جستجوی مقالات مرتبط با کلیدواژه « سیل » در نشریات گروه « آب و خاک »

Flooding in urban areas is increasing due to population growth with improper land use planning and climate change-induced increase in extreme rainfall events. Flood risks have affected the largest proportion of the world's population (45%) compared to other natural disasters and caused 5424 recorded deaths between 2000 and 2017, and if our cities are to be resilient and better able to deal with the consequences of floods. city to cope, it is expected that they will adopt sustainable strategies to adapt to the new climatic and socio-economic conditions. Accordingly, the purpose of this research is to evaluate the resilience of Shiraz city against floods in order to identify areas of low resilience and take the necessary measures to increase it. Their resilience should be done.

The research method of this research is descriptive-analytical and practical in terms of purpose. Based on the background of the research and refinement of the variables based on the conditions of the research area, four dimensions and 24 criteria were determined, including demographic (8 variables), economic (3 variables), environmental (4 variables) and infrastructure (9 variables).

AHP method was used for weighting and access to critical infrastructure for action and recovery has the highest weight with a weight of 0.115, and infrastructure with a weight of 0.395 has the highest weight. The estimated CR of the model is equal to 0.07, which it shows the acceptable accuracy of the model. For zoning, the Vikor model has been used, and based on the output of the model, 9.92% of the area is in the zone with low resilience, where about 16.63% of the population of Shiraz lives in this zone, and about 75.43% of the area is in the zone with medium resilience. 74.56% of the population lives in it, and the area with high resilience is 14.65%, where 8.81% of the population of Shiraz lives.

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.

Global warming in recent decades has caused significant changes in precipitation and temperature, including changes in the mean and standard deviation of these variables and changes in the intensity and frequency of climatic extremes (floods and droughts). Given the importance of these changes in water resources management, it is crucial to study the trends in these variables. In this study, in 12 selected stations in different climatic regions in Iran, the changes in monthly and annual precipitation and mean temperature during 1961-1990 and 1991-2020 were examined. The results of Mann-Kendall test showed in most stations precipitation had an increasing trend in the first period, and a decreasing trend in the second period; although in both periods the trend was not significant (Z<1.645). The mean temperature has increased in both periods, which in the second period has increased with a higher level of confidence (Z>2,576) and greater slope than in the first period. The average annual rainfall has decreased in most stations, and the average annual temperature has increased in all stations. The distribution of precipitation and temperature showed that in some stations, the probability of occurrence of extreme events and hot and cold periods in the second period has increased compared to the first period. In some other stations, droughts/floods are more/less likely to occur. This indicates that the activity of air masses affecting each station can be intensified or weakened due to climate change.

Floods have direct impacts on the organisms of rivers and indirect impacts on ecosystems of rivers. In this study we perform a water quality model of the zarjoob river by Hydrologic Engineering Center-River Analysis System (HEC-RAS) model, specifically for Carbonaceous Biochemical Oxygen Demand (CBOD) , Dissolved oxygen ( DO ) and nitrite ( No2 ) based on the hydrodynamic simulation for normal and flood condition  to determine the extent of pollution loading in the river. Results showed that trend of dissolved oxygen in flood conditions was reversed compared to dry conditions and showed a slight increase along the river. Downstream of zarjoob river on flood condition, whenever the BOD decreased, DO concentrations increased from 5.5 mg/L to 6.2 mg/L, indicating that water quality was not as sensitive to variations of the BOD as expected. This study showed that rainy events were important environmental factors positively affecting aeration, which raised the dissolved oxygen content of the Zarjoob River except for nitrite. This paper is the first report of the innovative use of HEC-RAS model Without the help of accompanying qualitative analysis models such as Qual2K, WASP … to simulate water quality in an urban river.

