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A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrological and Hydrodynamic Processes and Modelling".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 55877

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Special Issue Editors

Dr. Aristoteles Tegos

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Guest Editor

1. Laboratory of Hydrology and Water Resources Development, School of Civil Engineering, National Technical University of Athens, GR-15780 Athens, Greece
2. TOBIN, Block 10-4, Blanchardstown Corporate Park, D15 X98N Dublin, Ireland
Interests: hydrology; environmental; floods; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Alexandros Ziogas

E-Mail Website
Guest Editor

1. EMVIS S.A., Consultant Engineers - Environmental Services Research Information Technology & Services, 21, Paparrigopoulou Str., 153 43 Ag. Paraskevi, Greece
2. Hydraulic Engineering Laboratory, Department of Civil Engineering, University of Patras, 26500 Patras, Greece
Interests: groundwater hydraulics; surface hydrology; surface hydraulics; climate change impact/adaptation analysis; remote sensing

Dr. Vasilis Bellos

E-Mail Website
Guest Editor

Department of Environmental Engineering, Democritus University of Thrace, 12 Vas. Sofias Str., 67100 Xanthi, Greece
Interests: computational hydraulics; flood modeling; river engineering; urban drainage; machine learning; uncertainty quantification; water resources management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Floods are one of the most common natural hazards affecting substantially human lives and properties globally. Engineering is of key importance to cope with the flood risk by providing integrated solutions associated with hydrological–hydraulic and coastal-advanced techniques for analysing the flooding risk, for designing flood infrastructures for the direct protection, for providing natural retention measures enhancing environmental and river restoration, for developing flood warning systems, for presenting integrated constructional and non-constructional measures in order to adapt to emerging climatic challenges and develop resilience under the modern city-environment.

This Special Issue highlights current efforts in advancing the science and applications in flood engineering and more specifically in a wide spectrum of its related geosciences such as hydrology, hydraulics, sedimentation, river restoration. We, therefore, encourage researchers and experts to present their innovative contributions in the following areas:

Recent remote sensing dataset (rainfall, flood footage etc) and its use in flood modelling;
Compound events (fluvial, pluvial, coastal flooding and flooding due to a structure failure) and the integration of each driver, along with innovative ideas joint probability flooding models;
Advances in early flood forecasting systems;
Advanced approaches in erosion and sedimentation modelling;
New trends in dam break problems;
Extreme rainfall and runoff statistical analysis;
Climatological analysis on quantifying long term changes in annual maximum flooding patterns;
New developments on natural retention measures (green and green-grey approach);
Worldwide best practises highlighting the importance of integrated flood relief schemes for adapting city resilience.

Dr. Aristoteles Tegos
Dr. Alexandros Ziogas
Dr. Vasilis Bellos
Guest Editors

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Keywords

floods
extreme hydrology modelling
hydraulic modeling
urban flooding
dam break
sedimentation modelling
remote sensing
flood warning systems
natural retention measures

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Published Papers (14 papers)

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Editorial

Jump to: Research

5 pages, 180 KiB

Open AccessEditorial

Modern Developments in Flood Modelling

by Aristoteles Tegos, Alexandros Ziogas and Vasilis Bellos

Hydrology 2023, 10(5), 112; https://doi.org/10.3390/hydrology10050112 - 15 May 2023

Cited by 3 | Viewed by 2165

Abstract

Flood modelling is among the most challenging scientific task because it covers a wide area of complex physical phenomena associated with highly uncertain and non-linear processes where the development of physically interpretive solutions usually suffers from the lack of recorded data [...] Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

Research

Jump to: Editorial

44 pages, 147237 KiB

Open AccessFeature PaperArticle

CoastFLOOD: A High-Resolution Model for the Simulation of Coastal Inundation Due to Storm Surges

by Christos Makris, Zisis Mallios, Yannis Androulidakis and Yannis Krestenitis

Hydrology 2023, 10(5), 103; https://doi.org/10.3390/hydrology10050103 - 30 Apr 2023

