Flood Exposure Dynamics and Quantitative Evaluation of Low-Cost Flood Control Measures in the Bengawan Solo River Basin of Indonesia

Abstract

The frequent occurrence of floods puts additional pressure on people to change their activities and alter land use practices, consequently making exposed lands more vulnerable to floods. It is thus crucial to investigate dynamic changes in flood exposures and conduct quantitative evaluations of flood risk-reduction strategies to minimize damage to exposed items. This study quantitatively assessed dynamics of flood exposure and flood risk, and evaluated the effectiveness of flood control measures in the Bengawan Solo River basin, Indonesia. The Water and Energy Budget-Based Rainfall–Runoff–Inundation Model was employed for flood simulation for different return periods, and then dynamics of flood exposures and flood risk were assessed. After that, the effectiveness of flood control measures was quantitively evaluated. The results show that settlement/built-up areas and population are increasing in flood-prone areas. The flood-exposed paddy field and settlement areas for 100-year flood were estimated to be more than 950 and 212.58 km², respectively. The results also show that the dam operation for flood control in the study area reduces the flood damage to buildings, contents, and agriculture by approximately 21.2%, 20.9%, and 25.1%, respectively. The river channel improvements were also found effective to reduce flood damage in the study area. The flood damage can be reduced by more than 60% by implementing a combination of a flood control dam and river channel improvements. The findings can be useful for planning and implementing effective flood risk reduction measures.

Full text

Academic Editor: Aristoteles Tegos
Received: 17 January 2025
Revised: 10 February 2025
Accepted: 15 February 2025
Published: 17 February 2025
Citation: Shrestha, B.B.; Rasmy, M.;
Kuribayashi, D. Flood Exposure
Dynamics and Quantitative
Evaluation of Low-Cost Flood Control
Measures in the Bengawan Solo River
Basin of Indonesia. Hydrology 2025,12,
38. https://doi.org/10.3390/
hydrology12020038
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).
Article
Flood Exposure Dynamics and Quantitative Evaluation of
Low-Cost Flood Control Measures in the Bengawan Solo River
Basin of Indonesia
Badri Bhakta Shrestha * , Mohamed Rasmy and Daisuke Kuribayashi
International Centre for Water Hazard and Risk Management (ICHARM), Public Works Research Institute (PWRI),
Tsukuba 305-8516, Ibaraki, Japan; abdul@pwri.go.jp (M.R.); kuribayashi-d673cm@pwri.go.jp (D.K.)
*Correspondence: babhash@gmail.com; Tel.: +81-29-879-6815
Abstract: The frequent occurrence of floods puts additional pressure on people to change
their activities and alter land use practices, consequently making exposed lands more
vulnerable to floods. It is thus crucial to investigate dynamic changes in flood exposures
and conduct quantitative evaluations of flood risk-reduction strategies to minimize damage
to exposed items. This study quantitatively assessed dynamics of flood exposure and flood
risk, and evaluated the effectiveness of flood control measures in the Bengawan Solo River
basin, Indonesia. The Water and Energy Budget-Based Rainfall–Runoff–Inundation Model
was employed for flood simulation for different return periods, and then dynamics of
flood exposures and flood risk were assessed. After that, the effectiveness of flood control
measures was quantitively evaluated. The results show that settlement/built-up areas
and population are increasing in flood-prone areas. The flood-exposed paddy field and
settlement areas for 100-year flood were estimated to be more than 950 and 212.58 km
2
,
respectively. The results also show that the dam operation for flood control in the study
area reduces the flood damage to buildings, contents, and agriculture by approximately
21.2%, 20.9%, and 25.1%, respectively. The river channel improvements were also found
effective to reduce flood damage in the study area. The flood damage can be reduced by
more than 60% by implementing a combination of a flood control dam and river channel
improvements. The findings can be useful for planning and implementing effective flood
risk reduction measures.
Keywords: exposures; land use and land cover; flood damage; river channel improvement;
dam
1. Introduction
A growing population and increasing development activities often put pressure on
land systems, increasing environmental risks [
1
,
2
]. Changes in the land system in flood-
prone areas may expose the areas to even higher environmental risks. It is thus crucial
to understand the dynamics of flood exposure and corresponding flood-risk levels to
reduce environmental risks and provide useful information for informed decision-making
in practical environmental risk management and future land use planning [1].
The analysis of flood exposure changes, such as land use and land cover (LULC)
changes or population changes, is essential to gain a better understanding of environmental
issues [
3
], and it can provide more information for effective land use planning and manage-
ment and environmental risk reduction [
4
]. The dynamics of land cover changes can be
monitored and observed using historical land cover maps or remotely sensed satellite-based
Hydrology 2025,12, 38 https://doi.org/10.3390/hydrology12020038

Hydrology 2025,12, 38 2 of 25
images [
5
,
6
]. Numerous studies have focused on the analysis of LULC changes in different
parts of the world [
1
,
2
,
7
–
23
]. Land cover maps created in past years and their changes can
be the key information to understand the dynamics of flood exposures in flood-prone areas.
In addition to the changes in land cover, demographic changes and spatial distributions
also play an important role in flood risk assessment and planning adaptation measures.
Flooding can have severe impacts on humans, and it is thus important to understand how
exposed people in flood-prone areas are changing spatially and temporally in order to
define their vulnerability and improve adaptation measures by identifying current and
future risks [
24
]. Recent studies reported continuous increases in human exposure to
floods due to changes in the hydrological system and land use [
25
–
27
]. The increasing
rate of human exposure to floods might be higher in the future due to the combination of
social and climate changes unless proper mitigation measures are taken. Spatiotemporal
quantification of exposed people in flood-prone areas can be useful to improve emergency
preparedness, evacuation, and human-risk reduction.
Floods often threaten the environment of exposed lands in rural and urban flood-
prone areas, resulting in huge economic losses and disaster damage. The negative impact
of floods can be reduced through non-structural and structural approaches, and more
effectively by combining both approaches [
28
]. However, the efficient implementation of
preventive measures requires the quantitative assessment of flood risk. One of the most
important and informative non-structural approaches to reduce losses and prevent damage
from floods is mapping flood hazards and risks [
28
]. Structural approaches to preventing
floods include river channel improvements (e.g., channel deepening and widening, and
levee construction), flood diversions, and flood-detention dams. The construction of new
structural measures, such as flood storage dams, is challenging, particularly in developing
countries, because of financial, political, and social issues. As Kawasaki et al. [29] pointed
out, the construction of embankments would not be easy in some areas because it may
require a large investment not only for constructing embankments but also for resettling
people living on riverbanks. To address these issues, it is thus crucial to understand how
existing river structures, such as dams and reservoirs, can be effectively used for flood
control as low-cost flood control measures. In this study, low-cost flood control measures
refer to flood control measures that can be implemented with a relatively low construction
cost compared to large-scale structural measures, like building new flood control dams
or levees. In addition, it is also crucial to understand the effectiveness of river channel
improvements, such as channel deepening and widening, for reducing flood disasters.
Channel widening and deepening can also be considered a relatively low-cost flood control
method compared to other large-scale structural measures, such as construction of new
flood control dams. Figure 1shows the example of channel-dredging and -widening activi-
ties in the Pampanga River basin of the Philippines. Moreover, combining a flood control
dam with river channel-improvement works is considered the most effective approach to
significantly reduce flood damage. For example, in Japan, flood control has been dependent
primarily on the construction of dams in the upper reaches of rivers to reduce flood peaks
and on the construction of levees and floodways and channel dredging and widening in
the lower reaches to safely and quickly discharge floodwaters [30].
This study focused on the quantitative analysis of spatiotemporal changes in the
distributions of land cover areas and population exposed to flood risk, and it also aimed
to evaluate the effectiveness of flood control measures for reducing disaster damage.
The analyses were conducted using hydrologic–hydraulic model outputs in a flood loss
estimation model. This study selected the Bengawan Solo River Basin (BSRB) as the study
basin. First, Rainfall–Runoff–Inundation processes, using a hydrological–hydraulic model,
were simulated for flood events of different return periods, and flood hazards in the basin

Hydrology 2025,12, 38 3 of 25
were analyzed. Then, dynamic changes in land cover and population using historical data
were analyzed, and land cover areas or population exposed to flooding in the cases of flood
events of different return periods were also assessed. Finally, to evaluate the effectiveness
of flood control measures, flood hazards and damage were assessed for flood events of
different return periods with and without flood control measures. The effectiveness of
flood control measures for reducing damage was evaluated, focusing on the household and
agriculture sectors.
Hydrology 2025, 12, x FOR PEER REVIEW 3 of 26
Figure 1. Example of river channel-improvement activities in the Pampanga River basin of the Phil-
ippines. (Photos: Mr. Hilton Hernando, Pampanga River Basin Flood Forecasting and Warning Cen-
tre).
This study focused on the quantitative analysis of spatiotemporal changes in the dis-
tributions of land cover areas and population exposed to flood risk, and it also aimed to
evaluate the effectiveness of flood control measures for reducing disaster damage. The
analyses were conducted using hydrologic–hydraulic model outputs in a flood loss esti-
mation model. This study selected the Bengawan Solo River Basin (BSRB) as the study
basin. First, Rainfall–Runoff–Inundation processes, using a hydrological–hydraulic
model, were simulated for flood events of different return periods, and flood hazards in
the basin were analyzed. Then, dynamic changes in land cover and population using his-
torical data were analyzed, and land cover areas or population exposed to flooding in the
cases of flood events of different return periods were also assessed. Finally, to evaluate
the effectiveness of flood control measures, flood hazards and damage were assessed for
flood events of different return periods with and without flood control measures. The ef-
fectiveness of flood control measures for reducing damage was evaluated, focusing on the
household and agriculture sectors.
2. Materials and Research Methods
2.1. Study Area
The BSRB is the longest river on the island of Java in Indonesia, with a basin area of
15,839 km
2
. Figure 2a shows the location of the BSRB and the study-area boundary with a
topographical distribution. The average annual precipitation in the basin is approximately
2100 mm. The Wonogiri multipurpose dam with a reservoir capacity of 730 × 10
6
m
3
is
located in the upper part of the basin. The purposes of the Wonogiri dam are flood control
(flood storage capacity: 220 × 10
6
m
3
), irrigation, raw water supply, and hydropower gen-
eration. The upper catchment area of the dam is 1350 km
2
. The wet season usually lasts
from November to April, and floods typically occur between December and March.
Floods continue to be the most severe annual weather-related disaster in the basin, partic-
ularly in the lower part of the basin.
Figure 2b shows the spatial distribution of soil types in the study area based on the
FAO/UNESCO soil map. Vertisols, which are rich in clay, are the most widespread soil
type in the study area. Vertisols are the best soils for irrigated agricultural cultivation.
Fluvisols were found in the lowland area of farthest downstream of the Solo River and
also in the upstream part of the Madiun River. Both Vertisols and Fluvisols, which are
naturally good fertile soils, can be considered good for agricultural cultivation. Other soil
types found in the study area are Gleysols, Lithosols, Luvisols, Regosols, and Andosols.
Figure 1. Example of river channel-improvement activities in the Pampanga River basin of the Philip-
pines. (Photos: Mr. Hilton Hernando, Pampanga River Basin Flood Forecasting and Warning Centre).
2. Materials and Research Methods
2.1. Study Area
The BSRB is the longest river on the island of Java in Indonesia, with a basin area of
15,839 km
2
. Figure 2a shows the location of the BSRB and the study-area boundary with a
topographical distribution. The average annual precipitation in the basin is approximately
2100 mm. The Wonogiri multipurpose dam with a reservoir capacity of 730
×
10
6
m
3
is
located in the upper part of the basin. The purposes of the Wonogiri dam are flood control
(flood storage capacity: 220
×
10
6
m
3
), irrigation, raw water supply, and hydropower
generation. The upper catchment area of the dam is 1350 km
2
. The wet season usually lasts
from November to April, and floods typically occur between December and March. Floods
continue to be the most severe annual weather-related disaster in the basin, particularly in
the lower part of the basin.
Figure 2b shows the spatial distribution of soil types in the study area based on the
FAO/UNESCO soil map. Vertisols, which are rich in clay, are the most widespread soil type
in the study area. Vertisols are the best soils for irrigated agricultural cultivation. Fluvisols
were found in the lowland area of farthest downstream of the Solo River and also in the
upstream part of the Madiun River. Both Vertisols and Fluvisols, which are naturally good
fertile soils, can be considered good for agricultural cultivation. Other soil types found in
the study area are Gleysols, Lithosols, Luvisols, Regosols, and Andosols.
2.2. Methodology
This study primarily consisted of three components: (i) flood hazard and risk analysis;
(ii) analysis of social changes, such as land use and land cover, population changes, and the
assessment of the dynamics of flood exposure; and (iii) flood damage assessment and the
evaluation of the effectiveness of flood control measures (e.g., use of existing structures for
flood control or river channel improvements) for reducing flood damage. The methods are
described in detail in the following subsections.
2.2.1. Flood Hazard Analysis
For flood hazard and inundation analysis, this study employed the Water and En-
ergy Budget-Based Rainfall–Runoff–Inundation (WEB-RRI) model, a hydrologic–hydraulic
model, developed by Rasmy et al. [
31
]. The WEB-RRI model, which connects interactions

