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Year-round flow consistencies of the central Himalayan springs are extremely important for addressing rural water demand. As the prediction of Himalayan spring high-flows is expected to provide better opportunities for the management of excess runoff, this study aims to develop a data-driven model for predicting joint-fracture and depression type spring high-flows of the Kosi watershed of central Himalaya, India. Five machine learning algorithms are used with combinations of predictors, such as standardized anomaly of rainfall, pH, electrical conductivity and water quality index of spring water. The discharge and predictor parameters are used from a total of 06 springs distributed across the watershed, and monitored during January, 2019 to December, 2020 at monthly interval. Due to asymmetric relationships between model predictors and spring discharge, model performances are tested for the predictor time lags of 0–2 (= 60 days). A total of ten experiments are carried out, and model performances during training and testing are evaluated using receiver operator characteristics. The discriminant analysis classifier, in combination with rainfall and electrical conductivity as predictors, is found to be the best model for predicting spring high-flows. [ABSTRACT FROM AUTHOR]

Impacts of the changing climatic regime on the trends of Indian summer monsoon rainfall (ISMR) are explored in this study for the Indian Himalayan region (IHR). The analysis is carried out for the period of 1951-2007 using a daily high resolution gridded data from APHRODITE project. At first, the percent departures of decadal rainfall are estimated from the long-term June to September rainfall values for the western, central, and eastern Himalayan (WH, CH, and EH) regions. Next, changes in the frequency of strong and weak phases of monsoon intra-seasonal oscillation are investigated. A non-parametric statistical method (Sen's slope estimator) is applied to the seasonal (i) mean rainfall, (ii) maximum rainfall, and (iii) frequency of extreme rainfall events of WH, CH, and EH regions to identify changes in their decadal, multiple year normals (NY1; 1951-1980 and NY2; 1981-2007) and long-term (NY3; 1951-2007) trends. The inter annual to inter decadal variabilities of the frequency of extreme rainfall events are explored by analyzing statistically significant intrinsic mode functions of the empirical mode decomposition (EMD) method. Results of our analyses have revealed existence of an alternative decadal oscillation of scanty and excessive summer monsoon rainfall trends for the WH, whereas excessive rainfall is observed in the last three decades (1980-2007) over the CH region. It is also observed that the frequencies of both monsoon strong and weak phases are decreasing for the entire Himalayan region. No significant trend is observed for the WH and CH regions for the normal periods NY1, NY2, and NY3 when seasonal average rainfall is considered. However, a significant ( p value < 0.05) negative trend of −0.04 mm/day rain is observed for the EH region during NY1 period. Similarly, the seasonal maximum rainfall trends for all the normal periods are found to be negative of which trends of −0.12 and −0.43 mm/day during NY3 and NY1 are observed for WH and EH regions, respectively ( p value < 0.05). No significant enhancement in the extreme rainfall event frequencies is observed for the entire IHR during 1951-2007. However, a statistically insignificant positive trend in the extreme event frequencies is observed for the EH region. A dominant cycle of ∼ 2.7 years of high frequency of extreme rainfall events is observed for all the regions whereas, a 12.2-, 15.3-, and 5.8-year cycles are observed for the WH, CH, and EH regions, respectively. [ABSTRACT FROM AUTHOR]

A correction to the article "Summer monsoon rainfall trends in the Indian Himalayan region" that was published in a previous issue is presented.