Evaluation of AnnAGNPS Model for Runoff Simulation on Watersheds from Glaciated Landscape of USA Midwest and Northeast

Tamanna, Marzia; Pradhanang, Soni M.; Gold, Arthur J.; Addy, Kelly; Vidon, P.; Bingner, R. L.

doi:10.3390/w12123525

Cited by 8 publications

(5 citation statements)

References 53 publications

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“…Final values varied by watershed: calibrated CN was adjusted from baseline values by -7 units (North Fork), -5 units (Main Stem), and +6 units (Irish Creek), and calibrated initial soil moisture values were 0.7 (North Fork), 0.4 (Main Stem), and 0.8 (Irish Creek). These magnitudes of CN adjustment used to calibrate AnnAGNPS were consistent with Tamanna et al (2020), who adjusted contoured row crop CN by +6 to -7 with different adjustments for each of their three study watersheds, Karki et al (2017), who adjusted row crop CN by -9, and Parajuli et al (2009), who adjusted cropland CN by +4 to -4.…”

Section: Streamflowsupporting

confidence: 60%

Integrating Streambank Erosion with Overland and Ephemeral Gully Models Improves Stream Sediment Yield Simulation

Mankin¹,

Modala²

2022

Journal of the ASABE

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HighlightsSimulation of overland (AnnAGNPS) and ephemeral gully (REGEM) erosion underestimated watershed sediment yield.Three methods were used to disaggregate contribution of streambank sediment loads to daily streamflow events.A rainfall-based method, with scaling factor and threshold term, had good performance and low bias in all watersheds.Calibrated model results indicated that most sediment yield was from streambank sources (78% to 93%).Abstract. Models used to guide watershed management must account for sediment from all erosion sources (interrill and rill, ephemeral gully (EG), and streambank), each of which operates at different spatial and temporal scales. Our hypothesis was that the use of separate models to explicitly simulate sediment yield contributions for each of these three sources would improve model agreement with measured watershed sediment yield data. We tested this hypothesis using AnnAGNPS (overland flow/erosion model), REGEM (EG erosion model), and field-measured streambank erosion disaggregated to an event basis using three methods in three watersheds (North Fork, Main Stem, and Irish Creek, Kansas). AnnAGNPS alone and in combination with REGEM underestimated sediment yields and had a poorly calibrated performance in all three watersheds, which reinforced the need to include sediment loads from stream sources. The three streambank sediment disaggregation methods each included two calibration terms: a scaling factor and a threshold term, which disaggregated 2-year-total streambank erosion measurements into event-based values based on rainfall, streamflow, or stream power. All three methods gave “very good” sediment-yield calibration results (event-based Ef = 0.6, PBIAS near 0%) for Main Stem and Irish Creek but gave “satisfactory” results only for the rainfall-based streambank disaggregation method in the evaluation watershed (North Fork). Calibrated model results indicated that most of the measured outlet sediment yields in the study watersheds were from streambank sources (78% to 93%), with less from overland erosion (7% to 22%) and little from EG erosion (0% to 7%). These methods could be equally effective in scaling and disaggregating stream-based sediment contributions without measured streambank erosion, but the calibration terms would lose physical meaning in reference to measured streambank values. The skill demonstrated by all three streambank disaggregation thresholds may provide a foundation for building simple, process-based streambank sediment yield simulation models. Keywords: Ephemeral gully erosion, Interrill and rill erosion, Modeling, Streambank erosion, Watershed.

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Section: Streamflowsupporting

confidence: 60%

Integrating Streambank Erosion with Overland and Ephemeral Gully Models Improves Stream Sediment Yield Simulation

Mankin¹,

Modala²

2022

Journal of the ASABE

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“…However, it is limited to a single rainfall simulation and cannot evaluate the long-term impact of nonpoint-source pollution in the basin. The Annualized Agricultural Non-Point Source (AnnAGNPS) model (Tamanna et al, 2020) is based on this model and can be employed to evaluate surface runoff, sediment erosion, and nitrogen and phosphorus loss in the basin. This model is capable of simulating both the long-term runoff and sediment quantity of the basin and the runoff and sediment transport process within the watershed at multiple scales.…”

Section: Empirical Statistical Modelsmentioning

confidence: 99%

A review of the satellite remote sensing techniques for assessment of runoff and sediment in soil erosion