Cited by 11 | Viewed by 3862

Abstract

Storm surges due to severe weather events threaten low-land littoral areas by increasing the risk of seawater inundation of coastal floodplains. In this paper, we present recent developments of a numerical modelling system for coastal inundation induced by sea level elevation due to storm surges enhanced by astronomical tides. The proposed numerical code (CoastFLOOD) performs high-resolution (5 m × 5 m) raster-based, storage-cell modelling of coastal inundation by Manning-type equations in decoupled 2-D formulation at local-scale (20 km × 20 km) lowland littoral floodplains. It is fed either by outputs of either regional-scale storm surge simulations or satellite altimetry data for the sea level anomaly. The presented case studies refer to model applications at 10 selected coastal sites of the Ionian Sea (east-central Mediterranean Sea). The implemented regular Cartesian grids (up to 5 m) are based on Digital Elevation/Surface Models (DEM/DSM) of the Hellenic Cadastre. New updated features of the model are discussed herein concerning the detailed surveying of terrain roughness and bottom friction, the expansion of Dirichlet boundary conditions for coastal currents (besides sea level), and the enhancement of wet/dry cell techniques for flood front propagation over steep water slopes. Verification of the model is performed by comparisons against satellite ocean color observations (Sentinel-2 images) and estimated flooded areas by the Normalized Difference Water Index (NDWI). The qualitative comparisons are acceptable, i.e., the modelled flooded areas contain all wet area estimations by NDWI. CoastFLOOD results are also compared to a simplified, static level, “bathtub” inundation approach with hydraulic connectivity revealing very good agreement (goodness-of-fit > 0.95). Furthermore, we show that proper treatment of bottom roughness referring to realistic Land Cover datasets provides more realistic estimations of the maximum flood extent timeframe. Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

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27 pages, 16811 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

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

by Nikolaos Xafoulis, Yiannis Kontos, Evangelia Farsirotou, Spyridon Kotsopoulos, Konstantinos Perifanos, Nikolaos Alamanis, Dimitrios Dedousis and Konstantinos Katsifarakis

Hydrology 2023, 10(4), 91; https://doi.org/10.3390/hydrology10040091 - 12 Apr 2023

Cited by 20 | Viewed by 4637

Abstract

Floods are lethal and destructive natural hazards. The Mediterranean, including Greece, has recently experienced many flood events (e.g., Medicanes Zorbas and Ianos), while climate change results in more frequent and intense flood events. Accurate flood mapping in river areas is crucial for flood risk assessment, planning mitigation measures, protecting existing infrastructure, and sustainable planning. The accuracy of results is affected by all simplifying assumptions concerning the conceptual and numerical model implemented and the quality of geospatial data used (Digital Terrain Models—DTMs). The current research investigates flood modelling sensitivity against geospatial data accuracy using the following DTM resolutions in a mountainous river sub-basin of Thessaly’s Water District (Greece): (a) open 5 m and (b) 2 m data from Hellenic Cadastre (HC) and (c) 0.05 m data from an Unmanned Aerial Vehicle (UAV) topographical mission. RAS-Mapper and HEC-RAS are used for 1D (steady state) hydraulic simulation regarding a 1000-year return period. Results include flood maps and cross section-specific flow characteristics. They are analysed in a graphical flood map-based empirical fashion, whereas a statistical analysis based on the correlation matrix and a more sophisticated Machine Learning analysis based on the interpretation of nonlinear relationships between input–output variables support and particularise the conclusions in a quantifiable manner. Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

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16 pages, 5393 KiB

Open AccessArticle

Water Level Forecasting in Tidal Rivers during Typhoon Periods through Ensemble Empirical Mode Decomposition

by Yen-Chang Chen, Hui-Chung Yeh, Su-Pai Kao, Chiang Wei and Pei-Yi Su

Hydrology 2023, 10(2), 47; https://doi.org/10.3390/hydrology10020047 - 10 Feb 2023

