Showing 2 results for S. Dodangeh
S. Dodangeh, S. Soltani, A. Sarhadi,
Volume 15, Issue 58 (winter 2012)
Abstract
This study performs trend analysis of hydroclimatic varibles and their possible effects on the water resources variability. Nonparametric Mann-Kendall and spearman tests were used to investigate trend analysis of mean annual and 24-hr maximum rainfall, flood and low flow parameters of 23 hydrometery and 18 synoptic stations in Sefid-Roud basin. The results showed that mean annual and 24-hr rainfall parameters are decreasing in few stations while most of stations representing negative trend for low flow and flood time series. Applying Sequential Mann-Kendall test revelad that this negative trend is started from 1965 to 1970 for rainfall parameters and from 1970 to1980 for flow (low flow and flood) parameters. Results show that climate change has probability affected variability of climatic variables, while changing of land use may have aslo affeteced extreme flow trends during recent decads. Therefor it can be noted that combination of climate chanege effects and human activities on water recources have affected the negative trend of hydroclimatics variables.
S. Dodangeh, J. Abedi Koupai, S. A. Gohari,
Volume 16, Issue 59 (spring 2012)
Abstract
Due to the important role of climatic parameters such as radiation, temperature, precipitation and evaporation rate in water resources management, this study employed time series modeling to forecast climatic parameters. After normality test of the parameters, nonparametric Mann-Kendall test was used in order to do trend analysis of data at P-value<0.05. Relative humidity and evaporation (with significant trend, -0.348 and -0.42 cm, respectively), as well as air temperature, wind speed, and sunshine were selected for time series modeling. Considering the Autocorrelation function (ACF) and Partial Autocorrelation function (PACF) and trend of data, appropriate models were fitted. The significance of the parameters of the selected models was examined by SE and t statistics, and both stationarity and invertibility conditions of Autoregressive (AR) and Moving average (MA) were also tested. Then, model calibration was carried out using Kolmogorov-Smirnov, Anderson- Darling and Rayan-Joiner. The selected ARIMA models are ARIMA(0,0,11)*(0,0,1), ARIMA(2,0,4)*(1,1,0), ARIMA(4,0,0)*(0,1,1), ARIMA (1,0,1)*(0,1,1), ARIMA (1,0,0)*(0,1,1) for relative humidity, evaporation, air temperature, wind speed and sunshine, respectively. The fitted models were then used to forecast the parameters. Finally, trend analysis of forecasted data was done in order to investigate the climate change. This study emphasizes efficiency of time series modeling in water resources studies in order to forecast climatic parameters.
