JWSS - Journal of Water and Soil Science

Catchment prioritization in terms of natural disaster intensity as well as prevention and control practices plays a main role in the natural resources and watershed management. In this study, a total of 24 sub-catchments in the Zohreh-Jarrahi basin were prioritized according to their morphometric parameters and using the mixed model of TOPSIS-Multivariate regression. A total of 12 morphometric parameters including constant component of channel maintenance, drainage density, ruggedness number, infiltration index, stream power index, stream frequency, slope, drainage texture rate, relief rate, form factor, bifurcation ratio and topography wetness index, in addition to rainfall, were studied and scored. Parameters were weighted by using multivariate regression and the spatial distribution of the observed flood events. TOPSIS model was used for the prioritization process. The results obtained from the weighting analysis showed that the ruggedness number, slope and rainfall had the highest effect on flooding in the study area with the score of 0.068, 0.024, and 0.016, respectively. According to the prioritization results, sub-catchments of Seidoon, Emamzadeh Jafar, and Takht Deraz, which had the minimum distance to the positive optimum (0.0028, 0.0029, and 0.0029, respectively) and the maximum distance to the negative optimum (0.0097, 0.0098 and 0.0095, respectively), showed the highest flooding intensity with the score of 0.7745, 0.7690 and 0.7625, respectively. In order to validate the results, prioritization results were compared to the observed flood events. Validation results also indicated the efficiency of the mixed model in delineation of catchments prone to flooding. Three sub-catchments of Seidoon, Emamzadeh Jafar and Takht Deraz were observed to have the highest number of observed flood events, thereby showing the high effectiveness of the model and also, the role of the morphometric parameters in flooding.

Regional flood frequency studies are initialized by the delineation of the homogeneous catchments. This study was based on "Region of Influence" concept, aiming to find the similar catchments in the south of Caspian Sea. The methodology utilized the Particle Swarm Optimization Algorithm, PSO, to optimize the fuzzy system over a dataset of catchment properties. The main catchment variables in relation to flood were determined by the principle component analysis method and employed as the inputs in the fuzzy system. Catchments grouping was performed over these fuzzy input variables by the iterative process. The optimum similar groups were obtained by PSO, and the heterogeneous L-moment index was used as the termination criterion for the optimization process. A total of 61 hydrometric stations located in the study area were selected and their relevant catchments' physical, climatic and hydrologic properties in relation to flood were studied. Principle Component Analysis by Variomax Rotation Factor over the catchments datasets tended to four out of 16 physical variables, including area, mean elevation, Gravelious Factor and Form Factor, as the main parameters in terms of homogeneity with 84 percent of accumulative variance. These variables, as well as mean annual rainfall, were used as the input data to define the fuzzy system. PSO algorithm was then employed to optimize the developed fuzzy system. The developed algorithm tended to yield the best result in the 9th iteration with 26 and 22 for the minimum average and the optimum values of cost function, respectively. The topology of the resulting algorithm included inertia weight, local and acceleration rates, the number of generations and population size, with the values of 0.7298, 1.4962, 1.4962, 10 and 5, respectively. This study tended to a total of 61 regions of influence, proportional to the relevant 61 sites. According to the geographical location of the catchments in the region, it could be concluded that the geographical proximity doesn't necessarily involve homogeneity. The obtained results indicated the efficient potential of PSO-FES in the delineation of the homogenous catchments in the study area.

Simulation of the hydraulic behaviour of the river basin boundaries is important for river engineering projects, prediction of flood damages in different conditions and economic feasibility studies, flood control, and other social programs related to the system of the river. In this study, river bed and flood zoning map in the range of over 155 km from the Zayandehrud dam to Nekouabad Diversion Dam were addressed by using the software's ArcGIS, HEC-RAS and HEC-GeoRAS extension. For this purpose, a digital elevation map in 1: 250,000 scale was provided and cross-sectional area was divided in 1085. Manning roughness coefficient was determined by Cowan. Finally, data entered into the software HEC-RAS and was analysed. After determining the area of flood in the return periods of 2, 5, 10, 25, 50, 100 and 200 years in all cross sections, the results were entered into the ArcGIS environment and flood zoning maps were obtained. The results showed that of the 200-year flood lands, 96% of the land flood was related to the return period of 25 years.

