Table 5.

Evapotranspiration parameter values.

Process Modeling of Soil Thermal and Hydrological Dynamics DOI: http://dx.doi.org/10.5772/intechopen.84414

parameter values, representing the vegetation type of Figure 4(b), employed in the evapotranspiration process.

All the parameter values defined in Table 5 were taken from the literature, which are also defined in the GSSHA wiki https://www.gsshawiki.com. The literature values for albedo and vegetation height are defined in Eagleson [47]. The literature values for canopy resistance are defined in Monteith [39]. The literature values for the transmission coefficient are defined in Sutton [48].

### 5. Result and discussion

thermodynamics, are assigned based on the soil texture, shown in Figure 4a, and

Table 2 shows the soil thermal parameters with values, representing the silt loam [31, 42, 43] of Alaskan woodland and tundra ecosystem sites in permafrost-

Table 3 shows Manning's roughness parameter values, representing the vegeta-

Table 4 shows the soil hydraulic parameters with values, representing the silt loam ([46], https://www.gsshawiki.com) of Figure 4(a), employed in the infiltra-

In this study, Penman-Monteith method was employed as evapotranspiration process. Table 5 shows the Penman-Monteith parameters that includes vegetation transmission coefficient (light penetration through canopy), values of land surface albedo, vegetation height (for aerodynamic resistance term), and vegetation canopy

Land use type Manning's roughness values

Soil thickness (cm) 20 50 — Saturated hydraulic conductivity (cm/h) 0.5 0.5 0.5 Pore size distribution index 0.60 0.694 0.694 Wetting front suction head (cm) 8.0 6.0 6.0

) 0.42 0.40 0.40

0.15 10 120 0.18

) 0.04 0.045 0.045

Canopy resistance (s/m)

Top Middle Lower

Transmission coefficient

resistance (for stomatal control of the loss of water). Table 5 shows the ET

Coniferous open 0.17 Coniferous woodland 0.19 Deciduous closed 0.2 Shrub tall 0.25

active region, employed in the thermodynamics process.

Infiltration parameter Soil layer

/m<sup>3</sup>

(cm)

Coniferous open 0.15 10 120 0.18

Deciduous closed 0.2 12 200 0.18 Shrub tall 0.2 1.3 150 0.5

Porosity (m<sup>3</sup>

Coniferous woodland

Table 4.

Table 3.

Manning's roughness values.

Table 5.

114

/m<sup>3</sup>

Evapotranspiration parameter values.

Residual soil moisture content (m<sup>3</sup>

Soil parameter values for the Richards infiltration scheme.

Land use type Albedo Vegetation height

tion type of Figure 4(b) [44, 45], employed in the routing process.

land use, shown in Figure 4b.

Hydrology - The Science of Water

tion and soil water retention process.

#### 5.1 Soil thermodynamics

The simulation period was from May 1st to May 31st of 2002, a period during which air temperatures are beginning to rise above freezing. The daily maximum soil temperature obtained from the simulation is compared with the observed one in Figure 7. Both the observed and simulated temperatures in Figure 7 is effective at a depth of 10 cm in the soil profile. The root-mean-square error for this daily maximum soil temperature was 4.7°C. It was found that the soil thermal

Figure 7.

Comparison of the time series of observed and simulated temperature.

Figure 8. Hydraulic conductivity under freezing and thawing soil active layer.

conductivity parameter value was most sensitive and effective for the near-surface simulated temperature. It was also found that the temperature of the near-surface soil layer is directly influenced by the air temperature.

## 5.2 Soil's effective hydraulic conductivity

Figure 8 shows the simulation evolution of the effective hydraulic conductivity starting on May 1st of 2002. There are no observed hydraulic conductivity values to compare to, but the model intuitively represents the condition. Initially, the hydraulic conductivity in Figure 8 is very low, almost zero in the first 113 hours, the result of reduced effective saturation in the soil due to the soil being completely frozen (Figure 7). As the air and soil temperatures rise, the soil begins to thaw, with a resulting increase in hydraulic conductivity. The exponential rise of hydraulic conductivity is a consistent observation from freezing/thawing mineral and organic soils [33]. The simulated effective hydraulic conductivity in Figure 8 is from Eq. (11), where the effective soil hydraulic conductivity Ksoil at a given temperature (t) is a function of hydraulic conductivity of the unfrozen soil and the effective saturation SE.

shown in Figure 8, which results in a flashier response to the precipitation event as shown in Figure 9 graph with thermodynamics. On the other hand, loss of frozen soil and permafrost or without taking into account thermodynamics will lead to enhanced connectivity between the surface and groundwater storage regimes and decreased overland flow as shown by the graph without thermodynamics in

Comparison of distribution of overland runoff with and with thermodynamic process in the model simulation.

