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• Developed a Decision Support Framework to classify uncertainties and management options using participatory scoping for scenarios analysis. • DSS includes: reservoirs water balance, statistical downscaling, hydrological modelling, sediment assessment, water management and decision support for water management plans. • DSS framework was applied to Tandula reservoir Complex, India and performance was evaluated using multi-criteria decision support in the three-layer hierarchal process. • DSS framework provides a solution for strategic water resources planning and management in a basin using the participation of farmers, technocrats, and water resource managers. The climate uncertainties, data scarcity, and socio-techno challenges pose significant threats and options respectively to the future planning of complex water projects in developing countries. This paper attempts to develop a decision support system (DSS) framework to classify uncertainties and management options using participatory scoping for scenarios analysis with the aid of six interrelated modules namely (i) water balance of reservoirs for computation of runoff (ii) statistical downscaling for climate projection (iii) hydrological modelling (iv) sediment assessment (v) water management and (vi) decision support for the assessment of the performance of multiple management plans designed for three future assessment periods of 2020-35, 2046-64 and 2081-99. The proposed framework was applied to the Tandula complex system of Chhattisgarh state of India consists of three reservoirs namely Tandula, Kharkhara, and Gondli having water transfer in their commands and for industrial use. The developed DSS framework provides the opportunity to analyze the management plans with climate uncertainties for the complex water resources projects having data scarcity. Here, in the present paper, the developed DSS framework incorporates eighty-four diverse management plans devised from constant interaction with stakeholders and technocrats resulting to deal with climate uncertainties using possible improvement in efficiencies and consumptive use for three future assessment periods. Furthermore, the performance of the developed plans was evaluated using multi-criteria decision support in the three-layer hierarchal process. The decision support analysis suggested that the second assessment period (FP-2: 2046-64) will be more crucial from the management point of view where 54 out of 81 plans could not perform as par with the present performance of the system. The developed DSS framework provides a solution for strategic water resources planning and management under projected climate scenarios in a basin having problems of scarcity, surplus, and non-uniform distribution of surface water using the participation of farmers, technocrats, and water resource managers. [ABSTRACT FROM AUTHOR]

• Groundwater related research papers from 1978 to 2017 were collected. • The number of published studies increased by >10% per year on average. • Institutions based in developed countries have led groundwater research output. • China and India have experienced strong growth in output in recent years. • Depletion and sustainability have increased as a percentage of groundwater studies. Long-term over-exploitation and unsustainable practices have caused severe groundwater depletion and deterioration across many regions throughout the world. This has triggered a series of negative impacts on human populations and the natural environment. To understand groundwater-related processes and explore ways to mitigate pertaining issues, large numbers of groundwater-related studies have been performed by the research community. In this study, all research papers published between 1978 and 2017 incorporating "groundwater" in the title, keywords, or abstract, were retrieved from the Web of Science Core Collection (WSCC). Of these, a total of 80,763 papers were identified as being groundwater-related publications. Our analysis revealed that groundwater research output increased significantly during this period, at average annual rate of 10.1%, exceeding the general trend for the WSCC (3.4%). Institutions based in developed countries have held predominance in publishing groundwater research, however, research output from developing countries, such as China and India, experienced much growth in recent years. Development trends were assessed among the research topics of groundwater depletion, contamination, remediation technology, and sustainable management, all of which experienced increased research output. Among sub-topics, groundwater depletion and sustainable management research have increased as a percentage of total groundwater studies, while studies of arsenic, chlorinated solvents, BTEX, bioremediation, and natural attenuation have dropped off in recent years. Additionally, as groundwater-related research output has increased rapidly, robust secondary analyses and meta -analyses are called for in order to make sense of the rapidly expanding body of groundwater research literature. Moreover, owing to the many complexities surround groundwater issues, greater interdisciplinary collaboration is also called for. [ABSTRACT FROM AUTHOR]

