Seasonal Streamflow Forecasting Using Snow Budget and El Niño-Southern Oscillation Climate Signals: Application to the Salt River Basin in Arizona

Abstract

In this paper, a method for streamflow forecasting in arid and semiarid regions is presented. The proposed methodology consists of three steps. In the first step, hydrologic seasons are defined to capture coupled effects of climatic and hydrologic characteristics of a basin. For this purpose, the correlation between average snow water equivalent (SWE), the similarity in basic statistical characteristics of the streamflows in different months, and the general characteristics of the study area are considered for defining hydrologic seasons. In the second step, a seasonal streamflow forecast time series is developed using autoregressive integrated moving average (ARIMA) models. In the third step, the fuzzy rules are developed to modify the statistical forecasts, utilizing average snow budget over a watershed, El Niño–Southern Oscillation (ENSO) climate signals, historical streamflow data, and time series of forecasted streamflows. Application of the proposed algorithm to the Salt River Basin in Arizona has shown improvement in the statistical seasonal forecasts. The most important application of the proposed methodology is to forecast streamflows six to nine months ahead in the summer of each year, when the ENSO signals are determined. This is of significant importance in water resources planning and management.