Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

Abstract

<jats:p>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.</jats:p>

Assessing hydrologic and water quality effects of land use conversion to Brassica carinata as a winter biofuel crop in the southeastern coastal plain of Georgia, USA using the SWAT model

“…Here, we included relevant biophysical parameters during the streamflow calibration process using the aggregated (or lumped) approach (Chen et al, 2017; Cibin et al, 2016; Demissie et al, 2017; Holder et al, 2019). Recent research in watershed modeling has shown improvements in hydrology and water quality predictions with the assimilation of remotely sensed biophysical data (e.g., LAI; Ma et al, 2019; Rajib et al, 2020). Future research is needed to incorporate remotely sensed biophysical parameters in modeling the effect of bioenergy crops on hydrology and water quality with respect to crop yields in various geophysical regions.…”

Drought Early Warning in Agri-Food Systems

“…The remote sensing literature offers numerous examples proposing earth observation techniques to support assessment of drought conditions [23]. Increasing access to open data, high-resolution remote sensors, and enhanced computing facilities have led to a new set of sophisticated techniques for DEWS in agriculture experiencing drought stress conditions [24], which monitor evapotranspiration [25], soil moisture [26], ground water fluxes [26,27], and precipitation.…”

Impact of Real‐Time Satellite‐Derived Green Vegetation Fraction on National Water Model Fluxes in Humid Alabama Watersheds

“…Conversely, Parr et al (2015) found that interannually varying LAI better captured discharge variability and soil moisture estimates. Barbu et al (2011) found that assimilated in situ LAI corrects model deficiencies, particularly during senescence and the start of the growing season, and Rajib et al (2020) assimilated MODIS LAI leading to improved daily streamflow in medium‐to‐low flow conditions and realistic distributions of soil moisture during the growing season. It is the goal of this study to quantify the GVF‐related impacts to land surface fluxes and subsequent runoff within the NWM configuration.…”