Volumetric Liquid Water Content Research Articles

MVD and LWC prediction in icing environment based on genetic algorithm optimized neural network

Mean volumetric diameter (MVD) and liquid water content (LWC) are two important meteorological parameters that affect aircraft icing, but they are difficult to measure accurately in practice. If these two parameters can be obtained in real time and accurately, it can provide some guidance for ice accumulation prediction and the establishment of aircraft airworthiness certification standards. This paper proposes a prediction model for icing meteorological parameters based on genetic algorithm optimized neural network. With ice thickness and icing rate of different measurement point combinations, ambient temperature, flight speed and wing angle of attack as input parameters, and icing meteorological parameters MVD and LWC as output parameters, the genetic algorithm optimized icing meteorological parameter prediction model is constructed, and the prediction model is used to predict the icing meteorological parameters of the numerical calculation test group data and the icing wind tunnel experimental data. The results show that the prediction model based on genetic algorithm optimized Elman neural network predicts the icing meteorological parameters of the test group within the relative error of the test group 10%, and the relative error of the experimental data is within the relative error of the experimental data 20%. This method has certain feasibility.

Depth-specific distribution of bacterial MAGs in permafrost active layer in Ny Ålesund, Svalbard (79°N)

Arctic soil microbial communities may shift with increasing temperatures and water availability from climate change. We examined temperature and volumetric liquid water content (VWC) in the upper 80 cm of permafrost-affected soil over 2 years (2018–2019) at the Bayelva monitoring station, Ny Ålesund, Svalbard. We show VWC increases with depth, whereas in situ temperature is more stable vertically, ranging from −5°C to 5 °C seasonally. Prokaryotic metagenome-assembled genomes (MAGs) were obtained at 2–4 cm vertical resolution collected while frozen in April 2018 and at 10 cm vertical resolution collected while thawed in September 2019. The most abundant MAGs were Acidobacteriota, Actinomycetota, and Chloroflexota. Actinomycetota and Chloroflexota increase with depth, while Acidobacteriota classes Thermoanaerobaculia Gp7-AA8, Blastocatellia UBA7656, and Vicinamibacteria Vicinamibacterales are found above 6 cm, below 6 cm, and below 20 cm, respectively. All MAGs have diverse carbon-degrading genes, and Actinomycetota and Chloroflexota have autotrophic genes. Genes encoding β -glucosidase, N-acetyl-β-D-glucosaminidase, and xylosidase increase with depth, indicating a greater potential for organic matter degradation with higher VWC. Acidobacteriota dominate the top 6 cm with their classes segregating by depth, whereas Actinomycetota and Chloroflexota dominate below ∼6 cm. This suggests that Acidobacteriota classes adapt to lower VWC at the surface, while Actinomycetota and Chloroflexota persist below 6 cm with higher VWC. This indicates that VWC may be as important as temperature in microbial climate change responses in Arctic mineral soils. Here we describe MAG-based Seqcode type species in the Acidobacteriota, Onstottus arcticum, Onstottus frigus, and Gilichinskyi gelida and in the Actinobacteriota, Mayfieldus profundus.

Quantifying short-term changes in snow strength due to increasing liquid water content above hydraulic barriers

The rapid weakening of snow layers that accumulate infiltrating liquid water is a well-known, but poorly quantified, mechanism for wet-snow slab avalanche formation. Therefore, quantifying this mechanical process is a crucial part of forecasting these snow avalanches accurately. Currently, studies do not agree on how snow strength should change as a function of volumetric liquid water content (θ) and whether this relationship differs between snow types. Furthermore, strength measurements taken at or above θ=7% in snow are rare, so there is limited understanding at this end of the θ continuum. These levels of saturation can occur in the snow immediately above hydraulic barriers and are considered important for wet-snow slab avalanches to initiate. To address this knowledge gap, a blade hardness gauge (BHG, a.k.a. thin blade penetrometer) and SLF snow sensor were used to take 349 targeted paired measurements of strength and θ, respectively, in the snow above 19 manually wetted hydraulic (capillary) barriers. Using a multiple regression analysis, we developed an expression for the blade hardness of manually wetted snow as a function of θ, crystal form, and blade hardness prior to wetting (R2=0.85). To understand these results in terms of a common measure of snow strength, we also performed a comparison of the BHG and a shear frame in dry snow. The two instruments are highly linearly correlated (R2=0.94), allowing us to interpret our regression in terms of shear strength when compressive stress is low. Our results demonstrate that short-term (< 2 h) changes in snow strength due to increasing θ can differ between snow layers. These differences can be empirically modelled using easily measured dry snow properties (i.e. crystal form and blade hardness), which could allow avalanche forecasters to be more selective when identifying failure layers in advance of a wetting event.

