scholarly journals Catchment Hydrological Modeling with Soil Thermal Dynamics during Seasonal Freeze-Thaw Cycles

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 116 ◽  
Author(s):  
Nawa Pradhan ◽  
Charles Downer ◽  
Sergei Marchenko
Keyword(s):  
Soil Moisture ◽  
Numerical Model ◽  
Phase Change ◽  
Soil Temperature ◽  
Hydrological Modeling ◽  
Overland Flow ◽  
Thermal State ◽  
Richards Equation ◽  
Daily Maximum ◽  
Thermal Dynamics

To account for the seasonal changes in the soil thermal and hydrological dynamics, the soil moisture state physical process defined by the Richards Equation is integrated with the soil thermal state defined by the numerical model of phase change based on the quasi-linear heat conductive equation. The numerical model of phase change is used to compute a vertical soil temperature profile using the soil moisture information from the Richards solver; the soil moisture numerical model, in turn, uses this temperature and phase, information to update hydraulic conductivities in the vertical soil moisture profile. Long-term simulation results from the test case, a head water sub-catchment at the peak of the Caribou Poker Creek Research Watershed, representing the Alaskan permafrost active region, indicated that freezing temperatures decreases infiltration, increases overland flow and peak discharges by increasing the soil ice content and decaying the soil hydraulic conductivity exponentially. Available observed and the simulated soil temperature comparison analysis showed that the root mean square error for the daily maximum soil temperature at 10-cm depth was 4.7 °C, and that for the hourly soil temperature at 90-cm and 300-cm was 0.17 °C and 0.14 °C, respectively.

scholarly journals Modified Richards’ Equation to Improve Estimates of Soil Moisture in Two-Layered Soils after Infiltration

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1174 ◽  
Author(s):  
Honglin Zhu ◽  
Tingxi Liu ◽  
Baolin Xue ◽  
Yinglan A. ◽  
Guoqiang Wang
Keyword(s):  
Soil Moisture ◽  
Overland Flow ◽  
Hydrological Cycle ◽  
Soil Depth ◽  
Controlling Factors ◽  
Moisture Distribution ◽  
Richards Equation ◽  
Infiltration Process ◽  
Soil Moisture Distribution

Soil moisture distribution plays a significant role in soil erosion, evapotranspiration, and overland flow. Infiltration is a main component of the hydrological cycle, and simulations of soil moisture can improve infiltration process modeling. Different environmental factors affect soil moisture distribution in different soil layers. Soil moisture distribution is influenced mainly by soil properties (e.g., porosity) in the upper layer (10 cm), but by gravity-related factors (e.g., slope) in the deeper layer (50 cm). Richards’ equation is a widely used infiltration equation in hydrological models, but its homogeneous assumptions simplify the pattern of soil moisture distribution, leading to overestimates. Here, we present a modified Richards’ equation to predict soil moisture distribution in different layers along vertical infiltration. Two formulae considering different controlling factors were used to estimate soil moisture distribution at a given time and depth. Data for factors including slope, soil depth, porosity, and hydraulic conductivity were obtained from the literature and in situ measurements and used as prior information. Simulations were compared between the modified and the original Richards’ equations and with measurements taken at different times and depths. Comparisons with soil moisture data measured in situ indicated that the modified Richards’ equation still had limitations in terms of reproducing soil moisture in different slope positions and rainfall periods. However, compared with the original Richards’ equation, the modified equation estimated soil moisture with spatial diversity in the infiltration process more accurately. The equation may benefit from further solutions that consider various controlling factors in layers. Our results show that the proposed modified Richards’ equation provides a more effective approach to predict soil moisture in the vertical infiltration process.

scholarly journals Multi-source data assimilation for physically-based hydrological modeling of an experimental hillslope

2018 ◽  
Author(s):  
Anna Botto ◽  
Enrica Belluco ◽  
Matteo Camporese
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Hydrological Modeling ◽  
Nonlinear Models ◽  
Pressure Head ◽  
Richards Equation ◽  
Hydrological Models ◽  
Filter Performance ◽  
Trade Offs ◽  
Source Data

Abstract. Data assimilation has been recently the focus of much attention for integrated surface-subsurface hydrological models, whereby joint assimilation of water table, soil moisture, and river discharge measurements with the ensemble Kalman filter (EnKF) have been extensively applied. Although the EnKF has been specifically developed to deal with nonlinear models, integrated hydrological models based on the Richards equation still represent a challenge, due to strong nonlinearities that may significantly affect the filter performance. Thus, more studies are needed to investigate the capabilities of the EnKF to correct the system state and identify parameters in cases where the unsaturated zone dynamics are dominant, as well as to quantify possible tradeoffs associated with assimilation of multi-source data. Here, the model CATHY (CATchment HYdrology) is applied to reproduce the hydrological dynamics observed in an experimental two-layered hillslope, equipped with tensiometers, water content reflectometer probes, and tipping bucket flow gages to monitor the hillslope response to a series of artificial rainfall events. Pressure head, soil moisture, and subsurface outflow are assimilated with the EnKF in a number of scenarios and the challenges and issues arising from the assimilation of multi-source data in this real-world test case are discussed. Our results demonstrate that the EnKF is able to effectively correct states and parameters even in a real application characterized by strong nonlinearities. However, multi-source data assimilation may lead to significant trade-offs: the assimilation of additional variables can lead to degradation of model predictions for other variables that were otherwise well reproduced. Furthermore, we show that integrated observations such as outflow discharge cannot compensate for the lack of well-distributed data in heterogeneous hillslopes.

scholarly journals How do Soil Moisture and Vegetation Covers Influence Soil Temperature in Drylands of Mediterranean Regions?

