Location: Northwest Watershed Research Center
Project Number: 2052-13610-012-31-I
Project Type: Interagency Reimbursable Agreement
Start Date: Sep 25, 2020
End Date: Sep 30, 2021
Apply iSnobal for water supply forecasting, test and develop new and more effective simulation models that can be used by the National Water and Climate Center, Natural Resources Conservation Service (NRCS), for water supply forecasting.
Work cooperatively with Natural Resources Conservation Service (NRCS) to address issues, needs, and problems as they arise each year. With NRCS guidance, ARS will develop a research plan, including objectives, milestones and deliverables for each year of the project. ARS will provide the site, technical support, data management and analysis, and will publish findings as appropriate. NRCS will participate in project planning, analysis and publication of results. 1. Statistical Forecasting with iSnobal output using historical station measurement forcings with applications in the Reynolds Creek Basin: Correct errors with the wind redistribution model and generate iSnobal Reynolds Creek Basin simulation model output snow water equivalent (SWE) for 30+ years. Examine the statistical relationship between the mean spatial lumped model results and the seasonal streamflow volume. In addition, test spatial information from the model, distributing SWE and soil water input (SWI) output into elevation bands, and re-assess the statistical results. 2. Statistical Forecasting with iSnobal output using Weather Research Forecasting (WRF) forcings with applications in the Boise River Basin: Development of long time-series using station measurements is not feasible in most watersheds outside of the research basins due to lack of necessary long-term records. Therefore, ARS will investigate assimilating the 30-year WRF model dataset developed for iSnobal modeling, leading to developing seasonal volume supply forecasts. Initial gathering of data and iSnobal testing will be applied in the Boise River Basin, with an objective to develop an historical SWE time series for use in a statistical streamflow model.