JWSS - Journal of Water and Soil Science

Flood discharge estimation with different return periods is one of important factors for water structures design and installation. On the other hand, a lot of rivers existing in Iran watersheds have no complete and accurate hydrometric data. In these cases, one of the suitable solutions to estimate peak discharges with different return periods is the regional flood analysis. In this research, 55 hydrometric stations were used. For this purpose, at first, peak discharges in different return periods were estimated using the EasyFit software. Then, the effective variables on the peak discharges were collected and the input variables of the models were selected by using gamma test with the help of the WinGamma software. Finally, data modeling was performed using the support vector machine, artificial neural networks and nonlinear multivariate regression techniques. Quantitative and qualitative assessment of the results using various indices including Nash-Sutcliffe Efficiency Coefficient (NSC) showed that SVM modeling method had the most accuracy in comparison to the other two modeling methods to predict the peak discharges in the Namak Lake Watershed.

In the present study, we used 27 precipitation average monthly data from synoptic, climatologic, rain-guage and evaporative stations located in Zayandeh-Rud river basin for the period of 1970-2014. Before interpolating, the missing data in the time series of each station was reconstructed by the normal ratio method. Also, for the data quality control, the Dickey-Fuller and Shapiro-Wilk tests were used to check the data stationarity and normality. Then, these data were interpolated by six interpolation methods including   Inverse Distance Weighting, Natural Neighbor, Tension Spline, Regularized Spline, Ordinary Kriging and Universal Kriging; then each method was evaluated using the cross-validation technique with MAE, MBE and RMSE indices. The results showed that among the spatial interpolation methods, Natural Neighbor method with MAE of 0.24 had the best performance for interpolating precipitation among all of the methods. Also, among Ordinary Kriging, Universal Kriging, Spline and Inverse Distance Weighting methods, respectively, Exponential Kriging with MAE 0.54, Quadratic Drift Kriging with MAE of 0.5, Tension Spline with the MAE of 0.54 and Inverse Distance Weighting with the power of 4 with MAE of 0.57 had the least error compared to other IDW methods.

The current study was conducted to compute SPI and SPET drought indices due to their multi-scale concept and their ability to analyze different time-scales for selected meteorological stations in Karoon Basin. Regionalization of SPI and SPEI Drought indices based on clustering analysis was another aim of this study for hydrological homogenizing. Accordingly, to run test through data and determine similar statistical periods, 18 stations were selected. SPI and SPEI values were plotted in the sequence periods graphs and their relationships were analyzed using the correlation coefficient. The results were compared by Pearson correlation coefficient at the significance level of 0.01. The results showed that correlation coefficients (0.5-0.95) were positive and meaningful for all stations and the correlation coefficient between the two indices were increased by enhancing the time-scales. Also, time-scales enhancement decreased the frequency of dry and wet periods and increased their duration. Through regionalization of basin stations based on clustering analysis, the stations were classified into 7 classes. The results of SPEI regionalization showed that the frequency percentage of the normal class was more than those of dry and wet classes.

Creation and conservation of urban parks is challenging in arid environments where daily thermal extremes, water scarcity, air pollution and shortage of natural green spaces are more conspicuous. Water scarcity in the arid regions of Iran is major challenge for water managers. Accurate estimation of urban landscape evapotranspiration is therefore critically important for cities located in naturally dry environments, to appropriately manage irrigation practices. This study investigated two factor-based approaches, Water Use Classifications of Landscape Species (WUCOLS) and Landscape Irrigation Management Program (LIMP), to measure the water demand in a botanic garden. The irrigation water volume applied was compared with the gross water demand for the period from 2011 to 2013. On average, WUCOLS estimated an average annual irrigation need of 1164 mm which is 15% less than the applied value of 1366 mm while the LIMP estimate of 1239 mm was 9% less than the applied value. Comparison of estimated and applied irrigation showed that a water saving of 9% can be made by the LIMP method. The outcomes of this research stressed the need to modify the irrigation requirements based on effective rainfall throughout the year, rather relying on long-term average data.

The evaluation of climate change impact on hydrological cycle includes uncertainty. This study aimed to evaluate the uncertainty of climate change impact on the Zayandeh-Rud Reservoir inflow during the future period of 2020-2049. The outputs of 22 GCM models were used under the three emission scenarios including RCP2.6, RCP4.5 and RCP8.5. The Bayesian Model Averaging (BMA) was used as the uncertainty analysis for weighting the 22 GCM models based on their ability to simulate the baseline 1990-2005 period. Results showed that different GCM models had different abilities in estimating climatic and hydrological variables and the application of uncertainty analysis in climate change studies could be necessary. The monthly temperature in the upstream of Zayandeh-Rud reservoir could be raised by 0.85 to 1 ◦C; also, the precipitation might be increased by 2 to 3 percent. The high flow during winter season will increase under climate change, while the spring and autumn seasons’ low flows are expected to reduce. Additionally, the annual reservoir inflow may decrease by 1 to 8 percent, showing the necessity for change in Zayandeh-Rud reservoir’s rule curve and allocation of water resources.

The upcoming climate change has become a serious concern for the human society. These changes, caused and aggravated by the industrial activities of the international community and the increase in the concentration of greenhouse gases in the atmosphere, are seen as a threat to the food security and environment. Temperature change and precipitation are studied in the form of different probabilistic scenarios in order to have an outlook for the future. The present study was conducted to address the effects of climate changes on temperature and precipitation in Qazvin plain in the form of five AOGCMs including Hadcm3, CSIRO-MK3, GFDL, CGCM3 and MICROC3.2, and 3 greenhouse gas emission scenarios of A1B, A2 and B1, based on different possible scenario combinations in the next 30 years, 2021-2050 and 2051-2080 (near and far future). On basis of the study results, all 4 target stations, on average, will have experienced a change between two ratios of 0.5 and 1.4 of  the observed precipitation period  by the end of 2050, and the mean temperature will have had a change  between -0.1 to 1.6 °C, relative to the observed period.  By the end of 2080,  the  precipitation will also have fluctuated between the two proportions of 0.5 and 1.7 times of the observed precipitation period and the mean temperature will touch an increase between 0.6 and 2.6 °C. Both SPI and SPEI indices suggest the increment in the number of dry periods in the near and far future. However, the total number of negative sequences differed considering the 3, 12 and 24-month intervals at the stations level. Given the SPEI index, as compared to the base period, the total negative sequences of drought and number of dry periods will increase at 3 stations of Avaj, Bagh-Kowsar and Shahid-rajaei-powerhouse and decrease at Qazvin station in the future; however, SPI gives different results, such that  for Bagh-Kowsar, there will be an increase in both total negative sequences of drought and number of dry periods, as  compared to the baseline period; three other stations will have more dry periods, specifically, but less total negative sequences. The results reported that the drought events would become severe, and the wet events would become extreme in the future.