Today, the preparation of flood zoning maps is one of the basic and important issues in the study of development projects in the world; it is considered before any investment by the related organizations. In this paper, flood zoning was performed using the two-dimensional model HEC-RAS and GIS in order to assess the risk of the construction of a railway station near the bank of the Iranshahr River, in a range of 2500 meters. Two-dimensional hydraulic application could create a more accurate flow pattern in comparison to the one-dimensional model used in the previous studies, especially in the flood plain areas. In this paper, due to the important role of the topography of the area in ensuring the accuracy of the calculation, a Digital Elevation Model (DEM) was used with very high precision (about 2 meters), as obtained from aerial photos. The results of this study indicated the onrush of flood, depth and flow velocity in different return periods. Based on the comparison of water surface profiles in the floodplains with the return period of 100 and 25 years, the maximum difference between the water levels was 0.5 m, which seemed to be reasonable by considering the low slope of the studied area. The results of this paper, therefore, showed that the location of the railway station was in medium risk and the outskirt of floodplain.

One of the most important problems in flood manegment is the damages induced by this phenomenon. Expected annual damage (EAD) is an important index for basin vulnerability against flood. Prediction of flood damages requires the analysis of spatial and temporal risk and must be calculated by the combination of hydrologic, hydraulic and economic models. In this research, the uncertainty was considered in the flood risk analysis. The probability of flood occurrence was calculated by the parabolistic model. By using the river analysis systems software (HEC-RAS) and the geographic information system (GIS) and utilizing the Google-Earth software, the floodplains of Zayande Roud river in Esfahan province were investigated with the  return period of 25, 50, 100, 200 and 500 years. The Monte Carlo method was also sed to perform the uncertainty analysis in the proposed method. The logarithmic persion type III was selected as the best distribution of flood. The damage-stage relationship was calculated as well. Based on the uncertinity analysis, the river discharge could be regarded as the major parameter in the uncertainty of EAD.

Occurrence of heavy floods in rivers causes a lot of damages and losses. In this research, to highlight the river-training reaches in 10.9 km of Babolrud River, first, using topographic map of the area, the Tin layer was created in GIS software. Then, using the HEC-GeoRAS extension, the main route and cross sections of the river were prepared and introduced to the HEC-RAS model. River discharge with return periods of 2 to 200 years was calculated. Flow analysis in the agricultural and urban areas was performed and the areas which need training measures such as flood-retaining walls and levees were specified and designed. The structural design, stability control and sliding was performed based on the Standard No. 518, using RetainWall software, and design and control of levee stability was performed based on the Standard No. 214, using GeoStudio software. The cost of project implementation was estimated based on the Price List of 2017. The output of HEC-RAS software showed that height of flood-retaining wall in 3 urban reaches ranged between 1.73 to 2.8 m and in 5 agricultural reaches ranged between 1.46 to 2.25 m. It was concluded that the overall cost of levee implementation is about 9.01 billion Rials, of gravity concrete flood-retaining wall is about 9.26 billion Rials and of concrete cantilever inverted T shape flood-retaining wall is about 10.05 billion Rials. Thus, using flood-retaining levee is the most economical option.

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.

