ANALYSES AND IMAGES OF HYDROLOGICAL EXTREMES IN MEDITERRANEAN ENVIRONMENTS
FRIEND project AMHY group
ANALYSES AND IMAGES OF HYDROLOGICAL EXTREMES IN MEDITERRANEAN ENVIRONMENTS
International Hydrological Programme (IHP)
edited by
ENNIO FERRARI PASQUALE VERSACE
edited by E. FERRARI - P. VERSACE
ISBN 978-88-97181-00-2
9 788897 181002
3rd International Workshop on Hydrological Extremes AMHY-FRIEND group 30,00
EdiBios
EdiBios
University of Calabria, Cosenza (Italy) July 10-12, 2008
ANALYSES AND IMAGES OF HYDROLOGICAL EXTREMES IN MEDITERRANEAN ENVIRONMENTS edited by ENNIO FERRARI PASQUALE VERSACE
3rd International Workshop on Hydrological Extremes AMHY-FRIEND group
Department of Soil Protection ″V. Marone″ University of Calabria Cosenza (Italy) July 10-12, 2008
ISBN 978-88-97181-00-2
On the cover:
View of Raganello Creek from the “Devil Bridge” (photo by J. Plavsic)
©2010 by EDIBIOS Via G. De Rada, 10 87100 COSENZA ____________________________________________________________________________________ Tutti i diritti riservati – All rights reserved ____________________________________________________________________________________ Finito di stampare nel mese di Settembre 2010 ____________________________________________________________________________________
University of Calabria Cosenza (Italy) July 10-12, 2008
Analyses and images of hydrological extremes in Mediterranean environments 3rd International Workshop on Hydrological Extremes The workshop is a contribution of the AMHY-FRIEND group, ″Extreme events″ topic, to UNESCO IHP-VII (2008-2013)
Proceedings of the Workshop Edited by E. Ferrari and P. Versace Soil Protection Department Faculty of Engineering University of Calabria (Italy)
Scientific Committee Baldassare Bacchi (Brescia, Italy) Vito Copertino (Potenza, Italy) Maria Carmen Llasat (Barcelona, Spain) Eric Servat (Montpellier, France) Giuseppe Mendicino (Cosenza, Italy)
With the patronage of
Organizing Committee E. Ferrari, D. Biondi, G. Capparelli, V. Caputo, F. Cruscomagno, D.L. De Luca, S. Donato, L. Galasso, A. Senatore, T. Zaffino (www.camilab.unical.it)
Sponsorship
The authors are responsible for the choice and presentation of the viewpoints and information contained in their articles, which in no way commit UNESCO. The designations employed and the presentation of data throughout this publication do not imply the expression of any opinion whatsoever on the part of UNESCO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.
Contents IX
Foreword ADVANCES IN STATISTIC AND STOCHASTIC TOOLS FOR EXTREME EVENT MODELLING
Part 1 RAINFALLS Regional climate change projections for the Eastern Mediterranean/Middle East: expected changes in water availability and droughts Heckl A., Kunstmann H., Suppan P., Rimmer A., Laux P.
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Early warning by real-time forecasting models for landslides triggered by rainfalls Versace P., Capparelli G.
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Influence of threshold values on storm occurrence process modelled with a non-homogeneous Poisson distribution De Luca D.L., Ferrari E., Sirangelo B.
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A user-friendly tool for constant mean-segmentation of long time series Aksoy H., Gedikli A., Unal N.E.
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Monthly rainfall trends and teleconnections in Calabria Caloiero T., Coscarelli R., Ferrari E., Mancini M.
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First statistical analysis of extreme rainfalls in Slovenia on 18/9/2007 Meze M., Brilly M., Mikoš M.
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Part 2 DROUGHTS Climate change scenarios in Southern Italy and tools for drought assessment and management Mendicino G., Senatore A.
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Low-flow estimation in ungauged sites in Tuscany (Italy) using a regionalization by L-moments Rossi G., Caporali E.
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Persistency in wet and dry periods in Goztepe meteorological station in Istanbul, Turkey Eris E., Aksoy H.
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VII
Part 3 FLOODS A real time ensemble flood forecasting in the Alps and in the Apennines Grossi G., Bacchi B., Ranzi R.
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Testing FEST-WB, a continuous distributed model for operational quantitative discharge forecast in the upper Po river Ravazzani G., Rabuffetti D., Corbari C., Ceppi A., Mancini M.
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Effects of record length and period on design flood level estimation on the Danube River at Novi Sad Plavšić J., Milutinović R., Stanić N.
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Preliminary hydrological analysis of the extreme flood in Slovenia on 18/9/2007 Štrukelj M., Brilly M., Kobold M., Mikoš M.
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Experimental evidence on runoff generation mechanisms Onorati B., Margotta M.R., Carriero D., Manfreda S., Fiorentino M.
