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Received: 4 January 2025
Revised: 5 March 2025
Accepted: 11 March 2025
Published: 17 March 2025
Citation: Ucar, I.; Kapcak, M.;
Sonmez, O.; Dogan, E.; Turan, B.; Dal,
M.; Findik, S.B.; Yilmaz, M.; Sever, A.
From Hazard Maps to Action Plans:
Comprehensive Flood Risk Mitigation
in the Susurluk Basin. Water 2025,17,
860. https://doi.org/10.3390/
w17060860
Copyright: © 2025 by the authors.
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Article
From Hazard Maps to Action Plans: Comprehensive Flood Risk
Mitigation in the Susurluk Basin
Ibrahim Ucar 1, Masun Kapcak 1, Osman Sonmez 2, * , Emrah Dogan 2, Burak Turan 3, Mustafa Dal 4,
Satuk Bugra Findik 4, Mesut Yilmaz 4and Afire Sever 4
1Floodis Engineering, Ankara 06510, Türkiye; ucar@floodis.com (I.U.); masunkapcak@floodis.com (M.K.)
2Department of Civil Engineering, Faculty of Engineering, Sakarya University, Sakarya 54050, Türkiye;
emrahd@sakarya.edu.tr
3NFB Engineering, Ankara 06510, Türkiye; burak.turan@nfbproje.com
4
The Republic of Türkiye Ministry of Agriculture and Forestry, The General Directorate of Water Management,
Ankara 06560, Türkiye; dal.mustafa@tarimorman.gov.tr (M.D.); satukbugra.findik@tarimorman.gov.tr (S.B.F.);
yilmaz.mesut@tarimorman.gov.tr (M.Y.); afire.sever@tarimorman.gov.tr (A.S.)
*Correspondence: osonmez@sakarya.edu.tr
Abstract: Floods pose significant risks worldwide, impacting lives, infrastructure, and
economies. The Susurluk basin, covering 24,319 km
2
in Türkiye, is highly vulnerable
to flooding. This study updates the flood management plan for the basin, integrating
hydrological modeling, GIS-based flood mapping, and early warning system evalua-
tions in alignment with the EU Flood Directive. A total of 503 hydrodynamic models
(226 one-dimensional and 277 two-dimensional) were developed, analyzing 2116 km of
stream length. As a result of the evaluation, the capacities of only 33 streams were found to
be sufficient. Flood hazard and risk maps for the Q
50
, Q
100
, Q
500
, and Q
1000
return periods
identified the remaining 470 high-risk locations as requiring urgent intervention. Economic
risk assessments revealed significant exposure of critical infrastructure, especially in urban
areas with populations over 100,000. Furthermore, the study introduces a prioritization
framework for intervention that balances socioeconomic costs and environmental impacts.
Economic damage assessments estimate potential losses in critical infrastructure, including
residential areas, industrial zones, and transportation networks. The findings highlight
the importance of proactive flood risk mitigation strategies, particularly in high-risk urban
centers. Overall, this study provides a data-driven, replicable model for flood risk manage-
ment, emphasizing early warning systems, spatial analysis, and structural/non-structural
mitigation measures. The insights gained from this research can guide policymakers
and urban planners in developing adaptive, long-term flood management strategies for
flood-prone regions.
Keywords: hydrological modeling; hydrodynamic modeling; flood risk mitigation; risk
assessment; geospatial analysis
1. Introduction
Floods are among the most destructive natural disasters, causing significant loss of
life, property damage, and economic disruptions worldwide. As climate change intensifies
extreme weather events, the frequency and severity of floods are expected to increase,
necessitating a comprehensive approach to flood risk management [
1
,
2
]. The European
Union’s Flood Directive (2007/60/EC) has set out a framework for flood risk assessment
and management, emphasizing the need for a structured approach to mitigate the adverse
effects of floods on human health, infrastructure, and the environment [3].
Water 2025,17, 860 https://doi.org/10.3390/w17060860
Water 2025,17, 860 2 of 18
The Susurluk basin, which covers approximately 24,319 km
2
and represents 3.1% of
Türkiye’s surface area, is highly susceptible to flooding due to its hydrological and climatic
characteristics. With key urban centers, such as Balıkesir, Bursa, and Canakkale, within its
boundaries, floods in this basin have the potential to cause extensive socioeconomic and
environmental damage. Historical records indicate that major flood events in the region
have led to considerable financial losses and disruptions to local communities [4,5].
Recent advancements in hydrological modeling, geographic information systems
(GIS), and remote sensing technologies have significantly improved flood risk assessment
capabilities [
1
,
5
]. Two-dimensional (2D) hydrodynamic models, such as HEC-RAS and
HEC-HMS, have become essential tools in flood mapping and forecasting, allowing for
better prediction and management of flood-prone areas [
6
,
7
]. These models incorporate
hydrological and hydraulic parameters to simulate flood behavior under various scenarios,
providing crucial data for risk mitigation strategies.
The integration of GIS with hydrodynamic models has enabled more precise spatial
analyses of flood hazards, facilitating the development of flood susceptibility and vulner-
ability maps [
8
,
9
]. GIS-based models assist in identifying high-risk zones by analyzing
topography, land use, and historical flood data. Additionally, remote sensing technologies,
including satellite imagery and light detection and ranging (LiDAR), have enhanced the
accuracy of flood extent and inundation mapping by offering real-time and high-resolution
data [
9
–
11
]. Furthermore, the assimilation of multisource Earth observation data has con-
tributed to more reliable flood forecasting. Satellite missions, such as Sentinel-1 and Surface
Water and Ocean Topography (SWOT), provide valuable hydrological insights, improving
the calibration of hydrodynamic models [
10
]. The integration of these datasets helps refine
flood hazard assessments, especially in data-scarce regions.
As climate change continues to alter precipitation patterns and increase the frequency
of extreme weather events, adaptive and real-time flood risk management approaches are
becoming essential [
1
,
11
]. The combination of advanced hydrodynamic modeling, GIS
applications, remote sensing, and machine learning represents a promising direction for
enhancing flood resilience and protecting vulnerable communities.
In Türkiye, the integration of these methodologies within the national flood risk man-
agement framework has become a priority. The General Directorate of Water Management
under the Ministry of Agriculture and Forestry has been actively updating flood risk man-
agement plans for major river basins, including the Susurluk basin [
12
]. This study builds
on these efforts by incorporating multifaceted risk assessment methodologies, including
hazard mapping, socioeconomic impact analysis, and early warning system evaluations, to
develop a robust and adaptive flood management strategy.
By aligning with international best practices and leveraging state-of-the-art hydrologi-
cal modeling techniques, this study aims to enhance flood resilience in the Susurluk basin.
The boundary conditions of the study are determined by the Susurluk Basin, which is the
study area. In addition, among the curve number (CN) values used in hydrology studies
are Manning’s roughness coefficients, which are the boundary conditions of hydrodynamic
modeling studies, digital elevation model (DEM) resolution (0.2 m
×
0.2 m in settlements,
1 m
×
1 m in other areas), and art structures and obstructions. The advantages of the
study are that the basin is considered as a whole, upstream–downstream measures are
evaluated holistically, risk assessment is carried out on a basin basis, responsible and related
organizations are evaluated, and the flood management plan is studied as a whole.
The results will not only contribute to local flood mitigation efforts, but also serve as a
replicable model for other flood-prone regions, reinforcing the importance of data-driven,
evidence-based flood risk management in the face of evolving climatic and hydrologi-
cal challenges.
