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            Geoplanning: Journal of Geomatics and Planning, Vol. 11, No. 2, 2024, 189-204 

 

Original Research 

Utilizing Open Access Spatial Data for Flood 

Risk Mapping: A Case Study in the Upper Solo 

Watershed  

J. Jumadi1,2*, D. Danardono1,2, Kuswaji D. Priyono1,2, Efri Roziaty2,3, Heni 

Masruroh4, Arif Rohman5, Choirul Amin1, Hamim Zaky Hadibasyir1, Vidya 

N. Fikriyah1,6, Muhammad Nawaz7, Farha Sattar8, Aynaz Lotfata9 

1 Faculty of Geography, Universitas Muhammadiyah Surakarta, Surakarta 57162, Indonesia 
2 Center of Environmental Studies, Universitas Muhammadiyah Surakarta, Surakarta 57162, 

Indonesia 
3 Faculty of Education, Universitas Muhammadiyah Surakarta, Surakarta 57162, Indonesia;  
4 Department of Geography, Malang State University, Malang, Indonesia  
5 Department of Geomatic, ITERA, Lampung, Indonesia 
6 Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, 

Drienerlolaan 5, 7522 NB Enschede, The Netherland 
7 Department of Geography, National University of Singapore, 1 Arts Link, Block AS2, 

Singapore 117570 
8 Faculty of Arts & Society, Education & Enabling, Charles Darwin University, Ellengowan 

Drive, Casuarina NT 0810, Australia 
9 University of California, Davis, USA 

DOI: 10.14710/geoplanning.11.2.189-204 

Abstract 

Indonesia is experiencing a rise in natural disasters due to its geographical position within a tropical region, with the Upper 

Solo River watershed exhibiting a heightened risk of flooding. This region has already suffered numerous floods due to 

excessive precipitation and insufficient drainage. Susceptibility, hazard, and risk studies have been conducted to investigate 

this phenomenon but have been limited to specific regions within the catchment area. This study aims to construct a GIS-

based flood risk model using Open-Access Spatial Data (OASD) based on diverse physical characteristics, urbanization 

levels, and population. We used several OASD, including SRTM, Sentinel 2 MSI, GPM v6, NASA-USDA Enhanced SMAP 

Global Soil Moisture Data, GHS-SMOD R2023A - Global Human Settlement Layers, and GHSL: Global Population 

Surfaces 1975-2030 (P2023A). The model integrates the risk parameters to identify flood risk using a weighted overlay in 

ArcGIS. The results demonstrate spatial heterogeneity in flood risk throughout the watershed. The result also reveals that 

Surakarta City, with a high proportion of its area in the 'High' (57.3%) and 'Very High' (29.54%) risk categories, is at the 

highest risk of flooding within the watershed. The study enhances understanding of this topic by comprehensively 

evaluating flood hazards, vulnerabilities, and risks. It highlights the significance of utilizing low-cost OASD to improve 

flood preparedness and response strategies. 
Copyright © 2024 by Authors,  

Published by Universitas Diponegoro Publishing Group.  

This open access article is distributed under a  

Creative Commons Attribution 4.0 International license 

 

1. Introduction  

The frequency of natural catastrophes seems to rise due to natural phenomena and human activities, 

leading to significant human casualties, property damage, and material losses. Human activities, such as 

deforestation, land clearance on mountain slopes, and cultivation of steeply sloping lands, have the potential to 

give rise to natural disasters. Due to its geographical positioning within a region characterized by dynamic 

e-ISSN: 2355-6544 
 
Received: 03 September 2024;  
Revised:  29 October 2024;  
Accepted: 29 November 2024; 
Available Online: 30 November 2024; 
Published: 04 December 2024. 

 
Keywords:  
Natural Hazard, Flood, Risk, GIS, 
Solo River Watershed 
 
*Corresponding author(s)  
email: jumadi@ums.ac.id 
 
 

 

 

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tectonic activity and volcanic activity resulting from the convergence of three tectonic plates, namely the Indian-

Australian Plate, the Pacific Plate, and the Eurasia Plate, Indonesia is prone to disasters (Jumadi et al., 2016). 

Floods in the country are significant natural disasters that can have disastrous consequences (Susetyo, 2008), 

with Indonesia having the highest incidence of flood disasters in Southeast Asian countries from 1980 to 2018 

(Samphantharak, 2019).   

The origin of flood events, a phenomenon experienced in the region for many years, can be traced back. 

