Geospatial Technologies for Land and Water Resources Management

Land and water resources management are essential for the future sustainability of the environment. The studies on land and water resources require basic geo-referenced data, such as land use-land cover (LULC), soil maps, and digital elevation models (DEMs) for capturing the spatio-temporal variations of thematic layers. These data can be easily obtained from remote sensing images and limited ground truth. Hydro-meteorological data, such as precipitation, air, land surface temperature, solar radiation, evapotranspiration, soil moisture, river and lakes water levels, river discharge, and terrestrial water storage, can also be derived from remote sensing as well as from point-based ground instruments. Then, studies can be carried out at various spatio-temporal scales.

Land use and forest cover (LUFC) classification from satellite data in mountainous terrain offers challenge due to varied topography and complexities owing to different illumination conditions. Digital classification following supervised and/or unsupervised techniques in combination with/without ancillary information often does not provide acceptable level of accuracy. This chapter formulates and applies a hybrid approach for LUFC classification using moderate resolution satellite data. Both ‘elimination’ and ‘fishing’ approaches were used to classify the state of Sikkim into seventeen categories. The classification accuracy was estimated at 94.87% at 1:50,000 scale, which is suitable for utilization in further studies such as surface hydrological and energy fluxes. Further, the digital elevation model was utilized to derive the topographic units at 1000 m elevation steps, slope and aspect and their distribution across the seventeen LUFC classes. The distribution of various LUFC classes across different elevation, slope and aspects offers useful information for ecosystem planning and management.

The Himalayan reservoirs have immense significance from the point of view of water resources planning and management. However, natural and anthropogenic changes and their effects upon these reservoirs are often not explored, mainly due to limitations of data availability. This chapter presents an appraisal of land use/land cover (LULC) changes over the Tehri catchment located at the lower Himalayan region, using remote sensing and geographic information system (GIS). The imageries are collected for different years, i.e., 2008, 2014, and 2020 from the Landsat 5, Landsat 8, and Sentinel 2 satellites, respectively, with the objective of deriving information on different LULC classes. Following a supervised classification, the catchment area is divided into eight classes, viz. open forest, dense forest, water bodies, shrubland, agricultural land, settlements, barren land, and snow covers. The accuracy of classification is assessed with respect to the Google Earth images and ground truth verification. A comparison between the areal coverage of the LULC classes was analyzed for temporal LULC change detection over the catchment. Comparing 2008 and 2020, it is clear that the dense forests and barren land have decreased. On the other hand, an increase in the open forests, water bodies, shrubland, snow, and settlement is observed. The accuracy assessment results confirm that the LULC changes reported in this study are justifiably accurate and utilizable for further applications. The results reported in this study may be helpful to frame solutions to hydrological problems of the Tehri catchment. Moreover, this study highlights the usefulness of remote sensing and GIS in hydrological applications, even in mountainous catchments.

Land use and land cover are the two separate but related concepts that are predominant characteristics of the land. In general, land cover is defined as the observed surface cover on the ground, such as vegetation, water bodies, barren land, or man-made features, while land use refers to the purpose for which land is being used. Land use and land cover (LULC) change have a significant impact on water resources. Hence, it has become one of the critical components in most studies related to water resources. Remote sensing (RS) and Geographical Information System (GIS) techniques are extensively used to detect the location of changes, type of changes, and quantification of changes in LULC. In this study, an attempt is made to detect a change in LULC in the past three decades (1985–2015) for the Tons river basin, a sub-basin of the Ganges river basin. The supervised classification method was employed to classify the satellite images of the years 1985, 1995, 2005, and 2015 to study the change in LULC. The Maximum Likelihood Classification (MLC) method was used for the classification. Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) datasets were used to prepare LULC maps. Seven LULC class types are observed in the basin, namely agricultural land, barren land, built-up area, dense forest, open forest, shrubland, and water. The LULC study showed agricultural land is the dominant class in the basin, followed by forest land, the second dominant class. An increase in built-up area from 45.73 km² (1985) to 105.41 km² in 2015 shows rapid urbanization. Some change in water body class was also seen, which increased from 235.24 km² in 1985 to 255.96 km² in 1995, it showed a decrease in the area up to 211.38 km² in 2015. There was an increase in open forest area, and no significant change was observed for agricultural land. The overall classification accuracy varies from 85.4 to 90.6%, and the kappa coefficient value varies from 0.83 to 0.89 shows the satisfactory classification of the satellite-derived LULC maps.

