ترکیب فناوری‌های ماهواره‌ای و پهپادی برای شبیه‌سازی‌های هیدرولیکی با وضوح بالا: مطالعه موردی در حوضه مارون ایران

1.Soriano, E., Mediero, L., & Garijo, C. (2020). Quantification of Expected Changes in Peak Flow Quantiles in Climate Change by Combining Continuous Hydrological Modelling with the Modified Curve Number Method. Water Resources Management, 34 (14), 4381-4397. https://doi.org/10. 1007/s11269-020-02670-w.

2.Blöschl, G., Hall, J., Viglione, A., Perdigão, R. A. P., Parajka, J., Merz, B., Lun, D., Arheimer, B., Aronica, G. T., Bilibashi, A., Boháč, M., Bonacci, O., Borga, M., Čanjevac, I., Castellarin, A., Chirico, G. B., Claps, P., Frolova, N., Ganora, D., … Živković, N. (2019). Changing climate both increases and decreases European river floods. Nature, 573 (7772), 108–111. https://doi.org/10. 1038/s41586-019-1495-6.

3.Salarijazi, M. (2012). Trend and change-point detection for the annual stream-flow series of the Karun River at the Ahvaz hydrometric station. African Journal of Agricultural Research 7(32). https://doi. org/10.5897/AJAR12.650.

4.Bahrami, E., Salarijazi, M., & Nejatian, S. (2022). Estimation of flood hydrographs in the ungauged mountainous watershed with Gray synthetic unit hydrograph model. Arabian Journal of Geosciences, 15 (8), 761. https://doi.org/10. 1007/ s12517-022-10029-1.

5.Pedrozo‐Acuña, A., Rodríguez‐Rincón, J. P., Arganis‐Juárez, M., Domínguez‐Mora, R., & González Villareal, F. J. (2015). Estimation of probabilistic flood inundation maps for an extreme event:
P ánuco River, M éxico. Journal of
Flood Risk Management, 8(2), 177–192. https://doi.org/10.1111/jfr3.12067.

6.Li, B., Hou, J., Li, D., Yang, D., Han, H., Bi, X., Wang, X., Hinkelmann, R., & Xia, J. (2021). Application of LiDAR UAV for High-Resolution Flood Modelling. Water Resources Management, 35(5), 1433-1447. https://doi.org/10. 1007/s11269-021-02783-w.

7.Trepekli, K., Balstrøm, T., Friborg, T., Fog, B., Allotey, A. N., Kofie, R. Y., & Møller-Jensen, L. (2022). UAV-borne, LiDAR-based elevation modelling: A method for improving local-scale urban flood risk assessment. Natural Hazards, 113(1), 423-451. https://doi.org/10.1007/ s11069-022-05308-9.

8.Annis, A., Nardi, F., Petroselli, A., Apollonio, C., Arcangeletti, E., Tauro, F., Belli, C., Bianconi, R., & Grimaldi, S. (2020). UAV-DEMs for Small-Scale Flood Hazard Mapping. Water, 12(6), 1717. https://doi.org/10.3390/w12061717.

9.Escobar Villanueva, J. R., Iglesias Martínez, L., & Pérez Montiel, J. I. (2019). DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations. Sensors, 19(14), 3205. https://doi.org/10.3390/s19143205.

10.Mollaee, Z., Zahiri, J., Jalili, S., Ansari, M. R., & Taghizadeh, A. (n.d.-b). Estimating suspended sediment concentration using remote sensing and artificial neural network (case study: Karun river). Jwss, 22(2), 249–259.

11.Zolghadr, M., Rafiee, M. R., Esmaeilmanesh, F., Fathi, A., Tripathi, R. P., Rathnayake, U., Gunakala, S. R., & Azamathulla, H. M. (2022). Computation of Time of Concentration Based on Two-Dimensional Hydraulic Simulation. Water, 14(19), 3155. https://doi.org/10.3390/w14193155.

12.Sardemann, H., Eltner, A., & Maas, H.-G. (2018). Acquisition of geometrical data of small rivers with an unmanned water vehicle. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII–2, 1023-1027. https://doi.org/10. 5194/isprs-archives-XLII-2-1023-2018.

13.Fathi, A., & Zolghadr, M. (2024). A Novel Method for Estimating Time of Concentration in Ungauged Catchments. Water Resources Management, 38(11), 4003-4018. https://doi.org/10. 1007/s11269-024-03849-1.