In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications

Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L.

Light-scattering sensor for monitoring properties of snow

Real-time estimation of snow types and depth on a runway with stand-off sensing is essential for safe and efficient aircraft operations. Comprehensively identifying grain size, liquid water content (LWC), density and thickness is necessary for estimating the snow types and depth. However, off-the-shelf snow observation sensors are generally only optimized for one property of snow. In this study, we develop a light-scattering sensor consisting of lasers and image sensors, to obtain light-scattering images for comprehensively measuring the properties of snow. As a proof of concept demonstration, we measure snow samples with different grain size distribution, volumetric LWC, density and thickness, and obtain the relationship between the characteristics of snow and the optical scattering properties. For example, scattering intensity of the obtained image decreases as the grain size or volumetric LWC increases, and scattering area increases as the snow thickness increases. Additionally, given that multiple parameters can be extracted from a two-dimensional scattering image as well as with a different wavelength, by utilizing our sensor, we can simultaneously classify two properties of snow (e.g., grain size distribution and volumetric liquid water content, or density and thickness) on a scatter plot by extracting two indices from the obtained images. Our stand-off sensing technique shows great promise for improving safety and efficiency in aircraft operations during winter.

Application of a Magnetic Resonance Imaging Method for Nondestructive, Three-Dimensional, High-Resolution Measurement of the Water Content of Wet Snow Samples

The infiltration of melted snow water and rainwater into snow can drastically change the form of snow layers. This process is an important factor affecting wet snow avalanches. Accordingly, numerous field surveys and cold room experiments have been conducted to investigate the distribution of water in snow. The common methods of water content measurement (calorimetric and dielectric methods) are implemented by disturbing snow samples to measure them. However, the resolutions obtained are of the order of several centimeters, which hinders the continuous measurement of the water content of a particular sample. Magnetic resonance imaging (MRI), which is typically used in the medical field, can be used to generate a high-resolution three-dimensional (3D) image of the water distribution in samples without destructing them. The luminance of images produced by MRI depends on the volumetric water content of the sample, with luminance increasing with volumetric liquid water content. Therefore, the volumetric liquid water content of the sample can be estimated from its luminance value. Considering this concept, we developed a method to measure the volumetric liquid water content of wet snow samples using MR images. To evaluate the developed method, we prepared several wet snow samples and measured their various volumetric liquid water contents using MRI (θMRI) and the calorimetric method (θcal). θMRI and θcal showed good correlation when compared, with values in the range 0.02–0.46. Therefore, our system can accurately and nondestructively measure water content. The developed method using MRI can measure 3D volumetric liquid water contents with a high resolution (2 mm). Using the developed method, we investigated the hysteresis of the water retention curve of snow based on the measurements of a wetting process (boundary wetting curve) and a drying process (boundary drying curve) of the water retention curve for each sample. Our results indicate the existence of hysteresis in the snow water retention curves and the possibility of modeling it by adopting contexts of soil physics.

Snow gliding and glide-snow avalanches: recent outcomes from two experimental test sites in Aosta Valley (northwestern Italian Alps)

Abstract. Snow gliding and glide-snow avalanches are gaining importance among scientists as global warming might induce conditions favourable to those phenomena. Our aim is to analyse such processes with a particular focus on the potential driving factors associated with the soil conditions. We equipped two experimental test sites in the Aosta Valley region (NW Italy) with glide-snow shoes, temperature and volumetric liquid water content (VLWC) sensors in the soil and in the basal snowpack layer; snow and weather parameters were also collected by automatic weather stations and at manual snow measuring sites. In the two monitoring seasons 2013–2014 and 2014–2015 we registered nine glide-snow avalanches, two cold and seven warm events, which were characterized by different snow and soil conditions. In the only warm glide-snow avalanche event, which presented a continuous gliding before, the daily glide rate showed a significant exponential relationship with the soil VLWC. We also found, though without a general trend, that gliding and non-gliding periods (either considering warm and cold periods separately or together) were characterized by significantly different predisposing factors. This study contributes to the assessment of the importance of soil VLWC, which seems to be one of the most important driving factors for gliding processes. Therefore, it supports the need, already suggested by other scientists, for analysing such processes with an interdisciplinary approach which integrates snow and soil sciences.

