Application of Climate Assessment Tool (CAT) to estimate climate variability impacts on nutrient loading from local watersheds
A vast amount of future climate scenario datasets, created by climate models such as general circulation models (GCMs), have been used in conjunction with watershed models to project future climate variability impact on hydrological processes and water quality. However, these low spatial-temporal resolution datasets are often difficult to downscale spatially and disaggregate temporarily, and they may not be accurate for local watersheds (i.e., state level or smaller watersheds). This study applied the US-EPA (Environmental Protection Agency)’s Climate Assessment Tool (CAT) to create future climate variability scenarios based on historical measured data for local watersheds. As a case demonstration, CAT was employed in conjunction with HSPF (Hydrological Simulation Program-FORTRAN) model to assess the impacts of the potential future extreme rainfall events and
air temperature increases upon nitrate-nitrogen (NO3-N) and orthophosphate (PO4) loads in the Lower YazooRiver Watershed (LYRW), a local watershed in Mississippi, USA. Results showed that the 10 and 20% increases in rainfall rate, respectively, increased NO3-N load by 9.1 and 18% and PO4 load by 12 and 24% over a 10-year simulation period. In contrast, simultaneous increases in air temperature by 1.0 °C and rainfall rate by 10% as well as air temperature by 2.0 °C and rainfall rate by 20% increased NO3-N load by 12% and 20%, and PO4 load by 14 and 26%, respectively. A summer extreme rainfall scenario was created if a 10% increase in rainfall rate increased the total volume of rainwater for that summer by 10% or more. When this event occurred, it could increase the monthly loads of NO3-N and PO4, by 31 and 41%, respectively, for that summer. Therefore, the extreme rainfall events had tremendous impacts on the NO3-N and PO4 loads. It is apparent that CAT is a flexible and useful tool to modify historical rainfall and air temperature data to predict climate variability impacts on water quality for local watersheds.