Water 2025,17, 860 3 of 18
2. Study Area
The Susurluk basin is located in the north of Türkiye. The total precipitation area of
the basin is approximately 24,319 km
2
and the mean annual precipitation is 693.20 mm. The
Sakarya basin lies to the east, the Gediz basin to the south, the North Aegean basin to the
west, and the Marmara basin to the north. Balıkesir, Bursa, Kutuhya, ˙
Izmir, Manisa, Bilecik,
and Canakkale provinces are located in the basin [
12
]. The maximum height, minimum
height, maximum height at the boundary, and minimum height at the boundary of the
basin were calculated using the DEM. In addition, the DEM was used to create exposure
and slope maps of the basin.
The two most important factors affecting the CN, another important parameter of
the basin, are soil type and land use [
13
]. Land use and land cover are not homogenously
distributed within the borders of the Susurluk basin. While calculating the CN of the
Susurluk basin, 2018 CORINE data were used to obtain information on these factors, and
the CN was calculated by spatial weighting. The weighted average CN number in the basin
was found to vary between 77 and 78.
The Susurluk basin is located in the transition zone from the Black Sea climate to
the Mediterranean climate. The effects of this transition are also seen in precipitation.
The months with the highest precipitation are the winter months. When the temperature
changes of the basin are examined, little temperature difference is observed between
day and night. According to the data obtained from the website of the Turkish State
Meteorological Service (TSMS), the annual sum of monthly mean precipitation is 675.6 mm
in Balıkesir, 707.40 mm in Bursa, 624.40 mm in Canakkale, and 562.20 mm in Kutahya.
When the highest and lowest temperatures during the measurement period were evaluated,
it was determined that the highest temperature occurred in Bursa province at 43.80
◦
C,
while the lowest temperature occurred in Kutuhya province at −28.10 ◦C [12].
In the basin-wide rainfall assessment made within the scope of the Susurluk Basin
Flood Management Plan Preliminary Report, the number of stations evaluated within the
scope of the Thiessen polygon was 89. Approximately one-third of the weather stations
(WSs) entering the Thiessen polygon are mostly outside the basin. The TSMS operates
most of the stations. Previously closed stations have been activated as automatic weather
stations (AWSs). Most of the manual stations opened and operated by the state waterworks
(SWWs), mostly for project purposes, were closed in 2005.
In the hydrology studies carried out in the Susurluk basin, meteorological observation
stations with an observation period between 1980 and 2020 were identified, and 12 stations
(8 within the basin and 4 outside the basin) were selected to use meteorological observation
data. Some characteristic information about these selected stations is given in Table 1.
Table 1. Some characteristics of meteorological gauging stations used in hydrologic calculations.
Station No. Station Name Latitude Longitude
17704 Tavsanlı 39.550 29.500
17116 Bursa 40.233 29.017
17114 Bandırma 40.317 29.983
17695 Keles 39.917 29.067
17676 Uludag 40.117 29.017
17700 Dursunbey 39.583 28.617
17152 Balıkesir 39.650 27.867
17748 Simav 39.083 28.983
17184 Akhisar 38.917 27.817
17750 Gediz 39.050 29.417
17145 Edremit 39.583 27.017
17674 Balıkesir Gonen 40.100 27.650
Water 2025,17, 860 4 of 18
There are 162 stream gauging stations (SGSs) in the basin. SGSs with drainage areas
of 1000 km
2
or larger were selected for use in the HEC-HMS models established for the
Susurluk basin. In addition, the selected SGSs were required to have long-term data. In the
Susurluk basin, 11 SGSs were determined to meet the required criteria. Some characteristic
information about these stations is given in Table 2.
Table 2. Some characteristics of the stream gauging stations used in calculations.
ID Name Drainage
Area (km2)Elevation (m) Min
(m3/s)
Max
(m3/s)
Mean
(m3/s)
D03A004 Deveci
Konagı 4888 62 0 950 311.26
D03A034 Osmanlar 1253.9 271 0 430 162.91
D03A052 Sinderler 975,2 294 0 470 149.77
D03A089 Caltılıbuk 4631.37 65 0 530 141.88
E03A002 Dolluk 9629.2 40 0 3374 666.07
E03A014 Kayaca 2278 20 0 1693 521.11
E03A016 Yahyakoy 6454 32 0 2350 666.94
E03A017 Akcasusurluk 21,611.2 2 0 963 454.12
E03A021 Gecitkoy 1290.8 63 0 359 126.41
E03A024 Balıklı 1384 94 0 550 244.85
E03A028 Dereli 1125.6 557 0 312 91.19
The Susurluk basin, which covers an area of more than 2 million hectares, discharges
its rainfall into the Marmara Sea, and the Uluabat and Manyas lakes through many small
and large rivers. The basin contains many large and small rivers, flowing continuously or
for short periods of time. The rivers and lakes in the Susurluk basin are shown in Figure 1.
Water 2025, 17, 860 4 of 19
17152 Balıkesir 39.650 27.867
17748 Simav 39.083 28.983
17184 Akhisar 38.917 27.817
17750 Gediz 39.050 29.417
17145 Edremit 39.583 27.017
17674 Balıkesir Gonen 40.100 27.650
There are 162 stream gauging stations (SGSs) in the basin. SGSs with drainage areas
of 1000 km2 or larger were selected for use in the HEC-HMS models established for the
Susurluk basin. In addition, the selected SGSs were required to have long-term data. In
the Susurluk basin, 11 SGSs were determined to meet the required criteria. Some charac-
teristic information about these stations is given in Table 2.
Table 2. Some characteristics of the stream gauging stations used in calculations.
ID Name Drainage Area
(km²) Elevation (m)
Min
(m³/s)
Max
(m³/s)
Mean
(m³/s)
D03A004 Deveci Konagı 4888 62 0 950 311.26
D03A034 Osmanlar 1253.9 271 0 430 162.91
D03A052 Sinderler 975,2 294 0 470 149.77
D03A089 Caltılıbuk 4631.37 65 0 530 141.88
E03A002 Dolluk 9629.2 40 0 3374 666.07
E03A014 Kayaca 2278 20 0 1693 521.11
E03A016 Yahyakoy 6454 32 0 2350 666.94
E03A017 Akcasusurluk 21,611.2 2 0 963 454.12
E03A021 Gecitkoy 1290.8 63 0 359 126.41
E03A024 Balıklı 1384 94 0 550 244.85
E03A028 Dereli 1125.6 557 0 312 91.19
The Susurluk basin, which covers an area of more than 2 million hectares, discharges
its rainfall into the Marmara Sea, and the Uluabat and Manyas lakes through many small
and large rivers. The basin contains many large and small rivers, flowing continuously or
for short periods of time. The rivers and lakes in the Susurluk basin are shown in Figure
1.
Figure 1. The rivers and lakes in the Susurluk basin.
Figure 1. The rivers and lakes in the Susurluk basin.
There are many storage facilities (ponds or dams) in the basin, either existing or at
the plan–project stage. It was decided that the dams in the basin would be included in
the flood discharge calculations by performing a reservoir routing. Accordingly, the dams
in the basin were identified, and the necessary data (elevation–area–volume, spillway
information, spillway radial cover information (if any), spillway operating instructions,
etc.) were obtained to be used in the hydrology calculations of these dams.
