Hydrometeorological Resilience Assessment: The Case of the Veracruz–Boca del Río Urban Conurbation, Mexico

Abstract

Coastal regions in Mexico face significant exposure to hydrometeorological hazards, often resulting in severe flooding and socioeconomic disruption. This study assesses the hydrometeorological resilience of the Veracruz–Boca del Río Conurbation (VBC), a region comprising two coastal municipalities with shared hazard exposure despite distinct governance structures. The hydrometeorological resilience evaluation employs the City Resilience Index (CRI), developed by Bahena which integrates the Technical Resilience Index (TRI) and the Technical Profile of Resilience (TPR) across nine hierarchical indicators. Results reveal moderate resilience levels—59.83% for Veracruz and 58.32% for Boca del Río—with Disaster Risk Reduction Plans and Vital Services indicators as the strongest contributors, while Risk Assessments and Budget Allocation for Emergency Response indicators scored lowest due to limited municipal data. These findings highlight the need for enhanced data transparency, institutional coordination, and resource allocation in disaster management. Beyond its local significance, this study advances the global understanding of resilience assessment frameworks in data-scarce contexts, offering insights applicable to similar regions worldwide. As the first hydrometeorological resilience assessment for the VBC, this research provides a methodological and empirical foundation for future studies and informs targeted resilience strategies for Mexico’s coastal urban areas.

1. Introduction

Hydrometeorological resilience is the capacity of urban systems to withstand, absorb, and recover from hazards such as intense rainfall, floods, cyclones, and hurricanes, while maintaining essential functions and minimizing long-term disruptions [1]. Drawing on broader discussions of resilience in climate adaptation and disaster risk reduction (DRR) [2,3], this concept is specifically tailored to hydrological and meteorological stressors. Therefore, it must be understood as a multidimensional construct that includes physical dimensions, such as infrastructure robustness or emergency facilities, as well as socio-institutional capacities, such as urban planning strategies, policy frameworks, and resource allocation, which highlights the need for systematic assessments and metrics to translate resilience into actionable strategies. This makes hydrometeorological resilience a critical element in safeguarding urban communities’ well-being under conditions of climate uncertainty.

The urgency of advancing hydrometeorological resilience strategies is underscored by two interrelated drivers; first, climate change has intensified the frequency and severity of extreme weather events worldwide; second, rapid and often unplanned urbanization has increased exposure and sensitivity to hydrometeorological risks, particularly in coastal regions [4,5]. In Mexico, these factors are especially acute in the state of Veracruz, which is historically the most flood-prone region in the country. Between 1970 and 2015, approximately 40% of national flood events occurred there [6]. Moreover, records indicate that between 1951 and 2012, 86 hurricanes from the Atlantic basin made landfall in Veracruz [7,8], resulting in 4000 fatalities and $3.26 billion USD in economic losses only between 1955 and 2002 [7,8]. Within this state-level context, the Veracruz–Boca del Río Conurbation (VBC), located in the Coastal Zone of the Gulf of Mexico, emerges as a particularly exposed and vulnerable urban area. These combined conditions emphasize the critical importance of enhancing resilience strategies in the VBC to reduce hydrometeorological vulnerability.

Decision-Making Methods under uncertainty provide analytical tools for evaluating complex urban systems under risk. Classical approaches, such as Wald’s, Hurwicz’s, Savage’s, and Laplace’s Criteria, have historically informed decisions in uncertain scenarios [9,10,11,12]. However, these methods have inherent limitations, such as a tendency to consider costs and benefits separately and to overlook qualitative factors [13,14]. Multi-Criteria Decision-Making (MCDM) methods, such as the Weighted Sum Method and the Simple Ranking Method, facilitate more integrated assessments but are dependent on subjective inputs [15,16]. Alternatively, the Entropy Method is a large-scale, data-driven approach that reduces subjectivity by assigning weights based on data variability and richness, offering a robust framework for holistic evaluations [17,18].

To address these methodological limitations, Bahena [19] proposed a hybrid framework that integrates expert judgment with MCDM techniques such as the Weighted Sum Method, providing a replicable approach for data-limited contexts but urgent decision-making requirements. The framework is grounded in an extensive analysis of the Socioeconomic Impact of Disasters in Mexico reports published by CENAPRED, which detail the allocation of resources from the Natural Disaster Fund (FONDEN) between 1996 and 2014, and serve as the basis for defining the weights according to Bahena’s criteria. Based on this analysis, a quantitative Technical Resilience Index (TRI) is constructed, complemented by a Technical Profile of Resilience (TPR) that offers qualitative insights into the state of hydrometeorological threats around the area. These components form an articulate methodology, whose technical details are further elaborated in the Section 2.

Accordingly, the objective of this study is to assess the VBC’s hydrometeorological resilience through the application of Bahena’s [19] methodology. Instead of evaluating policy effectiveness directly, the analysis relies on existing, publicly accessible data across three general aspects: infrastructure condition, resource availability, and institutional capacity to respond to natural disasters. By taking this action, the study responds to the critical absence of systematic resilience-oriented assessments at the urban conurbation scale in Mexican coastal regions, despite their pronounced exposure to extreme events [20]. The study’s findings lay the groundwork for the formal assessment of hydrometeorological resilience in the VBC while also serving as a reference for conducting resilience evaluations in other coastal regions facing analogous challenges.

2. Methodology

As previously stated, the methodology developed by Bahena [19], known as the City Resilience Index (CRI), was applied to the VBC to estimate its Technical Resilience Index (TRI) and to construct its Technical Profile of Resilience (TPR). The framework is organized hierarchically into nine indicators, which are further structured into two levels of sub-indicators and ultimately into variables that operationalize their measurement. The purpose of these tools is to measure and monitor the capacity of resistance, adaptation, recovery, and preparedness in the face of hydrometeorological events. The indicators considered are:

• Infrastructure.
• Land use planning and ecological programs and building codes.
• Risk assessments.
• Disaster risk reduction plans.
• Budget assigned to emergency response.
• Institutions related to disaster risk reduction.
• Vital services.

