Analysing the spatiotemporal changes in long-term rainfall and extreme events at a river basin scale is crucial for optimal water resource management. This study examines trends in long-term rainfall and extreme event indices in the Krishna River Basin (KRB) using Indian Meteorological Department (IMD) 0.25° resolution daily precipitation data from 1951 to 2019. Methods include the Mann–Kendall trend test, Innovative Trend Analysis (ITA), Hurst's Rescaled Range (He) Analysis, and Wavelets.The results show variations in rainfall and extreme event patterns across KRB subsystems. Overall, Annual Rainfall (AR) is decreasing, with significant trends in Ghatprabha (K3), Lower Bhima (K6), and Vedavati (K9). Seasonal and monthly rainfall trends are not significant. Extreme event indices (Daily rainfall greater than 10 mm [R10], Daily rainfall greater than 20 mm [R20], and Daily rainfall greater than 40 mm [R40], the annual maximum for 1 to 7 days and starting days of such events showed the non-significant trend in some of the subsystems when analysed with statistical methods; however, graphical analysis using ITA indicates clear trends. He construed a sustainable decreasing trend and predicted a future rainfall reduction. The wavelet power spectra for different indices infer periods between 2 and 16, predominantly concentrated around 2–4 year bands. Decreasing annual rainfall in most headwater catchments, captured by most methods, suggests that KRB will experience less rainfall and fewer rainy days in the future.By discerning long-term rainfall trends and periodic patterns in the KRB, future water availability can be predicted, and extreme events can be better analysed. This analysis will be the basis for devising robust flood control measures, mitigating flood risks, and optimizing water resource allocation across sectors, thereby enhancing resilience to climate variability in future.

River Yamuna, flowing through the National Capital Territory (NCT) of Delhi, is considered as one of the worst polluted river stretches in the world. The river is primarily polluted through untreated sewage discharge from various point and non-point pollution sources that find their way through storm drains and join the river Yamuna. One of the major requirements to address urban water quality is to identify the potential sources of contamination in a large drainage basin, which is challenging as the basin area is so big that it is difficult to pinpoint the source of pollution. Invariably, as the pollutants move into the drains under gravity, it is possible to identify the drains and their corresponding catchment areas that have higher pollution load. Thus, the study aims to find the sources of pollution in a larger river basin by integrating field observation, GIS approach, and qualitative interviews. Water samples from 43 monitoring locations are collected in the various storm drains of the Barapullah drainage basin, in various primary, secondary, and tertiary drains, and analyzed the water quality parameters in the Delhi Jal Board (DJB) water quality laboratory. The locality-wise spatial contribution of pollution is mapped in the context of the Barapullah basin. The study results found excessive BOD values ranging from 37 to 430 mg/L, and six major pollution hotspots were identified. The qualitative interview conducted in the six major pollution hotspots regarding the source of pollution and results indicated that the discharge of domestic sewage from planned colonies, unauthorized colonies, and JJCs, as well as market waste disposal, are the potential reasons for pollution in the six major pollution hotspots. This pilot-level study provides a useful and effective approach to investigating and identifying pollution sources, mitigating the pollution at the source, and ultimately reducing the pollution level at river Yamuna.

Study region: Yamuna River (Delhi), India. Study focus: The anthropogenic activities within the vicinity of the floodplain reduce the river's margin and subsequently alter the magnitude of the river's flow. The encroachment of riverbeds leads to waterlogging and flooding in urban areas, thereby causing damage to property, human life, etc. It necessitates a comprehensive study of the floodplain and changes in its proximity such as encroachment of floodplains to carry out any further activities with certainty. This study employs a two-dimensional model to simulate the Yamuna River's (YR) hydrodynamic characteristics, focusing on India's Delhi region. New hydrological insights: Simulated flood flows are employed to evaluate floods of once in 10, 20, 25, and 30-year return periods using the flood frequency analysis for 1951-2013. The model validation results indicated that the model could mimic the flood depth in YR. Simulation results revealed that the floodplain's encroachment had increased the severity of the floods. The increase in the extremeness of flooding events, i.e., from once in a 10-year return period to a 30-year return period event, is expected to increase the areas at risk of floods by 12 %. The model also offers a potential platform for evaluating other alternatives, such as further encroachment, for a business-as-usual scenario or for restoring the Yamuna floodplains. With such a comprehensive perspective, floodplains' role enhances river basin resilience to climate and anthropogenic changes and increases flood safety.

