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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]

Studies on multivariate drought assessment and the climate change impact on a river basin scale are limited in India. Drought monitoring is a challenging subject due to its dependence on different climatic variables. To overcome this, a copula‐based probabilistic multivariate drought index (MDI) has been developed which simultaneously represents the meteorological, hydrological and agricultural drought phenomenon. The four variate Archimedean copula is used in this study to integrate the precipitation, evapotranspiration, soil moisture and streamflow. Hydrologic variables like evapotranspiration, soil moisture and streamflow were simulated using the Soil and Water Assessment Tool model. The future MDI is also analysed to identify the impact of climate change on drought phenomenon using Coupled Model Intercomparison Project‐6 Global Climate Models under four Socio‐Economic Shared Pathways. Drought characteristics like severity and duration were evaluated to identify the present and future drought events. The precipitation and minimum and maximum temperatures were identified to have increasing tendencies in the future scenarios. Most of the future scenarios showed lower drought duration and severity when compared to the reference period. The drought duration and severity are likely to decrease in the future time scales especially under the high emission scenarios. The present study used a novel approach to examine the drought from various perspectives and the study will be useful for drought mitigation and adaptation strategies over the basin. [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]

Analysis of climate change impacts on streamflow by perturbing the climate inputs has been a concern for many authors in the past few years, but there are few analyses for the impacts on water quality. To examine the impact of change in climate variables on the water quality parameters, the water quality input variables have to be perturbed. The primary input variables that can be considered for such an analysis are streamflow and water temperature, which are affected by changes in precipitation and air temperature, respectively. Using hypothetical scenarios to represent both greenhouse warming and streamflow changes, the sensitivity of the water quality parameters has been evaluated under conditions of altered river flow and river temperature in this article. Historical data analysis of hydroclimatic variables is carried out, which includes flow duration exceedance percentage (e.g. Q90), single low-flow indices (e.g. 7Q10, 30Q10) and relationships between climatic variables and surface variables. For the study region of Tunga-Bhadra river in India, low flows are found to be decreasing and water temperatures are found to be increasing. As a result, there is a reduction in dissolved oxygen (DO) levels found in recent years. Water quality responses of six hypothetical climate change scenarios were simulated by the water quality model, QUAL2K. A simple linear regression relation between air and water temperature is used to generate the scenarios for river water temperature. The results suggest that all the hypothetical climate change scenarios would cause impairment in water quality. It was found that there is a significant decrease in DO levels due to the impact of climate change on temperature and flows, even when the discharges were at safe permissible levels set by pollution control agencies (PCAs). The necessity to improve the standards of PCA and develop adaptation policies for the dischargers to account for climate change is examined through a fuzzy waste load allocation model developed earlier. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article presents a case study on hydrological modelling of the Kangsabati river under changed climate scenario in India. The study is committed towards the planning, management and development of water resources of the river through presenting detailed future scenarios of the river basin. Included in the findings are soil water content, potential evapotranspiration and lateral flow to reach.