Heavy rains often occur in small areas, but they may be the result of large-scale systems and their energy and moisture are provided from distant areas (Mohamadei et al., 2010). Therefore, identification of synoptic systems is of great importance in order to predict precipitation. Although rain has many positive effects on human life, heavy rain can cause one of the most dangerous and damaging natural disasters, namely floods. Every year, floods cause many human and financial losses in different regions of the world. Floods are more effective in vulnerable areas and cause the loss of human lives, damage to property and products, disruption of transportation and services, and other economic losses (Kheradmand et al., 2018). In March 2019, heavy rains occurred in Golestan province, which caused flooding in parts of this province, especially in the cities of Gonbad-Kavus and Aqqala. Most of this heavy rain and flood occurred in the Gorgan River basin. According to meteorological reports, the rain started from the night of 03.17.2019 and continued until 03.21.2019, although the heaviest rainfall occurred from the 03.18.2019. The volume of the flood was so great that the dams on the Gorgan River could not accommodate it. According to the reports of the regional water company of Golestan province, the flood entered the Bostan dam at 1 am on 03/19/2019, and after passing through it, entered the Vashmgir dam at 6 am, and then on 03.21.2019 entered the city of Aqqala. The damage of this flood was estimated at about 4800 billion Tomans, which includes damage to 17800 residential units, damage to farms, transportation infrastructure, 40% reduction in tourism, damage to industrial units, unemployment of about 3000 people, and damage to the nomads of the province. (Islamic Republic News Agency, 04.09.2019). Considering the heavy damage caused by the mentioned heavy rain and flood in Golestan province, it is necessary to identify and analyze the causes of its occurrence in order to plan and take the necessary measures to prepare and deal with such incidents.

The study area is Gorganrood watershed, most of this area is located in Golestan province. Golestan province is one of the northern provinces of the country and is located in the southeast of Caspian sea. In this research, in order to identify and analyze the heavy rain that occurred in Golestan province in March 2019, which led to severe flooding, several types of data were used (data from meteorological stations, NCEP/NCAR reanalyzed data, MODIS satellite images, GPM precipitation products). First, using the rainfall data of the synoptic stations located in the Gorgan River watershed, the time of heavy rainfall was identified, and then using the data of the aforementioned stations and several stations outside the basin, a rainfall zoning map was prepared. MODIS satellite images were also used to check the position of precipitation system and cloudiness of region. Using GPM satellite rainfall products called IMERG, which were extracted on a half-hourly basis, as well as the main synop reports of meteorological stations, which are reported on a six-hourly basis, the intensity of rainfall was investigated. In addition, the physical conditions of the basin were investigated using the topography and slope map of the basin prepared from the DEM layer of the region. In the following, using the reanalyzed data of the NCEP/NCAR database (National Center for Environmental Prediction - National Center for Atmospheric Research of the United States), synoptic maps including maps of land surface pressure, geopotential height of the upper atmosphere, Omega (indicates the speed of vertical movements of the atmosphere), wind direction and speed, moisture flux convergence function, frontal function, specific humidity, atmospheric precipitable water and Hoff-Müller diagram were drawn to identify the synoptic and dynamic factors of the mentioned precipitations.

The results of the present research in the analysis of flood factors can be summarized as follows:Survey of the topography and slope of the Gorganrood basin revealed that the physical conditions of the basin are such that the potential for flooding is high.The amount of rainfall in 24, 6 and a half hour intervals in the study area were investigated and it was shown that the rainfall occurred on March 17, 18 and 19, especially on March 18, in terms of the intensity of rainfall were very intense.Investigation of the state of the middle troposphere showed that the formation of the Rossby wave and the meridional expansion of one of its troughs, along with the creation of a positive vorticity that dominated the studied area on the seventeenth of March, are the main factors in the creation of a baroclinic atmosphere and the dynamic ascent of air.Investigation of the synoptic-dynamic conditions of the lower levels of the troposphere showed that in the lower levels of the low-altitude synoptic system with closed meters, at the same time as the deep trough reigns over the region, it has been formed and strengthened during peak rainfall times and has led to a strong rise of air.Investigating the state of atmospheric humidity in the study area and identifying sources of moisture supply using special humidity maps, moisture flux convergence function and atmospheric flow paths were carried out.Investigating the omega variable in the vertical profile of the atmosphere using the Hoff-Mueller diagram showed that during the times of precipitation events, upward movements prevailed in all levels of the troposphere, especially during the peak of precipitation, the upward movements became more intense in the lower levels.Identifying the type of clouds using MODIS products showed that during heavy rains, especially on March 18, deep convective clouds with a high density of water were formed in the region, which extended up to a height of 300 hectopascals and were very thick.

