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Uncertain Benefits of Using Remotely Sensed Evapotranspiration for Streamflow Estimation-Insights from a Randomized, Large-Sample Experiment
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  • Tam Van Nguyen,
  • Hung T.T Nguyen,
  • Vinh Ngoc Tran,
  • Manh-Hung Le,
  • Binh Quang Nguyen,
  • Hung Thanh Pham,
  • Tu Hoang Le,
  • Doan Van Binh,
  • Thanh Duc Dang,
  • Hoang Tran,
  • Hong Xuan Do
Tam Van Nguyen
Helmholtz Centre for Environmental Research
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Hung T.T Nguyen
Columbia University

Corresponding Author:[email protected]

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Vinh Ngoc Tran
University of Michigan
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Manh-Hung Le
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center
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Binh Quang Nguyen
The University of Danang - University of Science and Technology
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Hung Thanh Pham
The University of Danang
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Tu Hoang Le
Research Center for Climate Change, Nong Lam University - Ho Chi Minh City
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Doan Van Binh
Master Program in Water Technology, Reuse and Management, Vietnamese German University
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Thanh Duc Dang
University of South Florida
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Hoang Tran
Pacific Northwest National Laboratory
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Hong Xuan Do
Nong Lam University
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Remotely sensed evapotranspiration (ETRS) is increasingly used for streamflow estimation. Earlier reports are conflicting as to whether ETRS is useful in improving streamflow estimation skills. We believe that it is because earlier works used calibrated models and explored only small subspaces of the complex relationship between model skills for streamflow (Q) and ET. To shed some light on this complex relationship, we design a novel randomized, large sample experiment to explore the full ET-Q skill space, using seven catchments in Vietnam and four global ETRS products. For each catchment and each ETRS product, we employ 10,000 SWAT (Soil and Water Assessment Tool) model runs whose parameters are randomly generated via Latin Hypercube sampling. We then assess the full joint distribution of streamflow and ET skills using all model simulations. Results show that the relationship between ET and streamflow skills varies with regions, ETRS products, and the selected performance indices. This relationship even changes with different ranges of ET skills. Parameter sensitivity analysis indicates that the most sensitive parameters could have opposite contributions to ET and streamflow skills. Conditional probability assessment reveals that with certain ETRS products, the probabilities of having good streamflow skills are high and increase with better ET skills, but for other ETRS products, good model skills for streamflow are only achievable with certain intermediate ranges of ET skills, not the best ones. Overall, our study provides a useful approach for evaluating the value of ETRS for streamflow estimation.