Energy balance snowmelt modeling in the Echaurren Basin, Chilean Andes

Neural networks, binary regression trees, and simple interpolation methods were employed to create snow water equivalence (SWE) maps for the 4.7 km^2 Echaurren basin in the Chilean Andes (3300 m elevation, 33.58 S, 70.13 N). Distributed SWE is critical for forecsting seasonal runoff and provides the initial condition for forecasting the timing of runoff. Data from five annual peak-accumulation snow surveys (1992-1996) involving snow depth measurements at approximately 100 points and representative density measurements were used to estimate SWE at each point in the basin (5-m grid spacing). Independent variables in the regression tree and neural network were elevation, slope, aspect, mean daily rediation, and soil type. Results developed using a regression tree approach were found to be very sensitive to the accurate location of survey points. A shift of as small as 10-m in the placement of survey points in the regression calculation gave a considerably different distributed SWE map for the basin. Both regression trees and neural networks produced qualitatiely similar distributions of snow. Unlike neural networks, however, SWE maps from regression trees are limited to the range of input SWE values from field survey data. The neural network, on the other hand, can extrapolete SWE values in the basin. This is important in steeper slopes where the regression trees overetimated SWE. A comparison of errors using synthetic data for the catchment suggests that the neural networkds gives a more accurate estimation of the total SWE and distributedd SWE for this catchment. Kriging was attempted, but the field data were to sparse, and this approach was abandoned. Thiessen polygons showed similar SWE distributions to the regression tree and neural network distributed SWE maps. Due to the limited data points, polygons were constructed over extreme elevation ranges and may not accurately represent true basin SWE accumulation. An energy-balance model was used to simulate the melting of several distributed SWE maps. The snowmelt analysis was conducted on an hourly time step from peak-accumulation (September 30, 1992) until the snow completely melted in mid-summer. Simple hydrographs for the watershed were created. Preliminary results of an error analysis betweeen hydrographs generated with the regression tree, neural network, and Thiessen polygon method and the actual stream hydrograph suggest that the neural network method of SWE interpolation most accurately distributes SWE in the Echaurren basin.