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Simplified methods for identifying small catchments susceptible to generate flash floods Drobot R.
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Agenda of the workshop
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List of participants
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VIII
FOREWORD This book contains the proceedings of the 3rd International Workshop on Hydrological Extremes that was held at the University of Calabria in Cosenza (Italy) on 10th and 12th July, 2008, This scientific appointment was planned to exchange experiences and tools on the analysis of extreme hydrological and meteorological events among scientists coming from Mediterranean countries, as researchers involved in FRIEND project used to do from a long time. Actually, after about 20 years from the start, the FRIEND (Flow Regimes from International Experimental and Network Data) project is still one of the main themes of the IHP program of UNESCO (now in its VII phase: 2008-2013). The main objective of the workshop, focussed on ″Analyses and images of hydrological extremes in Mediterranean environments″, was to discuss advances in statistical analyses and monitoring of hydrological extremes by researchers involved in the ″Extreme events″ topic of AMHY (Alpine and Mediterranean Hydrology) group, as a contribution to the FRIEND project. The countries represented in this workshop were Germany, Greece, Italy, Romania, Serbia, Slovenia and Turkey. The significance of the workshop is closely connected to the increased vulnerability of people from hydrological events observed in the Mediterranean countries. On 10th July the workshop was mainly devoted to ″Advances in statistic and stochastic tools for extreme event modelling″, structured into 3 sessions respectively focussed on rainfalls, droughts and floods. A total of 15 works were presented during the conference, with enough time for discussion. On the next day a special attention was devoted to the subject ″Climatic disasters on the media″, designed as an open session supported by images and movies of extreme events recently happened in Mediterranean countries. The workshop was finally completed by a discussion guided by the national coordinators of the ″Extreme events″ topic about future outlooks for researches on extreme events. As for the previous appointments, this workshop was organized with the efforts of the personnel working in Camilab at the Soil Conservation Department of the University of Calabria which partially financed the workshop. Special thanks also to the Scientific Committee for judgments and suggestions on contributions and to all the people who gave logistic support to the organisation. The workshop, pleasantly concluded with a tour on the wilderness area of the Pollino National Park and to the fresh waters of the Raganello Creek, remembered to each of us once again the special opportunities our profession could offer for joyfully contributing to a better world.
Ennio Ferrari International coordinator of ″Extreme events″ topic AMHY-FRIEND group
ADVANCES IN STATISTIC AND STOCHASTIC TOOLS FOR EXTREME EVENT MODELLING Part 1 RAINFALLS
Analyses and images of hydrological extremes in Mediterranean environments Proceedings of the AMHY-FRIEND International Workshop on Hydrological Extremes,
held at University of Calabria, Cosenza (Italy), July 10-12, 2008
REGIONAL CLIMATE CHANGE PROJECTIONS FOR THE EASTERN MEDITERRANEAN/MIDDLE EAST: EXPECTED CHANGES IN WATER AVAILABILITY AND DROUGHTS A. Heckl (1), H. Kunstmann (1), P. Suppan (1), A. Rimmer (2), P. Laux (1) (1) Institute for Meteorology and Climate Research, Forschungszentrum Karlsruhe, Garmisch-Partenkirchen, Germany (2) Kinneret Limnological Laboratory, Israel Oceanographic & Limnological Research Ltd, Migdal, Israel
ABSTRACT The impact of expected climate change on water availability and droughts in the Eastern Mediterranean Middle East is investigated by regional climate modelling with the model MM5 and subsequent hydrological modelling. The meteorological model MM5 is forced with 2 IPCC emission scenarios (A2 and B2) derived from the global climate model ECHAM4 in two nesting steps of 54 km and 18 km resolution. The meteorological fields are used to drive a physically based hydrological model for the Upper Jordan catchment (UJC), computing in detail the surface and subsurface water flow and water balance of the UJC. Results of the joint regional climate-hydrology simulations indicate a decrease of winter precipitation up to 30% in the UJC and slightly increased spring precipitation for the scenario B2 and the time slice 2070 - 2099 compared to present climate (1961 - 1990). Temperature is concluded to increase up to 4.5°C. Total runoff in the UJC is expected to decrease, even in spring time. Snow storage is expected to decrease significantly. For investigating the impact of climate change on severity and length of droughts the Effective Drought Index (EDI) is calculated. It is found that in the future droughts are more intense and severe droughts are getting more frequent. 1. INTRODUCTION Sufficient freshwater availability is a central prerequisite for agricultural and industrial development in the water scarce environment of the Eastern Mediterranean and Near East (EM/NE). Political peace in the region is strongly linked to the satisfactory compliance of increasing water demands. Sustainable management of water resources requires scientific sound decisions on future freshwater availability, in particular under global climate change and increasing greenhouse gas emissions. Behind this background, the impact of climate change on water availability in EM/NE and in particular the Jordan River catchment is investigated within the framework of the GLOWA-Jordan river project (http://www.glowa-jordan-river.de). This article focuses on the Upper Jordan River catchment (UJC) as it provides 1/3rd of freshwater resources in Israel.