Soils pollution with heavy metals is due to the presence of various metals such as copper, nickel, cadmium, zinc, chromium and lead. Heavy metals have a negative effect on the biological parameters of soil, including size, activity and diversity of soil microbial population, as well as the enzymes involved in the deformation of such elements as P, N, C, and S. Thus, the activity of soil enzymes as a bioavailable agent is reflected as a cheap and fast method for the natural and anthropogenic distribution of heavy metals contamination. The purpose of this study was to investigate the effect of lead, humidity and their interaction on urease and phosphatase enzyme activity during a 10 week incubation period. Different levels of acetate lead (50,100, 150 and 200 mg/kg soil) were added to the plots containing two different moisture regimes (field capacity and flooding). The activity of urease and phosphatase (alkaline and acidity) was measured after 2,4,6,8 and 10 weeks of incubation. The results indicated different levels of lead had no significant effect on the activity of urease and acidity phosphatase. In contrast, high levels of lead significantly reduced the activity of alkaline phosphatase. Moreover, moisture served a different role in the activity of these enzymes, and it was related to the lead concentration and incubation time. Additionally, the function and interaction of lead, moisture and time were very influential on urease and phosphatase activity. Therefore, the above three characteristics are very important to study soil contamination for the polluted soils.

Selection of the appropriate distribution function and estimation of its parameters are two fundamental steps in the accurate estimation of flood magnitude. This study relied on the concept of optimization by meta heuristic algorithms to improve the results obtained from the conventional methods of parameter estimation, such as maximum likelihood (ML), moments (MOM) and probability weighted moments (PWM) methods. More specifically, this study aimed to improve flood frequency analysis using the Artificial Bee Colony algorithm (ABC). The overall performance of this algorithm was compared to the conventional methods by employing goodness of fit statistics, correlation coefficient (CC), coefficient of efficiency (CE) and root mean square error (RMSE). The study area, Babolrood catchment located in southern bank of Caspian Sea, has been subjected to annual flooding events. A total of 6 hydrometry stations in the study area were delineated and their data were used in the analysis of 6 distribution functions of Normal, Gumbel, Gamma, Pearson Type 3, General Extreme Value and General Logistic. This analysis indicated that Gamma and Pearson Type 3 were the most appropriate distribution functions for flood appraisal in the study area, according to the ABC and conventional methods, respectively. Also, the results showed that ABC outperformed ML, MOM and PWM; so, Gamma could be recommended as the most reliable distribution function for flood frequency analysis in the study area.

The study of floods has always been important for researchers due to the great loss of life and property. Investigation of flood bed can provide appropriate solutions to reduce this phenomenon to managers and researchers. In this research, the compound channel (with flood plain on one side of the main channel) Been paid, Therefore, two experimental models of compound channel in laboratory flume were examined by considering dimensional analysis. With the goal Investigation of lateral slope of flood wall in laboratory model In the first model, transverse slope 0 And in the second model, a value equal to 50% Was considered. Also in order to investigate the effect of longitudinal slope of river bed sediments Longitudinal slope in three steps 0.00 2, 0.004 and 0.006 Was changed. Examining the ADV speedometer data, the results showed that with increasing the longitudinal and transverse slope (slope of the flood wall) of the channel, the maximum longitudinal velocity changes to the floor of the channel. In order to investigate the effect of average sediment diameter on the scouring process during experiments Mm was used. The results showed that increasing the longitudinal and transverse slope had a great effect on increasing the volume of washed sediments 3 and 0.9 of sandy sediments with a diameter Along the canal and with the increase of these longitudinal and transverse slopes in the channel, more sediment transport volume occurs. In the following, using Investigation of dimensionless numbers obtained from dimensional analysis, dimensionless weight landing number was introduced to evaluate this value value of other hydraulic parameters and Was introduced. A relationship based on nonlinear regression with correlation coefficient Acceptable was introduced at around 0.88.

The basis of land management is the geomorphological zoning of the land surface, which is determined based on the same geomorphological characteristics of the zoning. Ground zoning detect land features by basic surface features such as height, slope, and slope direction. In this study, quantitative zoning of the land surface with small coefficients to the surface has been used to identify suitable areas for artificial feeding in the mountainous region of Gohar and Dasht-e Gorbayegan in Fars province. Quantitative zoning of the land surface has been performed by Evans-Shri coefficients due to the accurate determination and separation of types, faces, and surface features of the land has an important role in determining the exact land use. In this research basic models included linear, circular, and divergent models. These basic models with the dimensions of the final windows are ranked second in the MATLAB software to the level the ground is fitted to determine the fit of these models, the parameter of total squared difference has been used. In addition, the suitability of the study area for flood distribution in five different classes was determined using fuzzy logic. The most suitable areas for feeding downstream of the cones had five parameters with a maximum score of 20. The inappropriate class related to the lower plains of alluvial fans have a minimum score of five input classes in fuzzy logic, which is equal to zero.

