# An Operational Method for Flood Directive Implementation in Ungauged Urban Areas

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area

^{2}, 36 km

^{2}and 14 km

^{2}for Xerias, Krafsidonas and Anavros, respectively. The hydraulic-hydrodynamic modelling application involves three reaches that belongs to Xerias watershed, while Krafsidonas and Anavros streams consist of one reach per watershed (Figure 1). All selected streams drain through the city of Volos and contain multiple hydraulic structures and flood protection works. We point out that the city of Volos has experienced frequent flood events (e.g., 2003, 2006, 2009, 2012) due to heavy precipitation episodes that occurred in the last decades [4,10,25,26,34,35]. In particular, the extreme flash flood event that occurred in October of 2006 involved strong debris flow and mudslides that caused severe impacts on transportation networks, other technical infrastructures and agricultural areas. That specific event lasted from 06:00 UTC to 18:00 UTC, 9 October 2006, and generated a total rainfall amount of 232 mm [36].

## 3. Methodology

#### 3.1. Overview of Flood Modelling Approach

#### 3.2. Design Rainfall

**Step 1**:- Global estimations of parameters η and θ were extracted on the basis of pluviographic data, by optimizing the fitting metric known as Kruskal-Wallis statistic [39] against the compound (unified) sample of extreme rainfall intensities for all available time scales.
**Step 2**:- At each station, the shape parameter κ is initially obtained by fitting the GEV model to the maximum 24 h data and estimating its parameters by the L-moments method [40,41]. Next, we employ the correction technique developed by [42], in order to adjust the biased estimations of κ, thus prohibiting both the use of too high values and the generation of negative values, which are unfeasible, since the maximum rainfall cannot be bounded. We remark that such inconsistencies are mainly due to sample uncertainties, which are induced due to the small size of the observed rainfall maxima, the existence of outliers as well as measurement errors.
**Step 3**:- Based on their point values of parameter κ, we employed a geographical classification of the stations to obtain regional values that are associated with climatic and topographic characteristics.
**Step 4**:- For given parameters κ, η and θ, we employed the L-moments method to estimate the scale and location parameters, λ′ and ψ′, at each station.

**Step 1**:- Using an appropriate generator of random numbers following the desirable distribution ${F}_{X}$, we produce m synthetic samples ${x}_{i}$ = {${x}_{i1}$, ${x}_{i2}$, …, ${x}_{in}$}, where n is the length of the historical data.
**Step 2**:- From each synthetic sample
**x**_{i}we estimate its statistical characteristics and the corresponding sample parameters ${\theta}_{i}$ of ${F}_{X}$, by applying the same procedure with the historical data (e.g., method of moments, L-moments, maximum likelihood, etc.). **Step 3**:- For the desirable probability u, we generate m synthetic values using the inverse cumulative distribution function, i.e.:$${x}_{i}\left(u\right)\text{}=\text{}{F}_{X}^{-1}({\theta}_{i},\text{}u)$$
**Step 4**:- We estimate the confidence limits ${x}_{U}$(u) and ${x}_{L}$(u), by computing the larger m (1 − γ)/2 and smaller m (1 + γ)/2 values of the sorted sample of ${x}_{i}$(u).

#### 3.3. Hydrological Model Assumptions and Representation of Uncertainties

^{2}).

_{c}is the time of concentration (h), Α is the basin area (km

^{2}), L is the length of the longest runoff distance across the basin (km), and Δz is the difference between the mean elevation of the basin and the outlet elevation (m). This formula has been proved quite suitable for reproducing observed peak flood flows in a number of small river basins in Cyprus; in particular, its predictive capacity was by far superior with respect to other widely-used empirical formulas of the literature [13]. In the study, we used the time of concentration, estimated by the Giandotti formula, as the reference response time of each area of interest, i.e., the entire catchment and its sub-basins. The computation of the associated geometric quantities A, L and Δz was estimated via typical processing tools in GIS environment. In the case of complex river networks, i.e., with confluences, we considered the longest flow path across each corresponding area.

_{c}is definitely not a constant property of the basin, but it varies significantly with the flow [13,48]. In fact, the variability of t

_{c}is explained by the dependence of the kinematic wave celerity on the flow rate. Apparently, as surface runoff increases, the flow velocity across the river network and its tributaries also increases, which results in a faster response of the basin. For instance, [49] analyzed a large number of flood hydrographs and found that t

_{c}varied by even one order of magnitude across flood events of different intensities. To account for the dependence of the response time of the basin against runoff, we employed the following semi-empirical formula, which arises from the kinematic wave theory, considering that t

_{c}is inversely proportional to the design rainfall, i.e.,

#### 3.4. Hydraulic-Hydrodynamic Modelling

_{t}is the horizontal eddy viscosity coefficient; c

_{f}is the bottom friction coefficient; and f is the Coriolis parameter.

