Forecasting of SPI and SRI Using Multiplicative ARIMA under Climate Variability in a Mediterranean Region: Wadi Ouahrane Basin, Algeria
Mohammed Achite, Ommolbanin Bazrafshan, Zahra Azhdari, Andrzej Wałęga, Nir Y. Krakauer, Tommaso Caloiero
Abstract
Water resources have always been a major concern, particularly in arid and semiarid parts of the world. Low precipitation and its uneven distribution in Algeria, along with fast population and agriculture activity increase and, particularly, recent droughts, have made water availability one of the country’s most pressing issues. The objectives of the studies reported in this article are to investigate and forecast the meteorological and hydrological drought in Wadi Ouahrane basin (270 km2) using linear stochastic models known as Autoregressive Integrated Moving Average (ARIMA) and multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA). In particular, data from 6 precipitation stations and 1 hydrometric station for the period 1972–2018 were used to evaluate the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI) for 12 months. Then, the multiplicative ARIMA model was applied to forecasting drought based on SPI and SRI. As a result, the ARIMA model (1,0,1)(0,0,1)12 for SPI and (1,0,1)(1,0,1)12 for SRI were shown to be the best models for drought forecast. In fact, both models exhibited high quality for SPI and SRI of 0.97 and 0.51 for 1-month and 12-month lead time, respectively, based on validation R2. In general, prediction skill decreases with increase in lead time. The models can be used with reasonable accuracy to forecast droughts with up to 12 months of lead time.