Process Modeling of Soil Thermal and Hydrological Dynamics

DOI: http://dx.doi.org/10.5772/intechopen.84414

Unless the simulation discharge represents the distributed physical runoff process in a realistic way, even a well-calibrated simulated discharge at a catchment outlet may only be a right answer for wrong reasons. The modeling and the simulations in this study have explicitly taken into account frozen soil as an important hydrologic factor. The simulation results showed the variability of freezing condition in space and time. How this variability of freezing condition in space and time would affect the distribution of overland runoff is particularly important from the concern of climate change and land use change effect in local hydrology [7, 49]. Figure 10 shows the model simulated distribution of spatially distributed overland runoff, at the peak of the precipitation event of May 7th and May 8th of 2002 in CPEAK station, by taking into account thermodynamics and without taking into account thermodynamics. It is clear from Figure 10 that the simulation significantly

A coupled framework for simulating the interaction between soil temperature, including permafrost, and hydrology was developed by incorporating the soil temperature and permafrost model GIPL into the distributed, physics-based hydrologic model GSSHA. This chapter describes the architecture for numerically linking the GIPL thermodynamic model into GSSHA's hydrodynamic modeling framework. Deploying this enhanced capability showed that GSSHA hydrodynamics include soil moisture saturation feedback in the vadose zone as well as the corresponding soil ice content effects on hydraulic conductivity and transmissivity. In this study the coupled model was deployed in the headwater region at the peak of the Caribou-Poker Creeks Research Watershed that included the hydrometeorological and soil

underestimated the runoff when thermodynamic process was absent.

6. Recommendations and conclusions

property measurement station called CPEAK.

Figure 9.

117

Figure 10.

5.4 Discussion

## 5.3 Hydrologic runoff response

Figure 9 shows the comparison of GSSHA simulated discharge with and without taking into account freezing and thawing soil properties in the study area. While there are no measurements of runoff from the site, the results are consistent with the results presented for air and soil temperature and resulting hydraulic conductivity shown above. The freezing soil temperature (Figure 7) leads to increased coverage of frozen soil which in turn leads to less soil pore water storage. The reduced soil pore water storage capacity leads to decrease hydraulic conductivity as

Figure 9. Hydrograph with and without thermodynamics.

Process Modeling of Soil Thermal and Hydrological Dynamics DOI: http://dx.doi.org/10.5772/intechopen.84414

Figure 10. Comparison of distribution of overland runoff with and with thermodynamic process in the model simulation.

shown in Figure 8, which results in a flashier response to the precipitation event as shown in Figure 9 graph with thermodynamics. On the other hand, loss of frozen soil and permafrost or without taking into account thermodynamics will lead to enhanced connectivity between the surface and groundwater storage regimes and decreased overland flow as shown by the graph without thermodynamics in Figure 9.

#### 5.4 Discussion

conductivity parameter value was most sensitive and effective for the near-surface simulated temperature. It was also found that the temperature of the near-surface

Figure 8 shows the simulation evolution of the effective hydraulic conductivity starting on May 1st of 2002. There are no observed hydraulic conductivity values to

hydraulic conductivity in Figure 8 is very low, almost zero in the first 113 hours, the result of reduced effective saturation in the soil due to the soil being completely frozen (Figure 7). As the air and soil temperatures rise, the soil begins to thaw, with a resulting increase in hydraulic conductivity. The exponential rise of hydraulic conductivity is a consistent observation from freezing/thawing mineral and organic soils [33]. The simulated effective hydraulic conductivity in Figure 8 is from Eq. (11), where the effective soil hydraulic conductivity Ksoil at a given temperature (t) is a function of hydraulic conductivity of the unfrozen soil and the effective

Figure 9 shows the comparison of GSSHA simulated discharge with and without taking into account freezing and thawing soil properties in the study area. While there are no measurements of runoff from the site, the results are consistent with the results presented for air and soil temperature and resulting hydraulic conductivity shown above. The freezing soil temperature (Figure 7) leads to increased coverage of frozen soil which in turn leads to less soil pore water storage. The reduced soil pore water storage capacity leads to decrease hydraulic conductivity as

compare to, but the model intuitively represents the condition. Initially, the

soil layer is directly influenced by the air temperature.