Heavy rains combined with depreciative drainage are one of the main causes of flooding in the area. The Upper 

Solo River watershed's previous flood histories are represented by historical statistics that show many major 

floods. Especially in 1966, 2007, 2009, and 2010, the region suffered from severe flooding because of heavy rain 

(Damayanti, 2011; Fathimah & Dahroni, 2014; Gunawan, 2009), with these particular events being the most 

serious in recorded history. Heavy rains, therefore, can devastate infrastructure, such as roads and bridges, and 

lead to the loss of life. The region has undergone periodic flooding, mainly due to heavy precipitation and 

inadequate drainage, since the 2007 flood, which was by far the worst in fifty years and submerged 11,500 homes 

(Zein, 2010). Subsequently, the municipal authorities have begun this preparation coupled with upgrades to the 

drainage infrastructure within the area, with the intention of reducing flooding. However, despite such 

continuing attempts, the Upper Solo River watershed remains susceptible to flooding, especially during the rainy 

season. In recent years, intense and extreme weather conditions have increased; as a result, the area has been 

significantly affected in the form of a higher frequency of flooding. For this reason, it is vital to manage and 

develop disaster-fighting measures to keep the impact of the floods on the residents in the region to a minimum. 

Floods are natural calamities that must be prevented entirely. Complex hydrological, geological, and 

geomorphological circumstances, combined with deforestation and urbanization, contribute to floods, resulting 

in substantial social, economic, and environmental consequences (Amin et al., 2020; Curebal et al., 2016; 

Komolafe et al., 2020; Mukherjee & Singh, 2020; Mustikaningrum et al., 2023; Nada et al., 2023; Sejati et al., 

2023; Purwitaningsih et al., 2020; Saputra et al., 2022; Skilodimou et al., 2019). Factors like deforestation and 

urbanization disrupt soil absorption, resulting in unsuitable surfaces and causing further damage. As a 

consequence, rainwater collected from impervious surfaces accelerates the velocity and height of the intense 

flows that exacerbate flood events. Urbanization and land use alterations can modify the built environment, 

impacting hydrological systems and altering river flow rates, hence heightening the risk of catastrophic flooding 

in specific sensitive areas (Chagas et al., 2022). Flooding constitutes a disaster as it adversely affects society, the 

economy, and the environment (Komolafe et al., 2020; Mukherjee & Singh, 2020; Skilodimou et al., 2019). Floods 

result in fatalities, population displacement, infrastructure damage, agricultural and livestock losses, disease 

transmission, and water supply contamination (Rincón et al., 2018). Consequently, it is essential to provide flood-

related information to mitigate risks. 

A multitude of studies has been undertaken concerning flood analysis utilizing Geographic Information 

Systems (GIS) and remote sensing technologies (Elkhrachy, 2015; Greene & Cruise, 1995; Islam & Sado, 2000; 

Jumadi et al., 2024; Ozkan & Tarhan, 2016; Paudyal, 1996; Tehrany et al., 2017).Various methodologies have 

been employed, including the spatial multi-criteria method (Chen et al., 2012; Zhou et al., 2021), cellular 

automata (CA) (Ghimire et al., 2013), a one-dimensional hydraulic drainage network model (Jamali et al., 2018), 

and Analytical Hierarchical Processes (AHP) (Negese et al., 2022; Sarmah et al., 2020). Nonetheless, research 

employing comprehensive open access spatial data for all variables is still limited. Recent research has 

persistently utilized non-open access data sources for particular variables such as soil type (Diriba et al., 2024; 

Osman & Das, 2023); rainfall (Rana et al., 2024); flood depth (Ayenew & Kebede, 2023); flood-prone areas (Li et 

al., 2024); and geology (Osman & Das, 2023). Geographic Information Systems (GIS) and remote sensing offer 

effective frameworks for assessing flood hazards; nonetheless, the use of proprietary or restricted data sets may 

hinder accessibility and repeatability in this domain. In contrast, unrestricted open-access geographical data may 

serve as a more economical and transparent alternative to democratize flood risk analysis in low- and middle-

income nations.  

