JWSS - Journal of Water and Soil Science

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 region'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.

One of the major issues in the contemporary world is climate change. The behavior and characteristics of parameters affecting climate change can cause them to be seen and hidden. As one of the effective ways to detect overt and covert behaviors for periodic climatic data series, Spectral analysis can be used. It is the analysis of each of the wavelengths series, making this behavior clear. Accordingly, the present study was an attempt to use the method of spectral analysis, data cycles in the minimum temperature, maximum temperature and precipitation in Ramsar station (located in the western regions of Mazandaran province) an nd Babolsar (located in the central parts of this province) in a period from1961 to 2014. For this purpose, temperature and precipitation data were obtained from these stations; MATLAB software environment and the environment for the software were logged for each of the variable in the stations. The results revealed that the minimum temperature at both stations had significant cycles, with the return period being 2 to 5 years; Remote Link could be fit into the cycle parameters such as NAO, AO and ENSO. Analysis of the period gram showed cycles 8 and 5/13-year-old and 5-year-old period in Ramsar and Babolsar. During the rainy cycles, the difference between the two stations and the difference in the geographical position affected systems, and rain accounted for the difference in speed dual-zone climate indicator for Remote Link.

The effects of climate change can be released from the surface to the soil depth, thereby affecting soil thermal regime. Thermal energy in the soil plays a very important role in causing climate changes. In this study, for the assessment and detection of  the climate changes, soil depths temperature, the measured data related to the  daily air temperature at a height of 2 meters (screen) during the years (1951-2014), and the soil depths daily temperature (5-10-20-30-50 to 100 cm), for 3, 9 and 15 hours, were obtained during a period (1992-2014) in Shahrud station. The climate change detection was employed to compare the treatment mean. As well, for detection of trends related to the annual, seasonal and monthly time series and their relation to the soil depths temperature, parametric methods (regression analysis and Pearson) and nonparametric (Mann-Kendall, Spearman) were applied. The results showed that the soil temperature was increased in all months except January, February and March. Also, in the seasonal time series, the soil depths temperature was increased in all seasons except winter. In fact, based on the results, the soil temperature in spring, summer and autumn was increased. Detection trends of the annual soil depths temperature showed that, except for the Pearson correlation coefficient method, soil temperature was increased at all soil depths.

Drought as a natural hazard is a gradual phenomenon, slowly affecting an area; it may last for many years and can have devastating effects on the natural environment and in human lives. Although drought forecasting plays an important role in the planning and management of water resource systems, the random nature of contributing factors contributing to the occurrence of and severity of droughts causes some difficulties in determination of the time when a drought begins or ends. The present research was planned to evaluate the capability of linear stochastic models, known as multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) model, in the quantitative forecasting of drought in Isfahan province based on the Standardized Precipitation Index (SPI). To this end, the best SARIMA models were chosen for modelling the monthly rainfall data from 1990 to 2017 for every 10 synoptic stations in Isfahan province to forecast their monthly rainfall up to five years. The monthly time scale SPI values based on these predictions were used to assess the drought severity of different stations for the 2018- 2022 time period. The station results indicated a weak drought at the 2019- 2022 period for Isfahan, Kashan and Naeen, a severe drought in 2019 for Ardestan and Golpaygan, and a weak one in 2019 for the East of Isfahan, KabootarAbad and Shahreza stations. All other stations, except Golpayegan, Isfahan, Kashan and Naeen, faced a severe drought in 2018.

Expert aquatic designers face many problems; among these, in hydrology, defective occurrences in time-series can cause errors in the ultimate results of the study. This more often happens in the regions where the number of hydrometric and rain gauge stations is limited. In addition, assessing, developing and maintaining the use of water resources require accessible long-term and high-quality quality hydrological time-series. Thus, this necessitates correcting the statistical flaws and magnifies the importance of how to deal with the problems in the hydrological analyses. Statistical methods are, currently, used to infill data and statistical gaps. In this study, in order to introduce a multivariate method for estimating the missing data on rainfall and runoff, in a hydrologic homogeneous region in the Mazandaran province, self-organizing map methods were examined under two scenarios and some reliable estimates were obtained. In this regard, the correlation coefficients between the observational data and the model output were calculated for the precipitation data up to 0.92 and up to 0.95 for the runoff data. Therefore, to avoid the reduction of uncertainty caused by the inadequate data in water resource management, this method could be used.

Due to drought and climate change, estimation and prediction of rainfall is quite important in various areas all over the world. In this study, a novel artificial intelligence (AI) technique (WGEP) was developed to model long-term rainfall (67 years period) in Anzali city for the first time. This model was combined using Wavelet Transform (WT) and Gene Expression Programming (GEP) model. Firstly, the most optimized member of wavelet families was chosen. Then, by analyzing the numerical models, the most accurate linking function and fitness function were selected for the GEP model. Next, using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and different lags, 15 WGEP models were introduced. The GEP models were trained, tested and validated in 37, 20- and 10-years periods, respectively. Also, using sensitivity analysis, the superior model and the most effective lags for estimating long-term rainfall were identified. The superior model estimated the target function with high accuracy. For instance, correlation coefficient and scatter index for this model were 0.946 and 0.310, respectively. Additionally, lags 1, 2, 4 and 12 were proposed as the most effective lags for simulating rainfall using hybrid model. Furthermore, results of the superior hybrid model were compared with GEP model that the hybrid model had more accuracy.