Multicriteria parameter estimation for models of stream chemical composition

[1] The inability to develop an accurate and precise parameter estimation method for catchment hydrochemical models has been a persistent problem. We investigate the use of multicriteria calibration techniques and the selection of the criteria as a first step in solving this problem. We applied a multicriteria search algorithm to the Alpine Hydrochemical Model (AHM) of the Emerald Lake watershed, Sequoia National Park, California. A total of 21 chemical and hydrologic criteria were available for determining model performance. Four subsets of these criteria were selected for the multicriteria analysis using three different methods. The first set used the four least correlated observations of stream chemical composition. A second set of criteria was determined by using the four species with the least correlated root mean square error criteria values (p < 0.05 for each pair). Finally, two sets were chosen using the results of a multicriteria sensitivity analysis. The most accurate and precise results were observed using criteria selected using results from a sensitivity analysis, with the correlation analyses being a poor method for selecting criteria. This set of criteria emphasizes attributes of the model structure, the observations, and our understanding of the processes influencing watershed hydrology and water quality. Also, our results gave improved estimates of several hydrologic and biogeochemical processes in addition to identifying a flaw in the current representation of mineral weathering within the AHM, as applied to the Emerald Lake watershed.