Abstract
Abstract. Soils on the Qinghai–Tibetan Plateau (QTP) have distinct physical properties from agricultural soils due to weak weathering and strong erosion. These properties might affect permafrost dynamics. However, few studies have investigated both quantitatively. In this study, we selected a permafrost site on the central region of the QTP and excavated soil samples down to 200 cm. We measured soil porosity, thermal conductivity, saturated hydraulic conductivity, and matric potential in the laboratory. Finally, we ran a simulation model replacing default sand or loam parameters with different combinations of these measured parameters. Our results showed that the mass of coarse fragments in the soil samples (diameter >2 mm) was ∼55 % on average, soil porosity was less than 0.3 m3 m−3, saturated hydraulic conductivity ranged from 0.004 to 0.03 mm s−1, and saturated matric potential ranged from −14 to −604 mm. When default sand or loam parameters in the model were substituted with these measured values, the errors of soil temperature, soil liquid water content, active layer depth, and permafrost lower boundary depth were reduced (e.g., the root mean square errors of active layer depths simulated using measured parameters versus the default sand or loam parameters were about 0.28, 1.06, and 1.83 m). Among the measured parameters, porosity played a dominant role in reducing model errors and was typically much smaller than for soil textures used in land surface models. We also demonstrated that soil water dynamic processes should be considered, rather than using static properties under frozen and unfrozen soil states as in most permafrost models. We conclude that it is necessary to consider the distinct physical properties of coarse-fragment soils and water dynamics when simulating permafrost dynamics of the QTP. Thus it is important to develop methods for systematic measurement of physical properties of coarse-fragment soils and to develop a related spatial data set for porosity.
Highlights
Permafrost underlies 25 % of Earth’s surface
Our results showed that the root mean square errors (RMSEs) of active layer depth (ALD) and permafrost low boundary depth (PLB) were 0.55 and 4.78 m, whereas those calculated using φm were 0.28 and 6.71 m
It is possible to lose fidelity after daily interpolations, we still decided to use monthly driving data for the following reasons: (1) Zhuang et al (2001) performed a test with daily and monthly driving data sets, and the results showed that the RMSEs of ALD were about 3 cm; and (2) we intend to apply the model over large regions where reliable daily data sets might not be available
Summary
Permafrost underlies 25 % of Earth’s surface. Degradation of permafrost has been reported extensively in Alaska, Siberia. Permafrost dynamics have local to regional impacts on ecosystems by altering soil thermal and hydrological regimes (Salmon et al, 2015; Wang et al, 2008; Wright et al, 2009; Ye et al, 2009; Yi et al, 2014a). Degradation of permafrost affects infrastructure, such as QTP railways and roads (Wu et al, 2004) or the Trans-Alaska Pipeline System in Alaska (Nelson et al, 2001). It is critical to develop mitigation and adaptation strategies in permafrost regions for ongoing climate change. Accurate projection of the degree of permafrost degradation is a prerequisite for developing these strategies
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