Flow unsteadiness in flood events has a significant effect on the structure of the flow field and motion of sediment particles, thereby affecting dispersion of pollutants and river ecology. The aim of the present article was to evaluate state-of-the-art research efforts concerning flow characteristics and sediment transport in unsteady flow condition. The paper is organized in four sections: The first section deals with the unsteady parameters which affect sediment transport. In the second section, the flow characteristics in unsteady open channel flow are presented. Different studies showed that the flow characteristics which affect sediment transport including velocity distribution or shear stress during passage of a hydrograph differ from steady flow condition. In addition, measurements during passage of a hydrograph show that turbulence intensity is generally larger in the rising limb of the hydrograph rather than in the falling limb. This causes the peak of sediment load and pollutants occur during the rising limb of the storm hydrograph. The third and forth sections deal with bed load and suspended load in unsteady flow condition, respectively. Studies show that the methods which are based on steady flow conditions generally underestimate the sediment transport rates in unsteady flows. The larger the unsteadiness, the bigger is the difference. Finally, with considering different findings from previous studies, suggestions are presented for further research.

Although many studies have been carried out about dam break, this phenomenon still is one of the most important issues in the field of hydraulic engineering due to its hazards to human societies. Predicting the critical conditions including coincidence of flooding and dam break indicates more field studies requirement. In this research the mutual effects of coincidence of negative surge due to dam break and flash flood has been studied using under different stored water levels. In addition, the effects of bed slope, existence of reservoir sediment and different initial reservoir depth have also been studied. Due to unsteady condition of flow, the stereoscopy method was employed to measure the hydraulic parameters such as depth. Using two digital cameras, results showed that by increasing the initial depth in dam's reservoir and channel slope, the reservoir would evacuate faster. Furthermore, sediments in which act like step in the flow direction can cause an increase in speed of the positive surge toward downstream. In addition by increasing the maximum of flow rate and base time of hydrographs, water rise in downstream was monitored as well.

Extended AbstractIntroductionA flood is a widespread and dramatic natural disaster that affects the life, infrastructure, economy, and local ecosystems of the world. In this paper, a method for flood detection in urban (and suburban) environments using the intensity and coherence of SAR based on a convolutional neural network is introduced, and from the time series of SAR intensity and coherence to draw flood without obstruction (e.g. Flooded bare soils and short vegetation) are used. Non-cohesive areas blocked by floods (e.g., flooded vegetation) and cohesive areas with flood-blocked areas (e.g., frequently constructed flooded areas) are distinguished.This method is flexible according to the time period of the data sequences (at least one pair of pre-event and event intensities and one pair of pre-event and in-event coherence are required). The increasing number of SAR missions in orbit that have a fixed viewing scenario with a short retry time increases the chances of seeing a flood event, while also having a good pre-event scene achieved by the same sensor. This makes this method desirable for operational emergency responses.Materials & MethodsCNN algorithm is a multilayer perceptron that is designed to identify two-dimensional information of images and includes: input layer, convolution layer, sample layer, and output layer. The CNN algorithm has two main processes: collection and sampling.The convolution process involves the use of a trainable Fx filter, deconvolution of the input image (the first step of image input, input after image convolution, is the feature of each layer called Feature Map), then by adding bx can be hand convolution of the CX layer Found. Sampling process: n pixels are collected from each neighborhood to form a pixel, then weighted with a scalar weight of Wx + 1 and a bx + 1 bias is added, then a map of The Narrow n times feature map properties are generated.Three images of Sentinel-1A VV polarization, wide width interference (IW), and mode (SLC) data were used in this study. Intensity images were pre-processed with radiometric calibration, noise reduced with a spell-filter (window size 5.5 pixels), and converted from linear units to decibels. Coherent images were obtained with a pair of consecutive images with a window of 7.28 (range _ azimuth). Validation data set due to the lack of other data in two separate sections of ground data in the urban area of GonbadKavous that have been collected to identify homes damaged by floods and terrestrial reality data from gamma image thresholds for output validation were extracted.Results & DiscussionIn this section, the results of the study are qualitatively and quantitatively analyzed. Because the simultaneous display of SAR data over time in the form of RGB compounds is widely used in the qualitative interpretation of land cover and surface dynamics, RGB compounds are used to provide evidence of flood magnitude in terms of intensity and coherence. For both cases, the results of combining intensity and coherence and intensity alone and coherence alone are quantitatively analyzed. Overall accuracy (OA), kappa correlation coefficient, false-positive rate (FPR), precision (e.g., correctly predicted positive patterns out of the total predicted patterns in a positive class), recall (e.g., a fraction of properly classified positive patterns), and an F1 score (ie the harmonic mean between precision and recall). Flood reference and ground data are mentioned and reported based on the reference.ConclusionIn this paper, a method for mapping floods in urban environments based on SAR intensity and interferometry coherence was introduced. A combination of intensity and coherence extracts flood information in different types of land cover and outlet. This method was tested on the KavousGonbad flood incident obtained by various SAR sensors and the flood maps were confirmed by the flood reference resulting from thresholding and ground harvesting and satisfactory results were shown in this case study. The findings of this experiment show that the shared use of SAR intensity and coherence provides more reliable information than the use of SAR intensity and coherence alone in urban areas with different landscapes. In particular, flood detection in less cohesive / non-cohesive areas (e.g., bare soils, vegetation, vegetated areas) relies heavily on multi-temporality, while multi-temporal coherence provides more comprehensive flood information in areas Create coherence (e.g., mostly built-up areas). However, some flood-specific situations, such as flooded parking lots and flooded dense building blocks, are still challenging in terms of intensity and coherence. Also, since the proposed method is sensor and scene independent, with very frequent and regular observations of SAR missions such as Sentinel-1 and RADARSAT (RCM), there are opportunities to map global floods on a global scale, especially in small countries. Provides income.

