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Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure

Author

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  • Sangchul Lee

    (School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Korea)

  • Junyu Qi

    (Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA)

  • Hyunglok Kim

    (Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA
    School of Data Science, University of Virginia, Charlottesville, VA 22904, USA)

  • Gregory W. McCarty

    (USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA)

  • Glenn E. Moglen

    (USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA)

  • Martha Anderson

    (USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA)

  • Xuesong Zhang

    (Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
    Joint Global Change Research Institute, Pacific Northwest National Laboratory and University of Maryland, College Park, MD 20740, USA)

  • Ling Du

    (USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
    Department of Environmental Science & Technology, University of Maryland, College Park, MD 20742, USA)

Abstract

There is a certain level of predictive uncertainty when hydrologic models are applied for operational purposes. Whether structural improvements address uncertainty has not well been evaluated due to the lack of observational data. This study investigated the utility of remotely sensed evapotranspiration (RS-ET) products to quantitatively represent improvements in model predictions owing to structural improvements. Two versions of the Soil and Water Assessment Tool (SWAT), representative of original and improved versions, were calibrated against streamflow and RS-ET. The latter version contains a new soil moisture module, referred to as RSWAT. We compared outputs from these two versions with the best performance metrics (Kling–Gupta Efficiency [KGE], Nash-Sutcliffe Efficiency [NSE] and Percent-bias [P-bias]). Comparisons were conducted at two spatial scales by partitioning the RS-ET into two scales, while streamflow comparisons were only conducted at one scale. At the watershed level, SWAT and RSWAT produced similar metrics for daily streamflow (NSE of 0.29 and 0.37, P-bias of 1.7 and 15.9, and KGE of 0.47 and 0.49, respectively) and ET (KGE of 0.48 and 0.52, respectively). At the subwatershed level, the KGE of RSWAT (0.53) for daily ET was greater than that of SWAT (0.47). These findings demonstrated that RS-ET has the potential to increase prediction accuracy from model structural improvements and highlighted the utility of remotely sensed data in hydrologic modeling.

Suggested Citation

  • Sangchul Lee & Junyu Qi & Hyunglok Kim & Gregory W. McCarty & Glenn E. Moglen & Martha Anderson & Xuesong Zhang & Ling Du, 2021. "Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:2375-:d:504064
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    References listed on IDEAS

    as
    1. Sinnathamby, Sumathy & Douglas-Mankin, Kyle R. & Craige, Collin, 2017. "Field-scale calibration of crop-yield parameters in the Soil and Water Assessment Tool (SWAT)," Agricultural Water Management, Elsevier, vol. 180(PA), pages 61-69.
    2. Prem B. Parajuli & Priyantha Jayakody & Ying Ouyang, 2018. "Evaluation of Using Remote Sensing Evapotranspiration Data in SWAT," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 985-996, February.
    3. N. LeRoy Poff & Casey M. Brown & Theodore E. Grantham & John H. Matthews & Margaret A. Palmer & Caitlin M. Spence & Robert L. Wilby & Marjolijn Haasnoot & Guillermo F. Mendoza & Kathleen C. Dominique , 2016. "Sustainable water management under future uncertainty with eco-engineering decision scaling," Nature Climate Change, Nature, vol. 6(1), pages 25-34, January.
    4. Aouissi, Jalel & Benabdallah, Sihem & Lili Chabaâne, Zohra & Cudennec, Christophe, 2016. "Evaluation of potential evapotranspiration assessment methods for hydrological modelling with SWAT—Application in data-scarce rural Tunisia," Agricultural Water Management, Elsevier, vol. 174(C), pages 39-51.
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    Cited by:

    1. Lee, Sangchul & Qi, Junyu & McCarty, Gregory W. & Anderson, Martha & Yang, Yun & Zhang, Xuesong & Moglen, Glenn E. & Kwak, Dooahn & Kim, Hyunglok & Lakshmi, Venkataraman & Kim, Seongyun, 2022. "Combined use of crop yield statistics and remotely sensed products for enhanced simulations of evapotranspiration within an agricultural watershed," Agricultural Water Management, Elsevier, vol. 264(C).
    2. Bahi, Aya & Sauvage, Sabine & Payraudeau, Sylvain & Tournebize, Julien, 2023. "PESTIPOND: A descriptive model of pesticide fate in artificial ponds: II. Model application and evaluation," Ecological Modelling, Elsevier, vol. 484(C).

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