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Coho salmon ( Oncorhynchus kisutch ) are highly sensitive to 6PPD-Quinone (6PPD-Q). Details of the hydrological and biogeochemical processes controlling spatial and temporal dynamics of 6PPD-Q fate and transport from points of deposition to receiving waters (e.g., streams, estuaries) are poorly understood. To understand the fate and transport of 6PPD and mechanisms leading to salmon mortality Visualizing Ecosystem Land Management Assessments (VELMA), an ecohydrological model developed by US Environmental Protection Agency (EPA), was enhanced to better understand and inform stormwater management planning by municipal, state, and federal partners seeking to reduce stormwater contaminant loads in urban streams draining to the Puget Sound National Estuary. This work focuses on the 5.5 km2 Longfellow Creek upper watershed (Seattle, Washington, United States), which has long exhibited high rates of acute urban runoff mortality syndrome in coho salmon. We present VELMA model results to elucidate these processes for the Longfellow Creek watershed across multiple scales–from 5-m grid cells to the entire watershed. Our results highlight hydrological and biogeochemical controls on 6PPD-Q flow paths, and hotspots within the watershed and its stormwater infrastructure, that ultimately impact contaminant transport to Longfellow Creek and Puget Sound. Simulated daily average 6PPD-Q and available observed 6PPD-Q peak in-stream grab sample concentrations (ng/L) corresponds within plus or minus 10 ng/L. Most importantly, VELMA’s high-resolution spatial and temporal analysis of 6PPD-Q hotspots provides a tool for prioritizing the locations, amounts, and types of green infrastructure that can most effectively reduce 6PPD-Q stream concentrations to levels protective of coho salmon and other aquatic species.

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.

Low Impact Development (LID) is an alternative to conventional urban stormwater management practices, which aims at mitigating the impacts of urbanization on water quantity and quality. Plot and local scale studies provide evidence of LID effectiveness; however, little is known about the overall watershed scale influence of LID practices. This is particularly true in watersheds with a land cover that is more diverse than that of urban or suburban classifications alone. We address this watershed-scale gap by assessing the effects of three common LID practices (rain gardens, permeable pavement, and riparian buffers) on the hydrology of a 0.94 km2 mixed land cover watershed. We used a spatially-explicit ecohydrological model, called Visualizing Ecosystems for Land Management Assessments (VELMA), to compare changes in watershed hydrologic responses before and after the implementation of LID practices. For the LID scenarios, we examined different spatial configurations, using 25%, 50%, 75% and 100% implementation extents, to convert sidewalks into rain gardens, and parking lots and driveways into permeable pavement. We further applied 20 m and 40 m riparian buffers along streams that were adjacent to agricultural land cover. The results showed overall increases in shallow subsurface runoff and infiltration, as well as evapotranspiration, and decreases in peak flows and surface runoff across all types and configurations of LID. Among individual LID practices, rain gardens had the greatest influence on each component of the overall watershed water balance. As anticipated, the combination of LID practices at the highest implementation level resulted in the most substantial changes to the overall watershed hydrology. It is notable that all hydrological changes from the LID implementation, ranging from 0.01 to 0.06 km2 across the study watershed, were modest, which suggests a potentially limited efficacy of LID practices in mixed land cover watersheds.

Context The > 25,000 km 2 Flint Hills ecoregion in eastern Kansas and northeastern Oklahoma, USA, is an economically and ecologically important area encompassing the largest remaining tallgrass prairie ecosystem in North America. Prescribed fires are used routinely to control invasive woody species and improve forage production for the beef-cattle industry. However, burning releases harmful pollutants that, at times, contribute to air quality problems for communities across a multi-state area. Objectives Establish a modeling framework for synthesizing long-term ecological data in support of Flint Hills tallgrass prairie management goals for identifying how much, where, and when rangeland burning can be conducted to maximize ecological and economic benefits while minimizing regional air quality impacts. Methods We used EPA’s VELMA ecohydrology model to synthesize long-term experimental data at the 35 km 2 Konza Prairie Biological Station (KPBS) describing the effects of climate, fire, grazing, topography, and soil moisture and nutrient dynamics on tallgrass prairie productivity and fuel loads; and to spatially extrapolate that synthesis to estimate grassland productivity and fuel loads across the nearly 1000 times larger Flint Hills ecoregion to support prescribed burning smoke trajectory modeling using the State of Kansas implementation of the U.S. Forest Service BlueSky framework. Results VELMA provided a performance-tested synthesis of KPBS data from field observations and experiments, thereby establishing a tool for regionally simulating the combined effects of climate, fire, grazing, topography, soil moisture, and nutrients on tallgrass prairie productivity and fuel loads. VELMA’s extrapolation of that synthesis allowed difficult-to-quantify fuel loads to be mapped across the Flint Hills to support environmental decision making, such as forecasting when, where, and how prescribed burning will have the least impact on downwind population centers. Conclusions Our regional spatial and temporal extrapolation of VELMA’s KPBS data synthesis posits that the effects of integrated ecohydrological processes operate similarly across tallgrass prairie spatial scales. Based on multi-scale performance tests of the VELMA-BlueSky toolset, our multi-institution team is confident that it can assist stakeholders and decision makers in realistically exploring tallgrass prairie management options for balancing air quality, tallgrass prairie sustainability, and associated economic benefits for the Flint Hills ecoregion and downwind communities.

Modeling the spatial and temporal dynamics of soil temperature is deterministically complex due to the wide variability of several influential environmental variables, including soil column composition, soil moisture, air temperature, and solar energy. Landscape incident solar radiation is a significant environmental driver that affects both air temperature and ground-level soil energy loading; therefore, inclusion of solar energy is important for generating accurate representations of soil temperature. We used the U.S. Environmental Protection Agency’s Oregon Crest-to-Coast (O’CCMoN) Environmental Monitoring Transect dataset to develop and test the inclusion of ground-level solar energy driver data within an existing soil temperature model currently utilized within an ecohydrology model called Visualizing Ecosystem Land Management Assessments (VELMA). The O’CCMoN site data elucidate how localized ground-level solar energy between open and forested landscapes greatly influence the resulting soil temperature. We demonstrate how the inclusion of local ground-level solar energy significantly improves the ability to deterministically model soil temperature at two depths. These results suggest that landscape and watershed-scale models should incorporate spatially distributed solar energy to improve spatial and temporal simulations of soil temperature.