scholarly journals Use of a two-mode soil pore size distribution to estimate soil water transport in a land surface model

2003 ◽  
Vol 30 (6) ◽  
Author(s):  
Q. Liu ◽  
R. E. Dickinson
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Soil Water ◽  
Water Transport ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Soil Water Transport

Equations for the soil-water characteristic curve

1994 ◽  
Vol 31 (4) ◽  
pp. 521-532 ◽  
Author(s):  
D.G. Fredlund ◽  
Anqing Xing
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Soil Water ◽  
Characteristic Curve ◽  
Nonlinear Least Squares ◽  
Soil Water Characteristic Curve ◽  
Nonlinear Curve Fitting ◽  
Soil Behaviour ◽  
Nonlinear Curve

The soil-water characteristic curve can be used to estimate various parameters used to describe unsaturated soil behaviour. A general equation for the soil-water characteristic curve is proposed. A nonlinear, least-squares computer program is used to determine the best-fit parameters for experimental data presented in the literature. The equation is based on the assumption that the shape of the soil-water characteristic curve is dependent upon the pore-size distribution of the soil (i.e., the desaturation is a function of the pore-size distribution). The equation has the form of an integrated frequency distribution curve. The equation provides a good fit for sand, silt, and clay soils over the entire suction range from 0 to 106 kPa. Key words : soil-water characteristic curve, pore-size distribution, nonlinear curve fitting, soil suction, water content.

scholarly journals Prediction of soil water retention properties using pore-size distribution and porosity

2013 ◽  
Vol 50 (4) ◽  
pp. 435-450 ◽  
Author(s):  
Christopher T.S. Beckett ◽  
Charles E. Augarde
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Soil Water ◽  
Water Retention ◽  
Adsorbed Water ◽  
Retention Data ◽  
Soil Water Retention ◽  
Pore Size Distributions ◽  
Water Retention Properties

Several models have been suggested to link a soil's pore-size distribution to its retention properties. This paper presents a method that builds on previous techniques by incorporating porosity and particles of different sizes, shapes, and separation distances to predict soil water retention properties. Mechanisms are suggested for the determination of both the main drying and wetting paths, which incorporate an adsorbed water phase and retention hysteresis. Predicted results are then compared with measured retention data to validate the model and to provide a foundation for discussing the validity and limitations of using pore-size distributions to predict retention properties.

Using satellite data in the Land Surface Model to estimate water availability of arid agricultural region

2020 ◽  
Author(s):  
Eugene Muzylev ◽  
Zoya Startseva ◽  
Elena Volkova ◽  
Eugene Vasilenko
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Availability ◽  
Land Surface ◽  
Land Surface Model ◽  
Soil Surface ◽  
Surface Model ◽  
Heat Regime ◽  
Vegetation Characteristics

<p>The water availability of agricultural arid regions can be assessed at presence using the physical-mathematical model of water and heat exchange between land surface and atmosphere LSM (Land Surface Model) adapted to satellite-derived estimates of meteorological and vegetation characteristics. The LSM is designed to calculate soil water content W, evapotranspiration Ev, vertical heat fluxes and other water and heat regime elements. Soil and vegetation characteristics were used in the LSM as parameters and meteorological characteristics were utilized as input variables.</p><p>The case study was carried out for the territory of the Saratov and Volgograd Trans-Volga region (the left-bank part of the Saratov and Volgograd regions) of 66600 km<sup>2</sup> for the vegetation seasons 2016-2018.</p><p>The satellite measurement data from radiometers AVHRR/NOAA, SEVIRI/Meteosat-10, -11, -8, and MSU-MR/Meteor-M No. 2 in visible and IR ranges were thematic processed to built estimates of vegetation index NDVI, emissivity E, vegetation cover fraction B, leaf index LAI, land surface temperature LST and precipitation.</p><p>LAI and B estimates were obtained using empirical dependencies on NDVI. The adequacy of the LAI and B estimates obtained from all sensor data was verified when comparing the LAI time behavior built for named vegetation seasons. Errors of determining B and LAI were 15 and 20%, respectively.</p><p>Satellite-derived estimates of daily, decadal and monthly precipitation sums for each pixel were obtained using the Multi Threshold Method (MTM) for detecting clouds, identifying its types allocating precipitation zones and determining their maximum intensity. The MTM is based on the developed algorithm of the transition from the assessment of precipitation intensity to the assessment of their daily amounts. Testing of the method was carried out when comparing these amounts with observed at meteorological stations. The probability of satellite-detected precipitation zones corresponded to the actual ones was ~ 80% for all radiometers.</p><p>Based on the MTM, computational algorithm to evaluate the LST was developed and verified on the study region data. Comparison of ground-measured and satellite-derived LST showed that the latter estimates for the overwhelming number of observation turned out to be comparable in accuracy with each other and with the ground-based data.</p><p>Calculations of water and heat regime elements (being the final products of the simulation) were carried out when replacing ground-based estimates of precipitation, LST, LAI and B in the LSM by satellite-derived ones at each time step in all nodes of the computational grid. The efficiency of such replacement procedures was confirmed by comparing measured and calculated values of W and Ev (the difference between them didn’t exceed 15% for W and 25% for Ev).</p><p>The possibility of using soil surface moisture estimates obtained from all-weather measurements by the scatterometer ASCAT/MetOp in the microwave range when simulating soil water content was also revealed. These estimates may use to set initial conditions for the vertical soil water transfer equation, as well as for calculating evaporation from the soil surface and the subsequent formation of the upper boundary condition for this equation.</p><p>As a summary, the described approach can be considered as a method for assessing the water availability for agricultural arid region.</p>

