نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی آب-دانشگاه علوم کشاورزی و منابع طبیعی خوزستان
2 گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، خوزستان، ایران
3 گروه مهندسی طبیعت، دانشکده کشاورزی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، خوزستان، ایران
4 رئیس گروه نوآوری و توسعه فناوری- سازمان آب و برق خوزستان، خوزستان، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
One of the fundamental aspects of flood modeling is the digital elevation model (DEM) of the riverbed and its floodplains. Given that high-accuracy digital elevation maps (DEMs) are not available in many regions of the country, this study seeks to examine the impact of DEM quality of the Karun Riverbed and its floodplains, specifically from the Molasani to Farsiat stations, on flood inundation mapping. The study area encompasses approximately 110 kilometers of the Karun River, including three hydrometric stations: Molasani, Ahvaz, and Farsiat. For two-dimensional modeling in the HEC-RAS environment, the availability of an elevation map of the study area is essential. To this end, elevation maps of the Karun River were prepared with varying accuracies using methods such as existing survey data and aerial imagery. Due to the unavailability of riverbed elevation maps, riverbed reconstruction was conducted in a GIS environment. In this study, to evaluate the quality of DEMs on flood mapping, maps with resolutions of 30, 50, 100, and 150 meters were utilized. For analyzing different scenarios, Sentinel-2 satellite images, along with 12 quantitative indices, were employed. The analysis results show that the Threat Score (TS) decreased from 67% for 30-meter resolution to 66%, 59%, and 56% for 50 m, 100 m, and 150 m resolutions, respectively, indicating an 11% reduction in accuracy with a fivefold decrease in map resolution. The results of various quantitative criteria indicate that intermediate pixel sizes (50×50 or 100×50 meters) can provide reasonable accuracy while reducing computational efforts. This is particularly useful for regional-scale studies or trans-regional analyses. Overall, the findings emphasize the importance of adjusting pixel resolution in accordance with the specific objectives and constraints of flood modeling tasks.
کلیدواژهها [English]
Effective flood management requires flood mapping, estimating potential damages and risks in flood-prone areas, and designing a comprehensive plan to mitigate flood risks. Understanding the phenomena and the impacts of changes on flow conditions, as well as predicting hydraulic events in rivers, plays a significant role in minimizing damages and losses. Modern methodologies leverage techniques such as remote sensing, geographic information systems (GIS), and hydraulic and hydrological models to simulate river flows. Given the lack of high-accuracy topographic maps in many areas of the country, this study aims to investigate the impact of the quality of topographic maps for the Karun River basin, including the riverbed and floodplains areas from Molasani to Farsiat, on flood inundation mapping.
The study area covers approximately 110 km of the Karun River, including three hydrometric stations: Molasani, Ahvaz, and Farsiat. For two-dimensional modeling in the HEC-RAS environment, a digital elevation map (DEM) of the study area is essential. Therefore, DEMs of the Karun River with varying resolutions were prepared using existing survey data and aerial imagery. In aerial maps, riverbed elevation is represented as the water surface elevation. Due to the unavailability of a detailed elevation map for the Karun Riverbed, the riverbed was constructed in the GIS environment based on existing cross sections. For evaluating the impact of topographic map quality on floodplain mapping, maps with resolutions of 30 m, 50 m, 100 m, and 150 m were used. Subsequently, flood inundation map was generated using HEC-RAS model based on the different DEMs. To investigate the efficiency of the different DEMs with varying resolutions, Sentinel-2 satellite imagery and 12 quantitative metrics were employed. These metrics include Proportion Correct (PC), Threat Score (TS), Odds Ratio (θ), Bias, False Alarm Ratio (FAR), Hit Rate (H), False Alarm Rate (F), Extremal Dependence Index (EDI), Heidke Skill Score (HSS), Pierce Skill Score (PSS), Success Ratio, and Odds Ratio Skill Score (ORSS).
Analysis of the performance of 16 scenarios modeled in HEC-RAS with varying pixel resolutions for the river and floodplain, focusing on the PC metric, showed that models with smaller river pixel sizes (30 m and 50 m) consistently achieved the highest PC values. For 30 m pixels, the PC was approximately 0.799, while for 50 m pixels, it was slightly lower (ranging between 0.785 and 0.791). Examination of the TS metric, which is suitable for rare event prediction, revealed that models with the highest river pixel resolution (30 m) consistently achieved the highest TS values (approximately 0.67) across all floodplain pixel sizes, indicating strong performance. Additionally, higher-resolution river pixels consistently yielded the highest Odds Ratios (θ), reflecting high prediction reliability. For 30 m river pixels, θ started at 17.69 for 30 m floodplain pixels and slightly decreased to 17.51 for 150 m floodplain pixels. Increasing river pixel size from 30 m to 150 m led to a consistent rise in Bias, indicating over-prediction tendencies in larger pixel sizes. FAR also increased significantly with larger river pixels, signifying more false alarms. For smaller river and floodplain pixels, FAR remained relatively low, ranging from 0.25 to 0.29, indicating fewer false alarms at higher resolutions. The ORSS analysis showed that smaller pixel sizes for both river and floodplain consistently yielded higher ORSS values, demonstrating superior skill.
Based on the main effect analysis of river pixel size, PC and TS scores decreased as river pixel size increased, particularly for floodplain pixel sizes of 100 m and 150 m. The Heidke, Pierce, and Gilbert skill scores also decreased with larger river pixel sizes, with Gilbert’s score showing a steep decline for larger river pixels, reflecting weak flood prediction performance at lower resolutions. Bias increased with larger river pixel sizes, indicating a tendency for over-prediction. FAR followed a similar rising trend. Regarding the main effect of floodplain pixel size, PC and TS scores declined as floodplain pixel size increased, particularly for river pixel sizes of 100 m and 150 m. The drop in TS suggests that lower floodplain resolution reduces the model’s ability to accurately predict floods. All skill scores decreased with larger floodplain pixel sizes, especially in scenarios with 100 m and 150 m river pixels. Increased floodplain pixel sizes also resulted in higher Bias, indicating a greater tendency for over-prediction in lower floodplain resolutions.
Conceptualization, Methodology, Formal analysis, Writing Original Draft, J.Z.; Methodology, Writing - Review & Editing, A.J.; Software, Writing - Review & Editing, M.Ch. and MJ.N. All authors have read and agreed to the published version of the manuscript.
Data available on request from the authors.
The authors would like to thank the reviewers and editor for their critical comments that helped to improve the paper. The authors gratefully acknowledge the support and facilities provided by the Agricultural Sciences and Natural Resources University of Khuzestan [Grant number:1/411/1078].
The authors avoided data fabrication, falsification, plagiarism, and misconduct.
The author declares no conflict of interest.
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