Flooding is a significant problem across the United States, both in terms of contribution to overall risk (Doocy et al., 2013; Summers et al., 2017) and recovery costs (ASFPM, 2013). Four confounding trends are increasing the impact of flooding: changing occurrence and intensity of storm events (Milly et al., 2008), increased urbanization (Hollis, 1975; Shuster et al., 2005), increased value of assets vulnerable to flooding (Jongman et al., 2012), and decreased stormwater mitigation due to loss of green infrastructure (Ahern, 2007).

Green infrastructure refers to the systems that provide services to people—these systems can be managed to varying degrees from fully constructed systems, such as rain gardens, to existing, “natural” ecosystems, like floodplain wetlands (Burgess, 2015). The ability of wetlands to function by attenuating flood waters depends primarily on their type, condition, and landscape position (Brinson, 1993; Bullock and Acreman, 2003; Acreman and Holden, 2013; Kadykalo and Findlay, 2016). When a wetland attenuates flood water it has the potential to provide an ecosystem service, in the form of flood-reduction services. Despite the critical role wetlands play in the landscape, their extent in the contiguous United States has declined by more than half since colonization (Dahl, 1990). Most freshwater wetland losses are due to historic drainage to convert those areas to productive agricultural lands (Dahl and Allord, 1996) and continuing urban development (Stein and Ambrose, 1998; Kelly, 2001).

Recognizing both the magnitude of wetland losses, and the resulting loss in the vital ecosystem services those wetland systems supplied, national regulatory policies and community grassroots actions have focused on protecting remaining wetlands and restoring wetlands where feasible (Dahl and Allord, 1996). Federal programs include the Federal Emergency Management Agency (FEMA) Community Rating System, an incentive-based program that credits preservation or restoration of wetlands in floodplains, and the Department of Agriculture's Conservation Reserve Program, which funds wetland restoration projects nationally (USDA, 2015; FEMA, 2017). Community organizations restore, protect, and otherwise manage wetland resources through a variety of mechanisms, including mitigation banks, acquisitions, and easements (Environmental Law Institute, 2012).

With numerous protection or restoration candidate sites and limited funding, practitioners have developed many strategies to prioritize sites for restoration. Several states have developed their own tools or assessments, such as Rhode Island's adaptation of a rapid assessment tool (Miller and Golet, 2001), Oregon's Wetland Restoration Planning Tools¹, or Wisconsin's Wetlands and Watersheds Explorer (Miller et al., 2017). Most existing wetland assessment methods focus on wetland functions. Where methods do address benefits, it is typically in the form of a judgment regarding the “social significance” of each function (see King et al., 2000 and King and Price, 2004 for more on these approaches). Healy and Secchi (2016) reviewed seven ecosystem service modeling and decision support tools (e.g., Invest; Nelson et al., 2009) for their ability to evaluate different wetlands restoration scenarios. Only the ARIES model (Bagstad et al., 2014), incorporated benefits of inland flood-reduction services. However, the approach required semi-intensive modeling making it difficult to apply as a screening tool or at broader scales. Economic valuation studies are another approach that evaluate tradeoffs between alternative restoration sites by placing dollar values on wetlands or specific services of wetlands (Brander et al., 2006, 2013; Ghermandi et al., 2010). However, transferring the results of previous studies quickly and consistently across national or regional scales is difficult, because values are context and location-specific, requiring additional information for benefit transfer and use in local decision making.

The Rapid Benefit Indicators (RBI) approach (Mazzotta et al., 2016, 2018) attempts to directly capture the potential benefits to people from wetland restoration or protection efforts. It outlines an indicator set for prioritizing wetlands within a watershed informed by how people interact with, benefit from, and value the wetlands.

This paper details how we assembled a national dataset of spatial indicators developed through the RBI approach. We demonstrate how to use the developed indicators to inform decisions on wetland restoration and conservation priorities at multiple scales. Locations where there is demand for flood-reduction services by populations in potentially flooded areas downstream but there is limited supply of those services from wetlands upstream indicates restoration priorities. Locations where people currently benefit from reduced flooding through existing wetlands indicate conservation priorities.