The study of annual damage statistics due to floods in Iran and the world shows the extent of flood damage to natural and human resources in different regions. Determining the flood zone of rivers in order to protect national resources and reduce flood damage provides the possibility of protecting the river from encroachment and the construction of any unauthorized facilities in it. Therefore, in the present study, the capability of numerical models in simulating the flood zone of rivers was evaluated in the range of Azarshahr Qushqura river and the two-dimensional hydraulic model HEC-RAS 5.0.7 and one-dimensional HEC-RAS model were compared. Changes in the hydraulic characteristics of the flood flow including depth and velocity of the flow at different cross sections of the models were evaluated. The results showed that the water surface level (flow depth) of the two-dimensional model HEC-RAS compared to the one-dimensional model had the lowest error as compared to other hydraulic parameters of flood flow. The two-dimensional HEC-RAS model showed the highest error rate in the flow velocity parameter in comparison to the one-dimensional model. The results indicated that two-dimensional HEC-RAS model V5.0.7 determined the surface of the flood zone 12.46 % more than the one-dimensional HEC-RAS model. The confirmation of the resulting zones on the current state of the river and comparison with the river aerial photo of 1346 indicated the higher accuracy of the two-dimensional HEC-RAS model in estimating the flood zone of the river.

Population growth, urbanization, and land use change have increased disastrous floods. Iran is also among the countries at high risk of floods. The latest examples of flood damage are the devastating floods of the spring of 2019 with significant mortality and financial losses in more than ten provinces of the country. The purpose of this study is to prepare an urban flood risk map of District 4 City Shiraz. The vulnerability of the region was made using PROMETHEE Ⅱ and COPRAS multi-criteria decision-making models and urban flood hazard zones were prepared by partial least squares regression (PLSR) and ridge regression (RR) models and a risk map was obtained by multiplying the vulnerability and hazard in ArcGIS software. The highest percentage of the study area in the PROMETHEE Ⅱ and COPRAS models belongs to the moderate class of vulnerability. The evaluation of the vulnerability models using Boolean logic and RMSE and MAPE statistics, showed that the COPRAS model provided better results than the PROMETHEE model. The results of partial least square regression (PLSR) and ridge regression (RR) models in flood risk modeling were analyzed by the Taylor diagram, which showed the superiority of the ridge regression (RR) model and the accuracy of this model in preparing urban flood hazard maps. The risk map of the study area indicated that 34% of the area (973 ha) is in the range of high and very high flood risk.

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.

The objective of the current study was to zone flood probability in the Marzdaran watershed. Since the allocated budget for management work is limited and it is not possible to carry out operations in the whole area, having a map that has prioritized different areas in terms of the probability of flood occurrence will be very useful and necessary. A well-known data mining model namely MaxEnt (ME) is applied due to its robust computational algorithm. Flood inventories are gathered through several field surveys using local information and available organizational resources, and the corresponding map is created in the geographic information system. The twelve predisposing variables are selected and the corresponding maps are generated in the geographic information system by reviewing several studies. The area under the curve (ROC) is used to evaluate the modeling results. Then, the most prone areas of flood occurrence which are prioritized for management operations are identified based on the prepared map. Based on the results, about 100 km2 of the study area is identified as the most prone area for management operations. The results showed that the accuracy of the maximum entropy model is 98% in the training phase and 95% in the validation phase. The distance from the river, drainage density, and topographic wetness index are identified as the most effective factors in the occurrence of floods, respectively.