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Focal Statistic Modeling in order to Define Flood Interest in Hungary
Arif Rahmansyah DARANA1
1 Hydrogeological Engineering Program, Faculty of Earth Science and Engineering
University of Miskolc, Egyetemvaros, Miskolc, Hungary, 3515
*Corresponding author: [email protected]
Abstract
Based on the report of The European Environment Agency (2016) Hungary has approximately 18%
of the population, 1.8 million people, living on the flood-prone areas. That numbers refers to the one
of the most exposed country to suffer from flood in Central Europe. In facts, the losses could increase
up to fivefold by 2050. However, several techniques are existed to analyze the threat of the flood. One
of the techniques is remote sensing. That technique is one of the effective and efficient way to conduct
the analyses of the flood especially in big area study or in the low resolution, and it able to find the
flood interest. This study uses the ASTER image to generate the DEM, then the modeling of river and
geomorphological features are generated from it. The focal statistic modeling is run to locate the
interest of the flood prone area with drainage density and stream order as the approachming. The result
shows that from the middle to south east of Hungary, especially in Jász-Nagykun-Szolnok county,
Békés county and Bács-Kiskun county are the highest interest area for flood to occur, even the capital
city, Budapest, is one of the prone area. This method is still preliminary study; hence the advance
study is needed to make the better risk analyses.
Keywords: Flood, Hungary, Focal statistic Modeling, Remote Sensing, Natural Hazard
1. INTRODUCTION
Flood is one of destructive natural hazard. That event can cause abundant loss of life or property (Keller,
2011 P116). Flood is a disaster relating to hydro-meteorological event, thus it is included in the
hydrologic cycle. Flood is a big amount of runoff, which is a transport from land to ocean. Runoff water
will flow following the course determined by local topography and also it will infiltrate to the soils or
rocks. However due to the difference capability of earth materials the water just flows on surface to
transporting itself. In this case on the lowland area the runoff will be gather from the higher ground
surrounding (Keller,2011 P137).
Moreover, Individual flood waves emerge after prolonged high-intensity (up to a maximum of 260 mm
within 24 h) rainfall from local thunderstorms or following rapid temperature rises which cause
snowmelt over larger areas. Furthermore, the previous study (Dénes et al, 2009) also identified the
causes of destructive natural flood as:
1. Flood waves travelling down tributaries maybe often superimposed upon the flood of the main
river (Szlávik, 2001 in Dénes et al, 2009)..
2. Ice-jam floods (Kovács, 1987 in Dénes et al, 2009) are also common in this area, because the
permafrost blocks the water to infiltrate to subsurface.
3. Floods from impounding include the effect of a tributary joining a low-gradient trunk river
(Szlávik, 2001 in Dénes et al, 2009).
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Fig 1. Regional Map of Hungary with DEM (A) (Tamas et al, 2015), Geological Map of Hungary Surface
Lithology1 : 1,000,000 (EGDI, 2016).
Hungary is among the countries in Europe most severely endangered by floods. Fourteen rivers are
flowing in Hungary from the mountainous area of the Carpathian Basin which have high channel slope
gradients which serve as a source of considerable flood hazard (István, 2010). Hungary (Fig. 1) is placed
on the most lowland area with most of the lithology is floodplain sediment which contain clastic
sediment with wide range of particle sizes with muddy matrix (Table 1). The elevation difference in the
Great Hungarian Plain (GHP) is about 100 m, while its horizontal extent reaches 300 km. The elevation
differences of terrace levels within the Pleistocene–Holocene flood-plains are less than 20 m and the
width of these smooth plains is more than 10 km (Gábor and Gyula, 2008).
The report of floodlist (2013) the flood was occurred in June in Budapest while more than 1200 people
are forced to evacuate. Moreover, according to report of European Environment Agency about flooding
in Europe, the researchers have predicted the loses caused by flood will have increased fivefold, with
Hungary as one of the most exposed area due to approximately 1.8 of population live on flood plain
50 km
Nort
h Scale:
1 : 1,000,000
A
B
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area (Floodlist, 2016). Therefore, the study about flood is a must in order to reduce the risk, thus a flood
modeling to define the flood spot as a preliminary study is essential.
Table 1. Lithology Explanation of Geological Map of Hungary (EGDI,2016).
Legend Age Lithology
Holocene Epoch
Unsorted or poorly sorted, clastic sediment with a wide range of
particle sizes, including a muddy matrix.
Holocene Epoch Moisture-free basisorganic rich sediment.