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Nevertheless, the degree to which open-access data can be included into flood risk studies necessitates 

further investigation in this underutilized domain; hence, traditional data sources should be employed, since they 

have been previously validated in other contexts. Consequently, the integration of GIS and Remote Sensing (RS) 

data with additional databases, facilitated by contemporary technical improvements, enables the identification, 

monitoring, and assessment of flood disasters (Biswajeet & Mardiana, 2009; Haq et al., 2012; Pradhan et al., 

2009). The initial stage in comprehensively comprehending all aspects leading to flooding may entail the 

integration of diverse datasets into a broader framework for formulating flood control plans. Various strategies 

have been utilized to tackle the flooding issue, including a flood hazard mapping tool that identifies regions at 

elevated risk of flooding. This study aims to construct a GIS-based flood risk model using Open-Access Spatial 

Data (OASD) based on diverse physical characteristics, urbanization levels, and population. This study utilized 

physical characteristics and urbanization variables as stakeholders aim to produce a flood risk model for the 

upper reaches of the Solo River by integrating GIS and RS data in its construction process. 

In order to achieve the objective, the subsequent sections of this work are structured as follows. The 

following section presents a comprehensive overview of the methodologies, encompassing data collection 

techniques, descriptions of the parameters utilized for risk analysis, and the study framework employed. The 

subsequent section expounds on the outcomes and analysis, discussing the perils associated with flooding; 

vulnerability influenced by the degree of urban development, the associated risks; and the comprehensive 

findings. The concluding section presents a summary of the findings. 

2. Data and Methods 

2.1. Study Area  

The research was conducted in the upper part of the Bengawan Solo watershed (Figure 1), located between 

110º13'7.16" -110º26 '57.10" East and 7º26 '33.15" -8º6 '13.81" South. The watershed is the largest catchment 

area on Java Island, Indonesia, covering a total area of 16,100 km2, and it plays a significant role in providing 

water for the daily needs and agriculture of those living in the area. It comprises three sub-watersheds: the upper 

Bengawan Solo, Kali Madiun, and the lower Bengawan Solo. The upper Bengawan Solo area covers 

approximately 6,000 km2. The topographic condition of the study area is varied; it is dominantly flat but with 

relatively undulating terrain in the northeast and northwest parts of the watershed close to the mountains. Upper 

Bengawan Solo provides water from Mount Merapi and Mount Merbabu in the western part of the Bengawan 

Solo River and from Mount Lawu in the eastern region. 

 

Figure 1. The Study Area 

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2.2. Datasets  

The study used several spatial datasets to develop the risk level model. First, shuttle radar topographic 

mission (SRTM) data with 30m resolution was acquired as the digital elevation model. Land cover and land use 

information was then interpreted from remote sensing image Sentinel-2 (10 m spatial resolution). Global 

precipitation measurement (GPM) data were utilized to obtain rainfall information, while soil moisture active 

passive (SMAP) data were used to establish the soil moisture condition of the study area. GHSL and GHS-

SMOD R2023A datasets are also used in this study. These datasets aid in analyzing human settlement patterns, 

density, and spatial changes, enhancing the demographic characteristics of the study area. All dataset 

characteristics and their sources are provided in Table 1. 

Table 1. Flood Risk Parameter Data Sources 

No Data Description Source Derived Data 

1. DEM Shuttle Radar Topography 

Mission (SRTM). 

USGS (Earth Resources Observation and Science (EROS) 

Center, 2017) 

El, Sl, FA, DR, 

Cu, DD, TWI  

2. Images Sentinel 2 Multispectral 

Instrument 

ESA (ESA, 2023) LULC, NDVI 

3. Rainfall data Global Precipitation 

Measurement (GPM) v6. 

NASA (NASA, 2019) RF 

4. Soil moisture 

data 

NASA-USDA Enhanced 

SMAP Global Soil 

Moisture Data. 

NASA (Entekhabi et al., 2010) SM 

5. Level of 

urbanization 

GHS-SMOD R2023A - 

Global Human Settlement 

Layers. 

European Commission, Joint Research Centre (JRC) 

(Earth Engine Data Catalog, 2023; Pesaresi & Politis, 

2023; Santillan & Heipke, 2023; Schiavina et al., 2023) 

DoU 

6. Population GHSL: Global population 

surfaces 1975-2030 

(P2023A) 

Earth Engine Data Catalog (Google Earth Engine Data 

Catalog, 2023) 

Pop 

Note: El – elevation, Sl – slope, FA – flow accumulation, DR – distance to rivers, Cu – curvature, DD – drainage density, TWI – topographic 

wetness index, LULC – land use land cover, NDVI – normalized difference vegetation index, RF – rainfall, SM – soil moisture, URSD – Level of 

Urbanization, Pop - Population. 

