استخراج پتانسیل سیل خیزی حوضه سیمینه رود با کمک تصاویر ماهواره ای، شاخص رطوبت توپوگرافی و ویژگیهای مورفولوژیکی

شناسایی مناطق سیل گیر از راهکارهای اساسی در برنامه ریزی کاهش اثرات تخریبی سیل است. در نوشتار پیش رو از شاخص های توپوگرافی و مورفولوژیکی به منظور بررسی سیل زایی استفاده شده است. به دلیل اثرگذاری ویژگی های هیدروژئومورفیک در سیل‌زایی، این ویژگی ها با نرم افزارهای آرک‌جی‌.آی.‌اس. و سیستم مدل‌ساز حوضه ‌آبریز با کمک لایه های مدل رقومی ارتفاع و توپوگرافی استخراج شدند. به دلیل نبود اطّلاعات میدانی دقیق و به روز از رطوبت سطحی خاک، لایه پوشش گیاهی و آمار ثبتی بارش، از امکانات سنجش از دور اقدام به استخراج رطوبت خاک و بارش شده است. برای کنترل و مقایسه اطّلاعات به دست آمده از تصاویر ماهواره‌ای، بارش مورّخ 12 آذر 1395 به‌عنوان نمونه بارش سیلابی انتخاب شد. با توجّه به اهمّیت شاخص رطوبت توپوگرافی برای توصیف شرایط رطوبتی خاک و تخمین ویژگی های فیزیکی و هیدرولوژی از این شاخص استفاده شده و نقشه خروجی به صورت طبقه بندی در منطقه بررسی شد. برای نمایش نواحی مستعدّ سیلاب از مدل ترکیبی که در آن از لایه‌های رطوبت خاک سطحی (از مدل ذوزنقه ای فیزیکی از تصاویر لندست 8 استخراج شد)، پوشش گیاهی، بارش و شاخص رطوبت توپوگرافی استفاده شده است. پس از همسان سازی نقشه‌ها و وزن‌دهی، روی هم گذاری لایه ها در محیط سیستم اطّلاعات جغرافیایی انجام و نقشه پتانسیل سیل زایی حوضه استخراج شد و حوضه به ترتیب شدّت سیل زایی به پنج محدوده مستعدّ سیلاب، سیلاب متوسّط، تاحدودی سیلابی، سیلاب بسیارکم و فاقد سیلاب طبقه بندی شد. براساس نقشه استخراجی و تجزیه و تحلیل نتایج، از کل 3279 کیلومتر مربع منطقه، حدود 81.6 کیلومتر مربع (2.5%) مستعد سیلاب بوده و 1.9% از منطقه با میزان خطر متوسّط برای سیلاب شناسایی شد. محدوده‌های مستعدّ سیلاب بیشتر در نواحی دشتی مرکزی و شمال حوضه در زمین‌های مسطّح حاشیه‌ای رود سیمینه قرار گرفته است.

The Extraction of Flood Potential of Simineh River Basin Applying Satellite Images, Topographic Wetness Index and Morphological Features