Hydrology 2025,12, 38 4 of 25
between surface flow and river flow, groundwater flow and soil moisture contents, and
groundwater flow and river discharge, consists four components: (i) the Simple Biosphere
Model 2 (SiB2) module for the vertical energy and water flux transfer between land and
atmosphere, (ii) the vertical soil moisture distribution module for groundwater recharge,
(iii) the 2-D diffusive wave lateral flow module for surface flow and groundwater flow,
and (iv) the 1-D diffusive wave river flow module for river flow calculation [
31
]. The
details on the WEB-RRI model and required input data and their sources can be found in
Rasmy et al. [31]
. This study applied calibrated and validated WEB-RRI model parameters
used for the same basin by Shrestha et al. [
32
]. The details about model calibration and
validation can be found in Shrestha et al. [32].
Hydrology 2025, 12, x FOR PEER REVIEW 4 of 26
Figure 2. (a) Location of the Bengawan Solo River Basin (BSRB) and elevation distribution based on
HydroSHEDS digital elevation model (hps://www.hydrosheds.org/products/hydrosheds, ac-
cessed on 10 December 2023) and (b) soil types in the study area based on digital soil map of
FAO/UNESCO (hps://data.apps.fao.org/, accessed on 21 July 2022).
2.2. Methodology
This study primarily consisted of three components: (i) flood hazard and risk analy-
sis; (ii) analysis of social changes, such as land use and land cover, population changes,
and the assessment of the dynamics of flood exposure; and (iii) flood damage assessment
and the evaluation of the effectiveness of flood control measures (e.g., use of existing struc-
tures for flood control or river channel improvements) for reducing flood damage. The
methods are described in detail in the following subsections.
2.2.1. Flood Hazard Analysis
For flood hazard and inundation analysis, this study employed the Water and Energy
Budget-Based Rainfall–Runoff–Inundation (WEB-RRI) model, a hydrologic–hydraulic
model, developed by Rasmy et al. [31]. The WEB-RRI model, which connects interactions
between surface flow and river flow, groundwater flow and soil moisture contents, and
groundwater flow and river discharge, consists four components: (i) the Simple Biosphere
Model 2 (SiB2) module for the vertical energy and water flux transfer between land and
atmosphere, (ii) the vertical soil moisture distribution module for groundwater recharge,
(iii) the 2-D diffusive wave lateral flow module for surface flow and groundwater flow,
and (iv) the 1-D diffusive wave river flow module for river flow calculation [31]. The de-
tails on the WEB-RRI model and required input data and their sources can be found in
Rasmy et al. [31]. This study applied calibrated and validated WEB-RRI model parameters
used for the same basin by Shrestha et al. [32]. The details about model calibration and
validation can be found in Shrestha et al. [32].
Figure 2. (a) Location of the Bengawan Solo River Basin (BSRB) and elevation distribution based
on HydroSHEDS digital elevation model (https://www.hydrosheds.org/products/hydrosheds,
accessed on 10 December 2023) and (b) soil types in the study area based on digital soil map of
FAO/UNESCO (https://data.apps.fao.org/, accessed on 21 July 2022).
Previous researchers often used 500 m or 1000 m or larger grid sizes in the hydrologic–
hydraulic model simulation such as in Rainfall–Runoff–Inundation (RRI) model or WEB-
RRI model simulation for basin-level or areas with a larger catchment size, depending on
the catchment size for efficient computation [28,31–37]. In this study, the WEB-RRI model
was simulated for the entire river basin, and appropriate grid size, approximately 920 m
(
30-arc second)
, was used for the study area by referring to previous studies and consider-
ing the catchment area. The digital elevation model, flow direction, and flow accumulation,
which were downloaded from the HydroSHEDS (https://www.hydrosheds.org/, accessed
on 10 December 2023) at 30-arc second resolution, were used in the flood simulation model
as topographical feature inputs. The soil-type distribution and related soil-water parame-
ters (e.g., saturated hydraulic conductivity, saturated and residual social moisture contents,
and Van Genutchen parameters) were obtained from the Food and Agriculture Organi-
zation (FAO) (https://www.fao.org/home/en/, accessed on
5 July 2023
). Other forcing

Hydrology 2025,12, 38 5 of 25
inputs, such as air temperature, surface pressure, wind speed, radiation, and specific hu-
midity, were obtained from the Japan Meteorological Agency’s Japanese 55-year ReAnalysis
(JRA-55) (https://jra.kishou.go.jp/JRA-55/index_en.html, accessed on 15 May 2024).
The river channel locations and network were determined using the flow accumulation
and flow direction data downloaded from the HydroSHEDS. The resulting river network in
the study area was found to be reasonably consistent with the actual river network. In the
WEB-RRI model, the flow direction varies depending on local hydraulic gradients, and flow
direction and flow accumulation data were used to determine only river channel locations
and not for flood routing. The river cross-section was approximated as rectangular channel,
and the river channel width and depth at each river cell corresponding to bankfull discharge
were approximately calculated using empirical equations (Equations (1) and (2)) [33].
width =CwASw(1)
depth =CdASd(2)
where
A
is the upstream contributing catchment area in km
2
,
Cw
(=5.5) and
Sw
(=0.3) are the
empirical parameters of width, and
Cd
(=0.9) and
Sd
(=0.22) are the empirical parameters
of depth. The units of width and depth are meters. Firstly, the default values of the
parameters of these empirical relationships included in the RRI model were used, which
were determined using the data of the river basins in the Southeast Asian countries [
33
],
and these values were then adjusted or fine-tuned for the study area during the process of
calibration and validation and also based on Google Earth images.
Flood hazard or inundation maps, which usually show the flood extent and depth
caused by a flood of a target scale, are essential to implement practical land use management
and flood preventive measures. The target scale for flood hazard analysis is generally
decided based on the socio-economic conditions of the target area and the purpose of
research. In practice, the scale of the recorded largest flood in the past or the scale of a
50- or 100-year flood is used as the target scale. To understand the flood characteristics
of low-scale and extreme floods, this study simulated unsteady-state inundation during
flood events of different return periods, i.e., 5, 10, 25, 50, 100, 150, and 200 years, using the
calibrated and validated WEB-RRI model. Kudo et al. [
34
] found that the 4-day accumulated
rainfall was strongly correlated with peak discharge for the BSRB, and they used 4-day
annual maximum rainfall in the frequency analysis. Iwami et al. [
35
] also used 4-day
rainfall for frequency analysis for the same study basin. By referring to previous studies,
flood frequency analysis was thus conducted using the basin average (area average)
4-day
annual maximum rainfall (i.e., annual maximum of total rainfall accumulated over a
4-day
period) for the period from 1976 to 2009, based on the Gumbel distribution. The
worst flood events in history are often used to design rainfall for specific return period
in assessing flood hazards and damages because these events can help to estimate the
potential extent of extreme flood scenarios, which can help with disaster planning and
resource allocation [
29
,
36
–
38
]. Therefore, the spatial and temporal rainfall patterns of
December 2007–January 2008, which led to a worst flood event, were selected to determine
rainfall for flood events of different return periods. The rainfall intensity for each return
period was obtained from frequency analysis. The rainfall hyetograph for a specific return
period was estimated by multiplying the selected rainfall pattern of the 2007/2008 flood
event by a conversion factor. The conversion factor for each return period was calculated
as the ratio of the corresponding rainfall of the return period and the rainfall volume
of the selected 2007/2008 rainfall pattern [
28
]. Then, flood simulations were performed
using the WEB-RRI model by applying determined rainfall for a flood of a specific return
period. The initial setting of the time step for surface flow, groundwater flow, and river

