HR: 0830h
AN: C41B-0957    [PDF]
TI: Coupling a Physically Based and Spatially Distributed Snowmelt Model with a Flowpath Model, Green Lakes Valley, Colorado Front Range
AU: * Liu, F
EM: fengjing@snobear.colorado.edu
AF: Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO 80309 United States
AU: * Liu, F
EM: fengjing@snobear.colorado.edu
AF: Department of Geography, University of Colorado, Boulder, CO 80309 United States
AU: Williams, M W
EM: markw@snobear.colorado.edu
AF: Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO 80309 United States
AU: Williams, M W
EM: markw@snobear.colorado.edu
AF: Department of Geography, University of Colorado, Boulder, CO 80309 United States
AU: Ackerman, T
EM: Todd.Ackerman@Colorado.EDU
AF: Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO 80309 United States
AB: Spatially distributed estimates of snow deposition and melt allow us to better understand the interaction between hydrology and topographic structure and climate in mountain basins. Here we report on initial efforts to add a physical based and spatially distributed energy balance snowmelt model, ISNOBAL to the XTOP\_PRMS model, a flowpath model coupling the TOPMODEL and Precipitation Runoff Modeling System within the Module Modeling System (MMS). This procedure was demonstrated at the 8-ha Martinelli and the 220-ha Green Lake 4 (GL4) catchments in the Green Lakes Valley, Colorado Front Range. In the pilot study using a temperature index snowmelt algorithm in the XTOP\_PRMS model, streamflow discharge measured in 1996 was used to calibrate parameters and streamflow discharge was simulated from 1997 to 2000. The results showed that the discharge simulation was relatively successful at the 8-ha Martinelli catchment, with a Nash-Sutcliffe efficiency of 0.76. The {\it t}-test for paired means indicated that the prediction of discharge was not significantly different from the observation at $\alpha$ = 0.05 ({\it n} = 1611, {\it p} = 0.6). However, the discharge simulation was relatively poor at the GL4 catchment, with a Nash-Sutcliffe efficiency of only 0.54. Moreover, the difference between the predicted and observed values was significant at $\alpha$ = 0.05 ({\it n} = 1611, {\it p} = 0.005). It is believed that the poor performance of the XTOP\_PRMS model at the GL4 catchment was due to use of the temperature index snowmelt algorithm as well as the coarse (daily) temporal resolution of the air temperature in the model. To resolve this problem, we report on our efforts to incorporate the hourly mode ISNOBAL with the XTOP\_PRMS model within the MMS. High temporal resolution time-series images of climate surfaces such as air temperature, humidity, wind speed, precipitation, and solar and thermal radiation are generated using the Image Processing Workbench (IPW) and a combination of 3 to 5 point measurements, elevational gradients, and detrended Kriging. Through those efforts, we show that the discharge and flowpath simulations can be improved at both Martinelli and GL4 catchments.
DE: 1860 Runoff and streamflow
DE: 1863 Snow and ice (1827)
SC: Cryosphere [C]
MN: 2003 Fall Meeting