مجله علوم و مهندسی آبخیزداری ایران
پیاپی 61 (تابستان 1402)

Piping erosion, gully erosion, and mass movement have complexity that the best way to comprehensively identify them is to use new statistical tools. The purpose of this study is spatial analysis of soil landforms based on spatial statistics and field studies. In this research, a digital elevation model was prepared. Then, the spatial pattern and interaction of the three soil landforms were investigated and the spatial effect of slope degree was analyzed on the spatial distributions of them. The results showed the pair correlation function (g(r)) was clustered for both piping erosion and gully erosion. However, it was dispersed for mass movements up to spatial distance of 140 m. The bivariate statistic (g12(r)) showed there is a significantly positive correlation between gully erosion and piping erosion. The correlation between mass movements with each of gullies and piping erosions were positive at the near distances. In addition, increasing the slope had a large effect on the spatial distribution of mass movements and a small effect on the spatial distribution of piping erosion and gully erosion. Therefore, a comprehensive approach to a landscape using quantitative statistics can lead to better identification of processes affecting them, and then, reduction of natural hazards.

In this research, the weir-orifice composite structure was simulated using two water elevations and six analyzes in the Flow-3D model, and the results were validated with laboratory data. Three grid groups were used to select the optimal block mesh. However, the K-Ɛ and RNG turbulence models were used to select the appropriate turbulence model. The block mesh with 198750 cells was selected as the most suitable mesh for this simulation. The results showed that the RNG turbulence model with the correlation coefficient of 0.92 has the highest accuracy and speed. The numerical model investigated the velocity, pressure, water level profile, and flow lines, and the results were presented. Then, by using three water heights of 0.0756, 0.0973, and 0.1243 m and applying ten different angles at each height, by changing the location of the overflow from horizontal to diagonal in the cross-section of the circular channel, changes in the discharge coefficient of the case were investigated. Also showed that the diagonal weir had increased the discharge coefficient. The lowest and highest discharge coefficient values were obtained for angles of 2.5 and 12.5 degrees with values ​​of 0.67 and 0.84, respectively. Weir with an angle of 12.5 degrees with an increase of 13.27% of the discharge coefficient has the most increase in this coefficient among the applied angles.

Evapotranspiration (ET) is one of the most important components of the hydrological cycle and an important factor in determining water demand, its spatial distribution and accurate estimation for many studies such as water balance, crop production simulation, program Irrigation planning and water resources management are very important. Also, using of geostatistical methods is one of the ways to estimate evapotranspiration in places without stations. In this study, SWAT hydrological model was used to simulate evapotranspiration of Samalghan watershed . To evaluate the simulation results, indices R2 , bR2 , NS was used. In the calibration stage, the coefficients of R2, bR2 and NS were equal to 0.74, 0.71 and 0.68, respectively, and in the validation stage, they was equal to 0.69, 0.62 and 0.59. .Then, using the results of spatial analysis of data in this study, the exponantial model was selected as the most appropriate model for zoning due to the lower RMSe value and higher correlation.

The present research aims to determine areas with flood susceptibility using CART, RF and BRT models. Twelve factors affecting flood susceptibility including altitude )DEM), slope, aspect, distance from stream, lithology, rainfall, land use, SPI, TPI, TWI, curvature and RSP were selected. Out of 82 flood points, 70 percent to 30 percent were randomly classified as training and validation data. Also, random forest method was used to determine the most important parameters. The ROC curve was also used to validation the model. According to the random forest model, DEM, distance from stream, rainfall, land use and RSP were the most important factors affecting the susceptibility and probability of floods, respectively. According to the ROC chart, the accuracy of the RF model as a superior model has been very good in both training )0.884) and validation )0.856). According to the final flood susceptibility map, 32.7 percent of the study area has a medium to high flood susceptibility. The results showed due to the high accuracy of the spatial distribution map of flood susceptibility can be promising for decision makers, local managers and policymakers to reduce flood damages.

Floods are one of the most important natural hazards that have caused economic and social damage in most areas. Numerous climatic, hydrological, geomorphological and geological factors are involved in the occurrence of floods. Flood analysis, management and control can be done by preparing flood potential maps. The purpose of this study is to map the flood potential of Maroon watershed using random forest and support vector machine machine learning methods. For this purpose, 16 effective parameters in flood occurrence including altitude, slope and aspect, curvature, geological formations, land use, curve number, rainfall, temperature, stream power index )SPI), topographic wetness index )TWI), distance From stream, stream density, road distance, road density and NDVI index were considered. The mentioned parameters were prepared in ArcGIS 10.8, ENVI 5.3 and SAGA GIS 7.2 software environments and then converted to readable format for R software environment in order to implement the support vector machine and random forest models. Finally, RF and SVM models were implemented using SDM packages and evaluated using receiver operating characteristic )ROC). The results showed that RF and SVM models correctly predicted the flooding map of Maroon Basin with 0.997 and 0.947 percent accuracy, respectively

Watershed health provides an integrated view of the factors influencing the watershed conditions. In the current study, the data length effect on changes in SPI-based watershed health index was evaluated using the RelResVul framework for the period of 1961-2021 in Sahneh, Kermanshah, and Harsin stations in Kermanshah Province, Iran. For this purpose, reliability (Rel), resilience (Res), vulnerability (Vul) indicators, and mean watershed health (RelResVul) were calculated in different periods. According to the results, watershed health indicators were different in 1961-1980, 1981-2000, and 2001-2021. In addition, with the increase in the length of the statistical period, health indicators had different changes, so that in short-term data, the changes of the indicators mentioned above were more than the whole period. The maximum changes in the first 30 years of the statistical period, and with the increase in the length of the statistical period until the end of the period, changes decreased. Accordingly, the last 30 years of the statistical period have reliable results in calculating health indicators. Considering massive calculations, climatic data deficits, and limited stations while increasing the length of the statistical period, it is suggested to use the climatic data of the last 30 years to estimate watershed health.

The maximum canopy storage capacity is an important parameter in hydrological models. The aim of the present study, which was conducted in a region of Fars province, was to model the maximum canopy storage capacity for storing rainfall. Therefore, the parameters of leaf area index and branch area index were used as a pattern of the vertical distribution of canopy. Additionally, the ability of 10 vegetation indices produced from narrow- red edge bands compared to 10 vegetation indices produced from broad bands that resulted from Sentinel-2 satellite imagery in estimating branch and leaf area indices was investigated. Subsequently, using sampling in autumn and spring seasons, laboratory results and regression models, maps of maximum canopy storage capacity were drawn for these seasons. The results showed that the CIRE index, which was produced from narrow- red edge bands, was able to estimate both branch area index )R2=0.74) and leaf area index )R2=0.92) values very well and was the best index. Additionally, the modeling results of maximum canopy storage capacity showed that this capacity varied from zero to 1.32 mm per pixel in autumn and from zero to 1.74 mm per pixel in spring.