برآورد میزان دقت مدل رقومی ارتفاعی TanDEM-X در شبیه سازی مشخصات هیدرولیکی سیلاب (مطالعه موردی: حوضه رودخانه اترک) (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
مدل سازی هیدرولیکی سیلاب، نقش مهمی در مدیریت بهینه سیل و کاهش خطرات مرتبط دارد. منطقه موردمطالعه در این پژوهش، بازه 20 کیلومتری رودخانه اترک در بالادست شهر مراوه تپه است که یکی از مخاطره آمیزترین مناطق ایران از منظر سیلاب رودخانه ای می باشد. هدف از پژوهش حاضر، برآورد میزان دقت مدل رقومی ارتفاعی TanDEM-X با قدرت تفکیک 12 متر در شبیه سازی مشخصات هیدرولیکی سیلاب است. برای رسیدن به این هدف، مدل دوبعدی HEC-RAS در شرایط پایدار برای شبیه سازی سیلاب با دوره بازگشت 5، 10، 25، 50، 100 و 200 ساله استفاده شد؛ همچنین از تصاویر ماهواره ای Landsat-8 OLI برای استخراج پهنه سیلابی و اعتبارسنجی مدل هیدرولیکی بهره گرفته شد. نتایج مدل هیدرولیکی نشان دهنده آن است که وسعت پهنه سیلابی در محدوده 40/4 (دوره بازگشت 5 سال) تا 93/5 کیلومترمربع (دوره بازگشت 200 سال) متغیر است. از سوی دیگر، در دوره بازگشت 200 سال، میانگین عمق 9/67 درصد و سرعت جریان 5/49 درصد در مقایسه با دوره بازگشت 5 سال افزایش می یابد. تحلیل حساسیت ضریب مانینگ مؤید آن است که بیشترین حساسیت وسعت پهنه سیلابی، میانگین عمق و سرعت جریان نسبت به ضریب مانینگ 65/4، 84/4 و 23/12- درصد است. نتایج اعتبارسنجی مدل دوبعدی HEC-RAS با استفاده از پهنه سیلابی استخراج شده از تصاویر ماهواره ای Landsat-8 OLI برای دوره بازگشت 10 سال نشان از آن دارد که شاخص درصد تناسب 86 است. نتایج این پژوهش به عنوان نقطه عطفی برای مدل سازی هیدرولیکی مشخصات سیلاب با مدل رقومی ارتفاعی TDX در سایر مناطق ایران کمک می کند.Estimating the Accuracy of the TanDEM-X Digital Elevation Model in the Simulation of Flood Hydraulic Characteristics (Case Study: Atrak River Basin)
Hydraulic modelling of floods plays an important role in flood management and the related risk reduction. The case study in this research was a 20-km reach of the Atrak River in the upstream of Maraveh Tappeh City, which is one of the most hazardous regions of Iran from flood viewpoint. The aim of this research was to estimate the accuracy of the TanDEM-X digital elevation model with a resolution of 12 meters in simulating flood hydraulic characteristics. To achieve this aim, the HEC-RAS 2D model was used in steady conditions to simulate floods with a return period of 5, 10, 25, 50, 100, and 200 years. The results indicated that the inundation area varied in the range of 4.40 km 2 (return period of 5 years) and5.93 km 2 (return period of 200 years). In the return period of 200 years, the mean flow depth and velocity increased by 67.9 and 49.5% compared to the return period of 5 years, respectively. The sensitivity test also indicated that the maximum sensitivities of the inundation area, mean flow depth, and mean flow velocity to Manning’s coefficient were4.65, 4.84, and -12.23%, respectively. The validation results of the HEC-RAS 2D model by using the inundation area extracted from Landsat-8 OLI satellite images for a return period of 10 years showed that the fit percentage indicator was 86%. The results of this study indicated an initial effort for hydraulic modelling of flood characteristics with the TDX elevation digital model. Keywords : HEC-RAS 2D model, Frequency analysis, Hydraulic modelling, Landsat-8 satellite images Introduction Floods are among the most common and destructive natural disasters worldwide, imposing various adverse effects in different countries (Bui et al., 2018). These include fatalities, damage to infrastructures, people displacement, and environmental damages (Rahmati et al., 2020). Over the last decade, floods have affected millions of people worldwide and caused a damage of more than US$ 400 billion (Aerts, 2020). In Asia, more than 