JWSS - Journal of Water and Soil Science

Ziarat drainage basin, in the southern part of Gorgan city, is exposed to mass movement, especially landslide occurrence, due to geologic, geomorphologic, and anthropogenic reasons. The objectives of this study were to predict landslide susceptibility and to analyze the effective factors using rare events logistic regression. In view of this, the map layers of the variables including geology, land use, slope, slope aspect, distance of road, distance of fault and distance of river were prepared using topographic and geologic maps and aerial photo interpretation. In addition, the map layers of the soil variables including the percent of clay, silt, sand, and saturation water as well as plasticity limit index were determined based on the laboratory analysis of 32 soil samples collected from landslide sites and 32 soil samples obtained from non-occurrence landslide sites. The controlling factors of landslide were determined using rare events logistic regression analysis; then based on their coefficients, the landslide risk zoning map was prepared and validated. The landslide risk zoning map was classified in five different hazard classes ranging from very low risk to very high risk; the very high risk class with 16.8 km² was assigned as the having the highest percent of the catchment area. The results of the model validation showed that the rare events logistic regression model with the receiver operating characteristic (ROC) of 0.69 could be a suitable prediction model for the study area. The results of this study could be, therefore, useful for corrective actions and watershed management landslide high-risk zones.

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.

Flood is a natural disaster making the heavy humanistic and economic damages each year in most parts of Iran. In this research, the SWAT model performance in flood prediction and sub-basin priority was investigated in terms of flooding in Araz-Kose watershed in Golestan province. To calibrate the model, SUFI2 was applied. The calibration and validation were done for the 1991-1998 period based on the data of 2001-2009. After validation, the indices (R2, bR2, and NS) were estimated. They were equal to 0.81, 0.81 and 0.73 for calibration and 081, 0.78 and 0.64 for validation, respectively. The sensitivity analysis results showed 13 effective parameters. The curve number (CN2) was determined as the most effective parameter. For studying the flooding in a watershed, the Araz-Kose watershed was divided into six parts. Based on the obtained results from the SWAT model with different CN and F indexes (with/without considering the sub-watershed), the sixth sub-basin with 22.4% decrease in discharge was chosen as the most effective region in flooding. Meanwhile, the other sub-basins including 4, 1, 3, 5 and 2 had more flood potential, respectively.

In order to plan and manage the land and its changes, it is necessary to identify and evaluate the factors affecting it. Land use / cover changes are one of the main factors in global environmental change that is defined as a change in the type of land use; it is one of the major factors changing hydrologic flow, land erosion and destruction of biodiversity. The main purpose of this study was to assess the trends of land use changes in Beheshtabad Watershed of Chaharmahal and Bakhtiari Province with an area about 3847 square kilometers by using remote sensing and GIS during a 25-year period. In this research, first, analyzing and pre-processing the satellite images of Landsat 5 TM sensors from 1991 and 2008 were done, and Landsat 8 of OLI sensor of 2016 was applied. Then, by using the hybrid classification method, 5 land use classes including pasture lands, urban-building lands, agricultural lands, garden lands and bare lands, land use maps for the three time periods were prepared. The overall accuracy of the obtained land use maps for 1991, 2008 and 2016, was 92.17%, 94.29% and 93.41%, respectively, indicating the acceptable accuracy of the maps. Then, the process of land use change and the contribution of each land use classes and the percentage of changes in each land use class were determined in two study periods. The results of this study showed some changes occurred in the studied watershed. The total area of pasture lands during two periods indicated the decreasing trend, but urban-building and garden lands during two periods represented the increasing one. Agricultural lands during the first period indicated the decreasing trend and during the second period showed the increasing trend, while bare lands during the first period showed the increasing trend and during the second period, reflected the decreasing trend. In general, it should be noted that in the Beheshtabad watershed, we could see an increase in the replacement of pastures by urban-building class, rainfed agriculture, gardens, and bare lands, the incidence of destruction in the region.