A.

Predominant hazard.

B.

Recovery speed.

The selection and structure of these indicators follow the guidelines proposed in [21], while their association with the four main characteristics of resilience—preparedness, resistance, recovery, and adaptation—was established according to the framework presented in [22], as adapted by Bahena. This ensures alignment with internationally recognized resilience criteria and consistency in the conceptual linkage of indicators to resilience characteristics. A detailed description of their operationalization and specific contribution is provided in Appendix A.10.

2.1. Technical Resilience Index (TRI)—Quantitative Analysis

For the construction of the Technical Resilience Index (TRI), indicators 1 through 7 were assigned weights derived from the allocation of resources by the FONDEN between 1996 and 2014, as reported in the Socioeconomic Impact of Disasters in Mexico studies published by CENAPRED. These weights serve as the basis for calculating standardized scores (ranging from 0 to 1), which are then aggregated using the Weighted Sum Method. The general structure of the index composition is illustrated in Figure 1, while a detailed description of the variables employed in the analysis is provided in Appendix A.

How mentioned, the VBC comprises two municipalities that operate under independent governance structures. However, the geographical limits between them are diffuse, resulting in a shared exposure to the same hydrometeorological threats. For this reason, although the conurbation is analyzed as a single urban system in terms of hazard exposure, two separate hydrometeorological resilience indices were calculated, one for each municipality, to reflect their distinct administrative and institutional conditions.

Given that the methodology requires specific variables for which updated information is not always consistently available, the analysis relied on the most current data accessible for each variable. To ensure reliability and transparency, all information was obtained from recognized public sources and official databases. These sources include the DESINVENTAR disaster inventory system [23], national census data published by INEGI [24,25,26,27,28], municipal risk atlases [29,30], and official reports issued by local governments. When necessary, these were supplemented with validated information from local newspapers and government publications, enabling a comprehensive and contextually grounded assessment despite the inherent limitations in data availability.

Regarding data sources, two specific limitations should be acknowledged:

• For variables without publicly available information, values were conservatively set to zero. This does not imply that the condition is literally absent. Rather, it reflects a methodological decision to exclude indicators for which reliable data could not be obtained.
• Seven variables required estimation, notably those related to essential services (such as access to potable water, electricity, and sewage systems). These were only available in aggregated form for the municipalities of Veracruz, Boca del Río, and Medellín de Bravo. To address this limitation, and to avoid reporting implausible values (e.g., zero coverage of potable water), the aggregated data were proportionally disaggregated according to the relative population size of each municipality. This approach ensured a reasonable approximation that preserved consistency and enabled the inclusion of these critical variables. In Appendix A, these estimated variables are marked with an asterisk (*).

Finally, once the hydrometeorological resilience index has been calculated, each municipality of the VBC can be classified into one of the five resilience levels established by Bahena [19], which are presented in Figure 1b.

2.2. Technical Profile of Resilience (TPR)—Qualitative Analysis

In addition to the quantitative resilience index, a qualitative resilience profile was developed to provide contextual insights into the hydrometeorological vulnerability of the VBC. This profile is based on a structured literature review and the compilation of publicly available studies and reports. It also incorporates two complementary indicators that do not directly contribute to the numerical value of the index.

Indicator A identifies the main hydrometeorological hazard affecting the study area, indicating where prevention strategies should primarily be focused. Indicator B provides a record of socioeconomic damage caused by an extreme event, along with an evaluation of the recovery speed, which Bahena [19] recommends should be based on the most recent event available. These indicators are intended to strengthen the profile by linking hazard exposure with observed recovery capacity, thereby providing essential context that captures institutional, social, and environmental dimensions not fully reflected in the quantitative index. The analysis is organized around seven main components:

• Analysis of information on existing vulnerability, hazard, and risk in the city: Study of the main shortcomings and disadvantages of the urban area that increase exposure and susceptibility.
• Socioeconomic impacts of hydrometeorological events: An evaluation of the historical consequences of floods, hurricanes, and related phenomena on the city’s population, economy, and infrastructure.
• Predominant hazard analysis: Identification of the dominant hydrometeorological threats that shape local risk exposure.
• Water resource availability: Review of existing studies on the availability, use and distribution of water resources.
• Analysis of land use and ecological planning instruments, as well as of the regulation codes: Examination of the availability and currency of planning instruments, ecological frameworks and policies, and building codes that support urban development in the city.
• Statistics generation and updating: Production of new information from the literature review and resilience index, presented in graphs, maps, or tables.
• Proposal of structural and non-structural measures: Compilation of recommended strategies, both physical and institutional, aimed at strengthening resilience.

The combination of the TPR and the TRI provides a thorough diagnostic of the VBC’s hydrometeorological resilience. This dual approach enables the identification of institutional and governmental weaknesses across key dimensions. It also offers specific areas where greater emphasis is needed to enhance resilience capacities.

