2. Materials and Methods
2.1. Study Area
2.1.1. Remote Sensing Flooding Records
- MSWEP Multi-Source Weighted-Ensemble Precipitation
- GPM-IMREG (Global Precipitation Measurement) NASA
- CMORPH, Climate Prediction Center National Weather Service National Oceanic and Atmospheric Administration (NOAA)
- PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- CHRS Irvine)
- ERA5 land (ECMWF) (ERA5-Land hourly data from 1981 to present)
- MERRA land (Modern-Era Retrospective analysis for Research and Application) Global Modeling and Assimilation Office NASA.
2.1.2. Crowd Sourcing Data
2.2. Hydrological Model Set-Up
2.3. Hydrodynamic Model Set-Up
3.1. Extreme Statistical Analysis of Plastiras Reservoir Annual Runoff
3.2. Rainfall–Runoff Analysis
3.3. Flood Mapping
- Both the rainfall spatial and temporal variability is of high importance in order to reconstruct a past flood event with reliability, especially in small catchments with high complexity (Bellos et al., 2020 ). Topography in smaller scales (such as local horographic phenomena) is a factor that can significantly affect the meteorological conditions of the atmosphere. For this reason, the scientific community has turned its attention to meteorological satellite and meteorological radar products, in order to derive distributed rainfall data, both in space and time. However, in our study the remote sensing rainfall dataset at fine time scale (namely 30 min records) seemed to underestimate the order of magnitude of the storm, in comparison with the ground meteorological stations. This is probably due to the fact that the space step of the satellite product is rather large and smoothens the extreme rainfall intensity, which was (fortunately) captured by the ground observations. Needless to say, this is not a global conclusion, but an ad hoc remark based on our study. In the end, there are no doctrines for reconstructing a flood event: all the available rainfall products should be evaluated and used.
- The latter agrees with the recent global analysis presented by Pradhan et al., 2022 , an analysis of an extreme event in Mexico , highlighting the requirement for further improvements to achieve a higher rainfall estimate accuracy . Similar results were presented by a comparative evaluation of GPM IMERG Early, Late, and Final hourly precipitation products over the Sichuan Basin, China . Most interestingly, our findings, where the Early-run product was found to have a better performance than the Late and Final IMERG products, agrees with recent research on the Evaluation of IMERG GPM products during Tropical Storm Imelda . An additional source of rainfall data is also the meteorological radars, which can achieve a better accuracy and denser resolution for both space and time. Finally, the importance of expansion of the ground meteorological network should not be underestimated. In the end, ground observations are the only “reality”, compared with the proxy data provided by satellite and radar products.
- Integrated catchment modelling is the most significant tool for reconstructing a past flood event. In our case study, we linked a rainfall–runoff model with a 2D hydrodynamic model and compared the model output with crowdsourced data. Regarding rainfall–runoff modelling, caution should be taken when assessing the time of concentration of different sub-catchments and assuming the pre soil moisture conditions of the catchment. All these parameters are very sensitive for reliable flow estimates in ungauged catchments and have already been referred to in previous studies [41,42,43].
- The question raised by Apel et al., 2009  is still valid, in respect to how detailed we need to be in flood modelling. In our case, the 2D hydrodynamic modelling of an extended low-lying irrigated area exhibited a satisfactory performance in reproducing an extreme event; however, 2D-modelling has some shortcomings (e.g., of blockage bridge assessment under high debris rates). It is recommended that the modelling analysis should be always considered in conjunction with the available survey information, as well datasets for validation. We should highlight that we cannot exclude a priori any modelling option (2D or full 1D hydrodynamic modelling) and this is subject of the data availability in each case study.
- Our analysis introduced a data-driven integrated hydrological–hydrodynamic assessment of a major past fluvial event, including several datasets for validating our model approaches. It was based on a deterministic approach, which is the current practice for natural hazards and exhibited satisfactory results herein. However, given the complexity and uncertainty associated with the hydrological and hydrodynamic components, probabilistic flood mapping approaches [2,4], coupled hydrological–hydrodynamic physically-based numerical modelling , and the recent hybrid-stochastic approaches are strongly recommended , especially when we deal with real-world engineering design.
- It is generally accepted that flood studies suffer from a lack of data. The majority of the basins are ungauged, and in gauged basins an extreme and violent event, such as a flood, can destroy the monitoring system. Forensic hydrology gives a framework in which proxy data are mined from several sources, such as human observations (crowdsourced data). Recent technological advances, namely cell phones with good cameras, widespread internet access, and social media platforms, let us to derive this kind of data for flood studies more easily. A novel part of our study was the use of distributed public information posted on Facebook. This information seems to be a treasure trove for validating complex flooding events in data-scarce areas with unavailable gauged records. This has already been documented by several recent studies [47,48] and is strongly recommended for similar future studies.
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Informed Consent Statement
Conflicts of Interest
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