A Methodological Framework for Identification of Baseline Scenario and Assessing the Impact of DEM Scenarios on SWAT Model Outputs

Sukumaran, Harikrishna; Sahoo, Sanat Nalini

doi:10.1007/s11269-020-02691-5

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…The overall uncertainty of model outputs increased as the DEM resolution became coarser, evidenced by the absolute RD values (>15%). A minor impact was found on surface runoff, water yield, and ET, consistent with previous research [32,33,46,64,65]. For example, the results of Song et al [33] showed that the simulated values of surface runoff change gently, and this value dropped by 2% when DEM resolution increased from 10 m to 130 m. Lin et al [32] used the DEM resolution from 5 m to 140 m, and they also found similar results, namely that the relative difference (RD) of simulated runoff was less than 1%.…”

Section: Discussionsupporting

confidence: 91%

“…Di Luzio et al [78] explained that surface runoff was computed using the SCS curve number method, and DEM resolution did not affect the average curve number value. Sukumaran and Sahoo [64] pointed out that since surface runoff constituted a significant portion of water yield, variations in water yield were predominantly reflected by changes in surface runoff. Given the insensitivity of surface runoff to DEM resolutions, water yield was also not sensitive to the DEM resolutions.…”

Section: Discussionmentioning

confidence: 99%

“…The relative difference (RD) is a standard method to quantify the SWAT model output uncertainty due to different DEM scenarios [32,46,[64][65][66]. This approach was defined as:…”

Section: Quantification Of Model Uncertainties Due To Different Dem S...mentioning

confidence: 99%

“…where DEM baseline is the SWAT output derived from the baseline, and DEM x is the SWAT output generated from each different DEM scenario. A positive RD, as an uncertainty measure, indicated overestimation, and a negative RD, as an uncertainty measure, meant underestimation [64]. Moriasi et al [63] and Tan et al [46] suggested that if the RD values are >±15% than the baseline scenario, it is assumed that the DEM x scenario has a large impact on the SWAT model output.…”

Section: Quantification Of Model Uncertainties Due To Different Dem S...mentioning

confidence: 99%

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SWAT Model Performance Using Spatially Distributed Saturated Hydraulic Conductivity (Ksat) and Varying-Resolution DEMs

Jin,

Higgins,

Thompson

et al. 2024

Water

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Saturated hydraulic conductivity (Ksat) is a hydrologic flux parameter commonly used to determine water movement through the saturated soil zone. Understanding the influences of land-use-specific Ksat on the model estimation error of water balance components is necessary to advance model predictive certainties and land management practices. An exploratory modeling approach was developed in the physically based Soil and Water Assessment Tool (SWAT) framework to investigate the effects of spatially distributed observed Ksat on local water balance components using three digital elevation model (DEM) resolution scenarios (30 m, 10 m, and 1 m). All three DEM scenarios showed satisfactory model performance during calibration (R2 > 0.74, NSE > 0.72, and PBIAS ≤ ±13%) and validation (R2 > 0.71, NSE > 0.70, and PBIAS ≤ ±6%). Results showed that the 1 m DEM scenario provided more realistic streamflow results (0.315 m3/s) relative to the observed streamflow (0.292 m3/s). Uncertainty analysis indicated that observed Ksat forcings and DEM resolution significantly influence predictions of lateral flow, groundwater flow, and percolation flow. Specifically, the observed Ksat has a more significant impact on model predictive confidence than DEM resolution. Results emphasize the potential uncertainty of using observed Ksat for hydrological modeling and demonstrate the importance of finer-resolution spatial data (i.e., 1 m DEM) applied in smaller watersheds.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

“…The relative difference (RD) is a standard method to quantify the SWAT model output uncertainty due to different DEM scenarios [32,46,[64][65][66]. This approach was defined as:…”

Section: Quantification Of Model Uncertainties Due To Different Dem S...mentioning

confidence: 99%

Section: Quantification Of Model Uncertainties Due To Different Dem S...mentioning

confidence: 99%

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SWAT Model Performance Using Spatially Distributed Saturated Hydraulic Conductivity (Ksat) and Varying-Resolution DEMs

Jin,

Higgins,

Thompson

et al. 2024

Water

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“…The model is flexible and extensible, and it is computationally very efficient for doing continuous daily simulations for a longer period. However, the SWAT model cannot accurately simulate real-time crop water requirements (Harikrishna & Sanat, 2020;Navideh et al, 2020;Ferreira et al, 2021;Hanna et al, 2021). Therefore, in this study, the following modifications were made to the SWAT model:…”

Section: Improvement Of the Swat Modelmentioning

confidence: 99%

Dynamic characteristics of drought conditions during the growth of summer maize

Cui

Liu

et al. 2022

Water Policy

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This study aimed at investigating the applicability of a SWAT (Soil and Water Assessment Tool) model in understanding the effects of drought on summer maize. A real-time irrigation module was developed for the downstream irrigation area of the Yellow River to estimate the real-time irrigation of crops. By further simulating the dynamic evolution process of soil moisture content, a dynamic drought evaluation model of summer maize was established, and the relative soil moisture was set as the evaluation index to assess and analyze the dynamic variation of drought evolution during the growth of summer maize. The results showed that the improved SWAT model has strong applicability. During the growth of summer maize, the variation trend of drought is consistent with that of natural precipitation. Moreover, drought mainly occurs during the sowing-seedling and seedling-jointing stages, and the average frequency is 84.8 and 78.3%, respectively. Moderate drought is most likely to occur during the growth of summer maize and occurs mainly during the sowing-seedling and seedling-jointing stages, and the occurrence frequency is 55.3 and 32.6%, respectively. Extra-severe drought has the greatest impact, mainly in the jointing-tasseling, tasseling-milking and milking-maturity stages, and the occurrence frequency is 17.4, 15.2 and 10.9%, respectively.

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Hydrological Process Simulation of Sluice-Controlled Rivers in the Plains Area of China Based on an Improved SWAT Model

Gan

Chen

Tao

et al. 2021

Water Resour Manage

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A Methodological Framework for Identification of Baseline Scenario and Assessing the Impact of DEM Scenarios on SWAT Model Outputs

Cited by 10 publications

References 29 publications

SWAT Model Performance Using Spatially Distributed Saturated Hydraulic Conductivity (Ksat) and Varying-Resolution DEMs

SWAT Model Performance Using Spatially Distributed Saturated Hydraulic Conductivity (Ksat) and Varying-Resolution DEMs

Dynamic characteristics of drought conditions during the growth of summer maize

Hydrological Process Simulation of Sluice-Controlled Rivers in the Plains Area of China Based on an Improved SWAT Model

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