3. Results

The TRI and TPR are structured into a series of indicators, functions and variables. The indicators address the four basic characteristics outlined below. The indicators related to resistance typically include the structural integrity of critical infrastructure, land use planning and zoning regulations, among others. Adaptation is evaluated using indicators that demonstrate the city’s ability to modify its planning and infrastructure over time to cope with evolving risks. Recovery indicators show how quickly and effectively the city rebounds after a disaster. It means that the city demonstrates resilience through regeneration. Preparedness indicators assess a range of factors, including early warning systems, communication protocols, disaster education and drills, institutional coordination and contingency planning. They also examine whether the city has systems in place to reduce the impact of future events. As a starting point, the VBC results are shown below. For each indicator, a brief introductory paragraph outlines the main differences between Veracruz and Boca del Río, with scores expressed as percentages relative to their assigned weights to facilitate clearer comparison across resilience dimensions (${\displaystyle \frac{Total Score}{Indicator Weight}}·100$), followed by a table presenting the corresponding results.

3.1. Quantitative Component (TRI) Results

3.1.1. Infrastructure Indicator

The infrastructure indicator results (

Table 1. Results of the Infrastructure Indicator for the VBC.

Indicator	1. Infrastructure Weight = 30	Weight		Estimation			Score
	VBC	VM	BM	VM		BM	VM	BM
Sub-indicators	1.1. Investment in new infrastructure	7.0	7.0	0.69		0.59	4.86	4.16
	1.2. Investment in maintenance	7.0	7.0	0.01		1.00	0.08	7.00
	1.3. Supervision of the physical conditions of infrastructure	7.0	7.0	1.00		0.00	7.00	0.00
	1.4. Critical infrastructure	9.0	9.0
	1.4.1. Hospitals	5.0	5.0	1.00		1.00	5.00	5.00
	1.4.2. Schools	4.0	4.0	0.75		0.97	3.00	3.86
Total							19.94	20.03
Variables		Values
		VM			BM		Unit
$I_{ac}$	Investment in infrastructure in the current period	298,997,403.58 [31]			74,385,722.77 [31]		$MXN
$t_{ac}$	Growth rate in the current period	0			0		%
$I_{an}$	Investment in infrastructure in the previous period	430,692,590.10 [31]			125,108,916.07 [31]		$MXN
$t_{an}$	Growth rate in the previous period	0			0		%
$M_{ac}$	Investment in maintenance in the current period	580,000.00 [31]			56,329,022.58 [31]		$MXN
$M_{an}$	Investment in maintenance in the previous period	52,674,539.13 [31]			41,412,684.15 [31]		$MXN
$r$	Number of supervisions made per year	1172 [32]			0		supervisions
$F_{i1}$	Connecting factor in America between the number of hospital beds and population	24.47 [33,34]			24.47 [33,34]		beds/10,000 inhab
$P$	Number of inhabitants in the city	607,209 [35]			144,550 [36]		inhab
$N_b$	Number of hospital beds in the city	1724 [35]			534 [36]		beds
$P_{st}$	Number of basic education students in the city	86,522 [37]			19,706 [37]		students
$N_{sc}$	Number of basic education schools in the city	645 [37]			189 [37]		schools
$F_{i2}$	Connecting factor nationwide of student population and number of schools	101 [38]			101 [38]		students/schools

Note: In cases where information was unavailable, the corresponding variables were assumed to take a value of zero. For further details, see Appendix A.1.

) reveal a very similar overall performance between Veracruz (66.5%) and Boca del Río (66.8%). However, the distribution of sub-indicators highlights contrasting priorities. Veracruz allocated a higher score in new infrastructure (69.4%), but reported minimal investment in maintenance (1.1%), raising concerns about the long-term reliability of its infrastructure. In contrast, Boca del Río emphasized maintenance (100%) while allocating less to new investment (59.4%). These differences suggest distinct resilience strategies: one focused on expansion and the other on preservation. The lack of information on $t_{ac}$ and $t_{an}$ in both municipalities introduces uncertainty in these sub-indicators, and the actual scores could be different if such data were available. Furthermore, Veracruz achieved the maximum score in infrastructure supervision, whereas Boca del Río obtained the lowest, largely due to unavailable data. Critical infrastructure indicators, particularly hospitals and schools, generally reached maximum scores in both municipalities, underscoring their role as a shared priority in strengthening hydrometeorological resilience.

3.1.2. Indicator of Planning Programs and Building Codes

The results for the planning programs and building codes indicator (

Table 2. Results of the Indicator of planning programs and building codes for the VBC.

Indicator	2. Planning Programs and Building Codes Weight = 10		Weight		Estimation		Score
	VBC		VM	BM	VM	BM	VM	BM
Sub-indicators	2.1. Land use		2.50	2.50
	2.1.1. Existence		1.25	1.25	1.00	1.00	1.25	1.25
	2.1.2. Update		1.25	1.25	1.00	1.00	1.25	1.25
	2.2. Ecological		2.50	2.50
	2.2.1. Existence		1.25	1.25	0.00	0.00	0.00	0.00
	2.2.2. Update		1.25	1.25	0.00	0.00	0.00	0.00
	2.3. Regulation and building codes		2.50	2.50
	2.3.1. Existence		1.25	1.25	1.00	1.00	1.25	1.25
	2.3.2. Update		1.25	1.25	1.00	1.00	1.25	1.25
	2.4. Application of regulatory plans and codes		2.50	2.50	0.47	0.23	1.17	0.57
Total							6.17	5.57
Variables			Values
			VM		BM		Unit
$N_u$		Maximum number of years for considering a document to be updated	5		5		years
$A_a$		Year in which the assessment is performed	2025		2025		years
$A_e$		Urban Development, Territorial Planning, and Housing Law of the State of Veracruz de Ignacio de la Llave	2021 [39]		2021 [39]		years
$A_e$		Publication Year of the Ecological Planning Instrument	0		0		years
$A_e$		Regulations Governing Public and Private Construction in the Municipality of Veracruz	2020 [40]		2022 [41]		years
$O_e$		Number of works executed	30 [31]		22 [31]		works
$O_C$		Number of works executed under supervision of a Chief Construction	14 [31]		5 [31]		works

Note: In cases where information was unavailable, the corresponding variables were assumed to take a value of zero. For further details, see Appendix A.2.