14.Pandya, D., Rana, V. K., & Suryanarayana, T. M. V. (2024). Inter-comparison and assessment of digital elevation models for hydrological applications in the Upper Mahi River Basin. Applied Geomatics, 16(1), 191-214. https://doi.org/10. 1007/s12518-023-00547-2.

15.Xu, K., Fang, J., Fang, Y., Sun, Q., Wu, C., & Liu, M. (2021). The Importance of Digital Elevation Model Selection in Flood Simulation and a Proposed Method to Reduce DEM Errors: A Case Study in Shanghai. International Journal of Disaster Risk Science, 12(6), 890-902. https://doi.org/10.1007/s13753-021-00 377-z.

16.Azizian, A., & Brocca, L. (2020). Determining the best remotely sensed DEM for flood inundation mapping in data sparse regions. International Journal of Remote Sensing, 41(5), 1884-1906. https://doi.org/10.1080/01431161.2019.1677968.

17.Muthusamy, M., Casado, M. R., Butler, D., & Leinster, P. (2021). Understanding the effects of Digital Elevation Model resolution in urban fluvial flood modelling. Journal of Hydrology, 596, 126088. https://doi.org/10. 1016/ j.jhydrol.2021.126088.

18.Costabile, P., Costanzo, C., Ferraro, D., & Barca, P. (2021). Is HEC-RAS 2D accurate enough for storm-event hazard assessment? Lessons learnt from a benchmarking study based on rain-on-grid modelling. Journal of Hydrology, 603, 126962. https://doi.org/10.1016/ j.jhydrol.2021.126962.

19.Khattak, M. S., Anwar, F., Saeed, T., Sharif, M., Sheraz, K., & Ahmed, A. (2015). Floodplain Mapping Using HEC-RAS and ArcGIS: A Case Study of Kabul River. The Arabian Journal for Science and Engineering, 41, 1375-1390.

20.AL-Hussein, A. A. M., Khan, S., Ncibi, K., Hamdi, N., & Hamed, Y. (2022). Flood Analysis Using HEC-RAS and HEC-HMS: A Case Study of Khazir River (Middle East-Northern Iraq). Water, 14 (22), 3779. https://doi.org/ 10.3390/w14223779.

21.Iqbal, A., Mondal, M. S., Veerbeek, W., Khan, M. S. A., & Hakvoort, H. (2023). Effectiveness of UAV ‐based DTM and satellite‐based DEMs for local‐level flood modeling in Jamuna floodplain. Journal of Flood Risk Management,
16 (4), e12937. https://doi.org/10. 1111/jfr3.12937.

22.Zahiri, J., Ashnavar, M. (2019). Two-Dimensional Hydraulic Modeling of Karun River. Journal of Water and Soil Science, 23(4). https://doi.org/10. 47176/jwss.23.4.36151.

23.Liu, Z., Merwade, V., & Jafarzadegan, K. (2019). Investigating the role of model structure and surface roughness in generating flood inundation extents using one‐and two‐dimensional hydraulic models. Journal of Flood Risk Management, 12(1), e12347. https://doi. org/10.1111/jfr3.12347.

24.Karamuz, E., Romanowicz, R. J., & Doroszkiewicz, J. (2020). The use of unmanned aerial vehicles in flood hazard assessment. Journal of Flood Risk Management, 13(4), e12622. https://doi.org/10.1111/jfr3.12622.

25.Ozcan, O., & Akay, S. S. (2018). Modeling Morphodynamic Processes in Meandering Rivers with UAV-Based Measurements. IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 7886-7889. https://doi.org/10.1109/IGARSS.2018.8518221.

26.Dekker, R. J., Schuurmans, J. M., Berendrecht, W. L., Borren, W., Ven, T. J. M. van de, & Westerhoff, R. S. (2010). Improving hydrological models of the Netherlands using ALOS PALSAR. ESA Conference on Earth Observation and Water Cycle Science.

27.Langhammer, J., Bernsteinová, J., & Miřijovský, J. (2017). Building a High-Precision 2D Hydrodynamic Flood Model Using UAV Photogrammetry and Sensor Network Monitoring. Water, 9 (11), 861. https://doi.org/10.3390/ w9110861.

28.Massuel, S., Feurer, D., El Maaoui, M. A., & Calvez, R. (2022). Deriving bathymetries from unmanned aerial vehicles: A case study of a small intermittent reservoir. Hydrological Sciences Journal, 67 (1), 82-93. https://doi.org/10.1080/02626667.2021.1988614.