Increasingly forest land is diverted to different land uses leading to various levels of disturbances in a landscape. Any disturbance could affect the structure and functions of a landscape, their inherent properties and interactions, thereby could lead to temporal or irreversible changes. In this chapter, the spatial distribution of various forests and non-forest patches were combined with road and settlement proximity zones using remote sensing and GIS tools to generate disturbance index (DI) of a landscape, by adopting landscape ecological principles. Various landscape ecological matrices such as forest fragmentation, interspersion, juxtaposition patchiness, and porosity, were analyzed using spatial analysis. Field sampling data on species richness from 862 plots (nested quadrats of 20 × 20 m²) were analyzed to adjudge the correlation between different DI levels and their diversity content, and interestingly, higher species richness was observed for lower DI levels. The study was selected is the northeastern India of the eastern Himalaya accommodating Arunachal Pradesh, Assam, and Meghalaya states. DI demonstrated progressive disturbance in the forest structure and composition. Meghalaya state has better reflected the decreasing pattern of DI with species richness and its endemic subset. Disturbance Index, a landscape-based model proved to have well captured the patterns and processes, thereby advocating wider application and replication for conservation planning.

The Himalayan ecosystems have characteristics land and vegetation distribution pattern owing to its varied complexities in topography, seasonality, changing climate and socioeconomic interventions. The comprehensive mapping of land and vegetation cover in the Himalayas has always been a great challenge to the cartographers and remote sensing scientists. The focus of this chapter is to demonstrate a practical approach to map and understand land and vegetation cover distribution, and their dynamics over interval of three decades using earth observation data. The study provides an insight to characterize the vegetation pattern across an elevation gradient using geospatial techniques in a test site of Kargil district in the Ladakh Union Territory, India. Two set of images during August–November 1975 and 2005 were used in classification that provided high classification accuracy of >90% (overall, and 0.86 kappa), as per field correspondence. This spatial analysis has indicated that LULC demonstrated significant changes during 1975–2005. It was observed that the barren lands and the snow cover areas together contributed to nearly 80% of the total area in 1975, whereas in 2005, they contributed to nearly 60% area of the district. Variation in elevation range owing to distribution of the vegetation classes was realized for the eastern and western aspects. The separation of classes with a sharp boundary between two adjoining classes is absolutely impossible in nature. Therefore, some misclassification was noticed at the ecotone region between two spectrally similar classes such as agroforest-scrubs, scrubs-pastures. Another noticeable observation is the mapping of water body pixels in 2005 (58.88 Km²) in contrast to 1975, which may well be attributed to the low resolution (60 m) of satellite data used for 1975, and rise in the extent of water bodies such as alpine lakes, and more water flow through river channels owing to snow and glacier melting. The geospatial database integrated with field data plays an important role toward land and vegetation cover dynamics studies, useful for sustainable management.

Holistic planning approach for judicious use of natural resources is critical for development in the current depreciating global climate scenario. Identification of suitable areas/sites for planning land and water conservation measures is critical for long-term management and sustainable development. Information of natural resources inventoried using very high-resolution satellite images when integrated using Geographical Information Systems will provide an appropriate planning tool for sustainable management of natural resources. Particularly, planning activities that are carried out by the integration of geospatial technologies such as remote sensing and GIS help in achieving sustainable development goals. In this study, land and water resources development plans were generated for Chharba Gram Panchayat (GP) of the Dehradun district, Uttarakhand state in Northern India. Long-term surface runoff potential for different meteorological conditions was analysed spatially over the study GP. Suitable sites for land and water management practices were identified using a multi-criterion decision-based approach. Various basic and derived thematic layers that include ground water prospects, terrain characteristics and soil distribution were included in the planning. Land use land cover information generated using high-resolution satellite data at 1:2000 scale is of great help for developmental planning. Water Resource Development plan indicated that nearly 0.6% (8.43 ha) and 8% (121 ha) of the area is suitable for check dams and farm ponds, respectively. The analysis revealed that this area under single cropped areas can be converted to intensive agricultural areas while nearly 35 ha area under agricultural plantations can be converted to agro horticulture. Further, land use under sparse scrub land can be converted to agroforestry. Thus, the suggested land and water resources development plans are expected to convert the existing land use pattern into more suitable categories as per its potential without jeopardizing the environment.