- Hydraulic structures close to erroneous DEM area.
- Hydraulic structures close to historical flood points.
- Hydraulic structures inside the Potential High Flood Risk Areas [34].
- Hydraulic structures close to recently recorded flood episodes.
- Hydraulic structures that accurate topographical data are absent.
- Hydraulic structures within main water bodies.

#### 3.5. Hydraulic Simulation of Lower Course of Volos City Streams and Evaluation Procedure

## 4. Volos City: Application and Results of the Modelling Framework

#### 4.1. Semi-Distributed Hydrological Modelling of Volos City Watersheds

^{2}and d is the rainfall duration in h. The above empirical relationship has been formulated on the basis of tabular data by UK-NERC [70], which captures a wide range of durations (in particular, from 1 min to 25 days) and catchment sizes (from 1 to 30,000 km

^{2}). For instance, in the study basin of Xerias, with A = 116.8 km

^{2}, we employed φ = 0.788 and 0.930, in order to reduce the design rainfall estimations for durations 1 and 24 h, respectively.

#### 4.2. Hydraulic Modelling of Lower Course of Volos City Streams

_{I}, AMC

_{II}, AMC

_{III}) that correspond to lower, average and upper estimations of the design rainfall, respectively, and three roughness values (−50%, average, +50%). The examined scenarios aim to quantify the uncertainty induced to extreme rainfall analysis, antecedent soil moisture conditions and estimations of the roughness coefficient. Thus, this study investigates nine (9) different operational scenarios that incorporate important (yet not all) aspects of the total model uncertainty.

_{II}, T = 100, average roughness value) is examined in the validation procedure with the skill score Critical Success Index. Table 4 presents the flooded areas (km

^{2}) per river reach and the total flooded extent of Volos city for all examined hydrologic and hydraulic scenarios at the selected return periods. Flood extent variations that are presented in Table 4, and depicted in Figure 7 and Figure 8, show that the hydrologic conditions scenarios, accounting for both rainfall and initial soil moisture uncertainty, have much stronger impact on the flood extent than the return period itself, which is only an indicator of the rainfall risk. Specifically, the flood extent ranges between 0.068 km

^{2}and 2.76 km

^{2}for dry (AMC

_{I}) conditions, from 0.21 km

^{2}to 6.01 km

^{2}for average (AMC

_{II}) conditions, and from 0.77 km

^{2}to 9.7 km

^{2}for wet (AMC

_{III}) conditions (Table 4, Figure 7). This outcome is not surprising and confirms that the generation of a flood is strongly influenced by the soil moisture that is already stored at the beginning of rainfall.

^{2}and 5.3 km

^{2}for T = 50 years, from 0.081 km

^{2}to 6.34 km

^{2}for T = 100 years, and from 0.21 km

^{2}to 9.7 km

^{2}for T = 1000 years (Table 4, Figure 7 and Figure 8). Figure 9 presents the simulated velocities only for average configurations of input rainfall, soil moisture conditions and roughness coefficients of return period: (a) T = 50 years, (b) T = 100 years, (c) T = 1000 years. Finally, the validation procedure that is based on the comparison between Xerias stream flood extent of the designed flood of T = 100 by employing the average input rainfall, soil moisture conditions and roughness coefficients and simulated flood extent of the 2006 historical flash flood event achieved a score in Critical Success Index of 0.77 and shown on Figure 10. This high score of CSI justifies the accuracy of the proposed operational methodology for flood directive implementation in urban and ungauged areas.

