Deriving Disturbance Indices and Loading Factors Using Remote Sensing Data and Crop Models to Develop Derived Variables for SPARROW Water Quality Models in the Southeast USA

A dynamic version of the USGS southeast (SE) Spatially Referenced Regression on Watershed Attributes (SPARROW) water quality modeling system will be created for selected coastal watersheds through the use of remotely-sensed data products. SPARROW models are used throughout the United States for long term, steady state water quality analysis. The NASA Applied Sciences Program is supporting this effort to provide end users with a dynamic version of the model that can provide seasonal estimates of nutrients and suspended sediment to receiving waters, which can be achieved by using remotely sensed data plus associated derived products to enhance model development. Within SPARROW, remotely sensed products would constitute the independent variables in a regression analysis whose dependent variables are the water quality constituents' total nitrogen, total phosphorus, and suspended sediment. Our objectives are to describe the methods used to develop the various derived remote sensing products, illustrate the character of the products in the SE modeling domain and discuss their value to creating dynamic SPARROW models. Our hybrid approach uses remotely sensed data, geospatial analysis tools and Google Earth Engine (GEE) to develop innovative seasonal products/variables by catchment for inputs to SPARROW. Derived products that will be discussed include quarterly mean Enhanced Vegetation Index (EVI) values and a derived Normalized Difference Water Quality Index both developed with MODIS products for the SE regional model. MODIS land cover and derived EVI quarterly values were used to develop a seasonal Forest Disturbance Index (FDI) for SE catchments. The FDI was calculated based on MODIS EVI for the forested area normalized for temperature and precipitation. MODIS fire detections are being aggregated to estimate fire disturbance impacts on water quality too. Also, the expanded Decision Support System for Agrotechnology Transfer (DSSAT) crop model is being used to determine relationships between the residual soil nitrogen from DSSAT and nitrogen levels in the streams to refine loading factors currently used in the model. The enhanced SE regional and coastal watershed dynamic models will provide better tools for end users to evaluate water quality and inform natural resources planning.