Hydrology 2025,12, 38 6 of 25
flow calculations was 600 s; however, the adaptive Runge–Kutta algorithm was applied in
the model calculations, which may shorten the calculation time step if necessary [31].
ArcGIS 10.8, a geographic information system (GIS), was used to prepare a flood
hazard or inundation map by overlaying or classifying inundation depth. The calculated
flood inundation results from the model simulation were imported into ArcGIS, and then
spatial analysis tools in ArcGIS were used to classify the areas based on flood depth. The
flood inundation probability map was also prepared using ArcGIS by overlaying flood
inundated areas calculated for different return periods (i.e., 5-, 10-, 25-, 50-, 100-, 150-, and
200-year flood) (overlaying inundated areas for lower return periods on top of those for
higher return periods).
2.2.2. Social Changes and Exposure Assessment
This study analyzed changes in land cover and land use in the study basin using
land cover maps in 1990, 2006, and 2020 and changes in population using the WorldPop
population in 2000, 2010, and 2020 (Figure 3). Land cover maps of the study area were col-
lected from the Ministry of Environment and Forestry, Indonesia, and the population data
were downloaded from the WorldPop database (https://www.worldpop.org/, accessed
on 25 June 2024).
Hydrology 2025, 12, x FOR PEER REVIEW 7 of 26
Figure 3. Overview of flood exposure assessment.
2.2.3. Assessment of Flood Risk and Evaluation of Effectiveness of Low-Cost Flood
Control Measures
This study evaluated the effectiveness of low-cost flood control measures such as use
of the existing dam for flood control, focusing on flood damage to residential houses and
rice crops. The channel widening and deepening can also be considered as a relatively
low-cost flood control measure compared to other hard engineering options like building
large levees or building new flood control dams or constructing extensive flood relief
channels, as it primarily involves modifying the existing river channel without significant
infrastructure additions; however, the cost can vary depending on the scale of the project,
the topographical and geological characteristics, and the environmental factors. Thus, this
study also considered quantitative evaluation of river channel-improvement works, such
as channel widening and deepening for reducing the damage and risk.
To quantify damage with or without flood control measures, this study employed a
damage estimation method and flood damage curves presented in Shrestha et al. [32] for
houses and contents and those in Shrestha et al. [39] for rice crops. The total number of
houses at each calculation grid was estimated based on the population at the grid and the
average family size (3.7 people). The majority of the houses in the study area are masonry-
walled (approximately 79%) and wooden-walled (approximately 21%) [32]. The average
rebuilding value of a house was approximately IDR 106.64 million for masonry-walled
house and IDR 90 million for wooden-walled house. The average exposure value of house-
hold contents (replacement value) per household was IDR 32.37 million [32]. Flood dam-
age to houses and contents was defined as function of flood depth, while flood damage to
rice crops was defined as function of both flood depth and duration. The growth stage of
rice crops was considered the same as during the largest recorded flood event in
2007/2008. The volume and value of rice-crop loss were estimated using the following
values referring to previous research [39]: a rice yield of 5230 kg/ha, a farm gate price of
rice of 4650 IDR/kg, and a cost of input of 1,970,414 IDR/ha. The damage assessment meth-
ods were validated by comparing the calculated results of damage during past flood
events using reported data. The details on the validation of household damage, including
the calculation conditions, can be found in Shrestha et al. [32] and that of rice-crop damage
in Shrestha et al. [39].
To evaluate the effectiveness of flood control measures, first flood damage to build-
ings, contents, and rice crops was calculated without considering any flood control
Land Cover Maps for Past
Years: 1990, 2006, 2020
Flood Hazard Map
Change Analysis
Vector Data
Conversion of Vector
Data to Raster:
Land Cover Maps: 1990,
2006, 2020
Flood Exposure Assessment
Contribute to Flood Risk
Management and Land Use Planning
Flood Hazard Mapping
Z
Rainfall
2D Diffusion
on Land
Subsurface + Surface
Vertical Infiltration
1D Diffusion
in River
WEB-RRI Model Simulation
Z
Atmosphere-
land interactions
Evapotranspiration
Population for
Historical Years:
2000, 2010,
2020
Trend Change
Analysis
Figure 3. Overview of flood exposure assessment.
This study assessed social flood exposure, focusing on land cover areas and population,
considering changes in land cover, land use, and population. By overlaying the land cover
maps or population data with delineated flood hazard maps of different flood scales in a
GIS, this study estimated exposed areas of each land cover class in flooding or exposed
population in the flood-prone areas. To estimate exposed land cover areas or population in
the flood-prone areas, first the generated raster layer of flood inundation depth from flood
simulation for each return period flood was imported into ArcGIS, and then other relevant
data, such as land cover or population data, were overlayed with imported flood inundation
layer. Finally, the exposed land cover areas or population in the flood inundation areas
were extracted using spatial analysis tools in ArcGIS.

Hydrology 2025,12, 38 7 of 25
2.2.3. Assessment of Flood Risk and Evaluation of Effectiveness of Low-Cost Flood
Control Measures
This study evaluated the effectiveness of low-cost flood control measures such as use
of the existing dam for flood control, focusing on flood damage to residential houses and
rice crops. The channel widening and deepening can also be considered as a relatively
low-cost flood control measure compared to other hard engineering options like building
large levees or building new flood control dams or constructing extensive flood relief
channels, as it primarily involves modifying the existing river channel without significant
infrastructure additions; however, the cost can vary depending on the scale of the project,
the topographical and geological characteristics, and the environmental factors. Thus, this
study also considered quantitative evaluation of river channel-improvement works, such
as channel widening and deepening for reducing the damage and risk.
To quantify damage with or without flood control measures, this study employed
a damage estimation method and flood damage curves presented in Shrestha et al. [
32
]
for houses and contents and those in Shrestha et al. [
39
] for rice crops. The total number
of houses at each calculation grid was estimated based on the population at the grid and
the average family size (3.7 people). The majority of the houses in the study area are
masonry-walled (approximately 79%) and wooden-walled (approximately 21%) [32]. The
average rebuilding value of a house was approximately IDR 106.64 million for masonry-
walled house and IDR 90 million for wooden-walled house. The average exposure value of
household contents (replacement value) per household was IDR 32.37 million [
32
]. Flood
damage to houses and contents was defined as function of flood depth, while flood damage
to rice crops was defined as function of both flood depth and duration. The growth stage of
rice crops was considered the same as during the largest recorded flood event in 2007/2008.
The volume and value of rice-crop loss were estimated using the following values referring
to previous research [
39
]: a rice yield of 5230 kg/ha, a farm gate price of rice of 4650 IDR/kg,
and a cost of input of 1,970,414 IDR/ha. The damage assessment methods were validated
by comparing the calculated results of damage during past flood events using reported data.
The details on the validation of household damage, including the calculation conditions,
can be found in Shrestha et al. [32] and that of rice-crop damage in Shrestha et al. [39].
To evaluate the effectiveness of flood control measures, first flood damage to buildings,
contents, and rice crops was calculated without considering any flood control measures for
flood events of different return periods, i.e., 5-, 10-, 25-, 50-, 100-, 150-, and 200-year floods.
Then, flood damage was assessed using different flood control measures to evaluate the
effectiveness of each flood control measure for flood risk reduction. By using the calculated
results of flood events of different return periods, expected annual damage (EAD) with or
without flood control measures was calculated by integrating flood damage for all flood
probabilities, using Equation (3) [40].
EAD =1
2
n
∑
i=11
RPi
−1
RPi+1(Di+Di+1)(3)
where nis the number of return periods considered for damage calculations, RP is the
return period, and Dis the calculated damage cost.
This study evaluated flood damage reduction by (i) the use of the Wonogiri dam for
flood control using its current capacity and (ii) river channel improvements to increase
the channel’s transport capacity. To evaluate the effectiveness of the flood control dam,
this study considered the current flood control operation rules (FCOR) of the dam. Based
on the current FCOR, when the inflow into the reservoir exceeds 400 m
3
/s, the discharge
release from the dam should be kept at 400 m
3
/s until the reservoir water level reaches the
design flood water level. The effectiveness of damage reduction by the use of the Wonogiri

Hydrology 2025,12, 38 8 of 25
dam was evaluated by setting a discharge control rate based on FCOR in the river flow
simulation. The discharge release from the dam was maintained to be 400 m3/s when the
inflow into the reservoir exceeded 400 m
3
/s until the water storage volume reached the
maximum flood storage capacity (220
×
10
6
m
3
). When the inflow into the reservoir was
below 400 m
3
/s or when the dam water storage reached the flood storage capacity, the
dam’s outflow rate was set to be the same as the inflow rate.
River channel improvements, such as channel widening and deepening (dredging),
can increase its capacity to transport water, allowing it to carry a higher flow rate and
store a larger volume of water, helping prevent flooding, and potentially reducing flood
risk in certain areas. To evaluate the effectiveness of channel improvements for flood
control, channel widening or deepening along the river reach, as shown in Figure 4, was
considered based on flood inundation analysis. The river channel width or depth was
increased by 5% or 10% of the original river width or depth, which was calculated using
Equation (1) or Equation (2). Then, flood simulation was conducted with the scenarios
of channel widening or deepening. The construction of embankment can be low cost if it
uses local materials, lightweight materials, and construction methods that reduce labor and
materials and also no resettling of people living at riverbanks is required. Therefore, for our
better understanding, flood damage reduction by building embankments along the selected
river reach was also evaluated. The quantitative evaluation of flood damage reduction by
channel widening or deepening or building embankment provides scientific information
for choosing better options for reducing flood damage depending on availability of local
resources and materials. In addition, the combined effectiveness of use of existing Wonogiri
dam for flood control with channel widening or deepening or building embankment was
also evaluated quantitatively.
Hydrology 2025, 12, x FOR PEER REVIEW 9 of 26
Figure 4. River reach considered (red line) for river channel improvements in the analysis.
3. Results
3.1. Delineation of Flood-Prone Areas and Flood Hazard Analysis
The calibrated and validated WEB-RRI model parameters for the same basin by
Shrestha et al. [32] were used in this study. Figure 5 shows the comparison between cal-
culated discharges and observed discharges at the Cepu station for the flood events of
calibration and validation [32], and the calculated results agree well with the observations
with high Nash–Sutcliffe efficiency (NSE) and r-squared values. The calculated flood in-
undation area with a flood depth greater than 0.3 m varies from 219.5 km
2
to 1634.3 km
2
for 5-year to 200-year floods under the natural flow condition. Figure 6 shows the calcu-
lated flood inundation depth and extent for a 10-year flood (low-scale flood), 50-year flood
(high-scale flood), and 100-year flood (extreme flood). In the cases of high-scale and ex-
treme floods, inundation with high flood depths covers a large area located in the lower
reach of the basin and an area upstream of the confluence of the Madiun River (red circle
areas). Figure 7 shows the flood inundation probability from high frequency (frequently
inundated areas) to low frequency (rarely inundated areas), which was prepared by over-
laying inundated areas calculated for different return periods (i.e., 5-, 10-, 25-, 50-, 100-,
150-, and 200-year flood). The results of flood inundation probability are useful for flood
zoning that limits development activities in areas of high flood risk.
Figure 4. River reach considered (red line) for river channel improvements in the analysis.
3. Results
3.1. Delineation of Flood-Prone Areas and Flood Hazard Analysis
The calibrated and validated WEB-RRI model parameters for the same basin by
Shrestha et al. [
32
] were used in this study. Figure 5shows the comparison between
calculated discharges and observed discharges at the Cepu station for the flood events of
calibration and validation [
32
], and the calculated results agree well with the observations
with high Nash–Sutcliffe efficiency (NSE) and r-squared values. The calculated flood
inundation area with a flood depth greater than 0.3 m varies from 219.5 km
2
to
1634.3 km2
for 5-year to 200-year floods under the natural flow condition. Figure 6shows the calculated

Hydrology 2025,12, 38 9 of 25
flood inundation depth and extent for a 10-year flood (low-scale flood), 50-year flood (high-
scale flood), and 100-year flood (extreme flood). In the cases of high-scale and extreme
floods, inundation with high flood depths covers a large area located in the lower reach of
the basin and an area upstream of the confluence of the Madiun River (red circle areas).
Figure 7shows the flood inundation probability from high frequency (frequently inundated
areas) to low frequency (rarely inundated areas), which was prepared by overlaying
inundated areas calculated for different return periods (i.e., 5-, 10-, 25-, 50-, 100-, 150-, and
200-year flood). The results of flood inundation probability are useful for flood zoning that
limits development activities in areas of high flood risk.
Hydrology 2025, 12, x FOR PEER REVIEW 10 of 26
Figure 5. Time-series plots of calculated and observed daily discharges at Cepu station for flood
events in 2007/2008 and 2009.
Figure 6. Calculated flood inundation depth and extent areas for 10-, 50-, and 100-year floods.
0
40
80
120
160
2000
1000
2000
3000
4000
5000
Rainfall (mm/day)
Discharge (m
3
/s)
Date
0
40
80
120
160
2000
1000
2000
3000
4000
5000
Rainfall (mm/day)
Discharge (m
3
/s)
Date
Rainfall (basin avg.) Calculated discharge Observed discharge
NSE=0.82,
R
2
=0.84
NSE=0.81, R
2
=0.82
Calibration
Validation
2007/2008 Flood
2009 Flood
Figure 5. Time-series plots of calculated and observed daily discharges at Cepu station for flood
events in 2007/2008 and 2009.
3.2. Flood Exposure Assessment
Figure 8shows the land cover maps for 1990, 2006, and 2020. Large paddy-field areas
(PFA) lie in the low-land areas of the lower basin, where flooding occurs frequently. Table 1
shows the area of each land cover class and its increases or decreases between 1990 and
2006, between 2006 and 2020, and between 1990 and 2020. The rate of changes in LULC
was higher in 2006–2020 than in 1990–2006. Plantation forest (PF), settlement and built-up
area (SBA), dryland agriculture (DLA), and PFA significantly changed during the period
from 1990 to 2020. The study basin was largely dominated by PFA (38.8% of the total area)
and PF (23.5% of the total area) in 1990. The PFA decreased from 6155.92 km
2
in 1990 to
5687.02 km
2
in 2020, and PF from 3733.79 km
2
to 3413.91 km
2
, while SBA significantly
increased from 1714.67 km2in 1990 to 2518.34 km2in 2020.

