An ensemble of Bayesian SPARROW models for the Precambrian Shield in the eastern Georgian Bay drainage basin, Ontario, Canada

Neumann, Alex; Blukacz‐Richards, E. Agnes; Yao, Huaxia; James, April L.; Arhonditsis, George B.

doi:10.1016/j.jglr.2021.09.010

Cited by 6 publications

(1 citation statement)

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“…SPARROW [33] nutrients, pesticide, heavy metal, etc. watershed A regression statistical model of watershed spatial attributes combining empirical statistics and mechanisms; the statistical estimation of land and water parameters separately to quantitatively describe the migration rate of pollutants from source to river and the transport between upstream and downstream of the river network, which is advantageous in terms of data requirement and is also feasible for simulation in areas with uneven distribution of monitoring points.…”

Section: Nps Model Developmentmentioning

confidence: 99%

Review of Nonpoint Source Pollution Models: Current Status and Future Direction

Wang,

Chen,

et al. 2022

Water

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Modelling tools are commonly used for predicting non-point source (NPS) pollutants and it is timely to review progress that has been made in terms of the development of NPS models. This paper: (1) proposes a systematic description of model framework and generalizes some commonly used models; (2) identifies the common challenges in model structure and applications; (3) summarizes the future directions of NPS models. Challenges in model construction and application are based on the following: (1) limitations in understanding specific NPS pollution processes; (2) model expansion to different scales; (3) data scarcity and its impacts on model performance; (4) prediction uncertainty due to model input, parameter and model structure; (5) insufficient accuracy for decision-making. Finally, this paper proposes future directions for model development, including: (1) a source–flow–sink framework for model development; (2) standardization for model input and parameter; (3) reliable decision support for environmental management. The findings of this review provide helps in the accurate prediction and management of NPS pollution around the world.

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Section: Nps Model Developmentmentioning

confidence: 99%