Improved snow cover model in terrestrial ecosystem models over the Qinghai–Tibetan Plateau
Introduction
Snow cover has been found to be a critical component of terrestrial ecosystem and has a strong influence on carbon and water fluxes between atmosphere and land surface by altering soil temperature, moisture, roughness, and albedo (Stewart et al., 2004). Snow cover regulates the ecosystem water cycle by resisting moisture recharge and recharging soil moisture during thaws (Shanley and Chalmers, 1999). The insulating properties of snow cover substantially impact the processes of soil thawing and freezing (Mellander et al., 2004). Moreover, some studies showed that interannual variations in vegetation growth were primarily governed by year-to-year variations in the length of the snow-free period (Ryo, 2014, Choler, 2015). Numerous studies have highlighted that snow cover dynamics need to be simulated and incorporated into ecosystem models to provide an adequate assessment of the role of snow cover in terrestrial ecosystems (Douville et al., 1995, Zhuang et al., 2001, Walter et al., 2005, Trnka et al., 2010).
Many snow cover models have been developed and integrated into various models (Schlosser et al., 2000, Franz et al., 2008, Trnka et al., 2010, Jégo et al., 2014). Based on the observations from an alpine site, a recent comparison of 1701 snow models showed no best model if consideration of errors in simulations of snow mass, snow depth, albedo and surface temperature (Essery et al., 2013). More importantly, most of these modeling studies were conducted in high-latitude regions (Henderson-Sellers et al., 1995, Bowling et al., 2003, Nijssen et al., 2003), with few model studies in the Qinghai–Tibetan Plateau region. In such a low-latitude alpine region, the land surface and climate properties are different from those at high latitudes (Qin et al., 2006). The Qinghai–Tibetan Plateau is characterized by a semiarid climate with mean annual precipitation below 450 mm, as well as relatively little snowfall (Wang et al., 2009). The dominant plants in the Qinghai–Tibetan Plateau are very low, with a weak capability to block wind from the land surface (Li et al., 2006). Therefore, snow cover models need to be further examined and developed for use over the plateau.
Despite the fact that snow cover studies over the Qinghai–Tibetan Plateau are few, snow cover plays an important role in regulating major ecosystem structure and functions. Previous study showed snow melting date and vegetation green-up date were significantly correlated over large areas of the Qinghai–Tibetan Plateau (Shen, 2014). Seasonal snow cover durations substantially impacted vegetation growth over the Qinghai–Tibetan Plateau, and especially March–May snow cover dynamics played a significantly role in vegetation growth (Wang et al., 2014). Moreover, snow depth affected species composition of the Qinghai–Tibetan Plateau, and the highest species richness and species diversity occurred with an intermediate snow depth, showing a unimodal curve with the increase in snow depth (Chen et al., 2008).
The overarching goal of this study is to examine and improve a snow cover model over the Qinghai–Tibetan Plateau. In particular, the model should adequately incorporate the unique processes over the Qinghai–Tibetan Plateau, and model formulations should rely only on meteorological data that are commonly included in ecosystem models. Specific objectives are to (1) examine the accuracy of current snow cover models in the Qinghai–Tibetan Plateau, (2) improve snow cover models by considering the unique characteristics of the Qinghai–Tibetan Plateau, and (3) compare snow cover predictions with observations from meteorological stations and satellite products.
Section snippets
SnowMAUS model
The SnowMAUS model was originally developed by Running and Coughlan (1988) and modified and examined by Thornton et al. (2000) and Trnka et al. (2010). The SnowMAUS model operates with a daily time step and simulates three major processes of snow cover dynamics: snow accumulation, melting, and sublimation. The snow accumulation (SnowAccu, mm) process separates precipitation (Precip, mm) into snow and rain using a simple linear function of daily minimum air temperature (Tmin, °C):
Site comparison of snow cover models
All three previous models (SnowMAUS, revised SnowMAUS, and SnowFrostIce) showed similar performance for simulating snow cover dynamics, with low r and high predictive bias (PE and RMSE). Over all validation sites, on average, the correlation coefficients (r) of the three models were 0.30–0.49 and 0.59–0.71 for snow cover days and snow depth respectively (Fig. 2a and d). All three models overestimated the days and depth of snow cover at almost all sites. On average, the three models
Discussion
Simple snow cover models have been developed by a number of researchers; however, most of these models were used either in mountainous terrain or at high latitudes (Trnka et al., 2010, Jégo et al., 2014). None of them has been examined in the Qinghai–Tibetan Plateau. The results obtained here showed poor performance for three previous models which ignored the impact of wind speed (Fig. 2, Fig. 3). The Qinghai–Tibetan Plateau has unique characteristics of vegetation and climate condition. Except
Summary
This study aimed to examine the reliability of previous snow cover models in the Qinghai–Tibetan Plateau, which is characterized by high wind speeds and flat terrain. The results obtained here showed that three previous snow cover models which were validated and used in other regions overestimated snow cover depth and the number of days with snow because they ignored the impact of wind speed. Based on the SnowMAUS model, a new model (SnowWind) was developed in this research by incorporating
Acknowledgements
This study was supported by Key Project of Chinese Academy of Sciences (CAS) (KJZD-EW-G03-04), One Hundred Person Project of CAS, and National Science Foundation for Excellent Young Scholars of China (41322005). Snow depth dataset derived passive microwave remote-sensing data is provided by Cold and Arid Regions Sciences Data Center at Lanzhou (http://westdc.westgis.ac.cn). We have complied with AGU's Data Policy by providing information on how to obtain the data used to produce the results of
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