FLOOD INUNDATION MAPPING USING REMOTE SENSING AND HYDRAULIC MODELING: A CASE STUDY OF THE AKÇAY SUB-BASIN

Öz

This study investigates the flood event that occurred on 12–13 December 2022 in the Akçay Sub-Basin, located in the Western Mediterranean Basin of Türkiye, a region prone to flooding. The accuracy of flood inundation maps generated using the HEC-RAS 2D hydraulic model and Sentinel-1 SAR data was evaluated. Sentinel-1 imagery was processed on the Google Earth Engine platform, and approximately 379 hectares of land were identified as affected by the flood. The HEC-RAS model was calibrated using ground measurements and GPM-IMERG satellite precipitation data, with estimated flood depths ranging between 0.0016 and 2.1279 m. Additionally, soil moisture analysis with SMAP data revealed that surface and root zone saturation played a decisive role in flood development. A limited spatial agreement (26.17%) was observed between the flood inundation map derived from the HEC-RAS hydraulic model and the flood extent map generated from Sentinel-1 satellite imagery acquired two days after the flood event. The overall accuracy, sensitivity, and F1-score were calculated as 0.37, 0.34, and 0.36, respectively. These findings demonstrate that the integrated use of remote sensing data and hydraulic modeling approaches provides significant potential for identifying and analyzing the spatial extent of flash flood events.

Anahtar Kelimeler

• GPM-IMERG
• HEC-RAS
• Sentinel-1
• SMAP
• Flood Inundation Mapping

TAŞKIN YAYILIM HARİTALAMASINDA UZAKTAN ALGILAMA VE HİDROLİK MODELLEME: AKÇAY ALT HAVZASI ÖRNEĞİ

Öz

Bu çalışma, Türkiye’nin Batı Akdeniz Havzası’nda yer alan ve taşkına eğilimli bir bölge olan Akçay Alt Havzası’nda, 12–13 Aralık 2022 tarihlerinde meydana gelen taşkın olayını incelemekte; HEC-RAS 2B hidrolik modeli ve Sentinel-1 SAR verileri kullanılarak oluşturulan taşkın yayılım haritalarının doğruluğunu değerlendirmektedir. Sentinel-1 görüntüleri, Google Earth Engine platformunda işlenmiş ve yaklaşık 379 hektarlık alanın taşkından etkilendiği belirlenmiştir. HEC-RAS modeli, yer ölçümleri ve GPM-IMERG uydu yağış verileriyle kalibre edilmiş; tahmin edilen taşkın derinlikleri 0,0016–2,1279 m arasında hesaplanmıştır. Ayrıca SMAP verileriyle toprak nemi analiz edilmiş, yüzey ve kök bölgesi doygunluğunun taşkın gelişimi üzerinde belirleyici olduğu görülmüştür. HEC-RAS modelinden elde edilen taşkın yayılım haritası ile taşkın olayından 2 gün gecikmeli olarak Sentinel-1 uydu verileri kullanılarak türetilen taşkın yayılım haritası arasında sınırlı düzeyde mekansal uyum (%26,17) elde edilmiş; doğruluk, duyarlılık ve F1 skorları sırasıyla 0,37, 0,34 ve 0,36 olarak bulunmuştur. Elde edilen bulgular, uzaktan algılama verileri ile hidrolik modelleme yaklaşımlarının birlikte kullanılmasının, ani yağış kaynaklı taşkınların mekânsal yayılımının belirlenmesi ve değerlendirilmesinde önemli bir potansiyel sunduğunu ortaya koymaktadır.