Evaluation of damages caused by flash floods in urban areas is one of the most important concerns after their occurrence. Like other urban elements, vehicles suffer damage that should be considered for managing urban floods. The damage values ​​in most car damage models in flood are presented with the assumption of their stability and purely in terms of depth, while the flood velocity as flood important variable is also effective on the severity of damages. In the present research, while evaluating the eight theories presented in the Australian Rainfall and Runoff Guideline (AR&R), an attempt is made to provide the best theory to provide a simple and accurate algorithm for determining the damage to sedan vehicles in flood as a function of depth and velocity.

To achieve the goal of this research, the formulas and diagrams of the stability limit of eight theories presented in AR&R were evaluated and after deleting one of the theories and modifying the Melbourne Water presented in 1996 and DPW presented in 1986 formulas, the stability map of the sedan cars in Darwaze Quran flood on March 25, 2019 in Shiraz was produced for seven theories proposed in this guideline. Subsequently, by combining the stability limits of each theory with the HAZUS-MH depth-damage diagram and zoning below the stability limit diagram as stable areas, the risk map algorithm for stable sedan vehicles was provided separately for each theory. In the following, the risk map in the adjacent parking area of ​​the water pool upstream of the Quran Gate of Shiraz for each theory and, with their help, the total damage for the Pride_131 was calculated as a common vehicle in Iran. Finally, the measures of maximum damage, as well as the total damages were obtained from each one.

One of the main and most important results of this research is providing an algorithm for determining the damage of the sedan vehicles in a certain range of depth and speed for each of the theories proposed in AR&R, which were used for producing the risk map. In addition, the total damages for the Pride-131 as an index car were calculated by the proposed algorithms. The minimum damage was obtained by using AR&R (1987) theory algorithm equal to 10 billion and 265 million toman and the maximum amount of damage was obtained by using achieved by using AR&R (2011) theory algorithm equal to 14 billion and 32 million toman.

It was found that the use of the depth of flood as a hydrostatic index, which is now the criterion for calculating car damage, is not accurate enough and it is better to use velocity and depth composition as a hydrodynamic index for this purpose. At the same time, it was proved that among the other theories, the relation and the final limit of stability presented in the AR&R (2011) theory to provide a model for determining the damage to small and light vehicles in the flood as a function of depth and velocity has better and more reliable results. It is worth noting in order to achieve the more accurate damage amounts of vehicles in flood, more theoretical and experimental studies considering different types of vehicles are essential.

Variation of rainfall origin in different parts of Iran along with latitude, distance and proximity to moisture sources, etc. causes rainfall behaviors such as intensity and continuity to have temporal and spatial changes .Using statistical methods and discharge data of Dez river, severe floods of this river were extracted to identify the effective factors in their creation.In this research, the information of all effective atmospheric levels has been analyzed using a combination of illustrative and appropriate maps.And Synoptic and thermodynamic properties of heavy cloud precipitation in Dez catchment At the same time, the synoptic patterns lead to the prolongation of the systems on the catchment And its effect on flow rate was identified and presented.The purpose of this study was to identify the pattern or patterns of durable rainfall systems in the Dez River Basin. There is a deep landing in the eastern Mediterranean that has caused the cold European air to fall in the west of the ship, as well as the transfer of moisture from the Arabian Sea and the Persian Gulf in the east. And the conditions for creating an ascent in the Dez River Basin are the most important elements of the pattern of heavy and durable rainfall in this basin. Water resources planning, prevention and timely information, and flood control and control are among the results that can be expected from this research.

