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The need for and the use of different data assimilation techniques to improve the quality of streamflow forecast is now well established. In this paper, the goal is to demonstrate the power of a new class of methods known as the forward sensitivity method (FSM) which is based on the temporal evolution of model sensitivities with respect to the control variables consisting of initial conditions and parameters. FSM operates in two phases: the first phase provides a simple algorithm for placing observations at or near where the square of forward sensitivities attains their maximum values. Using only this selected subset of observations in a weighted least squares method, the second phase then provides an estimate of the unknown elements of the control variables. In this paper, FSM based assimilation is applied to a simple class of two parameter model in a medium‐sized agriculture dominant watershed lying in the Krishna River Basin, India. Four assimilation scenarios were tested to determine the effect of assimilating only sensitive observations as well as the impact of temporally evolving initial condition sensitivity. Sensitivity results showed that observations during the monsoon time alone are enough for assimilation purposes, which has helped in reducing the computational time greatly. Assimilation and forecast results also indicated that the scenarios which assimilated only sensitive observations are better in estimating daily streamflow. From the obtained results, it is concluded that FSM based assimilation has significant potential to improve the streamflow simulations, especially in places where data availability remains a major challenge. Key Points: The potential of assimilating only sensitive soil moisture observations on streamflow forecast was demonstrated using a novel forward sensitivity method (FSM) methodImpact of temporally evolving sensitivity of initial condition on hydrological model performance during assimilation was determinedThe sensitivity‐based soil moisture assimilation was effective up to 45 lead days during streamflow forecast [ABSTRACT FROM AUTHOR]

Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow‐fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7‐day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time. Key Points: Hybrid hydrological model integrates process‐based model with machine learning algorithm through data assimilation techniqueDynamic error correction framework capable of improving the streamflow forecast at longer lead time is proposedOverall the developed framework improves the forecast accuracy along with quantifying the model prediction uncertainty [ABSTRACT FROM AUTHOR]

Modeling hydrological processes for managing the available water resources effectively is often complex due to the existence of high nonlinearity, and the associated prediction uncertainty mainly arising from model inputs, parameters, and structure. Despite several attempts to quantify the model prediction uncertainty, reducing the same for improving the reliability of models is indispensable for their wider acceptance. This paper presents a novel modeling framework for minimizing the prediction uncertainty in the streamflow simulation of the conceptual hydrological model (HBV) by integrating with the Bayesian‐based Particle Filter technique (PF) and machine learning algorithm (Random Forest algorithm, RF). Initially, the streamflow prediction interval (PI) is derived from the stochastically estimated parameters of the HBV model through the PF technique (HBV‐PF model). As the HBV‐PF model quantifies only parametric uncertainty, the RF algorithm was employed (HBV‐PF‐RF model) for further minimizing the prediction uncertainty by inherently taking care of different sources of uncertainty. The RF algorithm inherently combines the physics of the hydrological system (i.e., process‐based variables) with machine learning‐based approach to minimize the overall prediction uncertainty. The proposed framework was analyzed on Nepal and India's Sunkoshi and Beas River basins, through several statistical performance indices for assessing the accuracy and uncertainty of the model prediction. The framework was observed to be consistently improving the model performance minimizing the uncertainty in both watersheds. Therefore, the proposed framework can be considered to be more reliable in improving the prediction capability of hydrological models. Key Points: Development of a physics‐aware machine learning based hydrological model for streamflow simulationThe proposed framework characterizes the model prediction uncertainty, accounting different sources of uncertainty implicit/explicitlySignificant reduction in overall prediction uncertainty is achieved [ABSTRACT FROM AUTHOR]