Anahtar Kelimeler

• GPM-IMERG
• HEC-RAS
• Sentinel-1
• SMAP
• Taşkın Yayılım Haritalaması

Kaynakça

1. Açıkgöz, N., Akköprü, E. 2024. Akçay Havzası Aşağı Çığırının (Antalya/Finike) Kuraklık Özelliği ve Su Hasadı Yapılabilecek Potansiyel Alanların Belirlenmesi. Journal of Geography (47), 85-100. https://doi.org/10.26650/JGEOG2023-1254965
2. Afzal, M. A., Ali, S., Nazeer, A., Khan, M. I., Waqas, M. M., Aslam, R. A., Shah, A. N. 2022. Flood inundation modeling by integrating HEC–RAS and satellite imagery: A case study of the Indus River Basin. Water, 14(19), 2984. https://doi.org/10.3390/w14192984
3. Aydın, M., & Çelik, S. 2019. Analysis of hydrological changes in the Akçay Sub-Basin using remote sensing and GIS techniques. Environmental Monitoring and Assessment, 191(12), 758. https://doi.org/10.1007/s10661-019-7958-9
4. Belay, H., Melesse, A. M., Tegegne, G., & Kassaye, S. M. 2025. Flood inundation mapping using the Google Earth Engine and HEC-RAS under land use/land cover and climate changes in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia. Remote Sensing, 17(7), 1283. https://doi.org/10.3390/rs17071283
5. Blašković, T. 2022. Raging floods hit Kumluca and Finike, Turkey. The Watchers. https://watchers.news
6. Boudaghpour, S., Bagheri, M., & Bagheri, Z. 2015. Estimation of flood environmental effects using flood zone. Arabian Journal for Science and Engineering, 40, 659–675.
7. Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104
8. Carreño Conde, F., De Mata Muñoz, M. 2019. Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study. Water, 11(12), 2454. https://doi.org/10.3390/w11122454