Ji,

Cao,

et al. 2024

Journal of Hydrology and Hydromechanics

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Soil erosion monitoring is essential for the ecological evaluation and dynamic monitoring of land resources via remote sensing technology. In this paper, we provide new insights into the existing problems and development directions of traditional models, which are supported by new technologies. An important role is played by remote sensing information acquisition technology in the qualitative and quantitative evaluation of soil erosion, and the data and technical support provided are systematically reviewed. We provide a detailed overview of the research progress associated with empirical statistical models and physically driven process models of soil erosion, and the limitations of their application are also summarized. The preliminary integration of remote sensing data sources with high spatial and temporal resolution and new technologies for soil erosion monitoring enables the high-precision quantitative estimation of sediment transport trajectories, the watershed river network density, and the terrain slope, enhancing the accuracy of erosion factor identification, such as spectral feature recognition from erosion information, gully erosion feature extraction, and vegetation coverage estimation. However, the current erosion models, driven by algorithms and models, are not comprehensive enough, particularly in terms of the spatial feature extraction of erosion information, and there are limitations in the applicability and accurate estimation of such models.

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“…In general, this information is missing from most REMM studies, which can hinder the ability of REMM to properly predict riparian functions at the site scale [39]. To offset this issue, a field-scale hydrological model, Annualized AGricultural Non-Point Source (AnnAGNPS), was used to predict the runoff and sediment loading to the riparian buffer [44,45]. AnnAGNPS [46,47] is a daily time step, watershed scale, pollutant-loading, distributed model developed to simulate long-term runoff, sediment, nutrients, and pesticide transport from agricultural watersheds [48][49][50].…”

Section: Upland Inputs (*Fin File)mentioning

confidence: 99%

“…For this study, AnnAGNPS was used with a cell size of 1.8-0.4 km 2 (interquartile range, IQR = Q 3 − Q 1, of cell size among a total of 185 cells in the watershed) to simulate input cells representing upland inputs to each of the eight riparian buffers within upper Pawcatuck River watershed. Details of the application of AnnAGNPS model on the study area can be found in our companion study [45]. Table 1 represents the upland characteristics for all the riparian sites.…”

Section: Upland Inputs (*Fin File)mentioning

confidence: 99%

Riparian Zone Nitrogen Management through the Development of the Riparian Ecosystem Management Model (REMM) in a Formerly Glaciated Watershed of the US Northeast

et al. 2021

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The Riparian Ecosystem Management Model (REMM) was developed, calibrated and validated for both hydrologic and water quality data for eight riparian buffers located in a formerly glaciated watershed (upper Pawcatuck River Watershed, Rhode Island) of the US Northeast. The Annualized AGricultural Non-Point Source model (AnnAGNPS) was used to predict the runoff and sediment loading to the riparian buffer. Overall, results showed REMM simulated water table depths (WTDs) and groundwater NO3-N concentrations at the stream edge (Zone 1) in good agreement with measured values. The model evaluation statistics showed that, hydrologically REMM performed better for site 1, site 4, and site 8 among the eight buffers, whereas REMM simulated better groundwater NO3-N concentrations in the case of site 1, site 5, and site 7 when compared to the other five sites. The interquartile range of mean absolute error for WTDs was 3.5 cm for both the calibration and validation periods. In the case of NO3-N concentrations prediction, the interquartile range of the root mean square error was 0.25 mg/L and 0.69 mg/L for the calibration and validation periods, respectively, whereas the interquartile range of d for NO3-N concentrations was 0.20 and 0.48 for the calibration and validation period, respectively. Moreover, REMM estimation of % N-removal from Zone 3 to Zone 1 was 19.7%, and 19.8% of N against actual measured 19.1%, and 26.6% of N at site 7 and site 8, respectively. The sensitivity analyses showed that changes in the volumetric water content between field capacity and saturation (soil porosity) were driving water table and denitrification.

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Evaluation of AnnAGNPS Model for Runoff Simulation on Watersheds from Glaciated Landscape of USA Midwest and Northeast

Cited by 8 publications

References 53 publications

Integrating Streambank Erosion with Overland and Ephemeral Gully Models Improves Stream Sediment Yield Simulation

Integrating Streambank Erosion with Overland and Ephemeral Gully Models Improves Stream Sediment Yield Simulation

A review of the satellite remote sensing techniques for assessment of runoff and sediment in soil erosion

Riparian Zone Nitrogen Management through the Development of the Riparian Ecosystem Management Model (REMM) in a Formerly Glaciated Watershed of the US Northeast

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