• UTFI reduces the intensity of seasonal floods by tapping and storing flood waters in aquifers for productive use. • Provides insights for scaling up the interventions in the Ganges and other flood prone river basins that face dry season water shortages. • Managing the interventions involves community participation on a regular basis. • Suggests short- and long-term institutional arrangements for managing UTFI. Protecting flood prone locations through floodwater recharge of the depleted aquifers and using it for protecting dry season irrigated agriculture is the rationale for a form of intervention termed as 'underground transfer of floods for irrigation' (UTFI). This helps reduce the intensity of seasonal floods by tapping and storing excess floodwater in aquifers for productive agricultural use. This paper presents a case study of managing the recharge interventions in the context of the Ramganga basin, India. Using a case study approach, this study determines the socio-economic and institutional context of the study area, proposes three potential routes to institutionalize UTFI, and provides insights for scaling up the interventions in the Ganges and other river basins that face seasonal floods and dry season water shortages. Managing the interventions involves community participation in regular operations and maintenance tasks. Given the limited scale of the pilot UTFI intervention implemented to date, and the socio-economic and institutional context of the case study region, the benefits are not conspicuous, though the piloting helped in identifying potential ways forward for the long-term management of the pilot site, and for scaling up the interventions. Initially pilot site management was handled by the project team working closely with the community leaders and villagers. As the intervention was demonstrated to perform effectively, management was handed over to the district authorities after providing appropriate training to the government personnel to manage the system and liaise with the local community to ensure the site is operated and managed appropriately. The district administration is willing to support UTFI by pooling money from different sources and routing them through the sub-district administration. While this is working in the short term, the paper outlines a programmatic longer term approach for wider replication. [ABSTRACT FROM AUTHOR]

• Shot-term reservoir inflow prediction accounting for hydrological and climate indices. • ML-based Ensemble methods to quantify forecasting uncertainty. • The Ensemble model performed better than the standalone ML models. • Two reservoirs of India and USA were chosen to test the robustness of ML models. Short and long range reservoir inflow forecast is essential for efficient real time operational planning, scheduling of hydroelectric power system and management of water resources. Large-scale climate phenomenon indices have a strong influence on hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. This study aims to explore the relevance of large-scale climate phenomenon indices in improving the reservoir inflow prediction at short-term time scales. This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting ensemble method to quantify forecasting uncertainty and to improve the model performance by combining the inflow results of the single ML model and the highest vote is chosen based on the weights assigned to the single ML model. The developed framework was examined in two distinct reservoirs located in India and California, USA. The ensemble model consistently outperformed the standalone RF, GBR, KNN, and LSTM in predicting high (flood control and monsoon seasons) and low flows (runoff and non-monsoon seasons) of both study reservoirs. The demonstrated short-term reservoir forecasting model allows reservoir operators to adapt and add regional hydrological and large-scale climate indices in real-time decision-making. The presented framework can be applied for any reservoir inflow forecasting. [ABSTRACT FROM AUTHOR]

Summary To face the problem of groundwater depletion, the Indian Government relies on large projects of Managed Aquifer Recharge (MAR). Numerous recharge structures such as percolation tanks exist but the impact of these structures on groundwater resources remains poorly understood. Although the evaporation/infiltration ratio of percolation tanks was determined in several studies in semi-arid contexts using surface water balance methods, few studies evaluated the impact on the aquifer recharge. However, knowledge on recharge dynamics over time and space is essential for (1) the quantitative evaluation of stored water volumes, (2) the identification of beneficiaries (farmers) and (3) the estimation of percolation tanks recharge zone to the extent that is required to define proper management regulations at basin scale. These three points are of prime importance in the case of semi-arid regions where a limited number of rain events determine the water stored over the entire year. Assessment of the stored groundwater is even more difficult in crystalline aquifers due to the heterogeneous structure of flow paths. To date no methodological guidelines exist for local assessment of percolation tanks in crystalline aquifers. In this paper, we develop a method for calculating a local groundwater budget and we compare it with a computed surface balance. The method is applied to a case study in semi-arid crystalline context. From the groundwater balance we draw conclusions on (1) the limited amount of stored water in the aquifer, (2) the delayed recharge of the aquifer highlighting temporary storage/slow groundwater movement in the unsaturated zone and (3) the limited number of beneficiaries in years of medium monsoon rainfall. These results complement the understanding of the hydrodynamic functioning of percolation tanks, and their impact on the local groundwater balance. [ABSTRACT FROM AUTHOR]