The plethora of remotely sensed datasets, specifically the Sentinel-2 data, provides an opportunity for assessment of waterlogging areas. Surface waterlogging is one of the main hazards in north Bihar, specifically in Gandak and Kosi command areas, which necessitates to map and monitor the waterlogging situation accurately at a fine scale. Present study is carried out with an objective of mapping the permanent and seasonal waterlogging areas in a flood-prone Vaishali district, Bihar state using Sentinel-2 multi-spectral data available at 10 m spatial resolution for 2020. The permanent and seasonal waterlogged area mapping were carried out using two approaches, namely spectral index (NDWI) and Otsu method based on the wetness of KT transformed images. The result suggests that both techniques could identify similar spatial patterns of waterlogged areas, while differed on area statistics. The total area under permanent and seasonal waterlogging using NDWI was less compared to the Otsu-KT approach. The higher estimate of Otsu-KT approach could be related to saturated areas, where pixels with higher moisture content during the binary thresholding approach may get classified. Although Otsu algorithm proved to be effective for delineation of water bodies and river network, its efficacy for delineation of waterlogged areas with saturated areas needs to be studied extensively.

Increasing competition for land and water resources is expected in future due to rising demands for food and bioenergy production, biodiversity conservation, and changing production conditions due to climate change. Growing competition for water in many sectors reduces its availability for irrigation. Thus, efficient approaches are required for effective management of water in every sector, particularly in agriculture. To achieve efficient and effective water use, it requires increasing crop water productivity (WP) and crop yield through improved crop varieties. Only high water productivity values carry little importance if they are not associated with increased or acceptable yields. Such association of high (or moderate) water productivity values with high (or moderate) yields has considerable implications on water’s effective use. Land productivity and water productivity increment are the most efficient solution for meeting increasing food demand and climate variation. Water consumption (evapotranspiration (ET)) is an influencing factor for productivity estimation across a command area. Thus, in the present analysis, ET was estimated first, followed by land and water productivity assessment for the Hirakud canal command situated in the Mahanadi basin. Water accounting plus (WA+) Framework, jointly developed by the UNESCO-IHE, IWMI and FAO, has been utilized to assess the total water consumptions, agricultural water consumptions (using green water and blue water), and estimation of land and water productivity for a period of 12 years, i.e., 2003–2014. WA+ is a python based framework designed to provide explicit spatial information on water depletion and the net withdrawal process of a region using globally available open access data. The results showed that land productivity and water productivity varies from 1973 to 2219 (kg/ha) and 0.41 to 0.55 (kg/m³) for irrigated cereals (paddy), respectively, across the Hirakud canal command area.

Food and fiber demand is increasing due to population growth, which compels us to optimize the irrigation system's performance to get more yield of food and fiber from the available resource. The high yielding varieties of crop requires a timely and adequate supply of water. A well-performing, well-managed irrigation system is a prerequisite to ensure a timely and proper water supply. The development of technology makes us ease in assessing the performance of the irrigation system. The availability of geospatial data in conjunction with the other ground data helps assess the performance minutely in spatial and temporal approaches. Performance evaluation of a canal irrigation system can be carried out by evaluating its actual water dynamics, water use, and productivity. Gadarjudda and Lakhnauta minor canal systems of the Upper Ganga Canal (UGC) are old systems and still performing in the right way with general maintenance works. The minor systems are decade old, and ungauged water management is carried out effectively. The physical condition of systems is not good; however, the canal carries the designed discharge. The results show that depleted fraction of Gadarjudda and Lakhnauta minor is nearly 1 to 1.30 kg/m³ for paddy productivity (actual water consumed), followed by Wheat and Sugarcane productivity by 1.61 kg/m³ and 6.08 kg/m³, respectively. The spatial maps of the indicator generated in the GIS environment give the information of the irrigation system's performance over command areas and help for evaluation. Thus, the study reveals the excellent performance of the UGC system.

Enhancement of water use efficiency in the agricultural field is essential for the sustainable management of water resources. This tedious task can only be possible by either increasing output (crop yield) or by decreasing input (irrigation) as per the water resources engineering perspective. Crop yield is restricted to various factors apart from irrigation viz. nutrient supplement, crop variety, property of soil, soil health, etc., which is challenging to manage. Irrigation can be managed by adopting various agricultural water management techniques (e.g. alternative wet and dry, spate irrigation, deficit irrigation, precision irrigation, etc.) at field or plot scale. However, agricultural water management techniques are challenging to adopt for a regional scale due to the lack of real-time soil moisture and evapotranspiration data. This difficulty can be overcome with the help of Geospatial Technology. In this chapter, a detailed role of remote sensing and GIS to enhance field water use efficiency is explained.

