ALBERT

Description: The inconsistent pattern of precipitation, a shift in the seasonality of river flows, and the early onset of snow and glacier melt in recent decades across river basins of High Mountain Asia (HMA) has compelled us to further investigate future variations in sources of runoff under projected climate change scenarios. This will help in determining the timing and magnitude of runoff components and this will help in management of future water resources. The current study employed the University of British Columbia Watershed Model (UBC WM) to estimate the spatiotemporal variations in simulated runoff components (i.e. snowmelt, glacier melt, rainfall-runoff, and baseflow) and their relative contribution to total runoff of Gilgit River regarding the baseline period (1981–2010) in near (2021–2050) and far future (2071–2100) under low (SSP1), medium (SSP2) and high (SSP5) emission scenarios. A significant increase in the magnitude of mean annual temperature and precipitation is expected in the near future (2021–2050) than far future (2071–2100) under most SSPs. Moreover, high-altitude stations of the Gilgit River basin are expected to experience more warming in the near and far future than low altitudes under all SSPs. On average, regarding the baseline period, the simulated runoff is projected to increase in the near (27%, 30%, and 33%) and far future (30%, 53%, and 91%) under SSP1, SSP2, and SSP5, respectively. Moreover, an early onset of snow/glacier melting is predicted in the far future due to an increase in summer air temperature and a decline in winter (DJF) precipitation. Besides, the rise in high altitude temperature is expected to cause the melting of snow/glaciers even above 6000 m elevation in the far future.

Description: Weather forecasts can enhance the utilization of scientific irrigation scheduling tools, crucial for maximizing agricultural water use efficiency. This study employed quantitative weather forecasts of 3-, 7- and 14-day lead times from a weather application programming interface (API) to generate irrigation schedules using the AquaCrop-OSPy model for maize, cotton and sorghum under different regulated deficit irrigation scenarios. The study aimed to determine the suitability of forecast lengths for irrigation scheduling under varying pumping capacities of center pivots (114 m3h−1, 182 m3 h−1 and 250 m3 h−1) in the Texas High Plains and Rio Grande Basin regions, United States. A comparative analysis was carried out to evaluate the irrigation schedules and corresponding crop yields simulated using forecasted and observed weather data. Results indicated that using shorter forecast time allowed the crop model to capture more precise variations in weather patterns, however, shorter lead times also caused over-irrigation in some scenarios. Use of longer lead times tended to be less suitable for scheduling irrigation during water-sensitive growth stages. Center pivots with large pumping capacities and application rates benefited more from longer forecast lengths due to their ability to adapt to weather fluctuations. Unplanned irrigation application occurred in some instances, primarily attributed to uncertainties in weather forecasts and limitations of the crop model. The approach developed and evaluated in this study supports water conservation efforts by promoting scientific irrigation scheduling in weather-data-poor and low adoption regions.