90% of human casualties resulting from natural disasters stem from flood events (Smith, 2003). Among several countries in Asia, Iran faces destructive floods each year due to its vast extent and heavy precipitations in most basins (Jahangir et al., 2019). Over the past 60 years, more than 3,700 flood events have been reported in Iran, while during the last decade, the damage caused by flooding has increased by 250% (Norouzi and Taslimi, 2012). Iran has recently experienced immense floods because of poor watershed management and climate change (Pouyan et al., 2021). In 2019, flooding events affected 25 out of 31 provinces, resulting in more than 77 human casualties and damage of US$ 2.2 billion (Khosravi et al., 2018). Even though we do not have an accurate answer to how climate change may impact flooding events, such as the ones that occurred in 2019(Sherpa and Shirzaei, 2021), a recent study has suggested that Iran will probably experience a higher frequency of floods in the future (Vaghefi et al., 2019). In addition, the growth of urbanization and increasing deforestation will make the condition worse (Arabameri et al., 2019). Methodology In the current study, the long-term (1977-2017) data of maximum discharge in the hydrometric station of Qazanqaya were used for the frequency analysis of Flood Peak Discharge (FPD). The stationarity in the time series of annual maximum peak discharge was checked before fitting the distribution. For computing FPD in the various return periods for the hydrometric station of Qazanqaya, the annual maximum discharge records were fitted via EasyFit software. Three goodness-of-fit criteria, including Anderson-Darling, Kolmogorov-Smirnov, and Chi-square, were adopted to select the best-fitted distribution. Finally, flood discharges with 5-, 10-, 25-, 50- 100-, and 200-yr return periods were estimated for the hydrometric station based on the corresponding best-fitted distribution. This study simulated 2D steady flow in a return period of 5-200 years using HEC-RAS 5.0 software (U.S. Army Corps of Engineering, 2016). Due to the complex numerical schemes, 2D diffusive wave equations could provide greater stability and faster calculation times (Li et al., 2020) and were thus used in this study to simulate 2D steady flows in a return period of 5 to 200 years. The peak flow discharges in the return periods of 5-200 years estimated from frequency analysis in the hydrometric station of Qazanqaya were considered as the upstream boundary conditions in the hydraulic model. Furthermore, the downstream boundary conditions were considered as normal depth conditions obtained based on the energy slope. Manning’s roughness coefficients of the main channel and floodplain were estimated based on the land cover mapand USGS method (Arcement and Schneider, 1989). In the previous studies, modification of Normalized Difference Water Index (NDWI) has been successfully done to map the flooding areas (Li et al., 2018). Hence, based on the date of the flood events, which were recorded in the hydrometric station of Qazanqaya, the flooded area was extracted from Landsat-8 OLI images. On the other hand, the fit percentage indicator proved to be useful for the validation of flood inundation models (Khojeh et al., 2022). A value of closer to 100% could denote a better agreement in flood extent modeling by TDX Digital Elevation Model. Discussion The results of hydraulic modelling indicated that the inundation area varied in the range of 4.40 square kilometers (return period of 5 years) and5.93 square kilometers (return period of 200 years). On the other hand, in the return period of 200 years, the