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2. REGIONAL CLIMATE MODELLING FOR THE EASTERN MEDITERRANEAN/NEAR EAST AND THE JORDAN RIVER REGION The Jordan catchment is located within a narrow transition zone of only a couple of hundred kilometres, with a Mediterranean climate in the west and an arid climate (Negev desert) in the east/southeast. The non-hydrostatic model MM5 (Grell et al., 1994) is applied to model atmospheric processes in the region. To investigate the impact of global warming on the water availability in the EM/NE and in particular the UJC, global climates scenarios are dynamically downscaled from 2.8° (roughly 300 km) in two nesting steps of 54 km and 18 km resolution (fig. 1). The model configuration is as follows: model top is set at 100 mbar. Terrain following coordinates and 26 vertical layers are used. Convective, subgrid-scale precipitation is parameterized according to Grell et al. (1994). Microphysics is calculated according to Reisner et al. (1998) which differentiates between water vapour, snow, ice, cloud water, rain water and graupel. The turbulent fluxes in the planetary boundary layer are parameterized according to Hong and Pan (1996). Feedback between soil moisture, temperature, vegetation, soil properties and atmosphere are accounted for by applying MM5 fully 2-way coupled with the Oregon State University-Land Surface Model (OSU-LSM) (Chen and Dudhia, 2001). The two IPCC-scenarios A2 and B2 simulated with the global climate model ECHAM4 are downscaled for the period 1961 – 2099, while the time slice 1961 -1990 represents current climate conditions. Fig. 2 shows a comparison of mean annual precipitation between 41 meteorological stations (located in domain 2 of fig. 1) and the corresponding grid pints in 18 km resolution indicating that the simulations reproduce observed climate satisfactorily.
Figure 1. ECHAM4 global climate scenarios are dynamically downscaled with MM5 using two nests of 54 km and 18 km resolution.
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Regional climate change projections for the Eastern Mediterranean/Middle East
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Figure 2. Comparison of modelled (1961-90) and long term observed (1961-90) mean annual precipitation [mm] for 41 meteorological stations in domain 2.
Fig. 3 shows expected changes in mean annual temperature and mean annual precipitation based on the results of the 18 km resolution (domain 2). It is seen that temperature increase of up to 3-5°C is expected and a decrease of mean annual precipitation of more than 40% for specific regions. Change in temperature A2 (2070-99)
5 4.75 4.5 4.25 4 3.75 3.5 3.25 3 2.75 2.5 2.25 2 1.75 1.5 1.25 1 0.75 0.5 0.25 0
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50 45 40 35 30 25 20 15 10 5 0 -5 -10 -15 -20 -25 -30 -35 -40 -45 -50
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Figure 3. Expected changes in mean annual temperature [°C] and precipitation [%] (2070-99 vs. 1961-90) for the scenarios A2 and B2.
Annual values of mean temperature and precipitations amounts for the Upper Jordan catchment (red rectangle) are shown in fig. 4. It can be seen that in both scenarios a steady increase of temperature is expected. The variability of temperature is small in contrast to annual precipitation. Until 2050 no significant changes can be seen, but in the second half of the century a significant reduction occurs.
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T mean [°C]
22 20 18 16 control scenario A2 scenario B2
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Figure 4. Expected change in temperature and precipitation in the Upper Jordan catchment.
To detect, whether under future climate conditions the severity and duration of droughts are changing, simulations results are analyzed by using the Effective Drought Index (EDI). This objective drought index, developed by Byun and Wilhite (1999) and used for the analysis of droughts in West Africa by Laux et al. (2009), is calculated with a daily time step. EDI is a function of precipitation needed for a return to normal conditions (PRN). PRN is precipitation which is necessary to recover from the accumulated deficit since the beginning of drought. PRN, in turn, effectively stems from daily effective precipitation (EP) and its deviation from the mean for each day (DEP). i
n
n 1
m 1
EPj [( Pm ) / n]
PRN j DEPj
EDI j
(1)
j
(1/ N)
(2)
N 1
PRN j SD(PRN j )
(3)
In the previous equations j is the index of a current day, i is the duration over which the sum is calculated, Pm is the precipitation m-1 days before the current day and SD(PRNj) is the standard deviation of each day’s PRN. The "drought range" of EDI indicates extremely dry conditions at EDI2 1 2 0 0 0 0 0 0 0
H3>2 2 1 1 0 1 0 0 0 0
D>2 10 3 2 2 2 1 0 1 0
D>3 5 3 3 1 1 0 0 1 0
The area is successively split into three different subregions, following previous studies on rainfall extreme values (Tartaglia et al., 2006; Caporali et al., 2008). With this subdivision there is a certain homogeneity, but some station still presents high values of discordancy. Thus a new subdivision is proposed based on 5 subregions (fig. 5), splitting the central and the northern region and following the main hydrological watersheds. With this subdivision the regions are more homogeneous, and the subdivision follows hydrological and precipitation features. 5. CONCLUSION AND DEVELOPMENTS Region Tuscany low flows are analyzed and a subdivision in homogeneous regions is evaluated with the L-moments method, using hydrologic characteristics of the area. With this method is possible to find a subdivision with good properties of homogeneity. Other physiographic characteristics, like soil characteristics, slope or land use of the basins have to take in account to improve the regionalization and to verify the new framework.
