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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]

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 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]