) reveal adequate performance, with Veracruz reaching 61.7% of the total weight and Boca del Río 55.7%. Both municipalities achieved the maximum scores for the existence and updating of land use documents and building codes, reflecting a suitable regulatory framework in these areas. However, the complete absence of ecological planning instruments critically undermines the indicator, suggesting a gap in integrating environmental considerations into urban resilience strategies. This omission may limit the capacity of both municipalities to anticipate and mitigate hydrometeorological risks. Additionally, Veracruz obtained a slightly higher score in the application of regulatory plans and codes, largely explained by a greater number of works considered under the supervision of Chief Construction. This suggests stronger enforcement mechanisms compared to Boca del Río, which may enhance compliance and structural safety in practice.

3.1.3. Indicator of Risk Assessments

Both municipalities in the conurbation recorded a similar score, achieving 45.20% of the indicator’s total weight (

Table 3. Results of the Indicator of risk assessments for the VBC.

Indicator	3. Risk Assessments Weight = 10		Weight		Estimation		Score
	VBC		VM	BM	VM	BM	VM	BM
Sub-indicators	3.1. Climate risk projections and trends		2.0	2.0
	3.1.1. Existence		1.0	1.0	1.00	1.00	1.00	1.00
	3.1.2. Update		1.0	1.0	0.26	0.26	0.26	0.26
	3.2. Hazard, exposure, and risk maps		2.0	2.0
	3.2.1. Existence		1.0	1.0	1.00	1.00	1.00	1.00
	3.2.2. Update		1.0	1.0	0.26	0.26	0.26	0.26
	3.3. Insurance coverage statistics		2.0	2.0
	3.3.1. Existence		1.0	1.0	0.00	0.00	0.00	0.00
	3.3.2. Update		1.0	1.0	0.00	0.00	0.00	0.00
	3.4. History of socioeconomic impacts		2.0	2.0
	3.4.1. Existence		1.0	1.0	1.00	1.00	1.00	1.00
	3.4.2. Update		1.0	1.0	1.00	1.00	1.00	1.00
	3.5. Population in risk areas		2.0	2.0	0.00	0.00	0.00	0.00
Total							4.52	4.52
Variables			Values
			VM		BM		Unit
$N_u$		Maximum number of years for considering a document to be updated	5		5		years
$A_a$		Year in which the assessment is performed	2025		2025		years
$A_e$		Year the document was issued	2006 [29]		2006 [30]		years
$A_e$		Year the document was issued	2006 [29]		2006 [30]		years
$A_e$		Year the document was issued	0		0		years
$A_e$		Year the document was issued	2024 [43]		2024 [43]		years
$P_r$		Number of inhabitants in the city	751,759 [26]		144,550 [26]		inhab
$P_{rk}$		Number of inhabitants settled in risk areas within the city	0		0		inhab

Note: In cases where information was unavailable, the corresponding variables were assumed to take a value of zero. For further details, see Appendix A.3.

). Both municipalities have risk atlases containing information on predominant threats, exposure levels, and the potential risks they might entail [29,30]. However, these are outdated, necessitating the use of the state of Veracruz’s risk atlas [42]. Therefore, information specific to the conurbation is lacking in terms of regionalization. This absence had a detrimental effect on the scores of sub-indicators 3.1 and 3.2. Furthermore, the lack of statistical information on local insurance coverage for the impacts caused by hydrometeorological disasters, as well as a figure on the population located in risk zones, contributed to the absence of scores for sub-indicators 3.3 and 3.5.

3.1.4. Indicator of Disaster Risk Reduction (DRR) Plans

As shown in

Table 4. Results of the Indicator of Disaster Risk Reduction (DRR) plans for the VBC.

Indicator	4. Disaster Risk Reduction (DRR) Plans Weight = 10		Weight		Estimation		Score
	VBC		VM	BM	VM	BM	VM	BM
Sub-indicators	4.1. Proactive		3.50	3.50
	4.1.1. Existence		1.75	1.75	0.50	0.50	0.88	0.88
	4.1.2. Update		1.75	1.75	1.00	1.00	1.75	1.75
	4.2. Reactive		3.00	3.00
	4.2.1. Existence		1.50	1.50	1.00	0.50	1.50	0.75
	4.2.2. Update		1.50	1.50	1.00	1.00	1.50	1.50
	4.3. Post-disaster		3.50	3.50
	4.3.1. Existence		1.75	1.75	1.00	1.00	1.75	1.75
	4.3.2. Update		1.75	1.75	1.00	1.00	1.75	1.75
Total							9.13	8.38
Variables			Values
			VM		BM		Unit
$N_u$		Maximum number of years for considering a document to be updated	5		5		years
$A_a$		Year in which the assessment is performed	2025		2025		years
$A_e$		Year the document was issued	2024 [44]		2024 [45]		years
$A_e$		Year the document was issued	2024 [44]		2024 [45]		years
$A_e$		Year the document was issued	2024 [44]		2024 [45]		years

Note: In cases where information was unavailable, the corresponding variables were assumed to take a value of zero. For further details, see Appendix A.4.

, Veracruz achieved a score of 91.30%, while Boca del Río attained 83.80% of the total weight. The region has DRR plans in place, but the lack of material and human resources has hindered their effective execution. This led to a reduction in the final scores of the sub-indicators (4.1.1 and 4.2.1), which reflects the existence of proactive and reactive plans on the part of both municipalities.

3.1.5. Indicator of Budget Assigned to Emergency Response

The results for the budget assigned to emergency response (

Table 5. Results of the Indicator of budget assigned to emergency response for the VBC.