29.Yalcin, E. (2018). Generation of high-resolution digital surface models for urban flood modelling using uav imagery. 357-366. https://doi.org/10. 2495/EID180321.

30.Tang, Q., Schilling, O. S., Kurtz, W., Brunner, P., Vereecken, H., & Hendricks Franssen, H. (2018). Simulating Flood‐Induced Riverbed Transience Using Unmanned Aerial Vehicles, Physically Based Hydrological Modeling, and the Ensemble Kalman Filter. Water Resources Research, 54 (11), 9342-9363. https://doi.org/10. 1029/2018WR023067.

31.Xafoulis, N., Kontos, Y., Farsirotou, E., Kotsopoulos, S., Perifanos, K., Alamanis, N., Dedousis, D., & Katsifarakis, K. (2023). Evaluation of Various Resolution DEMs in Flood Risk Assessment and Practical Rules for Flood Mapping in Data-Scarce Geospatial Areas: A Case Study in Thessaly, Greece. Hydrology, 10 (4), 91. https://doi.org/10.3390/ hydrology10040091.

32.Zhu, H., & Chen, Y. (2024). A Study of the Effect of DEM Spatial Resolution on Flood Simulation in Distributed Hydrological Modeling. Remote Sensing, 16(16), 3105. https://doi.org/10.3390/ rs16163105.

33.McClean, F., Dawson, R., & Kilsby, C. (2020). Implications of Using Global Digital Elevation Models for Flood Risk Analysis in Cities. Water Resources Research, 56(10), e2020WR028241. https://doi.org/10.1029/2020WR028241.

34.Parizi, E., Khojeh, SH., Hosseini, S. M., & Jouybari Moghadam, Y. (2022). Application of unmanned aerial vehicle DEM in flood modeling and comparison with global DEMs: Case study of
Atrak River Basin, Iran. Journal of Environmental Management, 317. https://doi.org/10.1016/j.jenvman.2022.114650.

35.Mazzoleni, M., Paron, P., Reali, A., Juizo, D., Manane, J., & Brandimarte, L. (2020). Testing UAV-derived topography for hydraulic modelling in a tropical environment. Natural Hazards, 103(1), 139-163. https://doi.org/10. 1007/s11069-020-03963-4.

36.Leitão, J. P., Moy de Vitry, M., Scheidegger, A., & Rieckermann, J. (2016). Assessing the quality of digital elevation models obtained from mini unmanned aerial vehicles for overland flow modelling in urban areas. Hydrology and Earth System Sciences, 20 (4), 1637-1653. https://doi.org/10. 5194/hess-20-1637-2016.

37.Jaramillo, G. V., & Bustán, G. A. (2024). Assessment of spatial data obtained by means of the use of unmanned aerial vehicle (UAV). Proceedings of International Structural Engineering and Construction. https://api.semanticscholar.org/Corpus ID:268671095.

38.Rudd, J. D., Roberson, G. T., & Classen, J. J. (2017). Application of satellite, unmanned aircraft system, and ground-based sensor data for precision agriculture: A review. 2017 Spokane, Washington July 16 - July 19, 2017. 2017 Spokane, Washington July 16 - July 19, 2017. https://doi.org/10. 13031/aim.201700272.

39.Adão, T., Hruska, J., Pádua, L., Bessa, J. E., Peres, E., Morais, R., & Sousa, J. J. (n.d.-a). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing.

40.Liao, X., Zhang, Y., Su, F., Yue, H., Ding, Z., & Liu, J. (2018). UAVs surpassing satellites and aircraft in remote sensing over China. International Journal of Remote Sensing, 39(21), 7138-7153. https://doi.org/10. 1080/01431161.2018.1515511.

41.Hashemi-Beni, L., Jones, J., Thompson, G., Johnson, C., & Gebrehiwot, A. (2018). Challenges and Opportunities for UAV-Based Digital Elevation Model Generation for Flood-Risk Management: A Case of Princeville, North Carolina. Sensors, 18(11), 3843. https://doi.org/ 10.3390/s18113843.

42.Peggy Zinke & Claude Flener. (n.d.). Experiences from the use of Unmanned Aerial Vehicles (UAV) for River Bathymetry Modeling in Norway. Water, 48(3), 351-360.

43.Notti, D., Giordan, D., Caló, F., Pepe, A., Zucca, F., & Galve, J. P. (2018). Potential and Limitations of Open Satellite Data for Flood Mapping. Remote Sensing, 10(11), 1673. https:// doi.org/10.3390/rs10111673.