Abstract
Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we 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 automatically override the model's built-in semiempirical LAI with spatially distributed and temporally continuous 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 the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, 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 fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.
Highlights
The big data evolution in recent years has paved the way for remote sensing-integrated watershed modeling
The few recent studies which have assimilated remotely sensed Leaf Area Index (LAI) data in traditional watershed modeling practices invariably showed that better LAI representation improves model predictability [24,25,27–30]. Whereas these results promote a widespread use of remotely sensed LAI data in watershed modeling, we identified two major knowledge gaps that require targeted investigations: (1) Previous studies were predominantly focused on hydrologic processes [24,25,27–29]
The specific objective of this study was to quantify the extent of improvements that the assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data would convey to streamflow, soil moisture, and nitrate load simulations at a daily timescale
Summary
The big data evolution in recent years has paved the way for remote sensing-integrated watershed modeling. To limit models’ perceived tendency to give the “right answers for wrong reasons” [1,2], and ensure realistic watershed management alternatives [3–5], the hydrogeoscience community is increasingly using remotely sensed big data. It is already evident that remotely sensed estimates of vertical water fluxes, such as soil moisture and evapotranspiration, offer realistic constraints to watershed models, leading to improved simulation of hydrologic processes [6–10]. Such improved simulation is feasible despite misrepresented vegetation dynamics in the model [7,8]. The implications of misrepresented vegetation dynamics on water quality simulations (e.g., in-stream nutrient load [13]) remains underexplored
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