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

Abstract

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.

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

“…The temperature lapse rate derived from the remote sensing based Ta ranges from −4 to −7 K/km, which is generally consistent with the reported observation values from independent station observations [61]. Due to the influence of terrain on air flow and surface radiation, the lapse rate varies with elevation and terrain aspect [64,65]. The lapse rates at different elevation bands and aspects derived from the remote sensing based daily Ta are presented in Tables 2 and 3.…”

Temporal and spatial changes in estimated near‐surface air temperature lapse rates on Tibetan Plateau

“…Considering the use of MODIS night-time LSTs, we should pay more attention to cloud cover, because the LRs in sunny days and cloudy days can be quite different. Wang et al (2016) found that the LRs in cloudy days were usually shallower than those in sunny days in the Yellow River source basin. Second, the influence of latitude and longitude on the calculation of LRs was not considered.…”

“…In general, LSTs and T_obs are closer at night due to the lack of the solar radiation effect (Kawashima et al, 2000;Wang et al, 2016), but other factors can also enhance/decrease the difference. In our study, the maximum temperature difference can sometimes reach 30 K in some stations, which is possibly affected by cloud cover.…”

Distributed Hydrological Modeling Framework for Quantitative and Spatial Bias Correction for Rainfall, Snowfall, and Mixed‐Phase Precipitation Using Vertical Profile of Temperature

“…Additionally, the DHMs are capable of coupling the remotely sensed products, GCM, RCM, NWPs, or atmospheric reanalysis. This model is known as WEB-DHM-S with diversified application especially mountainous basin with snow dynamics (e.g., Shrestha et al, 2011;Shrestha, Koike, et al, 2014;Shrestha, Wang, et al, 2014;Sixto et al, 2012;Wang et al, 2016). Shrestha et al (2012) improved the snow physics of water and energy budget-based distributed hydrological model (WEB-DHM) by embedding a three-layer energy balance snow scheme from Simplified Simple Biosphere Scheme 3 (Xue et al, 2003) and snow albedo as a prognostic state variable using Biosphere-Atmosphere Transfer Scheme (Dickinson et al, 1993) for a more realistic and accurate simulation of complex snow processes.…”