Evapotranspiration (ET) estimation at river basin scale with respect to various land use and land cover (LULC) provides useful conservation prescriptions. With the advancement of satellite remote sensing, ET estimation has gained tremendous attention. Satellite remote sensing-based methods can map spatially distributed ET over different land use and thus are helpful for inaccessible areas. In this chapter, a commonly used surface energy balance-based modified Priestley–Taylor algorithm was demonstrated to estimate LULC-wise ET in two eastern river basins of India, named Brahmani and Baitarani. The potential impact of cloud cover on the performance of the ET estimation was also assessed. The results showed that the forest accounted for the highest ET followed by water body/moist riverbed in both the river basins. The ET estimates were found reasonable during non-monsoon season; however, during monsoon season, an underestimation was observed due to cloud cover, revealing that a denser time-stack of satellite images is required for an accurate estimation of ET during monsoon season. The assessment of the effects of cloud cover on ET estimates revealed that the method used in the study require cloud-free satellite images for accurate estimates of ET. With the increased availability of data from different satellites from recent launches, a dense time-stack of data can be generated by fusing multisensor datasets. Such fusion may improve the accuracy of ET estimates considerably with better information about the spatio-temporal variability.

Sugarcane is one of India's most important cash crops and one of the major crops of Uttarakhand state. Accurate crop yield forecasting is essential for making appropriate government policies. Statistical regression method using meteorological parameters is one of the most widely used crop yield forecasting methods. With the help of statistical regression, it is possible to forecast the sugarcane yield a few months before the harvest. But there is no direct cause–effect relationship between meteorological parameters and crop yield, so uses of other independent parameters can increase the crop yield accuracy. Evapotranspiration is one of the most crucial independent parameters, which can be easily estimated using remote sensing. The benefit of remote sensing over other fields and empirical methods for evapotranspiration is the easy availability of data over a large area as data availability becomes critical in other methods. Crop water efficiency can be easily found by crop water productivity. The developed Sugarcane yield actual evapotranspiration (AET) model using regression techniques for the F2 stage and both with and without AET model for F3 stage except 2019–20 in Haridwar district and the developed sugarcane yield model with and without AET using regression techniques for the F2 and F3 stage in Dehradun district showed a good relationship between predicted and observed values of yield which is below 5% deviation. From the study of crop water productivity, we can easily mark the areas with low water productivity and used different planning to increase the water efficiency to fulfill the need of people in reducing water availability.

Natural Resource Conservation Services-Curve Number (NRCS-CN) method is commonly used in hydrologic and geological sciences for surface runoff estimation in ungauged watersheds for various hydrological applications because it requires very few parameters in comparison with other hydrological methods. Curve Number (CN) is one of the most sensitive parameters of the method because it accounts for most of the watershed runoff producing response characteristics. Therefore, this study aims to compute different CNs to estimate surface runoff in the Tilaiya catchment of the Barakar river basin, Jharkhand, in eastern India. For this, firstly, two approaches were applied to compute CNs values for different antecedent soil moisture conditions: (i) from observed rainfall-runoff (P–Q) events data (CN_PQ), (ii) GIS-based NRCS-CN method (CN_RS) using RS inputs in the observed period 2001–2010, and secondly, surface runoff was estimated by applying the computed CNs values at daily and event scale. As a result of calculating the datasets of CNs values for the years, 2001–2010 shows a large variability of CN. On the other hand, the GIS-based NRCS-CN method of estimating CNs presented in this study shows the characteristics of the catchment and actual rainfall. Estimated runoff using the CN_PQ exhibited better results than fixed values applied in the GIS-based NRCS-CN method and could be used for planning, developing, and managing the water resource in the Tilaiya catchment.

Monitoring the impact of hydrologic flux from river basin toward the coastal environment is useful for assessment of terrestrial impacts on coastal ecosystem. In this study, hydrologic flux from the Haldi catchment into Hooghly estuarine system was simulated for the period 1989–1994 using semi-distributed watershed model, the Soil and Water Assessment Tool (SWAT) integrated with ArcGIS, i.e., ArcSWAT (2009.93.5). Sensitivity and auto-calibration analysis was also carried out to investigate optimal model parameters, while global sensitivity was carried out using SWAT-CUP Sequential Uncertainty Fitting Algorithm (SUFI2) module. Prior to calibration, observed stream flow was partitioned into two major components namely surface runoff and base flow using an automatic recursive digital filter technique. Base flow ranges between 13 and 29% of the observed stream flow during the period 1990–94. A detailed uncertainty analysis was carried out to assess the performance of SWAT model over the study area. Higher surface runoff is inferred from the enhanced calibrated parameter CN2, whereas reduction in the soil evaporation compensation factor (ESCO) was observed. This signifies the increased extraction of water from the lower soil moisture profile that results in meeting the requirements of evaporative demand. Thus, the output of the model is substantially affected by soil water capacity (SOL_AWC), where lower SOL_AWC implies reduction of soil retention capacity associated with increased surface runoff. Model performance indicator parameters such as R² and NSE show that the model performed satisfactory and good respectively at daily and monthly time scales.

