Floods are the most impactful natural hazard to human infrastructure. However, they remain inevitable, so societies must adapt to them to reduce their outcomes. On the other hand, despite current satellite monitoring efforts worldwide, a lack of scalable analytics solutions and gaps in data availability limit their usefulness. That is why it is extremely urgent to improve impact estimations, in order to adapt urban planning to effectively handle floods. [1]

We aim to achieve Target D of the United Nations Office for Disaster Risk Reduction (UNISDR): “Substantially reduce disaster damage to critical infrastructure and disruption of basic services, among them health and educational facilities, including through developing their resilience by 2030”, referred to floods. The methodology, for monitoring global progress on this Target, constructs some indicators that measure damage over critical infrastructure, basic services disruptions and direct economic loss, attributed to disasters. In addition, they are related to the size of the population of each country, so as to reflect the relative importance of these disruptions and damages. The Index of Critical Infrastructure Damage (D1), the Index of Service Disruption (D5) and the Index of Direct Economic Loss resulting from Damaged or Destroyed Critical Infrastructure (C5) are computed as follows. [2]

D1 := number of infrastructure units and facilities damaged / population * 100,000

D5 := number of disruptions occurred / population * 100,000

C5 := Sum of direct economic loss of buildings, linear structures and other costs

Although these indicators try to reduce the amount of data required for measurement, today's national databases reports are highly biased, because their main source of information are the testimonies of eyewitnesses and rescuers. Therefore, a computing tool that takes advantage of available satellite images could process and provide more reliable information on a large scale and help governments make decisions.

The proposed development process for this tool consists of three stages, each one more advanced than the previous one: 1- study the impact of a flood in real time over a specific region, by measuring the rising level of the water; 2- analyze the historical record to estimate the effect of different levels of floods and their probability of occurrence; 3- calculate the direct and indirect economic impact of a flood, to determine which prevention and urban planning measures are the most appropriate to mitigate the damages that may occur on a future flood event. It is important to say that the last stage requires data from the economic matrix that is not necessarily freely accessible. However, as it is a tool to be used by governments, they could provide this information to complete the analysis.

The basic information required by the tool will be a map of the region to be analyzed, paying special attention to the critical infrastructure locations, essential services facilities, protective infrastructure, green infrastructure and topographic features. This can be obtained using satellite images and taking advantage of programs such as Google Maps [3]. Continuous monitoring will also be necessary to determine when the water level rises. ESA’s Copernicus Program (in particular Sentinel-1 Mission [4]) performs a real-time precipitation analysis. It is able to detect floods and estimate their intensity. NASA's GMFS [5] could be leveraged simultaneously, in order to reduce spatial and temporal limitations. Once the tool has all this information, it would be able to evaluate the scale of a flood and estimate the cost.

Now let's take a look at the different stages of project development.

1. First stage: Study the impact of a flood in real time over a specific region by measuring the rising level of the water. To determine the level of flooding, Sentinel-1 compares the images during the flood event with previous images of the area when it is not flooded. Then, it provides an image with a mask where the flooded areas are highlighted. Thus, areas with greater water depth will have more intense colors. With this information the tool will be able to estimate the economic valuation of damage by applying damage-depth functions. As shown by Orozco [6], we can develop a model that analyzes the exposure and fragility of goods and structures, based on an hydrologic model of the flooding area. Consequently, the tool can estimate costs based on the degree of degradation of each element and sum them up to obtain a total cost generated by the flood event.
2. Second stage: Analyze the historical record to estimate the effect of different levels of floods and their probability of occurrence. Although the real-time estimation generated in the first stage would allow us to determine the costs of a single event more reliably, it wouldn't be useful for forecasting future events by itself. Therefore, the next logical step in this tool development would be to include the ability to forecast future floods. By using the historical records, we can generate a scheme of floods occurrence (the greater floods will also have a greater return period). Thus, it is possible to forecast the probability of occurrence of events over a period of time and simulate a model of flood damage, so the total mean flood damage can be estimated. Once this mean damage over a period of time is known, it is possible to determine the benefits of various flood mitigation measures. [6]
3. Third stage: Calculate the direct and indirect economic impact of a flood, to determine which prevention and urban planning measures are the most appropriate to mitigate the damages that may occur on a future flood event. The last stage of development of the project would include the effects generated by the flood on the entire economy of the region. Thus, the analysis would use data from the complete economic matrix and would include the alterations produced by the flood in the demand vector, as Mendoza Tinoco [7] explains. The total economic impact of the event will be the sum of the direct costs and the indirect costs generated during each period of the recovery process. The indirect cost would be calculated as the accumulation of the differences between the production level before the disaster and the restricted production after the disaster in each period. Finally, the results can be broken down by sector to assess the impacts and adopt a mitigation plan that optimizes the allocation of resources to minimize damage and recovery time. It is important to say that this methodology could be applied both to floods in real time and to simulations to facilitate decision-making for the prevention of future events. This last stage would require data that is not necessarily freely accessible, but as this tool would be used by governments, they could provide this information to complete the analysis.

In conclusion, the proposed solution would take advantage of real-time satellite images to estimate the distribution and magnitude of a flood in a specific region, in order to estimate the costs generated by it. In addition, by developing the later stages, it would be able to simulate future events, estimate the damages, their impact on the economy and help to make decisions to mitigate the consequences of the floods.

The later stages carry an important added value, since they provide tools to facilitate planning. In particular, the third stage includes the analysis of indirect costs, which allow a more efficient allocation of resources when taken into account. However, it is not known which categories have been neglected when determining indirect costs, so it is always advisable to provide a range of upper and lower limits to the estimations.

Finally, it is important to highlight the emphasis made on economic estimation because, despite genuine interest in mitigating the effects of floods, stakeholders will be interested that the benefits obtained from flood prevention measures should at least exceed the costs involved. They will also consider investing in optimal flood prevention strategies where the marginal benefits equal the marginal costs. This is why free access to the information required for this type of tool should be ensured by the different agencies that provide it.