Indicator	5. Budget Assigned to Emergency Response Weight = 10	Weight		Estimation		Score
	VBC	VM	BM	VM	BM	VM	BM
Sub-indicators	5.1. Budget assigned to emergencies	5.00	5.00	0.0	0.0	0.0	0.0
	5.2. Budget assigned to prevention programs	5.00	5.00	0.0	0.0	0.0	0.0
Total						0.0	0.0
Variables		Values
		VM		BM		Unit
${Pr}_e$	Budget allocated for emergency response	0		0		$MXN
${Pr}_p$	Budget allocated to the development of DRR plans and programs	0		0		$MXN
${Pr}_c$	Budget of the city	175,245,285,470.00 [46]		554,370,785.00 [46]		$MXN
${\mathrm{\%}}_h$	Historic percentage of investment on DRR	1 [46]		1 [46]		%

Note: In cases where information was unavailable, the corresponding variables were assumed to take a value of zero. For further details, see Appendix A.5.

) are inconclusive, as no data was found for ${Pr}_e$ and ${Pr}_p$. Only two variables were available: ${Pr}_c$ and ${\%}_h$. The latter represented 0.0004% of total revenues and was rounded to 1% for integration into the index.

3.1.6. Indicator of Institution Related to DRR

The “Disaster Risk Reduction (DRR)” indicator, in the assessment framework, is based on UNISDR (2012b), which outlines a comprehensive strategy that includes preparedness, resistance, recovery and adaptation. However, when implementing the indicator (

Table 6. Results of the Indicator of institution related to DRR for the VBC.

Indicator	6. Institution Related to DRR Weight = 10	Weight		Estimation		Score
	VBC	VM	BM	VM	BM	VM	BM
Sub-indicators	6.1. Qualified personnel (emergency response)	2.5	2.5	1.00	1.00	2.5	2.5
	6.2. Equipment	2.5	2.5	0.00	0.00	0.00	0.00
	6.3. Units	2.5	2.5	1.00	1.00	2.5	2.5
	6.4. Early-Warning system	2.5	2.5	0.00	0.00	0.00	0.00
Total						5.00	5.00
Variables		Values
		VM		BM		Unit
$F_{i3}$	Connecting factor between the population and trained personnel	257.41 [35,47]		245.41 [36,47]		inhab/personnel
$F_{i4}$	Connecting factor between the population and number of units	5782.94 [35,47]		5559.61 [36,47]		inhab/ambulances
$P_r$	Number of inhabitants in the city	607,209 [35]		144,550 [36]		inhab
$N_p$	Number of trained personnel	2359 [47]		589 [47]		personnel
$I_e$	Budget allocated for equipment acquisition	0		0		$MXN
${\mathrm{\%}}_h$	Historic percentage of investment on DRR	1 [46]		1 [46]		%
${Pr}_c$	Budget of the city	175,245,285,470.00 [46]		554,370,785.00 [46]		$MXN
$N_a$	Number of ambulances	105 [47]		26 [47]		ambulances

Note: In cases where information was unavailable, the corresponding variables were assumed to take a value of zero. For further details, see Appendix A.6.

), the focus is exclusively on institutional emergency response. Therefore, The DRR institution indicator evaluates organizational response capacity, considering trained personnel, ambulances, equipment, and the presence of an early warning system. For both Veracruz and Boca del Río, the indicator value was 50% of the total weight, which is consistent given their conurbation and the low likelihood of significant variation between municipalities. Nevertheless, the absence of a functional early warning system remains a critical gap for strengthening institutional preparedness, and no data was available regarding emergency equipment funding.

3.1.7. Indicator of Vital Services

The results for the vital services indicator (

Table 7. Results of the Indicator of vital services for the VBC.

Indicator	7. Critical Services Weight = 20	Weight		Estimation		Score
	VBC	VM	BM	VM	BM	VM	BM
Sub-indicators	7.1. Drinking water	7.0	7.0
	7.1.1. Service coverage	1.0	1.0	0.99	0.99	0.99	0.99
	7.1.2. 24 h service coverage	1.0	1.0	0.77	0.63	0.77	0.63
	7.1.3. PIGOO overall efficiency	2.0	2.0	0.44	0.44	0.89	0.89
	7.1.4. Water stress degree PRONACOSE	2.0	2.0	0.00	0.00	0.00	0.00
	7.1.5. Supply	1.0	1.0	0.70	0.92	0.70	0.92
	7.2. Sanitation	7.0	7.0
	7.2.1. Sewerage service coverage	3.0	3.0	0.59	0.48	1.78	1.45
	7.2.2. Wastewater vs. Treated water	2.0	2.0	1.00	1.00	2.00	2.00
	7.2.3. Wastewater treatment plants	2.0	2.0	1.00	1.00	2.00	2.00
	7.3. Energy	6.0	6.0	0.98	0.98	5.93	5.93
Total						15.06	14.81
Variables		Values
		VM		BM		Unit
$P_{\mathrm{r}}$	Number of inhabitants in the city	607,209 [35]		144,550 [36]		inhab
$P_w$	Number of inhabitants with drinking water services	604,282 * [35,48,49]		144,066 * [36,48,49]		inhab
$P_{24\mathrm{w}}$	Number of inhabitants with 24 h drinking water services	470,851 * [35,48,49]		91,332 * [36,48,49]		inhab
${\mathrm{E}}_{\mathrm{g}}$	Indicator of global efficiency of Water Utilities Management	44.80 [50]		44.80 [50]		%
$RG$	Available guaranteed resources	78.69 * [35,48]		19.67 * [36,48]		hm3
${\mathrm{V}}_{\mathrm{n}\mathrm{a}}$	Environmental demand	0		0		hm3
${\mathrm{D}}_{\mathrm{u}}$	Demand for urban supply	0		0		hm3
$\mathrm{O}\mathrm{D}$	Other demands	0		0		hm3
$D_C$	Supply per inhabitant per day in the city	300 [35,48]		72 [36,48]		l/inhab/day
$D_O$	Supply per inhabitant per day recommended by the World Health	100 [51]		100 [51]		l/inhab/day
$P_s$	Number of inhabitants with sewerage services	361,795 * [35,48]		70,178 * [36,48]		inhab
${H_{2}O}_w$	Amount of water used by the city	46.44 * [35,48]		11.61 * [36,48]		hm3
${H_{2}O}_t$	Amount of water treated in the city	27.78 * [35,48]		6.95 * [36,48]		hm3
$T_s$	Number of treatment plants in operation	25 [52]		2 [52]		plants
$T_t$	Number of treatment plants in the city	25 [52]		2 [52]		plants
$P_e$	Number of inhabitants with electricity service	600,468 * [35,53]		142,945 * [36,53]		inhab