Cited by 8 | Viewed by 2428

Abstract

In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. Unlike more complex hydrological models, the main advantage of the proposed model is that the only required data are water level data. EEMD is used to decompose water level signals from a tidal river into several intrinsic mode functions (IMFs). These IMFs are then used to reconstruct the ocean and stream components that represent the tide and river flow, respectively. The forecasting model is obtained through stepwise regression on these components. The ocean component at a location 1 h ahead can be forecast using the observed ocean components at the downstream gauging stations, and the corresponding stream component can be forecast using the water stages at the upstream gauging stations. Summing these two forecasted components enables the forecasting of the water level at a location in the tidal river. The proposed model is conceptually simple and highly accurate. Water level data collected from gauging stations in the Tanshui River in Taiwan during typhoons were used to assess the feasibility of the proposed model. The water level forecasting model accurately and reliably predicted the water level at the Taipei Bridge gauging station. Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

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19 pages, 4635 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Assessing the Impact of the Urban Landscape on Extreme Rainfall Characteristics Triggering Flood Hazards

by Yakob Umer, Victor Jetten, Janneke Ettema and Gert-Jan Steeneveld

Hydrology 2023, 10(1), 15; https://doi.org/10.3390/hydrology10010015 - 6 Jan 2023

Cited by 5 | Viewed by 3367

Abstract

This study configures the Weather Research and Forecasting (WRF) model with the updated urban fraction for optimal rainfall simulation over Kampala, Uganda. The urban parameter values associated with urban fractions are adjusted based on literature reviews. An extreme rainfall event that triggered a flood hazard in Kampala on 25 June 2012 is used for the model simulation. Observed rainfall from two gauging stations and satellite rainfall from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) are used for model validation. We compared the simulation using the default urban fraction with the updated urban fraction focusing on extreme rainfall amount and spatial-temporal rainfall distribution. Results indicate that the simulated rainfall is overestimated compared to CHIRPS and underestimated when comparing gridcell values with gauging station records. However, the simulation with updated urban fraction shows relatively better results with a lower absolute relative error score than when using default simulation. Our findings indicated that the WRF model configuration with default urban fraction produces rainfall amount and its spatial distribution outside the city boundary. In contrast, the updated urban fraction has peak rainfall events within the urban catchment boundary, indicating that a proper Numerical Weather Prediction rainfall simulation must consider the urban morphological impact. The satellite-derived urban fraction represents a more realistic urban extent and intensity than the default urban fraction and, thus, produces more realistic rainfall characteristics over the city. The use of explicit urban fractions will be crucial for assessing the effects of spatial differences in the urban morphology within an urban fraction, which is vital for understanding the role of urban green areas on the local climate. Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

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31 pages, 2005 KiB

Open AccessEditor’s ChoiceArticle

Trivariate Joint Distribution Modelling of Compound Events Using the Nonparametric D-Vine Copula Developed Based on a Bernstein and Beta Kernel Copula Density Framework

by Shahid Latif and Slobodan P. Simonovic

Hydrology 2022, 9(12), 221; https://doi.org/10.3390/hydrology9120221 - 7 Dec 2022

Cited by 10 | Viewed by 2382

Abstract

Low-lying coastal communities are often threatened by compound flooding (CF), which can be determined through the joint occurrence of storm surges, rainfall and river discharge, either successively or in close succession. The trivariate distribution can demonstrate the risk of the compound phenomenon more realistically, rather than considering each contributing factor independently or in pairwise dependency relations. Recently, the vine copula has been recognized as a highly flexible approach to constructing a higher-dimensional joint density framework. In these, the parametric class copula with parametric univariate marginals is often involved. Its incorporation can lead to a lack of flexibility due to parametric functions that have prior distribution assumptions about their univariate marginal and/or copula joint density. This study introduces the vine copula approach in a nonparametric setting by introducing Bernstein and Beta kernel copula density in establishing trivariate flood dependence. The proposed model was applied to 46 years of flood characteristics collected on the west coast of Canada. The univariate flood marginal distribution was modelled using nonparametric kernel density estimation (KDE). The 2D Bernstein estimator and beta kernel copula estimator were tested independently in capturing pairwise dependencies to establish D-vine structure in a stage-wise nesting approach in three alternative ways, each by permutating the location of the conditioning variable. The best-fitted vine structure was selected using goodness-of-fit (GOF) test statistics. The performance of the nonparametric vine approach was also compared with those of vines constructed with a parametric and semiparametric fitting procedure. Investigation revealed that the D-vine copula constructed using a Bernstein copula with normal KDE marginals performed well nonparametrically in capturing the dependence of the compound events. Finally, the derived nonparametric model was used in the estimation of trivariate joint return periods, and further employed in estimating failure probability statistics. Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