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1747 ◽  
Author(s):  
Javier Lozano-Parra ◽  
Manuel Pulido ◽  
Carlos Lozano-Fondón ◽  
Susanne Schnabel
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Vegetation Cover ◽  
Daily Maximum ◽  
Local Climate ◽  
Tree Canopies ◽  
Soil Temperatures

Interactions between land and atmosphere directly influence hydrometeorological processes and, therefore, the local climate. However, because of heterogeneity of vegetation covers these feedbacks can change over small areas, becoming more complex. This study aims to define how the interactions between soil moisture and vegetation covers influence soil temperatures in very water-limited environments. In order to do that, soil water content and soil temperature were continuously monitored with a frequency of 30 min over two and half hydrological years, using capacitance and temperature sensors that were located in open grasslands and below tree canopies. The study was carried out on three study areas located in drylands of Mediterranean climate. Results highlighted the importance of soil moisture and vegetation cover in modifying soil temperatures. During daytime and with low soil moisture conditions, daily maximum soil temperatures were, on average, 7.1 °C lower below tree canopies than in the air, whereas they were 4.2 °C higher in grasslands than in the air. As soil wetness decreased, soil temperature increased, although this effect was significantly weaker below tree canopies than in grasslands. Both high soil water content and the effect of shading were reflected in a decrease of maximum soil temperatures and of their daily amplitudes. Statistical analysis emphasized the influence of soil temperature on soil water reduction, regardless of vegetation cover. If soil moisture deficits become more frequent due to climate change, variations in soil temperature could increase, affecting hydrometeorological processes and local climate.

scholarly journals Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope

2018 ◽  
Vol 22 (8) ◽  
pp. 4251-4266 ◽  
Author(s):  
Anna Botto ◽  
Enrica Belluco ◽  
Matteo Camporese
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Hydrological Modeling ◽  
Nonlinear Models ◽  
Pressure Head ◽  
Richards Equation ◽  
Test Case ◽  
Hydrological Models ◽  
Filter Performance ◽  
Source Data

Abstract. Data assimilation has recently been the focus of much attention for integrated surface–subsurface hydrological models, whereby joint assimilation of water table, soil moisture, and river discharge measurements with the ensemble Kalman filter (EnKF) has been extensively applied. Although the EnKF has been specifically developed to deal with nonlinear models, integrated hydrological models based on the Richards equation still represent a challenge, due to strong nonlinearities that may significantly affect the filter performance. Thus, more studies are needed to investigate the capabilities of the EnKF to correct the system state and identify parameters in cases where the unsaturated zone dynamics are dominant, as well as to quantify possible tradeoffs associated with assimilation of multi-source data. Here, the CATHY (CATchment HYdrology) model is applied to reproduce the hydrological dynamics observed in an experimental two-layered hillslope, equipped with tensiometers, water content reflectometer probes, and tipping bucket flow gages to monitor the hillslope response to a series of artificial rainfall events. Pressure head, soil moisture, and subsurface outflow are assimilated with the EnKF in a number of scenarios and the challenges and issues arising from the assimilation of multi-source data in this real-world test case are discussed. Our results demonstrate that the EnKF is able to effectively correct states and parameters even in a real application characterized by strong nonlinearities. However, multi-source data assimilation may lead to significant tradeoffs: the assimilation of additional variables can lead to degradation of model predictions for other variables that are otherwise well reproduced. Furthermore, we show that integrated observations such as outflow discharge cannot compensate for the lack of well-distributed data in heterogeneous hillslopes.

scholarly journals A numerical model and validation of phase change material integrated thermoelectric radiant cooling panel

2019 ◽  
Vol 111 ◽  
pp. 01001
Author(s):  
Hansol Lim ◽  
Hye-Jin Cho ◽  
Seong-Yong Cheon ◽  
Soo-Jin Lee ◽  
Jae-Weon Jeong
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Numerical Model ◽  
Surface Temperature ◽  
Phase Change ◽  
Transfer Coefficient ◽  
Phase Change Material ◽  
Overall Heat Transfer Coefficient ◽  
Radiant Cooling ◽  
Change Material