## 5. Concluding Remarks

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Tsakiris, G. Flood risk assessment: Concepts, modelling, applications. Nat. Hazards Earth Syst. Sci.
**2014**, 14, 1361–1369. [Google Scholar] [CrossRef] - Hall, J.; Arheimer, B.; Borga, M.; Brázdil, R.; Claps, P.; Kiss, A.; Kjeldsen, T.R.; Kriauĉuniene, J.; Kundzewicz, Z.W.; Lang, M.; et al. Understanding flood regime changes in Europe: A state-of-the-art assessment. Hydrol. Earth Syst. Sci.
**2014**, 18, 2735–2772. [Google Scholar] [CrossRef] [Green Version] - Kreibich, H.; Di Baldassarre, G.; Vorogushyn, S.; Aerts, J.C.J.H.; Apel, H.; Aronica, G.T.; Arnbjerg-nielsen, K.; Bouwer, L.M.; Bubeck, P.; Caloiero, T.; et al. Earth’s Future Special Section : Adaptation to flood risk : Results of international paired flood event studies. Earth’s Future
**2017**, 5, 953–965. [Google Scholar] [CrossRef] - Diakakis, M.; Mavroulis, S.; Deligiannakis, G. Floods in Greece, a statistical and spatial approach. Nat. Hazards
**2012**, 62, 485–500. [Google Scholar] [CrossRef] - Centre for Research on the Epidemiology of Disasters (CRED). Summarized Table of Natural Disasters in Greece from 1900 to 2017, EM-DAT: The CRED/OFDA International Disaster Database–www.emdat.be–Université Catholique de Louvain–Brussels–Belgium. Available online: http://www.emdat.be (accessed on 12 January 2018).
- Apel, H.; Thieken, A.H.; Merz, B.; Blöschl, G. Flood risk assessment and associated uncertainty. Nat. Hazards Earth Syst. Sci.
**2004**, 4, 295–308. [Google Scholar] [CrossRef] - Aronica, G.; Bates, P.D.; Horritt, M.S. Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE. Hydrol. Process.
**2002**, 16, 2001–2016. [Google Scholar] [CrossRef] - Aronica, G.T.; Franza, F.; Bates, P.D.; Neal, J.C. Probabilistic evaluation of flood hazard in urban areas using Monte Carlo simulation. Hydrol. Process.
**2012**, 26, 3962–3972. [Google Scholar] [CrossRef] - Dottori, F.; Di Baldassarre, G.; Todini, E. Detailed data is welcome, but with a pinch of salt: Accuracy, precision, and uncertainty in flood inundation modeling. Water Resour. Res.
**2013**, 49, 6079–6085. [Google Scholar] [CrossRef] - Papaioannou, G.; Loukas, A.; Vasiliades, L.; Aronica, G.T. Flood inundation mapping sensitivity to riverine spatial resolution and modelling approach. Nat. Hazards
**2016**, 83, 117–132. [Google Scholar] [CrossRef] - Teng, J.; Jakeman, A.J.; Vaze, J.; Croke, B.F.W.; Dutta, D.; Kim, S. Flood inundation modelling: A review of methods, recent advances and uncertainty analysis. Environ. Model. Softw.
**2017**, 90, 201–216. [Google Scholar] [CrossRef] - Soil Conservation Service (SCS). National Engineering Handbook; Section 4, Hydrology (NEH-4); U.S. Department of Agriculture: Washington, DC, USA, 1972.
- Efstratiadis, A.; Koussis, A.D.; Koutsoyiannis, D.; Mamassis, N. Flood design recipes vs. reality: Can predictions for ungauged basins be trusted? Nat. Hazards Earth Syst. Sci.
**2014**, 14, 1417–1428. [Google Scholar] [CrossRef] - Gr̈aler, B.; Van Den Berg, M.J.; Vandenberghe, S.; Petroselli, A.; Grimaldi, S.; De Baets, B.; Verhoest, N.E.C. Multivariate return periods in hydrology: A critical and practical review focusing on synthetic design hydrograph estimation. Hydrol. Earth Syst. Sci.
**2013**, 17, 1281–1296. [Google Scholar] [CrossRef] [Green Version] - Horritt, M.S.; Di Baldassarre, G.; Bates, P.D.; Brath, A. Comparing the performance of a 2-D finite element and a 2-D finite volume model of floodplain inundation using airborne SAR imagery. Hydrol. Process.
**2007**, 21, 2745–2759. [Google Scholar] [CrossRef] - Costabile, P.; Macchione, F. Enhancing river model set-up for 2-D dynamic flood modelling. Environ. Model. Softw.
**2015**, 67, 89–107. [Google Scholar] [CrossRef] - Horritt, M.S.; Bates, P.D. Evaluation of 1D and 2D numerical models for predicting river flood inundation. J. Hydrol.
**2002**, 268, 87–99. [Google Scholar] [CrossRef] - Merwade, V.; Cook, A.; Coonrod, J. GIS techniques for creating river terrain models for hydrodynamic modeling and flood inundation mapping. Environ. Model. Softw.
**2008**, 23, 1300–1311. [Google Scholar] [CrossRef] - Hunter, N.M.; Bates, P.D.; Horritt, M.S.; Wilson, M.D. Simple spatially-distributed models for predicting flood inundation: A review. Geomorphology
**2007**, 90, 208–225. [Google Scholar] [CrossRef] - Cook, A.; Merwade, V. Effect of topographic data, geometric configuration and modeling approach on flood inundation mapping. J. Hydrol.
**2009**, 377, 131–142. [Google Scholar] [CrossRef] - Mohammed, J.R.; Qasim, J.M. Comparison of One-Dimensional HEC-RAS with Two-Dimensional ADH for Flow over Trapezoidal Profile Weirs. Casp. J. Appl. Sci. Res.
**2012**, 1, 1–32. [Google Scholar] [CrossRef] - Néelz, S.; Pender, G.; Britain, G. Desktop Review of 2D Hydraulic Modelling Packages; DEFRA/Environment Agency: Bristol, UK, 2009; ISBN 9781849110792.
- Néelz, S.; Pender, G.; Wright, N.G. Benchmarking of 2D Hydraulic Modelling Packages; DEFRA/Environment Agency: Bristol, UK, 2010; ISBN 9781849111904.
