Changes to precipitation patterns and extremes over the Nepalese Himalayas were examined using a high‐resolution, station‐based daily dataset, Asian Precipitation‐Highly Resolved Observational Data Integration Towards Evaluation (0.05° × 0.05° APHRODITE) from 1951 to 2007. The annual statistics of extreme precipitation across Nepal show a significant increase since the end of the 20th century. However, seasonal mean precipitation shows a remarkable decrease in western Nepal, particularly since 1980, forming an east–west division in the precipitation change. This decreasing trend of precipitation led to a reduction to the dry‐season stream flow of Karnali River, the major river in western Nepal. At the same time, the increasing extreme precipitation produced greater threat of flash flood in Nepal. This east–west division of the precipitation trend agrees with the second leading mode of the mean precipitation variability, which was traced to the interannual variability of the Indian Ocean sea surface temperature that showed a slowdown of warming. Similar to the APHRODITE trends, precipitation simulated by the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models depicted the decreasing historical trend in western Nepal, but future projections reverse that trend towards an all‐Nepal increase. CMIP5 future climate projections depict continual warming in the Indian Ocean, potentially reversing the historical decreasing trends of precipitation in western Nepal.

The soil and water assessment tool (SWAT) hydrological model has been used extensively by the scientific community to simulate varying hydro-climatic conditions and geo-physical environment. This study used SWAT to characterize the rainfall-runoff behaviour of a complex mountainous basin, the Budhigandaki River Basin (BRB), in central Nepal. The specific objectives of this research were to: (i) assess the applicability of SWAT model in data scarce and complex mountainous river basin using well-established performance indicators; and (ii) generate spatially distributed flows and evaluate the water balance at the sub-basin level. The BRB was discretised into 16 sub-basins and 344 hydrological response units (HRUs) and calibration and validation was carried out at Arughat using daily flow data of 20 years and 10 years, respectively. Moreover, this study carried out additional validation at three supplementary points at which the study team collected primary river flow data. Four statistical indicators: Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), ratio of the root mean square error to the standard deviation of measured data (RSR) and Kling Gupta efficiency (KGE) have been used for the model evaluation. Calibration and validation results rank the model performance as “very good”. This study estimated the mean annual flow at BRB outlet to be 240 m3/s and annual precipitation 1528 mm with distinct seasonal variability. Snowmelt contributes 20% of the total flow at the basin outlet during the pre-monsoon and 8% in the post monsoon period. The 90%, 40% and 10% exceedance flows were calculated to be 39, 126 and 453 m3/s respectively. This study provides additional evidence to the SWAT diaspora of its applicability to simulate the rainfall-runoff characteristics of such a complex mountainous catchment. The findings will be useful for hydrologists and planners in general to utilize the available water rationally in the times to come and particularly, to harness the hydroelectric potential of the basin.

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.

Water resource is required for agricultural, industrial, and domestic activities and for environmental preservation. However, with the increase in population and growth of urbanization, industrialization, and commercial activities, planning and management of water resources have become a challenging task to meet various water demands globally. Information and data on streamflow hydrology are, thus, crucial for this purpose. However, availability of measured flow data in many cases is either inadequate or not available at all. When there is no gauging station available at the site of interest, various empirical methods are generally used to estimate the flow there and the best estimation is chosen. This study is focused on the estimation of monthly average flows by such methods popular in Nepal and assessment of how they compare with the results of hydrological simulation. Performance evaluation of those methods was made with a newly introduced index, Global Performance Index (GPI) utilizing six commonly used goodness-of-fit parameters viz. coefficient of determination, mean absolute error, root mean square error, percentage of volume bias, Nash Sutcliff Efficiency and Kling-Gupta Efficiency. This study showed that hydrological modeling is the best among the considered methods of flow estimation for ungauged catchments.

No abstract