**3. Flash flood modelling**

### **3.1 Objectives**

The above mentioned events directed the attention of water management experts and of the wider public to flash flood hazard. Consequently, flood prevention needs to be also

flood related disasters and their consequences have been appearing more and more

Some recent events that made news took place in the Mátra Mountains (North-Hungary) in 1999 (Koris & Winter, 1999) and again on 18 April 2005 (Horváth, 2005). The rainfall resulted from an atmospheric complex of several convective cells transporting moist air like a conveyor belt against the mountain slopes. Huge boulders of volcanic rock were transported by the local stream (Fig. 1). As an aftermath of the flood, slumps in 500 m length along the

Fig. 1. Deposits of debris flow after the Mátrakeresztes flash flood (by permission of the

Some of the most disastrous events in Hungary occurred on 15–16 May 2010, when a strong cyclone reached the Carpathian Basin. Hitherto virtually unknown stream names (e.g. Hábi Canal, Bükkösd Stream and Baranya Canal) appeared in the media. The ensuing floods caused significant economic losses in Southern Transdanubia (Southwest-Hungary), a region of mostly dissected hill topography and a dense drainage network (Fig. 2). Daily precipitation amounts, intensities and stream stages broke records and cumulative precipitation locally exceeded 300 mm in the Kapos drainage basin during May and June. In Csikóstőttős village 65 people were evacuated. A one metre high flood swept away a children's camp in Szekszárd, where firemen assisted to evacuate the campers. On 16 June 2010 182 mm of rainfall fell on the village of Iklódbördőce in the Zala Hills (Southwest-Hungary) and caused a mudflow. Estimated by the insurance companies, the May and June events caused ca HUF 100 billion (EUR 360 million) economic losses, at least 3,100 residential homes were damaged and the agricultural damage totals ca HUF 30 billion (EUR 110 million). A summary of water-related damage recorded by insurance companies shows the distribution of insurance events in Southern Transdanubia between 1980 and 2005 (Fig. 3). In the light of the 2010 floods, the number of events presented here seems to be underestimated (property insurance was probably not comprehensive), but the map is informative of the zones of highest flood risk.

The above mentioned events directed the attention of water management experts and of the wider public to flash flood hazard. Consequently, flood prevention needs to be also

frequently in the Hungarian media.

undercut bank are regularly generated.

Nógrád County Disaster Prevention Directorate)

**3. Flash flood modelling** 

**3.1 Objectives** 

Fig. 2. The drainage network of Southern Transdanubia with the catchments studied and an inserted location map (from the river network database of Hungary)

Flash Flood Hazards 35

The modelling of flash flood hazards requires a more complex approach than that of large riverine floods as more environmental factors have to be considered and regularly monitored. Flood modelling serves flood forecasting, i.e. the estimation of future flood conditions, while flood warning means the information of the public on the timing and location of a flood event allowing them sufficient time to take preparatory actions. A Decision Support System (DSS) in flood management assists authorities to make decisions based on forecast information (the expected characteristics of the flood, the number of inhabitants threatened and the evacuation infrastructure available) (Maarten et al., 2007).

Given the significance of catchment properties in the generation of floods, distributed hydrological models seem to be best suited for the purpose of flash flood prediction. These models of various levels of complexity are built on a grid-based network, small subbasins or triangulated irregular networks (TINs). Some frequently used examples of physically based,







The US Army Corps of Engineers (USACE) has played a vital role in the development and application of hydrological models in the Unites States since the early 1960s. USACE models are extensively used throughout the world. As a well-established standard model, HEC-HMS is widely used in the United States and worldwide for the simulation of surface runoff. For the mapping of inundated areas the models HEC-RAS, HEC-GeoRAS and ArcGIS 9.1 are useful. The HEC-HMS is designed to simulate the precipitation-induced runoff processes in catchments of dendritic drainage pattern. It is applicable in a wide range of geographical areas, equally for large drainage basins and small catchment for solving the widest possible range of problems (water availability, urban drainage, flow forecasting, future urbanization impact, reservoir spillway design, flood damage reduction, floodplain

The default model used in the European Flood Forecasting System (EFFS) is LISFLOOD (De Roo et al. 2000), a physically based catchment model, developed for the European river basins. As a rainfall-runoff model it has inputs of data on topography, precipitation amounts and intensities, antecedent soil moisture, land use type and soil type in the form of

distributed hydrological models (cited by Paudel 2010, see references there) are


Environmental and Natural Sciences, Lancaster University, UK;


**3.2 International models** 

Hydrology Laboratory;

Research Center;

Technology;


regulation etc.) (US Army Corps of Engineers, 2005).

Research Service (USDA-ARS)

extended to the previously neglected small mountainous catchments at relatively low elevations, which cover ca 30 per cent of the entire land area of Hungary, while the cumulative length of streams total more than 20,000 km (Kaliczka, 1998). It is recognized that flood assessment and prevention measures, such as the presently planned and constructed nationwide Flood Risk Information System (abbreviated from the Hungarian as ÁKIR), a model and software-based flood prediction system, also have to cover minor catchments potentially affected by flash floods (Pászthory & Szigeti, 2009).