Sampling depth of L-band radiometer measurements of soil moisture and freeze-thaw dynamics on the Tibetan Plateau

Knowing the exact sampling depth of microwave radiometry is essential for quantifying the performance and appreciation of the applicability of satellite soil moisture products. We investigate in this study the sampling depth (δSM) of the L-band microwave emission under frozen and thawed soil conditions on the Tibetan Plateau. Two years of diurnal brightness temperature (TBp) measurements at a time interval of 30 min are collected by the ELBARA-III radiometer deployed at a Tibetan meadow site. Vertical profiles of soil temperature and volumetric liquid water content (θliq) are measured simultaneously at soil depths up to 1 m below the surface. The impact of the θliq measured at different depths on the microwave emission simulations is assessed using the τ-ω emission model, whereby the permittivity of frozen and thawed soil is estimated by the four-phase dielectric mixing model. It is found that: 1) the sampling depth for the effective temperature depends on the magnitude of θliq, and is estimated to be, on average, about 50 and 15 cm for the cold dry and wet warm period, respectively, because of the seasonality in θliq; 2) the δSM is determined at 2.5 cm for both frozen and thawed soil conditions during both cold and warm periods, which is shallower than the commonly used θliq measurement depth (i.e. 5 cm) adopted for the in-situ monitoring networks across the globe; 3) the TBp simulations performed with the θliq measurements taken at the estimated δSM of 2.5 cm result in lower unbiased root mean squared errors, about 14% (3.16 K) and 22% (3.36 K) for the horizontal and vertical polarizations respectively, in comparison to the simulations with the θliq measurements taken from 5 cm soil depth; and 4) the θliq retrieved with the single channel algorithm from the ELBARA-III measured vertically polarized TBp are in better agreement with the θliq measured at 2.5 cm than the one measured at 5 cm. These findings are crucial for developing strategies for the calibration/validation as well as the application of satellite based soil moisture products relying on the L-band radiometry.

The role of soil volumetric liquid water content during snow gliding processes

In recent years, our understanding of snow gliding and glide-snow avalanches has improved; however, the contributing factors are still poorly understood and difficult to measure. In particular, the role of soil properties has not been considered as much as other environmental parameters (e.g. air temperature). Focusing on soil properties we established a monitoring site in the Italian Alps, in the release zone of a WSW-facing avalanche path. The area is typically characterized by intense snow gliding that results in the formation of large glide cracks, often leading to the release of a glide-snow avalanche. The site was equipped with four glide-snow shoes to measure snow gliding movement. Temperature and water content sensors were located at the snow-soil interface and at different depths within the soil. Meteorological data were recorded by a nearby automatic weather station, and snowpack properties were evaluated using manual snow profiles and SNOWPACK simulations; additionally, soils were characterized with special emphasis on the physical properties of the upper soil horizons. During two monitoring seasons, we registered a cold-temperature event characterized by gradual and continuous snow gliding and three warm-temperature events with glide-crack formation and evolution, in one case resulting in a glide-snow avalanche. Univariate (Mann-Witney U test) and multivariate (Classification Trees) analyses allowed us to find significant differences between gliding and non-gliding periods, and confirmed the importance of distinguishing between cold and warm-temperature events. In particular, for warm-temperature events we found that the most significant parameters were a large snow depth, strong settlement and high air temperature. For cold-temperature events we found that, together with a large snow depth, the volumetric liquid water content, both at the snow-soil interface and within the soil, played a fundamental role. Moreover, for the cold-temperature events we found a strong correlation between daily glide rates and the soil volumetric liquid water content, with an exponential relationship at the snow-soil interface and at 5cm depth within the soil. These results highlight the relationship between the snow gliding process and the soil conditions, which have been identified among the main environmental factors related to the development of snow gliding.

Assessing wet snow avalanche activity using detailed physics based snowpack simulations

AbstractWater accumulating on microstructural transitions inside a snowpack is often considered a prerequisite for wet snow avalanches. Recent advances in numerical snowpack modeling allow for an explicit simulation of this process. We analyze detailed snowpack simulations driven by meteorological stations in three different climate regimes (Alps, Central Andes, and Pyrenees), with accompanying wet snow avalanche activity observations. Predicting wet snow avalanche activity based on whether modeled water accumulations inside the snowpack locally exceed 5–6% volumetric liquid water content is providing a higher prediction skill than using thresholds for daily mean air temperature, or the daily sum of the positive snow energy balance. Additionally, the depth of the maximum water accumulation in the simulations showed a significant correlation with observed avalanche size. Direct output from detailed snow cover models thereby is able to provide a better regional assessment of dangerous slope aspects and potential avalanche size than traditional methods.