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22 pages, 9911 KiB

Open AccessFeature PaperArticle

Wetland Vulnerability Metrics as a Rapid Indicator in Identifying Nature-Based Solutions to Mitigate Coastal Flooding

by Narcisa Gabriela Pricope and Greer Shivers

Hydrology 2022, 9(12), 218; https://doi.org/10.3390/hydrology9120218 - 2 Dec 2022

Cited by 7 | Viewed by 4166

Abstract

Flood mitigation in low-gradient, tidally-influenced, and rapidly urbanizing coastal locations remains a priority across a range of stakeholders and communities. Wetland ecosystems act as a natural flood buffer for coastal storms and sea level rise (SLR) while simultaneously providing invaluable benefits to urban dwellers. Assessing the vulnerability of wetlands to flood exposure under different SLR scenarios and vegetation responses to climatic variability over time allows for management actions, such as nature-based solutions, to be implemented to preserve wetland ecosystems and the services they provide. Nature-based solutions (NBSs) are a type of green infrastructure that can contribute to flood mitigation through the management and restoration of the ecosystems that provide socio-environmental benefits. However, identifying the flood mitigation potential provided by wetlands and the suitability for NBS implementation depends on the ecological condition and environmental exposure. We propose that wetland vulnerability assessments can be used as a rapid method to quantify changes in ecosystem dynamics and flood exposure and to prioritize potential locations of NBSs implementation. We quantified exposure risk using 100- and 500-year special flood hazard areas, 1–10 ft of sea level rise scenarios, and high-tide flooding and sensitivity using timeseries analyses of Landsat 8-derived multispectral indices as proxies for wetland conditions at subwatershed scales. We posit that wetland areas that are both highly vulnerable to recurrent flooding and degrading over time would make good candidate locations for NBS prioritization, especially when they co-occur on or adjacently to government-owned parcels. In collaboration with local governmental agencies responsible for flood mitigation in the coastal sub-watersheds of the City of New Bern and New Hanover County, North Carolina, we conducted field verification campaigns and leveraged local expert knowledge to identify optimal NBS priority areas. Our results identified several government-owned parcels containing highly vulnerable wetland areas that can be ranked and prioritized for potential NBS implementation. Depending on the biophysical characteristics of the area, NBS candidate wetland types include brackish and freshwater marshes and riverine swamp forests, even though the predominant wetland types by area are managed loblolly pinelands. This study underscores the critical importance of conserving or restoring marshes and swamp forests and provides a transferable framework for conducting scale-invariant assessments of coastal wetland condition and flood exposure as a rapid method of identifying potential priority areas for nature-based solutions to mitigate coastal flooding. Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

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20 pages, 8537 KiB

Open AccessArticle

Numerical and Physical Modeling of Ponte Liscione (Guardialfiera, Molise) Dam Spillways and Stilling Basin

by Monica Moroni, Myrta Castellino and Paolo De Girolamo

Hydrology 2022, 9(12), 214; https://doi.org/10.3390/hydrology9120214 - 28 Nov 2022

Cited by 2 | Viewed by 2064

Abstract

Issues such as the design or reauditing of dams due to the occurrence of extreme events caused by climatic change are mandatory to address to ensure the safety of territories. These topics may be tackled numerically with Computational Fluid Dynamics and experimentally with physical models. This paper describes the 1:60 Froude-scaled numerical model of the Liscione (Guardialfiera, Molise, Italy) dam spillway and the downstream stilling basin. The k-ω SST turbulence model was chosen to close the Reynolds-averaged Navier–Stokes equations (RANS) implemented in the commercial software Ansys Fluent ^®. The computation domain was discretized using a grid with hexagonal meshes. Experimental data for model validation were gathered from the 1:60 scale physical model of the Liscione dam spillways and the downstream riverbed of the Biferno river built at the Laboratory of Hydraulic and Maritime Constructions of the Sapienza University of Rome. The model was scaled according to the Froude number and fully developed turbulent flow conditions were reproduced at the model scale (Re > 10,000). From the analysis of the results of both the physical and the numerical models, it is clear that the stilling basin is undersized and therefore insufficient to manage the energy content of the fluid output to the river, with a significant impact on the erodible downstream river bottom in terms of scour depths. Furthermore, the numerical model showed that a less vigorous jet-like flow is obtained by removing one of the sills the dam is supplied with. Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