Land use and land cover (LULC) change has one of the key modes of human modifications and has raised several queries related to its impact on hydrology and climate. Since the LULC plays a vital role in partitioning energy and water fluxes at the land surface into different components, the changes in LULC can impact the water and energy cycles to a significant level. However, at a basin scale, the severity of such consequences depends on the scale, type, and heterogeneity of LULC changes. An assessment of the impacts of LULC change on hydrology in three major river basins—Brahmaputra, Ganga, and Mahanadi of India is performed in this study using the variable infiltration capacity (VIC) model, a physically-based distributed hydrological model. The assessment reveals an important compensating effect in the hydrologic changes resulted from LULC transformations. The negative consequences (e.g., increased surface runoff due to urbanization) at one place are compensated by opposite positive changes (e.g., decreased surface runoff due to forest plantation) at other places at the basin scale. Such compensation leads to insignificant hydrologic changes at the basin scale; however, the consequences are significant at the local and sub-basin scales.

Satellite altimetry-based studies on monitoring the inland water bodies are limited in the Indian context due to small and medium size of these water bodies. The present study demonstrates the utilization of altimeters in monitoring reservoirs with a surface area <100 km² and Altimeter data from SARAL/AltiKa and RA-2 radar altimeter of ENVISAT having similar ground track were used in this study for the period 2002–2010 and 2013–2014, respectively. This study was carried out for both monsoon and non-monsoon seasons to assess the seasonal dependence between in-situ and satellite data. Reservoir water levels obtained from ENVISAT and SARAL were compared with in-situ gauge data. The root mean square error (RMSE) between the in-situ and ENVISAT data is 0.34 with R² of 0.97 during the monsoon season compared to the non-monsoon values of 0.70 and 0.88, RMSE and R², respectively) Further, it was observed that the 40 Hz data of SARAL/AltiKa is in good agreement (RMSE of 0.44 m and R² = 0.95) with in-situ data than ENVISAT 20 Hz data. Further, elevation-capacity rating curves were generated using both ENVISAT and SARAL/AltiKa observations. Overall, a 35-day time resolution of ENVISAT and SARAL/AltiKa data are insufficient for reservoir monitoring at a daily scale. However, this study has showcased the application of altimeter data in estimating the capacity of smaller water bodies.

Soil erosion in the catchment area of the reservoir catchment leads to sedimentation problem in the reservoir thereby affecting its both live and dead storage capacities that reduces the designed life span and planned economic benefits. Conventional techniques for assessment of reservoir sedimentation not only involve lot of manpower but also time intensive and costly to implement. Remote sensing techniques by virtue of its synoptic coverage and multidate observations are reported to be quite useful for computation of reservoir live capacity. These surveys are fast, economical and reliable. The present study was taken up to update the stage—area—capacity curves (estimating loss in the live storage capacity) for 30 reservoirs spread across India, where delineated waterspread area corresponding to satellite pass forms the important basis. The difference between the present satellite measured waterspread area and that of a previous survey (obtained through hydrographic survey) is the areal extent of silting at these levels. Integrating the area over different levels gives an estimate of volume of silting observed by satellite between the maximum and minimum reservoir level.

Identification of the critical areas is essential for the effective implementation of watershed management programs. In the present study, monitoring of runoff, sediment, and nutrient yield (NO₃–N and soluble P) was carried out from a small agricultural watershed to identify the critical areas on the basis of sediment and nutrient yield. Land use/land cover information was generated from Indian Remote Sensing Satellite data (IRS-1D-LISS-III). Universal Soil Loss Equation (USLE) was used to estimate soil loss from the study area, while remote sensing and GIS techniques were used for parameterization of USLE model. Delivery ratio was computed for the watershed to compare with the observed sediment yield. Soil samples were collected and tested from a selected portion of the wasteland for measuring soil fertility status (NPK), soil reaction (pH), organic carbon content, etc. The sediment delivery ratio in the study watershed ranges between 0.44 and 0.66. All the sub-watersheds fall within “slight” soil erosion class (0–5 ton ha⁻¹ year⁻¹). Sub-watershed I yielded more nutrient followed by Sub-watershed II and Sub-watershed III. The wastelands of the study area have a moderate amount of organic carbon (within 0.5–0.75%) and low available nutrient (NPK), initially, farmers can go for leguminous crop cultivation for increasing the nitrogen status of the field, and also, sabaigrass-based intercropping systems can be adopted in small plots of the wasteland.