Due to the limitation and scarcity of water supplies, it is essential to identify and reuse alternative water sources, particularly in the arid and semi-arid regions according to environmental conditions. Reuse of runoff is an adaptive management philosophy and approach to balance efforts and provide sustainable water services and manage hazards. The present study is aimed to design a suitable model of water resource management with an emphasis on crisis management. In this study, indices and dimensions of the water resources management model were determined via the Delphi method (based on Schmidt, et al.). Delphi panel members were identified and selected in three stages using random sampling. The faculty members of water resources and watershed management university specializing and passive defense experts of water organization were selected as the Delphy panel members. Initially, panel members identified the most important effective items on water resources security, based on their inference of the model. Based on the first evaluation and after eliminating duplicates, 43 items remained. Finally, 36 items remained in the analyses, when the items with low and medium significance coefficients were eliminated. The final model of water resources management was approved by the expert opinion based on the crisis management approach. According to the results, designing rainwater collection and storage systems in different parts of the city and installing tanks for roof rainwater collection in each home were selected as the best approach in the critical condition (with an average grade of 4.94), whereas the installation of the sensors in different parts of water pipe with an average grade of 2.10 was ranked in the 33rd selection. Finally, the items identified by the Delphi panel were classified into four general indices: retrofit, safety, culture, and planning. In general, it is necessary for the officials of urban water resources, as well as crisis management, must prepare for crisis conditions as well as the correct, principled, and scientific use of available water resources to extract and store rainwater and runoff to use it in crises and disrupt the general water supply system.

The simulation of the rainfall-runoff process in the watershed has particular importance for a better understanding of hydrologic issues, water resources management, river engineering, flood control structures, and flood storage. In this study, to simulate the rainfall-runoff process, rainfall and discharge data were used in the period 1997-2017. After data qualitative control, rainfall, and discharge delays were determined using the coefficients of autocorrelation, partial autocorrelation, and cross-correlation in R Studio software. Then, the effective parameters and the optimum combination were determined by the Gamma test method and used to implement the model under three different scenarios in MATLAB software. Gamma test results showed that today's precipitation parameters, precipitation of the previous day, discharge of the previous day, and discharge of two days ago have the greatest effect on the outflow of the basin. Also, the P_t Q_t-1 and P_t P_t-1 Q_t-1 Q_t-2 Q_t-3 combinations were selected as the most suitable input combinations for modeling. The results of the modeling showed that in the support vector machine model, the Radial Base kernel Function (RBF) has a better performance than multiple and linear kernels. Also, the performance of the Artificial Neural Network model (ANN) is better than the Support Vector Machine model (SVM) with Radial Base kernel Function (RBF).

Integrated assessment of the watershed is critical in arid and semi-arid areas due to the severe water stress in these regions. Data and information are an essential part of decision making and water governance to obtain integrated water resources management at the watershed scale. Water accounting is a helpful tool to organize information and present them as the standard indicators to achieve this goal. Therefore, the objective of this study is to implement the Water Accounting Plus framework (WA+) in the Ferizi watershed located in the Khorasan-e Razavi Province. In this study, water accounting indicators of the Ferizi watershed for a period of 28 years (1990-2017) and wet (1990-1997) and dry (1998-2009) periods were calculated using the SWAT model. The calculated indicators showed that the amount of manageable water and usefulness of consumption (transpiration) is low in the watershed and a large part of the share of irrigation in the watershed is provided by groundwater resources. Generally, the results of this study showed that the use of the SWAT model, WA+ framework, and analysis of water accounting indicators play a significant role in assessing the agricultural and hydrological conditions of the watershed. The proposed approach in this study can help managers make enlightened decisions to keep the sustainability of the watershed.

Introducing reliable regional models to predict the maximum discharge of floods using characteristics of sub-basins has special importance in terms of flood management and designing hydraulic structures in basins that have no hydrometric station. The present study has tried to provide appropriate regional flood models using generalized linear models (GLMs) to estimate 2-, 10-, 50-, and 100-year maximum daily discharges of 62 sub-basins in Great-Karoon and Karkhe basins. According to the results, the sub-basins were categorized into four sub-regions based on some physiographic and climatic characteristics of the study sub-basins. The results showed that regional flood modeling was successful in all sub-regions except sub-region II, which includes very large basins (A̅≈17300 km2). The adjusted R² of the best models in sub-regions I, III, and IV were estimated at around 82.4, 91.3, and 90.6 percent, and these models have a relative error (RRMSE) of around 9.5, 9.23, and 6.7 percent, respectively. Also, it was found that more frequent floods with 2- and 10-year return periods are influenced by properties such as basin’s length, perimeter, and area, while rare floods with 50- and 100-year return periods are mostly influenced by the river systems characteristics such as the main river length, total lengths of the river system, and slope of the main river. According to the research, it can be stated that the behavior of maximum daily discharges in the study area is extremely influenced by the different climatic and physiographic characteristics of the watersheds. Therefore, the maximum daily discharges can be estimated accurately at ungauged sites by appropriate modeling in gauged catchments.