Tortonian Age Mudstone
Aquitanian Age Diamictite
Carboniferous Period Granite
Miocene Epoch Tuff-breccia, agglomerate, or pyroclastic
Miocene Epoch Andesite-basalt
Cambrian Period Slate
Oligocene Epoch Impure dolomite
Triassic Period Limestone and dolomitic limestone.
2. METHODOLOGY
ASTER GDEM (2011) was used as a main data in this research. Through ArcMap 10.3 the modeling
was conducted to generate the flood interest map (Fig. 2).
Fig 2. Workflow Diagram of Applied Method.
ASTER GDEM (2011) was used as a main data in this research. Through ArcMap 10.3 the modeling
is conducted to generate the flood interest map (Fig. 2). The Aster images was exported to ArcMap
10.3. All of Images had to be processed by uniting all of them using “Mosaic to New Raster” tools then
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crop it with the study area border which in this case is Hungary country border by using “Clip Mask”
tools. The depression in the sink filled then, the flow direction and flow accumulation was generated
using hydrogeological tools. The conditional modeling of flow accumulation was run in order to
classified the stream Network. Next as the previous researcher (Ejikeme et al, 2015) conducted, the
focal statistic, was conducted and reclassified to different statement levels of flood.
The reclassified data were integrated with hill shade, the processed data from filled DEM, and also with
base map of Hungary (arivaIO, 2016 and openstreetmap.org, 2014). Hence the Flood Interest Map of
Flood in Hungary was created.
3. RESULT AND DISCUSSION
The Result is Shown on the Flood Interest Map of Hungary (Fig. 3). The flood interest is generated
from the flow direction which was continued by flow accumulation. It means the software generated
the place where the water could be accumulated, as a “stream network”, however it can be different
from river network, the focal statistic used it as an input. This modeling was classified using Jenks
natural break classification, because this statistical classification system determined the best
arrangement of values into different classes by minimizing each class’s average deviation from the class
mean, while maximizing each class’s deviation from the means of the other groups. In other words, the
method seeks to reduce the variance within classes and maximize the variance between classes (Jenks,
1967)
The focal statistic model is interpreted using drainage density and stream order from the morphometry
study of Purwanto (2013). The highest value of “stream network” means the highest stream order too.
Furthermore, the highest exposure of focal statistic modeling, signed by red color, the more streams are
close to each other which means higher drainage density.
Hence, the result of this model is defined into three categories of flood interest, there are
High Interest to flood
It has fast increasing flood water table, with low decreasing water table. The water flows
through the low resistant rocks, thus the transported sediment will be plentiful.
Moderate Interest to flood
It has moderate increasing and decreasing flood water table. The water flows through the
higher resistant rocks, thus the transported sediment is not too abundant
Low Interest to flood
It has fast increasing flood water table, with fast decreasing flood water table. The water flows
through hard rock, thus the transported sediment is just in little amount.
From the map (Fig 3), it can be seen that even in the heart of Budapest the high interest of flood is
placed on it, thus it will need extra treatment since it is a capital city of Hungary. Furthermore, the most
probable place to occur the flood is in Jász-Nagykun-Szolnok county, Békés county and Bács-Kiskun
county. Those two county are placed on very lowland area with or poorly sorted, clastic sediment with
a wide range of particle sizes, including a muddy matrix covering almost a whole area.
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Fig
3. F
loo
d I
nte
rest
Map
of
Hu
ng
ary
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4. CONCLUSION
All and all More than 60% of Hungary is lying on lowland and the major material is unsorted or poorly
sorted, clastic sediment with a wide range of particle sizes, including a muddy matrix. By interpreted
the model with the geomorphological aspect the interests of the flood are divided into high interest to
flood, moderate interest to flood, and low interest to flood. Furthermore, Jász-Nagykun-Szolnok county,
Békés county and Bács-Kiskun county has the most spot of highest interest to flood.
5. RECOMMENDATION FOR FUTURE STUDY
This Model is able to support the preliminary study of flood risk assessment in order to define the
probable spot of flood. However, further study is needed. For further study the high resolution DEM
will give better result, and the better quality data will give the better results, thus the area of the flood
interest can be defined, and more accurate flood interest can be generated too.
ACKNOWLEDGEMENT
Thanks to Prof. Dr. Szűcs Péter as the dean of Faculty of Earth Science and Engineering University
of Miskolc for providing and supporting this research with some references in English, and also for
the moral support.
REFERENCES
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