Wetlands serve an essential role in conserving the ecological balance of both biotic and abiotic lives in both inland and coastal environments. Hence, understanding their existence and the spatial extent of change in the wetland ecosystem is vital and monitored using remote sensing technology. A study was performed for the Ramganga reservoir, located in Uttarakhand, India, one of the important wetlands in Uttarakhand that comes under Ramsar sites with many bird species and is rich in biodiversity. The present study modeled the spatiotemporal changes of the reservoir using the multi-temporal Landsat TM (1992, 2000, and 2010) and Landsat OLI-TIRS (2020) imageries. While performing spatiotemporal analysis, the applicability of various satellite-derived indexes such as the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Turbidity Index (NDTI) has been employed for retrieval of wetland elements including the wetland extent boundary, water-spread area, aquatic vegetation, and turbidity level of the reservoir during pre- and post-monsoon seasons by using remote sensing and hierarchical decision tree algorithm. In the post-monsoon season, the minimum and maximum water-spread areas were 59.98 km² in 1992 and 74.30 km² in 2010, respectively. Similarly, in the pre-monsoon season, the reservoir had a minimum, and the maximum water-spread area was 28.697 km² in 2010 and 58.536 km² in 2020, respectively. The total aquatic vegetation of wetland was increased from 3.41 to 4.14 km² in the years 1992 and 2020 during post-monsoon. Throughout the study period, the medium turbidity level in the reservoir was observed to be less than 1 km². The result indicates that these different satellite-derived spectral indices are less time-consuming and provide more accurate mapping and wetlands monitoring. This requires constant monitoring of the structural components of wetlands and urgently focuses on wetland conservation, rehabilitation, and management.

Uttarakhand is a hill state of North India with a unique and diverse topographic, morphologic, and climatic setting. The higher elevation areas have been experiencing repeated flash flood events caused by cloud bursts or heavy precipitation. This study demonstrates the potential of remotely sensed data and geographical information system for assessing the flash flood risk in the Alaknanda River Basin (ARB) located in the Northwestern Himalayan Region in India. Multiple sub-watersheds in this river basin experience flash flood events almost every year, causing massive property damages and life loss. SRTM 30 m DEM was processed and utilized for conducting morphometric analysis to prepare a flash flood risk prioritization map for the 7 sub-watersheds of ARB. The morphometric parameters considered for this purpose were sub-watershed area, perimeter, stream order, stream length, basin length, bifurcation ratio, drainage density, stream frequency, texture ratio, form factor, circularity ratio, and elongation ratio. The correlation matrix for all these morphometric parameters for each sub-watershed was analyzed to obtain a compound parameter value using weighted sum approach (WSA), which was used for performing flash flood zonation (low, medium, high, and very high). The analysis results indicate that out of the 7 sub-watersheds, SW-2 and SW-3 were categorized under the high-risk zone, and SW-5 was categorized under the very high-risk zone.

With the increased flood havoc in many river basins worldwide, flood forecasting has been recognized as one of the feasible nonstructural measures of flood management. For accurate and reliable extreme flood forecasts, different artificial neural networks (ANNs)-based modelling approaches are developed in this study. For daily streamflow forecasting, a non-clustered feedforward backpropagation ANN (NCANN) model is developed using the daily observed streamflow training dataset. Further, in order to forecast high flow with ANN, two different types of models are developed, namely pre-classified ANN and post-processed ANN model. The pre-classified ANN models are basically the cluster-based ANN (CANN), and the post-processed ANN models are the ensemble ANNs (EANN). The high flow forecasting efficacy of the ANN models is compared for a common set of high flow regimes at one-, two- and three-day lead times in the flood-prone Mahanadi River basin in Eastern India. The results reveal that consideration of the training dataset consisting of various flow stratifications does not ensure a better forecasting of the high flow regime by NCANN model; rather the use of homogeneous set of training dataset gives improved forecasting during testing. Moreover, the high flow forecasting error is further reduced when the model ensembles are used. Seasonal flow assessment can be improved by using these developed models equally effectively.

