JWSS - Journal of Water and Soil Science

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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.

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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.

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Underground water resources considered as a major source of fresh water. Increasing need to water in Iran, causing to immensely utilization and ground water balance disorder, so that state of ground water in many of area is negative.The purpose of this study is to investigate the trend of changes in some of the characteristics of groundwater during the period 1374 to 1387 is Segzi plain. For this purpose, data gathered from the Organization of regional water and homogenous test with Tom test (Run-test) at 95% confidence level was performed on the data. The independence of data evaluated by time series auto correlated functions (ACF), to do this, the amount of auto correlated data computed in different time delays and finally Mann- Kendall test used to evaluate the trend of time series properties in groundwater. The results of Run-Test showed that all of used series in this study were homogenous (P value< 0.05). The result of trend analysis test for region’s wells showed a significant increase in chlorine in underground water resources (P value< 0.05). Calcium has an increasing Trend too about 3 units. Results also showed that all used series in this study are random and Mann- Kendall trend analysis test can be an appropriate for trend evaluating in data series. As regard to irregular utilization of underground water resources by increasing depth of water level, amount of different salts such as chlorine and sodium increased, that causing to surface source degradation like soil and plant cover in agricultural area.