Note: In cases where information was unavailable, the corresponding variables were assumed to take a value of zero. * These variables were appropriately approximated according to the author’s criteria. For further details, see Appendix A.7.

) show that Veracruz achieved 75.3% of the total weight, compared to 74.1% in Boca del Río. While the distinction between the two municipalities is not significant, it is primarily attributable to variations in the sub-indicators of 24 h service coverage, water supply, and sewerage coverage. These results are based on proportionally disaggregated values according to municipal population, which provides a reasonable approximation but may not fully capture the actual service coverage in this indicator. Additionally, the absence of municipal-level records on the allocation of water resources by type of demand prevented their consideration in the index, thereby lowering the scores for both municipalities. Overall, these findings highlight the need for more detailed and disaggregated data to strengthen the accuracy of resilience assessments in the VBC.

3.1.8. Hydrometeorological Technical Resilience Index (TRI)

The methodology employed to calculate the hydrometeorological resilience index for the technical component yielded a result of 59.83% for the municipality of Veracruz and 58.32% for the municipality of Boca del Río, respectively (

Table 8. Results of the Resilience Assessment of the VBC—Technical Component.

Indicator	Weight		Score		Score vs. Weight
VBC	VM	BM	VM	BM	VM	BM
1. Infrastructure	30	30	19.94	20.03	0.66	0.67
2. Planning programs and building codes	10	10	6.17	5.57	0.62	0.56
3. Risk assessments	10	10	4.53	4.53	0.45	0.45
4. Disaster Risk Reduction (DRR) plans	10	10	9.13	8.38	0.91	0.84
5. Budget assigned to emergency response	10	10	0.00	0.00	0.00	0.00
6. Institution related to DRR	10	10	5.00	5.00	0.50	0.50
7. Vital services	20	20	15.06	14.81	0.75	0.74
Total	100	100	59.83	58.32

). The mean average of these two values is 59.16%. The individual indicator ratings collectively form the hydrometeorological Technical Profile of Resilience (TPR) of the VBC, encompassing both municipalities as illustrated in Figure 2a,b.

3.2. Qualitative Component (TPR) Results

The historical disaster analysis for the VBC, based on the disaster inventory system [23], reveals a high degree of exposure and susceptibility to hydrometeorological phenomena. Flooding, heavy rainfall, storms, and strong winds constitute the primary threats defining urban vulnerability, overshadowing the historical impact of droughts. This recurrence is evidenced by 28 major storm events recorded between 1971 and 2013, which resulted in the destruction of at least 1320 dwellings and damage to over 44,227 others. The susceptibility is compounded by a pattern of severe flooding (recorded from 1979 to 2013) that has affected the Historic Center and peripheral neighborhoods, with water levels exceeding 1.5 m, leading to emergency declarations and the suspension of educational activities. The most devastating event was Hurricane Karl (2010), which destroyed at least 800 homes, damaged 40,000 additional dwellings, and caused damages totaling $25.032 billion MXN, resulting in the complete interruption of essential services, including education, transport, energy, and potable water supply [54,55]. This concentration of catastrophic events has led to systemic failures in essential infrastructure, with interruptions in energy, water, transport, and communications in 77% of rainfall-related disasters [55]. Furthermore, the high frequency of strong winds (84 events between 1981 and 2012) has caused the collapse of 70% of the VBC’s traffic lights, along with falling poles and trees, and power outages affecting approximately 25,000 people [56]. Due to the absence of accessible municipal-level records on the socioeconomic impacts of hydrometeorological phenomena from the State Civil Protection Directorate of Veracruz and the State Coordination of Civil Protection, the predominant hazard was identified qualitatively, rather than through the procedure stipulated in Indicator A (see Appendix A.8). Within this context, flooding emerges as the most recurrent and damaging hazard, not only because of its frequency but also due to its persistent impact on housing, infrastructure, and essential services [57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84]. Furthermore, since the last extreme event occurred in 2010 [85], the evaluation of recovery speed through Indicator B (see Appendix A.9) would not adequately reflect current conditions in the VBC; therefore, this indicator was not calculated.

The VBC water supply relies on the Coastal of Veracruz and Cotaxtla aquifers, which have been studied since 1985 and currently present annual deficits of −15.6 and −29.2 hm³, preventing the issuance of new concessions [86,87]. At the municipal level, Veracruz reports an estimated population of 607,209 inhabitants, while Boca del Río accounts for 144,550 [26]. This demographic concentration generates increasing demand for drinking water and sanitation services, intensifying pressure on the already overexploited aquifers. Although the municipalities report high levels of service coverage, persistent challenges such as intermittent supply, leakage, and aging infrastructure constrain effective access to water, underscoring the need for strengthened technical management and integrated strategies to ensure long-term sustainability and resilience.