9. Davies, R. 2022. Turkey – Floods hit Antalya for second time in 2 weeks. FloodList. https://floodlist.com
10. Elkhrachy, I., Pham, B. Q., Costache, R. D., Tran, D. A., et al. 2021. Sentinel-1 remote sensing data and Hydrologic Engineering Centres River Analysis System two-dimensional integration for flash flood detection and modelling in New Cairo City, Egypt. Journal of Flood Risk Management, 14(1), e12692. https://doi.org/10.1111/jfr3.12692
11. Erduran, K. S., Ünal, U., Dokuz, A. Ş., & Nas, M. Ç. 2023. Köprü ayak tipi ve verevliğinin su yüzü profilleri üzerindeki etkisinin deneysel ve sayısal olarak araştırılması. Konjes, 11(1), 41–58. https://doi.org/10.36306/konjes.1171227
12. Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., & Alsdorf, D. 2007. The Shuttle Radar Topography Mission. Reviews of Geophysics, 45(RG2004). https://doi.org/10.1029/2005RG000183
13. Fawcett, T. 2006. An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
14. Fedorov, M., Badenko, V., Maslikov, V., & Chusov, A. 2016. Site selection for flood detention basins with minimum environmental impact. Procedia Engineering, 165, 1629–1636. doi: 10.1016/j.proeng.2016.11.903
15. Friedl, M. A., et al. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1), 168–182. https://doi.org/10.1016/j.rse.2009.08.016
16. Guven, D. S., Yenigun, K., Isinkaralar, O., Isinkaralar, K. 2024. Modeling flood hazard impacts using GIS-based HEC-RAS technique towards climate risk in Şanlıurfa, Türkiye. Natural Hazards. https://doi.org/10.1007/s11069-024-05878-3
17. Karasaka, L., Güneş, M. 2024. Analysing the impact of urban growth on agricultural lands using SLEUTH model and Google Earth Engine. Konjes, 12(4), 1006–1021. https://doi.org/10.36306/konjes.1563738
18. Lakshmi, V., Kir, E. G., & Fang, B. 2025. Precipitation Data Accuracy and Extreme Rainfall Detection for Flood Risk Analysis in the Akçay Sub-Basin. Remote Sensing, 17(18), 3199. https://doi.org/10.3390/rs17183199
19. Liz, M. S. M. de, & Ribas, R. P. 2022. Flood extent delineation using Sentinel-1 data as information source: Systematic review of processing methods. Revista Brasileira de Geografia Física, 15(6), 3047–3076.
20. Manning, D. A. C., Sweeney, A., & Guo, Y. 2008. The F1 score and the balance between precision and recall in the evaluation of predictive models. Journal of Applied Statistics, 35(7), 835–847. https://doi.org/10.1080/02664760801998891
21. Mishra, K., & Sinha, R. 2020. Flood risk assessment in the Kosi megafan using multi criteria decision analysis: A hydro geomorphic approach. Geomorphology, 350, 106861. https://doi.org/10.1016/j.geomorph.2019.106861
22. Pekel, J.-F., Cottam, A., Gorelick, N., & Belward, A. S. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418–422. https://doi.org/10.1038/nature20584
23. Powers, D. M. W. 2011. Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation. Journal of Machine Learning Technologies, 2(1), 37–63.
24. Psomiadis, E., Diakakis, M., & Soulis, K. X. 2020. Combining SAR and optical earth observation with hydraulic simulation for flood mapping and impact assessment. Remote Sensing, 12(23), 3980. https://doi.org/10.3390/rs12233980
25. Pulvirenti, L., Chini, M., Pierdicca, N., & Boni, G. 2011. Use of SAR data for detecting floodwater in urban and agricultural areas: The role of the polarization, observed wavelengths and incidence angles. IEEE Transactions on Geoscience and Remote Sensing, 49(11), 4153–4162. https://doi.org/10.1109/TGRS.2011.2138720
26. Roohi, M., Ghafouri, H. R., Ashrafi, S. M., Motagh, M., & Haghshenas Haghighi, M. (2025). A hybrid approach for enhanced flood prediction and assessment: Leveraging physical models, deep learning and satellite remote sensing. Big Earth Data, 9(3), 439–472. https://doi.org/10.1080/20964471.2025.2530850
27. Roth, F. 2023. Sentinel-1-based analysis of the severe flood over Pakistan 2022. Natural Hazards and Earth System Sciences, 23, 3305–3317. https://doi.org/10.5194/nhess-23-3305-2023
28. Schiavina, F., Melchiorri, M., Pesaresi, M. 2019. GHSL Data Package 2019. EUR 29788 EN. Publications Office of the European Union, Luxembourg. https://doi.org/10.2760/290498
29. Shapovalova, A. A. 2025. Flood radar: Multi-sensor SAR-based flood mapping and evacuation modeling — A case study of the July 2025 Texas flood. Preprint. https://doi.org/10.31223/X5GJ08
30. Sy, H. M., Luu, C., Bui, Q. D., Ha, H., & Nguyen, D. Q. 2023. Urban flood risk assessment using Sentinel-1 on the Google Earth Engine: A case study in Thai Nguyen city, Vietnam. Remote Sensing Applications: Society and Environment, 30, 100987. https://doi.org/10.1016/j.rsase.2023.100987
31. Uddin, K., Matin, M. A., & Meyer, F. J. 2019. Operational flood mapping using multi-temporal Sentinel-1 SAR images: A case study from Bangladesh. Remote Sensing, 11(13), 1581. https://doi.org/10.3390/rs11131581
32. UN-SPIDER. 2021. Recommended practice: Flood mapping using Google Earth Engine and Sentinel-1 SAR data. https://un-spider.org
33. Zotou, I., Bellos, V., Gkouma, A., Karathanassi, V., & Tsihrintzis, V. A. 2020. Using Sentinel-1 imagery to assess predictive performance of a hydraulic model. Water Resources Management, 34(12), 4415–4430. https://doi.org/10.1007/s11269-020-02592-7
34. Zotou, I., Karamvasis, K., Karathanassi, V., & Tsihrintzis, V. A. 2022. Potential of Sentinel-1 data in assisting flood modeling procedures: A case study in Spercheios River, Central Greece. Water, 14(24), 4020. https://doi.org/10.3390/w14244020