MULTI-PARAMETER SENSITIVITY ANALYSIS AND OPTIMIZATION OF THE ALPINE HYDROCHEMICAL MODEL

Abstract

The University of Arizona's Alpine Hydrochemical Model (AHM) is an integrated set of algorithms for water and chemical balances that describes hydrologic and chemical processes in a headwater catchment. We developed AHM for use both as a research tool and as a predictive model for estimating effects of natural and anthropogenic changes in climate or in atmospheric -pollutant loading on alpine watersheds. We initially applied AHM to Emerald Lake watershed in the southern Sierra Nevada, and estimated model parameters by trial and error using a single water year of data and process -level studies. Using the same parameters, AHM successfully reproduced stream chemistry and discharge for a second water year. We have extended that empirical analysis by doing a systematic analysis of parameter sensitivity and an automatic optimization of model parameters. In the sensitivity analysis, a large number of Monte -Carlo simulations done on the multi -dimensional function field were used to identify the sensitive parameters and to set an appropriate range for each parameter. These results were then used to reduce the computational load in the automatic optimization, which is based on the downhill simplex method in multiple dimensions; we estimate the global optimum parameter set according to the fluctuation of the sum of squared errors between observed and modeled stream discharge and chemistry. Sensitive physical and chemical parameters were identified, including those describing evapotranspiration, hydraulic conductivity and soil depth or porosity; and those describing mineral weathering, ion release from the snow - pack, ion exchange, soil CO2 and nitrogen reactions. The automatic optimization method succeeded in estimating a global optimum parameter set from a single water year of data that improved the fitting compared to the set from trial and error manipulation.