- Néelz, S.; Pender, G. Benchmarking the Latest Generation of 2D Hydraulic Modelling Packages; DEFRA/Environment Agency: Bristol, UK, 2013; ISBN 9781849113069.
- Papaioannou, G.; Vasiliades, L.; Loukas, A.; Aronica, G.T. Probabilistic flood inundation mapping at ungauged streams due to roughness coefficient uncertainty in hydraulic modelling. Adv. Geosci.
**2017**, 44, 23–34. [Google Scholar] [CrossRef] - Papaioannou, G.; Loukas, A.; Vasiliades, L.; Aronica, G.T. Sensitivity analysis of a probabilistic flood inundation mapping framework for ungauged catchments. Eur. Water
**2017**, 60, 9–16. [Google Scholar] - Di Baldassarre, G.; Schumann, G.; Bates, P.D.; Freer, J.E.; Beven, K.J. Cartographie de zone inondable: Un examen critique d’approches déterministe et probabiliste. Hydrol. Sci. J.
**2010**, 55, 364–376. [Google Scholar] [CrossRef] - Sarhadi, A.; Soltani, S.; Modarres, R. Probabilistic flood inundation mapping of ungauged rivers: Linking GIS techniques and frequency analysis. J. Hydrol.
**2012**, 458, 68–86. [Google Scholar] [CrossRef] - Domeneghetti, A.; Vorogushyn, S.; Castellarin, A.; Merz, B.; Brath, A. Probabilistic flood hazard mapping: Effects of uncertain boundary conditions. Hydrol. Earth Syst. Sci.
**2013**, 17, 3127–3140. [Google Scholar] [CrossRef] - Dimitriadis, P.; Tegos, A.; Oikonomou, A.; Pagana, V.; Koukouvinos, A.; Mamassis, N.; Koutsoyiannis, D.; Efstratiadis, A. Comparative evaluation of 1D and quasi-2D hydraulic models based on benchmark and real-world applications for uncertainty assessment in flood mapping. J. Hydrol.
**2016**, 534, 478–492. [Google Scholar] [CrossRef] - Bates, P.D.; Wilson, M.D.; Horritt, M.S.; Mason, D.C.; Holden, N.; Currie, A. Reach scale floodplain inundation dynamics observed using airborne synthetic aperture radar imagery: Data analysis and modelling. J. Hydrol.
**2006**, 328, 306–318. [Google Scholar] [CrossRef] - Aggett, G.R.; Wilson, J.P. Creating and coupling a high-resolution DTM with a 1-D hydraulic model in a GIS for scenario-based assessment of avulsion hazard in a gravel-bed river. Geomorphology
**2009**, 113, 21–34. [Google Scholar] [CrossRef] - Bates, P.D.; Horritt, M.S.; Aronica, G.; Beven, K. Bayesian updating of flood inundation likelihoods conditioned on flood extent data. Hydrol. Process.
**2004**, 18, 3347–3370. [Google Scholar] [CrossRef] - Special Secretariat for Water, Ministry of Environment and Energy (SSW-MEE). Preliminary Assessment of the Flood Directive; Athens: Ministry of Environment and Energy; Available online: http://www.ypeka.gr/LinkClick.aspx?fileticket=T4DDG1hqQMY%3d&tabid=252&language=el-GR (accessed on 12 January 2018).
- Papaioannou, G.; Vasiliades, L.; Loukas, A. Multi-Criteria Analysis Framework for Potential Flood Prone Areas Mapping. Water Resour. Manag.
**2015**, 29, 399–418. [Google Scholar] [CrossRef] - Harats, N.; Ziv, B.; Yair, Y.; Kotroni, V.; Dayan, U. Lightning and rain dynamic indices as predictors for flash floods events in the Mediterranean. Adv. Geosci.
**2010**, 53, 57–64. [Google Scholar] [CrossRef] - Efstratiadis, A.; Papalexiou, S.M.; Markonis, I.; Mamassis, N. Ombrian curves. In Flood Risk Management Plan of River Basin District of Thessaly (GR08)–Phase A; Special Secretariat for Water, Ministry of Environment and Energy (SSW-MEE): Athens, Greece, 2016. [Google Scholar]
- Koutsoyiannis, D.; Kozonis, D.; Manetas, A. A mathematical framework for studying rainfall intensity-duration-frequency relationships. J. Hydrol.
**1998**, 206, 118–135. [Google Scholar] [CrossRef] - Hirsch, R.M.; Helsel, D.R.; Cohn, T.A.; Gilroy, E.J. Statistical analysis of hydrological data. In Handbook of Hydrology; Maidment, D.R., Ed.; McGraw-Hill: New York, NY, USA, 1993. [Google Scholar]
- Hosking, J.R.M. L-Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics. J. R. Stat. Soc. B
**1990**, 52, 105–124. [Google Scholar] - Vogel, R.M.; Fennessey, N.M. L moment diagrams should replace product moment diagrams. Water Resour. Res.
**1993**, 29, 1745–1752. [Google Scholar] [CrossRef] - Papalexiou, S.M.; Koutsoyiannis, D. Battle of extreme value distributions : A global survey on extreme daily rainfall. Water Resour. Res.
**2013**, 49, 187–201. [Google Scholar] [CrossRef] - Tyralis, H.; Koutsoyiannis, D.; Kozanis, S. An algorithm to construct Monte Carlo confidence intervals for an arbitrary function of probability distribution parameters. Comput. Stat.
**2013**, 28, 1501–1527. [Google Scholar] [CrossRef] - Sutcliffe, J.V. Methods of Flood Estimation: A Guide to Flood Studies Report; Institute of Hydrology: Wallingford, UK, 1978; Volume 49. [Google Scholar]
- Chow, V.T.; Maidment, D.R.; Mays, L.W. Applied Hydrology; McGraw-Hill: Singapore, 1988. [Google Scholar]
- Koutsoyiannis, D. A stochastic disaggregation method for design storm and flood synthesis. J. Hydrol.
