This Chapter provides the use of machine learning to map groundwater quality. To address this, focus on three effective methods: Artificial Neural Networks (ANNs), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBOOST). ANNs mimic biological neural networks, processing data through layers of interconnected neurons. Sensitivity to initial weights, biases, and activation functions. Small changes in input or model parameters can result in significant output variations. SVM uses optimal margins to separate different data classes, handling non-linear problems by projecting data into higher dimensions. SVMs generalize well and are suitable for low-data scenarios. Less sensitive to noise in data compared to ANNs. The choice of kernel function and its parameters can impact decision boundaries and classification outcomes. XGBOOST is a recently developed gradient-boosted tree technique with built-in regularization, efficient resource usage, and model interpretability. Fuzzy method and regression models also use for groundwater prediction. Grace satellite data set is used for storage of groundwater prediction. FEFLOW, MODFLOW, and MIKE SHE are used for groundwater quality and quantity assessment. These findings contribute to the advancement of water quality assessment and provide valuable insights for future research and practical applications. This book chapter will be helpful for students and researchers in the major fields of water resources management, agriculture, and environmental engineering as well as policymakers, particularly in developing countries.

Precise estimation of reference crop evapotranspiration (RET) is critical for effective water resource management and irrigation across various climatic regions. The present investigation used CROPWAT, a software tool developed by the Land and Water Development Division of Food and Agriculture Organization (FAO), employing the Penman–Monteith (FAO56-PM) method for RET calculation. However, the FAO56-PM method and related software are not practical when dealing with a restricted number of meteorological components. Hence, it is crucial to create a new method for measuring RET that uses fewer parameters. To address this challenge, climate data of 30-years were collected from a meteorological station of Skardu located in humid region of Pakistan. Firstly, CROPWAT 8.0 was used to compute the RET with the help of the weather factors as input. Secondly, correlation analysis (Pearson, Spearman, and Kendall) was performed to identify the prime climatic input factors. Afterward, two tree-based machine learning (ML) algorithms [extreme gradient boosting (XGBoost) and random forest (RF)] were employed to develop a model that describe relationship between weather observations and RET. The comparison between predicted (ML approaches) and actual (FAO56-PM) RET observations were evaluated using scatter plots and Taylor diagrams. Upon analysis, RET estimated by ML approaches coincide well with standard FAO56-PM method using effective parameters. In conclusion, this study recommends the use of ML approaches in water resource management across diverse climatic regions (humid, semi-arid, and arid conditions) using limited meteorological data (temperature and sunshine hours only). In order to achieve water sustainability, these ML techniques should give preference to areas with higher RET values and execute suitable irrigation scheduling strategies for crops.