Spatial Vulnerability Analysis of Tidal Flooding in Kandanghaur, Indramayu

Tidal flooding (rob) is a recurrent coastal hazard along the northern coast of Java, where low relief, sea-level rise, tidal variability, shoreline change, and land subsidence can combine to generate repeated inundation and cumulative damage [1-9]. Unlike short-duration flooding, recurrent tidal inundation progressively disrupts housing, public services, productive land, and ecosystem functions. Village-scale vulnerability analysis is therefore important because similar hazard footprints can produce different consequences depending on demographic structure, built assets, livelihood dependence, and ecological conditions [10-14].

Kandanghaur Subdistrict in Indramayu Regency is a relevant case because Eretan Wetan, Eretan Kulon, and Kertawinangun are directly exposed to the Java Sea and contain dense settlements, rice fields, and aquaculture ponds. Previous Indonesian studies demonstrate the usefulness of geographic information system (GIS) for coastal flood and building vulnerability assessment [1, 3, 15-24]. However, the traceability of composite vulnerability indices remains a methodological concern when component scores, weights, and monetary assumptions are not reported at comparable levels of detail.

The study evaluates social, economic, physical, and environmental vulnerability using the Indonesian National Disaster Management Agency (BNPB) framework. The analysis has three objectives: (1) to report village-level indicators and calculations symmetrically across all four dimensions; (2) to clarify the status and limitations of the hazard, OpenStreetMap (OSM), and Google Earth inputs; and (3) to test the robustness of composite rankings and monetary exposure to plausible changes in weights and housing-loss assumptions. This structure separates methodological assumptions from interpretation so that each vulnerability component and the resulting composite index can be independently audited.

1.1 Conceptual framework of multidimensional vulnerability

Disaster vulnerability is multidimensional because the consequences of a hazard depend not only on the intensity of the physical event but also on the people, assets, livelihoods, and ecosystems located within the exposed area. Social vulnerability reflects demographic and socioeconomic conditions that can constrain preparedness, evacuation, coping, and recovery. Physical vulnerability represents the susceptibility and value of buildings and infrastructure. Economic vulnerability captures the exposure of productive land and income-generating sectors, whereas environmental vulnerability represents natural assets that may be damaged while also contributing to coastal protection. Treating these dimensions separately before aggregation avoids interpreting a single composite score as if all villages were vulnerable for the same reasons [10-12, 25-30].

This distinction is particularly relevant to recurrent tidal flooding. Repeated inundation can degrade housing and public facilities, interrupt mobility and services, reduce agricultural and aquaculture productivity, and alter coastal ecosystems even when an individual event is not catastrophic. Consequently, vulnerability assessment must capture both direct asset exposure and the social and ecological conditions that influence longer-term recovery. The BNPB framework provides a policy-oriented basis for this multidimensional interpretation and allows the component indicators to be compared using a common scoring scale. Related studies have also examined critical-infrastructure impacts, physical vulnerability, socioeconomic exposure to sea-level rise and flooding, flood-related business resilience, and coastal-agriculture pressures [31-36].

1.2 Geographic information system-based coastal vulnerability and research contribution

GIS-based approaches are well suited to coastal vulnerability assessment because demographic statistics, building inventories, productive land, ecosystem classes, and hazard polygons can be spatially aligned and evaluated at a common administrative scale. Coastal studies in Indonesia and other settings have demonstrated the value of GIS overlays for identifying tidal-flood footprints, assessing building exposure, and integrating multiple vulnerability indicators [1-4, 15-16]. At the same time, composite mapping can become difficult to audit when the underlying indicator values, weights, and monetary assumptions are not reported at the same level of detail. GIS has also been applied to population-pattern analysis, flood-process modeling, and flood-depth exposure assessment in other settings [37-39].

The contribution of this study is therefore not limited to producing a final vulnerability map. It links the four BNPB dimensions to village-level indicator tables, exposes the calculation pathway from individual indicators to the composite index, quantifies physical and economic exposure, and evaluates the robustness of rankings and housing-loss assumptions through sensitivity tests. The analysis is intentionally village-specific because the three adjacent coastal settlements differ markedly in population density, productive-land extent, facility exposure, and environmental composition despite sharing the same broader coastal setting.

3.1 Tidal-flood hazard distribution

The supplied hazard layer shows that high-hazard areas dominate Eretan Wetan and Eretan Kulon, whereas Kertawinangun is dominated by the medium class. The three hazard classes reconcile exactly with each village area, providing a basic geometry-completeness check.

The spatial contrast is substantial. More than half of Eretan Wetan (55.26%) and Eretan Kulon (54.24%) falls within the high-hazard class, whereas 84.01% of Kertawinangun is classified as medium hazard and only 14.16% as high hazard. This pattern is consistent with the broader exposure of low-lying northern Java coastal settlements to recurrent tidal inundation reported in previous Indonesian [1-4]. The two Eretan villages therefore combine direct coastal proximity with a larger share of high-hazard land, while Kertawinangun experiences a wider but generally lower-intensity mapped hazard footprint.

Hazard class alone, however, does not determine the composite vulnerability result. Kertawinangun illustrates this distinction: its hazard map is dominated by the medium class, yet its extensive agricultural and aquaculture assets produce the largest economic exposure of the three villages. Conversely, the Eretan villages combine high-hazard coverage with dense settlements and critical facilities. This confirms the analytical value of separating the hazard layer from the four vulnerability dimensions rather than treating mapped inundation as a direct surrogate for overall disaster vulnerability. The village-level hazard areas are summarized in Table 2, while the spatial distribution is shown in Figure 1.