In recent years, due to drought in the country, the issue of management of available water resources is extremely important, and this attention is increasingly to the management of reservoirs and forecasting the volume of water in order to provide appropriate exploitation policies. On the other hand, seasonal and excessive rainfall caused dramatic changes in the bedding of rivers and catchments, which examines the forecasting models in the event of heavy rains, which in addition to preventing damage in addition to preventing damage. Due to the occurrence of floods, surplus water can also be used in the desired direction. Therefore, not developing a proper operation policy, especially in drought conditions, can cause a lot of damage to water-consuming sectors. Proper forecasting of water flows and reservoir inventories leads to the use of control curves for the optimal use of dams and reservoir systems. In this paper, due to the importance of the subject, a model based on deep learning and Mann-Kendall experimental test was used to estimate the flood rate in the Kan-Sulqan area. The results showed that the monthly difference in flood forecast for the convolution neural network is 0.00654 and for the Men-Kendall method is 0.19532. Also, the error rates of MSE, RMSE, MAE and MPE for the neural network were equal to 0.0019, 0.0439, 0.0239, and 0.0159, respectively, which shows the high accuracy of this method in estimating the flood rate in the region.

Iran is one of the countries exposed to many natural disasters, of which the flood phenomenon is one of the most serious. According to official statistics, more than 50% of casualties due to natural disasters in Iran are due to floods. Accurate and timely information is needed to plan any flood management measures. Currently, many researchers have examined the methods of obtaining water area information. Among these methods, land use classification and change analysis are the most important applications of remote sensing techniques. One of the most widely practical classifications is the separation of water class from other classes.Also, Landsat images are one of the most common data sources in this field. In this regard researches have been done in the field of identifying water areas, due to its high importance in flood crisis management with the advancement of satellite technology in remote sensing. In this study, an automated method has been developed to prepare flood change maps and use different capabilities of different water and humidity indicators to provide automated training data. This can reduce the challenge of operator interferences. Besides, the efficiency of the high level of automation in the extraction of flooded areas in accelerating and facilitating the management of this crisis is indisputable. Therefore, when manual training data is not available, applying a fully automated approach will be helpful. The integration of indicators can also be effective in improving the accuracy of existing water indicators. Floods are one of the most important natural disasters in the world that occurred due to different factors such as human encroachment on rivers and vegetation damages. The purpose of this study is to develop an automated method for preparing training data for supervised classification of images and using the capabilities of integrated indicators to identify flooded areas. Therefore, several approaches for flood in Golestan province, 2019, were implemented by Landsat-8 data and the results of each were examined. The Kmeans clustering algorithm, Otsu, Multi and Adaptive thresholds were used to generate automated training data based on different water indices (NDWI, AWEI and MNDWI; Then the ANN, SVM, ML, MD and BE classifications) was implemented to monitor flood changes. The results compared with the manual classification method indicated that the proposed approach, in addition to providing a high-level of automation in monitoring changes, also had high efficiency and accuracy. In another development approach, different water and moisture indices were combined with the aim of improving the production process of automated training data. Among the 85 tests performed, the combined approaches had the highest accuracy. Due to the nature of different water indicators, their selection and appropriate combination, in addition to reducing the noise in the water indicator image was also effective in increasing the ability to identify impure water areas. Finally, a comprehensive deductive analysis of automated methods and hybrid indicators was carried out to help to manage flood crisis, facilitate and accelerate the process. It can be said that with the advancement of technology and the emergence of various aerial / satellite images, many opportunities and possibilities have been created for effective and sustainable management. One of the best ways to extract information from digital images is to classify the image. One of the challenges in image classification is to reduce the interference of users using algorithms that have high level of automation. This issue has high importance due to the sensitivity and sudden occurrence of the flood crisis. Therefore, to promote this challenge, in this research, an automated and efficient approach for supervised classifications with emphasis on automatic production of training data to prepare a map of flood changes is presented. In total, 85 different studies were performed to obtain flood change maps, using hybrid and non-hybrid automated methods to prepare automated training data for supervised classifications. It is considered that the obtained results had acceptable accuracy. For example, in all approaches studied, ANN classification offered better accuracy, which could be seen from the diagrams provided. Also, due to the lack of significant difference in the accuracy of the maps, obtained from this classification with different indicators and thresholds, it can be said that this classification is also highly robust. The results indicate that ANN classification in combined approaches with Otsu and Adaptive thresholds has the highest accuracy and maximum robustness. In comparison with methods related to execution speed, ML and MD classifications also provided good performance. However, the difference in the accuracy of these classifications compared to the ANN classification is not significant in some cases and will be possible to replace due to higher execution speed. Finally, by analysis of the results, it can be stated that the automated approaches proposed in this research can be replaced by traditional classification methods.