Fig. 3. Water-related events with damage to property in Southern Transdanubia, 1980–2005, based on insurance data (after Varannai, 2005). The map shows the number of occurrences

In the following part of this chapter a proposed flood risk assessment mapping procedure and numerical flood forecasting system are outlined. Our objective is to identify flash flood risk in order to promote the development of a flood warning system and to mitigate floodrelated life and property losses. The screening of the country's territory for flash flood hazard and rating the risk in the various regions would also help insurance companies in estimating expectable damage.

extended to the previously neglected small mountainous catchments at relatively low elevations, which cover ca 30 per cent of the entire land area of Hungary, while the cumulative length of streams total more than 20,000 km (Kaliczka, 1998). It is recognized that flood assessment and prevention measures, such as the presently planned and constructed nationwide Flood Risk Information System (abbreviated from the Hungarian as ÁKIR), a model and software-based flood prediction system, also have to cover minor

Fig. 3. Water-related events with damage to property in Southern Transdanubia, 1980–2005, based on insurance data (after Varannai, 2005). The map shows the number of occurrences

In the following part of this chapter a proposed flood risk assessment mapping procedure and numerical flood forecasting system are outlined. Our objective is to identify flash flood risk in order to promote the development of a flood warning system and to mitigate floodrelated life and property losses. The screening of the country's territory for flash flood hazard and rating the risk in the various regions would also help insurance companies in

estimating expectable damage.

catchments potentially affected by flash floods (Pászthory & Szigeti, 2009).

### **3.2 International models**

The modelling of flash flood hazards requires a more complex approach than that of large riverine floods as more environmental factors have to be considered and regularly monitored. Flood modelling serves flood forecasting, i.e. the estimation of future flood conditions, while flood warning means the information of the public on the timing and location of a flood event allowing them sufficient time to take preparatory actions. A Decision Support System (DSS) in flood management assists authorities to make decisions based on forecast information (the expected characteristics of the flood, the number of inhabitants threatened and the evacuation infrastructure available) (Maarten et al., 2007).

Given the significance of catchment properties in the generation of floods, distributed hydrological models seem to be best suited for the purpose of flash flood prediction. These models of various levels of complexity are built on a grid-based network, small subbasins or triangulated irregular networks (TINs). Some frequently used examples of physically based, distributed hydrological models (cited by Paudel 2010, see references there) are


The US Army Corps of Engineers (USACE) has played a vital role in the development and application of hydrological models in the Unites States since the early 1960s. USACE models are extensively used throughout the world. As a well-established standard model, HEC-HMS is widely used in the United States and worldwide for the simulation of surface runoff. For the mapping of inundated areas the models HEC-RAS, HEC-GeoRAS and ArcGIS 9.1 are useful. The HEC-HMS is designed to simulate the precipitation-induced runoff processes in catchments of dendritic drainage pattern. It is applicable in a wide range of geographical areas, equally for large drainage basins and small catchment for solving the widest possible range of problems (water availability, urban drainage, flow forecasting, future urbanization impact, reservoir spillway design, flood damage reduction, floodplain regulation etc.) (US Army Corps of Engineers, 2005).

The default model used in the European Flood Forecasting System (EFFS) is LISFLOOD (De Roo et al. 2000), a physically based catchment model, developed for the European river basins. As a rainfall-runoff model it has inputs of data on topography, precipitation amounts and intensities, antecedent soil moisture, land use type and soil type in the form of

Flash Flood Hazards 37

precision GPS and SOKKIA surveying instruments were employed to improve the spatial resolution of the generated DEM to 1 m. This, however, was achieved only locally – usually

Surface runoff was simulated using HEC-HMS, which has the advantage of working with distributed precipitation data available from weather radar and a continuous soil-moistureaccounting model (Pirkhoffer et al., 2009b). Radar images and various meteorological data were obtained from the Hungarian Meteorological Service (OMSz), while hydrological data (e.g. water stage and discharge) were received from the Research Institute for Environmental Protection and Water Management (VITUKI Rt.), the South-Transdanubian Environmental Protection and Water Management Directorate (DDKÖVIZIG) and the MECSEKÉRC Rt., a successor enterprise to the former uranium mining company. To obtain field data we monitored soil moisture (using Time Domain Reflectrometry technique), canopy cover and precipitation at 14 monitoring stations in a 1.7 km2 pilot catchment (Pirkhoffer et al., 2009b; Czigány et al., 2010). Runoff output data were then compared with

The first comprehensive, but least detailed type of approach to flash flood modelling is rapid screening that usually employs ARC GIS and SGA GIS softwares (Pirkhoffer et al., 2009b; Czigány et al., 2011a). The input data for this analysis comprise various topographical, geological, soil and land use parameters. Rapid screening models serve to delineate the area with a natural hazard or rate vulnerability and risk in that area (Cobby et al., 2009; Czigány et al., 2008) in order provide a general overview of its level for experts,

Fig. 4. Hydrograph of a typical flash flood (a) and that of a flood event with saturated soils (b). Tc = time of concentration; LNQ = maximum discharge; LKQ = minimum discharge

in the immediate vicinity of watercourses.

**3.3.3 Rapid screening and GIS-based risk assessment** 

observed flow.

decision-makers and the public.

maps (topography, drainage network, CORINE land cover, soil depth, soil class). The meteorological variables required are rainfall, potential evaporation (for bare soil, closed canopy and water surfaces) and daily mean air temperature (De Roo et al., 2000).