Globally, floods are attributed to be one of the leading natural hazards responsible for recurrent major economic losses, population affected, and mortality. The rapid assessment of flood hazard dynamics at regional scale during flood crisis is one of the few elements which is required by the agencies involved on ground for relief and rescue operations. Due to the weather-independent and day and night acquisition capability offered through microwave sensors, space-borne remote sensing for flood hazard management has undergone a paradigm change. Today, globally data from synthetic aperture radar (SAR) has emerged as invaluable source for monitoring flood hazard. From demonstrating the proof of concept in its initial launch campaigns, the SAR technology has matured to be competent enough to provide operational support for major flood disasters. In recent times, the continuously streaming of free SAR datasets from Sentinel-1 mission and together with emergence of advanced cloud-based computing and processing technologies like the Google Earth Engine (GEE), automated, and quasi-real-time flood mapping services have evolved. The future missions like the NISAR in conjunction with Sentinel-1 C-band data will help in providing more accurate and faster response during flood crisis and see application of SAR data grow multi-fold in coming years for flood hazard mitigation. This chapter attempts to provide a broad overview of the active microwave remote sensing for flood hazard studies. The first part of the chapter discusses about the interaction of the SAR signal for flooding in open, vegetated, and urbanized areas, followed by the role of the sensor parameters like the wavelength, polarization, and incidence angle on the backscattering of SAR signal. The latter half of the chapter discusses about the flood mapping techniques, SAR satellite mission contributing to flood hazard mapping, various applications of SAR derived flood hazard information, the Indian nationwide near-real-time (NRT) flood hazard mapping under ISRO DMS program, and the emergence of Web-based cloud computing techniques and open-source data policies revolutionizing the flood hazard mitigation activities.

Assessment of surface water with higher accuracy is very critical in the present changing environment. Such assessment requires understanding of each and every hydrological process involved in hydrological cycle. The land characteristics along with climate variables make it a daunting task. With the advent of geospatial technology, both land surface and climate parameters may be studied with higher accuracy. Some of the hydrological parameters such as precipitation, soil moisture, evapotranspiration, and water level can now directly be retrieved using remote sensing data. However, other hydrological components such as rainfall-runoff, snowmelt-runoff, peak discharge, or flood hydrograph need modeling approach. Most of the surface and climate inputs required for hydrological and hydrodynamic (H&H) modeling nowadays can be quantified using the geospatial datasets. It makes modeling more realistic, and hydrological response of large basins can be studied. The H&H models are utilized for studying the hydrological extremes such as flood and droughts. Floods are one of the most naturally re-occurring hazards, which significantly impact the long-term sustainable use of land and water resources of a geographical region. The chapter focuses the use of geospatial technology for H&H modeling with relevant case studies on flood modeling. Further, the improvements in modeling outputs may be done by assimilating the geospatial inputs in near-real time. Moreover, these geospatial inputs are being updated and improved in spatial–temporal domain with the advancement in geospatial technology.

Remote sensing is a useful tool for flood monitoring and damage assessment. Unlike traditional survey methods, it provides cost-effective solution with wider coverage and frequent revisit cycle. In general, coarser sensors provide high repetitivity with lower spatial resolution, whereas constellation of finer spatial resolution sensors can be useful in continuous flood monitoring. Different methods and techniques are used for delineating the flood extent and damage assessment based on the type of sensors. Hence, it is necessary to carry out a detailed and in-depth review of remote sensing technologies and approaches available for processing and analysing satellite data for flood response studies. In the present study, automated procedures were used for generation of flood layers and flood persistence maps at Gram panchayat level in the part of Indo-Gangetic plains, Uttar Pradesh. Further, attempt was made to plan the measures that can be useful in relief operations based on the detailed analysis of persistence maps. Methods based on thresholding were improvised by applying unsupervised classification and online Geo-processing platform of Google Earth Engine. Historical flood events for the period 2010–2020 were generated over part of Indo-Gangetic basin and integrated with administrative layers for identifying the villages vulnerable to floods. Accuracy of flood maps were improved by applying the conditioning factors to remove misclassification of flood extents. Particularly, villages located in Ghazipur, Allahabad, Ballia, Gorakhpur, Bahraich and Balrampur districts of UP state indicated high Flood Vulnerability Index (FVI) values. FVI computed at village level using the historical flood events can be of great help for identifying and planning relief shelter locations in the study area. Remote sensing and GIS technologies were successfully envisaged in the identification and planning of relief shelters for the most vulnerable villages.

