Simulation of Spatiotemporal Annual Precipitation Using Stochastic Models

Precipitation is one of the most important components of water balance in any region and the development of efficient models for estimating its spatiotemporal distribution is of considerable importance. The goal of the present research was to investigate the efficiency of the first order multiple-site auto regressive model in the estimation of spatiotemporal precipitation in Kurdistan, Iran. For this purpose, synoptic stations which had long time data were selected. To determine the model parameters, data covering 21 years r (1992-2012) were employed. These parameters were obtained by computing the lag zero and lag one correlation between the annual precipitation time series of stations. In this method, the region precipitation in a year (t) was estimated based on its precipitation in the previous year (t-1). To evaluate the model, annual precipitation in the studied area was estimated using the developed model for the years 2013 and 2014; then, the obtained data were compared with the observed data. The results showed that the used model had a suitable accuracy in estimating the annual precipitation in the studied area. The  percentages of the model in estimating the regionchr('39')s  annual precipitation for the years 2013 and 2014 was obtained to be 7.9% and 17.3%, respectively. Also, the correlation coefficient between the estimated and observed data was significant at the significance level of one percent (R=0.978). Furthermore, the model performance was suitable in terms of data generation; so the statistical properties of the generated and historical data were similar and their difference was not significant. Therefore, due to the suitable efficiency of the model in estimating and generating the annual precipitation, its application could be recommended to help the better management of water resources in the studied region.