Temporal and spatial zoning of flood risk in Karganrood catchment using AWBM model and Fuzzy-ANP method

ABSTRACT
Evaluating the flood potential of different watersheds is one of the important measures in the field of reducing damages caused by floods. As one of the flood-prone basins of Gilan province, the River catchment has always caused a lot of damage to the residents of the region in recent years. Therefore, due to the lack of studies in this basin, the current research has attempted to zonate the risk of flooding, and for this purpose, the AWBM model and the Fuzzy-ANP method have been used. , 2014, 2017 and the end of 2018 are estimated to increase and equal to the floods of 1990, 1993, 199, and 1997. In the spatial zoning of flood risk, 10 effective factors incluincludingecipitation, temperature, distance from the river, slope, slope direction, height, land use, vegetation, geolo   and soil were used. In the analysis of the results, it is estimated that the highest risk of flooding is in the southern areas and the outlet of the basin to the Caspian Sea, and the settlement in the river, lack of attention to watershed management, destruction of forests and pastures, and land use change are among the most important factors affecting this issue. In total, 90.3% of the area of the basin has the highest risk of flooding from a geographical point of view
Extended Abstract
Introduction
Each of the effective factors in causing floods has a different contribution to the risk of its occurrence at the level of catchment basins so that each can be prioritized according to its importance in the region. Reclamation of watersheds for flood control in one project is impractical due to its large size, expansion of impervious areas, and economic and operational issues that can increase flooding. The temporal or spatial zoning of flood risk in this area has not been done with any of the selected models, and the relevant authorities rarely take measures to reduce flood damage. More research is needed in this field to study the basin properly. This study was conducted with the aim of flood risk zoning using two AWBM and Fuzzy-ANP models.
 
Methodology
Monthly evapotranspiration, river flow, and precipitation data from 2006 to 2018 were used to simulate the runoff volume of Kerganrood Talesh River using the AWBM model for time zoning. The research used data from synoptic and hydrometric stations in Hashtpar, Lisar, Mashinkhaneh, Sheikh Darun Shandol, Kishli, Khan Balaghi, Piseson, and Davor Ardabil, prepared by the synoptic and Hydrometrical stations of Gilan and Ardabil Provinces. The required data is first arranged in Excel, in the format of RRL software, then it is defined for the model, and based on 9 parameters determined by the software itself, the simulation has been done to estimate the runoff and flood of this basin. This method evaluates the model's accuracy in simulating the observational data using the Nash-Sutcliffe coefficient index (EN2) as the objective function and the explanation coefficient (R2). Additionally, the sensitivity level of the parameters used in the model is investigated in the studied area.
In order to conduct spatial zoning, Cochran's formula was used to calculate the statistical sample size. Then, 30 questionnaires were distributed among experts in the field based on the research objective. The purpose, criteria, and sub-criteria of the Super Decision software were determined, and 10 indicators were selected, which are factors such as geology, slope, land use, digital elevation model (DEM), precipitation, temperature, vegetation, soil, and distance from the river impact flood occurrences. This article will discuss the effect of each factor and how to map them.
 
Results and Discussion
According to the values estimated by the model and the graphs drawn in Excel, it can be concluded that the AWBM model has a relative ability to estimate the runoff trend in the Kerganrood watershed. The model attempts to replicate the highest river discharge points during the designated time series. Although it follows a uniform mode in simulation, it can compute the runoff flow process similar to reality. Therefore, the use of this model has been evaluated with average accuracy in monthly simulation and has obtained the required efficiency satisfactorily. In this model, the runoff graph shows an increasing trend over time in 2011, 2014, 2017 and 2018, which indicates the risk of flooding in the study area based on the floods recorded by the Water and Regional Organization of Gilan Province. This increasing trend and the peak of the river flow correspond to the floods of September and October 2011, 2014, 2017 and October 5, 2018, which caused significant damage to the area's residents.
In the spatial zoning of flood risk, the average data of Talesh synoptic station and 9 hydrometric stations recorded by the hydrometeorological and hydrological organization of Gilan province and Ardabil province were used to draw the map of the distribution of precipitation and temperature in the Kerganrood watershed. In the next step, the slope, slope direction, and topographic map were drawn using the digital elevation model (DEM) in the GIS software environment with a spatial resolution of 12.5 meters. Then, the waterways of the Karganrood basin have been classified after estimation by calculating the Euclidean distance from the river. In drawing the geological map, the 1:100,000 geological map of Talesh city was used, and for drawing the soil map, the soil map of the whole of Iran was used. Finally, to draw land use and vegetation maps, Landsat satellite images have been used as inputs for spatial zoning of flood risk.
First, experts' opinions are used to weight indicators through network analysis in Super Decisions software. Then, layers are classified using GIS software, and a flood risk zoning map is created through the Weighted Overlay and Raster Calculate tools. In the end, an output map was extracted as a spatial flood zoning map of the Kerganrood Talesh basin with very low, low, medium, high, and very high-risk points, and the area of each floor was calculated.
 
Conclusion
The spatial zoning of flood risk in the Karganrood Talesh watershed was done using AWBM and Fuzzy-ANP models. The Nash-Sutcliffe coefficient was 0.553 and 0.507 in the calibration and validation stages, respectively, based on the AWBM outputs. The estimated results have been evaluated as acceptable and show that in terms of time zoning, the trend of runoff in the years 2011, 2014, 2017 and 2018 is increasing and is in accordance with the floods that occurred in reality in the mentioned years. In the spatial zoning of flood risk using the fuzzy-ANP method, precipitation factors with 0.299856 and distance from the river with 0.150357 have the most significant impact compared to other factors about flood risk. However, the temperature factor with 0.0265413 has also obtained the least importance in causing floods in the studied basin. In the upper regions of the basin, the risk of flooding decreases as the height increases due to dense vegetation and resistant soil. 31.63% of the basin has very low flood risk, while 90.3% has a very high risk based on classification criteria. According to the results, it can be said that by taking appropriate measures and planning, the damage caused by floods in the studied area can be reduced as much as possible.
 
Funding
There is no funding support.
 
Authors’ Contribution
All of the authors approved the content of the manuscript and agreed on all aspects of the work.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.