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.

Surface runoff drainage is one of the management problems in Rasht City. Also, one of the urban flood factors is drainage channel obstruction through water level rising. The purpose of presented study is to investigate the probability of drainage channels obstruction and site selecting new outlets along Goharrood and Siahrood rivers. So, the rivers bed of Goharrood and Siahrood and their bank terrains were simulated by using HEC-GeoRAs extension and digital map (scale: 1000). Pick discharges with different return periods were estimated by using stochastic analysis. Then, river hydraulic behavior was simulated by using HEC-RAS model. Finally, unsuitable and suitable sites for runoff drainage were identified.

Studying the role of drops as one of the important hydraulic structures for flood control is essential. In the present study, the effects of drops on inundation depth and extension have been investigated in a reach of the Kan River with 7 Km in length. The hydraulic characteristics of flow were computed for floods with 5 to 700 year return periods using the HECRAS computer model for both upstream and downstream sections of the structures. The results of this study verified a different role of drops as well as an incremental and reductive impact on inundation depth and extension in upstream and downstream, respectively.

Understanding the spatial variation of the flood source area within watersheds as they affect of the flood characteristics at the outlet is an important issue in flood control studies. Determining the flood severity index in a watershed requires study of hydrogeomorphic properties, recorded rainfall-runoff events and use of mathematical models in the context of the methodology to delineate various watersheds areas with respect to the flood downstream. In this paper, Roodzard watershed was selected as the case study since it has suitable rainfall-runoff record. The watershed consists of five tributary subwatershed and three intermediate subwatersheds. ModClark distributed hydrologic model was calibrated in subwatersheds with hydrometric stations. Using HECRAS routing model the whole of the Roodzard watershed model was calibrated at Mashin Hydrometric Station at the outlet. Following the “Unit Flood Response” approach, 2*2 km2 grid squares within the watershed were removed one by one in the simulation process and their effect on the flood peak at the outlet was determined. Such effect was quantified by a flood index and used for preparing the map of “flood severity”. Furthermore, the profile of flood index along the main stream was plotted in grid-scale as well as for each sub-basin.