Water 2025,17, 860 5 of 18
According to land-use capability classification in the provincial land asset inventory
reports published by the General Directorate of Rural Services, lands are divided into eight
classes. The first four classes are mainly suitable for tillage farming. Classes 5, 6, and 7
identify lands that can be used as meadow, pasture, and woodland and are therefore not
suitable for arable agriculture. Class 8 lands have no chance of any crop production. The
land capability utilization classes of the Susurluk basin and the data belonging to these
classes are given in Table 3.
Table 3. Susurluk basin land-use capability classes.
Land-Use Capability Class Symbol Area (ha) Distribution (%)
Suitable for Tillage Farming I 90,843 3.74
Suitable for Tillage Farming II 172,533 7.09
Suitable for Tillage Farming III 121,203 4.98
Suitable for Tillage Farming IV 136,532 5.61
Unsuitable for Tillage Farming V 2398 0.10
Unsuitable for Tillage Farming VI 454,206 18.68
Unsuitable for Tillage Farming VII 1,346,667 55.38
Land Unsuitable for Agriculture VIII 107,546 4.42
Total 2.431.927 100.00
Large soil groups are classified by examining them under headings such as climate,
precipitation, temperature, vegetation cover, main material of the soil, and events that
provide soil formation. The most extensive soil group in the Susurluk basin is calcareous
brown forest soils without lime, covering an area of 1,036,001 ha. This soil group constitutes
43% of the basin. The second largest soil group in the basin is brown forest soils, with an
area of 537.699 ha [12]. Data on major soil groups in the basin are given in Table 4.
Table 4. Distribution of Large Soil Groups in Susurluk Basin.
Large Soil Group Symbol Area (ha) Distribution (%)
Alluvial Soils A 155,400 6.39
Brown Soils B 10,214 0.42
Chestnut Soils CE 3405 0.14
Reddish Chestnut Soils D 2918 0.12
Red Brown Mediterranean Soils E 76,849 3.16
Reddish Brown Soils F 243 0.01
Hydromorphic Alluvial Soils H 3162 0.13
Colluvial Soils K 51,314 2.11
Brown Forest Soils M 537,699 22.11
Calcareous Brown Forest Soils N 1,036,001 42.6
Organic Soils O 973 0.04
Sierozems S 1702 0.07
Rendzinas R 131,810 5.42
Calcareous Brown Soils U 241,490 9.93
Vertisols V 70,283 2.89
High Mountain Prairie Soils Y 1216 0.05
Areas Outside the Large Soil Group - 107,248 4.42
Total 2.431.927 100.00
3. Methodology
3.1. Hydrological Modeling
Within the scope of this study, flood hydrology studies were carried out using the
HEC-HMS (Hydrological Modeling System) model developed by the United States Army
Water 2025,17, 860 6 of 18
Corps of Engineers [
3
]. The HEC-HMS model is a simulation program that can perform
hydrological calculations, such as precipitation and runoff, in basin areas of different sizes
and characteristics. HEC-HMS can simulate hydrological elements, such as sub-basins,
streams, reservoirs, etc., in the catchment area. The HMS model calculates infiltration
losses and converts rainfall into runoff by different transform methods. It can use different
methods, such as the soil conservation service curve number (SCS-CN) and Green-Ampt
methods. It can run them together with Muskingum, Pulse, or Lag translational methods
using Clark, Snyder, or SCS unit hydrographs [13–15].
With the HEC-HMS model, hydrological calculations are defined within an interrelated
model network data structure. The calculations are made from upstream to downstream.
The HEC-HMS model components are as follows:
–
The basin model includes physical properties, such as basin area, stream connection
points, and reservoir data.
–
The meteorological model is the part that defines the basin meteorology (such as
precipitation, temperature, and flow values).
–
The control specifications contain information about the timing of the model, such as
the time of the flood and the time interval to be used in the model.
–
The time series data belong to the part in which the meteorological time series to be
used in the model are defined.
The sub-basin elements defined in the catchment area are used to convert precipitation
into runoff, so information on loss calculation methods, hydrograph transformations, and
base flow must be entered for each sub-basin. The loss method is used to select the method
of calculating the rainfall lost by absorption from the ground. In this study, the deficit and
constant method, which is also preferred in many applications, was selected as the loss
method. This method is based on continuous changes in soil moisture.
The transform method defines how to convert excess rainfall into direct runoff. The
Clark unit hydrograph method, which is widely used in modeling, was chosen. Clark (1945)
stated that two important parameters—the collection time (transition time) and storage
coefficient—in the calculation of the basin unit hydrograph are needed to determine the
area–time relationship of the basin [
15
]. When a baseflow is defined with the baseflow
method, the total hydrograph is obtained by adding it to the calculated surface flow
hydrograph. The linear reservoir is defined as the baseflow method in the model. This base
flow shows the movement of water along the underground layer. The canopy represents
the rainfall retained by the vegetation.
There are three different canopy methods in the HEC-HMS, namely dynamic, gridded
simple, and simple [
16
]. The simple canopy method was used in this modeling. This
method is a simple representation of the canopy. The canopy blocks all precipitation until
the storage capacity is filled. After the storage is filled, if no representation of the surface
is included, all other precipitation falls on the surface or directly on the soil. All potential
evapotranspiration is used to drain the canopy storage until the water in storage is used
up. The potential evapotranspiration is multiplied by the crop coefficient to determine
the amount of evapotranspiration from canopy storage and then from the surface and soil
components. Only the potential evaporation that is not used after the canopy storage is
emptied will be utilized by the surface and soil components [
17
–
19
]. The methodologies
and parameters used in the model are given in Figure 2, using the Orhaneli sub-basin, a
sub-basin of the Susurluk basin, as an example.
Water 2025,17, 860 7 of 18
Water 2025, 17, 860 7 of 19
components. Only the potential evaporation that is not used after the canopy storage is
emptied will be utilized by the surface and soil components [17–19]. The methodologies
and parameters used in the model are given in Figure 2, using the Orhaneli sub-basin, a
sub-basin of the Susurluk basin, as an example.
Figure 2. Susurluk basin HEC-HMS model installation scheme.
3.2. Hydraulic Modeling
The coupled model analysis can be performed by recognizing 1&2-dimensional
(1D&2D), and lateral weirs with the usage of the HEC-RAS program. Two model analyses
were conducted because it is not possible to define bridge or similar lateral factors in the
2D model in the old version of the HEC-RAS program. The 1D analysis is made through
the stream bed, the bridges are determined on a 1D model, and the 1D model is integrated
into a 2D model through lateral links. On the other hand, it has become possible to define
lateral factors, such as bridges, in a 2D model [20].
Both 1D and 2D models provide numerical solutions that consider the continuity and
conservation of momentum equations [1,2,4–7,21–23]. Two-dimensional continuity and
momentum equations can be wrien as follows:
𝜕𝐻
𝜕𝑡
+
𝜕
(
ℎ
𝑢
)
𝜕𝑥
+
𝜕
(
ℎ
𝑣
)
𝜕𝑦
+
𝑞
=
0
(1)
The continuity equation is solved with the finite volume method. As shown in the
flood modeling, the flow width is relatively greater than its depth, particularly in streams.