In the VBC, several instruments guide land use planning [88,89,90], complementing those assessed in the technical indicator and providing a broad framework for long-term spatial and territorial development, even in the absence of the document evaluated in Table 3. At the municipal level, only one building code exists per jurisdiction [40,41], ensuring a minimum regulatory baseline. The lack of an ecological planning instrument represents a critical gap. Despite this, land use documents implicitly contribute to environmental conservation by incorporating the spatial projection of social, economic, environmental, and cultural policies [91]. However, this approach remains insufficient to anticipate and mitigate the impacts of hydrometeorological hazards, as it does not consider the integration of explicit environmental policies into urban and territorial strategies, as well as the balancing between productive activities with the protection of natural resources [92].

Based on the results of the resilience profile and the identification of institutional and infrastructural gaps, a set of targeted non-structural and structural measures are proposed to strengthen the hydrometeorological resilience of the VBC. The selected measures focus on addressing critical weaknesses identified in planning, governance, and infrastructure, while ensuring alignment with evidence-based practices in DRR and urban resilience.

Non-structural measures

• Updating the VBC Municipal Risk Atlas: The current atlases date to 2006 [29,30] and do not reflect updated information on hazards, vulnerabilities, and risks. Detailed multi-hazard maps are essential tools for planning spatially targeted interventions, as they allow authorities to reduce exposure and vulnerability while improving adaptive capacity in areas subject to multiple threats [93]. Updating this instrument would therefore strengthen territorial planning and emergency preparedness.
• Establishing a municipal historical damage database: Current records of damage to housing, infrastructure, and services remain incomplete and dispersed. While national-level databases have proven useful for identifying recurring vulnerabilities and guiding cost-effective preventive actions [94], developing this type of system at the municipal scale would provide greater spatial precision, enabling local authorities to prioritize interventions more effectively and strengthen long-term disaster management capacities.
• Developing relocation strategies for high-risk settlements: Populations living in flood-prone areas face recurrent threats to life and property. Developing clear, gradual relocation protocols aligns with established practices in managed retreat, which emphasize reducing human exposure and minimizing long-term economic losses [95].

Structural measures

• Developing a new sewage and drainage system for vulnerable neighborhoods. Frequent flooding in low-income areas is exacerbated by insufficient or deteriorated drainage. Investments in improved sewerage and stormwater management systems, such as permeable pavements and communal rainwater harvesting, have been proven to reduce exposure and improve adaptive capacity in coastal cities [96,97].
• Strengthening wind-resistant design standards: Given the exposure of coastal infrastructure to strong winds, it is essential to update design criteria for non-structural elements (e.g., light poles, signage), since the wind code currently in force for the VBC [98] focuses on civil structures and does not provide specific guidance for these components.

Together, these measures complement the quantitative index and qualitative resilience profile by providing actionable pathways for local governments. Their implementation would help address institutional fragmentation, strengthen disaster management capacities, and improve both short-term preparedness and long-term resilience of the VBC.

Finally, an overall analysis of the hydrometeorological TPR for the municipalities of Veracruz (Figure 2a) and Boca del Río (Figure 2b) reveals a remarkable morphological similarity. As a functionally integrated conurbation, they not only share exposure to the same hazards but also, despite their administrative independence, face very similar structural and normative vulnerabilities. This resemblance is quantified by the final TRI scores, which are nearly identical at 59.83% for Veracruz and 58.32% for Boca del Río (see Table 8). Collectively, the profiles demonstrate a moderate level of resilience and common systemic vulnerabilities for the conurbation.

Figure 3 shows the geographic location of the VBC and its hydrometeorological resilience index, which reached a moderate level across both municipalities. As previously outlined, the level of resilience achieved is indicative of exposure to recurring threats such as cyclones and hurricanes, heavy rains and seasonal winds, and cold fronts, which have a significant impact on both the population, critical infrastructure, governance, among other indicators.

4. Discussion

The findings of this study reinforce the global consensus that hydrometeorological resilience is a key component of sustainable urban and coastal development. Consistent with observations reported in Padang, Indonesia [99], coastal Chile [100], Halmstad, Sweden [1], and Attica, Greece [101], the VBC results highlight the importance of strengthening local adaptive capacity and institutional coordination to effectively cope with recurrent floods and storms. While studies conducted in contexts such as Halmstad [1] emphasize technical and infrastructural vulnerabilities, the VBC presents a distinct scenario: the low scores in Risk Assessments and Budget Allocation reveal that the main constraints to resilience are socio-institutional fragmentation and limited data transparency. This aligns with the need for integrative and comprehensive approaches previously noted in Padang [99]. This convergence across regions underscores that resilience should not be viewed solely as an engineering objective but as a multidimensional challenge encompassing social and governance dimensions. By providing empirical evidence from a highly exposed urban–coastal area and identifying specific institutional limitations, this research contributes to the international understanding of how governance-driven strategies can drive global efforts to improve resilience. This alignment between global evidence and the VBC case further validates the suitability of Bahena’s hybrid framework for assessing hydrometeorological resilience in data-limited and institutionally complex urban environments.

Building upon this global evidence, the VBC represents one of the most densely populated and economically active regions in the state, with a high concentration of critical infrastructure, port operations, commercial hubs and tourism services [26,102]. Consequently, hydrometeorological events have a direct and measurable impact on both local and national economies (over $25,032,000,000 MXN only in 2010). Therefore, it is essential to implement integrated strategies that enhance the region’s resilience across multiple dimensions, particularly in terms of adaptive capacity, institutional robustness and risk-sensitive urban planning. In this context, the assessment of the City Resilience Index (CRI) for the VBC provides a technical foundation for identifying targeted measures to reduce economic losses, protect vulnerable populations, and improve recovery capacity following extreme hydrometeorological events.