Analysing the spatiotemporal changes in long-term rainfall and extreme events at a river basin scale is crucial for optimal water resource management. This study examines trends in long-term rainfall and extreme event indices in the Krishna River Basin (KRB) using Indian Meteorological Department (IMD) 0.25° resolution daily precipitation data from 1951 to 2019. Methods include the Mann–Kendall trend test, Innovative Trend Analysis (ITA), Hurst's Rescaled Range (He) Analysis, and Wavelets.The results show variations in rainfall and extreme event patterns across KRB subsystems. Overall, Annual Rainfall (AR) is decreasing, with significant trends in Ghatprabha (K3), Lower Bhima (K6), and Vedavati (K9). Seasonal and monthly rainfall trends are not significant. Extreme event indices (Daily rainfall greater than 10 mm [R10], Daily rainfall greater than 20 mm [R20], and Daily rainfall greater than 40 mm [R40], the annual maximum for 1 to 7 days and starting days of such events showed the non-significant trend in some of the subsystems when analysed with statistical methods; however, graphical analysis using ITA indicates clear trends. He construed a sustainable decreasing trend and predicted a future rainfall reduction. The wavelet power spectra for different indices infer periods between 2 and 16, predominantly concentrated around 2–4 year bands. Decreasing annual rainfall in most headwater catchments, captured by most methods, suggests that KRB will experience less rainfall and fewer rainy days in the future.By discerning long-term rainfall trends and periodic patterns in the KRB, future water availability can be predicted, and extreme events can be better analysed. This analysis will be the basis for devising robust flood control measures, mitigating flood risks, and optimizing water resource allocation across sectors, thereby enhancing resilience to climate variability in future.

Flood Frequency Analysis (FFA) is a process to relate the magnitude of extreme streamflows to their frequency of occurrence through distribution functions, and the results of FFA are useful in design flood estimation for various hydraulic structures. The FFA relies primarily on actual observed streamflow data (Qobts) and certain underlying assumptions that form the basis of FFA's philosophy. In today's world, Qobts time series are available for most river basins; however, it is an indubitable reality that Qobts do not satisfy the necessary requirements. Therefore, an alternative doctrine is needed to create such a dataset. This paper provides an overview of the limitations of utilising Qobts along with its drivers and suggests a surrogate mechanism to perform FFA in today's world.

The runoff from snow and glacier melt is one of the most important sources of freshwater for the ever-present Himalayan Rivers. Satellite imagery and Geographic Information System (GIS) tools like Digital Elevation Model (DEM) and simulation models have been highly useful in figuring out and connecting between theoretical concepts and actual conditions in hilly and difficult-to-reach places. The present study uses a variety of geospatial tools, including Remote Sensing (RS), GIS, and the snowmelt runoff model (SRM), to estimate snow and glacier melt runoff in the Beas River basin of the Western Himalayas. This research used Landsat-8 snow cover data, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEM, GIS, and ultimately the Windows Version of the Snowmelt Runoff Model (WinSRM) to estimate the snow and glacier melt runoff in the Beas River basin up to Pandoh Dam. The SRM has been done using hydro-meteorological data from the years 2013 to 2015. Remote sensing data was utilized to calculate the Beas River's temporal Snow Cover Area (SCA) from 2013 to 2015, and DEM was used to identify elevation zones and aspect maps. The findings showed that there is little difference between the calculated runoff (772.51 m3/s) and the observed runoff (802.47 m3/s). The data analysis shows that the SRM model is the most effective method for calculating runoff from snow and glacier melt in mountainous regions. For the period of April 2013 to October 2015, the overall coefficient of correlation (R2) accuracy of SRM for a portion of the Beas River basin is 0.77. Snowmelt accounts for around 6.68