Characterisation and quantification of drought are important considerations in the planning and management of water resources. Present study examines the impacts of meteorological droughts on various hydrological aspects of the Godavari River basin by characterising and quantifying the droughts using a geospatial approach. Standardised indicators such as Standardised Precipitation Index (SPI) and Standardised Stream Flow Index (SSI) were used to identify the meteorological and hydrological droughts, respectively at different accumulation periods (1 month to 24 months) for the years 1951–2018. The meteorological drought index was used to characterise the droughts in the Godavari River basin, considering their severity, spatial extent and duration at various time scales. The long-term hydrological regime of the basin (1951–2018) was simulated using the Variable Infiltration Capacity (VIC) hydrological model. The time periods (of each accumulation period) with normal rainfall were identified and segregated. The mean (average) discharge (both observed and modelled) of each accumulation period (1 month to 24 months) was estimated from discharge data of these normal meteorological periods. The percentage deviation of observed and simulated discharge values from the ‘mean’ values during each drought period for each station were obtained and their relation with the various category of meteorological drought was quantified. The hydrological drought index (SSI) was computed by applying the appropriate probability distribution (concluded from the KS test) for both observed and simulated discharge values for the Polavaram discharge station representing the whole basin. Majorly for all timescales, log-normal was observed as the optimum probability distribution for both the observed and modelled hydrological data. Hydrological drought events were then analysed in accordance with the meteorological droughts in order to examine their interrelationships. The results showed a linear relationship when we compared the meteorological droughts with percentage deviation in the modelled discharge. However, similar relation doesn’t exist in the case observed discharge values. This might be due to the fact that every basin will have its anthropogenic resilience towards droughts. The observed  discharge data from the Godavari basin does not describe the natural flow regime of the river, which is why the behaviour of hydrological droughts in the basin is anthropogenically controlled. The numerical impact of each category of meteorological drought on the hydrological regime of the Godavari basin and its sub-basins was quantified successfully for the virgin flow conditions.

Standard Precipitation Index (SPI) computed at multiple time scales is considered as a key indicator for short-term agricultural to long-term hydrological drought monitoring. SPI computed at multiple timescales namely 1, 3–6 and 12 months represent meteorological, agricultural and hydrological droughts, respectively. Traditionally, precipitation based SPI drought index is being computed using raingauge station data, which is often limited by sparse and uneven distribution of raingauge stations. However, uncertainty in aerial estimation of rainfall and data scarcity regions can be overcome by envisaging various quasi-global satellite derived precipitation products such as TRMM multi-satellite Precipitation Analysis (TMPA), Climate Hazards Group Infrared Precipitation with Stations data (CHIRPS), Climate Research Unit (CRU) data for drought monitoring. Hence in this study, characterization of spatially varying drought occurrences and its severity at different time scales was carried out using various precipitation gridded products (TRMM, CRU and CHIRPS) for part of Indo-Gangetic Plain, India. Further, drought severities assessed by these gridded products were relatively evaluated with reference to rain gauge based IMD gridded product. The spatial pattern of TRMM and IMD based SPI for all the time scales were observed to be similar for both wet and dry years. The spatial pattern of low and high number of drought events is mostly similar for CHIRPS, TRMM and IMD. Overall, it was observed that spatial pattern of drought frequency identified through CRU based SPI was completely distinct compared to other datasets. In general, CHIRPS appears to have overestimated the drought area and frequency compared to IMD, while TRMM data exhibited a similar pattern as that of IMD product which could be due to the fact that CHIRPS underestimates the rainfall.

Tropical cyclone (TC) landfalls are among the most damaging natural disasters. The North Indian Ocean (NIO) experiences ~12% of all cyclones every year. TC damage is primarily due to high wind gusts, rainfall, storm surges, waves and coastal flooding which pose serious risks to life, property and coastal ecosystems. Extreme wave activities, vegetation loss due to gale winds and saltwater intrusion during coastal inundation cause coastal erosion and turn agricultural land infertile over extended periods of time. The rate of TC devastation also depends on coastal Land Use and Land Cover (LULC: vegetation density, barren lands, agricultural fields, etc.). TCs in turn change the LULC and soil characteristics, thus modulating the land surface properties. The extent of physical and social vulnerability due to TCs are directly associated with population density, coastal infrastructure and TC frequency and intensity. Improved forecast and advanced preparedness are crucial to reduction in TC related fatalities with early risk assessment being key to disaster mitigation. The coastal vulnerability and impact of land-falling TCs in the NIO were analyzed. Assessment frameworks, observational tools and mitigation strategies were reviewed and critical factors for better disaster preparedness and mitigation of TC impacts in the coastal regions were identified.