The current study is aimed to zoning flood probability map in the Saliantapeh catchment is located in the Golestan Province. To this aim, two well-known data mining models namely Random Forest (RF) and Support Vector Machine (SVM) were applied due to their robust computational algorithm. Flood inventories were gathered through several field surveys using, local information and available organizational resources and corresponding map was created in the geographic information system. Reviewing several worldwide studies, 13 predisposing variables including proximity to stream, soil texture, lithological units, land use/cover, slope percent, elevation/DEM, slope aspect, plan curvature, profile curvature, stream power index and topographic wetness index were chosen and the corresponding maps were generated in the geographic information system. In this study, after preparing the predictor maps, SPSS software was used to analyze this data and testing Multi-collinearity. In order to evaluate models’ results the area under the receiver operating were used. Three different sample data sets (s1, s2, s3) including 70% for training and 30% for validation were randomly gathered to evaluate the robustness of the applied models. Results showed that the RF model with the area under curve value of 0.96 and robustness of 0,001 in validation step had better performance on flood probability zonation over the study area.

During periods of climate change, catastrophic floods have occurred, mainly due to extreme rainfalls, leading to widespread damages and heavy economic losses, the spread of epidemics, and the mortality of many people. Psychological research related to current global warming also indicates the appearance or exacerbation of mental disorders after the occurrence of this natural event. In this study, the socio-economic and health consequences of floods have been studied, and also, using paleoclimate, archeological, and historical researches, some severe and extensive flood events from prehistory to the present have been presented. Finding reports of flood events from historical documents and discovering evidence of floods among the cultural layers of ancient sites, along with paleoclimate and paleo-flood studies, can yield more accurate results from past climatic and environmental conditions. In the studies of environmental sedimentology of some ancient sites of Iran, evidence of catastrophic floods belonging to the mid-fourth millennium B.C. has been found and some have been reported in the historical books of the Islamic period. These events coincide with periods of climate change called medieval warming and the Little Ice Age and occurred mostly in Iran due to extreme rainfalls and flooding of rivers and seasonal streams.

In this study, first, the devastating social, economic, and health consequences of floods are explained. Then, archaeological evidence is examined, some of which are the result of field research. Finally, historical documents and reports that mention the occurrence of great and influential floods from the early Islamic period to the present are presented.

Floods kill more than 2,000 people each year and affect 75,000,000 of the world's population. The reason is the geographical distribution of alluvial fans and shorelines that have long been attractive for human habitation. The occurrence of floods, due to the extreme rainfalls related to climate change, mainly overlapped with drought periods. One of the most important archeological evidence of floods dates back to the fourth millennium B.C. According to the high-resolution paleoclimate research of Lake Neor in Ardabil, from about 4200 to 3000 B.C., there was a very dry period with increasing dust. During this period, at least two periods of severe drought occurred, 3600-3700 B.C. and 3150-3250 B.C., which are shown by the paleoclimate research of Soreq Cave in the west of Jerusalem. Archaeological evidence of floods in the middle and late fourth millennium B.C. as a result of environmental sedimentology and archaeological excavations in the sites of Mafin Abad Islamshahr, Meymanat Abad Robat Karim and Qara Tepe of Qomroud in North Central Iran, as well as in the sites of Shuruppak, Kish and Ur in Iraq have been identified. The flood of 628 A.D., which occurred due to the flooding of the Tigris and Euphrates rivers in Mesopotamia, was probably one of the main reasons for the fall of the Sassanid dynasty. Blazeri, the historian of the Islamic period, attributes the occurrence of this great flood to the end of the reign of Khosrow Parviz. This event has led to the death of many people, the destruction of crops, famine, displacement, and the spread of plague.