There is a heightened sensitivity about the risks posed by the prospect of climate change as deduced from the results of the global-regional climate models. Notwithstanding the moderating, albeit feebly articulated, acknowledgement of variability as being an intrinsic attribute of climate, it is often claimed that intensification of large hydrological extremes such as floods is indeed emerging as a new looming reality. This has aptly given rise to fears of prospective exacerbated socio-economic vulnerability. In an attempt to investigate the veracity of whether the hydrological flood events are really intensifying across the Krishna River Basin, the present study has examined the historical floods in Krishna River Basin (KRB) using available streamflow and precipitation records along with published reports and featured news articles. The approach followed is based on quantitative and qualitative analysis of floods in KRB at the subsystem scale (K1 to K12). The quantitative analysis involved i) Development of unregulated flow series through hydrological modelling, ii) Frequency analysis of unregulated flows and precipitation, iii) Threshold selection for defining the small, medium, and large floods, iv) Identification of flood events in observed streamflow series and v) Identification of causal rainfall and its relationship with flood peak. The qualitative analysis focused on published reports and news articles to attempt a multivariate characterization of flood flow attributes and the accompanying losses. The study concludes that the hypothesis that flood events are intensifying is untenable for most subsystems of KRB except K7 (Lower Krishna Basin) being the sole exception where, in sharp contrast, flood events show signs of moderation. Interestingly, cyclic patterns analogous to Noah and Joseph Effect are seen in the case of small floods for all the subsystems except K7. Non-recurrent, standalone extremes that bear the classical signature of Erratic Noah and Joseph elements have also been observed in medium and large floods in all the subsystems except the K12 subbasin (Munneru Basin). The study confirms that qualitative analysis alone cannot lead to an incontrovertible determination of trend like features in flood records as every event in the historical record bears a unique multi-dimensional footprint arising from a mix flood characteristics, associated losses and perceived short and long-term impacts. This study can provide a guideline to identify changes in flood typology especially in basins with altered hydrologic regimes and serve as an aid in planning and formulating policies for ameliorative flood management strategies as well as in policy restructuring when deemed to be necessary.

The reliable landslide hazard assessment entails a robust understanding of frequency-magnitude analysis of the landslide inventory. Previous studies proposed that the landslide frequency-size distribution follows a power-law distribution even though 75–90% of data deviated from the power-law fit. This deviation from the power-law fit was ubiquitous in various landslide studies irrespective of their triggering factor, spatial and temporal resolution of the landslide inventory and geological and morphological settings of the area. This study conducted a detailed frequency-size distribution of the landslides at four different locations in India’s Himalayan regions to check the validity of power law to represent the landslide data. The landslide inventory of Kathua, Shimla, Pithoragarh and Darjeeling regions are complied, consisting of 1942, 905, 2151 and 393 landslide events, respectively. The landslide frequency distribution is tested for the power-law, exponential and lognormal distribution. After a detailed statistical analysis, our finding suggests that medium-large landslide size events follow a power-law distribution, accounting for 8–25% of the total data; the rest deviates significantly from the power-law fit and follows a lognormal distribution. The cut-off between the lognormal and power-law distributions, or the minimum size for medium-large landslides, is 10 2.9 –10 4.3 m 2 . The Kolmogorov–Smirnov (KS) and Lilliefors tests are used to validate the findings. This study provides insight into the landslide size probability distribution in different locations situated in India’s Himalayan regions.