**1994**, 156, 193–225. [Google Scholar] [CrossRef] - National Regulator for Compulsory Specifications (NRCS). National Engineering Handbook: Part 630—Hydrology; NRCS: Washington, DC, USA, 2004. [Google Scholar]
- Michailidi, E.M.; Antoniadi, S.; Koukouvinos, A.; Bacchi, B.; Efstratiadis, A. Timing the time of concentration: Shedding light on a paradox. Hydrol. Sci. J.
**2018**, 63. [Google Scholar] [CrossRef] - Grimaldi, S.; Petroselli, A.; Tauro, F.; Porfiri, M. Time of concentration: A paradox in modern hydrology. Hydrol. Sci. J.
**2012**, 57, 217–228. [Google Scholar] [CrossRef] - Brunner, G. HEC-RAS River Analysis System: Hydraulic Reference Manual, Version 5.0. US Army Corps of Engineers–Hydrologic Engineering Center, 2016a; pp. 1–538. Available online: http://www.hec.usace.army.mil/software/hec-ras/documentation/HEC-RAS%205.0%20Reference%20Manual.pdf (accessed on 12 January 2018).
- Alfonso, L.; Mukolwe, M.M.; Di Baldassarre, G. Probabilistic Flood Maps to support decision-making: Mapping the Value of Information. Water Resour. Res.
**2016**, 52, 1026–1043. [Google Scholar] [CrossRef] - Patel, D.P.; Ramirez, J.A.; Srivastava, P.K.; Bray, M.; Han, D. Assessment of flood inundation mapping of Surat city by coupled 1D/2D hydrodynamic modeling: A case application of the new HEC-RAS 5. Nat. Hazards
**2017**, 89, 93–130. [Google Scholar] [CrossRef] - Vozinaki, A.E.K.; Morianou, G.G.; Alexakis, D.D.; Tsanis, I.K. Comparing 1D and combined 1D/2D hydraulic simulations using high-resolution topographic data: A case study of the Koiliaris basin, Greece. Hydrol. Sci. J.
**2017**, 62, 642–656. [Google Scholar] [CrossRef] - Afshari, S.; Tavakoly, A.A.; Rajib, M.A.; Zheng, X.; Follum, M.L.; Omranian, E.; Fekete, B.M. Comparison of new generation low-complexity flood inundation mapping tools with a hydrodynamic model. J. Hydrol.
**2018**, 556, 539–556. [Google Scholar] [CrossRef] - Brunner, G. Benchmarking of the HEC-RAS Two-Dimensional Hydraulic Modeling Capabilities. US Army Corps of Engineers—Hydrologic Engineering Center, 2016b; pp. 1–116. Available online: http://www.hec.usace.army.mil/software/hec-ras/documentation/RD-51_Benchmarking_2D.pdf (accessed on 12 January 2018).
- Tsubaki, R.; Fujita, I. Unstructured grid generation using LiDAR data for urban flood inundation modelling. Hydrol. Process.
**2010**, 24, 1404–1420. [Google Scholar] [CrossRef] - Md Ali, A.; Solomatine, D.P.; Di Baldassarre, G. Assessing the impact of different sources of topographic data on 1-D hydraulic modelling of floods. Hydrol. Earth Syst. Sci.
**2015**, 19, 631–643. [Google Scholar] [CrossRef] - Teng, J.; Vaze, J.; Dutta, D.; Marvanek, S. Rapid Inundation Modelling in Large Floodplains Using LiDAR DEM. Water Resour. Manag.
**2015**, 29, 2619–2636. [Google Scholar] [CrossRef] - Bates, P.D.; De Roo, A.P.J. A simple raster-based model for flood inundation simulation. J. Hydrol.
**2000**, 236, 54–77. [Google Scholar] [CrossRef] - Chow, W. Open-Channel Hydraulics, 1st ed.; McGraw-Hill: New York, NY, USA, 1959. [Google Scholar]
- Hunter, N.M.; Bates, P.D.; Neelz, S.; Pender, G.; Villanueva, I.; Wright, N.G.; Liang, D.; Falconer, R.A.; Lin, B.; Waller, S.; et al. Benchmarking 2D hydraulic models for urban flooding. Proc. Inst. Civ. Eng. Water Manag.
**2008**, 161, 13–30. [Google Scholar] [CrossRef] [Green Version] - Bellos, V. Ways for flood hazard mapping in urbanised environments : A short literature review. Water Util.
**2012**, 25–31. [Google Scholar] - Schubert, J.E.; Sanders, B.F. Building treatments for urban flood inundation models and implications for predictive skill and modeling efficiency. Adv. Water Resour.
**2012**, 41, 49–64. [Google Scholar] [CrossRef] - Bellos, V.; Tsakiris, G. Comparing Various Methods of Building Representation for 2D Flood Modelling In Built-Up Areas. Water Resour. Manag.
**2015**, 29, 379–397. [Google Scholar] [CrossRef] - Brunner, G. HEC-RAS River Analysis System 2D Modeling User’s Manual. US Army Corps of Engineers—Hydrologic Engineering Center, 2016c; pp. 1–171. Available online: http://www.hec.usace.army.mil/software/hec-ras/documentation/HEC-RAS%205.0%202D%20Modeling%20Users%20Manual.pdf. (accessed on 12 January 2018).
- Horritt, M.S.; Bates, P.D. Predicting floodplain inundation: Raster-based modelling versus the finite-element approach. Hydrol. Process.
**2001**, 15, 825–842. [Google Scholar] [CrossRef] - Alfieri, L.; Salamon, P.; Bianchi, A.; Neal, J.; Bates, P.; Feyen, L. Advances in pan-European flood hazard mapping. Hydrol. Process.
**2014**, 28, 4067–4077. [Google Scholar] [CrossRef] - Jolliffe, I.T.; Stephenson, D.B. Forecast Verification; Wiley-Blackwell: Chichester, UK, 2012; ISBN 9780470660713. [Google Scholar]
- Koutsoyiannis, D.; Xanthopoulos, Th. Engineering Hydrology, 3rd ed.; National Technical University of Athens: Athens, Greece, 1999; p. 418. (In Greek) [Google Scholar]
- U.K. National Environmental Research Council (UK-NERC). Flood Studies Report; Institute of Hydrology: Wallingford, CT, USA, 1975.

