River flooding causes significant losses to crops and negatively affects local agriculture economies, particularly in rural riverine areas. In this work, a techno-economic methodology for the monetary estimation of crop losses due to flash flooding is presented. The methodology takes into account flood depth and flow velocity, as provided by MIKE FLOOD, as well as the season of flood occurrence, and provides monetary estimates of crop damage based on synthetic logistic flow velocity-flood depth-crop damage surfaces. The development of the flood damage surfaces involved a questionnaire survey targeting practicing and research agronomists. Subsequently, a weighted Monte Carlo simulation was performed in order to enhance the questionnaire-based loss estimate information. Finally, synthetic flow velocity-flood depth-crop damage surfaces were developed for every crop under study and for every month using logistic regression analysis. The damage surfaces are an essential component of the developed model which was implemented in Python, enabling the GIS visualization of the estimated agricultural damage. The aforementioned methodology was applied for estimating the damage caused by a flash flood that took place in the Koiliaris River Basin in Crete for which no historical data exist. The novelty of the proposed methodology is the development of local synthetic flow velocityflood depth-crop damage surfaces. Furthermore, the velocity parameter, which is taken into account, makes the methodology suitable for flash flood events, where significant discharges and high velocities dominate, or for flood event cases which are characterized by high flow velocities. The methodology identifies rural areas and agricultural land uses that are most prone to flooding and serious crop damages and thus require greater attention. Furthermore, the methodology aptitude for developing local damage surfaces could be modulated in order to confront different flood scenarios on various crops distributions and be used to address agricultural planning activities.
The present work deals with the reconstruction of the flood wave that hit Mandra town (Athens, Greece) on 15 November 2017, using the framework of forensic hydrology. The flash flood event was caused by a huge storm event with a high level of spatial and temporal variability, which was part of the Medicane Numa-Zenon. The reconstruction included: (a) the post-event collection of 44 maximum water depth traces in the town; and (b) the hydrodynamic simulation employing the HEC-RAS and MIKE FLOOD software. The derived open dataset (which also includes additional data required for hydrodynamic modeling) is shared with the community for possible use as a benchmark case for flood model developers. With regards to the modeling issues, we investigate the calibration strategies in computationally demanding cases, and test whether the calibrated parameters can be blindly transferred to another simulator (informed modeling). Regarding the calibration, it seems that the coupling of an initial screening phase with a simple grid-search algorithm is efficient. On the other hand, the informed modeling concept does not work for our study area: every numerical model has its own dynamics while the parameters are of grey-box nature. As a result, the modeler should always be skeptical about their global use.
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