5.2 Soil's effective hydraulic conductivity

Hydrology - The Science of Water

saturation SE.

Figure 9.

116

Hydrograph with and without thermodynamics.

5.3 Hydrologic runoff response

Unless the simulation discharge represents the distributed physical runoff process in a realistic way, even a well-calibrated simulated discharge at a catchment outlet may only be a right answer for wrong reasons. The modeling and the simulations in this study have explicitly taken into account frozen soil as an important hydrologic factor. The simulation results showed the variability of freezing condition in space and time. How this variability of freezing condition in space and time would affect the distribution of overland runoff is particularly important from the concern of climate change and land use change effect in local hydrology [7, 49]. Figure 10 shows the model simulated distribution of spatially distributed overland runoff, at the peak of the precipitation event of May 7th and May 8th of 2002 in CPEAK station, by taking into account thermodynamics and without taking into account thermodynamics. It is clear from Figure 10 that the simulation significantly underestimated the runoff when thermodynamic process was absent.

#### 6. Recommendations and conclusions

A coupled framework for simulating the interaction between soil temperature, including permafrost, and hydrology was developed by incorporating the soil temperature and permafrost model GIPL into the distributed, physics-based hydrologic model GSSHA. This chapter describes the architecture for numerically linking the GIPL thermodynamic model into GSSHA's hydrodynamic modeling framework. Deploying this enhanced capability showed that GSSHA hydrodynamics include soil moisture saturation feedback in the vadose zone as well as the corresponding soil ice content effects on hydraulic conductivity and transmissivity. In this study the coupled model was deployed in the headwater region at the peak of the Caribou-Poker Creeks Research Watershed that included the hydrometeorological and soil property measurement station called CPEAK.

The model captured the seasonal rise of soil temperatures and thaw of frozen soils. The model showed intuitively correct representations of soil hydraulic conductivity and runoff, consistent with the observed rise of soil temperatures. Numerical simulations showed the hydrologic importance of frozen soils with the implication that climate change could have large effects on hydrology as air and soil temperatures rise near the poles resulting in loss of permafrost and increase seasonal thawing of soils.

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DOI: http://dx.doi.org/10.5772/intechopen.84414

Process Modeling of Soil Thermal and Hydrological Dynamics

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The main use of the "process modeling of soil thermal and hydrological dynamics" has been to generate spatial and temporal dataset of permafrost distribution and ground temperature dynamics as well as the active layer thickness, the depth of the seasonal thaw. Such dataset would be useful in the assessments of a wide range of thermo-hydrodynamic related fields, including ecology, climatology, and socioeconomy, in the cold regions.

#### Acknowledgements

Pradhan et al. [13] was the initial hydrodynamic and thermodynamic model integration effort which was then funded by Strategic Environmental Research and Development Program (SERDP) under Project Number 11 RC-2110. Theoretical and technical contributions of Dr. Anna Liljedahl, at the University of Alaska Fairbanks are significant for that initial hydrodynamic and thermodynamic model integration effort. Article Processing Charges (APCs) for the publication were supported by the U.S. Army Corps of Engineers Flood and Coastal Storm Damage Reduction Research and Development Program under the technical direction of Dr. Julie Rosati and Mr. Ian E. Floyd. The Bonanza Creek Long-Term Ecological Research program (BNZ LTER) at the University of Alaska Fairbanks is thanked for making and sharing the hydrology and climate data set of the CPCRW in Alaska. Constructive comments from anonymous reviewers are greatly appreciated.

#### Author details

Nawa Raj Pradhan<sup>1</sup> \*, Charles W. Downer<sup>1</sup> and Sergei Marchinko<sup>2</sup>

1 Coastal and Hydraulics Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA

2 Geophysical Institute, University of Alaska Fairbanks, USA

\*Address all correspondence to: nawa.pradhan@usace.army.mil

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Process Modeling of Soil Thermal and Hydrological Dynamics DOI: http://dx.doi.org/10.5772/intechopen.84414
