• Application of snow distribution models within the laguna Negra basin, Chile

      Cadle, Brad J.; Bales, Roger C.; Department of Hydrology & Water Resources, The University of Arizona (Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1997-08)
      Spectral linear unmixing and binary regression trees were used to estimate the distribution of snow within the Laguna Negra basin in Chile. Spectral linear unmixing was performed for multi-band Landsat 5 images for the determination of sub-pixel snow fractions. We were interested in determining the number of bands needed for an adequate distribution of SCA. Results showed that for winter scenes (scenes with greater than 90% snow cover and portions of the basin covered by shadows) linear spectral unmixing can be used to model SCA using at least four bands with a rock, a snow and a shaded snow endmember, but that five bands, using two rock endmembers, a snow and a shaded rock endmember, are needed for the fall scenes (scenes with less than 10% snow cover and portions of the basin covered by shadows). The spring scenes (scenes with 50 percent and higher snow cover and no shadows) showed plausible results with three bands, but the need for a second rock endmember in the fall scenes suggest 4 bands may give a more accurate result. A binary regression tree model was used to determine distributed SWE at peak accumulation in the Echaurren basin, a sub basin of Laguna Negra. Regression trees grown from field snow survey data did an excellent job at explaining the variation of SWE in two of the three surveys examined when resubstitution was used to evaluate the model, but did a poor job in all cases when cross validation was used. However, cross validation may over estimate the errors associated with the model. Basin-wide SWE maps resulting from the application of the regression trees formed plausible structures. Normalized snow distribution was sufficiently different between years such that a "typical" SWE map could not be developed. Nonetheless, there were identifiable patterns that did occur in the SWE distributions from different years that gave insight into the factors affecting SWE in the basin. Such factors include a strong dependance on radiation in the lower portion of Echaurren for two of the years, and the presence of heavy SWE regions near cliffs. Insights such as these provided useful information on how the type of data and method of collection might be improved. The large SWE values near cliffs, for instance, suggest that use of an avalanche map might improve the modeled SWE distribution. The dependance of SWE on radiation in the lower basin suggest the SWE data should be obtained over the entire range of radiation values in the lower basin.
    • Hydrologic resource assessment of upper Sabino Creek basin, Pima county, Arizona

      Peters, Christopher J.; Bales, Roger C.; Department of Hydrology & Water Resources, The University of Arizona (Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 2001-01)
      A hydrologic resource assessment was performed for upper Sabino Creek basin, using data from a variety of local, state, and Federal agencies and organizations. Hydrologic fluxes were identified and quantified in order to create a monthly water budget. Snowmelt and rainfall are the major inputs to the watershed. Evapotranspiration accounts for the greatest loss of water. Human consumption and streamflow, while important for regulatory and aesthetic reasons, are relatively minor components of the water budget. Evapotranspiration, precipitation, and groundwater recharge / soil moisture account for the greatest fluxes of water in the basin. Precipitation is the most variable hydrologic process in the study area. Over a 47-year period, the greatest amount of water moving through the system in any one month was 6,300 acre-feet in October of 1983. The month with the lowest movement of water was December 1996, with 400 acre-feet. A comparison of Sabino Creek data with the El Niño Southern Oscillation phenomenon shows a strong correlation with precipitation and streamflow in upper Sabino Creek basin.

      Ohte, Nobuhito; Bales, Roger C.; Department of Hydrology & Water Resources, The University of Arizona (Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1994)
      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.