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The availability of detailed surface runoff and river flow data across large geographic areas is needed for several scientific applications, such as refined freshwater environmental risk assessments. Some limiting factors in developing detailed river flow datasets over large spatial scales have been paucity of detailed input spatial data and challenges in processing of these data. The well-established USDA Curve Number (CN) method was applied for spatially distributed hydrologic processing to estimate surface runoff. Publicly available global datasets for hydrologic soil groups, land cover, and precipitation were spatially processed by applying the CN equations to create a global mean annual surface runoff grid of 50 meters. Runoff was spatially combined with global hydrology of catchments and rivers from publicly available datasets to estimate daily mean annual flow (MAF) across the globe. Estimated daily MAF were compared with measured gauge flow at rivers in several countries which showed good correlation (R2 of 0.76 - 0.98). These flow estimates can be used for diverse applications at local watersheds to larger regions across the globe. The two spatial data products of this project representing MAF at the global scale are publicly available for download at https://doi.org/10.6084/m9.figshare.22694146 (Heisler, et al., 2023). [ABSTRACT FROM AUTHOR]

A modeling framework was created for the development of spatially explicit aquatic exposure models for any region or country of interest for chemicals disposed of down the drain. The framework relies on globally available data sets for river flow and population, and locally available data sets for wastewater treatment infrastructure and domestic water use, and leverages the iSTREEM® chemical routing algorithm. The framework was applied to China and Japan as case study countries. Spatially explicit population data were obtained from WorldPop. River flows covering the spatial extent of the two countries were derived from a high‐resolution surface runoff gridded data set that was based on the Curve Number approach and combined with the hydrology network for catchments and rivers from HydroBASINS and HydroSHEDS data sets. Publicly available data from government sources were used for estimating per capita water use and wastewater treatment infrastructure. To demonstrate the framework, the China model was used to predict the levels of the antifungal agent climbazole in rivers across the country, and the Japan model was used to predict river concentrations of linear alkylbenzene sulfonate. For both chemicals, the comparison of measured to modeled values showed good agreement, using linear regression analysis (R2 ≥ 0.96). The framework presented in this study provides a systematic and robust approach to develop spatially resolved exposure models that can be extrapolated to any country or region, allowing more accurate risk assessment of chemicals disposed down the drain by leveraging concentration distributions generated by the model. Integr Environ Assess Manag 2022;18:722–733. © 2021 SETAC Key Points: Modeling framework created for the development of spatially explicit aquatic exposure models for any region or country of interest for chemicals disposed of down the drain.Framework relies on the use of a high‐resolution global flow dataset developed as part of this research.Framework also relies on globally available datasets for population and locally available datasets for wastewater treatment infrastructure and domestic water use.Model framework was successfully applied to two case study countries, China and Japan, which differ significantly in population density, river flow (due to climate and geography), and wastewater treatment infrastructure. [ABSTRACT FROM AUTHOR]

Despite widespread acceptance of the utility of population modeling and advocacy of this approach for a more ecologically relevant perspective, it is not routinely incorporated in ecological risk assessments (ERA). A systematic framework for situation‐specific model development is one of the major challenges to broadly adopting population models in ERA. As risk assessors confront the multitude of species and chemicals requiring evaluation, an adaptable stepwise guide for model parameterization would facilitate this process. Additional guidance on interpretation of model output and evaluating uncertainty would further contribute to establishing consensus on good modeling practices. We build on previous work that created a framework and decision guide for developing population models for ERA by focusing on data types, model structure, and extrinsic stressors relevant to anuran amphibians. Anurans have a unique life cycle with varying habitat requirements and high phenotypic plasticity. These species belong to the amphibian class, which is facing global population decline in large part due to anthropogenic stressors, including chemicals. We synthesize information from databases and literature relevant to amphibian risks to identify traits that influence exposure likelihood, inherent sensitivity, population vulnerability, and environmental constraints. We link these concerns with relevant population modeling methods and structure in order to evaluate pesticide effects with appropriate scale and parameterization. A standardized population modeling approach, with additional guidance for anuran ERA, offers an example method for quantifying population risks and evaluating long‐term impacts of chemical stressors to populations. Integr Environ Assess Manag 2020;16:223–233. © 2019 SETAC KEY POINTS: Various stressors and life‐history strategies that make amphibian populations uniquely vulnerable on a global scale are reviewed.Agreement on population modeling methods for ecological risk assessment is advanced, building on previously published decision guide steps and discussing modifications relevant to anuran populations.Guidance for inclusion of relevant data in model development at the appropriate spatial and temporal scale for risk assessment needs is outlined. [ABSTRACT FROM AUTHOR]

ABSTRACT The 'in-stream exposure model' iSTREEM®, a Web-based model made freely available to the public by the American Cleaning Institute, provides a means to estimate concentrations of 'down-the-drain' chemicals in effluent, receiving waters, and drinking water intakes across national and regional scales under mean annual and low-flow conditions. We provide an overview of the evolution and utility of the iSTREEM model as a screening-level risk assessment tool relevant for down-the-drain products. The spatial nature of the model, integrating point locations of facilities along a hydrologic network, provides a powerful framework to assess environmental exposure and risk in a spatial context. A case study compared national distributions of modeled concentrations of the fragrance 1,3,4,6,7,8-Hexahydro-4,6,6,7,8,8,-hexamethylcyclopenta-γ-2-benzopyran (HHCB) and the insect repellent N,N-Diethyl- m-toluamide (DEET) to available monitoring data at comparable flow conditions. The iSTREEM low-flow model results yielded a conservative distribution of values, whereas the mean-flow model results more closely resembled the concentration distribution of monitoring data. We demonstrate how model results can be used to construct a conservative estimation of the distribution of chemical concentrations for effluents and streams leading to the derivation of a predicted environmental concentration (PEC) using the high end of the concentration distribution (e.g., 90th percentile). Data requirements, assumptions, and applications of iSTREEM are discussed in the context of other down-the-drain modeling approaches to enhance understanding of comparative advantages and uncertainties for prospective users interested in exposure modeling for ecological risk assessment. Integr Environ Assess Manag 2016;12:782-792. © 2016 SETAC [ABSTRACT FROM AUTHOR]