Nearly one billion people worldwide receive water through piped networks that are not continually pressurized and operate intermittently. The prevalence and persistence of these Intermittent Water Supplies (IWS) is surprising as this mode of operation induces water contamination and customer equity issues. Shortages of source water, customers' water demand, and leaking pipes are frequently cited as necessitating IWS. We propose a framework for understanding the persistence and operation of IWS. The supply system is represented by an average customer and a spatially averaged leakage rate. With this macroscopic hydraulic model, we relate customer demand satisfaction, source water availability, customer demand, and leakage. While this approach ignores the complexities of network topology, we find that the model approximates real systems well (calibrating to four intermittent reference networks achieved R2>0.87). The calibrated model is robust to moderate changes in demand and leakage (maintaining R2>0.81). Using the model, we show that the tipping point between satisfied demand and unsatisfied demand is a local optimum for utilities, which may explain the persistence of IWS. Beyond this point, the volume received by customers does not increase, but utilities must supply more water to the network. The generality of the proposed model enables its use when regulating and upgrading IWS. We demonstrate the latter by critiquing a performance‐based contract that was intended to improve an intermittent supply in India. Demand satisfaction has profound implications for hydraulics and human welfare. We propose the degree of demand satisfaction as a metric for evaluating IWS and for tracking the United Nations Sustainable Development Goal 6.1. Plain Language Summary: While most people get drinking water through underground water pipes, in some areas these pipes only provide water for a few hours each week. Globally, more than one billion people are served by these "intermittent water supply systems". In severely degraded systems, keeping pipes empty most of the time can reduce leakage and conserve source water, but it can also allow mud or sewage to enter through holes or cracks in the pipes, contaminating the water. This paper presents a simple model (an equation) that approximates how intermittent systems behave. The model suggests why intermittent operation is so common and highlights the challenges of improving such systems. The model relates hours of operation to factors such as source water supply, consumer demand, leakage, and if consumer demand is satisfied. The model implies that once consumer demand is satisfied, a system crosses a threshold and behaves differently: leakage, rather than demand, determines most of a system's water requirements. Measuring this threshold could identify systems that leave people thirsty and prioritize such systems for improvements. The model, therefore, provides new tools to support global commitments outlined in the United Nations' Sustainable Development Goal 6.1 and the Human Right to Water. Key Points: Without topology details, our proposed model of intermittent water supplies (IWS) matches closely with more complete network modelsWhile inadvisable, IWS provide utilities traditionally ignored, short‐term benefits (e.g., supply timing will change less due to drought)We propose a new demand satisfaction metric to quantify supply availability and help track Sustainable Development Goal 6.1 in IWS [ABSTRACT FROM AUTHOR]

In this paper, we discuss a challenging water resources problem in a developing world city, Chennai, India. The goal is to reconstruct past system behavior and diagnose the causes of a major water crisis. In order to do this, we develop a hydrologic-engineering-economic model to address the complexity of urban water supply arising from consumers' dependence on multiple interconnected sources of water. We integrate different components of the urban water system: water flowing into the reservoir system; diversion and distribution by the public water utility; groundwater flow in the aquifer beneath the city; supply, demand, and prices in the informal tanker-truck-based water market; and consumer behavior. Both the economic and physical impacts of consumers' dependence on multiple sources of water are quantified. The model is calibrated over the period 2002-2006 using a range of hydrologic and socio-economic data. The model's results highlight the inadequacy of the reservoir system and the buffering role played by the urban aquifer and consumers' coping investments during multiyear droughts. [ABSTRACT FROM AUTHOR]

Recent studies suggest that there is a strong linkage between the moisture uptake over the equatorial area of the Somali low level jet (SLLJ) and the rainfall variability over most of continental India. Additionally, the Madden-Julian Oscillation (MJO) strongly modulates the intraseasonal variability of the Indian summer monsoon rainfall, since the northward propagation of the boreal summer MJO is closely associated with the active and break phases of monsoon rainfall. But a question remains open: is there a relationship between the moisture transported by the SLLJ and the MJO evolution? In this paper, a Lagrangian approach is used to track the evaporation minus precipitation (E − P) evolution along trajectories of particles initially situated over the equatorial region of SLLJ. The impact of the MJO on the water budget transport of the SLLJ is examined by making composites of the obtained (E-P) fields for the different MJO phases. The spatial structures of the boreal summer intraseasonal oscillation are revealed in our results, which strongly suggest that the main responsible for the rainfall variability associated to the MJO in these regions are the changes in the moisture advected by the SLLJ. In order to assess the MJO-SLLJ interaction, an analysis of the total-column mass and the total-column specific humidity transported by the SLLJ during the MJO life cycle is performed. While a systematic difference between air mass advected to India during active and break phases of MJO is not detected, changes in the moisture of particles are found, with wet (dry) anomalies over enhanced (suppressed) convection region. This result implicitly leads to assume air-sea interaction processes. [ABSTRACT FROM AUTHOR]