Hydrology 2025,12, 38 10 of 25
Hydrology 2025, 12, x FOR PEER REVIEW 10 of 26
Figure 5. Time-series plots of calculated and observed daily discharges at Cepu station for flood
events in 2007/2008 and 2009.
Figure 6. Calculated flood inundation depth and extent areas for 10-, 50-, and 100-year floods.
0
40
80
120
160
2000
1000
2000
3000
4000
5000
Rainfall (mm/day)
Discharge (m
3
/s)
Date
0
40
80
120
160
2000
1000
2000
3000
4000
5000
Rainfall (mm/day)
Discharge (m
3
/s)
Date
Rainfall (basin avg.) Calculated discharge Observed discharge
NSE=0.82,
R
2
=0.84
NSE=0.81, R
2
=0.82
Calibration
Validation
2007/2008 Flood
2009 Flood
Figure 6. Calculated flood inundation depth and extent areas for 10-, 50-, and 100-year floods.
Hydrology 2025, 12, x FOR PEER REVIEW 11 of 26
Figure 7. Flood inundation probability from high frequency to low frequency.
3.2. Flood Exposure Assessment
Figure 8 shows the land cover maps for 1990, 2006, and 2020. Large paddy-field areas
(PFA) lie in the low-land areas of the lower basin, where flooding occurs frequently. Table
1 shows the area of each land cover class and its increases or decreases between 1990 and
2006, between 2006 and 2020, and between 1990 and 2020. The rate of changes in LULC
was higher in 2006–2020 than in 1990–2006. Plantation forest (PF), selement and built-up
area (SBA), dryland agriculture (DLA), and PFA significantly changed during the period
from 1990 to 2020. The study basin was largely dominated by PFA (38.8% of the total area)
and PF (23.5% of the total area) in 1990. The PFA decreased from 6155.92 km
2
in 1990 to
5687.02 km
2
in 2020, and PF from 3733.79 km
2
to 3413.91 km
2
, while SBA significantly in-
creased from 1714.67 km
2
in 1990 to 2518.34 km
2
in 2020.
Figure 8. Land cover maps for past years (source: Ministry of Environment and Forestry, Indonesia).
Figure 7. Flood inundation probability from high frequency to low frequency.
Figure 9shows the loss and gain in the area of LULC during two periods: 1990–2006
and 2006–2020. In the period between 1990 and 2006, the loss areas of PFA were mainly
converted to PF and SBA, whereas the gain areas in PFA were mainly from the PF. The loss
areas of PF were converted to PFA, DLA, and mixed dryland and shrub (MDS), whereas
the gain areas were from DLA, PFA, MDS, secondary dryland forest (SDF), and shrubland
(SL). The gain areas of SBA were largely from PFA, MDS, and DLA. In the period between
2006 and 2020, large areas of PFA and PF were converted to other LULCs. The loss areas
of PFA in this period were mainly converted to SBA, whereas the gain areas were from
MDS, DLA, and SBA. The loss areas of PF were mainly converted to DLA, MDS, and
PFA, whereas the gain areas in PF were mainly from MDS. The gain areas of SBA were
mainly from PFA, MDS, and DLA. The net changes (increase or decrease) in PF during three
periods, 1990–2006, 2006–2020, and 1990–2020, were 164.81, (
−
) 484.69, and (
−
)
319.88 km2
,
respectively (“
−
” means decrease in area). They were about (
−
) 57.18., (
−
) 411.72, and
(−) 468.9 km2
in the case of PFA. The net increased area in SBA was 83.83 km
2
from 1990
to 2006, 719.84 km2from 2006 to 2020, and 803.67 km2from 2006 to 2020.

Hydrology 2025,12, 38 11 of 25
Hydrology 2025, 12, x FOR PEER REVIEW 11 of 26
Figure 7. Flood inundation probability from high frequency to low frequency.
3.2. Flood Exposure Assessment
Figure 8 shows the land cover maps for 1990, 2006, and 2020. Large paddy-field areas
(PFA) lie in the low-land areas of the lower basin, where flooding occurs frequently. Table
1 shows the area of each land cover class and its increases or decreases between 1990 and
2006, between 2006 and 2020, and between 1990 and 2020. The rate of changes in LULC
was higher in 2006–2020 than in 1990–2006. Plantation forest (PF), selement and built-up
area (SBA), dryland agriculture (DLA), and PFA significantly changed during the period
from 1990 to 2020. The study basin was largely dominated by PFA (38.8% of the total area)
and PF (23.5% of the total area) in 1990. The PFA decreased from 6155.92 km
2
in 1990 to
5687.02 km
2
in 2020, and PF from 3733.79 km
2
to 3413.91 km
2
, while SBA significantly in-
creased from 1714.67 km
2
in 1990 to 2518.34 km
2
in 2020.
Figure 8. Land cover maps for past years (source: Ministry of Environment and Forestry, Indonesia).
Figure 8. Land cover maps for past years (source: Ministry of Environment and Forestry, Indonesia).
Table 1. Area of each land cover class and changes in area (note: the FPA was not observed in the
1990 and 2006 LULC maps, and the MA was also not observed for 1990).
LULC Type
Area (km2) Change in Area (km2)
1990 2006 2020
1990
−
2006 2006
−
2020
1990−2020
SDF 170.02 129.86 146.90 −40.16 17.04 −23.12
PF 3733.79 3898.60 3413.91 164.81 −484.69 −319.88
SL 154.76 152.52 106.03 −2.24 −46.49 −48.73
EC 50.70 50.70 116.87 0.00 66.17 66.17
SBA 1714.67 1798.50 2518.34 83.83 719.84 803.67
BL 0.69 21.40 7.76 20.71 −13.64 7.07
WB 121.48 112.41 130.40 −9.07 17.99 8.92
DLA 1811.94 1713.34 1568.31 −98.60 −145.03 −243.63
MDS 1922.58 1860.09 1954.44 −62.49 94.35 31.86
PFA 6155.92 6098.74 5687.02 −57.18 −411.72 −468.9
ASA 2.501 2.50 4.40 0.00 1.90 1.899
MA - 0.37 19.60 - 19.23 -
FPA - - 165.02 - - -
Figure 10 shows the calculated flood-exposed area of the LULC class for different
flood scales (10-, 50-, and 100-year floods), using the 1990, 2006, and 2020 land cover maps.
PFA is highly vulnerable to floods. The calculated flood-exposed PFA for 10-, 50-, and
100-year floods is more than 250, 700, and 950 km
2
, respectively. The flood exposure of
PFA in the cases of 50- and 100-year floods slightly decreases because of the conversion
of PFA to other land cover classes, such as SBA. On the other hand, the flood exposure of
SBA increases. The calculated flood-exposed area of SBA for a 100-year flood using the
1990, 2006, and 2020 land cover maps were 129.03, 137.99, and 212.58 km
2
, respectively,

Hydrology 2025,12, 38 12 of 25
indicating a rapid expansion of SBA in the flood-prone areas. In the study basin, the areas
of MDS, DLA, and PF were found to be exposed to flooding. The calculated flood-exposed
areas of MDS, DLA, and PF for a 100-year flood using the 1990 land cover map were 142.24,
49.27, and 49.12 km
2
, respectively, while they were about 65.72, 53.95, and 43.94 km
2
, using
the 2020 land cover map.
Hydrology 2025, 12, x FOR PEER REVIEW 12 of 26
Table 1. Area of each land cover class and changes in area (note: the FPA was not observed in the
1990 and 2006 LULC maps, and the MA was also not observed for 1990).
LULC
Type
Area (km
2
) Change in Area (km
2
)
1990 2006 2020 1990−2006 2006−2020 1990−2020
SDF 170.02 129.86 146.90 −40.16 17.04 −23.12
PF 3733.79 3898.60 3413.91 164.81 −484.69 −319.88
SL 154.76 152.52 106.03 −2.24 −46.49 −48.73
EC 50.70 50.70 116.87 0.00 66.17 66.17
SBA 1714.67 1798.50 2518.34 83.83 719.84 803.67
BL 0.69 21.40 7.76 20.71 −13.64 7.07
WB 121.48 112.41 130.40 −9.07 17.99 8.92
DLA 1811.94 1713.34 1568.31 −98.60 −145.03 −243.63
MDS 1922.58 1860.09 1954.44 −62.49 94.35 31.86
PFA 6155.92 6098.74 5687.02 −57.18 −411.72 −468.9
ASA 2.501 2.50 4.40 0.00 1.90 1.899
MA - 0.37 19.60 - 19.23 -
FPA - - 165.02 - - -
Figure 9 shows the loss and gain in the area of LULC during two periods: 1990–2006
and 2006–2020. In the period between 1990 and 2006, the loss areas of PFA were mainly
converted to PF and SBA, whereas the gain areas in PFA were mainly from the PF. The
loss areas of PF were converted to PFA, DLA, and mixed dryland and shrub (MDS),
whereas the gain areas were from DLA, PFA, MDS, secondary dryland forest (SDF), and
shrubland (SL). The gain areas of SBA were largely from PFA, MDS, and DLA. In the
period between 2006 and 2020, large areas of PFA and PF were converted to other LULCs.
The loss areas of PFA in this period were mainly converted to SBA, whereas the gain areas
were from MDS, DLA, and SBA. The loss areas of PF were mainly converted to DLA,
MDS, and PFA, whereas the gain areas in PF were mainly from MDS. The gain areas of
SBA were mainly from PFA, MDS, and DLA. The net changes (increase or decrease) in PF
during three periods, 1990–2006, 2006–2020, and 1990–2020, were 164.81, (−) 484.69, and
(−) 319.88 km
2
, respectively (“−” means decrease in area). They were about (−) 57.18., (−)
411.72, and (−) 468.9 km
2
in the case of PFA. The net increased area in SBA was 83.83 km
2
from 1990 to 2006, 719.84 km
2
from 2006 to 2020, and 803.67 km
2
from 2006 to 2020.
Figure 9. Loss and gain areas of each land cover class during 1990–2006 and 2006–2020 (ploed
using circlize–Circular Visualization R-package by Gu et al. [41]).
Figure 9. Loss and gain areas of each land cover class during 1990–2006 and 2006–2020 (plotted using
circlize–Circular Visualization R-package by Gu et al. [41]).
Figure 11 shows the population distributions in the study area for 2000, 2010, and 2020,
based on WorldPop data. The estimated population over the study area in 2000, 2010, and
2020 was 11.71, 11.94, and 12.24 million. The population mainly increased in Surakarta City
and the districts located in the low-land areas. Figure 12 shows dynamic changes in basin
population and flood-exposed population in the flood-prone areas. Both cases show an
increasing trend. The flood-exposed population is increasing due to demographic changes.
3.3. Evaluation of Flood Control Measures for Damage Reduction
To evaluate the effectiveness of flood control measures for flood damage reduction,
household and rice-crop damage by different return period floods was calculated based on
the 2020 population data and the 2020 PFA data, respectively. Figures 13 and 14 show the
spatial distributions of calculated flood damage to household buildings and contents and
rice crops, respectively, for 10-, 50-, and 100-year floods without flood control measures.
The estimated number of houses for 10-, 50-, and 100-year floods was 45,970, 190,015, and
247,030, respectively. The calculated values of flood damage to buildings and contents
for a 10-year flood were IDR 222.1 billion and IDR 198.2 billion, respectively, while they
were IDR 1057.1 billion and IDR 940.3 billion for a 50-year flood and IDR 1616.9 billion
and IDR 1407.04 billion for a 100-year flood. The increase in the value of both building and
content damage between 10- and 50-year floods is more than 370%. The value increase
in building and contents damage between 50- and 100-year floods is approximately 50%.
The flooded paddy area and the calculated value of rice-crop damage for a 10-year flood
were 18,479 ha and IDR 7.68 billion, respectively, while they were about 62,813 ha and
IDR 78.48 billion for a 50-year flood and 79,342 ha and IDR 138.13 billion for a 100-year
flood. The value increase in rice-crop damage between 10- and 50-year floods is 921%
and approximately 76% between 50- and 100-year floods. The results show that the flood
damage to households and agricultural crops in the case of a small-scale flood mainly
occurred in the low-land areas of the lower basin and the areas immediately upstream of
the Madiun River confluence point. However, in the case of high-scale and extreme floods,