Figure 1. Tidal-flood hazard distribution in the study villages

Table 2. Tidal-flood hazard area by village

3.2 Social vulnerability

Social vulnerability is highest in the two coastal Eretan villages because population density and poverty dominate the weighted index. Eretan Wetan also has a high vulnerable-age ratio. Kertawinangun is classified as medium because its lower population density reduces the 60% density contribution. The social index for Eretan Kulon is 0.833, obtained directly from the five indicator contributions shown in Table 3; presenting those contributions makes the village-level score fully traceable.

Table 3. Social indicators and weighted contributions by village

Population density is the strongest differentiating social indicator because it carries a 60% weight. Eretan Wetan reaches 53 persons/ha and Eretan Kulon 26 persons/ha, both classified as high, whereas Kertawinangun records 9 persons/ha and is classified as medium. The difference substantially lowers Kertawinangun's social index despite all three villages having high poverty ratios. Eretan Wetan also records a vulnerable-age ratio of 41, compared with 40 in Eretan Kulon and Kertawinangun, reinforcing the concentration of residents who may require additional assistance during evacuation and recovery.

From a disaster-management perspective, high social vulnerability means that an equivalent flood footprint can create different demands for evacuation support, temporary shelter, health services, livelihood assistance, and post-disaster recovery. This interpretation is consistent with multidimensional vulnerability research, which emphasizes that demographic structure and socioeconomic disadvantage shape the capacity to anticipate, cope with, and recover from hazards [10, 11, 12]. The sex-ratio indicator contributes only 0.033 in each village under the BNPB operational classification, so the social differences observed here are driven principally by density, poverty, and vulnerable age rather than by sex ratio. The resulting spatial pattern of social vulnerability is shown in Figure 2.

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Figure 2. Social vulnerability map

3.3 Economic vulnerability

Economic vulnerability is high in all three villages. Productive land is heavily exposed to medium and high tidal-flood classes, and the estimated agricultural/fishery GRDP exceeds the BNPB high-class threshold in every village. Kertawinangun has the largest economic exposure because of its extensive agricultural and aquaculture area.

The economic results reveal a different spatial ordering from the social dimension. Productive-land exposure rises from IDR 11.216 billion in Eretan Wetan to IDR 24.925 billion in Eretan Kulon and IDR 43.131 billion in Kertawinangun. The estimated village agricultural/fishery GRDP follows the same direction, from IDR 10.679 billion to IDR 19.594 billion and IDR 25.110 billion. Consequently, Kertawinangun has the highest combined economic exposure (IDR 68.240 billion) even though its social and physical indices are lower than those of the Eretan villages. The corresponding economic indicators and exposure values are summarized in Table 4.

Table 4. Economic indicators and exposure by village

This finding demonstrates why productive-land exposure should not be inferred solely from settlement density or the proportion of high-hazard land. Large agricultural and aquaculture areas can generate substantial livelihood risk under recurrent medium-hazard inundation, particularly when saline water affects crop productivity, pond management, access, and the timing of harvesting. The monetary values in Table 4 should therefore be interpreted as spatial exposure proxies for economic assets and activity, not as realized post-event losses. Their main analytical value is to identify where disruption to productive sectors could have the greatest consequences for local income and livelihood continuity. The spatial pattern of economic vulnerability is shown in Figure 3.

3.4 Physical vulnerability

Physical vulnerability is high in all villages. Housing and public-facility losses exceed the high-class thresholds throughout. Eretan Wetan and Eretan Kulon also contain critical facilities in high-hazard areas, whereas Kertawinangun has no critical facility in the mapped exposure inventory. The resulting Kertawinangun physical index is 0.799 rather than the rounded 0.79 used previously.

Table 5. Physical indicators, losses, and indices by village

The housing inventory shows the concentration of built assets within mapped hazard zones. Eretan Wetan contains 4,039 OSM building footprints, of which 2,324 intersect the high-hazard class and 1,713 the medium class. Eretan Kulon contains 3,865 buildings, including 2,941 in high hazard, 883 in medium hazard, and 21 in low hazard. Kertawinangun contains 1,852 buildings, including 383 in high hazard, 1,375 in medium hazard, and 79 in low hazard. The small differences between total and classified OSM counts are explicitly retained as boundary/no-data residuals in the QA/QC procedure rather than forced into a hazard class. The corresponding physical indicators, loss estimates, and indices are summarized in Table 5.

Public-service exposure further strengthens the physical vulnerability of the study area. Field mapping recorded 26 public facilities in Eretan Wetan, 19 in Eretan Kulon, and 15 in Kertawinangun, including educational, religious, and administrative facilities. Eretan Wetan and Eretan Kulon also contain critical health or fish-marketing facilities within the mapped exposure inventory, while none was recorded for Kertawinangun. Damage or inaccessibility affecting such facilities can create indirect and cascading consequences by reducing emergency response capacity, interrupting basic services, and constraining local economic recovery [31]. The spatial pattern of physical vulnerability is shown in Figure 4.

This work was supported by the DPA of the Institute for Research and Community Service, Universitas Negeri Semarang (LPPM UNNES), under Research Contract No. 761.14.3/UN37/PPK.11/2025. Administrative maps, secondary data, and fieldwork support were provided by relevant agencies of Indramayu Regency and village authorities in Eretan Wetan, Eretan Kulon, and Kertawinangun.