• Studied the effect of catchment size and simulation time steps on parameter sensitivity. • Hydrological model is developed for 10 different watersheds of Narmada River basin. • Two Global Sensitivity Analysis methods used to identify the sensitive parameters. • To avoid over parameterization, most and least sensitive parameters are identified. • Identifying a set of sensitive parameters that covers all the hydrological processes. Hydrological models have many parameters representing various hydrological processes. These parameters are effective at different spatial and temporal resolution. Most of the parameters can be measured such as slope, elevation, area, vegetation type etc., but others cannot be estimated from the available data. Thus, they remain unknown in a hydrological simulation. Local and Global Sensitivity Analyses (LSA and GSA respectively) are therefore used to reduce the number of parameters that need to be equipped with input–output data. GSA also improves the efficiency of the model calibration and validation which in turn increases the reliability of the model. In this paper, we investigate into the effect of size of a catchment (small, medium, and large) time steps of simulation (Daily and Monthly), and the choice of GSA method in performing sensitivity analysis. A Soil and Water Assessment Tool (SWAT) hydrologic model is developed for 10 subbasins of Narmada River basin at daily and monthly temporal simulations from January 1991 to December 2000. The sensitivity of 29 input parameters have been analysed with two GSA methods: the multilinear regression SA used by sequential uncertainty fitting algorithm (SUFI-2) in SWAT – Calibration and Uncertainty procedure (SWAT-CUP), and the first order sensitivity indices driven by Fourier Amplitude Sensitivity Test (FAST). Our results show that a hydrological model set up with only sensitive parameters can also produce similar results as when all the parameters are considered. We concluded that the selection of catchment size and simulation time step strongly influence the parameter sensitivity. Our results show that the sensitivity of some of the parameters like Curve number for moisture condition (CN2.mgt), baseflow alpha factor for bank storage (ALPHA_BNK.rte) and lateral flow travel time (LAT_TTIME.hru) are independent of catchment size and simulation time step. Moreover, baseflow alpha factor for the recession constant (ALPHA_BF.gw), manning's value for overland flow (OV_N.hru) and soil erodibility factor (USLE_K(..).sol) are not sensitive for large and medium range watersheds but those are sensitive for small watershed at a monthly time step. Ground water delay (GW_DELAY.gw), average slope steepness (HRU_SLP.hru) and support practice (p) factor of USLE soil equation (USLE_P.mgt) are not sensitive for large watersheds but sensitive for small and medium range watersheds at a daily time step. Our results strongly recommends that a detailed parameter sensitivity analysis is an important step in setting up any hydrological model to reduce the number of parameters while addressing all relevant hydrological processes. [ABSTRACT FROM AUTHOR]

• Hydrological responses to land use and climate change are simulated using integrated modelling. • Scoring method is applied to select representative climate models. • Mean temperature is likely to increase by 1.79 °C (3.57 °C) under mid (high) emission scenarios. • Annual and monsoon flow is likely to increase during the 2050s and 2080s. • Hydrological sensitivity of the basin is analysed against changes in climatic variables. Accurate spatial and temporal measurement of water budget play a crucial role in achieving the sustainable planning and management of water resources. This paper presents a comprehensive integrated approach to simulate hydrological responses under dynamics of land use land cover (LULC) and climate change scenarios over Upper Narmada Basin, India. In order to select the representative climate models, scoring method was applied at regional scale. Best five performing climate models (MIROC5, CNRM-CM5, MPI-ESM-LR, GFDL-ESM2G and IPSL-CM5A-MR) were coupled with semi-distributed hydrological model to simulate the hydrological responses. Considering the heterogeneity of the large basin, multi-site calibration and parameters sensitivity analysis were performed using Sequential Uncertainty Fitting (SUFI-2) algorithm. To compute the hydrological sensitivity against land use change, calibrated model was coupled with historical and futuristic land use scenarios (1990, 2000, 2010 and 2030). The results indicate intensified precipitation towards the late 21st century, whereas annual mean temperature could be raised by 1.79 °C and 3.57 °C under mid and high emission scenarios respectively, at the end of century. Annual and monsoon flow in the basin likely to increase during the 2050 s (2041–2070) and 2080 s (2071–2100). Moreover, the relation between climate variables and water budget components were identified to analyse the hydrological sensitivity of the basin under changing climate. [ABSTRACT FROM AUTHOR]

• It is to estimate river discharges solely using satellite images only without ground data. • Monitoring upstream discharges using this approach will help in flood early warning. • Flows in ungauged and in transboundary rivers can be monitored. Many countries experiences floods due to heavy flows carried by trans -boundary Rivers. Hydrological modelling in un-gauged catchments and trans -boundary Rivers has become a challenging task for hydrological modellers. This research aims at estimating river discharges computed solely using satellite images, with no ground-based or a prior information at all. It is also aimed at testing the applicability of approach in very well defined channel to braided channels and in very wide range of discharge conditions. The approach works owing to principle of hydraulic geometry of natural rivers. Monitoring these discharges in upstream areas will help in forecasting flows in the downstream areas. In this paper, an attempt is made to estimate the river discharge using river flow width information derived from temporal satellite remote sensing data alone. The approach is based on Many Stations Hydraulic Geometry (AMHG) principle through Genetic Algorithm Technique. The technique is demonstrated in four major rivers in India for various discharge values using temporal remote sensing data of Indian Remote Sensing Satellites (IRS) and LANDSAT. Computed discharges are validated with observed discharges in all the identified river reaches using statistical method and found to have very good match. This novel approach is useful globally for quantifying river discharges with continuous imaging of the rivers and has strong advantage in flood early warning and water resources assessment. [ABSTRACT FROM AUTHOR]