2.3. Research Framework 

The study area flood-risk levels were determined using the general risk function (Equation 1) (Sar et al., 

2015; UNISDR, 2004). The study was separated into three main sections: flood hazard, vulnerability and risk 

analysis (Figure 2). Flood hazard was determined by weighted overlay operation of certain physical parameters 

related to flood occurrences (Table 2). Similarly, vulnerability was indicated by using weighted overlay by 

parameters related to population and level of urbanization (Table 3). Finally, risk was determined by multiplying 

hazard by vulnerability (Samarasinghea et al., 2010).  

𝑅 = 𝐻 𝑥 𝑉……………. (Equation. 1) 

Various parameters are used to define flood hazards, such as El, Sl, FA, DR, Cu, DD, TWI, LULC, NDVI, 

RF, and SM are modified from Negese et al. (2022) and Purwanto et al. (2023). Low-lying locations exhibit 

significant water accumulation, heightening flooding threats due to markedly reduced flow velocity in the flat 

portions of the terrain. The proximity of a river increases the likelihood of floods. Areas at lower elevations near 

rivers are more susceptible to flooding due to elevated discharge rates and reduced water velocity. Flood events 

are precipitated by flow accumulation and drainage density; elevated levels of flow accumulation and drainage 

density augment the probability of flooding. Consequently, Land Use and Land Cover (LULC) are critical to 

assessing flood risk, as areas with high vegetation density demonstrate reduced susceptibility due to delayed 

water movement and enhanced infiltration rates. Consequently, soil qualities are crucial; fine soils are recognized 

for accelerating surface runoff while diminishing permeability, thereby heightening the likelihood of inundation. 

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Table 2. Flood Hazard Indicators 

No Factor Classification Degree of  Flood 
Hazard 

Score Weight (%) 

1. Slope (Sl) (Degree) 

> 45 Very Low 1 

15 
25-45 Low 2 
15-25 Moderate 3 
8-15 High 4 
0-8 Very High 5 

2. Rainfall (Rf) (mm) 

1,696-1,728 Very Low 1 

11 
1,728-1,761 Low 2 
1,761-1,793 Moderate 3 
1,793-1,825 High 4 
> 1,825 Very High 5 

3. Drainage Density (DD) (km/km2) 

0-0.372 Very Low 1 

8 
0.372-0.754 Low 2 
0.754-1.106 Moderate 3 
1.106-1.519 High 4 
>1.519 Very High 5 

4. Soil Moisture (SM)(m3/m3) 

15.4-16.14 Very Low 2 

4 
16.14-16.68 Low  
16.68-17.42 Moderate  3 
17.42-18.49 High 4 
18.49-20.53 Very High 5 

5. Land Use/ Land Cover (LULC) 

Dense Vegetation Very Low 1 

7 
Bare land Low 2 
Open Mining Moderate 3 
Cropland High 4 
Built-up area Very High 5 

6. Elevation (El) (m amsl) 

558.8 - 697 Very Low 1 

18 
420.6 - 558.8 Low 2 
282.4 - 420.6 Moderate 3 
144.2 - 282.4 High 4 
6 - 144.2 Very High 5 

7. Distance to river (DR) (m) 

0.372 - 1.2976 Very High 1 

11 
1.2976 - 2.2232 High 2 
2.2232 - 3.1488 Moderate 3 
3.1488 - 4.0744 Low 4 
4.0744 - 5 Very Low 5 

8. NDVI 

– 0.16–0.29 Very Low 1 

3 
0.29–0.38 Low 2 
0.38–0.45 Moderate 3 
0.45–0.51 High 4 
0.51–0.59 Very High 5 

9. Curvature (Ct) 
Convex (positive) Moderate 1 

2 Concave (negative) High 2 
Flat Very High 3 

10. Flow Accumulation (FA) 

< 250 Very Low 1 

15 
250–2195 Low 2 
2195–3415 High 4 

3415–15,125 Very High 5 

11. Topographic Wetness Index (TWI) 

2.48–5.91 Very Low 1 

6 
5.91–8.09 Low 2 
8.09–10.18 Moderate 3 
10.18–12.63 High 4 
12.63–22.77 Very High 5 

Additional factors encompass NDVI and curvature, which further assessing an area's vulnerability to 

flooding events. Regions with greater vegetation cover exhibit prolonged precipitation runoff, whereas flat plain 

areas are especially susceptible to flooding. Finally, precipitation and TWI indices were utilized, revealing that 

significant rainfall provided substantial water, which, when combined with TWI, indicated regions likely to 

possess saturated soils prone to flooding. Curvature is a fundamental characteristic of the Earth's surface, 

articulated by geomorphometric techniques. In this instance, it may serve as an effective instrument for flood 

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194 

management because to its correlation with water distribution and concentration (Faisal & Hayakawa, 2023; 

Sofia, 2020; Xiong et al., 2022).  