The CRI, built up by the TRI and TPR, were structured into a series of indicators, functions and variables, where historical records of local impacts caused by hydrometeorological hazards were also taken into consideration. Therefore, CRI was based on four fundamental characteristics (preparedness, resistance, recovery, adaptation). Consequently, the findings contribute to evidence-based decision-making in urban risk governance. Finally, as illustrated in Figure 2, it is possible to discuss areas for improvement that have the potential to impact on the preparedness, resistance, recovery, and adaptation of the VBC. For instance, preparedness can be augmented through the implementation of contemporary and efficacious preventive measures. These include formal early warning systems, pre-allocated budgets, the availability of trained personnel, and systematic documentation of identified threats and localized risk characterizations. In terms of promoting resilience, this characteristic necessitates investment in ongoing maintenance, periodic structural monitoring, or tangible reinforcement of critical infrastructure, including hospitals, schools, and essential services, with a view to strengthening them. The establishment of documented and functional protocols is imperative for the expeditious restoration of essential services and operations in the case of disruptive events. This necessitates emergency budgets and the establishment of trained response teams. Adaptation can be defined as a process that requires updated regulatory instruments, clearly defined plans, and strategic processes based on experience, leading to accurate diagnoses of future climate projections.

This paper provides valuable insights into urban resilience in relation to a specific case and hazard type. However, further research is required to explore its applicability to other geographic contexts, hazard profiles and governance systems. However, the integration of a comparative perspective drawing on international frameworks, such as the Sendai Framework for Disaster Risk Reduction and ISO 37123 [21,103,104], would be a valuable improvement. When considering cross-cutting themes, it is important to consider institutional capacity, multi-hazard planning and socio-spatial vulnerability. This would position the study not only as a local assessment tool but also as a reference point for cities facing diverse hazards.

5. Conclusions

The main objective of this research was to evaluate urban resilience to hydrometeorological threats using a set of indicators considering four key characteristics: resistance, adaptation, recovery, and preparedness. The hydrometeorological resilience evaluation is based on the methodology proposed by Bahena [19], based on a dual-tool framework (TRI and TPR). The TRI provides a clear overview of the current resilience conditions of the VBC in the face of recurrent hydrometeorological hazards. Veracruz obtained a score of 59.83, while Boca del Río achieved a score of 58.32, with both corresponding to a moderate level of resilience (see Figure 3). The study identified the strengths and weaknesses in terms of institutional, physical, and social performance of the VBC based on results examined indicator by indicator, where several critical areas emerge that require attention to enhance the municipalities’ capacity to cope with extreme events.

Among the indicators, a score–to–weight ratio below 40% can be considered insufficient for resilience performance. According to this criterion, based on the TPR results, four out of the seven indicators exceeded 60% of their assigned contribution, demonstrating acceptable performance in several critical dimensions of resilience. The strongest contributions relative to their assigned weight came from DRR Plans, with 66% in Veracruz and 67% in Boca del Río, and Vital Services, with 75% and 74%, respectively. Conversely, the lowest scores were noted in Risk Assessments (45% in both municipalities) and Budget Assigned to Emergency Response, which received a 0% rating across both cases. The latter indicates the absence of municipal-level records on financial resources allocated to emergency response plans. The establishment of such records could substantially raise the contribution of this indicator.

This study demonstrates that resilience must not be understood solely as post-disaster response capacity, but rather as a multidimensional attribute supported by functional infrastructure, updated regulatory frameworks, effective territorial planning, and governance grounded in prevention. In this regard, transparency plays a key role, since the dispersion and limited availability of municipal data constrained the accuracy of the evaluation and likely reduced the final index values. Even so, the main contribution of this work is to provide the first systematic assessment of hydrometeorological resilience in the VBC. This baseline not only highlights the dimensions where governmental and institutional action should be prioritized but also underscores the persistent challenge posed by limited access to socioeconomic data of direct relevance for resilience assessment studies.

Given that this research represents an initial intervention and a preliminary attempt to assess hydrometeorological resilience in the VBC, several lines of future inquiry are suggested. First, subsequent evaluations should aim to complement the present analysis by obtaining missing information or refining approximate data across the indicators. In addition, it is recommended to systematically collect information on the economic resources allocated to the management of different hydrometeorological threats, which would enable a more quantitative identification of the predominant hazard. Second, although Bahena’s methodology [19] stipulates that recovery speed must be assessed using the most recent extreme event, this was not feasible for the VBC since the last major event (Hurricane Karl) occurred in 2010 and does not adequately reflect the current recovery capacity [85]. Nevertheless, incorporating such historical events in future applications would still provide valuable benchmarks, even if they do not capture present-day conditions. It is also recommended that future studies apply large-scale data-driven approaches, such as the Entropy Method, to generate a resilience index from a broader methodological perspective, thereby deepening the analysis and offering complementary insights. Finally, it is recommended that this methodological approach be carried out in other municipalities where no study has been conducted to assess resilience to hydrometeorological phenomena. From a theoretical point of view, this study will allow for the drawing of conclusions about its application in international contexts with socioeconomic and geographical characteristics that differ from those of the VBC. In practical terms, the results provide a valuable contribution to the development of risk management strategies, territorial planning, and institutional strengthening. Finally, regarding the lack of certain municipal-level data; the use of approximate values in critical indicators; and the absence of updated records on economic resources and recovery speed assessments, applicability to other geographic contexts, hazard profiles, and governance systems, rather than presenting these issues as shortcomings, they are presented as opportunities for further work.