Influence of Frozen Soil on Rangeland Erosion

Abstract. Snowmelt is a major source of groundwater recharge in cold regions. Throughout many landscapes snowmelt occurs when the ground is still frozen; thus frozen soil processes play an important role in snowmelt routing, and, by extension, the timing and magnitude of recharge. This study investigated the vadose zone dynamics governing snowmelt infiltration and groundwater recharge at three grassland sites in the Canadian Prairies over the winter and spring of 2017. The region is characterized by numerous topographic depressions where the ponding of snowmelt runoff results in focused infiltration and recharge. Water balance estimates showed infiltration was the dominant sink (35 %–85 %) of snowmelt under uplands (i.e. areas outside of depressions), even when the ground was frozen, with soil moisture responses indicating flow through the frozen layer. The refreezing of infiltrated meltwater during winter melt events enhanced runoff generation in subsequent melt events. At one site, time lags of up to 3 d between snow cover depletion on uplands and ponding in depressions demonstrated the role of a shallow subsurface transmission pathway or interflow through frozen soil in routing snowmelt from uplands to depressions. At all sites, depression-focused infiltration and recharge began before complete ground thaw and a significant portion (45 %–100 %) occurred while the ground was partially frozen. Relatively rapid infiltration rates and non-sequential soil moisture and groundwater responses, observed prior to ground thaw, indicated preferential flow through frozen soils. The preferential flow dynamics are attributed to macropore networks within the grassland soils, which allow infiltrated meltwater to bypass portions of the frozen soil matrix and facilitate both the lateral transport of meltwater between topographic positions and groundwater recharge through frozen ground. Both of these flow paths may facilitate preferential mass transport to groundwater.

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Projected decrease in wintertime bearing capacity on different forest and soil types in Finland under a warming climate

Hydrology and Earth System Sciences ◽

10.5194/hess-23-1611-2019 ◽

2019 ◽

Vol 23 (3) ◽

pp. 1611-1631 ◽

Cited By ~ 5

Author(s):

Ilari Lehtonen ◽

Ari Venäläinen ◽

Matti Kämäräinen ◽

Antti Asikainen ◽

Juha Laitila ◽

...

Keyword(s):

Bearing Capacity ◽

21St Century ◽

Climate Model ◽

Thick Layer ◽

Timber Harvesting ◽

Snow Accumulation ◽

Point Of View ◽

Frozen Soil ◽

Soil Types ◽

Soil Frost

Abstract. Trafficability in forest terrain is controlled by ground-bearing capacity, which is crucial from the timber harvesting point of view. In winter, soil frost affects the most the bearing capacity, especially on peatland soils which have in general low bearing capacity. Ground frost similarly affects the bearing capacity of forest truck roads. A 20 cm thick layer of frozen soil or 40 cm thick layer of snow on the ground may already be sufficient for heavy forest harvesters. In this work, we studied the impacts of climate change on soil frost conditions and, consequently, on ground-bearing capacity from the timber harvesting point of view. The number of days with good wintertime bearing capacity was modelled by using a soil temperature model with a snow accumulation model and wide set of downscaled climate model data until the end of the 21st century. The model was calibrated for different forest and soil types. The results show that by the mid-21st century, the conditions with good bearing capacity will decrease in wintertime in Finland, most likely by about 1 month. The decrease in soil frost and wintertime bearing capacity will be more pronounced during the latter half of the century, when drained peatlands may virtually lack soil frost in most of winters in southern and western Finland. The projected decrease in the bearing capacity, accompanied with increasing demand for wood harvesting from drained peatlands, induces a clear need for the development of sustainable and resource-efficient logging practices for drained peatlands. This is also needed to avoid unnecessary harvesting damages, like rut formation on soils and damage to tree roots and stems.

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Power Sampler for Frozen Soil

Soil Science Society of America Journal ◽

10.2136/sssaj1962.03615995002600020034x ◽

1962 ◽

Vol 26 (2) ◽

pp. 209-209

Author(s):

Arthur W. Krumbach

Keyword(s):

Frozen Soil

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Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods

Advances in Civil Engineering ◽

10.1155/2020/8898126 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Fu-Qing Cui ◽

Wei Zhang ◽

Zhi-Yun Liu ◽

Wei Wang ◽

Jian-bing Chen ◽

...

Keyword(s):

Thermal Conductivity ◽

Support Vector Regression ◽

Nonlinear Regression ◽

Prediction Accuracy ◽

Prediction Models ◽

Frozen Soil ◽

Support Vector ◽

Soil Thermal Conductivity ◽

Fine Grained ◽

Fine Grained Soil

The comprehensive understanding of the variation law of soil thermal conductivity is the prerequisite of design and construction of engineering applications in permafrost regions. Compared with the unfrozen soil, the specimen preparation and experimental procedures of frozen soil thermal conductivity testing are more complex and challengeable. In this work, considering for essentially multiphase and porous structural characteristic information reflection of unfrozen soil thermal conductivity, prediction models of frozen soil thermal conductivity using nonlinear regression and Support Vector Regression (SVR) methods have been developed. Thermal conductivity of multiple types of soil samples which are sampled from the Qinghai-Tibet Engineering Corridor (QTEC) are tested by the transient plane source (TPS) method. Correlations of thermal conductivity between unfrozen and frozen soil has been analyzed and recognized. Based on the measurement data of unfrozen soil thermal conductivity, the prediction models of frozen soil thermal conductivity for 7 typical soils in the QTEC are proposed. To further facilitate engineering applications, the prediction models of two soil categories (coarse and fine-grained soil) have also been proposed. The results demonstrate that, compared with nonideal prediction accuracy of using water content and dry density as the fitting parameter, the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils (more than 98% of the soil specimens’ relative error are within 20%). The SVR model can further improve the frozen soil thermal conductivity prediction accuracy and more than 98% of the soil specimens’ relative error are within 15%. For coarse and fine-grained soil categories, the above two models still have reliable prediction accuracy and determine coefficient (R2) ranges from 0.8 to 0.91, which validates the applicability for small sample soils. This study provides feasible prediction models for frozen soil thermal conductivity and guidelines of the thermal design and freeze-thaw damage prevention for engineering structures in cold regions.

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