Hydrology 2025,12, 38 13 of 25
flood damage occurred along the Solo River, and the flood damage was severe in areas in
the lower basin and immediately upstream of the confluence point.
Hydrology 2025, 12, x FOR PEER REVIEW 13 of 26
Figure 10 shows the calculated flood-exposed area of the LULC class for different
flood scales (10-, 50-, and 100-year floods), using the 1990, 2006, and 2020 land cover maps.
PFA is highly vulnerable to floods. The calculated flood-exposed PFA for 10-, 50-, and 100-
year floods is more than 250, 700, and 950 km2, respectively. The flood exposure of PFA in
the cases of 50- and 100-year floods slightly decreases because of the conversion of PFA to
other land cover classes, such as SBA. On the other hand, the flood exposure of SBA in-
creases. The calculated flood-exposed area of SBA for a 100-year flood using the 1990,
2006, and 2020 land cover maps were 129.03, 137.99, and 212.58 km2, respectively, indicat-
ing a rapid expansion of SBA in the flood-prone areas. In the study basin, the areas of
MDS, DLA, and PF were found to be exposed to flooding. The calculated flood-exposed
areas of MDS, DLA, and PF for a 100-year flood using the 1990 land cover map were
142.24, 49.27, and 49.12 km2, respectively, while they were about 65.72, 53.95, and 43.94
km2, using the 2020 land cover map.
Figure 10. Calculated flood exposed areas of each land cover class in the cases of 10-, 50- and 100-
year floods.
Figure 11 shows the population distributions in the study area for 2000, 2010, and
2020, based on WorldPop data. The estimated population over the study area in 2000, 2010,
and 2020 was 11.71, 11.94, and 12.24 million. The population mainly increased in Sura-
karta City and the districts located in the low-land areas. Figure 12 shows dynamic
changes in basin population and flood-exposed population in the flood-prone areas. Both
cases show an increasing trend. The flood-exposed population is increasing due to demo-
graphic changes.
0 200 400 600 800 1000
SDF
PF
SL
EC
SBA
DLA
MDS
PFA
MA
FPA
Calculated Flood Exposed Areas (km
2
)
Land Cover Class
1990
2006
2020
0 200 400 600 800 1000
SDF
PF
SL
EC
SBA
DLA
MDS
PFA
MA
FPA
Calculated Flood Exposed Areas (km
2
)
Land Cover Class
1990
2006
2020
0 200 400 600 800 1000
SDF
PF
SL
EC
SBA
DLA
MDS
PFA
MA
FPA
Calculated Flood Exposed Areas (km
2
)
Land Cover Class
1990
2006
2020
10-Year Flood 50-Year Flood
100-Year Flood
Figure 10. Calculated flood exposed areas of each land cover class in the cases of 10-, 50- and
100-year floods.
Hydrology 2025, 12, x FOR PEER REVIEW 14 of 26
Figure 11. Spatial distribution of population over the basin based on WorldPop Population for 2000,
2010, and 2020.
Figure 12. (a) Total estimated population in the study area for 2000, 2010, and 2020; and (b) calcu-
lated flood exposed population using different years’ population data (2000, 2010, and 2020) for 10-
, 50-, and 100-year flood event cases.
3.3. Evaluation of Flood Control Measures for Damage Reduction
To evaluate the effectiveness of flood control measures for flood damage reduction,
household and rice-crop damage by different return period floods was calculated based
on the 2020 population data and the 2020 PFA data, respectively. Figures 13 and 14 show
the spatial distributions of calculated flood damage to household buildings and contents
and rice crops, respectively, for 10-, 50-, and 100-year floods without flood control
measures. The estimated number of houses for 10-, 50-, and 100-year floods was 45,970,
190,015, and 247,030, respectively. The calculated values of flood damage to buildings and
contents for a 10-year flood were IDR 222.1 billion and IDR 198.2 billion, respectively,
while they were IDR 1057.1 billion and IDR 940.3 billion for a 50-year flood and IDR 1616.9
billion and IDR 1407.04 billion for a 100-year flood. The increase in the value of both
10
10.5
11
11.5
12
12.5
2000 2010 2020
Total Population in Basin (mil)
Yea r
0
0.4
0.8
1.2
1.6
10-YF 50-YF 100-YF
Flood Exposed Population (mil)
Flood Scale
2000
2010
2020
(a) (b)
Figure 11. Spatial distribution of population over the basin based on WorldPop Population for 2000,
2010, and 2020.

Hydrology 2025,12, 38 14 of 25
Hydrology 2025, 12, x FOR PEER REVIEW 14 of 26
Figure 11. Spatial distribution of population over the basin based on WorldPop Population for 2000,
2010, and 2020.
Figure 12. (a) Total estimated population in the study area for 2000, 2010, and 2020; and (b) calcu-
lated flood exposed population using different years’ population data (2000, 2010, and 2020) for 10-
, 50-, and 100-year flood event cases.
3.3. Evaluation of Flood Control Measures for Damage Reduction
To evaluate the effectiveness of flood control measures for flood damage reduction,
household and rice-crop damage by different return period floods was calculated based
on the 2020 population data and the 2020 PFA data, respectively. Figures 13 and 14 show
the spatial distributions of calculated flood damage to household buildings and contents
and rice crops, respectively, for 10-, 50-, and 100-year floods without flood control
measures. The estimated number of houses for 10-, 50-, and 100-year floods was 45,970,
190,015, and 247,030, respectively. The calculated values of flood damage to buildings and
contents for a 10-year flood were IDR 222.1 billion and IDR 198.2 billion, respectively,
while they were IDR 1057.1 billion and IDR 940.3 billion for a 50-year flood and IDR 1616.9
billion and IDR 1407.04 billion for a 100-year flood. The increase in the value of both
10
10.5
11
11.5
12
12.5
2000 2010 2020
Total Population in Basin (mil)
Yea r
0
0.4
0.8
1.2
1.6
10-YF 50-YF 100-YF
Flood Exposed Population (mil)
Flood Scale
2000
2010
2020
(a) (b)
Figure 12. (a) Total estimated population in the study area for 2000, 2010, and 2020; and (b) calculated
flood exposed population using different years’ population data (2000, 2010, and 2020) for 10-, 50-,
and 100-year flood event cases.
Figure 15 compares the calculated inflow discharge into reservoir and outflow dis-
charge from the dam reservoir with a discharge control rate based on FCOR in the river
flow simulation for 10-, 50-, and 100-year floods. The estimated peak inflow discharge into
reservoir for 10-, 50-, and 100-year floods was 850, 1003, and 1068 m
3
/s, respectively, which
was comparatively higher than the set discharge control rate 400 m
3
/s. According to the
study by JICA [
42
], the reservoir of Wonogiri dam in the basin often experienced inflow
of large-scale flood with peak discharge exceeded 1000 m
3
/s. The largest inflow flood
peak discharge in 1988 was recorded at 2880 m
3
/s, which was more than two times higher
than the calculated peak inflow discharge into reservoir for 100-year flood presented in
Figure 15. The results clearly indicate that the operation of Wonogiri dam based on current
FCOR and available flood storage reservoir capacity can effectively control the inflow flood
volume and peak discharge, even during a 100-year flood event.
Hydrology 2025, 12, x FOR PEER REVIEW 15 of 26
building and content damage between 10- and 50-year floods is more than 370%. The
value increase in building and contents damage between 50- and 100-year floods is ap-
proximately 50%. The flooded paddy area and the calculated value of rice-crop damage
for a 10-year flood were 18,479 ha and IDR 7.68 billion, respectively, while they were about
62,813 ha and IDR 78.48 billion for a 50-year flood and 79,342 ha and IDR 138.13 billion
for a 100-year flood. The value increase in rice-crop damage between 10- and 50-year
floods is 921% and approximately 76% between 50- and 100-year floods. The results show
that the flood damage to households and agricultural crops in the case of a small-scale
flood mainly occurred in the low-land areas of the lower basin and the areas immediately
upstream of the Madiun River confluence point. However, in the case of high-scale and
extreme floods, flood damage occurred along the Solo River, and the flood damage was
severe in areas in the lower basin and immediately upstream of the confluence point.
Figure 13. Calculated flood damage to buildings and contents for 10-, 50-, and 100-year floods, with-
out any flood control measures: (a) building damage and (b) content damage.
Figure 14. Calculated flood damage to agricultural crops (rice crops) for 10-, 50-, and 100-year floods,
without any flood control measures.
Figure 15 compares the calculated inflow discharge into reservoir and outflow dis-
charge from the dam reservoir with a discharge control rate based on FCOR in the river
flow simulation for 10-, 50-, and 100-year floods. The estimated peak inflow discharge into
reservoir for 10-, 50-, and 100-year floods was 850, 1003, and 1068 m
3
/s, respectively, which
was comparatively higher than the set discharge control rate 400 m
3
/s. According to the
study by JICA [42], the reservoir of Wonogiri dam in the basin often experienced inflow
of large-scale flood with peak discharge exceeded 1000 m
3
/s. The largest inflow flood peak
discharge in 1988 was recorded at 2880 m
3
/s, which was more than two times higher than
the calculated peak inflow discharge into reservoir for 100-year flood presented in Figure
15. The results clearly indicate that the operation of Wonogiri dam based on current FCOR
Figure 13. Calculated flood damage to buildings and contents for 10-, 50-, and 100-year floods,
without any flood control measures: (a) building damage and (b) content damage.

