نشریه هیدروژیومورفولوژی
پیاپی 33 (زمستان 1401)

From the point of view of fractal behaviors, the property of replication over time creates patterns in the bed of the basin that show unique performance during the maturity of a basin, depending on the reproductive characteristics and how they change. For this reason, in this study, investigates the bifurcations of drainage networks of the Ramhormoz basin using Tokunaga model and fractal dimension correlation, has been done. Ramhormoz River is one of the sub-basins of Jarahi basin located in the southwest of the country. This river originates from 50 km southeast of Izeh and flows to the southwest. In this study, river networks were extracted from Arc GIS software, and then input data were entered into the software to calculate two-dimensional fractal numbers using correlation function. In this regard, first Ramhormoz basin is divided into eastern and western parts and then using the Tokonaga method, irregular network and curve of Ramhormoz river are drawn in a regular, geometric and corresponding tree form. According to the length of the streams, the fractal dimension was calculated for both parts of the Ramhormoz basin. The fractal dimension of the correlation between the Ramhormoz basin and its eastern and western parts has been calculated between (1.42 to 1.68) with a high correlation coefficient, which indicates a relatively high turbulent behavior of the basin and its parts. The calculated fractal dimension represents the average bifurcation ratio and the short time to reach a constant flow.

During the occurrence of hydrological drought, the river flow will be reduced and the quality of the river ecosystem will be degraded. Determining the temporal and spatial variations of the hydrological drought makes it possible to plan for the optimal use of river water. In this study, temporal and spatial changes of river flow index (SDI) in watersheds of Ardabil province were evaluated. In this regard, daily flow time series of 28 hydrometric stations in Ardabil province were analysed. River flow drought index was calculated using DrinC software and then hydrological drought characteristics were analyzed and spatial variations of SDI index were determined in 1-month, 3-month, 6-month and annual temporal scales in GIS software. 1-month SDI values (short-term droughts) showed that severe droughts were more common in rivers with low discharge values. However, most rivers with low and high amounts of river flow discharge have experienced severe seasons. The annual (long-term) hydrological drought index in most stations was in the range of -1.5 SDI greater than or equal to -1, which indicates the occurrence of mild drought. Spatial changes of SDI showed that the spatial variations of drought index on a 1-month scale are completely different from the annual scale. The lowest occurrence of hydrological drought was in upstream stations and flowing rivers in Sabalan hillsides. Severe and very severe droughts occur in low discharge rivers. The higher the river discharge, the lower the incidence of severe droughts, and the greater its flow stability than low-discharge rivers.

Due to their geomorphological characteristics, alluvial fans are part of the high flood risk area. Placement of human phenomena in flood zones is a factor that intensifies the instability of currents. For this purpose, in this research, we applied the location of human phenomena in the geographical space of Pardisan in the flood zone with an applied-experimental method. To achieve this goal, the HEC-RAS-6 hydraulic model has been used as a working tool. Due to the size of the area, the area was divided into 15 sub-basins. First, the sub-layers of rivers and floodplain network were extracted, then human phenomena in the studied space were extracted and located on the RAS background map. Then the measured data and values ​​were considered and implemented in the model. By locating the phenomena and considering the conditions of the alluvial fan flood, it was determined that the 100-year-old flood in the area of ​​railway and communication lines, stairs in the west of Pardisan, west side of Payamnoor University, upstream of Pardisan town and also agricultural lands, has a high vulnerability rate. In general, despite the newly established Pardisan town, urban design and subsequent study and modification of the route did not match the geographical features of the region and the prospect of instability has prevailed in the geographical space of the area. It is suggested that for the future development of the city, the flood route be improved and monitored upstream to maintain environmental sustainability.

The aim of this study was to predict changes in temperature, precipitation and evaluate the effects of climate change on the status of surface runoff in the Aras catchment. Climatic conditions were simulated in LARS-WG software environment under RCP8.5 scenario. Using the modified Trent White experimental model, the amount of potential evapotranspiration was estimated for both observation and simulation periods. To ensure the validity of the model, the mean error orthography (RMSE), and the determination coefficient and Nash-Sutcliffe efficiency coefficient (ENS) were used. Also, modeling of surface runoff changes in GIS software environment and SWAT plugin was performed. After forming hydrological units (HRU), the baseline model for surface runoff changes was selected to calibrate and validate the model. The results show that by modeling climatic data during the simulation period, the amount of temperature, evapotranspiration and transpiration will increase, and in contrast, the amount of precipitation has occurred and the flow rate will decrease superficially. The results of validation showed that the accuracy of the model in the selected stations was high and for the precipitation parameter due to its discontinuous nature, the correlation between the data is less than the temperature parameter and different. The results of hydrometric modeling of the basins showed that the Nash-Sutcliffe value is close to 1 and the correlation coefficient between the data is 0.99, which indicates the high efficiency of the model for simulating and estimating climate change and its effects on surface runoff.