A phase change material based radiant cooling panel with thermoelectric module (PCM-TERCP) is proposed in this study. It consists of two aluminium panels, and phase change materials (PCMs) sandwiched between the two panels. Thermoelectric modules (TEMs) are attached to one of the aluminium panels, and heat sinks are attached to the top side of TEMs. PCM-TERCP is a thermal energy storage concept equipment, in which TEMs freeze the PCM during the night whose melting temperature is 16○C. Therefore, the radiant cooling panel can maintain a surface temperature of 16◦C without the operation of TEM during the day. Furthermore, it is necessary to design the PCM-TERCP in a way that it can maintain the panel surface temperature during the targeted operating time. Therefore, the numerical model was developed using finite difference method to evaluate the thermal behaviour of PCM-TERCP. Experiments were also conducted to validate the performance of the developed model. Using the developed model, the possible operation time was investigated to determine the overall heat transfer coefficient required between radiant cooling panel and TEM. Consequently, the results showed that a overall heat transfer coefficient of 394 W/m2K is required to maintain the surface temperature between 16○C to 18○C for a 3 hours operation.

scholarly journals Analysis of an Extreme Weather Event in a Hyper Arid Region Using WRF-Hydro Coupling, Station, and Satellite data

2018 ◽  
Author(s):  
Youssef Wehbe ◽  
Marouane Temimi ◽  
Michael Weston ◽  
Naira Chaouch ◽  
Oliver Branch ◽  
...  
Keyword(s):  
Soil Moisture ◽  
United Arab Emirates ◽  
Hydrological Modeling ◽  
Flash Flood ◽  
Pearson Correlation ◽  
Global Radiation ◽  
Wrf Model ◽  
Extreme Weather ◽  
Extreme Weather Event ◽  
Weather Event

Abstract. This study investigates an extreme weather event that impacted the United Arab Emirates (UAE) in March 2016 using the Weather Research and Forecasting (WRF) model version 3.7.1 coupled with its hydrological modeling extension package (Hydro). Six-hourly forecasted forcing records at 0.5o spatial resolution, obtained from the NCEP Global Forecast System (GFS), are used to drive the three nested downscaling domains of both standalone WRF and coupled WRF/WRF-Hydro configurations for the recent flood-triggering storm. Ground and satellite observations over the UAE are employed to validate the model results. Precipitation, soil moisture, and cloud fraction retrievals from GPM (30-minute, 0.1o product), AMSR2 (daily, 0.1o product), and MODIS (daily, 5 km product), respectively, are used to assess the model output. The Pearson correlation coefficient (PCC), relative bias (rBIAS) and root-mean-square error (RMSE) are used as performance measures. Results show reductions of 24 % and 13 % in RMSE and rBIAS measures, respectively, in precipitation forecasts from the coupled WRF/WRF-Hydro model configuration, when compared to standalone WRF. The coupled system also shows improvements in global radiation forecasts, with reductions of 45 % and 12 % for RMSE and rBIAS, respectively. Moreover, WRF-Hydro was able to simulate the spatial distribution of soil moisture reasonably well across the study domain when compared to AMSR2 satellite soil moisture estimates, despite a noticeable dry/wet bias in areas where soil moisture is high/low. The demonstrated improvement, at the local scale, implies that WRF-Hydro coupling may enhance hydrologic forecasts and flash flood guidance systems in the region.

scholarly journals A soil moisture accounting-procedure with a Richards' equation-based soil texture-dependent parameterization

2015 ◽  
Vol 51 (1) ◽  
pp. 506-523 ◽  
Author(s):  
Simon A. Mathias ◽  
Todd H. Skaggs ◽  
Simon A. Quinn ◽  
Sorcha N. C. Egan ◽  
Lucy E. Finch ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Texture ◽  
Richards Equation ◽  
Soil Moisture Accounting ◽  
Accounting Procedure

Ecological responses of Stipa steppe in Inner Mongolia to experimentally increased temperature and precipitation. 3. Soil respiration

2018 ◽  
Vol 40 (2) ◽  
pp. 153 ◽  
Author(s):  
Xuexia Wang ◽  
Yali Chen ◽  
Yulong Yan ◽  
Zhiqiang Wan ◽  
Ran Chao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Respiration ◽  
Soil Temperature ◽  
Respiration Rate ◽  
Inner Mongolia ◽  
Growing Season ◽  
Climatic Warming ◽  
Soil Respiration Rate ◽  
Natural Rainfall ◽  
Temperature And Precipitation

The response of soil respiration to simulated climatic warming and increased precipitation was evaluated on the arid–semi-arid Stipa steppe of Inner Mongolia. Soil respiration rate had a single peak during the growing season, reaching a maximum in July under all treatments. Soil temperature, soil moisture and their interaction influenced the soil respiration rate. Relative to the control, warming alone reduced the soil respiration rate by 15.6 ± 7.0%, whereas increased precipitation alone increased the soil respiration rate by 52.6 ± 42.1%. The combination of warming and increased precipitation increased the soil respiration rate by 22.4 ± 11.2%. When temperature was increased, soil respiration rate was more sensitive to soil moisture than to soil temperature, although the reverse applied when precipitation was increased. Under the experimental precipitation (20% above natural rainfall) applied in the experiment, soil moisture was the primary factor limiting soil respiration, but soil temperature may become limiting under higher soil moisture levels.

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