Understanding the riverine thermal regimes is challenging due to sparse spatiotemporal data of river water temperatures (RWTs). The development of systematic models combined with machine learning models under data‐limited context has not been intensively studied for the prediction of RWT. The present study developed hybrid models using long short‐term memory (LSTM), integrated with (a) k‐nearest neighbor (k‐NN) bootstrap resampling algorithms (kNN‐LSTM) to address the data limitations of RWT prediction and (b) discrete wavelet transform (WT) approach (WT‐LSTM) to address the time–frequency localized features of RWT prediction. The study assessed the climate change impacts on RWT using an ensemble of National Aeronautics Space Administration Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway scenarios 4.5 and 8.5 for seven major polluted river catchments of India. The hybrid kNN‐LSTM has effectively predicted RWT at monthly time scales under data limitations and outperformed the standalone LSTM, WT‐LSTM, and hybrid three‐parameter version air2stream models. The RWT increase for Tunga‐Bhadra, Musi, Ganga, and Narmada basins is predicted as 3.0, 4.0, 4.6, and 4.7°C, respectively, for 2071–2100. Key Points: Hybrid deep learning models coupled with resampling algorithms and wavelet transforms to predict river water temperatures under sparse dataThe hybrid models effectively predicted river water temperatures at monthly time scales under data limitationsAssessed the climate change impacts on river water temperatures in seven major polluted river catchments of India [ABSTRACT FROM AUTHOR]

A two‐level clustering approach is proposed for optimal design/expansion of a ground‐based precipitation monitoring network (GPN). It harnesses the advantages of Infinite Bayesian fuzzy clustering in the first level to partition the study area into homogeneous precipitation zones by considering structural/statistical characteristics and temporal variability of the observed precipitation. In the second level, an ensemble of hierarchical and partitional clustering techniques is considered in the time domain to effectively partition each zone into groups by considering weighted inter‐site dissimilarities of precipitation. The dissimilarities account for correlation, temporal dynamics, and fuzzy mutual information of precipitation at existing stations and possible new gauge locations. Key station's location in each group is identified by a proposed ranking procedure that accounts for population density, land‐use/landcover, and fuzzy marginal entropy of precipitation. For use with the approach, information on precipitation was derived for fine resolution ungauged grids covering the study area using random forest‐based regression relationships developed for gauged grids between merged multiple satellite‐based precipitation products (CHIRPS, IMERG) and ground‐based precipitation measurements. The potential of the proposed approach over other clustering‐based procedures is illustrated through a case study on a GPN comprising 1,128 gauges in Karnataka state (191,791 km2) of India. Potential locations for installing new gauges and areas where there is scope for relocating existing stations are identified. The proposed methodology appears promising and could be extended to design networks monitoring various other hydrometeorological variables. Key Points: Proposes a two‐level clustering approach for optimal design/expansion of ground‐based precipitation monitoring network (GPN)Approach harnesses advantages of infinite Bayesian fuzzy clustering and an ensemble of hierarchical and partitional clustering methodsMerged ground and multiple satellite‐based precipitation derived using random forest method is used to locate key stations to redesign GPN [ABSTRACT FROM AUTHOR]

Basin-scale streamflow is influenced by numerous local and global climate inputs. In this paper, genetic programming (GP) is combined with "importance analysis" to identify the important global climate inputs and local meteorological variables needed for prediction of weekly streamflow at the basin scale. The analysis is carried out for the Mahanadi River in India using global climate inputs, namely, the El Niño-Southern Oscillation (ENSO) index and equatorial Indian Ocean Oscillation (EQUINOO) index; local meteorological inputs, including outgoing longwave radiation (OLR), total precipitable water (TPW), temperature anomaly (TA), and pressure anomaly (PA); and streamflow information from previous time steps. The rainfall information over the basin is intentionally not utilized so that the procedure may be applicable to basins with little or no rain gauge information and to achieve a longer prediction lead time. The Birnbaum importance measure is used to assess the importance of each input. Results of this study show that the relative importance of individual input variables is influenced by time lags. It is observed that among various local meteorological inputs, OLR and PA are more important than TA and TPW. Among large-scale circulation indices, ENSO index is important for previous 5th to 7th week, whereas EQUINOO index is important for previous 3rd to 6th week. On the basis of their importance measures, 15 indices were selected from the initial group of 30 indices. The GP-derived streamflow forecasting models could predict weekly streamflow with good accuracy (correlation coefficient r = 0.821) for such a complex system. [ABSTRACT FROM AUTHOR]

