Optimizing wetland restoration to improve water quality at a regional scale

Published by IOP Publishing Ltd. Excessive phosphorus (P) export to aquatic ecosystems can lead to impaired water quality. There is a growing interest among watershed managers in using restored wetlands to retain P from agricultural landscapes and improve water quality. We develop a novel framework for prioritizing wetland restoration at a regional scale. The framework uses an ecosystem service model and an optimization algorithm that maximizes P reduction for given levels of restoration cost. Applying our framework in the Lake Champlain Basin, we find that wetland restoration can reduce P export by 2.6% for a budget of $50 M and 5.1$200 M. Sensitivity analysis shows that using finer spatial resolution data for P sources results in twice the P reduction benefits at a similar cost by capturing hot-spots on the landscape. We identify 890 wetlands that occur in more than 75% of all optimal scenarios and represent priorities for restoration. Most of these wetlands are smaller than 7 ha with contributing area less than 100 ha and are located within 200 m of streams. Our approach provides a simple yet robust tool for targeting restoration efforts at regional scales and is readily adaptable to other restoration strategies

Optimizing investments in national-scale forest landscape restoration in Uganda to maximize multiple benefits

Forest loss and degradation globally has resulted in declines in multiple ecosystem services and reduced habitat for biodiversity. Forest landscape restoration offers an opportunity to mitigate these losses, conserve biodiversity, and improve human well-being. As part of the Bonn Challenge, a global effort to restore 350 million hectares of deforested and degraded land by 2030, over 30 countries have recently made commitments to national forest landscape restoration. In order to achieve these goals, decision-makers require information on the potential benefits and costs of forest landscape restoration to efficiently target investments. In response to this need, we developed an approach using a suite of ecosystem service mapping tools and a multi-objective spatial optimization technique that enables decision-makers to estimate the potential benefits and opportunity costs of restoration, visualize tradeoffs associated with meeting multiple objectives, and prioritize where restoration could deliver the greatest benefits.Wedemonstrate the potential of this approach in Uganda, one of the nations committed to the Bonn Challenge. Using maps of the potential benefits and costs of restoration and efficiency frontiers for optimal restoration scenarios, we were able to communicate how ecosystem services benefits vary spatially across the country and how different weights on ecosystem services objectives can affect the allocation of restoration across Uganda. This work provides a generalizable approach to improve investments in forest landscape restoration and illuminates the tradeoffs associated with alternative restoration strategies.UKAid from the UK government through the International Union for Conservation of Nature’s KnowFor program as well as by the Natural Capital Project, a partnership between the University of Minnesota, Stanford University, the World Wildlife Fund, and the Nature Conservancy. MG was supported by the National Research Foundation of South Africa (Grant Number 98889).http://http://iopscience.iop.org1748-9326am2017Plant Scienc

Federal incentives for community-level climate adaptation: an evaluation of FEMA’s Community Rating System

In response to growing threats of climate change, the US federal government is increasingly supporting community-level investments in resilience to natural hazards. As such federal programs become more widespread, evaluating their efficiency and equity is essential. The Community Rating System (CRS), which is part of the National Flood Insurance Program (NFIP), is a promising example of a federal policy designed to reduce flood losses by providing financial incentives for local climate adaptation. In exchange for community engagement in a range of risk communication and risk reduction activities, CRS provides discounts on NFIP premiums ranging from 5% to 45%. Using national-scale NFIP claims, policies, and CRS data between 1998 and 2020, we assess the program, asking whether it has been effective in reducing flood losses, how it can be improved, and what lessons it holds for similar programs. We find that participation in CRS is associated with reduced flood damage, with the percent reduction in claims roughly proportional to NFIP premium discounts. Among CRS activities, those related to ‘Flood Damage Reduction’ are most effective in reducing flood losses and are associated with a 20%–30% decrease in NFIP claims. Between 1998 and 2020, cumulative damage reductions attributable to CRS were $11.4 billion; over the same period, cumulative NFIP premium discounts were$12.1 billion. This close match is an endorsement of CRS historically and supports its future continuation. To improve the efficiency and equity of CRS, we recommend that Federal Emergency Management Agency: (a) reexamine the surcharge levied on NFIP premiums that cross-subsidizes premium discounts, and (b) allocate greater resources towards supporting participation among smaller, under-resourced communities. In general, CRS serves as an effective model for other federal market-based programs seeking to stimulate community-level investment in climate resilience

Improving flood hazard datasets using a low-complexity, probabilistic floodplain mapping approach.

As runoff patterns shift with a changing climate, it is critical to effectively communicate current and future flood risks, yet existing flood hazard maps are insufficient. Modifying, extending, or updating flood inundation extents is difficult, especially over large scales, because traditional floodplain mapping approaches are data and resource intensive. Low-complexity floodplain mapping techniques are promising alternatives, but their simplistic representation of process falls short of capturing inundation patterns in all situations or settings. To address these needs and deficiencies, we formalize and extend the functionality of the Height Above Nearest Drainage (i.e., HAND) floodplain mapping approach into the probHAND model by incorporating an uncertainty analysis. With publicly available datasets, the probHAND model can produce probabilistic floodplain maps for large areas relatively rapidly. We describe the modeling approach and then provide an example application in the Lake Champlain Basin, Vermont, USA. Uncertainties translate to on-the-ground changes to inundated areas, or floodplain widths, in the study area by an average of 40%. We found that the spatial extent of probable inundation captured the distribution of observed and modeled flood extents well, suggesting that low-complexity models may be sufficient for representing inundation extents in support of flood risk and conservation mapping applications, especially when uncertainties in parameter inputs and process simplifications are accounted for. To improve the accuracy of flood hazard datasets, we recommend investing limited resources in accurate topographic datasets and improved flood frequency analyses. Such investments will have the greatest impact on decreasing model output variability, therefore increasing the certainty of flood inundation extents

Projected losses of ecosystem services in the US disproportionately affect non-white and lower-income populations

Social inequalities may be reflected in how ecosystem services are distributed among groups of people. Here the authors estimate the distribution of three ecosystem services across demographic and socioeconomic groups in the US between 2020 and 2100, finding that non-white and lower-income groups disproportionately bear the loss of ecosystem service benefits

Increasing decision relevance of ecosystem service science

The ecosystem service (ES) community aspires to illuminate how nature contributes to human well-being, and thereby elevate consideration of nature in decision making. So far, however, policy impact of ES research has been limited. To understand why, we identify five key elements of ES research that help inform decisions by connecting the supply of ES to those who benefit from them. Our structured review of the ES literature reveals that only 13% of assessments included the full ES chain from place to value. Only 7% of assessments considered the distribution of ES benefits explicitly across demographic or other beneficiary groups (for example, private landowners versus the broader public), although disaggregation across regions or spatial units was more common (44%). Finally, crucial mediating factors that affect who benefits and how (for example, the vulnerability of beneficiaries or the availability of substitutes for ES) were considered in only 35% of assessments. Our results suggest that increasing the decision relevance of ES research requires more effectively predicting the impacts of specific decisions on the value and distribution of ES across beneficiary groups. Such efforts will need to integrate ecological models with socioeconomic and cultural dimensions of ES more closely than does the current ES literature