Publications

Wang, L; Sun, LT; Shrestha, M; Li, XP; Liu, WB; Zhou, J; Yang, K; Lu, H; Chen, DL (2016). Improving snow process modeling with satellite-based estimation of near-surface-air-temperature lapse rate. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 121(20), 12005-12030.

Abstract
In distributed hydrological modeling, surface air temperature (T-air) is of great importance in simulating cold region processes, while the near-surface-air-temperature lapse rate (NLR) is crucial to prepare T-air (when interpolating T-air 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.

DOI:
10.1002/2016JD025506

ISSN:
2169-897X