Elsevier

Science of The Total Environment

Volume 670, 20 June 2019, Pages 1190-1203
Science of The Total Environment

Hydro-thermal boundary conditions at different underlying surfaces in a permafrost region of the Qinghai-Tibet Plateau

https://doi.org/10.1016/j.scitotenv.2019.03.090Get rights and content

Highlights

  • The hydro-thermal boundary conditions were modeled at different underlying surfaces for permafrost engineering.

  • The regional climate downscaling method was combined with multiple linear regression method in the study.

  • The predicted and simulated models were calibrated and validated by the monitored data.

  • The developed approach was compared with the other methods for predicting hydro-thermal boundary conditions.

Abstract

Hydro-thermal properties of permafrost and its distribution are sensitive to climate changes and human activities. Accurate and reasonable prediction on aforementioned information is important for eco-environment construction and vital infrastructures development. To model the current and future states of permafrost, it is a key challenge to effectively determine the upper hydro-thermal boundary conditions for permafrost models under changing climate and different underlying surfaces at proper spatial and temporal scales. An approach, combined regional climate downscaling method with model output statistics method, was developed to produce a time series of air temperature, surface temperatures, and surface unfrozen water contents for different underlying surfaces. It provided various climate and surface parameters at a spatial scale on the order of 102 m2 for engineering designs, which was used to predict boundary conditions under possible climate scenarios. The predicted and simulated models were calibrated and validated by the monitored data at an experimental site in Chumar, China, close to the Qinghai-Tibet Railway and the Qinghai-Tibet Highway. Results show that the multiple linear regression model (MLRM) can predict the current states and future changes of upper hydro-thermal boundary conditions for permafrost while the original states of natural surface are modified by natural or human factors on the condition of complicated climatic and complex topography regions. The statistical regression model (SRM) based on the outputs of regional climate model (RCM) and MLRM provides a simple method for the convenience of numerical calculation. These results also indicate the possible applications to other areas and situations.

Introduction

Permafrost regions constitute 25% of the Earth's land area, with the percentage growing to 50 for the seasonally frozen regions (Zhou et al., 2000). The distribution and developing tendency of permafrost are influenced by many factors, including hydrologic and geologic characteristics, topographic features, climate changes, surficial conditions, biological and human activities (Nelson, 2003). In general, climatic characteristics and human activities play a primary role for the distribution and stability of permafrost. Hence, permafrost is regarded as an important indicator of climate changes. Furthermore, many critical transportation infrastructures have been constructed in these regions, such as the Qinghai-Tibet Railway and the Qinghai-Tibet Highway in China, the Alaska Railway and the Alaska Highway in USA, the Baikal-Amur Railway and the East-Siberian Railway in Russia, the Hudson Bay Railway in Canada (Cheng, 2005; Reimchen et al., 2010; Kondratiev, 2013; Addison et al., 2016). The construction of embankment modifies the pre-existing ground surface conditions and the original hydro-thermal state of underlying layer, which varies the vegetation fraction, the solar absorption, the potential evaporation, the sensible and latent heat flux at the surface (Zhang et al., 2008). Moreover, the changes of hydro-thermal state are aggravated by the climate warming, the precipitation changes and other anthropogenic perturbations. These changes not only disturb the eco-hydrological characteristics in permafrost regions, but also influence the freezing-thawing process within the active layer, which have a significant impact on the permafrost properties and the stability and sustainability of infrastructure.

Numerous approaches have been carried out to model permafrost condition and distribution related to climate system, such as analytical and physical models (Lunardini, 1996), empirical or semi-empirical models (Jin et al., 2000; Li and Cheng, 2002) and numerical models (Yang, 2006; Riseborough et al., 2008; Li et al., 2009; Wang et al., 2009; Zhang et al., 2013; Rasmussen et al., 2018). Compared with above models, numerical models, which combine the theoretical approaches and observed data, can be used to estimate the current conditions and predict the future changes in permafrost for scientific researches and engineering applications. Once a permafrost model is set up, it is also difficult to constrain the upper hydro-thermal boundary conditions for the specified models under different underlying surfaces and climate conditions. Generally, both the thermal and moisture boundary conditions can be summed up as three types (Li et al., 2014). In terms of upper thermal boundary condition, it is an available method to use long-term observed data in situ; however, it is hard to obtain long-term, accurate and complete observations for most of permafrost regions, especially at high latitudes and high altitudes. Moreover, this method is unavailable for future prediction and regions without observations. Researchers have put forward some empirical and statistical methods for upper temperature boundary conditions. One way is to make direct regression of observed air and surface temperatures (Beltrami, 1996; Niu et al., 2008). An alternative approach is to build relationships between air and ground temperature by using n factor (Duchesne et al., 2008; Luo et al., 2018a, Luo et al., 2018b) and adherent layer theory (Zhu, 1988) or the thermal orbit regression method (Hu et al., 2017). Besides, a more elaborate method combining with surface energy balance equation can calculate the equilibrium temperature (Zhang et al., 2003; Ling and Zhang, 2004). But, this method is limited for lack of parameters in field.