Figure 5. Subdivision in hydrologically homogeneous regions.
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REFERENCES Caporali E., Cavigli E., and A. Petrucci (2008). The index rainfall in the regional frequency analysis of extreme events in Tuscany (Italy). Environmetrics 19, 714-724. Castellarin A., Camorani G., and A. Brath (2006). Predicting annual and long-term flow-duration curves in ungauged basins. Adv. Water Resour. 30, 937 – 953. Chokmani K., and T.B.M.J. Ouarda (2004). Physiographical space-based kriging for regional flood frequency estimation at ungauged sites. Water Resour. Res. 40, 1-12. Garrote L., Martin-Carrasco F., Flores-Montoya F., and A. Iglesias (2006). Linking drought indicators to policy actions in the Tagus basin drought management plan. Water Resour. Manag. 21, 873–882. Greenwood J.A., Landwehr J.M., Matalas N.C., and J.R. Wallis (1979). Probability weighted moments: definition and relation to parameters of several distributions expressible in inverse form. Water Resour. Res. 15, 1049–1054. Gustard A., Bullock A., and J.M. Dixon (1992). Low flow estimation in the United Kingdom. Report 40, n. 108, Institute of Hydrology, Wallingford, UK. Hayes, D.C. (1991). Low-flow characteristics of streams in Virginia. U.S. Geological Survey Water-Supply Paper 2374, 89-586. Hisdal H., and L. Tallaksen (2000). Drought Event Definition. ARIDE Technical Report n°6. Hosking J.R.M., and J.R. Wallis (1993). Some statistical useful in regional frequency analysis. Water Resour. Res. 29, 271-281. Hosking J.R.M., Wallis J.R., and E.F. Wood (1985). Estimation of the Generalized ExtremeValue Distribution by the Method of Probability-Weighted Moments. Technometrics 27(3), 251-261. Hosking, J.R.M. (1990). L-moments: analyzing and estimation of distributions using linear combinations of order statistics. J. Roy. Stat. Soc. B 52, 105–124. Laaha G., and G. Bloschl (2005). A comparison of low flow regionalization methods – catchment grouping. J. Hydrol. 323, 193–214. Laaha G., and G. Bloschl (2007). A national low flow estimation procedure for Austria. Hydrol. Sci. J. 52, 625-644. Menedez, M. (1995). Aspectos Hidrologicos de las Sequias. Las sequias en España. Centro de Estyudios Hidrograficos del Cedex. Modarres, R. (2008). Regional Frequency Distribution Type of Low Flow in North of Iran by Lmoments. Water Resour. Manag. 22, 823–841. Pyrce, R. (2004). Hydrological Low Flow Indices and their uses. Watershed Science Centre Report n. 04-2004. Regione Toscana, Unioncamere Toscana, ISTAT – Istituto Nazionale di statistica (2009). Annuario Statistico Regionale, Toscana 2008. Pubblicazioni del Sistema Statistico Regionale. Riggs, H.C. (1973). Regional Analysis of streamflow characteristics. US Geological Survey Techniques of Water Resources, US Government Printing Office, Washington, 1982. Smakhtin, V.U. (2001). Low flow hydrology: a review. J. Hydrol. 240, 147–186. Tallaksen, L.M., and H.A.J. van Lanen (2004). Hydrological Drought: Processes and Estimation Methods for Streamflow and Groundwater. Developments in Water Science, 48, Elsevier Science. Tallaksen, L.M., Madsen, H., and B. Clausen (1997). On the definition and modeling of streamflow drought duration and deficit volume. Hydrol. Sci. J. 42(1), 15-34
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Tartaglia, V., Caporali, E., Cavigli, E., and A. Moro (2006). L-moments based assessment of a mixture model for frequency analysis of rainfall extremes. Advances in Geosciences ADGEO (2), 6th PLINIUS Conference on Mediterranean Storms, Ferraris L. (Ed.), 331-334. Zaidman, M.D., Keller, V., Young, A.R., and D. Cadman (2003). Flow-duration-frequency behaviour of British rivers based on annual minima data. J. Hydrol. 277, 195–213.