A novel Bayesian Hierarchical Network Model (BHNM) for ensemble forecasts of daily streamflow is presented that uses the spatial dependence induced by the river network topology and hydrometeorological variables from the upstream contributing area between station gauges. Model parameters are allowed to vary with time as functions of selected covariates for each day. Using the network structure to incorporate flow information from upstream gauges and precipitation from the immediate contributing area as covariates allows one to model the spatial correlation of flows simultaneously and parsimoniously. An application to daily monsoon period (July–August) streamflow at three gauges in the Narmada basin in central India for the period 1978–2014 is presented. The best set of covariates include daily streamflow from upstream gauges or from the gauge above the upstream gauges depending on travel times and daily precipitation from the area between two stations. The model validation indicates that the model is highly skillful relative to a null‐model of generalized linear regression, which represents the analogous non‐Bayesian forecast. The ensemble spread of BHNM accounts for the forecast uncertainty leading to reliable and skillful streamflow predictions. Key Points: We developed a Bayesian Hierarchical Network Model for ensemble forecasts of daily streamflow with the attendant uncertaintiesThe model provides ensemble forecasts at all the locations on a river network simultaneously, capturing the spatial and temporal correlationThe framework can be applied to any river network and with appropriate covariates [ABSTRACT FROM AUTHOR]

In the past, many mathematical models based on the dual‐porosity (DP) concept were developed to describe the groundwater flow in fractured aquifer systems. Most of them seemingly have problems in predicting accurate drawdown at the early and/or intermediate times as compared with field measured data. Thus, this study proposes a new analytical model with a generalized transfer term (GTT) to describe the flow induced by pumping in such systems. The new model is nonlinear because the GTT representing the matrix‐to‐fracture flux gives different weights to the fracture and matrix drawdowns. The GTT reduces to the existing first‐order transfer term if the weight equals zero and second‐order term if the weight is one. The present model also includes a leakage term accounting for flow from the overlain or underlain acquitard. The drawdown solution of the model is developed based on the Laplace transform method and integration by parts formula and then verified through the comparison with the finite‐element solution. The effect of different weight values in the GTT on the DP flow is investigated. Additionally, the sensitivity analysis is performed to assess the impact of the change in each of the aquifer parameters on the flow. Furthermore, the present solution is used to analyze two sets of pumping drawdown data from test sites in Canada and India. We found that the drawdown predictions from the present solution fit field measured data very well, suggesting that the present model can adequately describe the real‐world DP flow system. Key Points: A generalized nonlinear water transfer term is proposed for describing the matrix‐to‐fracture flux in a dual‐porosity (DP) system subject to pumpingThe present term reduces to the existing first‐order or second‐order transfer term for weighting factor of 0 or 1, respectivelyThe drawdown solution of the model is used to analyze field drawdown data for estimating the aquifer parameters [ABSTRACT FROM AUTHOR]

A realistic evaluation of drought onset is inevitable for the effective implementation of mitigation strategies. Meteorological droughts indicating the onset of drought propagation are usually quantified through unbiased indices that consider the month‐wise magnitude variations in historical climatic variables, while completely ignoring their intramonthly distributions. However, the applicability of such indices under the present scenario of changing climate, where intense short wet spells are reportedly increasing, is questionable. Such changes in the wet spells could eventually lead to prolonged intramonthly dry spells, which in turn will pose many agricultural and socioeconomic risks. To monitor the changed scenario realistically, we propose a new drought index—the Standardized Net‐Precipitation Distribution Index (SNEPI), which incorporates the distribution characteristics of the daily net‐precipitation variable. The applicability of SNEPI is critically evaluated using synthetically generated and observed precipitation series over six diverse climatic locations of India, at 1‐, 3‐, 6‐, and 12‐month time scales. The utility of SNEPI is more profound for longer time scales, understandably due to the enhanced emphasis imparted on the distribution of rainfall spells by the longer periods. Standardized Net‐Precipitation Distribution Index proves to be efficient in capturing the present scenario of increasing dryness (wetness) in wet (dry) regions when compared with the traditional Standardized Precipitation Evapotranspiration Index. Hence, the proposed index, which incorporates the changing precipitation characteristics, may aid in better quantification and monitoring of meteorological drought under the scenario of changing climate. Key Points: Revisited the concept of drought indices by accounting the precipitation characteristics such as intensity, frequency, and uniformityProposed index shows profound influence for longer time scales, implying possible utility in drought propagation to hydrological levelsProposed index may prove beneficial under the present scenario of abruptly changing climate [ABSTRACT FROM AUTHOR]

