Till sidans topp

Sidansvarig: Webbredaktion
Sidan uppdaterades: 2012-09-11 15:12

Tipsa en vän
Utskriftsversion

Improving snow process mo… - Göteborgs universitet Till startsida
Webbkarta
Till innehåll Läs mer om hur kakor används på gu.se

Improving snow process modeling with satellite-based estimation of near-surface-air-temperature lapse rate

Artikel i vetenskaplig tidskrift
Författare L. Wang
L. Sun
M. Shrestha
X. Li
W. Liu
J. Zhou
K. Yang
H. Lu
Deliang Chen
Publicerad i Journal of Geophysical Research - Atmospheres
Volym 121
Nummer/häfte 20
Sidor 12,005-12,030
ISSN 0148-0227
Publiceringsår 2016
Publicerad vid Institutionen för geovetenskaper
Sidor 12,005-12,030
Språk en
Länkar dx.doi.org/10.1002/2016JD025506
Ämnesord distributed hydrological model, lapse rate, MODIS land surface temperature, snow process, water and energy cycle
Ämneskategorier Geofysik

Sammanfattning

In distributed hydrological modeling, surface air temperature (Tair) is of great importance in simulating cold region processes, while the near-surface-air-temperature lapse rate (NLR) is crucial to prepare Tair (when interpolating Tair from site observations to model grids). In this study, a distributed biosphere hydrological model with improved snow physics (WEB-DHM-S) was rigorously evaluated in a typical cold, large river basin (e.g., the upper Yellow River basin), given a mean monthly NLRs. Based on the validated model, we have examined the influence of the NLR on the simulated snow processes and streamflows. We found that the NLR has a large effect on the simulated streamflows, with a maximum difference of greater than 24% among the various scenarios for NLRs considered. To supplement the insufficient number of monitoring sites for near-surface-air-temperature at developing/undeveloped mountain regions, the nighttime Moderate Resolution Imaging Spectroradiometer land surface temperature is used as an alternative to derive the approximate NLR at a finer spatial scale (e.g., at different elevation bands, different land covers, different aspects, and different snow conditions). Using satellite-based estimation of NLR, the modeling of snow processes has been greatly refined. Results show that both the determination of rainfall/snowfall and the snowpack process were significantly improved, contributing to a reduced summer evapotranspiration and thus an improved streamflow simulation. ©2016. American Geophysical Union. All Rights Reserved.

Sidansvarig: Webbredaktion|Sidan uppdaterades: 2012-09-11
Dela:

På Göteborgs universitet använder vi kakor (cookies) för att webbplatsen ska fungera på ett bra sätt för dig. Genom att surfa vidare godkänner du att vi använder kakor.  Vad är kakor?