**Figure 1.**Study watersheds of Xerias, Krafsidonas and Anavros and the junction points and stream reaches that have been selected for flood inundation modelling and mapping.

**Figure 3.**PRF484 unit hydrograph, corresponding to T = 5 years, and adjusted design hydrograph, corresponding to T = 200 years, in a hypothetical basin of 100 km

^{2}, considering a reference time of concentration of 3.0 h and adjusted time of 1.8 h.

**Figure 4.**Fitting of GEV distribution to daily rainfall maxima and estimation of associated parameter values through the L-moments method (resulting to biased values) and the correction technique by Papalexiou and Koutsoyiannis [42], at the rainfall stations of (

**a**) Makrynitsa; (

**b**) Agchialos.

**Figure 5.**Maps of distributed values of IDF parameters over the Water District of Thessaly: (

**a**) scale parameter λ′; (

**b**) location parameter ψ′.

**Figure 6.**Design hydrographs at the outlet of Xerias river basin for average moisture conditions (CN II) and the study return periods.

**Figure 7.**Box and Whisker plots of all examined scenarios according to flood extent (km

^{2}): (

**a**) for all return periods (50, 100, 1000 years) and, (

**b**) for all hydrologic conditions (AMC

_{I}, AMC

_{II}, AMC

_{III}).