GIS-based flood hazard maps were generated using ArcGIS overlays of flood components assigned specific 

weights. Flood formation occurs due to eleven primary parameters: land slope (Sl), elevation (El), flow 

accumulation (FA), rainfall (Rf), drainage density (DD), distance to rivers (DR), topographic wetness index 

(TWI), normalized difference vegetation index (NDVI), land use land cover (LULC), soil type (ST), and 

curvature of water surface (Cu)—consequently, raster data formats generated spatially refined information 

regarding these 11 parameters. Low-lying areas exhibit significant water accumulation, heightening the risk of 

floods due to markedly reduced flow velocity in the flat portions of the terrain. The proximity of rivers increases 

the likelihood of floods. Areas at lower elevations near rivers are more susceptible to flooding due to elevated 

discharge rates and reduced water velocity. Flood events are precipitated by flow buildup and drainage density; 

elevated levels of both factors augment the probability of flooding. Consequently, Land Use and Land Cover 

(LULC) are critical to assessing flood risk, as areas with high vegetation density demonstrate reduced 

susceptibility due to delayed water movement and enhanced infiltration rates. Consequently, soil properties 

emerge as a significant factor: Fine soils are recognized for accelerating surface runoff while reducing 

permeability, heightening the likelihood of inundation. 

Additional criteria encompass NDVI and curvature, further influencing an area's vulnerability to flooding 

events. Regions with greater vegetation cover experience prolonged rainwater runoff, whereas flat land areas 

are especially susceptible to flooding. Finally, precipitation and TWI indices were utilized, as significant rainfall 

provided additional water, which, when combined with TWI, indicated regions likely to possess saturated soils 

susceptible to flooding. Curvature is a fundamental characteristic of the Earth's surface, articulated by 

geomorphometric methods. In this context, it may serve as an effective instrument for flood management because 

to its correlation with water distribution and concentration (Faisal & Hayakawa, 2023; Sofia, 2020; Xiong et al., 

2022). GIS-based flood hazard maps were generated using ArcGIS overlays of flood components assigned 

specific weights. Flood formation occurs due to eleven primary parameters: land slope (Sl), elevation (El), flow 

accumulation (FA), rainfall (Rf), drainage density (DD), distance to rivers (DR), topographic wetness index 

(TWI), normalized difference vegetation index (NDVI), land use land cover (LULC), soil type (ST), and 

curvature of water surface (Cu). Consequently, raster data formats were employed to generate spatially refined 

information regarding these 11 parameters. 

Table 3. Vulnerability Indicators 

No. Factor Classification Degree of  Vulnerability Score 
Weight 

(%) 

1. Population 

0 - 12 Very Low 1 

50 
13 - 37 Low 2 
38 - 75 Moderate 3 

76 - 127 High 4 
128 - 290 Very High 5 

2. 
Degree of  
urbanization 

Water Unclassified 0 

50 

Very low density rural Very Low 1 
Low density rural Low 2 
Rural cluster Low 2 
Suburban or peri-urban Moderate 3 
Semi-dense urban cluster High 4 
Dense urban cluster High 4 
Urban center Very high 5 

In addition, we quantified vulnerability based on the density of inhabitants. The higher the density, the 

higher the vulnerability. The GHS-SMOD R2023A dataset was used to provide this data. This is a spatial dataset 

that delineates the distribution and evolution of human settlements across the globe, tracking changes from 1975 

through to 2030 in five-year increments. The dataset is instrumental in evaluating the susceptibility of various 

settlement types, highlighting an increased vulnerability in more densely populated areas (Melchiorri, 2022). 

The 2023A release of the Global Human Settlement Layer GHS-SMOD settlement segments were classified 

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into eight distinct types (Earth Engine Data Catalog, 2023; European Commission, 2023) (Table 3). For each 

type, a specific vulnerability level was determined, alongside a score that progressed from "Very Low" (1) for 

areas with very low density rural to "High" (5) for urban centers. This segmentation indicated a direct correlation 

between the compactness of the population of a settlement and its vulnerability to risks. In addition, the GHSL-

derived global population surfaces from 1975-2030 (P2023A) classify flood vulnerability levels into five 

categories: Very Low (0-12), Low (13-37), Moderate (38-75), High (76-127), and Very High (128-290), based on 

population density. The impact of this analysis is significant, as it provides understanding of vulnerability that 

can inform more effective flood risk management. By leveraging this data, policymakers and emergency 

responders can prioritize resources and interventions in high-density areas, ultimately reducing the potential for 

loss of life and damage to property during flood events. 