Bulk volumetric liquid water content in a seasonal snowpack: modeling its dynamics in different climatic conditions

We focus on the dynamics of volumetric liquid water content in seasonal snow covers. This is a key variable describing the fate of snowpacks during the melting season. However, its measurement and/or prediction by means of models at high spatial and temporal resolutions is still difficult due to both practical and theoretical reasons. To overcome these limitations in operational applications, we test the capability of a one-dimensional model to predict the dynamics of bulk volumetric liquid water content during a snow season. Multi-year data collected in three experimental sites in Japan are used as an evaluation. These sites are subjected to different climatic conditions. The model requires the calibration of one or two parameters, according to the degree of detail used. Either a simple temperature-index or a coupled melt–freeze temperature-index approach are considered to predict melting and/or melt–freeze dynamics of liquid water. Results show that, if melt–freeze dynamics are modeled, median absolute differences between data and predictions are consistently lower than 1 vol% at the sites where data of liquid water content are available. In addition, we find also that the model predicts correctly a dry condition in 80% of the observed cases at a site where calibration data are scarce. At the same site, observed isothermal conditions of the snow cover at 0 °C correspond to predictions of bulk volumetric liquid water content that are greater than 0.

Seasonal and diurnal cycles of liquid water in snow—Measurements and modeling

AbstractEvaluating and improving snow models and outflow predictions for hydrological applications is hindered by the lack of continuous data on bulk volumetric liquid water content (θw) and storage capacity of the melting snowpack. The combination of upward looking ground‐penetrating radar and conventional snow height sensors enable continuous, nondestructive determinations of θw in natural snow covers from first surficial wetting until shortly before melt out. We analyze diurnal and seasonal cycles of θw for 4 years in a flat study site and for three melt seasons on slopes and evaluate model simulations for two different water transport schemes in the snow cover model SNOWPACK. Observed maximum increases in θw during a day are below 1.7 vol % (90th percentile) at the flat site. Concerning seasonal characteristics of θw, less than 10% of recorded data exceed 5 vol % at the flat site and 3.5 vol % at slopes. Both water transport schemes in SNOWPACK underestimate maximum θw at the flat site systematically for all observed melt seasons, while simulated θw maxima on slopes are accurate. Implementing observed changes in θw per day in outflow predictions increases model performance toward higher agreement with lysimeter measurements. Hence, continuously monitoring θw improves our understanding of liquid water percolation and retention in snow, which is highly relevant for several aspects of the cryosphere such as avalanche formation, catchment hydrology, and ice sheet mass balances.

Influence of Experimental Warming on Heat and Water Fluxes of Alpine Meadows in the Qinghai-Tibet Plateau

In order to predict the response of alpine ecosystems to global warming and to provide the experimental data needed for Atmosphere Global Circulation Models (AGCMs) coupled with Land Surface Models (LSMs), a warming experiment using infrared heaters was conducted in the alpine meadow ecosystem of the Qinghai-Tibet Plateau. Five replicate blocks with three 2 × 2 m treatment plots in each block were randomly installed. The treatment plots were control plots (C) at ambient temperatures, moderately warmed plots (W1), and intensely warmed plots (W2), manipulated by using 130 W m−2 and 150 W m−2 infrared heaters, respectively. The results showed that when significant warming increased the daily mean soil surface temperature by 1–3 °C compared to temperatures in the control plots during the warm season (seasonal frozen soil thaw), the soil temperature gradients from depths of 0 to 100 cm significantly increased by 0.02–0.04 °C cm−1 during the day and 0.01–0.03 °C cm−1 at night. Volumetric soil liquid water content significantly decreased by 2.6–3.4% in shallow soil (5–15 cm) and significantly increased by 0.7–5.1% in deep soil (100 cm) compared to the control plots. Likewise, soil liquid water content gradients at depths between 10 and 20 cm significantly decreased by 0.2–0.3% cm−1 and significantly increased by 0.01–0.08% cm−1 at depths between 20 and 100 cm. Warming did not cause significant vapor water change in the atmosphere near the soil surface. Based on these results, it can be concluded that increasing soil temperatures accelerated the processes of ground heat flux, sensible heat, and latent heat, which caused significant change in soil water content and in its gradients.