Introduction Urban floods have been exacerbated by climate change and urbanization, as well as restrictions on the drainage of urban infrastructure, and over the past decade have had many negative effects on cities around the world [4]. As a result, the demand for more resilience has not been successful in many cases [2]. Accordingly, the resilience of key urban buildings is one of the necessities of urban resilience [3]. In this regard, research on urban resilience in events such as floods was reviewed, some of the most important of which are mentioned below. In 2019, Wang and colleagues evaluated the resilience of the urban basin to floods, and the CADDIES model was used to simulate floods. Based on the results, vulnerable basins were identified and strategies were developed to increase the city's resilience to floods [4]. In 2019, Barajas et al. worked on an article on the resilience of urban buildings in the face of flood risk in the Mexican metropolitan area, and addressed the resilience of buildings in Mexico City during the floods of recent decades. Findings show that building resilience is a complex and sequential process that of course depends on social, economic and institutional conditions [1]. Research Methods In this research, in order to achieve the model of resilience of important buildings against floods, data analysis is performed in several stages, which include the following: 1- Identification of significant assets 2- Modeling river flow using HecRAS software 3- Adaptation of assets and modeling results from rivers in different return periods 4- Counting assets affected by floods 5- Modeling of building resilience components using structural equation modeling of LISREL software 6-Counting and ranking the components extracted from the model using AHP-TOPSIS combined method 7- Ranking of key buildings affected by floods using AHP-TOPSIS combined method Discussion and conclusion The asset layers of the city of Hamedan and the rivers of the city have been adapted in the GIS context and five buildings of the University of Technology, the Faculty of Art and Architecture, Payam-e Noor University, the Blood Transfusion Building and the Amiran Hotel have been identified as vulnerable centers of Hamedan. Conclusion Components (adaptability-flexibility, connection of failure-safe feedback, dependence on environmental ecosystems, diversity, learning-memory-prediction, performance, response speed, fragmentation redundancy, resourcefulness, and robustness) are effective variables on flood resilience of buildings. In testing the hypothesis using the structural equation model, the software output indicates the suitability of the fitted structural model to test the research hypotheses. Weighting indicators Resilience components Sub-components of resilience Weight Compatibility - Flexibility Change while maintaining or improving performance 0.049 Evolution 0.045 Adopt alternative strategies quickly 0.05 Timely response to changing circumstances 0.027 Open design and flexible structures 0.049 Connection - Feedback - Safety - Failure Shock absorption 0.007 Absorb the cumulative effects of challenges with a slow start 0.012 Avoid catastrophic failure if you exceed the threshold 0.007 Gradual failure instead of sudden 0.013 Failure without cascading effects (demino effect) 0.024 Parallel analysis of technology system - human 0.005 Identify locking effects and possible discrepancies with reduction 0.014 Identify synergies with other city policies, value added estimation 0.015 Dependence on local ecosystems Flood control 0.012 Bioclimatic design and management 0.006   Resilience components Sub-components of resilience Weight Variety Spatial diversity - key assets and tasks that are physically distributed and not all of them are affected by a specific event at any time 0.0146 Functional Diversity - Multiple methods of dealing with a particular need 0.021 Balance variation with potential cascading effects 0.013 Learning-Memory - Prediction Learn from past experiences and failures 0.003 Use information and experience to create fresh compatibility 0.003 Avoid repeating past mistakes 0.005 Collect, store, and share experiences 0.009 Construction based on long-term value and city history 0.007 Integrate resilience into long-term development scenarios 0.02 Function Performance capacity 0.056 System quality in a suitable and efficient way 0.013 Self-sufficiency - reducing external dependence 0.019 It performs better than other buildings 0.039 Response speed In taking casualties, including mortality and disease 0.007 Reorganize 0.015 Maintain performance and re-establish it 0.032 Restore structure 0.017 Establish public order 0.013 Prevent disruption in the future 0.005 Redundancy - fragmentation Systems replacement or systems agents 0.054 Buffer from external shocks or changes in demand 0.013 Replacing components with modular parts 0.026 Balance redundancy with potential cascading effects 0.077 plan Identify and predict problems 0.013 Prioritize 0.011 Mobilize resources of visualization, planning, collaboration and action 0.014 re-evaluation 0.006 Integrate resilience into work and management processes 0.052 Getting cooperation from citizens 0.03 Strength Surface resistance to stress 0.003 No degradation and loss of performance 0.015 Capacities that ensure adequate margins 0.006

This study aims to determine the design strategy of a nuclear power plant near the river by assessing flood risk as a design precondition and the Darkhovin Nuclear Site near the Karoun River in Khuzestan Province was considered as a case study. In this study, by sampling the probabilistic space fitted to the flow rate and by filtering and removing flood flows that does not overflow from the river to the flood plain, the two-dimensional HEC-RAS hydraulic model was used to determine the depth and flow velocity within the power plant site. Frequency analysis of flood depth simulated by the model for different discharges showed that the frequency distribution of flow depth and the generating flood are different from each other. The safe design of a power plant site requires consideration of the many uncertainties that make it difficult to use conventional methods. In this research, for the first time, the Rosenbluet technique was used to evaluate the uncertainty and finally to determine the maximum possible water level for locating the reactor core. The results show that to create the maximum probable depth with a return period of 100 years, there should be a flood with a return period of 10,000 years in Karoun downstream of Ahvaz. The method presented in this research can be the basis of a standard for the safe design of nuclear power plants in the vicinity of rivers considering flood hazards.