Introduction Large severe floods can have enormous influence on the fluvial system in comparison with the floods with lower magnitude and more frequencies. This work addresses the geomorphic response of mountainous rivers to extreme floods to explore the relationships between morphological changes and controlling factors. In October 2015, following the occurrence of a sudden extreme rainfall, a large and devastating flood occurred in Ilam province. The flood caused major changes in the morphology of Ilam's rivers. The rate of channel expansion is various in different sections of the studied rivers. Thus, we can examine the influential and controlling factors that led to diversity of river behavior. The hypothesis of this research is that explanation of geomorphic effects requires models that include other variables, e. g., lateral confinement, degree of sediment, besides hydraulic related variables (cross-sectional or unit stream power). The main purpose of this research is to explore the relationship between channel widening and arrange of controlling factors. We have addressed channel width (i. e: pre-or post-flood width) to calculate unit stream power in order to have a better explanation of channel response? Since few studies have been done in this field, this research was conducted with the aim of investigating the factors controlling the response of Mountain Rivers to extreme flood events in upstream of Ilam dam. Materials and methods The research has examined three tributaries of the Konjancham River (upstream of Ilam dam) whose catchments were affected by an extreme flood on 7th October, 2015. An integrated approach was taken to study this flood, including (i) Analysis of channel width changes by comparing aerial photographs before and after the flood, (ii) Estimation of peak discharges in studied reaches, and (iii) Determining the degree of sedimentation in studied reaches. Delineation of spatial units was carried out according to the approach proposed by Rinaldi et al. (2013), which is a modification of the approach by Brierley and Fryirs (2005). According to the approach, stream sectors were defined as macro reaches having similar characteristics in terms of lateral confinement, while the reaches are homogeneous in terms of channel morphology (channel pattern, width, and slope) and hydrology. We have used the reach scale (reach length was commonly from 200 to 1300 m) for an overall assessment of magnitude of channel changes and for a preliminary investigation of controlling factors. The dominant process observed in the study reaches was channel widening, which was analyzed by comparing aerial photographs taken before and after the flood. To assess the changes in channel width, channel banks, and islands, these features were digitized on pre-and post-flood orthophotos. The channel width was calculated by dividing channel area by the length of the reach, and changes in channel width were expressed as a width ratio (ratio of channel width after the flood to channel width before that flood). The estimation of peak discharges has been used to calculate cross-sectional stream power and unit stream power. The last part of the methodological section deals with statistical analysis carried out to explain channel response to the flood event by exploring the relationships between the changes in channel width and controlling factors. Results and discussion The relationships between the degree of channel widening and possible controlling factors were explored using multiple regression analysis. The analysis was carried out for the widening (width ratio) at reach scale. The entire data set includes 38 reaches. We analyzed seven controlling variables including confinement index, percentage of reach length with artificial structures, degree of sedimentation, channel slope, cross-sectional stream power, and unit stream power using pre-flood and post-flood channel width. Each regression model included only three to four variables. Each model included only one of the variables expressing potential or flood flow energy, e. g., channel slope, cross-sectional stream power, unit stream power. All four multiple regression models turned out to be significant (p< 0. 001) and gave high coefficients of multiple determinations. The values of R2and adjusted R2 are ranged between 0. 73 and 0. 8 and between 0. 69 and 0. 77, respectively. The best model embraced unit stream power calculated based on pre flood channel width and confinement index as explanatory variables. Conclusion The results confirmed the main hypothesis of this work that hydraulic variables alone are not sufficient to explain channel response to an extreme flood event. The inclusion of other factors, specifically lateral confinement, degree of sedimentation, and percentage of reach length with artificial structures can lead to satisfactory models explaining the observed variability in the degree of channel widening. These results suggest that the widening process is essentially controlled by two factors: flood power and valley confinement. Flood duration exceeding a critical threshold was not included in our analysis, but it is a variable that very likely would increase the robustness of regression models in these reaches. The analysis carried out in the three subcatchments of the Konjancham River basin showed that unit stream power calculated based on pre-flood channel width has stronger relations with channel widening in comparison with that based on post-flood channel width and cross-sectional stream power. Because peak discharge was used for stream power calculation, we are aware that neither pre-flood nor post-flood channel width is actually appropriate for the estimation of unit stream power, as the most appropriate would be the (unknown) width at the flood-peak time. The pre-flood width has stronger relations with the degree of channel widening (width ratio). This could suggest the width changes occurred after the flood peak.