Continuous-time monitoring of liquid water content in snowpacks using capacitance probes: A preliminary feasibility study

Liquid water in snowpacks rules wet snow avalanche formation, surface albedo and snowmelt runoff timing. By now, volumetric liquid water content (LWC) measurements are collected mainly with destructive methods, while continuous-time and non-invasive measurements would be preferable to track its time dynamics. Here, we assess the feasibility of continuous-time monitoring of LWC using EnviroSMART® capacitance sensors. These were conceived to track liquid water dynamics in soils, and their use in snow is evaluated here for the first time, as far as we know. A field case study was settled up in April 2013 within an Italian Alpine valley. An instrumental set up with eight capacitance sensors was installed. Two time-domain reflectrometers were added to the aim of comparison. To assist in interpreting the signal of the capacitance sensors, two laboratory tests were run, and a FEM model was implemented. This preliminary study demonstrates that capacitance sensors are sensitive to increasing LWC, although their long-term installation in snow entails the development of an air gap around them, due to localized melting, air turbulence and solar radiation absorption, which hinders following LWC variations. As a result, capacitance sensors readings are challenging to be interpreted quantitatively. Perspectives on future investigation are discussed to bring the proposed procedure towards long-term applications in snowpacks.

Continuous snowpack monitoring using upward-looking ground-penetrating radar technology

AbstractSnow stratigraphy and water percolation are key contributing factors to avalanche formation. So far, only destructive methods can provide this kind of information. Radar technology allows continuous, non-destructive scanning of the snowpack so that the temporal evolution of internal properties can be followed. We installed an upward-looking ground-penetrating radar system (upGPR) at the Weissfluhjoch study site (Davos, Switzerland). During two winter seasons (2010/11 and 2011/12) we recorded data with the aim of quantitatively determining snowpack properties and their temporal evolution. We automatically derived the snow height with an accuracy of about ± 5 cm, tracked the settlement of internal layers (± 7 cm) and measured the amount of new snow (± 10 cm). Using external snow height measurements, we determined the bulk density with a mean error of 4.3% compared to manual measurements. Radar-derived snow water equivalent deviated from manual measurements by 5%. Furthermore, we tracked the location of the dry-to-wet transition in the snowpack until water percolated to the ground. Based on the transition and an independent snow height measurement it was possible to estimate the volumetric liquid water content and its temporal evolution. Even though we need additional information to derive some of the snow properties, our results show that it is possible to quantitatively derive snow properties with upGPR.

Dependence of the water retention curve of snow on snow characteristics

AbstractThe water retention curve (WRC), which shows the relationship between the volumetric liquid water content, θv, and suction, h, is a fundamental part of the characterization of hydraulic properties. Therefore, the formulation of the WRC as a function of snow characteristics is essential for establishing a model of water movement through the snow cover. In this study, we measured the WRC of several snow samples, which had different characteristics (grain size, bulk dry density and grain type), using a gravity drainage column experiment and then analysed these data using the Van Genuchten soil physics model (VG model). The shape of the WRC depended strongly on both the sample grain size, d, and bulk dry density, ρ. Therefore, we introduced the parameter ρ/d to model the WRC of snow. The relationships between the parameters α and n of the VG model and ρ/d change with grain type. For melt forms, α, which is related to the inverse value of the air-entry suction, increases quickly as ρ/d decreases, whereas n, which is related to the gradient of θv vs h, increases with ρ/d. Conversely, neither of these parameters of the VG model for rounded grains showed obvious dependence on ρ/d. These results suggest that water movement through snow cover can be modelled using grain size, bulk dry density and grain type based on the soil physics model.

Upward-looking ground-penetrating radar for measuring wet-snow properties

Abstract Snow stratigraphy information is among other sources the key data for assessing avalanche danger—not only for dry snow but also for wet-snow conditions. Until now this information is obtained by traditional snow pit observations or more recently by applying more quantitative methods such as the snow micro-penetrometer or dielectric devices. All these methods are destructive and only provide a snap shot in time of snowpack evolution. We used an upward-looking ground-penetrating radar system (upGPR) to monitor snowpack evolution on a daily or, whenever necessary, hourly basis to obtain information on wet-snow properties. We focused on determining the volumetric liquid water content ( θ w ) by calculating the effective permittivity ( e eff ) of the wet snow above the radar antennas, the advance of a wetting front and the wet-snow stratigraphy. e eff was obtained using the signal velocity and snow depth recorded with nearby ultrasonic gauges; θ w was calculated with different mixing model approaches. Results were compared to in-situ measured permittivity, modelled wetting front advance and modelled and measured outflow at the bottom of the snowpack. The upGPR system clearly showed the advance of a wetting front and the arrival time was similar to the one recorded with a nearby lysimeter. Possibly weak wet layers with high liquid water content ( θ w > 6%) were detected within the radar signal by multiple reflections. However, determining the exact amount of liquid water for each layer separately is still a task for future research.

