Modeling the hydrologic effects of watershed-scale green roof implementation in the Pacific Northwest, United States.

Green roofs are among the most popular type of green infrastructure implemented in highly urbanized watersheds due to their low cost and efficient utilization of unused or under-used space. In this study, we examined the effectiveness of green roofs to attenuate stormwater runoff across a large metropolitan area in the Pacific Northwest, United States. We utilized a spatially explicit ecohydrological watershed model called Visualizing Ecosystem Land Management Assessments (VELMA) to simulate the resulting stormwater hydrology of implementing green roofs over 25%, 50%, 75%, and 100% of existing buildings within four urban watersheds in Seattle, Washington, United States. We simulated the effects of two types of green roofs: extensive green roofs, which are characterized by shallow soil profiles and short vegetative cover, and intensive green roofs, which are characterized by deeper soil profiles and can support larger vegetation. While buildings only comprise approximately 10% of the total area within each of the four watersheds, our simulations showed that 100% implementation of green roofs on these buildings can achieve approximately 10-15% and 20-25% mean annual runoff reductions for extensive and intensive green roofs, respectively, over a 28-year simulation. These results provide an upper limit for volume reductions achievable by green roofs in these urban watersheds. We also showed that stormwater runoff reductions are proportionately smaller during higher flow regimes caused by increased precipitation, likely due to the limited storage capacity of saturated green roofs. In general, green roofs can be effective at reducing stormwater runoff, and their effectiveness is limited by both their areal extent and storage capacity. Our results showed that green roof implementation can be an effective stormwater management tool in highly urban areas, and we demonstrated that our modeling approach can be used to assess the watershed-scale hydrologic impacts of the widespread adoption of green roofs across large metropolitan areas.

A review of regulatory modeling frameworks supporting numeric water quality criteria development in the United States.

The US Environmental Protection Agency (USEPA) has a long history of leveraging environmental models and integrated modeling frameworks to support the regulatory development of numeric ambient water quality criteria for the protection of aquatic life and human health. Primary modeling types include conceptual, mechanistic, and data-driven empirical models; Bayesian and probabilistic models; and risk-based modeling frameworks. These models and modeling frameworks differ in their applicability to and suitability for various water quality criteria objectives. They require varying knowledge of system processes and stressor-response relationships, data availability, and expertise of stakeholders. In addition, models can be distinguished by their ability to characterize variability and uncertainty. In this work, we review USEPA recommendations for model use in existing regulatory frameworks, technical support documents, and peer-reviewed literature. We characterize key attributes, identify knowledge gaps and opportunities for future research, and highlight where renewed USEPA guidance is needed to promote the development and use of models in numeric criteria derivation. These outcomes then inform a decision-based framework for determining model suitability under particular scenarios of available knowledge, data, and access to technical resources. Integr Environ Assess Manag 2023;19:191-201. © 2022 SETAC.

Ambient water quality criteria derived using probabilistic risk assessment.

National recommendations for numeric human health ambient water quality criteria (AWQC) for toxic substances are derived by the US Environmental Protection Agency (USEPA) using a deterministic approach that combines point estimates for exposure, toxicity, and acceptable risk. In accordance with the Clean Water Act, states, territories, and authorized tribes must either adopt these recommendations or modify and replace them with criteria using an alternative, scientifically defensible method. Recent reports have criticized the deterministic approach, stating that it suffers from compounded conservatism by selecting upper percentiles or maximum values for multiple inputs and that it cannot directly determine what portion of the population a given criterion protects. As an alternative, probabilistic risk assessment (PRA) has been promoted as a more transparent and robust method for deriving AWQC. Probabilistic risk assessment offers several advantages over the deterministic approach. For example, PRA uses entire data distributions rather than upper-percentile point estimates to specify exposures, thereby reducing compounded conservatism. Additionally, because it links acceptable risk targets with specific segments of the exposed population, PRA-based AWQC demonstrably protects multiple subsets of the population. To date, no study has quantitatively compared deterministic and PRA approaches and resulting AWQC using national inputs consistent with USEPA guidance. This study introduces a PRA method for deriving AWQC and presents case studies to compare probabilistically derived AWQC with USEPA's 2015 recommendations. The methods and results of this work will help federal and state regulators, water quality managers, and stakeholders better understand available approaches to deriving AWQC and provide context to assumption- and method-specific differences between criteria. Integr Environ Assess Manag 2023;19:501-512. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Deriving Human Health and Aquatic Life Water Quality Criteria in the United States for Bioaccumulative Substances: A Historical Review and Future Perspective.