Accelerated soil erosion poses a serious threat to land management sustainability and water resource utilization in many areas of the world. The objective of this study was to apportion surface (cropland and rangeland) and subsurface (channel bank) sources relative contributions to the supply of suspended sediment during the storm event in Kamish mountainous catchment, using a geochemical fingerprinting approach and Bayesian un-mixing model. To this end, thirty-four geochemical tracers were measured as potential tracers to evaluate surface and subsurface sediment sources (69 samples), including 10 target suspended sediments samples collected across the hydrographs of a flood event at the overall catchment outlet. In total, two statistical methods Kruskal– Wallis H test and discriminant function analysis (DFA) were used to select the optimum tracer composition. The results of Bayesian un-mixing model indicated that mean (uncertainty range) relative contribution of cropland and rangeland (surface land-use) and channel bank (subsurface) sources are 31. 8 (12. 7-50. 3), 33. 2 (17. 5-49), 35. 2 (25. 6-44. 6) percent, respectively. These results indicate surface and subsurface sediment sources have the same contribution to the supply of suspended sediments during the basin flood event. Although sediment sources contribution in during the flood event change intermittently between subsurface and surface soils. As a result, targeted management practices should focus through erosion and land use control of these sources for minimizing their effects on fine sediment deposition.

Prediction of flood peak discharge and runoff volume is one of the major challenges in the management of watersheds. The present study was carried out to estimate event flood peak discharge and runoff volume using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in Kasilian watershed, Iran. For this purpose, 15 rainfall characteristics were considered for 60 storms from 1975 to 2009. Statistical indices of mean square error (RMSE), coefficient of efficiency (CE) and the coefficient of determination (R2) were used to assess models performance. The results showed that flood peak discharge variable, ANFIS with RMSE=1.28m3s-1, CE=%82 and R2=0.86 has better performance than ANN with RMSE=1.22m3s-1, CE=%82 and R2=0.95 and for runoff volume variable, ANFIS with RMSE=2369.54 m3, CE=%99 and R2=0.99 has better performance than ANN with RMSE=10282.82m3, CE=%98 and R2=0.98. Also, the results of the sensitivity analysis indicated that the most sensitive factor is excess rainfall for runoff flood peak discharge and runoff volume estimation.