As a result, the velocity toward depth (the vertical component of velocity) is assumed to
be lower. Thus, the integral of the momentum equation in the direction of depth is appro-
priate for the modeling of floods. The integral of the momentum equation with respect to
depth can be wrien in the following form:
𝜕𝑢
𝜕𝑡
+
𝑢
𝜕𝑢
𝜕𝑥
+
𝑣
𝜕𝑢
𝜕𝑦
=
−
𝑔
𝜕𝐻
𝜕𝑥
+
𝑣
𝜕
𝑢
𝜕
𝑥
+
𝜕
𝑢
𝜕
𝑦
−
𝐶
𝑢
+
𝑓𝑣
(2)
𝜕𝑣
𝜕𝑡
+
𝑢
𝜕𝑣
𝜕𝑥
+
𝑣
𝜕𝑣
𝜕𝑦
=
−
𝑔
𝜕𝐻
𝜕𝑦
+
𝑣
𝜕
𝑣
𝜕
𝑥
+
𝜕
𝑣
𝜕
𝑦
−
𝐶
𝑣
+
𝑓𝑢
(3)
𝑢, 𝑣 = 𝑉𝑒𝑙𝑜𝑐𝑖𝑡𝑦 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡𝑠 𝑖𝑛 𝑡ℎ𝑒 𝐶𝑎𝑟𝑡𝑒𝑠𝑖𝑎𝑛 𝑑𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛
𝑔 = 𝐺𝑟𝑎𝑣𝑖𝑡𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑎𝑐𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛
𝑣= 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙 𝑒𝑑𝑑𝑦 𝑣𝑖𝑠𝑐𝑜𝑠𝑖𝑡𝑦
𝑐= 𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑜𝑓 𝑓𝑟𝑖𝑐𝑡𝑖𝑜𝑛
𝑅= 𝐻𝑦𝑑𝑟𝑎𝑢𝑙𝑖𝑐 𝑟𝑎𝑑𝑖𝑢𝑠
𝑓= 𝐶𝑜𝑟𝑖𝑜𝑙𝑖𝑠 𝑝𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟
Figure 2. Susurluk basin HEC-HMS model installation scheme.
3.2. Hydraulic Modeling
The coupled model analysis can be performed by recognizing 1&2-dimensional
(1D&2D), and lateral weirs with the usage of the HEC-RAS program. Two model analyses
were conducted because it is not possible to define bridge or similar lateral factors in the
2D model in the old version of the HEC-RAS program. The 1D analysis is made through
the stream bed, the bridges are determined on a 1D model, and the 1D model is integrated
into a 2D model through lateral links. On the other hand, it has become possible to define
lateral factors, such as bridges, in a 2D model [20].
Both 1D and 2D models provide numerical solutions that consider the continuity and
conservation of momentum equations [
1
,
2
,
4
–
7
,
21
–
23
]. Two-dimensional continuity and
momentum equations can be written as follows:
∂H
∂t+∂(hu)
∂x+∂(hv)
∂y+q=0 (1)
The continuity equation is solved with the finite volume method. As shown in the
flood modeling, the flow width is relatively greater than its depth, particularly in streams.
As a result, the velocity toward depth (the vertical component of velocity) is assumed to be
lower. Thus, the integral of the momentum equation in the direction of depth is appropriate
for the modeling of floods. The integral of the momentum equation with respect to depth
can be written in the following form:
∂u
∂t+u∂u
∂x+v∂u
∂y=−g∂H
∂x+vt∂2u
∂x2+∂2u
∂y2−Cfu+f v (2)
∂v
∂t+u∂v
∂x+v∂v
∂y=−g∂H
∂y+vt∂2v
∂x2+∂2v
∂y2−Cfv+f u (3)
u,v=Velocity components in the Cartesian direction
g=Gravitational acceleration
vt=Horizontal eddy viscosity
cf=Coefficient of friction
R=Hydraulic radius
f=Coriolis parameter
The floodplain area should be divided into small polygons to solve the above equa-
tions [19]. In HEC-RAS 2D modeling, the grid geometry supports up to octagons.
The use of different geometrical solution elements provides flexibility, and field condi-
tions are represented in the most appropriate way [
4
]. The DEM is an important base
Water 2025,17, 860 8 of 18
for hydrodynamic modeling. A well-prepared sample DEM is suitable for hydrody-
namic modeling.
4. Modeling Stages and Results
In order to determine the floods that may occur in the basin, it is necessary to work with
appropriate data [
1
,
2
,
4
,
21
,
22
]. Within the scope of the studies, the physical characteristics of
the basin and meteorological–hydrological data in time series were collected in appropriate
formats and in sufficient lengths.
The length and reliability of the dataset used in modeling and statistical studies
directly affect the accuracy of the results. For this reason, meteorological and hydrological
data, the physical characteristics of the basin, the size, length, slope, soil structure, soil
use, and accumulation structures in the basin, the characteristics of these structures, the
operating rules, and the past operating results were obtained from the relevant institutions
and organizations in the most precise manner [1,2,7,23].
4.1. Preliminary Flood Risk Assessment (PFRA)
In the Flood Risk Preliminary Assessment Report, locations with flood risk were identified
within the Susurluk Basin with the criteria specified by anthropogenic effects and disaster
risk factors. Accordingly, 1543 settlements were examined throughout the Susurluk basin.
Of these, 298 settlements were determined to be at preliminary risk of flooding, and 2116 km
of hydrodynamic modeling studies were regarded as necessary in 466 creeks [12].
According to the results of the analyses performed for the Susurluk basin Flood Man-
agement Plan Preliminary Flood Risk Assessment Report, seven river classes were determined
using the Horton–Strahler method. River lengths for each class are given in Table 5. Also,
the settlements derived from the preliminary flood risk assessment are shown in Figure 3.
Table 5. Horton–Strahler classes in the Susurluk basin.
Horton–Strahler Class 1 2 3 4 5 6 7
Length (km) 6928.10 3283.30 1503.60 596.40 647.35 179.90 29.80
Water 2025, 17, 860 8 of 19
The floodplain area should be divided into small polygons to solve the above equa-
tions [19]. In HEC-RAS 2D modeling, the grid geometry supports up to octagons.
The use of different geometrical solution elements provides flexibility, and field con-
ditions are represented in the most appropriate way [4]. The DEM is an important base
for hydrodynamic modeling. A well-prepared sample DEM is suitable for hydrodynamic
modeling.
4. Modeling Stages and Results
In order to determine the floods that may occur in the basin, it is necessary to work
with appropriate data [1,2,4,21,22]. Within the scope of the studies, the physical charac-
teristics of the basin and meteorological–hydrological data in time series were collected in
appropriate formats and in sufficient lengths.
The length and reliability of the dataset used in modeling and statistical studies di-
rectly affect the accuracy of the results. For this reason, meteorological and hydrological
data, the physical characteristics of the basin, the size, length, slope, soil structure, soil use,
and accumulation structures in the basin, the characteristics of these structures, the oper-
ating rules, and the past operating results were obtained from the relevant institutions
and organizations in the most precise manner [1,2,7,23].
4.1. Preliminary Flood Risk Assessment (PFRA)
In the Flood Risk Preliminary Assessment Report, locations with flood risk were identi-
fied within the Susurluk Basin with the criteria specified by anthropogenic effects and
disaster risk factors. Accordingly, 1543 selements were examined throughout the Susur-
luk basin. Of these, 298 selements were determined to be at preliminary risk of flooding,
and 2116 km of hydrodynamic modeling studies were regarded as necessary in 466 creeks
[12].