**Figure 8.**Flood extent and water depths for all configurations of input rainfall, soil moisture conditions and roughness coefficients of return periods: (

**a**) T = 50, (

**b**) T = 100, and (

**c**) T = 1000 years.

**Figure 9.**Simulated velocities only for average configurations of input rainfall, soil moisture conditions and roughness coefficients of return periods: (

**a**) T = 50, (

**b**) T = 100, and (

**c**) T = 1000 years.

**Figure 10.**Xerias stream flood extent of the designed flood of T = 100 years, by employing the average input rainfall, soil moisture conditions and roughness coefficients and simulated flood extent of the 2006 historical flash flood event (CSI = 0.77).

LABEL1 | LABEL2 | LABEL3 | Mannings n |
---|---|---|---|

1 Artificial surfaces | 1.1 Urban fabric | 1.1.1 Continuous urban fabric | 0.013 |

1.1.2 Discontinuous urban fabric | |||

1.2 Industrial, commercial and transport units | 1.2.1 Industrial or commercial units | 0.013 | |

1.2.2 Road and rail networks and associated land | |||

1.2.3 Port areas | |||

1.2.4 Airports | |||

1.3 Mine, dump and construction sites | 1.3.1 Mineral extraction sites | 0.013 | |

1.3.2 Dump sites | |||

1.3.3 Construction sites | |||

1.4 Artificial, non-agricultural vegetated areas | 1.4.1 Green urban areas | 0.025 | |

1.4.2 Sport and leisure facilities | |||

2 Agricultural areas | 2.1 Arable land | 2.1.1 Non-irrigated arable land | 0.03 |

2.1.2 Permanently irrigated land | |||

2.1.3 Rice fields | |||

2.2 Permanent crops | 2.2.1 Vineyards | 0.08 | |

2.2.2 Fruit trees and berry plantations | |||

2.2.3 Olive groves | |||

2.3 Pastures | 2.3.1 Pastures | 0.035 | |

2.4 Heterogeneous agricultural areas | 2.4.1 Annual crops associated with permanent crops | 0.04 | |

2.4.2 Complex cultivation patterns | 0.04 | ||

2.4.3 Land principally occupied by agriculture, with significant areas of natural vegetation | 0.05 | ||

2.4.4 Agro-forestry areas | 0.06 | ||

3 Forest and semi natural areas | 3.1 Forests | 3.1.1 Broad-leaved forest | 0.1 |

3.1.2 Coniferous forest | |||

3.1.3 Mixed forest | |||

3.2 Scrub and/or herbaceous vegetation associations | 3.2.1 Natural grasslands | 0.04 | |

3.2.2 Moors and heathland | 0.05 | ||

3.2.3 Sclerophyllous vegetation | 0.05 | ||

3.2.4 Transitional woodland-shrub | 0.06 | ||

3.3 Open spaces with little or no vegetation | 3.3.1 Beaches, dunes, sands | 0.025 | |

3.3.2 Bare rocks | 0.035 | ||

3.3.3 Sparsely vegetated areas | 0.027 | ||

3.3.4 Burnt areas | 0.025 | ||

3.3.5 Glaciers and perpetual snow | 0.01 | ||

4 Wetlands | 4.1 Inland wetlands | 4.1.1 Inland marshes | 0.04 |

4.1.2 Peat bogs | |||

4.2 Maritime wetlands | 4.2.1 Salt marshes | 0.04 | |

4.2.2 Salines | |||

4.2.3 Intertidal flats | |||

5 Water bodies | 5.1 Inland waters | 5.1.1 Water courses | 0.05 |

5.1.2 Water bodies | |||

5.2 Marine waters | 5.2.1 Coastal lagoons | 0.07 | |

5.2.2 Estuaries | |||

5.2.3 Sea and ocean |

**Table 2.**Estimation of maximum 24-h rainfall at the two stations that are neighboring to Volos city, by employing the IDF relationship, and 80% confidence intervals (low and up values).

Station | T = 50 Years | T = 100 Years | T = 1000 Years | ||||||
---|---|---|---|---|---|---|---|---|---|

20% low | IDF | 80% up | 20% low | IDF | 80% up | 20% low | IDF | 80% up | |

Makrynitsa | 208.6 | 238.0 | 263.6 | 230.9 | 272.9 | 311.9 | 300.5 | 406.1 | 530.0 |

Agchialos | 105.1 | 140.5 | 168.8 | 113.8 | 162.9 | 207.7 | 134.8 | 248.3 | 407.3 |

**Table 3.**Characteristic geomorphological properties and input parameters of Xerias sub-basins (A: sub-basin area; z

_{m}: mean elevation; z

_{o}: outlet elevation; L

_{max}: maximum flow length; λ′ and ψ′: spatially-averaged scale and location parameters of IDF relationship; CN

_{I}, CN

_{II}, and CN

_{III}: runoff curve number values for AMC type I, II and III, respectively).

