Quantifying the social benefits and costs of reducing phosphorus pollution under climate change.

Excess phosphorus loading to waterbodies has led to increasing frequency and severity of harmful algal blooms, negatively impacting economic activity and human health. While interventions to improve water quality can create large societal benefits, these investments are costly and the value of benefits is often unknown. Understanding the social and economic impacts of reduced phosphorus loading is critical for developing effective land use policies and for generating public and political support for these initiatives. Here, we quantify the social benefits and costs of improving water quality in Lake Champlain under a range of phosphorus reduction and climate change scenarios between 2016 and 2050. We use statistical models to link water quality outputs from an established integrated assessment model with three categories of benefits: tourism expenditures, property sales, and avoided human health impacts. We estimate the costs of reducing phosphorus loading using data reported by the State of Vermont. We find that under the most aggressive phosphorus reduction scenario, the total benefits of improved water quality are $55 to $60 million between 2016 and 2050. Over this 35 year time horizon, the combined benefits do not outweigh the costs under any scenario. If the time horizon is extended to 2100 or beyond, however, the benefits may exceed the costs if the applied discount rate is less than 3%. Importantly, we almost certainly underestimate the value of clean water, due to the omission of other types of benefits. Despite this uncertainty, our study provides a tractable framework for disentangling the complex relationships between water quality and human well-being, and illuminates the value of reductions in phosphorus loading to society.

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

Addressing how ecosystem services (ES) are distributed among groups of people is critical for making conservation and environmental policy-making more equitable. Here, we evaluate the distribution and equity of changes in ES benefits across demographic and socioeconomic groups in the United States (US) between 2020 and 2100. Specifically, we use land cover and population projections to model potential shifts in the supply, demand, and benefits of the following ES: provision of clean air, protection against a vector-borne disease (West Nile virus), and crop pollination. Across the US, changes in ES benefits are unevenly distributed among socioeconomic and demographic groups and among rural and urban communities, but are relatively uniform across geographic regions. In general, non-white, lower-income, and urban populations disproportionately bear the burden of declines in ES benefits. This is largely driven by the conversion of forests and wetlands to cropland and urban land cover in counties where these populations are expected to grow. In these locations, targeted land use policy interventions are required to avoid exacerbating inequalities already present in the US.

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.

The climate sensitivity of carbon, timber, and species richness covaries with forest age in boreal-temperate North America.

Climate change threatens the provisioning of forest ecosystem services and biodiversity (ESB). The climate sensitivity of ESB may vary with forest development from young to old-growth conditions as structure and composition shift over time and space. This study addresses knowledge gaps hindering implementation of adaptive forest management strategies to sustain ESB. We focused on a number of ESB indicators to (a) analyze associations among carbon storage, timber growth rate, and species richness along a forest development gradient; (b) test the sensitivity of these associations to climatic changes; and (c) identify hotspots of climate sensitivity across the boreal-temperate forests of eastern North America. From pre-existing databases and literature, we compiled a unique dataset of 18,507 forest plots. We used a full Bayesian framework to quantify responses of nine ESB indicators. The Bayesian models were used to assess the sensitivity of these indicators and their associations to projected increases in temperature and precipitation. We found the strongest association among the investigated ESB indicators in old forests (>170 years). These forests simultaneously support high levels of carbon storage, timber growth, and species richness. Older forests also exhibit low climate sensitivity of associations among ESB indicators as compared to younger forests. While regions with a currently low combined ESB performance benefitted from climate change, regions with a high ESB performance were particularly vulnerable to climate change. In particular, climate sensitivity was highest east and southeast of the Great Lakes, signaling potential priority areas for adaptive management. Our findings suggest that strategies aimed at enhancing the representation of older forest conditions at landscape scales will help sustain ESB in a changing world.

The social costs of nitrogen.

Despite growing recognition of the negative externalities associated with reactive nitrogen (N), the damage costs of N to air, water, and climate remain largely unquantified. We propose a comprehensive approach for estimating the social cost of nitrogen (SCN), defined as the present value of the monetary damages caused by an incremental increase in N. This framework advances N accounting by considering how each form of N causes damages at specific locations as it cascades through the environment. We apply the approach to an empirical example that estimates the SCN for N applied as fertilizer. We track impacts of N through its transformation into atmospheric and aquatic pools and estimate the distribution of associated costs to affected populations. Our results confirm that there is no uniform SCN. Instead, changes in N management will result in different N-related costs depending on where N moves and the location, vulnerability, and preferences of populations affected by N. For example, we found that the SCN per kilogram of N fertilizer applied in Minnesota ranges over several orders of magnitude, from less than $0.001/kg N to greater than $10/kg N, illustrating the importance of considering the site, the form of N, and end points of interest rather than assuming a uniform cost for damages. Our approach for estimating the SCN demonstrates the potential of integrated biophysical and economic models to illuminate the costs and benefits of N and inform more strategic and efficient N management.