Under climate change and pressure from human activities, extreme flood events are becoming a significant concern. The study involves setting up a coupled hydroclimate-hydrodynamic models over the Ganga River Basin by coupling the CORDEX regional climate models to the physically based Soil Water Assessment Tool-based hydrological model and MIKE 21C-based hydrodynamic model. The coupled model was employed to explore the flood inundation and dynamics of sediment mobilization amid various extreme events under future conditions. The water level, particularly for the 40-year return period for the late century (2061–2100), is estimated to rise in the future (0.9–1.2m). Since extreme streamflow has increased in the future, morphological transformations will also aggravate. Moreover, during the extreme events, the peaks in shear stress and velocity profile distribution have a propensity to coincide with the areas of the meander bend, implying that the river meander processes govern the shifts in the dynamics of the sediment transport. Additionally, the developed model framework has been employed to determine the areas affected and inundation pattern due to the 18 August 2008 flood in the Kosi River due to the embankment breach. Simulation exhibited that there is a good agreement between the simulated and observed inundation areas. Together with the evaluation of the susceptibility corresponding to lives and livelihoods, this information may facilitate decision-makers to develop ameliorative policies and adopt meaningful measures to alleviate the impacts.

The assessment of climate and land-use transformations upon the hydrologic response is crucial for decisionmakers to accomplish various adaptation strategies. The Regional Climate Models (RCMs) have been extensively employed to study the impact of climate change on various hydrologic components. However, these climate models are subjected to a large number of uncertainties, which demands a careful selection of an appropriate climate model. To rationalize such uncertainties and select suitable models, a multi-criteria ranking technique has been employed. Ranking of RCMs has been done on its capability to simulate hydrologic components, i.e., simulations of the surface runoff by employing Soil Water Assessment Tool (SWAT), exercising Entropy, and PROMETHEE-2 approach. The spatial extent of changes in the hydrologic components is examined over the Ganga river basin, using the top three ranked RCMs, for a period from January 2021-December 2100. For the monsoon months (June-September), the future annual mean surface runoff will decrease substantially (-50% to -10%), while the flows for post-monsoon months (October-December) are projected to increase (10-20%). Extremes are noted to increase during the non-monsoon months, while a substantial decrease in medium events is also highlighted. Snow-melt is projected to increase during the months of November-March (50% to 400%). Major loss of recharge is expected to occur in the central part of the basin. The investigation presents not only a reliable impact assessment but also the valuation of future alterations in individual hydrological components and will furnish the administrators with substantive information, a prerequisite to formulating ameliorative policies.

The paper comprises of an application of a multi-faceted physically based two-dimensional (2D) hydrodynamic model to simulate the transport phenomena of Loktak Lake, including the water quality of Loktak Lake, for which there is consensus that it is deteriorating due to river discharge from sub-catchments carrying sewage loads, soil sediments and agricultural fertilizers, and therefore, has emerged as a serious environmental concern. Accordingly, the study attempts to understand the overall environmental quality of the Loktak system and in particular simulate Loktak Lake water quality (state) variables by coupling through MIKE 21 ECO Lab. The model simulated dissolved oxygen and biochemical oxygen demand throughout the lake.

Deterioration of water quality condition of lakes and rivers can be harmful to the health of aquatic life forms. This study presents habitat suitability analysis of Pengba fish (Osteobrama belangiri) species in Loktak Lake and its river basin. Pengba is the state fish of Manipur with a restricted distribution range in India, Myanmar and China. This fish species is endemic to a limited habitat range and is currently assessed as a near threatened species and has been reduced dramatically from the Northeast region of India. Water quality parameters of the lake and the nine rivers were used to assess habitat suitability using MIKE 11 ECO Lab and MIKE 21 ECO Lab. Simulated water quality parameters were integrated using a geographic information system (GIS)-based, weighted overlay tool. The suitability of the fish species in the lake and nine rivers draining into the lake were divided into different zones of habitat suitability. This will help in framing conservation strategies, which, in turn, is expected to facilitate and aid in harmonizing eco-sustainability services.