Despite the remarkable improvements in skillful weather forecasts, their uses in irrigation water management at a farm scale are still limited. This is attributable to the scale mismatch between weather and hydrologic models as well complexities in farmscale ecohydrological processes. Here, we have developed a simulation‐optimization algorithm for minimizing irrigation water application using short to medium range forecasts by determining the conditional probability density functions of the rainfall and subsequently the soil moisture for the days in forecast range. With the forecasted soil moisture information, the model ensures that the probability of crops undergoing water stress is less than a prescribed threshold. The optimization model includes a farmscale ecohydrological model, which computes daily evapotranspiration, runoff, and leakage based on a probabilistic description of rainfall through Monte‐Carlo simulations. After calibrating and validating the ecohydrological model with past data obtained from the farmers, this proposed optimization framework was employed to test the outcome for two pilot sites in Nashik, Maharashtra, India. We found that using the proposed framework, irrigation water use can be reduced by 10%–30% as compared to that resulting from the conventional strategies used by the farmers, without significant loss in crop yields, by almost always maintaining the soil moisture at or above the prescribed threshold. Considering that irrigation accounts for over 80% of the total water use worldwide, the value of such an approach as a decision‐support tool for irrigation optimization is self‐evident. We also posit that the co‐production of this tool with the farmers increases its usability and credibility. Plain Language Summary: A competent irrigation water management should minimize water application, avoid water stress in plants, and stabilize crop productivity and revenues. As there are substantial monetary investments and water use for irrigation, planned management is essential for sustainable use of water, without a loss in yield or economic benefit, especially considering the uncertainties in climatic drivers. To deliver a suitable scheme, we adopt a probabilistic approach based on the probability density function (pdf) of soil moisture at a farm scale. A stochastic ecohydrological model simulates the probabilistic soil moisture considering irrigation and rainfall input. The model uses the conditional probability of rainfall, given past forecasts, and hence, takes care of uncertainties and errors therein. The stochastic modeling is represented by Monte‐Carlo simulations, which are inbuilt within the optimization model. The model's objective function is to minimize the irrigation amount with a constraint that on a specific day, given a forecast, the probability of water stress is less than a user‐defined value. The framework thus co‐developed with the farmers is found to have considerable potential for reducing irrigation water use compared to the farmers' approach over the chosen test sites. The usability of the tool is enhanced by the farmers' involvement from the beginning. Key Points: Use of weather/climate forecasts for analytically deriving irrigation requirements at farm‐scaleDevelopment of water use management strategy at farm‐scale addressing uncertainty in weather forecastsReducing water use for irrigation with weather forecasts and stochastic ecohydrological model at farm‐scale [ABSTRACT FROM AUTHOR]

Synthetic daily streamflow generation requires a critical understanding of the underlying dynamics resulting from the inherent temporal asymmetry (time irreversibility) in the rising and falling limbs of the hydrograph. Most models considering the time irreversibility deal with single‐site streamflow simulation. Addressing intersite dependencies is, however, crucial for interconnected stream networks. This study presents a time irreversibility dynamics‐based multisite streamflow generating framework to simulate concurrent streamflow sequences at multiple sites in a basin. This framework explicitly takes into account the spatial correlation and time irreversible dynamics of streamflow. To demonstrate the utility and applicability, a few selected streamflow gauging stations in the Godavari River Basin, located in southern India, are considered. The proposed framework shows the ability to adequately generate multisite simulations capturing at‐site statistics as well as intersite correlations for the case study. Furthermore, the approach ensures that the simulated flow values are not merely resampled from the historical data, uses the physical features of the hydrograph, and shows variability beyond that observed in the historical sequence. Such a rich variety of streamflow sequences can help water managers to investigate how existing water resources systems on interconnected stream networks will operate in scenarios which have not been observed in the historical record. Key Points: A three‐stage algorithm for generating concurrent multisite streamflow is proposedTime irreversible dynamics of hydrologic processes at daily scale are accounted forThe algorithm results in simulated sequences that capture variability beyond that represented in the historical streamflow record [ABSTRACT FROM AUTHOR]

