Evaluation of Statistically Downscaled GCM Output as Input for Hydrological and Stream Temperature Simulation in the Apalachicola–Chattahoochee–Flint River Basin (1961–99)

• Published in: Earth interactions Vol. 18; no. 9; pp. 1 - 32
• Main Authors: Hay, Lauren E, LaFontaine, Jacob, Markstrom, Steven L
• Format: Journal Article
• Language: English
• Published: Washington American Meteorological Society 01.04.2014
• Subjects: Air temperature; Armed forces; Climate; Climate and hydrology; Climate and land use; Climate change; Climate models; Climatic conditions; Components; Daily; Daily precipitation; Evaluation; Freshwater; General circulation models; Groundwater; Heat transport; Historic temperatures; Hydrologic models; Hydrology; Land use; Modelling; Mollusks; Precipitation; Process parameters; Rainfall-runoff relationships; Regression models; River basins; Rivers; Runoff; Simulation; Spatial analysis; Stream discharge; Stream flow; Temperature; Temperature requirements; Water temperature; Watersheds; United States > US; Atlanta Georgia; Chattahoochee River; Apalachicola River; Gulf of Mexico; Georgia; Florida; Alabama; Flint River; Lake Seminole; USA, Southeast
• ISSN: 1087-3562; 1087-3562
• DOI: 10.1175/2013EI000554.1

Abstract The accuracy of statistically downscaled general circulation model (GCM) simulations of daily surface climate for historical conditions (1961–99) and the implications when they are used to drive hydrologic and stream temperature models were assessed for the Apalachicola–Chattahoochee–Flint River basin (ACFB). The ACFB is a 50 000 km2 basin located in the southeastern United States. Three GCMs were statistically downscaled, using an asynchronous regional regression model (ARRM), to ⅛° grids of daily precipitation and minimum and maximum air temperature. These ARRM-based climate datasets were used as input to the Precipitation-Runoff Modeling System (PRMS), a deterministic, distributed-parameter, physical-process watershed model used to simulate and evaluate the effects of various combinations of climate and land use on watershed response. The ACFB was divided into 258 hydrologic response units (HRUs) in which the components of flow (groundwater, subsurface, and surface) are computed in response to climate, land surface, and subsurface characteristics of the basin. Daily simulations of flow components from PRMS were used with the climate to simulate in-stream water temperatures using the Stream Network Temperature (SNTemp) model, a mechanistic, one-dimensional heat transport model for branched stream networks. The climate, hydrology, and stream temperature for historical conditions were evaluated by comparing model outputs produced from historical climate forcings developed from gridded station data (GSD) versus those produced from the three statistically downscaled GCMs using the ARRM methodology. The PRMS and SNTemp models were forced with the GSD and the outputs produced were treated as “truth.” This allowed for a spatial comparison by HRU of the GSD-based output with ARRM-based output. Distributional similarities between GSD- and ARRM-based model outputs were compared using the two-sample Kolmogorov–Smirnov (KS) test in combination with descriptive metrics such as the mean and variance and an evaluation of rare and sustained events. In general, precipitation and streamflow quantities were negatively biased in the downscaled GCM outputs, and results indicate that the downscaled GCM simulations consistently underestimate the largest precipitation events relative to the GSD. The KS test results indicate that ARRM-based air temperatures are similar to GSD at the daily time step for the majority of the ACFB, with perhaps subweekly averaging for stream temperature. Depending on GCM and spatial location, ARRM-based precipitation and streamflow requires averaging of up to 30 days to become similar to the GSD-based output. Evaluation of the model skill for historical conditions suggests some guidelines for use of future projections; while it seems correct to place greater confidence in evaluation metrics which perform well historically, this does not necessarily mean those metrics will accurately reflect model outputs for future climatic conditions. Results from this study indicate no “best” overall model, but the breadth of analysis can be used to give the product users an indication of the applicability of the results to address their particular problem. Since results for historical conditions indicate that model outputs can have significant biases associated with them, the range in future projections examined in terms of change relative to historical conditions for each individual GCM may be more appropriate.