 

Figure 2. Research Framework 

3. Result and Discussion 

3.1 Flood Hazard Model 

Figure 3 shows all the parameters used to define flood hazard, while the distribution of flood indicator 

classes in determining the hazard can be seen in Figure 4. Figure 5 shows the distribution of environmental and 

geographical factors across different flood hazard levels, providing quantitative insight into their impact on flood 

risk. Elevation is included in 57.64% of the "Very High" flood risk areas, with slopes in 49.15%. Distance to 

rivers is a critical factor in flood risk, with 42.24% of these areas near water bodies. Flow accumulation is high 

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in 99.81% of the "Very Low" risk areas, indicating efficient water dispersal mechanisms. The NDVI shows that 

vegetation cover decreases significantly in "Very High" risk areas, indicating the protective role of vegetation 

against flooding. Rainfall contributes differently across hazard levels, with a notable percentage of 28.27% in 

"Moderate" risk areas. Soil moisture is higher in lower-risk areas and decreases to 2.85% in "Very High" areas, 

highlighting the influence of soil water content on flood susceptibility. The Topographic Wetness Index (TWI) 

indicates potential water accumulation based on topography, with high levels in "Very Low" risk areas, but lower 

levels in "Very High" areas. Land use land cover (LULC) falls from 34.97% in "Very Low" risk areas to 7.76% 

in "Very High" ones, illustrating the impact of land cover and human land use on flood risk.  

 

Figure 3. Flood Hazard Parameters. Note: (a) Sl, (b) RF, (c) DD, (d) SM, (e) LULC,  

(f) El, (g) DR, (h) NDVI, (i) Cu, (j) FA, (k) TWI 

 

Figure 4. The Percentage of Hazard Classes based on the Parameters 

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Figure 5. Reclassified Parameters. Note: (a) Sl, (b) RF, (c) DD, (d) SM, (e) LULC,  

(f) El, (g) DR, (h) NDVI, (i) Cu, (j) FA, (k) TWI 

 

Figure 6. Flood Hazard Map 

3.2 Flood Vulnerability 

Figures 7 and 8 show the vulnerability indicators and their classification, respectively. Figure 7a shows 

the level of urbanization, which is classified into vulnerability levels from very low to very high in Figure 8a. 

Figure 7b shows population density, while Figure 8b shows the classification from very low to very high. 

Vulnerability distribution across the research area is shown in Figure 8. Based on weighted overlay, this map 

was produced as a composite from Figures 7b and 7c. The figure shows that urban areas are moderate to very 

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highly vulnerable. For example, Surakarta, Boyolali, Karanganyar, Klaten, and Sukoharjo exhibit a high presence 

within the ‘Very High’ classification and a significant one within the 'Very High' and 'Moderate' classifications.  

 

Figure 7. Vulnerability Factors and their Classification 

 

Figure 8. Vulnerability Factors and their Classification 

 

Figure 9. Vulnerability Class 

3.3 Flood Risk Model 

Table 4 indicates the flood risk in different cities and regencies within the watershed, whereas the spatial 

distribution is presented in Figure 9. Boyolali possesses a substantial proportion of land categorized as 'Moderate' 

risk, indicating a comprehensive approach to risk management. At the same time, Karanganyar exhibits diverse 

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risk levels, displaying an apparent propensity towards the 'Very Low' and 'High' classifications. The majority of 

the Kota Surakarta area, 57.3%, is classified as 'High' risk, with 29.54% classified as 'Very High,' highlighting the 

urgent need for comprehensive urban planning and measures to mitigate flood disasters.  

 

Figure 10. Flood Risk Map  

Table 4. Area risk classes for each region. 

