Data & Risk Assessment
States must understand what is at risk before they invest in flood adaptation. The first step in conducting a risk assessment is identifying and collecting the correct data. Such data will be used to develop and carry out a strong plan for managing flood risk.
Plans based on good data enable states and communities to make smarter development decisions and build infrastructure that will last. Plans based on outdated, incomplete, or inaccurate data, however, may waste valuable public resources.
Key data for risk assessments include:
Bathymetry: The underwater geography of rivers, lakes, and oceans, which affects how water flows.
Topography: Elevation data of physical landforms of an area, often gathered by LiDAR (light detection and ranging), which affects how water flows.
Rainfall: Historical records of precipitation amount and duration, gathered from monitoring stations.
Design Storm: Estimates of precipitation amount, calculated at a particular location, for a given amount of time, and of a certain intensity (known as an intensity-duration-frequency curve). This estimate, which can be used to determine a flood hazard map, should be for projections of at least 30 years.
Sea Level Rise: Estimate of the high and low tide point, projected at least 30 years, which can be used to map coastal flood hazards and groundwater conditions.
Building Stock: The location and physical attributes of residential, government, commercial, and other built structures, which can be used to determine what is at risk.
Critical Infrastructure: The location and physical attributes of critical infrastructure used to assess whether infrastructure is at risk of flooding; key critical infrastructure assets include bridges, stormwater pipes, and levees–which can affect how water flows– as well as electrical, communications, and water treatment facilities.
Social Vulnerability: Data on factors that may weaken a community’s ability to prevent human suffering and financial loss from a hazard; such data can include economic status, racial and ethnic demographics, age, and gender.
Regulatory Data: Local (or regional) regulatory buffers, overlays, or other features that prohibit or restrict activity in flood-prone areas.
Local Planning Data: Comprehensive master plans, zoning, planned capital improvement plans, and similar documents that provide guidance on how a community wants to develop.
A flood risk assessment looks at the likelihood of a hazard (e.g., a flood) combined with a population’s vulnerability and exposure (or, how that flood will affect people and property).
While flood hazard maps estimate the boundary and depth of a flood, flood risk assessments combine these maps with building stock and social vulnerability data to determine who and what will be affected by a flood. Because risk is the combination of likelihood and consequence, taking proactive measures based on this risk can better prepare communities for floods.
For example, elevated buildings in a floodplain have a lower flood risk than surrounding buildings at ground-level. While the likelihood of a flood remains the same for all structures in the floodplain, the consequence is drastically lower for those buildings built above the base flood elevation. Holistic risk assessments enable states to develop and carry out data-driven plans for long-term flood resilience.
HYDROLOGIC & HYDRAULIC MODELING
Two important sciences make up flood risk modeling:
Hydrology: the science of the occurrence, distribution, movement, and properties of water
Hydraulics: the evaluation of the physical interaction of water with various systems (such as culverts, pipes, rivers, canals, open channels, pumps, structures, and bridges).
In other words, hydrology helps us understand where water is, and hydraulics tells us where water will go.
Hydrologic & hydraulic (H&H) modeling is computer-based flood modeling that includes hydrologic and hydraulic simulations. This modeling tends to focus on the relationship between rainfall and runoff, but it can cover virtually all aspects of the hydrologic cycle. Such modeling is generally conducted by civil and environmental engineers, hydrogeologists, and hydrologists and can be used for the design of water infrastructure, analysis of natural systems and rivers, and various regulations. Planners also use this class of modeling to map floodplains, including many aspects of FEMA’s floodplain maps.
Because flooding follows watershed dynamics and ignores jurisdictional boundaries, states should model flooding at the watershed level. To understand and compare flood risk across watersheds, states must take a standardized large-scale watershed approach to modeling, mapping, and using risk information to make decisions. As states often include multiple large watersheds, state governments are best positioned to ensure coordination of flood risk assessments that use a consistent methodology across different types of modeling (e.g., inundation modeling, H&H modeling).
Data collection, modeling, mapping, and risk assessment can be extremely cost and labor intensive. Without state governments taking the lead, low-resourced communities cannot afford such processes. By standardizing these processes and making raw data and modeling outputs available to communities, states can reduce the cost and time of local planning and engineering, helping build local capacity and more equitable flood resilience across the state.
To help states improve data collection and risk assessment, the State Resilience Partnership created the Flood Risk Data Collection & Assessment Framework, organized into four topics:
Each topic is crucial to building a mature flood risk data collection and risk assessment process, but states can choose the order in which they implement them.
Within each topic are four building blocks that increase in complexity as states improve data collection and risk assessments. These building blocks can be applied to any state, but individual regions with unique hydrologies or governance structures may need additional tasks not included here.
Collecting data and assessing risk can be technical and hard to navigate for those not trained in modeling, engineering, or geographic information systems (GIS). The following recommendations can help states avoid common errors that governments make when approaching flood data and modeling.
Avoid having no or poor data standards, even if developing these data standards causes schedule delays.
Establish a peer review process to ensure model quality.
Build a process that values public engagement and input.
Properly incorporate future data and/or conditions.
Use models that are tested against reality.
Select scenarios and storms early in the modeling process to avoid generating too much data and information.
Selecting the wrong model can lead to many difficulties, including a decreased useful life of the model or data, budget issues, schedule delays, and stakeholder disputes.
Understand how modeling fits into the broader flood resilience framework.