Hydrology 2025,12, 38 15 of 25
Hydrology 2025, 12, x FOR PEER REVIEW 15 of 26
building and content damage between 10- and 50-year floods is more than 370%. The
value increase in building and contents damage between 50- and 100-year floods is ap-
proximately 50%. The flooded paddy area and the calculated value of rice-crop damage
for a 10-year flood were 18,479 ha and IDR 7.68 billion, respectively, while they were about
62,813 ha and IDR 78.48 billion for a 50-year flood and 79,342 ha and IDR 138.13 billion
for a 100-year flood. The value increase in rice-crop damage between 10- and 50-year
floods is 921% and approximately 76% between 50- and 100-year floods. The results show
that the flood damage to households and agricultural crops in the case of a small-scale
flood mainly occurred in the low-land areas of the lower basin and the areas immediately
upstream of the Madiun River confluence point. However, in the case of high-scale and
extreme floods, flood damage occurred along the Solo River, and the flood damage was
severe in areas in the lower basin and immediately upstream of the confluence point.
Figure 13. Calculated flood damage to buildings and contents for 10-, 50-, and 100-year floods, with-
out any flood control measures: (a) building damage and (b) content damage.
Figure 14. Calculated flood damage to agricultural crops (rice crops) for 10-, 50-, and 100-year floods,
without any flood control measures.
Figure 15 compares the calculated inflow discharge into reservoir and outflow dis-
charge from the dam reservoir with a discharge control rate based on FCOR in the river
flow simulation for 10-, 50-, and 100-year floods. The estimated peak inflow discharge into
reservoir for 10-, 50-, and 100-year floods was 850, 1003, and 1068 m
3
/s, respectively, which
was comparatively higher than the set discharge control rate 400 m
3
/s. According to the
study by JICA [42], the reservoir of Wonogiri dam in the basin often experienced inflow
of large-scale flood with peak discharge exceeded 1000 m
3
/s. The largest inflow flood peak
discharge in 1988 was recorded at 2880 m
3
/s, which was more than two times higher than
the calculated peak inflow discharge into reservoir for 100-year flood presented in Figure
15. The results clearly indicate that the operation of Wonogiri dam based on current FCOR
Figure 14. Calculated flood damage to agricultural crops (rice crops) for 10-, 50-, and 100-year floods,
without any flood control measures.
Hydrology 2025, 12, x FOR PEER REVIEW 16 of 26
and available flood storage reservoir capacity can effectively control the inflow flood
volume and peak discharge, even during a 100-year flood event.
Figure 15. Calculated inflow discharge into reservoir and outflow discharge from the dam for 10-,
50-, and 100-year flood cases.
Figure 16 shows the calculated EAD for household buildings and contents and
agricultural crops with and without the use of the Wonogiri dam for flood control. EAD
was calculated using the calculated damage cost for different return period floods, i.e., 5-
, 10-, 25-, 50-, 100-, 150-, and 200-year floods. The results show that the percentages of EAD
reduction in flood damage to residential buildings and contents by discharge control at
the Wonogiri dam are 21.2% and 20.9%, respectively. The EAD reduction in rice-crop
damage by using the Wonogiri dam for flood control is 25.1%. Even though the Wonogiri
dam is located in the upper part of the basin, the use of this dam for flood control based
on the current FCOR can reduce EAD by more than 20%.
Figure 16. Calculated expected annual damage (EAD) of building, content, and rice-crop damages,
with and without dam control function and percentage reduction in EAD by the use of dam for
flood control: (a) buildings and contents and (b) rice crops.
Figure 17 shows the calculated EAD of building damage, content damage, and rice-
crop damage with and without different options of river channel improvement for
damage reduction. The figure also shows the EAD reduction by each river channel
0
200
400
600
800
1000
1200
2007/12/102007/12/152007/12/202007 /12/252007/12/302008/1 /42008/1/920 08/1/142008/1
/
Discharge (m
3
/s)
Day
Dam Inflow
Dam Outflow
5 10 15 20 25 30 35 40
0
200
400
600
800
1000
1200
2007/12/102007/12/152007/12/202007/12/252007/12/302008/1/42008/1/92008/1/142008/1/
1
Discharge (m
3
/s)
Day
Dam Inflow
Dam Outflow
5 10 15 20 25 30 35 40
0
200
400
600
800
1000
1200
2007/12/102007/12/152007/12/202007/12/252007/12/302008/1/42008/1/92008/1/142008/1
/
Discharge (m
3
/s)
Day
Dam Inflow
Dam Outflow
5 10 15 20 25 30 35 40
10-Year Flood 50-Year Flood
100-Year Flood
0
200
400
600
800
1000
1200
2007/12/102007/12/152007/12/202007/12/2520 07/12/302008/1/42008/1/92008/1/142008/1
/
Discharge (m
3
/s)
Day
Dam Inflow
Dam Outflow
5 10 15 20 25 30 3 5 40
100-Year Flood
Figure 15. Calculated inflow discharge into reservoir and outflow discharge from the dam for 10-,
50-, and 100-year flood cases.
Figure 16 shows the calculated EAD for household buildings and contents and agri-
cultural crops with and without the use of the Wonogiri dam for flood control. EAD was
calculated using the calculated damage cost for different return period floods, i.e., 5-, 10-,
25-, 50-, 100-, 150-, and 200-year floods. The results show that the percentages of EAD
reduction in flood damage to residential buildings and contents by discharge control at the
Wonogiri dam are 21.2% and 20.9%, respectively. The EAD reduction in rice-crop damage
by using the Wonogiri dam for flood control is 25.1%. Even though the Wonogiri dam is
located in the upper part of the basin, the use of this dam for flood control based on the
current FCOR can reduce EAD by more than 20%.
Figure 17 shows the calculated EAD of building damage, content damage, and rice-
crop damage with and without different options of river channel improvement for damage
reduction. The figure also shows the EAD reduction by each river channel improvement
option. The EAD of building damage decreases by 17–31% through 5–10% river channel
deepening compared with the EAD of building damage without river channel improvement,
and approximately 10–20% by 5–10% river channel widening. The reduction in the EAD
of contents damage by 5–10% river channel deepening or widening is 16–30% or 10–19%.
Similarly, the reduction in the EAD of rice-crop damage by 5–10% river channel deepening
or widening is 15–28% or 13–17%. The construction of 3-m-high levees can reduce the EAD
of building, contents, and rice-crop damage by 40%, 40%, and 37%, respectively.

Hydrology 2025,12, 38 16 of 25
Hydrology 2025, 12, x FOR PEER REVIEW 16 of 26
and available flood storage reservoir capacity can effectively control the inflow flood vol-
ume and peak discharge, even during a 100-year flood event.
Figure 15. Calculated inflow discharge into reservoir and outflow discharge from the dam for 10-,
50-, and 100-year flood cases.
Figure 16 shows the calculated EAD for household buildings and contents and agri-
cultural crops with and without the use of the Wonogiri dam for flood control. EAD was
calculated using the calculated damage cost for different return period floods, i.e., 5-, 10-,
25-, 50-, 100-, 150-, and 200-year floods. The results show that the percentages of EAD
reduction in flood damage to residential buildings and contents by discharge control at
the Wonogiri dam are 21.2% and 20.9%, respectively. The EAD reduction in rice-crop
damage by using the Wonogiri dam for flood control is 25.1%. Even though the Wonogiri
dam is located in the upper part of the basin, the use of this dam for flood control based
on the current FCOR can reduce EAD by more than 20%.
Figure 16. Calculated expected annual damage (EAD) of building, content, and rice-crop damages,
with and without dam control function and percentage reduction in EAD by the use of dam for
flood control: (a) buildings and contents and (b) rice crops.
Figure 17 shows the calculated EAD of building damage, content damage, and rice-
crop damage with and without different options of river channel improvement for dam-
age reduction. The figure also shows the EAD reduction by each river channel
0
200
400
600
800
1000
1200
2007/12/102007/12/152007/12/202007 /12/252007/12/302008/1 /42008/1/920 08/1/142008/1
/
Discharge (m
3
/s)
Day
Dam Inflow
Dam Outflow
5 10 15 20 25 30 35 40
0
200
400
600
800
1000
1200
2007/12/102007/12/152007/12/202007/12/252007/12/302008/1/42008/1/92008/1/142008/1/
1
Discharge (m
3
/s)
Day
Dam Inflow
Dam Outflow
5 10 15 20 25 30 35 40
0
200
400
600
800
1000
1200
2007/12/102007/12/152007/12/202007/12/252007/12/302008/1/42008/1/92008/1/142008/1
/
Discharge (m
3
/s)
Day
Dam Inflow
Dam Outflow
5 10 15 20 25 30 35 40
10-Year Flood 50-Year Flood
100-Year Flood
0
200
400
600
800
1000
1200
2007/12/102007/12/152007/12/202007/12/2520 07/12/302008/1/42008/1/92008/1/142008/1
/
Discharge (m
3
/s)
Day
Dam Inflow
Dam Outflow
5 10 15 20 25 30 3 5 40
100-Year Flood
Figure 16. Calculated expected annual damage (EAD) of building, content, and rice-crop damages,
with and without dam control function and percentage reduction in EAD by the use of dam for flood
control: (a) buildings and contents and (b) rice crops.
Hydrology 2025, 12, x FOR PEER REVIEW 17 of 26
improvement option. The EAD of building damage decreases by 17–31% through 5–10%
river channel deepening compared with the EAD of building damage without river chan-
nel improvement, and approximately 10–20% by 5–10% river channel widening. The re-
duction in the EAD of contents damage by 5–10% river channel deepening or widening is
16–30% or 10–19%. Similarly, the reduction in the EAD of rice-crop damage by 5–10%
river channel deepening or widening is 15–28% or 13–17%. The construction of 3-m-high
levees can reduce the EAD of building, contents, and rice-crop damage by 40%, 40%, and
37%, respectively.
Figure 17. Calculated expected annual damage with and without river channel-improvement op-
tions and percentage reduction in EAD by the river channel-improvement options: (a) building-
damage case, (b) content-damage case, and (c) rice crop-damage case. (Note: Riv in the figures
means River).
The above-discussed results are based on the evaluation of individual effectiveness
of the Wonogiri dam or each river channel improvement option for flood control. How-
ever, a combination of flood control dam (Wonogiri dam) and river channel improvement
works can further reduce the damage or loss. Figure 18 shows calculated results of build-
ings, contents, and rice-crop damage and reduction of damage by combining the use of
0
20
40
60
80
100
0
20
40
60
80
100
No Riv
Improvement
Depth_5% Depth_10% Width_5% Width_10% Levee_3m
Percentage Reduction in EAD (%)
Expected Annual Damage (EAD)
(bil. IDR/Year)
River Channel Imporvement Options
EAD
Percentage Reduction
0
20
40
60
80
100
0
20
40
60
80
100
No Riv
Improvement
Depth_5% Depth_10% Width_5% Width_10% Levee_3m
Percentage Reduction in EAD (%)
Expected Annual Damage (EAD)
(bil. IDR/Year)
River Channel Imporvement Options
EAD
Percentage Reduction
0
20
40
60
80
100
0
1
2
3
4
5
6
No Riv
Improvement
Depth_5% Depth_10% Width_5% Width_10% Levee_3m
Percentage Reduction in EAD (%)
Expected Annual Damage (EAD)
(bil. IDR/Year)
River Channel Imporvement Options
EAD
Percentage Reduction
(b)
(a)
(c)
Figure 17. Calculated expected annual damage with and without river channel-improvement options
and percentage reduction in EAD by the river channel-improvement options: (a) building-damage
case, (b) content-damage case, and (c) rice crop-damage case. (Note: Riv in the figures means River).

