The goal of this project is to develop a dynamic decision support system to enhance the USGS spatially referenced regression on watershed attributes (sparrow) water quality model for the…

The goal of this project is to develop a dynamic decision support system to enhance the USGS spatially referenced regression on watershed attributes (sparrow) water quality model for the southeastern united states through the use of remotely-sensed data and other NASA land information system (LIS) products. Sparrow is widely used throughout the united states for long term, steady state water quality analysis. However, users are increasingly asking for a dynamic version of the model that can provide seasonal estimates of nutrients and suspended sediment to receiving waters. Time varying sparrow outputs would aid water managers in decision making regarding allocation of resources in protecting aquatic habitats, planning for harmful algal blooms, and restoration of degraded habitats, stream segments, or lakes. The spatial and temporal scale of satellite remote sensing products and LIS modeling data make these sources ideal for the purposes of development and operation of the dynamic sparrow model. The proposed project is a joint effort between the US geological survey, remote sensing scientists and engineers at the national space science and technology center, and managers at the mobile BAY national estuary program, the tampa BAY estuary program, the sarasota BAY estuary program, and both the weeks BAY and north inlet winyah BAY national estuarine research reserves (nerrs). Remote sensing products including modis vegetation indices, smap surface soil moisture, and OMI atmospheric chemistry along with LIS derived ET and soil temperature and moisture products will be included in model development and operation. Landsat data will also be used to map land cover/land use in the study areas as well as identify disturbed areas that might be sources of sediment and increased phosphorus loading through exposure of the bare soil. These data, and others constitute the independent variables in a regression analysis whose dependent variables are the water quality constituents total nitrogen, total phosphorus, and suspended sediment. Sparrow variables must either be direct observations of processes affecting nutrient or sediment generation and transport, or indirect proxies of these processes. Remotely-sensed variables such as vegetation indices and ET can be proxies for nutrient uptake by vegetation, modis LAI can indicate sources of phosphorus from vegetation, soil moisture and temperature that are known to control rates of denitrification, and bare soil areas serve as sources of enhanced nutrient and sediment production. The spatial relations and temporal frequency of all of these factors and more can be obtained from NASA remote sensing and LIS products and can provide added dimensions to sparrow in terms of the timing of processes on the landscape and the resultant nutrient and sediment loading to the receiving waters. Water managers in the southeastern US and elsewhere continually express their strategic needs for this information.