According to the results of the analyses performed for the Susurluk basin Flood Man-
agement Plan Preliminary Flood Risk Assessment Report, seven river classes were determined
using the Horton–Strahler method. River lengths for each class are given in Table 5. Also,
the selements derived from the preliminary flood risk assessment are shown in Figure 3.
Table 5. Horton–Strahler classes in the Susurluk basin.
Horton–Strahler Class 1 2 3 4 5 6 7
Length (km) 6928.10 3283.30 1503.60 596.40 647.35 179.90 29.80
Figure 3. Selements derived from the Preliminary Flood Risk Report for the Susurluk basin.
4.2. Hydrology Studies
Figure 3. Settlements derived from the Preliminary Flood Risk Report for the Susurluk basin.
4.2. Hydrology Studies
Within the scope of hydrology studies, flood hydrographs were calculated using
stochastic and statistical methods and hydrological models (HEC-HMS) for all necessary
outlet points on the streams under flood risk. A lumped hydrological model solution
method within the HEC-HMS model was used for hydrological modeling. Hydrological
analyses of creek tributaries with sufficient basin size were determined by establishing a
Water 2025,17, 860 9 of 18
hydrological model. The Thornthwaite method was selected to calculate potential evap-
otranspiration in this study. For the creeks in all the settlements at risk (466 creeks),
hydrological studies were conducted by flood frequency at a gauging station, regional
flood frequency, and synthetic methods accepted in the literature, such as SCS, Mockus, and
Snyder unit hydrographs. In flood frequency analyses, Gumbel and Log-Pearson Type-3
distributions are generally found to be appropriate. Chi-square (X
2
) and Kolmogorov–
Smirnov goodness-of-fit-tests were performed to determine the best distribution. Peak
flows and hydrographs of floods with return periods of 2, 5, 10, 50, 100, 500, and 1000 years
were calculated by the selected classical method that best fit the observed stream hydro-
graphs. The results of the synthetic methods applied for rainfall runoff analysis were
used more than the results of the flood frequency analyses. To put it more explicitly, over
700 flood hydrograph calculations were performed for approximately 503 hydrodynamic
models. There is a limited number of flow observation stations with long-term data on the
main river branches. However, there are several meteorological observation stations with
records covering 60 years and more throughout the Susurluk basin.
The obtained flood hydrographs were used as upstream boundary conditions in the
hydrodynamic models. The HEC-HMS model of the Orhaneli sub-basin is given in Figure 4
as an example of hydrologic studies.
Water 2025, 17, 860 9 of 19
Within the scope of hydrology studies, flood hydrographs were calculated using sto-
chastic and statistical methods and hydrological models (HEC-HMS) for all necessary out-
let points on the streams under flood risk. A lumped hydrological model solution method
within the HEC-HMS model was used for hydrological modeling. Hydrological analyses
of creek tributaries with sufficient basin size were determined by establishing a hydrolog-
ical model. The Thornthwaite method was selected to calculate potential evapotranspira-
tion in this study. For the creeks in all the selements at risk (466 creeks), hydrological
studies were conducted by flood frequency at a gauging station, regional flood frequency,
and synthetic methods accepted in the literature, such as SCS, Mockus, and Snyder unit
hydrographs. In flood frequency analyses, Gumbel and Log-Pearson Type-3 distributions
are generally found to be appropriate. Chi-square (X2) and Kolmogorov–Smirnov good-
ness-of-fit-tests were performed to determine the best distribution. Peak flows and hydro-
graphs of floods with return periods of 2, 5, 10, 50, 100, 500, and 1000 years were calculated
by the selected classical method that best fit the observed stream hydrographs. The results
of the synthetic methods applied for rainfall runoff analysis were used more than the re-
sults of the flood frequency analyses. To put it more explicitly, over 700 flood hydrograph
calculations were performed for approximately 503 hydrodynamic models. There is a lim-
ited number of flow observation stations with long-term data on the main river branches.
However, there are several meteorological observation stations with records covering 60
years and more throughout the Susurluk basin.
The obtained flood hydrographs were used as upstream boundary conditions in the
hydrodynamic models. The HEC-HMS model of the Orhaneli sub-basin is given in Figure
4 as an example of hydrologic studies.
Figure 4. Orhaneli sub-basin HEC-HMS model.
4.3. Hydraulic Studies
In the generation of flood hazard maps and flood risk maps for the Susurluk basin,
maps were obtained according to three different scenarios. Since the effects of maximum
flood flows that may occur in the study area are evaluated, this value is determined as
Q500, Q100 for selements in Türkiye, Q1000 for selements with a population over 100,000
and Q50 or Q10 + hf for non-selement areas. During the study period, the occurrence and
effects of Q500 flood flows in at least five areas were observed and used for validation. In
these scenarios, hydrographs calculated according to 50-, 100-, and 500-year recurrence
intervals were entered into the model as limit values, and flood simulations were per-
formed. According to the water levels of the floods obtained as a result of these studies,
the hazard and risk situations of the regions in the basin were determined. In the flood
hazard maps and flood risk maps produced in this way, results were obtained that
Figure 4. Orhaneli sub-basin HEC-HMS model.
4.3. Hydraulic Studies
In the generation of flood hazard maps and flood risk maps for the Susurluk basin,
maps were obtained according to three different scenarios. Since the effects of maximum
flood flows that may occur in the study area are evaluated, this value is determined as
Q
500
, Q
100
for settlements in Türkiye, Q
1000
for settlements with a population over 100,000
and Q
50
or Q
10
+ hf for non-settlement areas. During the study period, the occurrence and
effects of Q
500
flood flows in at least five areas were observed and used for validation. In
these scenarios, hydrographs calculated according to 50-, 100-, and 500-year recurrence
intervals were entered into the model as limit values, and flood simulations were performed.
According to the water levels of the floods obtained as a result of these studies, the hazard
and risk situations of the regions in the basin were determined. In the flood hazard maps
and flood risk maps produced in this way, results were obtained that identified the areas
under threat, and response capacity analysis and flood risk analysis were then carried out
by evaluating these results.
In all, 503 hydrodynamic models (226 1-dimensional and 277 2-dimensional) were
performed. It was determined that the capacities of 33 of the 503 models studied were
sufficient, and in the other models, the capacities of the creek beds were determined.
Water 2025,17, 860 10 of 18
The data produced for the workspace in 2D models are projected with 2-dimensioned
surface grid and mesh elements. The grid elements may be defined as rectangular. Model-
ing of the flows is achieved through the numerical solution of 2-dimensional Saint-Venant
equations for any grid element. For the modeling of floods within residential areas and
structures, it is known that 1D and 2D combined modeling is the most appropriate.
For the 1D and 2D combined models, the main channel and the hydraulic structures
(bridges, dikes, weirs, floodgates, etc.) on the main channel are defined within the 1D
model, and flood modeling is made with the establishment of a dynamic link with the
2D model. This integrated method targets the comprehensive, efficient, and accurate
presentation of the hydraulic system to benefit from both the 1D and 2D models. The flows
are modeled using the HEC-RAS coupled model in such a way that the flows within the
river will be modeled in 1D and the flows within the floodplain area will be modeled in 2D.
4.4. Flood Inundation Maps
4.4.1. Map Studies
Cross-sectional readings of hydraulic structures were surveyed for the creeks passing
through streams in the study area that were in the last three branches according to the
Horton–Strahler method and within the scope of the study.
A 406.78 km long strip map was produced at a scale of 1/1000 for residential areas
with a population of less than 500 and the streams within the scope of the Türkiye flood
protection law (Law no. 4373) that were determined as risky within the scope of the study.