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Analyses and images of hydrological extremes in Mediterranean environments Proceedings of the AMHY-FRIEND International Workshop on Hydrological Extremes, held at University of Calabria, Cosenza (Italy), July 10-12, 2008
PERSISTENCY IN WET AND DRY PERIODS IN GOZTEPE METEOROLOGICAL STATION IN ISTANBUL, TURKEY E. Eris, H. Aksoy Istanbul Technical University, Department of Civil Engineering, Maslak, Istanbul, Turkey
ABSTRACT Monthly precipitation data of Goztepe meteorological station in Istanbul is analyzed for persistency by fitting Markov chain of first order based on standardized precipitation index (SPI). The SPI can be defined as an index used for monitoring both dry and wet conditions. Positive SPI values indicate wet conditions where precipitation is greater than the long-term mean value whereas negative SPIs indicate dry conditions. The SPI can be calculated for different time scales; usually 1, 3, 6, 12 and 24 months are used. In this study, only the 1-month time scale is used together with a Markov chain model to define (a) steady-state class probability which is the probability of occurrence of each class; (b) expected residence time in each class of severity which represents the average time the process stays in a particular class before moving to another class; (c) short term class prediction defined as the most probable class 1, 2 and 3 months ahead. From the results, the “near normal” class is found to have the highest steady-state class probability. The longest expected residence time is found approximately 3 months for the “near normal” class. Transition probability from any class to the “near normal” has always found the highest. 1. INTRODUCTION It is well known that extremes are realities facing many parts over the world and also reasons to social and economical response. Prediction tools for extreme events therefore become more and more important. The first classical approach of extreme analysis has been made by Gumbel (1963). There are various drought indices in use for drought, the lower extreme. The Palmer drought severity index (PDSI) is a well-proved index, used mainly in USA (Palmer, 1965). The standardized precipitation index (SPI), although recently developed (McKee et al., 1993, 1995), is now widely used, because it allows the comparison between different locations and climates (Paulo et al., 2005). SPI is also simpler than PDSI and can be calculated on any timescale. For comparison, combination and choosing the drought indicators, a multi-state homogenous Markov model which included the Palmer Hydrologic Drought Index (PHDI), PDSI and SPI was developed by Steinemann (2003). In order to understand the stochastic characteristics of droughts, Markov chain approach was applied to drought class transitions derived from the SPI time series (Paulo and Pereira, 2007, 2008). The distribution of drought interval time, mean drought interval time, transition probabilities of drought events were investigated using SPI series by Mishra et al. (2007). To extend SPI series, the first order Markov model was used and then an analysis of drought characteristics was performed. 93
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2. STUDY AREA AND DATA Monthly precipitation data series taken from Goztepe meteorological station operated by State Meteorological Works (DMI) of Turkey were used in this study. The station is located in the Asian part of Istanbul City with the latitude 40058’ N and longitude 29005’ E (fig. 1).
Figure 1. Location of the Goztepe station.
The data series include monthly total precipitation in mm for a period of 64 years in length from 1929 to 1992. Variation of mean monthly precipitation in the Goztepe station is shown in fig. 2. It shows a typical Mediterranean rainfall pattern (Paulo and Pereira, 2007), higher precipitation in autumn and winter and lower in summer. 3. METHODS 3.1 Standardized Precipitation Index (SPI)
SPI originally developed by McKee et al. (1993) can be used for monitoring both dry and wet conditions. A drought event occurs at the time when the value of SPI is continuously negative. The event ends when the SPI becomes positive (Mishra and Desai, 2006). The SPI classification is shown in tab. 1. SPI is based on the probability distribution of the long term precipitation records and can be computed with different time scales such as 1, 3, 6, 12 or 24 months. Guttman (1998) showed that the use of SPI at longer time scales was not advisable as the sample size reduces even with originally long-term data sets.
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Mean monthly precipitation (mm)
Persistency in wet and dry periods in Goztepe meteorological station in Istanbul, Turkey
120 100 80 60 40 20 0 Oct Nov Dec Jan Feb Mar Apr May Jun
Jul Aug Sep
Figure 2. Mean monthly precipitation in Goztepe station for 1929-1992.