id | A (km^{2}) | z_{m} (m) | z_{o} (m) | L_{max} (km) | λ′ | ψ′ | t_{c} (h) | CN_{I} | CN_{II} | CN_{III} |
---|---|---|---|---|---|---|---|---|---|---|

1 | 6.1 | 66.0 | 0.0 | 5.6 | 698.1 | 0.757 | 2.81 | 49.3 | 69.8 | 84.2 |

2 | 1.4 | 26.4 | 8.7 | 1.7 | 695.4 | 0.754 | 2.17 | 62.5 | 79.9 | 90.1 |

3 | 20.4 | 199.8 | 21.3 | 8.9 | 613.6 | 0.738 | 2.94 | 48.8 | 69.4 | 83.9 |

4 | 8.0 | 140.0 | 21.3 | 5.1 | 686.9 | 0.749 | 2.17 | 46.6 | 67.5 | 82.7 |

5 | 7.5 | 73.3 | 8.7 | 5.2 | 754.0 | 0.763 | 2.91 | 65.8 | 82.1 | 91.3 |

6 | 2.2 | 130.2 | 51.9 | 3.1 | 789.5 | 0.768 | 1.50 | 60.5 | 78.5 | 89.4 |

7 | 22.3 | 447.7 | 58.7 | 10.6 | 808.6 | 0.771 | 2.20 | 29.2 | 49.6 | 69.4 |

8 | 13.6 | 338.4 | 170.7 | 7.7 | 697.7 | 0.743 | 2.54 | 31.3 | 52.0 | 71.4 |

9 | 20.0 | 722.7 | 170.7 | 15.1 | 788.1 | 0.766 | 2.15 | 32.4 | 53.3 | 72.4 |

10 | 15.3 | 1236.7 | 800.1 | 7.0 | 825.0 | 0.775 | 1.57 | 49.3 | 69.8 | 84.2 |

**Table 4.**Flooded areas (km

^{2}) per river reach and total flooded extent of Volos city for all examined hydrologic and hydraulic scenarios at the selected return periods.

Code | River Name | Hydrologic Conditions/Roughness Coefficient Conditions | Return Period (Years) | ||
---|---|---|---|---|---|

50 | 100 | 1000 | |||

GR0817FR00700 | Xerias | Dry (AMC_{I})/ −50% | 0.42 | 0.49 | 1.79 |

Average (AMC_{II})/ Average | 2.15 | 2.63 | 4.84 | ||

Wet (AMC_{III})/ +50% | 3.69 | 4.49 | 6.33 | ||

GR0817FR00800 | Krafsidonas | Dry (AMC_{I})/ −50% | 0.085 | 0.087 | 0.75 |

Average (AMC_{II})/ Average | 0.34 | 0.45 | 0.99 | ||

Wet (AMC_{III})/ +50% | 0.93 | 1.34 | 2.91 | ||

GR0817FR00900 | Anavros | Dry (AMC_{I})/ −50% | 0.068 | 0.081 | 0.21 |

Average (AMC_{II})/ Average | 0.21 | 0.25 | 0.33 | ||

Wet (AMC_{III})/ +50% | 0.77 | 0.82 | 1.2 | ||

Entire Volos city | Xerias & Krafsidonas & Anavros | Dry (AMC_{I})/ −50% | 0.57 | 0.66 | 2.76 |

Average (AMC_{II})/ Average | 2.68 | 3.32 | 6.01 | ||

Wet (AMC_{III})/ +50% | 5.3 | 6.34 | 9.7 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Papaioannou, G.; Efstratiadis, A.; Vasiliades, L.; Loukas, A.; Papalexiou, S.M.; Koukouvinos, A.; Tsoukalas, I.; Kossieris, P.
An Operational Method for Flood Directive Implementation in Ungauged Urban Areas. *Hydrology* **2018**, *5*, 24.
https://doi.org/10.3390/hydrology5020024

**AMA Style**

Papaioannou G, Efstratiadis A, Vasiliades L, Loukas A, Papalexiou SM, Koukouvinos A, Tsoukalas I, Kossieris P.
An Operational Method for Flood Directive Implementation in Ungauged Urban Areas. *Hydrology*. 2018; 5(2):24.
https://doi.org/10.3390/hydrology5020024

**Chicago/Turabian Style**

Papaioannou, George, Andreas Efstratiadis, Lampros Vasiliades, Athanasios Loukas, Simon Michael Papalexiou, Antonios Koukouvinos, Ioannis Tsoukalas, and Panayiotis Kossieris.
2018. "An Operational Method for Flood Directive Implementation in Ungauged Urban Areas" *Hydrology* 5, no. 2: 24.
https://doi.org/10.3390/hydrology5020024