mean flow depth and velocity increased by 67.9 and 49.5% compared to the return period of 5 years, respectively. The validation results of the HEC-RAS 2D model by using the inundation area extracted from Landsat-8 OLI satellite images for a 10-yr return period indicated that the fit percentage indicator was 86%, indicating a high agreement of flood modeling results based onthe TDX digital elevation model. Conclusion The results of the frequency analysis and estimation of flood peak discharges with a return period of 5 to 200 years in the Atrak River Basin showed that this basin with peak discharges between 487.8 m 3 /s (5-year flood) and 1605.6 m 3 /s (200-year flood) could be considered as one of the most dangerous basins in Iran, which could cause a lot of human and financial losses, especially for floods with a high return period. Although HEC-RAS 2D modeling based on the TDX digital elevation model with a resolution of 12 m indicated that this digital elevation model with an accuracy of 86% (14% error) was probably better than digital elevation models, such as SRTM, ASTER, and ALOS, with a resolution of 30 m , its validation for other flood-prone areas of Iran was necessary. References - Aerts, J. C. J. H. (2020). Integrating agent-based approaches with flood risk models: A review and perspective. Water Security, 11 , 100076. - Arabameri, A., Rezaei, K., Cerdà, A., Conoscenti, C., & Kalantari, Z. (2019). A comparison of statistical methods and multi-criteria decision-making to map flood hazard susceptibility in Northern Iran. Science of the Total Environment, 660 , 443-458. - Arcement, G. J., & Schneider, V. R. (1989). Guide for selecting Manning's roughness coefficients for natural channels and flood plains . Available from the US Geological Survey, Books, and Open-File Reports Section, Box 25425, Federal Center, Denver, CO 80225-0425, Water-Supply Paper, 2339, 1989, p. 38, Fig. 22, Tab 4, Ref. 23. - Bales, J. D., & Wagner, C. R. (2009). Sources of uncertainty in flood inundation maps. Journal of Flood Risk Management, 2 (2), 139-147. - Brunner, G.W. (2016). HEC-RAS River Analysis System, 2D Modeling User’s Manual, Version 5.0 . Davis, CA. - Bui, D. T., Panahi, M., Shahabi, H., Singh, V. P., Shirzadi, A., Chapi, K., & Ahmad, B. B. (2018). Novel hybrid evolutionary algorithms for spatial prediction of floods. Scientific Reports, 8 (1), 1-14. - Costabile, P., Costanzo, C., Ferraro, D., Macchione, F., & Petaccia, G. (2020). Performances of the new HEC-RAS,Version 5 for 2-D hydrodynamic-based rainfall-runoff simulations at basin scale: Comparison with a state-of-the art model. Water, 12 (9), 2326. - Dong, Y., Zhao, J., Floricioiu, D., Krieger, L., Fritz, T., & Eineder, M. (2021). High-resolution topography of the Antarctic Peninsula combining the TanDEM-X DEM and Reference Elevation Model of Antarctica (REMA) mosaic. The Cryosphere, 15 (9), 4421-4443. - Falter, D., Vorogushyn, S., Lhomme, J., Apel, H., Gouldby, B., & Merz, B. (2013). Hydraulic model evaluation for large-scale flood risk assessments. Hydrological Processes, 27 (9), 1331-1340. - Golshan, M., Jahanshahi, A., & Afzali, A. (2016). Flood hazard zoning using HEC-RAS in GIS environment and impact of manning roughness coefficient changes on flood zones in Semi-arid climate. Desert, 21 (1), 24-34. - Grohmann, C. H. (2018). Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM, and ALOS AW3D30. Remote Sensing of Environment, 212 , 121-133. - Gu, X., Zhang, Q., Singh, V. P., & Shi, P. (2017). Changes in magnitude and frequency of heavy precipitation across China and its potential links to summer temperature. Journal of Hydrology, 547 , 718-731. - Hamed, K. H. (2008). Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis. Journal of hydrology, 349 (3-4), 350-363. - Jahangir, M. H., Reineh, S. M. M., & Abolghasemi, M. (2019). Spatial predication of flood zonation mapping in Kan River Basin, Iran, using artificial neural network algorithm. Weather and Climate Extremes, 25 , 100215. - Janizadeh, S., Pal, S. C., Saha, A., Chowdhuri, I., Ahmadi, K., Mirzaei, S., & Tiefenbacher, J. P. (2021). Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. Journal of Environmental Management, 298 , 113551. - Karamouz, M., & Mahani, F. F. (2021). DEM uncertainty based coastal flood inundation modeling considering water quality impacts. Water Resources Management, 35 (10), 3083-3103. - Khojeh, S., Ataie-Ashtiani, B., & Hosseini, S. M. (2022). Effect of DEM resolution in flood modeling: Acase study of Gorganrood River, Northeastern Iran. Natural Hazards , 1-21. - Khosravi, K., Pham, B. T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., & Bui, D. T. (2018). A comparative assessment of decision tree algorithms for flash flood susceptibility modeling at Haraz Watershed, northern Iran. Science of the Total Environment, 627 , 744-755. - Leitao, J. P., Boonya-Aroonnet, S., Prodanović, D., & Maksimović, Č. (2009). The influence of digital elevation model resolution on overland flow networks for modeling urban pluvial flooding. Water Science and Technology, 60 (12), 3137-3149. - Li, J., Yang, X., Maffei, C., Tooth, S., & Yao, G. (2018). Applying independent component analysis on Sentinel-2 imagery to characterize geomorphological responses to an extreme flood event near the non-vegetated Río Colorado terminus, Salar de Uyuni, Bolivia. Remote Sensing, 10 (5), 725. - Li, J., Zhao, Y., Bates, P., Neal, J., Tooth, S., Hawker, L., & Maffei, C. (2020). Digital Elevation Models for topographic characterization and flood flow modeling along low-gradient, terminal dryland rivers: A comparison of spaceborne datasets for the Río Colorado, Bolivia. Journal of Hydrology, 591 , 125617. - Modarres, R., Sarhadi, A., & Burn, D. H. (2016). Changes of extreme drought and flood events in Iran. Global and Planetary Change, 144 , 67-81. - Muench, R., Cherrington, E., Griffin, R., & Mamane, B. (2022). Assessment of Open Access Global Elevation Model Errors Impact on Flood Extents in Southern Niger. Frontiers in Environmental Science , 547. - Muthusamy, M., Casado, M. R., Butler, D., & Leinster, P. (2021). Understanding the effects of Digital Elevation Model resolution in urban fluvial flood modeling. Journal of Hydrology, 596 , 126088. - Norouzi, G., & Taslimi, M. (2012). The impact of flood damages on the production of Iran’s Agricultural Sector. Middle East J Sci. Res., 12 , 921-926. - Papaioannou, G., Loukas, A., Vasiliades, L., & Aronica, G. T. (2016). Flood inundation mapping sensitivity to riverine spatial resolution and modeling approach. Natural Hazards, 83 (1), 117-132. - Phillips, J. D. (1988). Incorporating fluvial change in hydrologic simulations: Acase study in coastal North Carolina. Applied Geography, 8 (1), 25-36. - Pinos, J., & Timbe, L. (2019). Performance assessment of two-dimensional hydraulic models for generation of flood inundation maps in mountain river basins. Water Science and Engineering, 12 (1), 11-18. - Pouyan, S., Pourghasemi, H. R., Bordbar, M., Rahmanian, S., & Clague, J. J. (2021). A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran. Scientific Reports, 11 (1), 1-19. - Rahmati, O., Darabi, H., Panahi, M., Kalantari, Z., Naghibi, S. A., Ferreira, C. S. S., & Haghighi, A. T. (2020). Development of novel hybridized models for urban flood susceptibility mapping. Scientific Reports, 10 (1), 1-19. - Rangari, V. A., Umamahesh, N. V., & Bhatt, C. M. (2019). Assessment of inundation risk in urban floods using HEC RAS 2D. Modeling Earth Systems and Environment, 5 (4), 1839-1851. - Rizzoli, P., Martone, M., Gonzalez, C., Wecklich, C., Tridon, D. B., Bräutigam, B., & Moreira, A. (2017). Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS Journal of Photogrammetry and Remote Sensing, 132 , 119-139. - Saksena, S., & Merwade, V. (2015). Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping. Journal of Hydrology, 530 , 180-194. - Sheikh, V. (2014). Analysis of hydroclimatic trends in the Atrak River Basin, North Khorasan, Iran (1975–2008). Environmental Resources Research, 2 (1), 1-14. - Sherpa, S. F., & Shirzaei, M. (2022). Country-wide flood exposure analysis using Sentinel-1 synthetic aperture radar data: Case study of 2019 Iran flood. Journal of Flood Risk Management, 15 (1), e12770. - Shi, X., Qin, T., Nie, H., Weng, B., & He, S. (2019). Changes in major global river discharges directed into the ocean. International Journal of Environmental Research and Public Health, 16 (8), 1469. - Smith, K. (2003). Environmental hazards: Assessing risk and reducing disaster . Routledge. - Srinivas, V. V., Tripathi, S., Rao, A. R., & Govindaraju, R. S. (2008). Regional flood frequency analysis by combining self-organizing feature map and fuzzy clustering. Journal of Hydrology, 348 (1-2), 148-166. - Tamiru, H., & Wagari, M. (2022). Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia. Modeling Earth Systems and Environment, 8 (2), 2291-2303. - Tayefi, V., Lane, S. N., Hardy, R. J., & Yu, D. (2007). A comparison of one- and two-dimensional approaches to modeling flood inundation over complex upland floodplains. Hydrological Processes: An International Journal, 21 (23), 3190-3202. - U.S. Army Corps of Engineering. (2016). HEC-RAS 5.0 Hydraulic Reference Manual . U.S. Army Corps of Engineers, Institute for Water Resources, Hydrologic Engineering Center, Davis, CA, USA, CPD-68. - Utlu, M., & Özdemir, H. (2020). How much spatial resolution do we need to model a local flood event? Benchmark testing based on UAV data from Biga River (Turkey). Arabian Journal of Geosciences, 13 (24), 1-14. - Vaghefi, S. A., Keykhai, M., Jahanbakhshi, F., Sheikholeslami, J., Ahmadi, A., Yang, H., &Abbaspour, K. C. (2019). The future of extreme climate in Iran. Scientific Reports, 9 (1), 1-11. - Wessel, B. (2016). TanDEM-X Ground Segment–DEM Products Specification Document . German Space Center. - Wessel, B., Huber, M., Wohlfart, C., Marschalk, U., Kosmann, D., & Roth, A. (2018). Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data. ISPRS Journal of Photogrammetry and Remote Sensing, 139 , 171-182. - Xu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27 (14), 3025-3033. - Xu, K., Fang, J., Fang, Y., Sun, Q., Wu, C., & Liu, M. (2021). The Importance of Digital Elevation Model Selection in Flood Simulation and AProposed Method to Reduce DEM Errors: A Case Study in Shanghai. International Journal of Disaster Risk Science, 12 (6), 890-902. - Zhang, K., Gann, D., Ross, M., Biswas, H., Li, Y., & Rhome, J. (2019a). Comparison of TanDEM-X DEM with LiDAR data for accuracy assessment in a coastal urban area. Remote Sensing, 11 (7), 876. - Zhang, K., Gann, D., Ross, M., Robertson, Q., Sarmiento, J., Santana, S., & Fritz, C. (2019b). Accuracy assessment of ASTER, SRTM, ALOS, and TDX DEMs for Hispaniola and implications for mapping vulnerability to coastal flooding. Remote Sensing of Environment, 225 , 290-306.
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