Morphometric analysis of a drainage basins and river network plays an vital role in understanding the hydrogeological behavior of the drainage basin . Therefore, the current research was conducted with the aim of modeling flood and its relationship with morphometric variables using multivariate regression. The research method is based on the destruction of morphometric parameters and statistical analysis, including correlation coefficient and linear regression analysis. In this regard, first, the studied basins have been selected using ARC GIS 10.5 software, and the limits of each one has been determined. Then, their morphometric characteristics have been extracted. The Flood discharge has also been reconstructed in return periods of 2 to 500 years using easy-fit software. For modeling, first, the linear relationship between each morphometric variable as an independent variable with flood discharge as a dependent variable was investigated. After ensuring the linear relationship between variables and flood discharge, correlation between each variable and flood discharge was calculated. The variables with the highest correlation were watershed area, basin length, and surface flow length. Using multivariate regression, modeling was used for independent variables and flood discharge. The results show that as the return period increases, the relationship between independent variables and flood discharge increases, So that the correlation of the area with the return period of 25 years is 0.609 and with the return period of 200 years is 0.677.also, the proposed models have more validity for flood prediction using multivariate regression analysis in return periods above 25 to 500 years.

Changes in erosion and sedimentation of the basin are one of the most important factors that affect different parts of human life and natural life. it is very necessary to receive these changes quantitatively, which mainly take place under temperature fluctuations and climate changes in different regions, in order to be more prepared to deal with its negative consequences. In this research, erosion and sedimentation changes in Hajiler watershed were investigated and predicted using GeoWEPP and SWAT models. Based on this, first, by using the data of the current situation of the Ahar synoptic station and using the SDSM model, the changes of the statistical period2020-2040 in three scenarios RCP2.6-RCP4.5-RCP8.5 were investigated, then simulation and prediction of erosion changes was carried out. and sedimentation was done under the influence of climate change by using popular models. The output of the SDSM model indicates an increase in temperature and a decrease in rainfall for the basin until 2040.And the analysis of the simulation results of the sedimentation rate of the models showed that in the studied basin, the GeoWEPP with the selection of the domain method has a suitable level in estimating the sedimentation rate compared to observational statistics. The final model was chosen to predict the amount of sediment in the mentioned period of the basin. Using the downscaled results of the atmospheric general circulation model, the sediment changes in the statistical period of 2020-2040 under the above mentioned three scenarios were estimated as -1.97, 4.45, and 2.98, respectively.

Floods are one of the most important natural hazards that often affect millions of people around the world annually with huge impacts. In recent years, due to the occurrence of frequent floods in the watershed of Cheshmekile River and the subsequent damage caused by floods, the need to pay attention to the zoning of the flood risk in the investigated basin is becoming more apparent. Among the different methods for preparing flood zoning maps, statistical methods are more important due to their simplicity and acceptable accuracy. The aim of this research is to compare the reliability of Shannon entropy models, frequency ratio and witness weight in the context of flood zoning in Cheshmekile watershed. In this research, the criteria of slope, elevation classes, soil type, topographic humidity index, distance from the river, geology, land use, watercourse density, NDVI and rainfall have been used. The probability of flood occurrence has been calculated for each class of each parameter. The calculated weights for each class were applied in the ARC GIS software in the relevant layers and flood zoning maps of the area were obtained. The final maps resulting from the implementation of these three models in the region were divided into 3 low risks, medium and high-risk classes. And finally, the reliability of each model was evaluated using the system performance characteristic curve (ROC). The results have shown that frequency ratio (FR), weight of evidence (WOE) and Shannon entropy (SE) techniques have the highest accuracy in predicting the occurrence of floods.

In this research, in order to simulate the underground water level of Khorramabad plain, the performance of hybrid models of bat support vector regression, bat support vector regression, gray wolf support vector regression for four piezometric wells of Sarab Pardah, Naservand, Sally and Baba Hossein Bridge, which have homogeneous statistics. and it was done without missing data. For modeling, rainfall (P), temperature (T) and underground water level (H) and withdrawal from water resources (q) have been used in the monthly report of the models during the period of 1380-1400. It should be noted that for modeling, 80% of the data is chosen for training and the remaining 20% ​​for testing, randomly, which covers a wide range of data types. Correlation coefficient (R), root mean square error (RMSE), mean absolute value of error (MAE) and Sutcliffe coefficient of vitality (NS) were used to evaluate and compare the performance of the models. The results showed that the combined structure provides better performance than other structures in all the investigated models. Also, the results showed that the wavelet support vector regression model based on the evaluation indicators, in the piezometric well of Sarab Pardah has R=0.978, RMSE=0.221 m, MAE=0.011 m, NS=0.985 and also in the piezometric well of Naservand has R=0.981 . 010 m, NS=0.986 and finally piezometric well, Baba Hossein Bridge has 5 R=0.98, RMSE=0.101 m, MAE=0.007 m, NS=0.995, compared to other models, it can be used to create a favorable result.