Identifying floodprone areas is one of the essential strategies in planning to mitigate the damaging effects of floods. In this study, topographic and morphological indices were used to investigate flooding. Due to the effect of hydrogeomorphic features on flooding, these features were extracted by ARCGIS software and watershed modeling system with the help of digital elevation model and topography layers. Due to lack of accurate field data and incidence of soil moisture, vegetation layer and precipitation statistics from remote sensing facilities, soil moisture and precipitation were extracted. In order to control and compare the information extracted from precipitation satellite images dated December 03, 2016, it was selected as a flood sample. Due to the importance of topographic moisture index to describe soil moisture conditions and estimation of physical and hydrological characteristics, this index was used and the output map was classified according to the area. To illustrate floodprone areas, a hybrid model was used in which the soil surface moisture layers (optical trapezoidal model was extracted from Landsat 8 images), vegetation, precipitation and topographic moisture index were applied. After mapping the maps and weighting, the layers were merged into GIS environment and the flood potential map of the basin was extracted and the basin was flooded with five flood susceptibility ranges, moderate floods, partly floods and floods, respectively. The flood was classified. According to the extraction map and analysis, out of 3279 km2, about 81.6 km2 (2.5%) were susceptible to flood and 1.9% of the area with moderate risk of flood was identified. Most flood prone areas are located in the central plain and the north of the basin in the flat marginal lands of the Simineh River.Extended Abstract1IntroductionFlooding is a natural hazard in many parts of Iran and has been increasing in intensity and frequency in recent years. Studies show that flood damage is not caused by increased frequency, or magnitude of floods, but by the extensive use of floodplain lands. Therefore, it is necessary to formulate a comprehensive plan with the aim of controlling, inhibition and optimizing the use of management measures appropriate to all the factors involved in the creation of regional floods.For flood control, flood zoning is one of the best methods for planning and identifying flood sensitive areas with the aim of reducing flood hazards, and identifying flood zones using mathematical models that combine topographic and morphological indicators can be used in the program. Plans can be very effective in reducing risks and injuries.Laval and Omodoji (2017) studied, correlated and significantly correlated vertical roughness measurement surface roughness index (VRM), topographic roughness index (TRI) and topographic wetness index (TWI) in the detection of flood and nonflood areas. The effect of topographic wetness index was higher than other indices.Given the limitations and scarcity of field record information in developing countries, the use of satellite imagery, including TRMM satellite data, can fill the gap.2Materials and MethodsThe study area of the Simineh river basin is about 2977 km2 from the sub basins of the Urmia Lake Basin. Hydro geomorphic features of the basin have a significant effect on the flood potential and are affected by two factors causing flood. One is the topographic and altitude features that increases rainfall and the flow of water. The other is the use of geomorphic features of the basin to estimate the flood potential that has been used in many parts of the worldIn this study, a digital elevation model (DEM) with a resolution of 30 m extract of ASTER image was used. The physical properties of the basin were extracted using Archydro application in the ARCGIS10.2 software environment. In the next step, the necessary analyses for the preparation of the topographic humidity index and other parameters were extracted according to Horton parameters.Among topographic characteristics, the topographic Wetness index is a useful and common tool to describe the humidity conditions in the scale of the basin. In the extraction map, the highest value is for the areas with a higher TWI and lower values related to the areas with a lower TWI index.3Results and DiscussionThe results of TRMM satellite images were used to control and verify the recorded statistics of ground stations. For this incident, 02 December 2016 precipitation were selected as a partial incident. Several factors such as the occurrence of heavy rainfall, the sudden melting of snow in the mountainous region, or the simultaneous operation of both factors have been noted in flood formation in a basin.In this study, the precipitation layer of the meteorological stations is used and compared with the information obtained from TRMM satellite images. Soil moisture index from Landsat 8 satellite images was extracted and used by OPTRAM model on December 19, 2016 and NDVI index was extracted using Landsat 8 images on the same date.After extracting the required maps of the model, all maps were matched in order to match the nearest neighboring method. After implementation of the model, a map of flood risk distribution was prepared. According to the extracted map, and based on the experience and the results of other studies, the total area of ​​3279 square kilometers is about 81.6 km2 (2.5%) prone to floods and 62 km2 (1.9%) with a moderate risk of flood. The area is more prone to floods in the plains of the central and northern basin.Descriptive and spatial information on flood prone areas was collected to identify and obtain the information from high risk areas. These areas are mostly located in the plain and north of the basin on the flat marginal lands of the Simineh River, with 18 villages in the area prone to flooding4ConclusionThe study showed that high resolution satellite images such as TRMM are suitable to estimate and severe precipitation, as Islam and colleagues (2010) concluded in their study. The topographic wetness index (TWI) is derived from a digital elevation model (DEM) and hydrological features can be extracted using ARCGIS and WMS software. As Banon et al. (1979) extracted the hydrological characteristics using the topographic index.To complete the information layers used in the model to extract the floodprone area, precipitation data were extracted from TRMM satellite images and soil moisture using OPTRAM model and vegetation from Landsat8 satellite images. After combining the layers, three layers of classification including topographic moisture, precipitation and soil moisture mapping were assembled using raster analysis with different weights. Finally, spatial hazard distribution map of floods was extracted, with flood intensity of five flood prone areas, respectively, partly flooded, with very low floods and no floods.