Point observations of liquid water content in wet snow – investigating methodical, spatial and temporal aspects

Abstract. Information about the volume and the spatial and temporal distribution of liquid water in snow is important for forecasting wet snow avalanches and for predicting melt-water run-off. The distribution of liquid water in snow is commonly estimated from point measurements using a "hand" squeeze test, or a dielectric device such as a "Snow Fork" or a "Denoth meter". Here we compare estimates of water content in the Swiss Alps made using the hand test to those made with a Snow Fork and a Denoth meter. Measurements were conducted in the Swiss Alps, mostly above tree line; more than 12 000 measurements were made at 85 locations over 30 days. Results show that the hand test generally over estimates the volumetric liquid water content. Estimates using the Snow Fork are generally 1 % higher than those derived from the Denoth meter. The measurements were also used to investigate temporal and small-scale spatial patterns of wetness. Results show that typically a single point measurement does not characterize the wetness of the surrounding snow. Large diurnal changes in wetness are common in the near-surface snow, and associated changes at depth were also observed. A single vertical profile of measurements is not sufficient to determine whether these changes were a result of a spatially homogeneous wetting front or caused by infiltration through pipes. Based on our observations, we suggest that three measurements at horizontal distances greater than 50 cm are needed to adequately characterize the distribution of liquid water through a snowpack. Further, we suggest a simplified classification scheme that includes five wetness patterns that incorporate both the vertical and horizontal distribution of liquid water in a snowpack.

Analyses and development of a hierarchy of frozen soil models for cold region study

Numerous frozen soil models currently in use differ in the complexity of their governing equations or/and in the processes being considered. It is important to comprehensively examine and categorize these on the basis of physical principles, assumptions, and relationship to each other. In this paper frozen soil models are classified into different levels according to the complexity of the governing equations. On the basis of scale analysis, models with different levels of complexity were derived from the most complicated frozen soil model. Rationales for the simplification of models at different levels are discussed. To overcome the difficulties in achieving numerical solutions, a new method of substituting soil enthalpy and total water mass for soil temperature and volumetric liquid water content in governing equations is introduced for each level of the frozen soil models. Models with different complexity levels are assessed with observational data. The preliminary monthly and seasonal evaluation shows that the results from the models with different complexity are generally similar but with substantial differences at the Tibetan D66 site during the melting and freezing period. The model including the contribution of vapor flux due to the matric potential gradient to the water balance performs the best at the D66 site. Compared to the corresponding original models, the frozen soil model versions with enthalpy and total water mass for governing equations appear to produce consistently better performance. Furthermore, the rationale of different methods for the freezing‐melting process in frozen soil is discussed. It has been noted that the model derived from the freezing point depression equation and the soil matric potential equation is supported by both thermodynamic equilibrium theory and the simulation results.

A fully coupled thermo-hydro-mechanical model for unsaturated porous media

In examining potential host rocks for such purposes as the disposal of high-level radioactive wastes, it is important to understand the coupled thermo-hydro-mechanical (THM) behavior of a porous medium. A rigorous and fully unified coupled thermo-hydro-mechanical model for unsaturated porous media is required to simulate the complex coupling mechanisms involved. Based on modified Darcy’s and Fourier’s laws, equations of mechanical equilibrium, mass conservation and energy conservation are derived by introducing void ratio and volumetric liquid water content into the model. The newly derived model takes into account the effects of temperature on the dynamic viscosity of liquid water and void ratio, the influence of liquid flow on temperature gradient (thermo-osmosis), the influence on mass and heat conservation equations, and the influence of heat flow on water pressure gradient and thermal convection. The new coupled THM constitutive model is constructed by a finite element program and is used to simulate the coupled behavior of a tunnel during excavation, ventilation and concrete lining stages. Oil and gas engineering, underground disposal of nuclear waste and tunnel engineering may be benefited from the development of the new model.