Therefore, it can be said that if flood prevention and control in Iran are not managed efficiently and effectively, extreme rainfalls related to current climate change (global warming) can cause serious damages and irreparable losses.

Runoff estimation is one of the main concerns of hydrologists and plays a key role in various engineering calculations and designs. Many factors such as climate, topography, soil properties, land cover, etc, are involved in producing surface runoff. Land use and land cover changes have a direct impact on the hydrological cycle in the ecosystem. The most common model of surface runoff estimation is the curve number model developed by the US Soil Conservation Service (SCS-CN). Accurate estimation of its important parameters increases its precision and performance. Land use is one of the most important parameters of this model.Remote sensing (RS) and geographic information system (GIS) technologies are used in order to increase its speed and accuracy of estimation. One of the problems that have occurred in the Kashaf-Rood Basin is the extensive land use changes that may cause changes in peak discharge and surface runoff volume. In this study, due to the great importance and impact of land cover change on increasing flood risk, the effects of land use change over 28 years (from 1987 to 2015) on flood hydrograph characteristics were investigated.

The Kashaf-Rood basin is a part of the Ghara-Ghum basin. The total area of the basin is 16779 square kilometers with the highest and lowest elevation of 3235 and 378 meters above sea level, respectively . The length of the Kashaf-Rood River from the highest point to the outlet of the basin is about 374 km and its average and gross river slope are 0.0028 and 0.0043 m/m, respectively. The digital elevation model was used to calculate the topographical properties, hydrological properties and geometrical corrections required on satellite images. In this research, the data of the Global Digital Elevation Model (ASTER) with a spatial accuracy of 30 m was used. Also, the soil hydrologic group map prepared in Ghara-Ghum water resources balance studies was used. Since no land use change occurs in the short term and can be detected at long intervals, a 28-year interval was chosen for satellite imagery. In general, five images of Landsat satellite are needed for full coverage of the Kashaf-Rood Basin. For the oldest data, Landsat 5 images and for the latest data, Landsat 8 images were used. ERDAS IMAGINE 2014 software was used to digitally process satellite images. The images were classified in three

The Minimum distance, Mahalanobis distance and the Maximum Likelihood. In order to select the appropriate method, after applying different classification algorithms for the image of 2015, the accuracy of their classification was evaluated and, the image of 1987 was also classified based on the selected method. By combining soil hydrological group and land use map derived from Landsat satellite imagery using ArcGIS 10.3 software, the curve number maps for 1987 and 2015 were prepared. In the present study, the US soil conservation service standard curve number method (SCS-CN) was used to calculate the amount of rainfall and losses in the HEC-HMS model. For the calibration of the HEC-HMS model, four flood events at the bridge of Khatun Kashaf-Rood hydrometric station with relatively concomitant precipitation were selected. Three flood events were used for calibration and one flood event for validation.

The images were classified into three methods: The Minimum distance, Mahalanobis distance, and the Maximum Likelihood. Comparing the results of these three methods showed that their overall accuracy in evaluating and identifying land use was 78.5, 83.7 and 87.3, respectively. Thus, the maximum likelihood algorithm was used to classify the images and the image of the year 1987 was classified with this method. Ten land use classes were identified in the study area. The results showed that during the 28 years of study, the area of rocky lands and rangelands did not change. The highest percentage of change was due to water zones, poor rangelands and residential lands, which increased by 189, 143 and 50 percent, respectively. The highest amount of increase in the area occurred in the poor rangelands, which 1514 km2, and the highest decrease occurring in moderate rangelands which is 1278 km2. By combining soil hydrological group maps and land use maps in ArcGIS software and using standard tables, the curve number maps for 1987 and 2015 were prepared. The weighted average of the curve number in the mean moisture conditions for 1987 and 2015 was 77.5 and 78.4 units, respectively. After performing the calibration and validation steps, the HEC-HMS hydrological model was used to investigate the impact of land-use change on the flood hydrograph of the Kashaf-Rood River between 1987 and 2015. According to the results, in all four events which were studied, land-use changes have increased the peak of discharge and the flood volume over the 28 years of study. On average, the peak flood discharge in 2015 was 15.2% higher than the peak flood discharge in 1987, and similarly, the flood volume increased by 13.7% during the study period.