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22 pages, 9056 KiB

Open AccessArticle

Differentiated Spatial-Temporal Flood Vulnerability and Risk Assessment in Lowland Plains in Eastern Uganda

by Godwin Erima, Isa Kabenge, Antony Gidudu, Yazidhi Bamutaze and Anthony Egeru

Hydrology 2022, 9(11), 201; https://doi.org/10.3390/hydrology9110201 - 9 Nov 2022

Cited by 11 | Viewed by 3969

Abstract

This study was conducted to map flood inundation areas along the Manafwa River, Eastern Uganda using HECRAS integrated with the SWAT model. The study mainly sought to evaluate the predictive capacity of SWAT by comparisons with streamflow observations and to derive, using HECRAS, the flood inundation maps. Changes in Land-use/cover showed by decrease in forest areas and wetlands, and conversions into farmlands and built-up areas from 1995 to 2017 have resulted in increased annual surface runoff, sediment yield, and water yield. Flood frequency analysis for 100-, 50-, 10-, and 5-year return periods estimated peak flows of 794, 738, 638, and 510 m³/s, respectively, and total inundated areas of 129, 111, 101, and 94 km², respectively. Hazard classification of flood extent indicated that built-up areas and commercial farmlands are highly vulnerable, subsistence farmlands are moderately to highly vulnerable, and bushland, grassland, tropical high forest, woodland, and wetland areas are very low to moderately vulnerable to flooding. Results demonstrated the usefulness of combined modeling systems in predicting the extent of flood inundation, and the developed flood risk maps will enable the policy makers to mainstream flood hazard assessment in the planning and development process for mitigating flood hazards. Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

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17 pages, 16645 KiB

Open AccessFeature PaperArticle

Identifying Modelling Issues through the Use of an Open Real-World Flood Dataset

by Vasilis Bellos, Ioannis Kourtis, Eirini Raptaki, Spyros Handrinos, John Kalogiros, Ioannis A. Sibetheros and Vassilios A. Tsihrintzis

Hydrology 2022, 9(11), 194; https://doi.org/10.3390/hydrology9110194 - 31 Oct 2022

Cited by 9 | Viewed by 2675

Abstract

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. Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

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14 pages, 8400 KiB

Open AccessEditor’s ChoiceArticle

Flood Exposure of Residential Areas and Infrastructure in Greece

by Stefanos Stefanidis, Vasileios Alexandridis and Theodora Theodoridou

Hydrology 2022, 9(8), 145; https://doi.org/10.3390/hydrology9080145 - 13 Aug 2022

Cited by 30 | Viewed by 7014

Abstract

Worldwide, floods are the most common and widespread type of disaster during the 21st century. These phenomena have caused human fatalities, destruction of infrastructures and properties, and other significant impacts associated with human socioeconomic activities. In this study, the exposure of infrastructure (social, industrial and commercial, transportation) and residential areas to floods in Greek territory was considered. To accomplish the goal of the current study, freely available data from OpenStreetMap and Corine 2018 databases were collected and analyzed, as well as the flood extent zones derived under the implementation of the European Union’s (EU) Floods Directive. The results will be useful for policy-making and prioritization of prone areas based not only on the extent of flood cover but also on the possible affected infrastructure types. Moreover, the aforementioned analysis could be the first step toward an integrated national-wide flood risk assessment. Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

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18 pages, 5614 KiB

Open AccessEditor’s ChoiceArticle

Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network

by Raphaël A. H. Kilsdonk, Anouk Bomers and Kathelijne M. Wijnberg

Hydrology 2022, 9(6), 105; https://doi.org/10.3390/hydrology9060105 - 10 Jun 2022