We present a sociohydrological model that can help us to better understand the system dynamics of a smallholder farmer. It couples the dynamics of the six main assets of a typical smallholder farmer: water storage capacity, capital, livestock, soil fertility, grazing access, and labor. The hydroclimatic variability, which is a main driver and source of uncertainty of the smallholder system, is accounted for at subannual scale. The model incorporates rule-based adaptation mechanisms of smallholders (for example, adjusting expenditures on food and fertilizers and selling livestocks) when farmers face adverse sociohydrological conditions, such as low annual rainfall, occurrence of dry spells, or variability of input or commodity prices. We have applied the model to analyze the sociohydrology of a cash crop producing smallholder in Maharashtra, India, in a semisynthetic case study setting. Of late, this region has witnessed many suicides of farmers who could not extricate themselves out of the debt trap. These farmers lacked irrigation and were susceptible to fluctuating commodity prices and climatic variability. We studied the sensitivity of a smallholder's capital, an indicator of smallholder well-being, to two types of cash crops (cotton and sugarcane), water storage capacity, availability of irrigation, initial capital that a smallholder starts with, prevalent wage rates, and access to grazing. We found that (i) smallholders with low water storage capacities and no irrigation are most susceptible to distress, (ii) a smallholder's well-being is low at low wage rates, (iii) wage rate is more important than absolution of debt, (iv) well-being is sensitive to water storage capacity up to a certain level, and (v) well-being increases with increasing area available for livestock grazing. Our results indicate that government intervention to absolve the debt of farmers or to invest in local storage to buffer rainfall variability may not be enough. In addition, alternative sources of income may need to be provided, for instance by ensuring minimum wages or by providing more access to grazing areas. [ABSTRACT FROM AUTHOR]

Investigating changes in terrestrial water storage (TWS) is important for understanding response of the hydrological cycle to recent climate variability worldwide. This is particularly critical in India where the current economic development and food security greatly depend on its water resources. We use 129 monthly gravity solutions from NASA's Gravity Recovery and Climate Experiment (GRACE) satellites for the period of January 2003 to May 2014 to characterize spatiotemporal variations of TWS and groundwater storage (GWS). The spatiotemporal evolution of GRACE data reflects consistent patterns with that of several hydroclimatic variables and also shows that most of the water loss has occurred in the northern parts of India. Substantial GWS depletion at the rate of 1.25 and 2.1 cm yr−1 has taken place, respectively in the Ganges Basin and Punjab state, which are known as the India's grain bowl. Of particular concern is the Ganges Basin's storage loss in drought years, primarily due to anthropogenic groundwater withdrawals that sustain rice and wheat cultivation. We estimate these losses to be approximately 41, 44, and 42 km3 in 2004, 2009, and 2012, respectively. The GWS depletions that constitute about 90% of the observed TWS loss are also influenced by a marked rise in temperatures since 2008. A high degree of correspondence between GRACE-derived GWS and in situ groundwater levels from observation well validates the results. This validation increases confidence level in the application of GRACE observations in monitoring large-scale storage changes in intensely irrigated areas in India and other regions around the world. [ABSTRACT FROM AUTHOR]

Water storage is an important way to cope with temporal variation in water supply and demand. The storage capacity and the lifetime of water storage reservoirs can be significantly reduced by the inflow of sediments. A global, spatially explicit assessment of reservoir storage loss in conjunction with vulnerability to storage loss has not been done. We estimated the loss in reservoir capacity for a global data set of large reservoirs from 1901 to 2010, using modeled sediment flux data. We use spatially explicit population data sets as a proxy for storage demand and calculate storage capacity for all river basins globally. Simulations suggest that the net reservoir capacity is declining as a result of sedimentation (∼5% compared to the installed capacity). Combined with increasing need for storage, these losses challenge the sustainable management of reservoir operation and water resources management in many regions. River basins that are most vulnerable include those with a strong seasonal flow pattern and high population growth rates such as the major river basins in India and China. Decreasing storage capacity globally suggests that the role of reservoir water storage in offsetting sea-level rise is likely weakening and may be changing sign. [ABSTRACT FROM AUTHOR]