McKee et al. (1993, 1995) originally used an incomplete gamma distribution to calculate the SPI. Probability density function is then transformed into the standardized normal distribution. In this study, two-parameter gamma function was used and 1-month time scale was adopted. The probability density function of gamma distribution is defined as: 1 for x>0 (1) f (x) x 1e x where, α > 0 represents the shape factor; β > 0 the scale factor, and x > 0 the precipitation. Γ(α) is the gamma function which is defined as:
0 y1e y dy
(2)
It should be noted from Equation (1) that the gamma distribution is valid for non-zero values. However, precipitation values may contain zeros after which the cumulative probability distribution becomes: H x q (1 q)F x (3) where q stands for the zero-precipitation probability and F(x) the cumulative probability distribution of f(x) and H(x) the cumulative probability distribution considering the zeroprecipitation probability. H(x) is then transformed to the standard normal random variable Z with mean zero which is the value of SPI. The following approximate conversion provided by Abramowitz and Stegun (1965) was used in this study as suggested by Edwards and McKee (1997), Hughes and Saunders (2002) and Mishra and Desai (2006). co c1t c 2 t 2 Z SPI t , for 0 H x 0.5 2 3 1 d t d t d t 1 2 3
(4)
co c1t c 2 t 2 Z SPI t , for 0.5 H x 1 2 3 1 d t d t d t 1 2 3
(5)
where: 95
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1 , for 0 H x 0.5 t In 2 H x
(6)
1 , for 0.5 H x 1 (7) t In 2 1 H x and co=2.515517, c1=0.802853, c2=0.010308, d1=1.432788, d2=0.189269, d3=0.001308. Table 1. SPI drought classifications SPI >2 1.5 to 1.99 1.0 to 1.49 -0.99 to 0.99 -1.0 to -1.49 -1.5 to -1.99 2m) will be represented using different colours. A vulnerability assessment for the flooded areas is reccommended (Oprisan, 2006). This method allows for a quick evaluation of the relationship between predefined thresholds or precipitation corresponding to 1% probability of exceedance and the maximum discharges in the rivers crossing human settlements. The flooded areas and the water depths are also obtained. 4. TORRENTIAL CATCHMENT CHARACTERIZATION The following parameters are proposed to characterize the torrential catchments: - the flooding susceptibility coefficient τf , - the sediment discharge τs , - the flood danger coefficient . 4.1. The flooding susceptibility coefficient τf The proposed flooding coefficient τf represents a measure of overpassing the river transportation capacity as a result of the 100-year return period precipitation and is defined as the ratio: h (6) f p1% h tc where hp1% is the precipitation characterized by 1% probability of exceedance and htc is the precipitation of the same duration occurring in the same initial conditions, which leads to the formation of the maximum discharge without overflowing the river banks (the transportation capacity of the river). This value may be obtained by trial and error method using distributed hydrological models. The flooding coefficient τf allows the comparison of torrential catchments and leads to their hierarchization based on the flooding susceptibility of the flood plain. According to the torrential rain duration, different values may be obtained for τf. 4.2. The flood danger coefficient The flood danger coefficient is directly proportional with the increment Δh (the difference between the maximum water level in the river during the flood event and the water river level before the flood), the increment ΔQ (the difference between the maximum discharge and the river discharge before the flood), and conversely proportional with tincr (the increasing time of the flood, meaning the interval of time during which the discharge increased with ΔQ). As a consequence, the flood danger coefficient will be a function of the ratio Δh∙ΔQ/tincr. Using for normalization the ratio (Δh∙ΔQ/tincr)max corresponding to the most severe flash flood registered in the country and introducing a proportionality coefficient, the proposed flood danger coefficient has the following expression: lg h Q t incr (7) 10 lg h Q t incr max The ratio of logarithms is less than 1. The proportionality coefficient has the role to extend the scale of flood danger to a maximum value equal to 10. Since the same 165
R, Drobot
variables are used both at the numerator and at the denominator, it is no need for other transformation factors. Thus, the discharge increment is expressed in m3/s, the water level increase in cm, and the increasing time of the flood in hours. Again, the magnitude of Δh, ΔQ and tincr are obtained by mathematical modelling, using preferably hydrological distributed models. In order to assure similar conditions for comparison, in all mathematical models the precipitation will correspond to 1% probability of exceedance as for the simplified methodologies. 4.3. The sediment discharge τs According to the sediment load, the small catchments may be classified as it follows (Gaspar, 1967, Clinciu, 2006): - class 1: 0 – 0.5 t/ ha year - class 2: 0.5 – 1.0 t/ ha year - class 3: 1.0 – 2.0 t/ ha year - class 4: 2.0 – 4.0 t/ ha year - class 5: 4.0 – 8.0 t/ha year - class 6: 8.0 – 16 t/ha year The same classes may be used to characterize the sediment load of the flash floods, but concerning strictly the extreme events in the catchment. 