The study focuses on climate change impacts on the environmental flow indicators from hydrologic method point of view using IWMI’s Global Environmental Flow Calculator and Indicators of Hydrologic Alteration. It also discusses how the changes in flow magnitude and duration of annual extreme conditions, timing of annual extreme water condition, frequency and duration of high and low pulses, rate and frequency of water condition changes will affect the ecosystem. Climate change disturbs the ecology by directly affecting the functions of individual organisms (growth and behavior), modifying the population (size and age structure), and altering the ecosystem structure, functioning (e.g., decomposition, nutrient cycling, water flows, and species composition and species interactions) and its distribution within landscapes (Gitay et al., 2002). Ecosystem regime shifts can occur naturally and by anthropogenic factors (Muenich et al. in Ecol Model 340:116–125, 2016). Climate change effects on flow regime are expressed by different indicators such as mean annual runoff, mean river discharge, low and high flows, mean seasonal discharge, and changes from permanent to intermittent flow or vice versa. Understanding of changes in flow regimes is important for the well-being of humans and freshwater-dependent biota with respect to water and habitat availability (Döll and Schmied in Environ Res Lett 7(1):14037, 2012). Even though the basin is rich in fish species, peoples living in lower Omo-Gibe basin and Turkana region are undertaking a traditional fishing culture. Wildlife in the parks, pastoralist communities using the flood recession farming and livestock farming are dependent on the river. The environmental flow that sustains these activities is inevitably necessary for the survival of the biodiversity. Understanding of the flow variability helps to protect the freshwater biodiversity and maintenance of goods and services that the river provides.

Landslides are one of the severe natural hazards induced by heavy rainfall, deforestation, slope failure and urban expansion. It can lead to significant loss of life and property in hilly and gully regions. Field studies that identify and map landslides are expensive and time-consuming as it includes the cost of the survey, travelling, workforce, and instrument. Although progression in technology and availability of high-resolution remote sensing data has now made it possible to identify landslides (satellite images and aerial photographs), accessibility to high-resolution satellite data is still an expensive and tedious procedure. Several studies have conducted in a GIS environment to map landslide zones, but the resolution of the open-source data is commonly coarse (30 m), which adds to the uncertainty of the outcome. In this study, application of the appropriate rule set with object-based image analysis (OBIA) technique has been used to identify landslides zones, through a combination of spectral, textural and geometrical properties of imagery and topographic data. It overcomes the shortcomings induced by pixel-based classification. For the current study, High spatial resolution data such as Google Earth imagery and CartoDEM (30 m) has been used. This approach shows an excellent prospect for quick and near-to-actual assessment of landslides zones which are generally induced by extreme rainfall events in the hilly regions of India. The methodology used has the potential to facilitate more reliable disaster management strategies. This study shows the potential of open-source data and emerging technology in the field of landslide assessment.

Loktak Lake is an internationally important, Ramsar designated, fresh water wetland system in the state of Manipur, India. The lake has also been listed under Montreux Record on account of the ecological modifications that the lake system has witnessed over time. Discharges from nine rivers namely Khuga, Thongjaorok, Awang Khujairok, Nambol, Nambul, Imphal, Kongba, Iril, and Thoubal have a great impact on the habitats and the overall ecological status of the lake. Monitoring of water quality at the catchment scale can be considered as an essential step towards the eventual goal to design effective conservation and management practices for the entire Loktak Lake ecosystem. This article presents the status of nine rivers draining into the Loktak Lake and correlation with land use patterns which can be used as support for making sound decisions regarding the management of the lake ecosystems. Flows were modelled using a combination of soil and water assessment tool (SWAT) and MIKE SHE, abbreviated as hybrid SHE-SWAT. Water quality models were established using MIKE 11 ECO Lab. Water quality parameters such as biological oxygen demand, dissolved oxygen and water temperature were simulated. Water quality models were calibrated using available measured water quality data procured from State Pollution Control Board and validated using observed water quality collected during the field study.

