An integrated approach of flash flood analysis in ungauged Mediterranean watersheds using post‐flood surveys and unmanned aerial vehicles

Abstract

This study analyzes the flash flood event of two ungauged ephemeral streams in Olympiada region (Chalkidiki, North Greece), which occurred at the 21-22 of November 2019. Aim of the study is to reconstruct the specific flash flood event, investigate the causes of flood generation mechanisms, evaluate the performance of SCS-CN hydrological and HEC-RAS hydraulic models, investigate the relation between extreme flash floods and human intervention, using the combination of ground and aerial observations obtained from the field survey and unmanned aerial vehicles (UAVs), respectively. The results of the specific discharge ranged between 9 and 11 m 3 s −1 km 2 , values that are typical for flash flood events in Mediterranean region. The comparison between the observed and simulated values of flood extent showed sufficiently good performance of the hydraulic model (CSI = 82%). However, the statistical analysis of the observed and simulated flood depths displayed a flood depth overestimation by the applied model, despite that the values of the used statistic indexes are acceptable (RMSE = 0.35 m, SD = 0.53, NSE = 0.56, PBIAS = 11.26%). The model overestimation of flood depth was attributed to the DEM low resolution and quality. Ground and aerial observations depicted the alluvial fan activation, the alternation of flow paths and the huge sediment transport. Human intervention in main streams, urban sprawl, wet AMC and sediment transport were among the main factors that contributed to the flash flood generation. This integrated approach revealed the necessity of the constant evaluation and validation of hydrological and hydraulic models in small ungauged Mediterranean watersheds and ephemeral streams. The use of UAVs in combination with ground observations and hydraulic simulation could significantly contribute to the enhanced understanding of flash flood mechanisms, in the direction of flood risk mitigation, improvement of the planning efficiency of flood prevent measures, flood hazard estimation, evolution of flood warning systems and floodplain geomorphology analysis.

Large-Scale Particle Image Velocimetry to Measure Streamflow from Videos Recorded from Unmanned Aerial Vehicle and Fixed Imaging System

“…Among the various video shooting vehicles available, unmanned aerial vehicles (UAVs), which possess highly maneuverable characteristics, can overcome terrain constraints, have orthographic capabilities, and provide high economic benefits (i.e., low cost), are being favored by an increasing number of researchers [10][11][12][13][14][15][16][17][18][19][20]. Because UAVs can overcome terrain obstacles and perform aerial photography operations, UAVs have been widely utilized in agricultural and ecological surveys [21][22][23][24][25], environmental monitoring projects [26][27][28][29], disaster monitoring efforts [30][31][32][33], and other tasks [34].…”

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

“…Nevertheless, although UASs have demonstrated to be a very useful tool in flood monitoring and delineation, they cannot substitute ground-truth measurements in this field since they cannot provide data on flood depth and the quality of the topographic products is compromised when the terrain is masked by vegetation or anthropogenic features. This issue has been recently addressed by [112], who reconstructed the flash flood events of two ungauged ephemeral streams in Olympiada region (North Greece) through the combination of ground-based and UAS observations and hydraulic (HEC-RAS) with hydrological (SCS-CN) models. Although the comparison between the modeled and observed flow extents showed a good performance of the hydraulic model, the modeled flood depths displayed an overestimation attributable to the low resolution and quality of the DEM especially in the urbanized and vegetated areas of the floodplain.…”

UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection

“…In addition, various techniques involving machine learning have been explored for efficient flood disaster management through UAVs. The specific applications include ANN, CNN, SVM, Image segmentation, and others [60][61][62]. The latest research focuses on CNN applications in flood disaster management using UAVs [63].…”