2021
DOI: 10.1016/j.scitotenv.2020.143920
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Simulating internal watershed processes using multiple SWAT models

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Cited by 25 publications
(24 citation statements)
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“…The model's HRUs were specially delineated following Teshager et al. (2016) using land use, roadway, and stream network spatial data to create near field‐scale HRUs (i.e., HRUs that depicted the approximate size and location of distinct agricultural fields within the basin) (Apostel et al., 2021). The near field‐scale resolution of the model's HRUs facilitated improved representation of basin heterogentity with respect to soils, slopes, land use, and agricultural management practices (Apostel et al., 2021).…”
Section: Methodsmentioning
confidence: 99%
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“…The model's HRUs were specially delineated following Teshager et al. (2016) using land use, roadway, and stream network spatial data to create near field‐scale HRUs (i.e., HRUs that depicted the approximate size and location of distinct agricultural fields within the basin) (Apostel et al., 2021). The near field‐scale resolution of the model's HRUs facilitated improved representation of basin heterogentity with respect to soils, slopes, land use, and agricultural management practices (Apostel et al., 2021).…”
Section: Methodsmentioning
confidence: 99%
“…Our specific objectives were to (a) develop a method to more accurately represent the expected soil property changes associated with the soil health‐improving practices (specifically, no‐till and cover crops), (b) incorporate the changes into a watershed scale hydrologic model and apply the model to simulate effects on field‐ and basin‐scale nutrient export, and (c) assess the degree to which changing soil properties affected field‐ and basin‐scale nutrient export. To accomplish our objectives, we used a previously constructed SWAT model for the ∼17,000 km 2 Maumee River watershed, a predominantly agricultural basin that is a significant source of nutrient loads to Lake Erie (see Apostel et al., 2021).…”
Section: Introductionmentioning
confidence: 99%
“…However, variations in watershed models that all perform well at the outlet can have significant differences in process representation upstream (Apostel et al. 2021). Hence, we then consider each SWAT model as unique.…”
Section: Methodsmentioning
confidence: 99%
“…All models were calibrated to a single station (Maumee at Waterville, USGS # 04193500) near the watershed outlet and achieved good standards of performance for discharge and nutrients (Moriasi et al 2007(Moriasi et al , 2015; Table S1). However, variations in watershed models that all perform well at the outlet can have significant differences in process representation upstream (Apostel et al 2021). Hence, we then consider each SWAT model as unique.…”
Section: Watershed Modelsmentioning
confidence: 99%
“…For such processes, bio-optical and bio-geo-optical models that mechanistically link the irradiancereflectance ratio across multiple spectral bands to the bulk water column properties are a reliable alternative [19]. For impairments such as harmful algal blooms and oxygen depletion, more sophisticated statistical (e.g., [20]) or mechanistic models (e.g., [21]) are used to relate watershed nutrient loads with impairment. For water quality parameters that do not directly affect the optical properties of the water column and spectral properties of the reflectances (e.g., mercury and heavy metals), statistical, machine learning and artificial intelligence models relating the quantities of interest with other water constituents (e.g., turbidity) are becoming increasingly common [15].…”
Section: Introductionmentioning
confidence: 99%