HR: 0800h
AN: C21A-1066    [Abstracts]
TI: Lapse Rates and Spatial Interpolation of Air Temperature in Mountainous Terrain
AU: * Blandford, T R
EM: troy.blandford@uidaho.edu
AF: University of Idaho, Department of Geography, Moscow, ID 83844-3021
AU: Humes, K S
EM: khumes@uidaho.edu
AF: University of Idaho, Department of Geography, Moscow, ID 83844-3021
AU: Harshburger, B J
EM: brian.harshburger@uidaho.edu
AF: University of Idaho, Department of Geography, Moscow, ID 83844-3021
AU: Moore, B C
EM: brandonmoore@uidaho.edu
AF: University of Idaho, Department of Geography, Moscow, ID 83844-3021
AU: Walden, V P
EM: vonw@uidaho.edu
AF: University of Idaho, Department of Geography, Moscow, ID 83844-3021
AB: Interpolation of near-surface air temperature is a key part of all approaches to modeling snowmelt processes. Linear lapse rates are commonly used in models to extrapolate near-surface air temperature measurements from weather stations to unmeasured locations. Use of a constant environmental lapse rate is sometimes problematic at short temporal and fine spatial scales. We used 14 meteorological stations (2 COOP and 12 SNOTEL) located within 1/2° radius of the center of our mountainous study basin in south-central Idaho. Daily lapse rates were computed using linear regression between station elevation and near-surface air temperature (Tmax and Tmin) for the period October 1993 to December 2003. Variations in lapse rates were investigated by season, synoptic classification, and a combination of these factors. Results indicate a significant seasonal trend exists and synoptic weather types may help explain within-month variation in lapse rates. Errors in temperature interpolation were evaluated using cross-validation and compared to errors incurred with the use of a constant lapse rate. Lapse rates are also used in more complex spatial interpolation schemes such as detrended kriging. Results from detrended kriging were compared to the simple lapse rate approaches. Examples of the impact of the different interpolation schemes on snowmelt runoff predictions are presented.
DE: 0740 Snowmelt
DE: 1833 Hydroclimatology
DE: 1840 Hydrometeorology
SC: Cryosphere [C]
MN: Fall Meeting 2005