Incorporating antecedent soil moisture into streamflow forecasting within the North Platte River Basin, Wyoming

A study of incorporating antecedent (preceding) soil moisture into forecasting streamflow volumes within the North Platte River Basin is presented. By integrating antecedent soil moisture as a predictor to forecast streamflow, processes that determine the amount of streamflow, such as infiltration and runoff can be better accounted for. Current Natural Resource Conservation Service (NRCS) forecasting methods were replicated and a comparison was drawn between current NRCS forecasts and the proposed forecasting methods. Current predictors used by the NRCS in regression based streamflow forecasting include precipitation, streamflow persistence (previous season streamflow volume) and snow water equivalent (SWE) from SNOTEL (snow telemetry) sites. Proposed methods include utilizing antecedent soil moisture as a predictor variable and extending the period of record. By extending the period of record, an expert system was utilized to segregate data based on antecedent soil moisture conditions (e.g., dry, wet or normal). Correlation techniques were applied to determine ideal predictors. Principal component analysis and stepwise linear regression were applied to generate streamflow forecasts and various validation statistics were determined. The incorporation of antecedent soil moisture and the expert system approach improved forecast skill for each of the stations evaluated. The research presented shows the importance of including antecedent soil moisture as a predictor variable in streamflow forecasting models.