Flooding has caused immense damage to the people as well as to the property. Flooding in urban areas mostly occurs due to increased urbanization, low rate of infiltration and poor infrastructure for stormwater drainage network. Stormwater Management Model (SWMM) is found to be very dynamic hydrology-hydraulic water quality simulation model for modeling of the urban stormwater drainage network. In the present study, PCSWMM model is used for modeling the stormwater drainage network for the southern part of Delhi, the capital city of India. PCSWMM is developed by Computational Hydraulics International (CHI), Canada. PCSWMM uses the same SWMM engine for the modeling work; the only advantage is that it is GIS compatible software which makes this model more efficient. The model required following input information for simulation, i.e., land-use for calculating impervious and previous area, soil type, 15-minute interval precipitation data, temperature, humidity, and three-dimension cross-sectional geometry of the existing drainage network. A field survey was carried out for data collection, and in the process, it was found that most of the storm-water drains are choked, have improper flow gradient 370or damaged. All the collected field details of the storm-water drains were incorporated in ArcMap 10.1 and then imported in PCSWMM to develop a hydrology-hydraulic model for surface runoff. The simulated results of the model were further calibrated and validated with the available flooding locations data obtained from the Delhi Traffic Police Department. The simulated results were in close agreement with the observed flooding locations. Thus PCSWMM model can be applied to any urban/rural areas for designing stormwater drains or drainage network.

The studies pertaining to urban storm water drainage system have picked up importance lately in light of pluvial flooding. The flooding is mostly due to urban expansion, reduction in infiltration rate and environmental change. In order to minimize flooding, hydrologists are using conceptual rainfall–runoff models as a tool for predicting surface runoff and flood forecasting. Manual calibration is often a tedious process because of the involved subjectivity, which makes the automatic approach more preferable. In this study, three evolutionary algorithms (EAs), namely SFLA, GA and PSO, were used to calibrate SWMM parameters for the two study areas of the highly urbanized catchments of Delhi, India. The work incorporates auto-tuning of a widely used SWMM, via internal coupling of SWMM with all three EAs in MATLAB environment separately. Results were tested using statistical parameters, i.e., Nash–Sutcliffe efficiency (NSE), Percent Bias (PBIAS) and root-mean-square error–observations standard deviation ratio (RSR). GA results were in good agreement with the observed data in both the study area with NSE and PBIAS values lying between 0.60 and 0.91, and 1.29 and 7.41%, respectively. Also, RSR value was near zero, indicating reasonably good model performance. Subsequently, the model reasonably predicted the flooding hotspots that should be controlled to prevent any possible inundation of the surrounding areas. SFLA results were also promising, but better than PSO. Thus, the approach has demonstrated the potential use and combination of single-objective optimization algorithms and hydrodynamic models for assessing the risk in urban storm water drainage systems, providing valuable information for decision-makers.

Groundwater has emerged as an important source to meet the water requirements of various sectors including the major consumer of water like irrigation, domestic and industries. To meet the water requirement of rapidly expanding urban, industrial and agricultural sector of the country in a sustainable manner, optimum groundwater utilization is of fundamental importance. Reliable and periodic estimation of groundwater resource, is, therefore, a prime necessity for planning groundwater management such as artificial recharge, regulation of groundwater use, etc. These estimates form one of the key indices for identification of areas for implementation of various government-sponsored schemes/programmes. Groundwater being a dynamic system, the methodology for assessment requires continuous updating keeping abreast with the evolution in technologies. At grassroots, the technologies should be cheaper, reliable and simpler in usage. In this mammoth exercise of assessment of water resources, various organizations such as government and non-government join hands for this cause. Rural Technology Action Group (RuTAG) at IIT Delhi is an initiative of the Principal Scientific Adviser to GOI in providing technical interventions at grassroots for rural betterment, has taken up initiative to improve a low-cost and robust groundwater-level measuring device as requested by an NGO working for state water board in collecting data of groundwater in and around Chirawa, Rajasthan. The existing gadget developed by the NGO was unreliable and often give a false reading. This paper presents an overview of the development of robust and low-cost groundwater-level measuring device and its field testing.