Regency/City 
Area Class 

Total 
WB % VL % L % M % H % VH % 

Boyolali 1.88 0.46 165.64 40.31 137.14 33.38 64.56 15.71 40.07 9.75 1.62 0.39 410.9 

Gunung Kidul 0 0 34.28 91.35 3.23 8.6 0.02 0.05 0 0 0 0 37.52 

Karanganyar 1.3 0.24 261.23 47.74 170.08 31.09 63.66 11.64 48.5 8.86 2.36 0.43 547.14 

Klaten 1.88 0.29 191.88 29.75 346.11 53.66 79.84 12.38 25.27 3.92 0 0 644.99 

Surakarta 0.21 0.45 0 0 1.93 4.21 3.89 8.5 26.24 57.3 13.53 29.54 45.79 

Pacitan 0 0 66.71 97.21 1.91 2.79 0 0 0 0 0 0 68.62 

Semarang 0 0 2.47 16.27 9.43 62.07 2.91 19.15 0.29 1.9 0.09 0.62 15.19 

Sleman 0 0 4.13 99.17 0.03 0.83 0 0 0 0 0 0 4.17 

Sragen 0 0 0 0 0 0 0.01 100 0 0 0 0 0.01 

Sukoharjo 1.7 0.35 59.76 12.33 232.44 47.96 104.5 21.56 78.52 16.2 7.74 1.6 484.64 

Wonogiri 54.15 3.92 983.05 71.1 365.41 26.43 30.5 2.21 9.22 0.67 0 0 1382.57 

Note: WB – water body (sq km), VL – very low risk (sq km), L – low risk (sq km), M – moderate risk (sq km), H – high risk (sq km), VH – very high 

risk (sq km). 

3.4 Discussion 

The study has revealed varied flood vulnerabilities across the Upper Solo River watershed, highlighting 

regions of significant susceptibility and the necessity for customized flood mitigation strategies based on the 

data analysis. This analysis demonstrates a multifaceted and diverse environment in which flood risk differs 

considerably among the various cities/regencies across the watershed (Anna, 2021).Additionally, a large river 

passes through Surakarta (Absori et al., 2023) which makes the place prone to floods. A study conducted by 

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Hussain et al. (2021), pointed out that closeness to rivers, high rainfalls, elevation, and numerous socio-economic 

factors were the main determinants of the seriousness of flood risk. It should be known that increasing the level 

of awareness and understanding of flood risks by the local government (Cisternas et al., 2024), such as in 

Surakarta, is one way of increasing resilience while at the same time mitigating vulnerabilities (Muryani et al., 

2021). More work must be done to design safety measures against floods that consider flood vulnerability 

features outlined in this research. Risk factors and vulnerabilities should be assessed when formulating relevant 

mitigation plans for flooding (Orru et al., 2023). By integrating components of physical, economic, and societal 

vulnerability within an interconnected system, there is a more extensive scope through which disaster impacts 

can be analysed, providing the direction for further improvement of mitigation strategies (Ward et al., 2012). 

Additionally, using GIS-based multi-criteria approaches can help determine flood risks among vulnerable areas 

to favour mitigation choices and resource distribution (Chakraborty et al., 2023). 

Using spatial data can benefit decision-makers in designing mitigation plans precisely (Bakhtiari et al., 

2024; Rezvani et al., 2023). For targeted mitigation measures, creation purposes like geographical location or 

attributes may identify at-risk areas (Liu & Li, 2016; Tellman et al., 2020). Concerning social aspects, this study 

looks into urbanization and population density levels. This work has added new dimensions to our understanding 

of flood vulnerability dynamics, especially in the Upper Solo basin, which helps us think about what we can face 

here. Therefore, it strengthens the use of GIS and remote sensing data, especially OASD to analyse flood risk. 

Determining where the danger lies encourages focused and efficacious efforts to address flooding problems 

(Chakraborty et al., 2023). 

On the contrary, some limitations existed during the study. For example, long-term climatic change 

variables and fast-growing urbanization characteristics were not fully integrated in designing the risk model 

formulation. Therefore, future flood estimates should include hydrological data and other factors to maintain 

accuracy. However, these results come with limitations. Changes in climate over several decades or the rapid 

expansion of urban areas within the Upper Solo basin may not have been considered by this risk model 

(Marhaento et al., 2021). Information constraints or inadequate integration of non-hydrological variables could 

lead to less accurate forecasts of forthcoming flood risks. Besides, there has been an argument that risk maps 

alone might not properly illustrate susceptible regions unless they undergo supplementary validation processes. 

In any given research area, the social vulnerability of local populations is a crucial consideration that would 

require identifying probable locations to inform mitigation strategies. Consequently, a GIS-based methodology 

is useful because it helps us create flood risk maps needed for targeted interventions. After all, they show certain 

areas where more resources should be directed. The high-risk results for Surakarta substantiate the necessity for 

evidence-based mitigation actions to enhance the area’s resilience against future flooding. 