In conclusion, it can be derived that in recent decades, land-use changes which were caused by human interference, affected the flood characteristics and increased the risk of flooding in the Kashaf-Rood river. Therefore, land use must be managed and prevented further destruction of natural resources to prevent flooding in the area.

River flow prediction is one of the most important key issues in the management and planning of water resources, in particular the adoption of proper decisions in the event of floods and the occurrence of droughts. In order to predict the flow rate of rivers, various approaches have been introduced in hydrology, in which intelligent models are the most important ones. The application of artificial neural networks (ANNs) to various aspects of hydrological modeling has undergone much investigation in recent years. This interest has been motivated by the complex nature of hydrological systems and the ability of ANNs to model non-linear relationships. ANNs are essentially semi-parametric regression estimators and well suited for hydrological modeling, as they can approximate virtually any (measurable) function up to an arbitrary degree of accuracy (Hornik et al., 1989). A significant advantage of the ANN approach in system modeling is that one need not have a well-defined process for algorithmically converting an input to an output.

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.

Estimation of flood discharge and return period of extreme events is one of the most important factors in design of hydraulic structures. Therefore, conducting researches for improving the accuracy of estimation methods is very important. Nowadays, the fractal behavior of all earth science phenomena, including precipitation and runoff has been accepted. This method is used for modeling of natural phenomena because of its strong physical background. As power law is derived from fractal theory, it can be a suitable method for estimation of design flood. In the annual maxima method, a maximum value is extracted among all the recorded discharge occurred during a year. Therefore, in this method, out of a number of extreme events happened at the end of the year, only one value is chosen and other values are eliminated. The positive point of this method is that the extracted data are independent of each other. However, this method has its own limitations. For example, in years with high flood occurrences, the values that stand in the second and third places in terms of magnitude lose the chance to participate in the annual maxima series. On the other hand, in dry years, the occurred maximum values are far lower than the second or third ranked discharge of wet years. Thus, correspondence of the annual maximum time series with reality is not suitable, and such a selection may result in neglecting the real pattern in the recorded values. In another method, partial time series (the time series greater than a given threshold) is employed. In this sampling method, a certain threshold of flow rate is considered and all flow rates that are above the threshold, are taken into consideration. In this study, power law has been employed to select the flood partial series. Therefore, the main objective of this study is to use power law in a) selection of partial series of flood in Dez Basin, and b) flood frequency analysis. In this study, flood frequency analysis of Dez watershed during the period of 1956- 2012 was performed using two approaches of power law (PL) and ordinary statistical distributions. At first, the partial-duration flood series were extracted using power law with the intervals of 7, 14, 30, and 60 days, and then compared with the annual maxima series. The results indicated that the annual maxima series were not suitable for frequency analysis of the flood in Dez Basin, and the 30-day partial duration series obtained from the power law has a better correspondence with the flow and properties of the Dez Basin. Examining of the independence and stationarity of the 30-d partial duration series by Wald-Wolfowitz (W-W) test, confirmed the independence of the considered series. In the next step, the power distribution and ordinary statistical distributions were fitted onto the flood data of the Dez Basin, and performance of each distribution was investigated using Normalized Root Mean Square Error (NRMSE) and Nash-Sutcliffe (NS) criteria. The results showed that in the Tange Panj Bakhtiari station (TPB), power distribution had a better performance than other considered distributions, and in the Tange Panj Sezar (TPS) and Tale Zang (TZ) stations the power distribution stood in the second rank in terms of the best distribution with a slight difference. The results indicated that the annual maxima within shorter return periods estimate lower values for the flood of the studied stations. However, the partial series extracted using power law solved the problem in the annual maxima, and are in full correspondence with the physics of Dez Basin. Following selection of suitable statistical distributions, flood quintiles were calculated for return periods of 2, 5, 10, 20, 50, and 100 years in each station. Based on the calculated quintiles, it was observed that TPB station is more vulnerable to the flood occurrence, where the amount of flood increases in it with the prolongation of return period. In other words, in basins culminating in TPB station, development of warning systems and implementation of flood control programs are more needed. Since the performance of power distribution in estimating the flood in Dez Basin has been very satisfactory and calculation of its parameters and its application is easier than ordinary probability distributions, it can be suggested as the superior distribution for flood frequency analysis in Dez Basin.