1800 hydraulic structure surveys were measured on 250 creeks. Sample images of the field
studies are given in Figure 5.
Water 2025, 17, 860 11 of 19
and 535 surface control points were taken. In addition, orthophoto, DEMs, and triangle
models for 268 creeks were taken within map studies.
4.4.2. Hydrodynamic Model Studies
Within the scope of the study, hydrodynamic modeling studies were carried out us-
ing the best computer conditions in today’s conditions in order to determine the flood
distribution areas, the height and velocity of the water at the time of flood, and the capac-
ity of the river bed to prepare flood water depth and flood hazard maps in each stream at
risk.
In this context, cross-sections were taken in the field in order to best represent the
creek bed. Potential changes in the creek bed were tried to be represented as much as
possible, and cross-section intervals were reduced and cross-sections densified where nec-
essary. In addition, for the best representation of the hydraulic structures to be used in the
model, at least two sections—one immediately before and one immediately after the sur-
veys—were taken to ensure that the modeling studies were realistic. Surveys of all hy-
draulic structures on the creek bed were taken. Within the scope of hydrodynamic mod-
eling studies, 1D, 2D, and integrated 1D/2D models were created with HEC-RAS (Version
6.1.0).
In addition to the cross-sectional surveys, very detailed DEMs were prepared as a
basis for 2D hydrodynamic modeling studies (Figure 5).
Figure 5. Sample digital elevation model.
Hydrodynamic modeling studies were integrated with data from the field, and gov-
ernment agencies and modeling studies were carried out. The results of these modeling
studies were validated with the floods experienced, with the model results in line with
the information obtained from news sources, official sources, field observations, and local
people living in the region. In addition, calibration and validation of the studies were
completed with the help of real-time observation data by evaluating the observations
made in large rivers from the key curves of SGSs.
Flood inundation and flood hazard maps were created using the flood water depth,
velocity, and spreading areas obtained as a result of calibrated modeling studies [25]. Q50,
Q100, and Q500 flood discharges were used in selements and economic activity areas where
the population is less than 100,000 people, Q1000 flood discharge for selements with a
central population of 100,000 people or more, and Q10, Q50, and Q100 flood discharges in
agricultural areas.
Figure 5. Sample digital elevation model.
Current 1:1000 scale map studies were carried out in provincial and district centers
and settlements with a population of over 500. Map studies with a resolution of at least
1:5000 in settlement centers with a population of less than 500, economic activity areas,
agricultural areas, etc., were also conducted. Cross-sections were taken to represent the
stream bed, at most every 50 m in the provincial and district centers and settlements with
a population over 500, and at most every 1000 m in settlements with a population of
less than 500, in economic activity areas, and in agricultural areas. In settlements with a
population of 100,000 and above, instead of cross-sections that should be taken every 10 m
at most, 1:1000 scale strip maps were prepared with the approval of the administration.
Cross-sectional readings were made on these maps. At least two cross-sections were
taken upstream and downstream of force majeure points, such as section constrictions,
section obstructions, hydraulic structures, water accumulation structures, water diverting
structures, slope change, etc. For streams passing through settlements with a population of
less than 500, measurements were made using global positioning system (GPS) measuring
Water 2025,17, 860 11 of 18
devices. Readings were made considering the specified survey and cross-section locations
for each stream, and photographs and/or videos were taken for each cross-section and
for hydraulic structures, such as bridges, culverts, and drops [
24
]. For streams passing
through settlements with a population of 500 or more, flights were made using a manned
aircraft and a 150 MP camera to take aerial photographs of 8–10 GSD (ground sample
distance). Approximately 535 ground control point (GCP) reconnaissances were made
for aerial photography. Orthophoto, DEM, and triangular models were produced using
the data obtained from these flights. Along the 298 km long risky settlements (463 creeks
approximately 2115.61 km in length), 3080 hydraulic structure surveys, 3713 cross-sections,
and 535 surface control points were taken. In addition, orthophoto, DEMs, and triangle
models for 268 creeks were taken within map studies.
4.4.2. Hydrodynamic Model Studies
Within the scope of the study, hydrodynamic modeling studies were carried out
using the best computer conditions in today’s conditions in order to determine the flood
distribution areas, the height and velocity of the water at the time of flood, and the capacity
of the river bed to prepare flood water depth and flood hazard maps in each stream at risk.
In this context, cross-sections were taken in the field in order to best represent the creek
bed. Potential changes in the creek bed were tried to be represented as much as possible,
and cross-section intervals were reduced and cross-sections densified where necessary. In
addition, for the best representation of the hydraulic structures to be used in the model, at
least two sections—one immediately before and one immediately after the surveys—were
taken to ensure that the modeling studies were realistic. Surveys of all hydraulic structures
on the creek bed were taken. Within the scope of hydrodynamic modeling studies, 1D, 2D,
and integrated 1D/2D models were created with HEC-RAS (Version 6.1.0).
In addition to the cross-sectional surveys, very detailed DEMs were prepared as a
basis for 2D hydrodynamic modeling studies (Figure 5).
Hydrodynamic modeling studies were integrated with data from the field, and gov-
ernment agencies and modeling studies were carried out. The results of these modeling
studies were validated with the floods experienced, with the model results in line with
the information obtained from news sources, official sources, field observations, and local
people living in the region. In addition, calibration and validation of the studies were
completed with the help of real-time observation data by evaluating the observations made
in large rivers from the key curves of SGSs.
Flood inundation and flood hazard maps were created using the flood water depth,
velocity, and spreading areas obtained as a result of calibrated modeling studies [25]. Q50,
Q
100
, and Q
500
flood discharges were used in settlements and economic activity areas where
the population is less than 100,000 people, Q
1000
flood discharge for settlements with a
central population of 100,000 people or more, and Q
10
, Q
50
, and Q
100
flood discharges in
agricultural areas.
The flood hazard maps were prepared using the results of the hydrodynamic modeling
studies. Some examples of flood inundation and flood hazard maps obtained as a result of
the modeling studies are presented in Figures 6–8.
Using the flood hazard maps report, 503 hydrodynamic models were established
(226 1D and 277 2D hydrodynamic modeling), and the capacities of 33 streams were found
sufficient. A total of 470 separate model results, excluding 33 hydrodynamic models,
were evaluated, flood inundation and hazard maps were produced, and the creek bed
capacities that can pass without causing flooding were found for each stream section related
to settlements.
Water 2025,17, 860 12 of 18
Water 2025, 17, 860 12 of 19
The flood hazard maps were prepared using the results of the hydrodynamic mod-
eling studies. Some examples of flood inundation and flood hazard maps obtained as a
result of the modeling studies are presented in Figures 6–8.
Figure 6. Balıkesir Province Erdek District (Western Part) flood hazard map (Q500).
Figure 7. Bursa Province Karacabey District flood hazard map (Q500).
Figure 8. Kutahya Cavdarhisar District flood hazard map (Q500).
Using the flood hazard maps report, 503 hydrodynamic models were established (226
1D and 277 2D hydrodynamic modeling), and the capacities of 33 streams were found
sufficient. A total of 470 separate model results, excluding 33 hydrodynamic models, were
evaluated, flood inundation and hazard maps were produced, and the creek bed capaci-
ties that can pass without causing flooding were found for each stream section related to
selements.
Figure 6. Balıkesir Province Erdek District (Western Part) flood hazard map (Q500).
