Study region: Koshi basin, Nepal. Study focus: While rainfall estimates based on satellite measurements are becoming a very attractive option, they are characterized by non-negligible biases. As such, we assessed the accuracy of two satellite products of the Climate Hazard Group (CHG) – (a) a satellite-only Climate Hazards Group InfraRed Precipitation (CHIRP) product, and (b) a CHIRP blended with ground-based station data (CHIRPS) – at a monthly time scale from 1981 to 2010 in the Koshi basin of Nepal using ground-based measurements. A separate analysis was also made for the data set after 1992, as the number of stations used in the blending has significantly reduced since 1992. Next, both CHG data sets were used to calculate one of the most popularly-used precipitation-based drought indicators – the Standardized Precipitation Index (SPI). New hydrological insights for the study region: The accuracy of the CHG data set was found to be better in low-lying regions, while it was worse in higher-elevation regions. While the CHIRPS data set was better for the whole period, the CHIRP data set was found to be better for the period after 1992. Physiographic region-wise bias correction has improved the accuracy of the CHG products significantly, especially in higher-elevation regions. In terms of SPI values, the two CHG data sets indicated different drought severity when considering the whole period. However, the SPI values, and hence the drought severity were comparable when using the data from after 1992

This paper focuses on understanding the effects of projected climate change on streamflow dynamics of the Grand and Thames rivers of the Northern Lake Erie (NLE) basin. A soil water assessment tool (SWAT) model is developed, calibrated, and validated in a base-period. The model is able to simulate the monthly streamflow dynamics with ‘Good’ to ‘Very Good’ accuracy. The calibrated and validated model is then subjected with daily bias-corrected future climatic data from the Canadian Regional Climate Model (CanRCM4). Five bias-correction methods and their 12 combinations were evaluated using the Climate Model data for hydrologic modeling (CMhyd). Distribution mapping (DM) performed the best and was used for further analysis. Two future time-periods and two IPCC AR5 representative concentration pathways (RCPs) are considered. Results showed marked temporal and spatial variability in precipitation (−37% to +63%) and temperature (−3 °C to +14 °C) changes, which are reflected in evapotranspiration (−52% to +412%) and soil water storage (−60% to +12%) changes, resulting in heterogeneity in streamflow (−77% to +170%) changes. On average, increases in winter (+11%), and decreases in spring (–33%), summer (−23%), and autumn (−15%) streamflow are expected in future. This is the first work of this kind in the NLE and such marked variability in water resources availability poses considerable challenges to water resources planners and managers.