Event studies have found several applications in financial and accounting researches as the standard method to assess the average effect of some types of announcement on stock prices. In a large sample of announcements, event window length can be standardized (fixed) across observations, because the errors from having too long or too short event window should have small impact in average by the Law of Large Numbers. But in small samples (in emerging markets) we cannot use this procedure. Here, we examine various potential rules for determining the length of an event window when looking at limited number of observations. We consider the announcements of adjustment of predicted earnings (by companies) in Tehran Stock Exchange during 1386 to 1388. To determining the length of time period affecting the market, tree methods are used. We also examine the relationship between the length of event window and the amount of unexpected earnings.

Experiment design is believed to be an important part of investigating an engineering phenomenon for characterizing and optimizing the process. In this study, the Taguchi method (TM) reduced the number of experiments and was used to analyze the results of an artificial neural network (ANN) and find the optimal combination of the relevant parameters in the ANN. Accordingly, the phenomenon of the local scour depth around the bridge during flood events was considered as a case study. The study results indicated that TM could reduce the number of experiments compared to the previous original study and the full factorial method by 28% and 67%, respectively. According to TM, the flow intensity at the hydrograph peak was the most effective parameter providing the optimal state (minimum scour depth). Additionally, an ANN with three hidden layers and the main parameters, including several neurons in the first and second hidden layers, training function, and transfer function, was introduced. Adjusting the input parameters of the ANN, TM led to the emergence of networks with a reasonable correlation coefficient of R= 0.952. Finally, the results demonstrated that the transfer function had the most significant effect on the results of the ANN.

Experiment design is believed to be an important part of investigating an engineering phenomenon for characterizing and optimizing the process. In this study, the Taguchi method (TM) reduced the number of experiments and was used to analyze the results of an artificial neural network (ANN) and find the optimal combination of the relevant parameters in the ANN. Accordingly, the phenomenon of the local scour depth around the bridge during flood events was considered as a case study. The study results indicated that TM could reduce the number of experiments compared to the previous original study and the full factorial method by 28% and 67%, respectively. According to TM, the flow intensity at the hydrograph peak was the most effective parameter providing the optimal state (minimum scour depth). Additionally, an ANN with three hidden layers and the main parameters, including several neurons in the first and second hidden layers, training function, and transfer function, was introduced. Adjusting the input parameters of the ANN, TM led to the emergence of networks with a reasonable correlation coefficient of R= 0. 952. Finally, the results demonstrated that the transfer function had the most significant effect on the results of the ANN.

Flood damage assessment is often necessary for early flood management. To this end, this paper provides a framework of rapid estimation of flood damage and identification the flooded areas in March 2020 using Sentinel-1 satellite data. To this end, in the present study, after applying the necessary pre-processing in SNAP6 software, the backscattering coefficient, or sigma naught for two images related to before and after the flood occurrence was extracted. The backscattering coefficient histogram was used to separate the image into two classes including water and non-water and the threshold of 0. 01 was obtained based on it. Then, by applying mathematical operations on both backscattering images, the binary image of water and non-water was prepared and the flooded areas were determined based on the difference between the two images. After detecting the flooded areas, Sentinel images were classified into three classes including waterbody before flood, flooded area and other lands using supervised classification algorithms. The results indicated the high accuracy of the Random Forest algorithm with kappa of 0. 92 compared to other algorithms. By overlaying the land use and flooded areas maps, the inundation percentage for each land use was determined. According to the results, bare lands with 27. 9 percent, residential land with 16 percent and rangelands with 12 percent had the highest inundation percentage, respectively.