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Effect of Remotely Sensed Vegetation in Hydrology and Water Quality Predictions: New Evidence from Large-scale Watershed Modeling
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  • I Luk Kim,
  • Adnan Rajib,
  • Heather Golden,
  • Charles Lane,
  • Sujay Kumar
I Luk Kim
Purdue University

Corresponding Author:[email protected]

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Adnan Rajib
Texas A&M University Kingsville
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Heather Golden
US Environmental Protection Agency, Office of Research & Development
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Charles Lane
US Environmental Protection Agency, Office of Research & Development
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Sujay Kumar
NASA Goddard Space Flight Center
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Traditional watershed modeling often overlooks the role of vegetation dynamics. While past studies indicate possible improved hydrologic predictions by increasing the physical realism of vegetation dynamics in process-based models, there has been little quantitative evidence to support similar improvements in water quality predictions. To fill this knowledge-gap, we recently applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to directly insert spatially distributed and temporally continuous LAI estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.