Watershed development (WSD) is an important and expensive rural development initiative in India. Proponents of the approach contend that treating watersheds will increase agricultural and overall biomass productivity, which in turn will reduce rural poverty. We used satellite-measured normalized differenced vegetation index as a proxy for land productivity to test this crucial contention. We compared microwatersheds that had received funding and completed watershed restoration with adjacent untreated microwatersheds in the same region. As the criteria used can influence results, we analyzed microwatersheds grouped by catchment, state, ecological region, and biogeographical zones for analysis. We also analyzed pre treatment and posttreatment changes for the same watersheds in those schemes. Our findings show that WSD has not resulted in a significant increase in productivity in treated microwatersheds at any grouping, when compared to adjacent untreated microwatershed or the same microwatershed prior to treatment. We conclude that the well-intentioned people-centric WSD efforts may be inhibited by failing to adequately address the basic geomorphology and hydraulic condition of the catchment areas at all scales. [ABSTRACT FROM AUTHOR]

The excessive exploitation of groundwater aquifers is emerging as a worldwide problem, but it is nowhere as dramatic and consequential as it is in India, the world's largest consumer, where hundreds of millions of people depend on it. Usually the problem is framed in terms of a long-term decline in water tables and its consequence for extraction costs, resource depletion, and the sustainability of irrigated agriculture. Here a comparative analysis is provided of coupled groundwater, energy, and irrigation dynamics in two groundwater intensive regions in India that differ in their underlying hydrogeology-the Indian Punjab with its deep alluvial aquifers and the Telangana region in south-central India with its shallow hard rock aquifers. Using a simple modeling framework and piezometric and agricultural time series, we show that in shallow aquifers the sense in which extraction is excessive is different, and is related to the short-term reliability of water supply rather than long-term sustainability. This has important repercussions for irrigated agricultural economies. [ABSTRACT FROM AUTHOR]

Hydrogen (2H/1H) and oxygen (18O/16O) isotopic ratios were measured in precipitation (900 samples) collected from several locations in India during the period 2003-2006 (12 locations in 2003 and 18 locations in 2004-2006). The amount of rainfall along with air temperature and humidity were also measured. The meteoric water line developed for India using isotopic data of precipitation samples, namely, δ2H = 7.93(±0.06) × δ18O + 9.94(±0.51) ( n = 272, r2 = 0.98), differs slightly from the global meteoric water line. Regional meteoric water lines were developed for several Indian regions (i.e., northern and southern regions of India, western Himalayas) and found to be different from each other (southern Indian meteoric water line, slope is 7.82, intercept or D excess is 10.23; northern Indian meteoric water line, slope is 8.15, intercept is 9.55) which is attributed to differences in their geographic and meteorological conditions and their associated atmospheric processes (i.e., ambient temperature, humidity, organ, and source of vapor masses). The local meteoric water lines developed for a number of locations show wide variations in the slope and intercept. These variations are due to different vapor sources such as the northeast (NE) monsoon that originates in the Bay of Bengal; the southwest monsoon (SW) that originates in the Arabian Sea; a mixture of NE and SW monsoons; retreat of NE and SW monsoons and western disturbances that originate in the Mediterranean Sea. The altitude effect in the isotopic composition of precipitation estimated for western Himalayan region also varies from month to month. [ABSTRACT FROM AUTHOR]

Indian megacities face severe water supply problems owing to factors ranging from growing population to high municipal pipe leakage rates; no Indian city provides 24/7 water supply. Current approaches to addressing the problem have been 'utility centric,' overlooking the significance of decentralized activities by consumers, groundwater extraction via private wells, and aquifer recharge by rainwater harvesting. We propose a framework that makes it possible to evaluate a wider range of centralized and decentralized policies than previously considered. The framework was used to simulate water supply and demand in a simulation model of Chennai, India. Three very different policies, supply augmentation, efficiency improvement, and rainwater harvesting, were evaluated using the model. The model results showed that none of the three policies perfectly satisfied our criteria of efficiency, reliability, equity, financial viability, and revenue generation. Instead, a combination of rainwater harvesting and efficiency improvement best meets these criteria. [ABSTRACT FROM AUTHOR]