Water 2025, 17, 860 12 of 19
The flood hazard maps were prepared using the results of the hydrodynamic mod-
eling studies. Some examples of flood inundation and flood hazard maps obtained as a
result of the modeling studies are presented in Figures 6–8.
Figure 6. Balıkesir Province Erdek District (Western Part) flood hazard map (Q500).
Figure 7. Bursa Province Karacabey District flood hazard map (Q500).
Figure 8. Kutahya Cavdarhisar District flood hazard map (Q500).
Using the flood hazard maps report, 503 hydrodynamic models were established (226
1D and 277 2D hydrodynamic modeling), and the capacities of 33 streams were found
sufficient. A total of 470 separate model results, excluding 33 hydrodynamic models, were
evaluated, flood inundation and hazard maps were produced, and the creek bed capaci-
ties that can pass without causing flooding were found for each stream section related to
selements.
Figure 7. Bursa Province Karacabey District flood hazard map (Q500).
Water 2025, 17, 860 12 of 19
The flood hazard maps were prepared using the results of the hydrodynamic mod-
eling studies. Some examples of flood inundation and flood hazard maps obtained as a
result of the modeling studies are presented in Figures 6–8.
Figure 6. Balıkesir Province Erdek District (Western Part) flood hazard map (Q500).
Figure 7. Bursa Province Karacabey District flood hazard map (Q500).
Figure 8. Kutahya Cavdarhisar District flood hazard map (Q500).
Using the flood hazard maps report, 503 hydrodynamic models were established (226
1D and 277 2D hydrodynamic modeling), and the capacities of 33 streams were found
sufficient. A total of 470 separate model results, excluding 33 hydrodynamic models, were
evaluated, flood inundation and hazard maps were produced, and the creek bed capaci-
ties that can pass without causing flooding were found for each stream section related to
selements.
Figure 8. Kutahya Cavdarhisar District flood hazard map (Q500).
4.5. Flood Risk Maps
Using the results obtained from 2D hydrodynamic models (flood inundation maps),
flood risk maps (FRMs) were prepared for all relevant settlements. All structures under
flood were classified according to their intended use [
8
,
26
–
30
]. Although there are different
methodologies in the literature on flood risk maps, the risk values were calculated with the
help of the values determined for the European continent from the depth–damage curves
given in the report published by the Joint Research Center (JRC) in 2017 [31].
While determining the economic damage values, building unit costs were taken from
the document entitled Communiqué on 2022 Building Approximate Unit Costs to be Used in
the Calculation of Architecture and Engineering Service Fees, published by the Ministry of
Water 2025,17, 860 13 of 18
Environment, Urbanization, and Climate Change. Economic damage maps (EDMs) were
created using these values [32].
In contrast to economic risk maps, since human life cannot be valued, population risk
maps are created assuming that all people who may be located in areas where floodwaters
spread are affected, regardless of water depth. The population affected by the flood was cal-
culated on the basis of neighborhoods and distributed according to the volumetric weights
obtained from the floor heights and areas of all buildings in the relevant neighborhood. The
number of people affected for each settlement was determined by using the flood extent
areas found for different recurrence periods and the address-based population registra-
tion system (ADKNS) data announced by the Türkiye Statistical ˙
Institute, and affected
population maps (APMs) were created.
In the risk calculations, it was considered that critical places, such as educational
institutions, health institutions, bus stations, places of worship, parks, industrial facilities,
shopping malls, stations, airports, terminals, fuel stations, and veterinarians, all “social hot
spots” from which people cannot leave very quickly during flooding, may be more affected
by flooding, so the total risk value of these places was multiplied by 1.5.
It is thought that the damage and negative consequences that will occur due to the
exposure to flood disasters of structures such as educational institutions, health institutions,
and places of worship would be higher than for other structures. Therefore, strategic facility
(SF) maps were created to show how many strategic facilities would be affected at different
flood flow rates.
It is considered that environmental damage may occur if parks, forests, treatment
plants, storage facilities, and similar facilities are exposed to flood disasters. Therefore,
environmental damage magnitude (EDM) maps were created to show how many of these
facilities would be affected at different flood return periods in flood areas.
As in the maps showing the strategic facilities and the magnitude of environmental
damage, commercial facilities, industrial facilities, terminals, highways, railways, and
similar facilities that may adversely affect the economy in flood-prone areas were identified,
and economic activity (EA) maps were created.
Sample maps of Balıkesir Province Erdek District Center (Western Part), which is only
one of the settlements for which flood risk maps were produced, are shared in Figure 9.
For each settlement center, detailed information on the affected building polygons
(building type, activity area, number of stores, etc.) was determined and digitized, and their
impact status was evaluated. For Balıkesir Province Erdek District Center (Western Part),
the affected building types, numbers, and expected damage values are given in Table 6as
an example.
Table 6. Balıkesir Province Erdek District Center (Western Part) affected building types, numbers,
and expected damage values.
Flood Recurrence Period Structure
Type
Economic
Loss ($)
Rate
(%)
Structure Num. Expected
to be Affected
Q500
Other 27,348.00 0.6 8
Education 92,554.00 1.9 6
Industrial 3742.00 0.1 3
Worship Places 27,383.00 0.6 2
Administrative 129,052.00 2.6 10
Building 3,267,155.00 66.0 605
Health 197,485.00 4.0 5
Sport 7165.00 0.1 2
Commercial 765,019.00 15.4 125
Touristic 435,497.00 8.8 19
Water 2025,17, 860 14 of 18
Water 2025, 17, 860 14 of 19
Figure 9. Balıkesir Province Erdek District Center (Western Part) flood risk map (a), Economic dam-
age map (b), Affected population map (c), Strategic facilities map (d), Strategic facilities map (e),
Economic activity areas map (f) (Q500).
For each selement center, detailed information on the affected building polygons
(building type, activity area, number of stores, etc.) was determined and digitized, and
their impact status was evaluated. For Balıkesir Province Erdek District Center (Western
Part), the affected building types, numbers, and expected damage values are given in Ta-
ble 6 as an example.
Table 6. Balıkesir Province Erdek District Center (Western Part) affected building types, numbers,
and expected damage values.
(a) (b)
(c) (d)
(e) (f)
Figure 9. Balıkesir Province Erdek District Center (Western Part) flood risk map (a), Economic
damage map (b), Affected population map (c), Strategic facilities map (d), Strategic facilities map (e),
Economic activity areas map (f) (Q500).
Response Capacity Analysis
The main purpose of the Susurluk basin flood management plan in the long term is to
prevent flood hazards in flood-prone areas and to eliminate the need for the construction of
flood control facilities in advance, which will be very expensive to build in the future. In the
short and medium term, the plan aims to reduce the potential loss of life and property in
flood-prone areas and to significantly reduce the need for emergency response by the public
during floods. Although it is possible to reduce this need through pre-flood protection and
prevention works, it is not possible to eliminate it completely. Therefore, while flood risk is
reduced and prevented, the response capacity for potential floods needs to be continuously
improved, as summarized in Table 7.
Water 2025,17, 860 15 of 18
Table 7. Classification and Criteria Used in the Preparation and Assessment of Flood Response
Capacity Maps.