Hydrology 2025,12, 38 17 of 25
The above-discussed results are based on the evaluation of individual effective-
ness of the Wonogiri dam or each river channel improvement option for flood control.
However, a combination of flood control dam (Wonogiri dam) and river channel im-
provement works can further reduce the damage or loss. Figure 18 shows calculated
results of buildings, contents, and rice-crop damage and reduction of damage by com-
bining the use of the Wonogiri dam for flood control with various river channel im-
provement options, in the case of 100-year flood. The results show that the flood dam-
age can be reduced by more than 60% by implementing combined flood control option
C5 (i.e.,
Dam +Levee_3m)
, C6 (i.e.,
Dam +Depth_5% +Width_5%
, + Levee_3m), or C7 (i.e.,
Dam +Depth_10%
+Width_10%, + Levee_3m). The reduction in damage by other com-
bined flood control options, such as C1 (Dam +Depth_5%), C2 (Dam +Depth_10%) or C3
(
Dam +Width_5%
), or C4 (
Dam +Width_10%
) ranges from 28 to 42% of building damage,
from 28 to 41% of content damage, and from 37 to 43% of rice-crop damage.
Hydrology 2025, 12, x FOR PEER REVIEW 18 of 26
the Wonogiri dam for flood control with various river channel improvement options, in
the case of 100-year flood. The results show that the flood damage can be reduced by more
than 60% by implementing combined flood control option C5 (i.e., Dam + Levee_3m), C6
(i.e., Dam + Depth_5% + Width_5%, + Levee_3m), or C7 (i.e., Dam + Depth_10% + Width_10%,
+ Levee_3m). The reduction in damage by other combined flood control options, such as
C1 (Dam + Depth_5%), C2 (Dam + Depth_10%) or C3 (Dam + Width_5%), or C4 (Dam +
Width_10%) r ange s from 2 8 to 4 2% of buildi ng da mage, f rom 2 8 to 4 1% of co nten t damag e,
and from 37 to 43% of rice-crop damage.
Figure 18. Calculated flood damage in the cases of combination of flood control dam with river
channel improvement options for 100-year flood: (a) buildings damage, (b) contents damage, and
(c) rice-crop damage (C1, Dam + Depth_5%; C2, Dam + Depth_10%; C3, Dam + Width_5%; C4, Dam +
Width_10%; C5, Dam + Levee_3m; C6, Dam + Depth_5% + Width_5% + Levee_3m; and C7, Dam +
Depth_10% + Width_10% + Levee_3m).
0
20
40
60
80
100
0
40
80
120
160
No Flood
Control
With
Dam only
C1 C2 C3 C4 C5 C6 C7
Percentage Reduction (%)
Calculated Damage (bil. IDR)
Flood Control Options
Calculated Damage
Percentage Reduction in Damage
0
20
40
60
80
100
0
300
600
900
1200
1500
1800
No Flood
Control
With
Dam only
C1 C2 C3 C4 C5 C6 C7
Percentage Reduction (%)
Calculated Damage (bil. IDR)
Flood Control Options
Calculated Damag e
Percentage Reduction in Damage
0
20
40
60
80
100
0
300
600
900
1200
1500
1800
No Flood
Control
With
Dam only
C1 C2 C3 C4 C5 C6 C7
Percentage Reduction (%)
Calculated Damage (bil. IDR)
Flood Control Options
Calculated Damage
Percentage Reduction in Damage
(a)
(b)
(c)
Figure 18. Calculated flood damage in the cases of combination of flood control dam with river
channel improvement options for 100-year flood: (a) buildings damage, (b) contents damage, and
(c) rice-crop damage (C1, Dam +Depth_5%; C2, Dam +Depth_10%; C3, Dam +Width_5%; C4,
Dam +Width_10%
; C5, Dam +Levee_3m; C6, Dam +Depth_5% +Width_5% +Levee_3m; and C7,
Dam +Depth_10% +Width_10% +Levee_3m).

Hydrology 2025,12, 38 18 of 25
4. Discussion
4.1. Dynamic Changes in Flood Exposure
This study presented the dynamic changes in flood exposures and quantitative ev-
idence on how low-cost flood control measures, such as the use of existing structures
like dams and river channel improvements, can effectively reduce flood damage. Large
PFAs and SBAs are exposed to flooding in the study area, posing a greater impact on
agricultural production and residential areas. Urbanization and development activities are
rapidly increasing in the flood-prone areas of the study basin. Flood-exposed population
is also increasing in the study area due to demographic changes in flood-prone areas.
Swain et al. [43]
reported that the key factors for the increased flood-exposure population
in the United States are climate change and population growth. Population growth in
flood-prone areas puts more people at risk of flooding. Climate change is expected to
increase rainfall intensity and flood risk in the study area [
39
], which may further increase
flood exposure. Effective preventive measures are thus necessary to mitigate flood risk in
areas with growing populations and development activities.
4.2. Flood Damage Assessment and Quantitative Evaluation of Flood Control Measures
The estimated EAD of residential households (both house and contents damage)
without flood control measure for BSRB was IDR 148.38 billion per year (USD 9.1 million
per year). In the case of rice-crop damage, it is approximately IDR 4.74 billion per year
(USD 0.29 million per year). Shrestha and Kawasaki [
28
] estimated EAD value of household
damage for Bago River basin in Myanmar at MMK 69.8 billion per year (USD 33.2 million
per year), and at MMK 5.8 billion per year (USD 2.7 million per year) in the case of rice-
crop damage. Budiyono et al. [
44
] estimated total EAD for Jakarta in Indonesia at USD
333 million per year. Yamamoto et al. [
45
] estimated the EAD value of direct flood damage
to general properties and agriculture in the late 20th century for Japan at JPY 3259 billion
per year (USD 21,162 million per year). The estimated EAD values significantly differ
between locations due to the varying levels of flood exposure and their exposed value at
risk, depending on the location and size of the study area.
To reduce the flood damage, the construction of new flood preventive structural mea-
sures might be challenging due to various social, political, and financial issues. However,
if existing structures are effectively used by maximizing their current capacity for flood
control, flood damage can be reduced without building additional structures. Our findings
show that the use of the Wonogiri dam with its current storage capacity can reduce flood
damage to the residential and agricultural sectors by more than 20%. Flood damage can
be further reduced by releasing reservoir water before a flood starts. The pre-releasing
of reservoir water can increase the flood storage capacity, thus further improving flood
damage prevention [46], which can be performed using rainfall forecasts [47]. In addition
to using existing dams for flood control, river channel improvements have also been prac-
ticed in many developing countries to prevent flooding by increasing the river channel
capacity. River channel improvements can help control flooding by stabilizing a river
channel. The findings also indicated that river channel improvements (e.g., deepening or
dredging, widening, and levee construction) aimed at increasing the channel capacity to
transport more floodwaters can also reduce flood impact and damage. A recent study by
Syaifurahman et al. [
48
] for the upper part of the BSRB reported that the existing channel
cannot accommodate extreme flood discharges and pointed out the importance of channel
deepening and widening to reduce flood damage in the study area. The findings of this
study show that the reduction in EAD by levee construction is higher than the channel deep-
ening or widening, but investment costs may vary depending on selection of construction
materials and methods. In addition, if houses are on riverbanks, extra investment will be

Hydrology 2025,12, 38 19 of 25
necessary to relocate residents. River channel dredging can be implemented with less effort
than the construction of embankments and channel widening, as dredged sediments are
usually deposited on riverbanks, particularly in developing countries. However, dredging
may need to be performed annually.
River channel widening or deepening increases the flow area, which results in a de-
crease in the flow speed for the same flow rate. Meanwhile, in the case of levee construction,
the flow speed remains unchanged up to the bank stage, but it increases when the river
overflows its bank stage. When river water flows higher than the bankfull discharge, the
flow area is reduced due to the existence of levee, which leads to an increase in the flow
speed. If river channel-improvement works are implemented only in the middle reach of a
river, they may increase flood risk in areas downstream of the new structures. To overcome
this issue, river channel-improvement works should be carried out continuously from the
middle to lower reaches or only in the lower reaches [30].
The use of Wonogiri dam and the river channel-improvement works for flood control in
the study area can significantly reduce the flood damage to buildings, household contents,
and agricultural crops. In addition, nature-based solutions like floodplain restoration,
use of retention ponds, and the use of wetlands for flood regulation can also reduce the
flood risk, which are more eco-friendly flood defenses than structural measures and also
enhance biodiversity. However, these solutions may require replanting trees, creating
retention ponds and wetlands, and preserving natural floodplains; and also, they may not
be effective for large-scale flooding. Moreover, elevating and floodproofing solutions by
households living in the flood-prone areas (e.g., elevating the building by raising the plinth
level, building new houses on elevated ground above the flood level, building houses
with flood resistance materials, and building barrier walls) are also other possible feasible
options for reducing the flood impact [
49
,
50
]. In the case of agricultural damage reduction,
establishment of adequate drainage capacity, the use of submergence-tolerant rice varieties
in flood-prone areas during the monsoon season, and changing the cropping pattern or
schedule to avoid the flood can also help in reducing the crop loss [51].
Increasing flood exposure due to demographic changes has significant social and
economic implications, including widespread physical property damage, agricultural crop
damage, and disruption to daily life, while flood control measures presented in this study
can reduce these impacts by protecting properties, reducing economic losses, and enhancing
community resilience. The implementation of effective flood control measures can not only
reduce flood damage but also contribute to the long-term improvement of livelihoods and
hereafter the socioeconomic development in the flood-prone areas, allowing for more stable
economic activity and improved quality of life [
52
]. The flood control options presented
in this study for the BSRB can significantly impact local communities by reducing flood
damage, which will contribute to improving the living conditions of people living in the
flood-prone areas. However, when implementing river channel improvements, particularly
constructing embankment, displacement or relocation of houses located along the river
might be required.
4.3. Long-Term Strategies for Reducing Flood Risk
Quantifying exposed people in flood-prone areas spatiotemporally and evaluating the
effectiveness of flood control measures provide scientific information that can be useful for
preparing, planning, and implementing flood preventive measures and reducing property
and human risks. The quantitative results of flood exposure and damage can be used
to build a resilient society and also to help policy- and decision-makers establish flood
adaptation measures and policies required for risk reduction, such as land use regula-
tions, guidelines for building constructions, and restrictions on development activities