The Rapid Benefit Indicators (RBI) approach is a beneficiary-centric, non-monetary way to quickly compare restoration sites using indicators that address several questions related to the aspects of benefits that make them valuable. Spatial tools developed to apply the RBI approach answer these questions by assessing indicators for the area surrounding potential restoration sites from spatial datasets (Bousquin et al., 2017). Most of the spatial input datasets have suggested national defaults for use where locally relevant data are not available. We developed a new national dataset as a more accessible alternative to the spatial analysis tools. The dataset has the added benefit that practitioners can use it at multiple scales. The new dataset is specific to flood-reduction services and answers two of the RBI questions.

The first question addressed here is: how many people benefit? In contrast to some other benefits of freshwater wetlands, flood-reduction is particularly dependent on the spatial and hydrological characteristics of the landscape, because sites providing flood-reduction services benefit people some distance downstream. The value of flood-reduction services is highly dependent on the number of people who benefit as well as the value and level of vulnerability of assets protected from flooding. During a storm event people benefit from flood-reduction services in the form of flood damage reductions. When planning for future storms, the benefit of future flood-reduction services can take the form of flood-risk reductions. Here, we use the term flood-reduction benefits to refer to both benefits interchangeably. The number of people downstream who are currently exposed to flooding and may experience flood-reduction benefits after site restoration indicates how many people benefit. For simplicity this will be referred to as the demand indicator.

The other question addressed here is: by how much do people benefit? This question has four parts, however the only one addressed here is service scarcity. For flood-reduction services, people downstream may already be receiving benefits from existing wetlands or gray infrastructure, such as dams and levees. Where there are fewer substitute sources of flood-reduction services, those services are scarcer and the value of flood-reduction services from a restored site is greater. The prevalence of wetlands upstream provides an indication of the existing flow of benefits. For simplicity this will be referred to as the scarcity indicator.

The national dataset we developed characterizes two metrics: the population in flood-prone areas and the prevalence of existing wetlands. However, the spatial relationship of a restoration site, either upstream or downstream of these landscape characterizations is what identifies the beneficiaries or service scarcity. We used the perspective of a practitioner evaluating a potential restoration site to frame the spatial relationship. From that perspective the indicators are (1) downstream beneficiaries, i.e., how many people are in flood-prone areas that would benefit from restoring the site, and (2) local scarcity, i.e., how many wetlands are already supplying the same flood-reduction services. The alternative perspective is that of a potential beneficiary and could be explored using the same national dataset and methods (see Supplemental Materials). From that perspective the indicators would be (1) local beneficiaries and (2) upstream scarcity.

Another requirement for this national dataset was scalability. When a county, municipality, watershed manager, or other community decision maker is trying to prioritize wetlands restoration or conservation projects on the ground, the data used to inform that decision needs to be high resolution and aggregated to a scale appropriate to discern differences within that management area. Both regional- and local-scale decision makers need to be able to use the dataset to prioritize restoration or conservation. We developed the national dataset for use at a local-scale but demonstrate a method for aggregating information for use in regional prioritization.

This study presents results from national, regional, and local analysis. At the national scale, results focus on catchment characterizations. At the regional scale, results focus on the same characterizations but aggregated to HUC-8 for the Gulf of Mexico. At the local scale, results include catchment landscape characterizations, upstream/downstream characterizations, and quartile designations. We use an example application in Harris County, Texas to demonstrate how practitioners could use local-scale data for restoration and conservation decisions.

Datasets have previously characterized NHDPlusV2 catchments using census data (Hill et al., 2016). Those have been with the intended purpose of characterizing potential stressors on the landscape. Here the intended purpose was to define human beneficiaries for ecosystem services. The new dataset generated by this work was for this express purpose, and results show the utility of the dataset for characterizing a specific aspect of wetland restoration and conservation prioritization.

Characterizing NHDPlusV2 catchments with the two indicators, wetlands and population in flood-prone areas, allowed analysis and visualizations for decision-making at a variety of scales. The connection between catchments and the WBD allowed for aggregation to HUC-8, making it easier to see regional trends. The upstream and downstream relationships in the NHDPlusV2 allowed connections between wetlands, the flood-reduction services they provide, and the population in flood-prone areas downstream.