Methods used to derive water quality regulations for persistent, bioaccumulative, and toxic substances (PBTs) in the United States have evolved substantially over the past 50 yr, leveraging current understandings and assumptions about the nature and magnitude of partitioning and accumulation of substances in water, sediments, and organisms. In the United States and across the world, environmental regulations continue to evolve into more refined water quality criteria protective of aquatic life and human health. The present review provides historical context on the establishment of aquatic life and human health water quality criteria in the United States by compiling information from regulatory agencies and peer-reviewed literature on methods used to characterize and quantify bioaccumulation of substances in aquatic organisms and humans. Primary data needs and assumptions for various methods, as well as their application in setting criteria by the US Environmental Protection Agency over the past half century, are highlighted. Our review offers an important retrospective on the data and methods used to derive water quality criteria for PBTs and provides commentary on the future of US criteria development. Environ Toxicol Chem 2021;40:2394-2405. © 2021 SETAC.

Modeling Landscape Change Effects on Stream Temperature Using the Soil and Water Assessment Tool.

Stream temperature is one of the most important factors for regulating fish behavior and habitat. Therefore, models that seek to characterize stream temperatures, and predict their changes due to landscape and climatic changes, are extremely important. In this study, we extend a mechanistic stream temperature model within the Soil and Water Assessment Tool (SWAT) by explicitly incorporating radiative flux components to more realistically account for radiative heat exchange. The extended stream temperature model is particularly useful for simulating the impacts of landscape and land use change on stream temperatures using SWAT. The extended model is tested for the Marys River, a western tributary of the Willamette River in Oregon. The results are compared with observed stream temperatures, as well as previous model estimates (without radiative components), for different spatial locations within the Marys River watershed. The results show that the radiative stream temperature model is able to simulate increased stream temperatures in agricultural sub-basins compared with forested sub-basins, reflecting observed data. However, the effect is overestimated, and more noise is generated in the radiative model due to the inclusion of highly variable radiative forcing components. The model works at a daily time step, and further research should investigate modeling at hourly timesteps to further improve the temporal resolution of the model. In addition, other watersheds should be tested to improve and validate the model in different climates, landscapes, and land use regimes.

Penumbra: A spatially distributed, mechanistic model for simulating ground-level incident solar energy across heterogeneous landscapes.

Landscape solar energy is a significant environmental driver, yet it remains complicated to model well. Several solar radiation models simplify the complexity of light by estimating it at discrete point locations or by averaging values over larger areas. These modeling approaches may be useful in certain cases, but they are unable to provide spatially distributed and temporally dynamic representations of solar energy across entire landscapes. We created a landscape-scale ground-level shade and solar energy model called Penumbra to address this deficiency. Penumbra simulates spatially distributed ground-level shade and incident solar energy at user-defined timescales by modeling local and distant topographic shading and vegetative shading. Spatially resolved inputs of a digital elevation model, a normalized digital surface model, and landscape object transmittance are used to estimate spatial variations in solar energy at user-defined temporal timesteps. The research goals for Penumbra included: 1) simulations of spatiotemporal variations of shade and solar energy caused by both objects and topographic features, 2) minimal user burden and parameterization, 3) flexible user defined temporal parameters, and 4) flexible external model coupling. We test Penumbra's predictive skill by comparing the model's predictions with monitored open and forested sites, and achieve calibrated mean errors ranging from -17.3 to 148.1 µmoles/m2/s. Penumbra is a dynamic model that can produce spatial and temporal representations of shade percentage and ground-level solar energy. Outputs from Penumbra can be used with other ecological models to better understand the health and resilience of aquatic, near stream terrestrial, and upland ecosystems.