Hydrology 2025,12, 38 20 of 25
in flood-prone areas [
49
]. A comprehensive flood management strategy combining both
structural measures (like those presented in this study) with non-structural approaches,
like hazard and risk mapping, land use planning, urban-planning regulations, and resilient
infrastructure investments, is crucial for long-term flood mitigation.
Land use planning, such as land use restrictions in flood-prone areas through land
use zoning with building restrictions also plays a key role in reducing damage by extreme
flood events. Relocating existing buildings from flood-prone areas can be expensive, but
restrictions on constructing new buildings in flood-prone areas anticipating high flood
depths can help reduce damage [
49
]. Flood damage reduction is also possible in paddy
fields in flood-prone areas with high flood depths by introducing submergence-tolerant rice
varieties, new cropping practices, or different rice-cropping calendars, avoiding flooding
and other adverse effects taking place potentially due to climate change. Figure 19 shows a
decrease in damage owing to land use restrictions in the study flood-prone areas with high
flood depths (2.5, 2.0, and 1.5 m). Flood damage was calculated in the case of applying
land use restrictions, relocating existing buildings; adjusting the rice-cropping calendar; or
changing cropping practices for the flood-prone areas with high flood depths (
≥
2.5, 2.0, or
1.5 m), assuming 50- and 100-year floods.
In addition, establishment of policies for urban planning regulations (e.g., zoning
restrictions in flood-prone areas, mandatory building elevation requirements in flood-prone
areas, regulations on impervious surfaces to reduce runoff, and guidelines for stormwater
management systems) and investment in sustainable and resilient infrastructures (e.g.,
allocating funds or budget for infrastructures designed for reducing the flood impact are
also crucial for long-term flood control because they help communities withstand and
recover from extreme weather events. Furthermore, mapping flood hazards and risks
can also be important and informative non-structural approaches to reducing losses and
preventing damage from floods [28].
Hydrology 2025, 12, x FOR PEER REVIEW 21 of 26
structural measures (like those presented in this study) with non-structural approaches,
like hazard and risk mapping, land use planning, urban-planning regulations, and resili-
ent infrastructure investments, is crucial for long-term flood mitigation.
Land use planning, such as land use restrictions in flood-prone areas through land
use zoning with building restrictions also plays a key role in reducing damage by extreme
flood events. Relocating existing buildings from flood-prone areas can be expensive, but
restrictions on constructing new buildings in flood-prone areas anticipating high flood
depths can help reduce damage [49]. Flood damage reduction is also possible in paddy
fields in flood-prone areas with high flood depths by introducing submergence-tolerant
rice varieties, new cropping practices, or different rice-cropping calendars, avoiding
flooding and other adverse effects taking place potentially due to climate change. Figure
19 shows a decrease in damage owing to land use restrictions in the study flood-prone
areas with high flood depths (2.5, 2.0, and 1.5 m). Flood damage was calculated in the case
of applying land use restrictions, relocating existing buildings; adjusting the rice-cropping
calendar; or changing cropping practices for the flood-prone areas with high flood depths
(≥2.5, 2.0, or 1.5 m), assuming 50- and 100-year floods.
Figure 19. Calculated damage with and without land use restriction (LUR) alone in the flood-prone
areas with high flood depth: (a) 50-year flood case and (b) 100-year flood case.
In addition, establishment of policies for urban planning regulations (e.g., zoning re-
strictions in flood-prone areas, mandatory building elevation requirements in flood-prone
areas, regulations on impervious surfaces to reduce runoff, and guidelines for stormwater
management systems) and investment in sustainable and resilient infrastructures (e.g.,
allocating funds or budget for infrastructures designed for reducing the flood impact are
also crucial for long-term flood control because they help communities withstand and re-
cover from extreme weather events. Furthermore, mapping flood hazards and risks can
also be important and informative non-structural approaches to reducing losses and pre-
venting damage from floods [28].
0
400
800
1200
1600
2000
Buildings Contents
Calculated Damage
(bil. IDR)
0
40
80
120
160
Rice Crops
Calculated Damage
(bil. IDR)
0
400
800
1200
1600
2000
Buildings Contents
Calculated Damage
(bil. IDR)
0
40
80
120
160
Rice Crops
Calculated Damage
(bil. IDR)
Without LUR With LUR 2.5m depth With LUR 2m depth With LUR 1.5m dep th
(a)
(b)
Figure 19. Calculated damage with and without land use restriction (LUR) alone in the flood-prone
areas with high flood depth: (a) 50-year flood case and (b) 100-year flood case.

Hydrology 2025,12, 38 21 of 25
4.4. Uncertainties and Further Improvements
Flood damage assessment results are often subject to significant uncertainties due
to a variety of factors, such as data limitations, quality of topographical data used in the
flood hazard prediction, selection of spatial scale in flood modeling, assumptions of data
used in damage estimation, variations in exposure characteristics, and the lack of detailed
information on characteristics of exposures. This study used average exposed values of
buildings, contents, and agricultural crops in assessing the damage, and the use of average
exposed values for large areas can also lead to uncertainty in the results; to reduce this
uncertainty, it is important to consider a wider range of data from various locations in
further studies.
This study used globally available topographical data at 30-arc second spatial reso-
lution, focusing on hazard and risk assessment at basin level. For local community-level
evacuation purposes, detailed flood inundation simulation with an even finer spatial reso-
lution can be beneficial. In addition, the reliability of flood inundation results can be further
improved by incorporating ground-based topographical data. The river channel geometry
was assumed to be rectangular, and its shapes were defined by depth and width. Because
the river cross-sectional data can hardly be obtained, this study used regime equations
to calculate river depth and width. However, incorporation of river cross-sectional data
with the actual geometry shape of a river cross-section in a model simulation can further
enhance the reliability of the flood simulation results.
In this study, the number of distributed houses was estimated based on the gridded
population counts and the average family size. When estimating the number of houses
based on population, uncertainty can arise due to several factors, such as variations in fam-
ily size, demographic shifts and migrations, and data quality issues in gridded population
counts. For example, the number of inundated houses for 100-year flood (without flood
control-measures case) using the average family size of 3.7 people was 247,030; however,
it varies to 261,146 when using an average family size of 3.5 people and to 234,361 using
an average family size of 3.9 people. However, Shrestha et al. [
32
] validated the estimated
number of inundated houses for the 2007/2008 flood event with the reported inundated
houses and found estimated inundated houses (162,505) to be reasonably agreeable with
the reported inundated house (165,117). The reliability of the flood damage estimation
for households can be further improved by incorporating houses data and information
available from the local government. In addition, the use of the average value of rice yield,
farm gate price of rice, and cost of input to calculate volume and value of rice-crop loss can
also lead to uncertainty in the rice crop-damage estimation, as these values vary from place
to place, and this uncertainty can be reduced by incorporating a wider range of data from
different locations.
This study specifically focused on flood damage to residential buildings and assets,
and rice crops. However, it is also recommended to consider flood damage to other sectors,
such as roads, bridges, and other types of infrastructure, as well as the risk of human loss. In
addition, this study did not take into account the potential future impact of climate change,
and in further studies, it will also be crucial to incorporate the impacts of climate change,
particularly the projected future climate change in the assessment of the effectiveness of
flood control measures and evaluating potential future damage.
5. Conclusions
This study focused on the quantitative analysis of spatiotemporal changes in land
cover areas and people exposed to flood risk, and it also aimed to evaluate the effectiveness
of the use of an existing dam for flood control and river channel-improvement works for
reducing flood damage. This study revealed that the PF, SBA, DLA, and PFA in the study

Hydrology 2025,12, 38 22 of 25
basin significantly changed during the period of 1990—2020. The areas of PF, DLA, and
PFA decreased from 1990 to 2020, whereas the area of SBA increased rapidly. The large
paddy fields are at high risk of flooding, indicating a significant impact of flooding on
agricultural production; however, the extent of flood impact on agricultural production
depends on several factors, such as crop type, growth stage, timing of flooding, and flood
characteristics. The findings of this study also indicated that the SBA areas are rapidly
expanding in the flood-prone areas. Individual protection measures should be taken if
settlements expand into flood-prone areas. The flood-exposed population also relatively
increased from 1990 to 2020 in the study areas. The exposure assessment focusing on LULC
and population presented in this study enables us to understand spatiotemporal changes
in flood exposure, thus helping is plan land use regulations.
The quantitative estimation of flood damage in monetary values with and without
flood control options provides scientific information to understand the effectiveness of
flood control options in reducing flood damage and also to help policy- and decision-
makers implement preventive measures to reduce damage more effectively. The findings
show that the increases in rainfall intensity are also one of the key drivers of increased
flood damage. The increase in the monetary value of the damage to buildings and contents
between 10- and 50-year floods was more than 370% and about 50% between 50- and
100-year floods. The increase in the monetary value of rice-crop damage between 10- and
50-year floods was more than 921%, and it was about 76% between 50- and 100-year floods.
The findings show that the use of the Wonogiri dam for flood control can reduce the flood
damage in its downstream areas by more than 20%. The results also revealed that the
reduction rate in EAD due to levee construction is higher than that due to channel dredging
or widening. The results also show that the flood damage can be reduced by more than 60%
by implementing a combination of flood control dam with river channel improvements.
The estimation of flood damage with different scenarios of flood control options
enables us to better manage flood risk in the future. The results presented in this study
can be useful to establish flood control measures and policies required for reducing flood
risk, such as land use regulations and guidelines for better preparedness and emergency
response activities.
Author Contributions: Conceptualization, B.B.S., M.R. and D.K.; methodology, B.B.S., M.R. and D.K.;
validation, B.B.S. and M.R.; formal analysis, B.B.S.; investigation, B.B.S.; data curation, B.B.S.; writing—
original draft preparation, B.B.S.; writing—review and editing, B.B.S., M.R. and D.K.; visualization,
B.B.S. All authors have read and agreed to the published version of the manuscript.
Funding: This work was conducted by Theme 4 of the Advanced Studies of Climate Change
Projection (SENTAN Program), Grant Number JPMXD0722678534, supported by the Ministry of
Education, Culture, Sports, Science, and Technology (MEXT), Japan.
Data Availability Statement: The sources data that support the findings of this study are available
from the related organizations and the sources mentioned in this article.
Acknowledgments: The authors would like to thank the Research and Development Center for
Water Resources (PUSAIR), the Bengawan Solo River Basin Organization (BBWS Bengawan Solo),
Ministry of Public Works and Housing, Indonesia, for providing rainfall and discharge data; and the
Directorate of Forest Resources Inventory and Monitoring, Directorate General of Forestry Planning
and Environmental Administration, Ministry of Environment and Forestry, Indonesia, for providing
land cover map data for the study area.
Conflicts of Interest: The authors declare no conflicts of interest.

Hydrology 2025,12, 38 23 of 25
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