• Fuzzy entropy method is proposed for design and retrofitting of hydrometric network. • Proposed method alleviates shortcomings of Shannon and exponential entropy methods. • Method is used to assess performance of networks in 16 river basins of India. • Optimal monitoring network in Mahanadi basin is obtained using the methodology. • Effect of various entropy methods and bin size estimation rules is analysed. This paper proposes a fuzzy entropy-based methodology for optimal design and performance assessment of a hydrometric monitoring network that provides information essential for hydrological studies. The methodology does not require choice of bin size for discretization of data to estimate entropy measures/indices. Therefore, it alleviates the uncertainty associated with bin size estimation which is a concern in analysis with conventional Shannon entropy-based methodology (SEBM). Advantage of the proposed methodology over SEBM and its related theoretical improvement EEBM (exponential entropy-based methodology) in arriving at optimal design of streamflow monitoring network is demonstrated through performance investigation on Mahanadi basin of India, which is frequently prone to floods. Following this, the methodology is used to assess the performance of streamflow monitoring networks in 16 river basins of Peninsular India and identify stream gauge deficit zones. This study is first of its kind which evaluates the effect of different entropy estimation methods and bin size estimation rules on: (i) prioritization of stream gauge stations for their importance in data collection, (ii) identification of stream gauge deficit zones, and (iii) design of optimal hydrometric network in a river basin. Proposed methodology appears promising and offers scope for application to networks monitoring various other hydrometeorological variables. [ABSTRACT FROM AUTHOR]

• Disparity analysis of two and multi parametric models is performed with 8 Budyko based equations. • The study is carried out over 19 catchments across India. • Higher discrepancy in percentage contributions values for the case of multi-parameter model. Water availability is crucial for sustaining the development and even existence of human civilization. Identifying major sources of its variability is of paramount importance. The climate elasticity method provides a suitable platform to quantify the relative influences of climatic variables and anthropogenic stresses using Budyko hypothesis to the changes in runoff but judicious selection of Budyko based equations with relevant runoff elasticities is vital. In this paper, comparative study is carried out using climate elasticity approach in 19 catchments across India, to evaluate the disparity among runoff elasticities and percentage contributions of climatic variables (precipitation, maximum and minimum temperature, wind speed, sunshine duration, and relative humidity) and anthropogenic stress in runoff alterations. Among climatic parameters, precipitation has shown the maximum influence in 16 catchments. In 15 catchments, maximum temperature has higher relative contribution than minimum temperature. In addition, anthropogenic influence is higher in 9 catchments for Two-parameter approach (precipitation and potential evapotranspiration) whereas it has higher impact in 10 catchments as per Multi-parameter (including evapotranspiration elasticities) approach. Decomposing evapotranspiration elasticity to five climatic variables has been proven to be unproductive as it has produced more disparity among the percentage contribution values. The standard deviation values in contributions are more in the case of the Multi-parameter model. Thus, it is pragmatic to adopt Multi-parameter model that constitutes runoff elasticities to different climatic variables, when assignment demands the individual influences of these variables on hydrology. Adding more parameters into the framework introduces more error in the assessment of impacts of climatic variabilities. [ABSTRACT FROM AUTHOR]

• Temperature trends alter rainfall uniformity by modifying characteristics of rainfall events. • A significant relationship between rainfall uniformity and low rainfall intensity is observed. • Rainfall uniformity, high and low intensity of rainfall show a decreasing order of sensitivity to annual mean temperature. Understanding response of temporal distribution, timing, frequency and amount of high and low intensity rainfall to warming is important in water resources management. In this paper, Relative Entropy is used to investigate the spatial variability and change in uniformity of rainfall distribution over India. Temporal trends in atmospheric temperature can alter the frequency and amount of high and low intensity rainfall events, which influence the uniformity of rainfall distribution. The study is divided into two time periods, 1951–1980 and 1981–2010 based on time trend in annual mean temperature. The sensitivity of rainfall uniformity and high and low intensity rainfall events to annual mean temperature and the degree of coherence between them are investigated. The uniformity of rainfall distribution shows a significant spatial variability. Significant changes are observed in both the amount and timing of rainfall across India. A significant association between rainfall uniformity and low intensity of rainfall is observed in the recent past over a larger aerial extent compared to the distant past. It is concluded that rise in temperature modifies both high and low intensity rainfall events, thus altering the uniformity in rainfall distribution. A regionally varied strength of coherence between rainfall uniformity and high and low intensity rainfall is observed which may be due to regionally dependent soil moisture-precipitation feedbacks. [ABSTRACT FROM AUTHOR]