4.4. Complex characterization of the torrential catchments For a more complex characterization, the following combinations of the previous indicators to which the runoff coefficient is added can be imagined: (α, τs) = (runoff coefficient during extreme events; sediment discharge), (τf, τs) = (flooding susceptibility coefficient; sediment discharge), (π, τs) = (flood danger coefficient; sediment discharge). Other combinations can also be imagined. At the limit, all these coefficients are coupled together, leading to (α, τf, π, τs), which is a complex indicator of the torrential catchments. REFERENCES ANAR (2004). Efectele viiturile din anul 2004 in bazinul raului Trotus. Raport preliminar (in Romanian). Chendes, V. (2007). Scurgerea lichida si solida in Subcarpatii de la curbura. Teza de doctorat, Institutul de Geografie, Academia Româna, pp. 352 (in Romanian). Clinciu, I. (2006). Padurea si regimul apelor, de la primele abordari ale inaintasilor, la recentele preocupari de exprimare cantitativa si de zonare a riscului la viituri si inundatii. Silvologie. Padurea si regimul apelor. Sub redactia Victor Giurgiu si Ioan Clinciu. Editura Academiei Romane, Vol. V, 107-154 (in Romanian). Cocean, P. and G. Cocean (2006). Cauzele şi efectele viiturii catastrofale de la Târlişua, Judeţul Bistriţa-Năsăud, din 20 iunie 2006. Studia Universitatis Babeş-Bolyai. Geographia. LII. nr. 1. Cluj-Napoca. 47-55 (in Romanian). Diaconu, C., and P. Serban (1994). Sinteze si Regionalizari Hidrologice, Ed. Tecnica, pp. 388 (in Romanian). Gaspar, R. (1967). Contributii la determinarea gradului de torentialitate a bazinelor hidrografice
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si a eficientei hidrologice a lucrarilor de corectare a torentilor. Revista padurilor, 8, 410-414, (in Romanian). Government of Romania (2007). Ordinul nr. 326 din 12 martie 2007 privind aprobarea Metodologiei pentru delimitarea albiilor minore ale cursurilor de apa care apartin domeniului public al statului, pp. 9 (in Romanian). Hong, Y., Adler, R.F., Hossain, F., Curtis, S., and G.J. Huffman (2007). A first approach to global runoff simulation using satellite rainfall estimation. Water Resour. Res., 43(8), W08502. Hong, Y., and R.F. Adler (2008). Estimation of global SCS curve numbers using satellite remote sensing and geospatial data. Int. J. Remote Sens. 29(2), 471-477. National Institute of Hydrology and Meteorology (currently National Institute of Hydrology and Water Management) (1994). Zonation maps of precipitation in Romania. Bucuresti (in Romanian). National Institute of Hydrology and Meteorology (currently National Institute of Hydrology and Water Management) (1997). Instructiuni pentru calculul scurgerii maxime in bazine mici, pp. 39 (in Romanian). Miţă, P. (1996). Representative basins in Romania. Research achievements. I.N.H.G.A., pp. 33. Oprisan, E. (2006). Gestionarea situatiilor de criza. Vulnerabilitatea la inundatii. Teza de doctorat. Universitatea Tehnica de Constructii Bucuresti, pp. 186 (in Romanian). USDA (1997). National Engineering Handbook. Part 630, Hydrology, Natural Resources Conservation Service, US Department of Agriculture, Washington DC, pp. 20.
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Analyses and images of hydrological extremes in Mediterranean environments Proceedings of theAMHY-FRIEND International Workshop on Hydrological Extremes University of Calabria, Cosenza (Italy), July 10-12, 2008
Agenda of the Workshop THURSDAY July 10, 2008 9:15 Welcome addresses of University Authorities 9:30 Introduction by the IC of topic “Extreme events”. Session: ″ADVANCES IN STATISTIC AND STOCHASTIC TOOLS FOR EXTREME EVENT MODELLING″ a. RAINFALLS Heckl A., Kunstmann H., Suppan P., Rimmer A., Laux P. (Germany), Regional climate change projections for the Eastern Mediterranean/Middle East: expected changes in water availability and droughts. Versace P., Capparelli G. (Italy), Early warnings by real-time forecasting model for landslides triggered by rainfalls. De Luca D.L., Ferrari E., Sirangelo B. (Italy), Influence of threshold values on storm occurrence process modelled with a non-homogeneous Poisson distribution. Aksoy H., Gedikli A., Unal N.E. (Turkey), A user-friendly tool for constant meansegmentation of long time series. Vafiadis M. (Greece), The problems and inadequacies in spatial variability of extreme phenomena assessments. Caloiero T., Coscarelli R., Ferrari E., Mancini M. (Italy), Monthly rainfall trends and teleconnections in Calabria. Meze M., Brilly M., Mikoš M. (Slovenia), First statistical analysis of extreme rainfalls in Slovenia on 18/9/2007. b. DROUGHTS Mendicino G., Senatore A. (Italy), Climate change scenarios in Southern Italy and tools for drought assessment and management Rossi G., Caporali E. (Italy), Low-flow estimation in ungauged sites in Tuscany (Italy) using a regionalization by L-moments. Eris E., Aksoy H. (Turkey), Persistency in wet and dry periods in Goztepe meteorological station in Istanbul, Turkey. 169
c. FLOODS Grossi G., Bacchi B., Ranzi R. (Italy), A real time flood forecasting in the Alps and in the Apennines. Ravazzani G., Rabuffetti D., Corbari C., Ceppi A., Mancini M. (Italy), Testing FESTWB, a continuous distributed model for operational quantitative discharge forecast in the upper Po river. Plavšić J., Milutinović R., Stanić N. (Serbia), Effects of record length and period on design flood level estimation on the Danube River at Novi Sad. Štrukelj M., Brilly M., Kobold M., Mikoš M. (Slovenia), Preliminary hydrological analysis of the extreme flood in Slovenia on 18/9/2007. Onorati B., Margotta M.R., Carriero D., Manfreda S., Fiorentino M. (Italy), Experimental evidence on runoff generation mechanisms. Drobot R. (Romania), Simplified methods for identifying small catchments susceptible to generate flash floods.