Moreover, Surakarta is situated next to a notable river; hence, it is at risk of flooding. The study by Hussain 

et al. (2021) underscored the role of the proximity of rivers, high precipitation levels, relief levels, and many 

socio-economic factors in determining a location’s criticality. Realizing that people’s knowledge about places like 

Surakarta, prone to floods, and their understanding of river inundation risks are essential to increase the 

population resilience and reduce vulnerabilities, as Muryani et al. (2021) indicate. It is further evident from 

research findings that more effort is needed to develop flood safety measures suited to the characteristics 

associated with flood vulnerability identified here. If we wish to protect our cities from going under, we must 

know what puts them there and what makes them vulnerable. The integration of physical, economic, and societal 

vulnerability components results in cohesive framework that enhances disaster impact analysis, thereby offering 

more than adequate guidance for mitigation strategies (Ward et al., 2012). In addition, GIS-enabled multi-

criteria approaches can quantify flood risks within exposed zones, thereby giving preference to appropriate 

mitigation over available resources (Chakraborty et al., 2023). 

High-risk regions' geographical attributes are very important in designing custom-tailored mitigation 

plans. This work’s urban dimension hinged on levels of urbanization and population density. For example, in the 

Upper Solo watershed, this study provides a new perspective on the risk to floods among different populations 

thus guiding risk understanding. Therefore, this brings out the importance of detailed regional spatial analysis 

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using GIS data and remote sensing techniques. Identification of potential danger spots is thus supportive of 

specific and efficient strategies to counteract flooding (Chakraborty et al., 2023). Additionally, the historical data 

available serves as an extra reassurance for these conclusions. 

4. Conclusion 

This study uses OASD to build a GIS-based flood risk model based on population, urbanization levels, and 

various physical factors. Flood risk assessment in the Upper Solo River watershed has been successfully achieved 

using remote sensing data and geographic information systems. The results suggest that specific areas, namely 

Wonogiri, Karanganyar, and Boyolali, exhibit a lower risk of floods. Conversely, Sukoharjo, Surakarta, and 

certain parts of Karanganyar have an elevated risk, with Surakarta being the highest. The research highlights 

the need to incorporate physical and rural-urban attributes when evaluating flood risk, offering significant 

perspectives for formulating efficient approaches to preventing catastrophic events. The integrated 

methodology, which combines GIS technology with an analysis of physical and social factors, highlights the 

interconnectedness of natural and human elements in shaping the probability of floods. The research findings 

significantly contribute to understanding the specific geographical area and to the broader scholarly debate on 

mitigating catastrophic risks. The possible application of the research methodologies and results to comparable 

locations further increases the global importance and impact of the research. 

The research on flood risk models has several limitations, mainly limited field validation. The model may 

not be sensitive to future factors like climate change, accelerating extreme rainfall patterns, and rapid land 

development in urban areas. Additionally, remote sensing data and GIS may not reflect actual field conditions 

fully. Future research should consider long-term climate change scenarios, updated high-resolution data, 

population growth, and land use change to produce more accurate flood risk projections. A collaborative 

approach involving field data and hydrological modelling based on climate data could improve the validity of the 

findings. Additionally, risk mapping at the micro or neighbourhood level could help develop more targeted 

mitigation strategies for local-scale implementation. 

The study recommends flood mitigation strategies in the study area, including developing green 

infrastructure, sustainable drainage systems, and increased collaboration with the private sector. Green 

infrastructure, such as urban forests and infiltration parks, can reduce flooding impact and increase groundwater 

absorption. Sustainable drainage systems optimize water management and reduce surface water flow. Flood risk 

zoning should be implemented throughout the watershed, dividing areas based on vulnerability and potential 

flooding. Development and spatial planning regulations should be strengthened to align with flood mitigation 

efforts. Local governments should tighten building permits in flood-prone areas and require flood-resistant 

designs for new construction in high-risk areas. Community participation in flood risk management programs is 

crucial. Utilizing remote sensing technology and GIS can help predict potential flooding risks more accurately 

and support risk-based spatial planning. Capacity building of local governments and institutions is essential for 

supporting flood mitigation strategies, including training technical staff, strengthening coordination, and 

increasing disaster mitigation budgets. 

5. Acknowledgments 

The authors gratefully acknowledge Dirjend DIKTI, Kemendikbud for funding the research through the 

PFR Scheme, grant number 108/E5/PG.02.00.PL/2024, 007 /LL6/PB/AL.04/2024, 196.28/A.3-

III/LRI/VI/2024. 

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