1. Introduction Flood zoning determines flood advance, elevation, and characteristics in different return periods. Flood zoning tends to subdivide all areas around the river and flood plains into different hazardous areas in order to control land use and land development. These areas are used to determine land use, identify the area in flood insurance, and establish mandatory restrictions in hazardous areas. Rasht, with an annual precipitation of nearly 1, 400 mm, is one of the rainiest areas in Iran. In high intensity and in short time, these precipitations cause river flood in ranges of the river and flooded areas of the city. Flooded streets, houses and urban roads, slowing down of vehicle movements, destruction of urban facilities and many other problems are all results of these precipitations. Using mathematical models, digital elevation models (DEMs), GIS software and HECGeo-RAS extension, flooding zone map is discussed in a part of the main range of the Goharrood River at the beginning of the entrance to its outlet out of Rasht. 2. Materials and Methods The studied area was a range of the Goharrood River, one of the main branches of the Pirbazar River in Rasht, north of Iran. According to objectives of the study and investigation of the flood zone at the Goharrood riverside, the data included hydrometric statistics of the Lakan station during the statistical period of 1989-2013 (25 years) and river hydrometric characteristics using a 1: 2000 map and field surveys and layers were required to plot a flood zoning map. Using mapped data of the area, including river planning maps at a scale of 1: 2000, bed conditions, such as main flow line of the river, sides and cross sections, etc., geometric data required for simulation determined by TIN map was obtained; then, data were inserted into the HEC-RAS model. Subsequently, flow data and boundary conditions were included in the system and hydraulic calculations were carried out. The results were presented in the form of input formats into the GIS environment and the necessary processing was done using HEC-GeoRas extension. Finally, maps of water depth, water velocity, shear stress and flow power along the river range were plotted. These maps were presented in both GIS and Google Earth for better clarity. These maps precisely presented flood zone with different return periods. Moreover, the ranges of the river in which flood spread and caused watering were identified with the depth of watering and areas with low, medium and high risk of flood. 3. Results and Discussion To determine flood status of Rasht and Pirbazar Basin, a precipitation map of Rasht was plotted in GIS environment. The results show that the average annual precipitation is 1350 mm in Rasht which can cause flooding and problems such as flooding of streets due to precipitation regime and severe rainfall. Using statistics of the Lakan hydrometric station, maximum instantaneous discharges were determined by Smada software; after analyzing the distribution of Log Pearson Type III, the best statistical distribution was determined to estimate maximum instantaneous discharges in different return periods. Using geographic information system (HEC-GeoRAS), the river planning maps were plotted for physical model of the bed and riversides. Based on mathematical simulations performed by HEC-RAS software and results of the obtained data, the model was developed to determine hydraulic conditions. For this purpose, sections of the river were first identified in the urban environment by considering longitudinal and transverse profiles of the river as well as field observations. Next, the sections were modified according to the shape of adjacent sections and engineering judgment. Finally, the model was run for this condition and the results and output of hydraulic parameters of the model were obtained for discharge with a return period of 50 years. The files generated in the GIS environment using the HECGeoRAS extension included flood maps with a 50-year return period of discharge, indicating a river flood in a 500-meter range in the northern part of the city called Siah Estalakh. This hazardous area is located where the river passes the Shohadaye Gomnam Blvd. Extreme changes in the river's width in this area indicate a flood in this part of the river range. 4. Conclusion Plotting of flood zoning maps for identifying hazardous areas is one of the first tasks of responsible organizations to deal with floods. Data analysis and results of the model show that the only part of the 15 km range of the Goharrood River which is at risk of flooding with a 50-year return period of discharge is a 500 m range of the river after the Shohadaye Gomnam Blvd northward in Siah Estalakh. Considering land uses of the area, flood-exposed regions were identified as low-risk, moderate-risk and high-risk regions. Thus, any structural attempt on this river should be prioritized in this area.