The two indicators we developed here may inform different decisions. The local scale example in Harris County, Texas showed how practitioners might prioritize restoration sites in the county to increase flood-reduction services where people need them most. The demonstration also showed how practitioners might prioritize existing freshwater wetlands for conservation to help ensure maintenance of important flood-reduction services. At this localized scale, we considered catchment data alongside other risk and socio-demographic indicators characterized for county and census-tract jurisdictional boundaries. The low natural infrastructure domain score in CRSI for Harris County identified the county as a good candidate for ecosystem restoration to increase resilience (Summers et al., 2017). Decision makers could use the indicators developed here to prioritize restoration projects to increase that domain score and increase resilience to flood events. Decision makers could also compare catchments identified as priorities for restoration or conservation in comparison to downstream demographics-based characterizations such as SoVI (Cutter et al.), to further differentiate specific catchments or projects as higher priority.

Actual restoration decisions should consider priority catchments identified here alongside additional characterizations of project feasibility and restorable wetland function. It is important to remember the limits of the prioritization method presented here. The method evaluates only two aspects, how many people may benefit and scarcity, of only one type of ecosystem service, flood-reduction. Our hope is that characterizing our metrics for NHDPlusV2 catchments and aggregating to HUC-8 will make it easier to integrate results with other datasets (e.g., EnviroAtlas; Pickard et al., 2015) or indicator frameworks (e.g., Recovery Potential Screening Tool; Norton et al., 2009) that provide additional information on wetland function and restoration feasibility at larger watershed scales.

In the example indicator application, we made several assumptions that may need adjustment depending on the context of the local decision. The first was the choice to use USEPA flood model results in lieu of FEMA flood zones. The demonstration here used the flood model results, because those are more comprehensive and therefore are more generally applicable. However, the FEMA flood zones are more specific to riverine flooding, better account for existing gray infrastructure, and should be more accurate where available. Characterizing the population in both these flood-prone areas allows future users to choose the flood definition that best suits their location and decision contexts. Both definitions for flooding rely on modeling and may not accurately represent where actual flood risk is highest (Batker et al., 2010; Yang et al., 2010).

The analysis here only considered the population in flood-prone areas, but uninhabited structures and infrastructure are exposed to flooding as well. The addition of these built environment assets may change restoration and conservation priorities. In their more localized RBI application, Mazzotta et al. (2018) used point datasets defining addresses for individual structures rather than population counts. Substituting actual flood occurrence data would be preferable where available.

The distance flood-reduction services flow downstream will impact where upstream freshwater wetland restoration and protection priorities are. In different parts of the United States and during different storms this distance will likely vary, and it is important to consider both local hydrologic characteristics and objectives. The brief sensitivity analysis performed here showed how changes in the downstream extent of flood reduction service flows may alter estimates of the number of people those services benefit, potentially altering priorities. However, the aggregation of datasets to NHDPlusV2 catchments with upstream and downstream relationships allows for these assumptions of downstream distances to be more easily and quickly manipulated.

The choice of method for binning combined indicators—in our case, based on sub-region quartiles—greatly influences the resulting prioritization. It is important to consider local conditions and, potentially, supplemental information when translating these metrics into indicators for prioritizing projects. For example, Harris County has a dense population compared to most of the Texas 12a sub-region, meaning a disproportionate number of catchments will be higher priority (i.e., have population in flood-prone areas) than in other less populated parts of the sub-region. Further, using a statistical distribution such as quartiles in prioritization does not account for potential thresholds. For example, in our Texas 12a sub-region demonstration the demand indicator (population in flood-prone areas) threshold between medium (Priority B) and low (Priority C) priorities was 5 people. In an actual decision, local context will dictate the required number of people serviced by a project for it to be a priority.

This analysis is very timely as Harris County voters recently approved 2.5 billion in bonds for flood risk reduction projects throughout the county and many of those proposed projects are nature-based, including wetlands restoration³. Benefit assessments can help inform decisions on where to prioritize for flood-reduction benefits and who might benefit from proposed projects. Decision makers can use national datasets such as the one generated here to make these benefit assessments more feasible and adapt them to fit the needs of a variety of different communities.

JB: conceptualization, planning, data acquisition and processing, analysis, interpretation and writing. KH: planning, drafting introduction, and interpretation of results. All authors contributed to manuscript revision, read and approved the submitted version.