The purpose of this study is to evaluate the land use changes on flood hydrograph at Abolabbas basin during the period of 1990-2009. In this paper, the techniques of Remote sensing, Geographic Information System (GIS) and HEC-HMS hydrologic model was used to achieve the purpose intended. At the first step, Landsat satellite images used for the preparation the land use maps of 1990 and 2009, Then images were classified using artificial network algorithm with fine accuracy in eight class of land use. Then these land use maps with the hydrologic soil map were integrated using GIS tools to produce CN maps of 1990 and 2009. The HEC-HMS rainfall-runoff model using of the SCS-CN method for estimating runoff in Abolabbas basin was calibrated and validated for 4 storms occurred at study area. Results of the simulation showed that changes in land use that occurred in the direction the reduction in the level of forest and increased level of rangelands and residential areas has caused peak flow and flood volume during the study period increased 5.5 and 5.1 percent, respectively. On the other hand the results showed, this land use change not affect on the time of peak in flood hydrograph.

Check dams are considered as main measures for flood and sediment control in watersheds، and their uses have been rapidly increased from 1990 onward in Iran. This research is done in Darjazin watershed in north of semnan city. The check dams have been constructed from 15 years ago in two sub basins of the watershed for flood control in Mahdishar. More than 650 check dams were evaluated for effects on flood. The collected data in the field was fed to ArcGIS software. The effects of these structures on flood reduction were evaluated by HEC-GeoHMS extension and HEC-HMS model. Because of homogeneity of watershed management projects in the basin due to building more check-dams in different watercourses، any flood discharge is related to check dams. Evaluating the effects of check dams on flood by t-test showed significant differences between flood discharge before and after construction of check dams at 5 percent level. So، check dams have been able to reduce flood discharge by 16. 7 percent on average.

Knowledge of variation of suspended sediment due to sudden and gradual fluctuations of river discharge is one of the important subjects in water studies specially watershed management. For this purpose sediment hysteretic loops based on hysterics theory have been utilized for last decade. Due to lack of such studies in Iran, this study attempts to investigate variations of suspended sediment hysteretic loops of 13 runoff events along 4 years for the first time. Therefore, after simulation of instantaneous suspended sediment concentration using Artificial Neural Networks and regression methods, hysteretic loops of suspended sediment plotted based on the best method from which 6 cases of single valued, 4 cases of eight-shaped, 2 cases of clockwise and one case of counter-clockwise were observed. The results showed that the linear hysteretic loops occurred in spring were because of low intensity precipitations and the 8-shaped loops mostly occurred in dry season with low river discharge and intense precipitations.

Flood is one of the catastrophic events that has attracted the hydrologists’ attention. In this research one of the important flood indices, i.e. maximum-daily mean-discharge, was determined for several western Iran watersheds, namely, in the catchments of Gamasiab, Qarasou, Saimare, Kashkan, Sezar and Abshineh. Daily data were prepared from stream-gauging stations and a 30-year concurrent period was selected. Flood frequency analysis was performed using HYFA and TR computer programs and optimum distributions were chosen by goodness of fit tests. Extreme flow values having different return periods of 2, 5, 10, 25, 50, 100, 500 and 1000 years were calculated. Modeling was done with regional analysis using multiple regression technique between maximum-daily mean-discharge and physiographic characteristics of the basins. The most important parameter for the selection of the model was the adjusted coefficient of determination while significant level, standard error and observed discharger vs. computed discharge plot acted as controlling parameters. Finally, different models with different parameters were selected from power, exponential, linear and logarithmic forms. The results showed the power model to be the best among the four types. The main channel length, drainage density and time of concentration were the most effective parameters on flow. After analyzing the errors, it appeared that increasing the return period would cause an increase in the model error. At 1000-year return period, the error reached 32.2%.