Assessment of flash flood susceptibility potential in Moldavian Plain (Romania)

Abstract

Concentration of time (Tc) is a frequently used parameter in the evaluation of the hydrological response of different sizes hydrographic basin in case of rainfall events. The present study is innovative, because it has created an index that identifies the small-sized hydrographic basins that are exposed to the risk of flooding. The Moldavian Plain is an area located in the northeast of Romania where the local population is frequently affected by floods and flash floods caused by heavy rainfall events. The main purpose of the current study is to identify the settlements located in the small-sized hydrographic basins, which are associated with low concentration times and powerful surface runoff. The empirical method was applied in order to calculate the Tc for rainfall water, for each drainage basin, for a time class less than 6 hr. Calculations of the runoff water were also done for a theoretical extreme precipitation event, corresponding to the 1% occurrence probability. A total number of 312 basins were identified that are smaller than 30 km 2 , out of which 112 have Tc of less than 6 hr. These basins, in particular, pose flood risk for 12.4% of the villages and towns in the study area.

Remote Sensing Methods for Flood Prediction: A Review

“…The results were validated using GPS technology which shows accuracy that exceeds 30 cm [54]. Lamichhane and Sharma [55] developed a flood warning system using a DEM derived from LIDAR. The acquired LIDAR data were also integrated with some field data related to flooding in the target area to determine the evacuation time required by the people.…”

“…The acquired LIDAR data were also integrated with some field data related to flooding in the target area to determine the evacuation time required by the people. Flood risk maps were produced by an HEC-GeoRAS, a software that allows the processing of geospatial data in ArcGIS [55]. The flood risk maps were then combined with digital orthographic maps to construct a real-time online flood warning system for the public [56].…”

Using 1D HEC-RAS Modeling and LiDAR Data to Improve Flood Hazard Maps Accuracy: A Case Study from Jijia Floodplain (NE Romania)

“…According to the latest demographic studies and national census [54], in the Jijia floodplain, there are 92,826 inhabitants, of which 24,309 inhabitants are only in Dorohoi City, and the population density is 653.06 inhabitants/km 2 (2630 inhabitants/km 2 in Dorohoi) [54]. Concerning the causes and extent of flood hazard within the inhabited territory of the Jijia floodplain, the most catastrophic events took place due to intense anticyclone activity in the June-July interval [55]. The last example is the flash flood which took place in 2018 (Figure 2b,c) as a result of heavy rains generated by the intense activity of Azores anticyclone and its influence on Eastern Europe [19].…”

Estimation of flood influencing characteristics of watershed and their impact on flooding in data-scarce region

“…As a result, for ungauged watersheds or where data are not available, several researchers use runoff modelling to gain a inclusive understanding of the hydrological response of the watershed (Karmokar and De 2020). Several methods, like Soil Conservation Service (SCS) -Curve Number method, Cook's method, and unit hydrograph method, have been developed by engineers to estimate the discharge for an ungauged watershed (Gioti et al 2013;Wakode et al 2013;Elkhrachy 2015;Sudhakar, Anupam, and Akshay 2015;Abuzied et al 2016;Iosub et al 2020).…”