The study of resistance to flow in open channels as well as rivers has a long history, and applying those studies on a river or channel with specific properties in order to manage the river is a necessity. An optimum and proper foresight of resistance to flow has a direct impact on the estimation of river flow rate, which is one of the most important factors in a decision of hydro-project developments. This research work tries to identify items which are influencing on the flow resistance by handling properties of the river in specific intervals. In this research, the best Manning coefficient of “ Shahar-Chay” river by means of hydraulic characteristics and aggregation of river bed has been estimated. The Manning roughness coefficient estimated by several methods and using pictures and properties of a river reach with a specific Manning’ s coefficient, by comparing to the river conditions with relative pictures and estimating approximate range of the Manning roughness coefficient (by considering similarities in between river reach) has been verified. By preparing 3D drawings from region using AutoCad, ArcGIS and creating lateral sections using Hec-GeoRas, geometric specifications of “ Shahar-Chay” river used as input in order to complete hydraulic simulation of HEC-RAS model. By running the software for different values of Manning’ s coefficient and hydraulic specifications output from modeling compared to the hydraulic specifications collected from the river reach area. It is found that the most proper value for the Manning coefficient at “ Keshtiban” reach is about 0. 032. The results of the present research work reveal that the methods, namely “ Chow” , “ Cowan” and “ comparison of pictures” are more accurate for estimating the Manning roughness coefficient in rivers, because of introducing several factors taking into account.

Introduction: Flood is a natural disaster that threatens the lives of millions of people yearly. Obtaining the flood zone and consequently obtaining the flood zone maps with a specific return period for a reach is one of the important issues. Therefore, estimation of flood maps is required for accurate river engineering studies, flood control projects and planning to reduce the economic and social flood damages. In most parts of the world, hydraulic models are the best ones for flood inundation mapping. However, due to the lack of data and numerical problems, it is not possible to use them for hydraulic simulations. Different of these models, topographically based models such as HAND due to their simple structure and minimum data requirements are the best choice for data sparse regions. Hence, evaluating the performance of the HAND model, which relies solely on topographic features, is one of the objectives of the present project. Methodology: In this study, the flood maps are determined using the HAND model with a calibration based on satellite observations. Seimareh river is selected as a case study to challenge the performance of the model in relation to the observational data and HEC-RAS hydraulic model. In addition, the efficiency of HAND model in low and high flows compared with a 1D and 2D hydraulic model to evaluate the performance of the model in different flow conditions. Results and Discussion: The most important results can be summarized as follows: • The results obtained from the HAND model indicate that this model has the high potential for flood inundation mapping in Seimareh river. The similarity percentages of the estimated and observed flood extents are higher than 92%. Also, the average relative error (ARE) between HAND model and observed flood extents through the study reach is 8. 5%. • The results in Seimareh river show that changing the discharge value does not change the performance of HAND model and the model has a good capability compared to HEC-RAS model. Based on the findings, the similarity percentage of estimated (based on HEC-RAS) and observed flood extents is limited to 83%. The ARE value of HECRAS model in simulation of flood extents is 13%. • Despite the excellent performance of the HAND model in estimating the flood maps, in some parts of the study reach there are differences between the HAND model, satellite images, and the hydraulic model. The main reason of this discrepancy can be related to the extraction and using only one rating-curve for Seimareh river. Therefore, dividing the river into different segments, which contain similar roughness coefficient and river geometry, can significantly increase the performance of the model. • Hydraulic model compared to HAND has more error in estimating flood extents. The main reason for this can be related to important factors such as distance between cross sections, computational dimensions, numerical parameters used in the 1D and 2D hydraulic models (such as θ parameter and currant number), boundary conditions, computational time step and Manning roughness coefficient for each cross section. In general, more factors affect the performance of the hydraulic models and affect theirs outputs, while the HAND model experiences relatively better conditions in this regard. Conclusion: Flood inundation mapping (FIM) is one the key parts of river engineering and flood control studies. Therefore, using a reliable and robust method for calculation of FIM is paramount of importance. In this research, the applicability of a topographic-based method (HAND model) is investigated in Seimareh River. In addition, the performance of is compared with HEC-RAS model and observed flood extents. Findings clearly showed that the HAND model, in spite of having simple structure, performed better than HEC-RAS and estimated the FIM as well as observed flood maps. This model can be used in data sparse regions or large scale reaches in which setting up or running hydrodynamic models is a daunting and time consuming task. Finally, coupling HAND approach with flood warning models can be used as an applicable system for flood emergency management and flood control studies.