جغرافیا و برنامهریزی محیطی (Jun 2022)
Frequency Analysis and Investigation of the Factors Affecting 100-yr Peak-Flood in Iran’s Watersheds
Abstract
AbstractThe purpose of the current study is to analyze the frequency of peak flood discharge with a 100-year return period in 206 Iran watersheds and to quantify it based on the most important factors. In this regard, flood frequency analysis was performed based on annual maximum discharge data and fitting of conventional continuous distributions in hydrology and fitting statistical tests. Then, for modeling, 8 parameters affecting the flood peak discharge including heavy daily rainfall, average vegetation, area, perimeter, average slope, average elevation, length of the main river, and the slope of the main river at the catchment area leading to the extraction of selected hydrometric stations. Also, the stepwise regression analysis technique was used to determine the factors affecting the production of flood peak discharge in the selected stations. The results of the study showed that the southwestern, southern, and southeastern basins of Iran with peak discharges of more than 4000 m3/s had the highest 100-year peak discharges among the study basins. The results of the stepwise regression model indicated that five parameters of area, heavy rainfall, elevation, vegetation, and slope of the basin with an adjusted coefficient of determination of 0.72, standard error of estimation of 132.7, Akaike's information criterion of 1.62, and variance inflation factor of 0.62 had the best performance in estimating the flood peak discharge. The results of this study, considering its large spatial scale, which includes the whole of Iran, can be used as a practical guide by the hydrologists and decision-makers in estimating the 100-year flood peak discharge in ungauged watersheds based on the most important factors affecting its generations. IntroductionFlood is one of the most important natural hazards that has attracted a lot of attention from managers and planners due to the heavy damage it has caused to human societies (Jahangir et al., 2019). In fact, floods, as a type of natural disaster, have a significant negative impact on regional development, and its catastrophes are characterized by sudden water flow, high intensity, uncontrollable factors, and serious damages (Miceli et al., 2008). On the other hand, among various types of natural disasters such as earthquakes, landslides, soil erosion, and tsunamis, floods are considered to be the most common and destructive phenomena of the earth that affect the lives of many people every year (Doocy et al., 2013; Salvati et al., 2018; Yari et al., 2019). High socio-economic losses, human casualties, widespread destruction, and threatening living conditions are some of the damages that floods can cause (Turgut & Tevfik, 2012). It can be stated that half of the deaths occur due to floods (FitzGerald et al., 2010; Lee & Vink, 2015). In recent years, Iran has experienced very destructive floods due to climate change and poor watershed management (deforestation, overgrazing, and lack of flood control measures). For example, the recent floods (2019) in Iran have affected 25 provinces, killed 77 people, and caused about $ 2.2 billion in damage to these 25 provinces (Khosravi et al., 2020). MethodologyIn the first step, the Iran hydrometric stations that had discharge data with maximum long-term annual peak records (at least 30 years) were collected from the Iran Water Resources Management Company. In the next step, flood frequency analysis was performed based on the fitting of conventional continuous distributions in hydrology and fitting statistical tests. After performing flood frequency analysis and estimating peak discharge for 100 year return period, the watersheds boundary of hydrometric stations was determined. In this regard, using a digital elevation model with 12.5 m resolution and ARC GIS, Global Mapper, and Surfer software, the boundaries of the studied watersheds were extracted. Then, using the watersheds boundary and digital elevation model, the geomorphic parameters of the watershed such as perimeter, area, average slope, average elevation, length of the main river, and the slope of the main river were calculated. In the next step, long-term daily precipitation data of synoptic stations were collected from Iran Meteorological Organization. Then, 95% of the non-zero daily precipitation series was calculated for heavy precipitation (Gu et al., 2017). Using the IDW method, the long-term amount of heavy rainfall for each watershed was determined in GIS software. The NDVI index was used to determine the mean annual vegetation. In this regard, the vegetation time series for each watershed was extracted using Landsat images from 2000 to 2019 with a resolution of 30 m on the Google Earth Engine platform. After calculating the 100-year return period and possible parameters influencing the flood in the study watersheds, using Pearson bivariate analysis and stepwise regression model, the most suitable models for estimating flood peak discharge were determined. DiscussionThe results of the study show that the southwestern, southern, and southeastern watersheds of Iran with peak discharges of more than 4000 cubic meters per second have the highest peak discharges of 100 years among the study watersheds. Meanwhile, the Minab watershed, which ends in the Persian Gulf, has a maximum peak flow of 100 years with a peak flow of 12,614 cubic meters per second. On the other hand, the northwestern and northern watersheds of Iran with a peak discharge of less than 300 cubic meters per second have the lowest peak discharge, with a minimum discharge of 20.7 cubic meters per second related to the Solan watershed in Hamadan province. The findings of the stepwise regression model indicated that the five parameters of the watershed, including area, heavy rainfall, mean elevation, vegetation, and mean slope with R2 = 0.72 and significance level of 0.01, are the most influential factors in the estimation of flood peak discharge. In addition, the results showed that the three factors of watershed area, heavy rainfall, and mean slope have a direct relationship with peak discharge but mean elevation and vegetation have an inverse relationship. ConclusionThis study quantified the relative contribution of driving factors influencing the flood peak discharge over 100 years across Iran. 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