Cited by 19 | Viewed by 4251

Abstract

Extreme precipitation events can lead to the exceedance of the sewer capacity in urban areas. To mitigate the effects of urban flooding, a model is required that is capable of predicting flood timing and volumes based on precipitation forecasts while computational times are significantly low. In this study, a long short-term memory (LSTM) neural network is set up to predict flood time series at 230 manhole locations present in the sewer system. For the first time, an LSTM is applied to such a large sewer system while a wide variety of synthetic precipitation events in terms of precipitation intensities and patterns are also captured in the training procedure. Even though the LSTM was trained using synthetic precipitation events, it was found that the LSTM also predicts the flood timing and flood volumes of the large number of manholes accurately for historic precipitation events. The LSTM was able to reduce forecasting times to the order of milliseconds, showing the applicability of using the trained LSTM as an early flood-warning system in urban areas. Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

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19 pages, 14666 KiB

Open AccessArticle

Forensic Hydrology: A Complete Reconstruction of an Extreme Flood Event in Data-Scarce Area

by Aristoteles Tegos, Alexandros Ziogas, Vasilis Bellos and Apostolos Tzimas

Hydrology 2022, 9(5), 93; https://doi.org/10.3390/hydrology9050093 - 20 May 2022

Cited by 33 | Viewed by 5853

Abstract

On 18 September 2020, the Karditsa prefecture of Thessaly region (Greece) experienced a catastrophic flood as a consequence of the IANOS hurricane. This intense phenomenon was characterized by rainfall records ranging from 220 mm up to 530 mm, in a time interval of 15 h. Extended public infrastructure was damaged and thousands of houses and commercial properties were flooded, while four casualties were recorded. The aim of this study was to provide forensic research on a reconstruction of the flood event in the vicinity of Karditsa city. First, we performed a statistical analysis of the rainfall. Then, we used two numerical models and observed data, either captured by satellites or mined from social media, in order to simulate the event a posteriori. Specifically, a rainfall–runoff CN-unit hydrograph model was combined with a hydrodynamic model based on 2D-shallow water equations model, through the coupling of the hydrological software HEC-HMS with the hydrodynamic software HEC-RAS. Regarding the observed data, the limited available gauged records led us to use a wide spectrum of remote sensing datasets associated with rainfall, such as NASA GPM–IMREG, and numerous videos posted on social media, such as Facebook, in order to validate the extent of the flood. The overall assessment proved that the exceedance probability of the IANOS flooding event ranged from 1:400 years in the low-lying catchments, to 1:1000 years in the upstream mountainous catchments. Moreover, a good performance for the simulated flooding extent was achieved using the numerical models and by comparing their output with the remote sensing footage provided by SENTINEL satellites images, along with the georeferenced videos posted on social media. Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

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22 pages, 9379 KiB

Open AccessEditor’s ChoiceArticle

Regional Ombrian Curves: Design Rainfall Estimation for a Spatially Diverse Rainfall Regime

by Theano Iliopoulou, Nikolaos Malamos and Demetris Koutsoyiannis

Hydrology 2022, 9(5), 67; https://doi.org/10.3390/hydrology9050067 - 23 Apr 2022

Cited by 17 | Viewed by 3670

Abstract

Ombrian curves, i.e., curves linking rainfall intensity to return period and time scale, are well-established engineering tools crucial to the design against stormwaters and floods. Though the at-site construction of such curves is considered a standard hydrological task, it is a rather challenging one when large regions are of interest. Regional modeling of ombrian curves is particularly complex due to the need to account for spatial dependence together with the increased variability of rainfall extremes in space. We develop a framework for the parsimonious modeling of the extreme rainfall properties at any point in a given area. This is achieved by assuming a common ombrian model structure, except for a spatially varying scale parameter which is itself modeled by a spatial smoothing model for the 24 h average annual rainfall maxima that employs elevation as an additional explanatory variable. The fitting is performed on the pooled all-stations data using an advanced estimation procedure (K-moments) that allows both for reliable high-order moment estimation and simultaneous handling of space-dependence bias. The methodology is applied in the Thessaly region, a 13,700 km² water district of Greece characterized by varying topography and hydrometeorological properties. Full article

(This article belongs to the Special Issue Modern Developments in Flood Modelling)

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