FRIDAY July 11, 2008 Special session on: ″CLIMATIC DISASTERS ON THE MEDIA ″ 9:00
Opening of the multi-media session
Slovenia (Mikos): Flash flood in September 18, 2007.
Turkey (Aksoy): Flood characteristics and disasters.
Greece (Vafiadis): Evolution of a localized flood.
Italy (Versace): Mud flow in Sarno (southern Italy)
Italy (Carriero): Hydrological extreme events in Puglia.
Italy (Grossi): Flood events in northern Italy.
Italy (Caporali): Social perception of floods.
Italy (Arnone): Flood events in Sicily.
Italy (Fusto): Damaging heavy rains in southern Italy.
Italy (Ferrari): Heavy storm in Vibo Valentia on July 3, 2006.
SATURDAY July 12, 2008 One-day tour on Calabria mountains (Pollino National Park, old town of Morano Calabro and Raganello Creek)
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LIST OF PARTICIPANTS Aksoy Hafzullah (
[email protected]) Department of Civil Engineering, Istanbul Technical University, Maslak, Istanbul, Turkey Arnone Elisa (
[email protected]) Università di Palermo, Italy Bacchi Baldassare (
[email protected]) DICATA, University of Brescia, Italy Balistrocchi Matteo (
[email protected]) DICATA, University of Brescia, Italy Bussi Giambattista DICATA, University of Brescia, Italy Caloiero Tommaso (
[email protected]) DIIAR, Polytechnic University of Milan, Italy Caporali Enrica (
[email protected]) Department of Civil and Environmental Engineering, University of Florence, Italy Capparelli Giovanna (
[email protected]) Department of Soil Protection, University of Calabria, Cosenza, Italy Carriero Domenico (
[email protected]) Department of Engineering and Physics of Environment, University of Basilicata, Potenza, Italy Coscarelli Roberto (
[email protected]) CNR-IRPI, Rende (CS), Italy De Luca Davide Luciano (
[email protected]) Department of Soil Protection, University of Calabria, Cosenza, Italy Di Piazza Annalisa (
[email protected]) Università di Palermo, Italy Eris Ebru (
[email protected]) Department of Civil Engineering, Istanbul Technical University, Maslak, Istanbul, Turkey Ferrari Ennio (
[email protected]) Department of Soil Protection, University of Calabria, Cosenza, Italy Fusto Francesco (
[email protected]) Functional Centre of Calabria Region, Catanzaro, Italy Grossi Giovanna (
[email protected]) 171
DICATA, University of Brescia, Italy Kunstmann Harald (
[email protected]) Institute for Meteorology and Climate Research (IMK-IFU), Forschungszentrum Karlruhe, Garmisch-Partenkirchen, Germany Mendicino Giuseppe (
[email protected]) Department of Soil Protection, University of Calabria, Cosenza, Italy Mikos Matjas (
[email protected]) Faculty of Civil and Geodetic Engineering, University of Ljubljana, Slovenia Plavšić Jasna (
[email protected]) Faculty of Civil Engineering, University of Belgrade, Serbia Ravazzani Giovanni (
[email protected]) DIIAR, Polytechnic University of Milan, Italy Rossi Giuseppe (
[email protected]) Department of Civil and Environmental Engineering, University of Florence, Italy Senatore Alfonso (
[email protected]) Department of Soil Protection, University of Calabria, Cosenza, Italy Sirangelo Beniamino (
[email protected]) Department of Soil Protection, University of Calabria, Cosenza, Italy Stanić Nikola (
[email protected]) Faculty of Civil Engineering, University of Belgrade, Serbia Vafiadis Marios (
[email protected]) Division of Hydraulics and Environmental Engineering, Department of Civil Engineering, Aristotle University of Thessaloniki, Greece Versace Pasquale (
[email protected]) Department of Soil Protection, University of Calabria, Cosenza, Italy
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