Compared with thermal boundary condition, the moisture boundary condition is more complex due to the strong interplay between atmosphere and frozen ground. Using long-term observations is always one of the direct methods; however, this is typically not the case due to budget and temporal-spatial limitations. Some researchers regard the saturation moisture content as the surface moisture boundary (Bense et al., 2012; McKenzie and Voss, 2013). While, others consider volumetric unfrozen water content (VUWC) as a known function of space and time. The value is equal to the water content in unfrozen zone but is described as a function of temperature in freezing zone (Andersland and Ladanyi, 2004). Another available method is using the in-site moisture data to make regression, which obtains a piecewise function distinguished by freezing point (Zhang et al., 2017). But this method makes the assumption that the unfrozen water content is treated as a constant during unfrozen period, which deviates from the practical situation. Los Alamos National Laboratory adopts a constant water infiltration rate as the moisture boundary (Frampton et al., 2011; Painter, 2011) without considering other factors, e.g. precipitation and evaporation. Ignoring the influence of snowfall and surface runoff on surface moisture, the upper boundary condition for water flux can be described by evaporation rate and rainfall rate (Zhang et al., 2016). Recently, satellite remote sensing (Njoku et al., 2003), land surface modeling (Dirmeyer et al., 2004) and data assimilation techniques (Tian et al., 2009) have been used to obtain continuous soil moisture values at large spatial scale with high temporal resolution. Besides, the different soil moisture products have been compared over the Qinghai-Tibet Plateau, which shows significant uncertainties in the remote sensing and reanalysis data use (Ullah et al., 2018).

Although a series of calibration models have been established for past and current conditions, there is another major challenge to obtain sufficiently detailed climate and surface parameters at suitable spatial scale and temporal interval to drive the permafrost models when considering the probable changes of future climate scenarios and land use types. In the previous studies, the thermal changes in upper boundary are reflected by using a constant warming rate under different future climate scenes (Wen et al., 2005; Chou et al., 2009).

The current and future climate conditions and permafrost states have also been simulated by many other climatic and remote sensing-based models (Wu et al., 2000; Westermann et al., 2015; Yin et al., 2017). The weather forecast models can provide more detailed climate parameters for planning and using, however, the reliable time series are too short to predict long-term changes. Besides, the spatial variability of future climates is uncertain due to the downscaling methods and other factors; thus, it is indispensable to predict and evaluate such changes as well. Global climate models (GCMs) are useful tools to make longer simulations for climate changes and permafrost distribution (Lawrence et al., 2012); nevertheless, the coarse spatial resolution limits their applications in specific regions and situations. Regional climate models (RCMs), on the other hand, which can be driven by the GCMs outputs or reanalysis data, can provide the outputs that are continuous and have physical meaning for regional and engineering studies (Liang et al., 2012). But, the spatial resolution of RCM outputs is still too low for related applications of permafrost models, for example, evaluation of infrastructure stability, predictions for changes and differences in the upper boundary conditions. Besides, it is difficult for RCM to describe the changes between originally natural ground conditions and modified surface conditions at local scale. Once the type of underlying surface is changed by natural force or human activities, the upper boundary conditions of permafrost always need to be determined by long-term observations. It is also unavailable for the areas that are remote or changed at short time scale.

Our objective is to determine the further changes in the upper boundary conditions under possible climate scenarios and land use changes by combining the regional downscaling method with statistical downscaling method. According to model output statistics (MOS) method, multiple linear regression models can obtain particular time series of climate and surface parameters to predict the hydro-thermal boundary conditions at properly spatial and temporal scales for engineering designs and eco-environment construction. This method can provide effective boundary conditions for permafrost models, which leads to more accurate predictions of distribution for permafrost. It is important for evaluating the stability of permafrost and infrastructures in permafrost regions. Meanwhile, this study attempts to determine the upper boundary conditions for regions where the long-term observations are unobtainable and the surface conditions are changed. The model performances were verified by a series of observations and evaluation indices. These results also indicate the possible applications to other areas and situations.

Section snippets

Study area

Chumar experiment site is a typical warm and ice-rich permafrost region. It is located at the Qinghai-Tibet Plateau (QTP) near Wudaoliang with an elevation of 4538 m above sea level. According to in-site investigation, the permafrost table is at the depth of 2–3 m under the gravel surface and the mean annual ground temperature ranges from −0.8 °C to −1.0 °C. A silty sand with gravel is below the gravel surface (0–3.3 m), while a strongly weathered mudstone is below the layer (Zhang et al., 2017

Simulation of regional climate model

In this study, the simulation is continuous from September 2013 to April 2018. We concentrated on air temperatures, surface temperatures and volumetric unfrozen water contents at the experimental site. These data are indispensable for building the upper hydro-thermal boundary conditions. The observed data for air temperature, gravel surface and cement concrete pavement are from 1 October 2013 to 1 April 2018, while 1 September 2014 to 1 April 2018 for asphalt pavement. RCM outputs are available

Results and analysis

Ignoring the slight differences of monitoring depth between gravel surface and pavements, we compared the changes of surface temperatures and volumetric unfrozen water contents under different surfaces. Table 3, Table 4 indicate that the construction of pavements modified the hydro-thermal conditions. The mean annual surface temperatures of pavements are higher than gravel surface. But, the mean volumetric unfrozen water content of concrete and asphalt pavements in thawing period are about 3.2%

Discussion and conclusion

The exchange of heat and moisture between atmosphere and ground surface is a complex process influenced by a variety of factors. Thus, it is hard to determine the thermal and moisture fields of natural or modified ground surface without monitored data. When the observations are unavailable, numerical models are proved to be an effective method to study the interactions between atmosphere and land surface, especially for remote regions. The Qinghai-Tibet Plateau is a typical case where the

Acknowledgements

This research was supported by the National Science Fund for Distinguished Young Scholars (Grant No. 41825015), the Program of the State Key Laboratory of Frozen Soil Engineering (Grant No. SKLFSE-ZT-23), the Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (QYZDY-SSW-DQC015), and the National Key Research and Development Program of China (Grant No. 2018YFC0809605).

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