Classification Sub-Classification Maps Mapping and Assessment Parameters
CAPACITY
(response and ability to cope
with flooding)
Flood Control Structures and
Early Warning
Existing and under construction Flood Protection Structures,
Hydro-meteorological Gauging Network, Siren,
Communication and Local Media Tools
Evacuation Evacuation Zones, Open and Closed Gathering Areas for
People and Animals, Emergency Transportation Routes
Emergency Facilities and
Services
Hospitals, School Build., Fire Sta. Police Sta., Bakeries, Dry
Stores, Cold Storage, Some Public Build. Facilities such as
Stadiums, Main Transp. Routes, Stations where Transp. Types
intersect, Bridges, Tunnels, Energy Transfer Sta., Water Reser.
Debris and Recycling Sites Abandoned mines and quarries, etc. suitable for debris and
waste storage and recycling
After the flood risk maps were completed, the preparation of the flood evacuation
maps was started. With the flood evacuation plan maps, for all settlements with a popula-
tion over 500 (for which 2D hydrodynamic modeling was performed) that are expected to
be affected by floods, in case of early warning before the flood, the places to be evacuated
in a coordinated manner before or during the flood, the roads to be used (by vehicle and
on foot), the duration, etc., were determined and mapped. In this way, an infrastructure
was created that decision makers in charge of disaster coordination could directly use
before and during floods. Within the scope of this study, 458 flood evacuation points were
identified. The evacuation map created for Balıkesir Province Erdek District Center is given
in Figure 10.
Water 2025, 17, 860 16 of 19
Figure 10. Balıkesir Province Erdek District Center (Western Part) flood evacuation plan map (Q500).
As a result of all the studies carried out, measures were determined in order to elim-
inate the flood risk in the areas in the basin determined to have flood risk. The measures
identified are given in the table that follows.
Structural measures (1159 in total) that should be implemented before flooding in
selements where flooding is expected to occur were identified. Summary information
about the determined structural measures and distribution of risk classes is given in Fig-
ures 11 and 12.
Figure 10. Balıkesir Province Erdek District Center (Western Part) flood evacuation plan map (Q
500
).
Water 2025,17, 860 16 of 18
As a result of all the studies carried out, measures were determined in order to
eliminate the flood risk in the areas in the basin determined to have flood risk. The
measures identified are given in the table that follows.
Structural measures (1159 in total) that should be implemented before flooding in
settlements where flooding is expected to occur were identified. Summary information
about the determined structural measures and distribution of risk classes is given in
Figures 11 and 12.
Water 2025, 17, 860 16 of 19
Figure 10. Balıkesir Province Erdek District Center (Western Part) flood evacuation plan map (Q500).
As a result of all the studies carried out, measures were determined in order to elim-
inate the flood risk in the areas in the basin determined to have flood risk. The measures
identified are given in the table that follows.
Structural measures (1159 in total) that should be implemented before flooding in
selements where flooding is expected to occur were identified. Summary information
about the determined structural measures and distribution of risk classes is given in Fig-
ures 11 and 12.
Figure 11. Summary of information about the determined structural measure (Q500).
Water 2025, 17, 860 17 of 19
Figure 11. Summary of information about the determined structural measure (Q500).
Figure 12. Distribution of risk classes (Q500).
5. Conclusions
This study presents a comprehensive approach to flood risk management in the Su-
surluk basin, integrating hydrological modeling, GIS-based flood mapping, and evalua-
tion of early warning systems. The analysis covered 8 cities and 1153 selements within
these cities. Taking into account the assessments, 503 hydrodynamic models were created.
Hydrological and hydraulic analyses of the Susurluk basin and all related maps necessary
for flood risk assessment were prepared. According to the study criteria, 226 of these anal-
yses were 1D and 277 were 2D, identifying 470 high-risk locations requiring immediate
intervention. Flood hazard and risk maps for multiple return periods (Q50, Q100, Q500,
and Q1000) highlighted the significant exposure of critical infrastructure, particularly in
densely populated urban areas.
The results emphasize the need for proactive flood risk reduction strategies, includ-
ing both structural and non-structural measures. The proposed prioritization framework
ensures the optimal allocation of resources by balancing socioeconomic impacts and en-
vironmental concerns. Furthermore, the study highlights the importance of integrating
remote sensing, machine learning techniques, and real-time monitoring systems into flood
risk management to improve forecasting accuracy and preparedness.
By aligning with the European Union’s Flood Directive (2007/60/EC) and national
water management policies, this research provides a replicable model for other flood-
prone regions. Future studies should focus on refining early warning systems, incorpo-
rating climate change projections, and improving community engagement to build long-
term flood resilience.
Author Contributions: Conceptualization, A.S., S.B.F., M.D. and M.Y.; methodology, M.D., B.T.,
O.S. and E.D.; software, M.K. and I.U.; validation, M.K. and I.U; writing—original draft preparation,
O.S. and I.U.; writing—review and editing, S.B.F. and M.D.; visualization, M.K. and I.U.; supervi-
sion, O.S. and E.D. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: The data are unavailable due to privacy.
Acknowledgments: This study was prepared within the scope of the project of updating the Susur-
luk Basin Flood Management Plans of the General Directorate of Water Management of the Republic
Figure 12. Distribution of risk classes (Q500 ).
5. Conclusions
This study presents a comprehensive approach to flood risk management in the
Susurluk basin, integrating hydrological modeling, GIS-based flood mapping, and evalua-
tion of early warning systems. The analysis covered 8 cities and 1153 settlements within
these cities. Taking into account the assessments, 503 hydrodynamic models were created.
Hydrological and hydraulic analyses of the Susurluk basin and all related maps necessary
for flood risk assessment were prepared. According to the study criteria, 226 of these
analyses were 1D and 277 were 2D, identifying 470 high-risk locations requiring immediate
intervention. Flood hazard and risk maps for multiple return periods (Q50, Q100, Q500,
Water 2025,17, 860 17 of 18
and Q1000) highlighted the significant exposure of critical infrastructure, particularly in
densely populated urban areas.
The results emphasize the need for proactive flood risk reduction strategies, includ-
ing both structural and non-structural measures. The proposed prioritization framework
ensures the optimal allocation of resources by balancing socioeconomic impacts and en-
vironmental concerns. Furthermore, the study highlights the importance of integrating
remote sensing, machine learning techniques, and real-time monitoring systems into flood
risk management to improve forecasting accuracy and preparedness.
By aligning with the European Union’s Flood Directive (2007/60/EC) and national
water management policies, this research provides a replicable model for other flood-prone
regions. Future studies should focus on refining early warning systems, incorporating
climate change projections, and improving community engagement to build long-term
flood resilience.
Author Contributions: Conceptualization, A.S., S.B.F., M.D. and M.Y.; methodology, M.D., B.T., O.S.
and E.D.; software, M.K. and I.U.; validation, M.K. and I.U; writing—original draft preparation, O.S.
and I.U.; writing—review and editing, S.B.F. and M.D.; visualization, M.K. and I.U.; supervision, O.S.
and E.D. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: The data are unavailable due to privacy.
Acknowledgments: This study was prepared within the scope of the project of updating the Susurluk
Basin Flood Management Plans of the General Directorate of Water Management of the Republic
of Türkiye Ministry of Agriculture and Forestry. We would like to express our gratitude to all the
individuals and organizations who contributed to the realization of this study.
Conflicts of Interest: Authors Ibrahim Ucar and Masun Kapcak were employed by the company
Floodis Engineering. Author Burak Turan was employed by the company NFB Engineering. The
remaining authors declare that the research was conducted in the absence of any commercial or
financial relationships that could be construed as a potential conflict of interest.
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