Evaluating Soil Water Content in a WRF-Noah Downscaling Experiment

2013 ◽  
Vol 52 (10) ◽  
pp. 2312-2327 ◽  
Author(s):  
Peter Greve ◽  
Kirsten Warrach-Sagi ◽  
Volker Wulfmeyer
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Root Zone ◽  
Regional Scale ◽  
Land Surface Model ◽  
Surface Model ◽  
Flux Partitioning

AbstractSoil water content (SWC) depends on and affects the energy flux partitioning at the land–atmosphere interface. Above all, the latent heat flux is limited by the SWC of the root zone on one hand and radiation on the other. Therefore, SWC is a key variable in the climate system. In this study, the performance of the Weather Research and Forecasting model coupled with the Noah land surface model (WRF-Noah) system in a climate hindcast simulation from 1990 to 2008 is evaluated with respect to SWC versus two reanalysis datasets for Europe during 2007 and 2008 with in situ soil moisture observations from southern France. When compared with the in situ observations, WRF-Noah generally reproduces the SWC annual cycle while the reanalysis SWCs do not. The biases in areal mean WRF-Noah SWCs relate to biases in precipitation and evapotranspiration in a cropland environment. The spatial patterns and temporal variability of the seasonal mean SWCs from the WRF-Noah simulations and from the two reanalyses correspond well, while absolute values differ significantly, especially at the regional scale.

scholarly journals Development of a Land Surface Model Including Evaporation and Adsorption Processes in the Soil for the Land–Air Exchange in Arid Regions

2007 ◽  
Vol 8 (6) ◽  
pp. 1307-1324 ◽  
Author(s):  
Genki Katata ◽  
Haruyasu Nagai ◽  
Hiromasa Ueda ◽  
Nurit Agam ◽  
Pedro R. Berliner
Keyword(s):  
Water Vapor ◽  
Soil Water ◽  
Land Surface ◽  
Soil Layer ◽  
Land Surface Model ◽  
Ground Surface ◽  
Surface Model ◽  
Water Vapor Flux ◽  
Vapor Flux ◽  
Adsorption Processes

Abstract A one-dimensional soil model has been developed to better predict heat and water exchanges in arid and semiarid regions. New schemes to calculate evaporation and adsorption in the soil were incorporated in the model. High performance of the model was confirmed by comparison of predicted surface fluxes, soil temperature, and volumetric soil water content with those measured in the Negev Desert, Israel. Evaporation and adsorption processes in the soil have a large impact on the heat and water exchange between the atmosphere and land surface and are necessary to accurately predict them. Numerical experiments concerning the drying process of soil are performed using the presented model and a commonly used land surface model. The results indicated that, when the dry soil layer (DSL) develops, water vapor flux to the atmosphere is caused by evaporation in the soil rather than evaporation at the ground surface. Moreover, the adsorption process has some impact on the water and heat balance at the ground surface. The upward water vapor flux during the daytime is due to evaporation of soil water in the DSL, which is stored during the night due to adsorption. When the DSL progresses sufficiently, almost the same amounts of water are exchanged between the air and the soil surface by daytime evaporation and nighttime adsorption. In such conditions, latent heat due to evaporation and adsorption in the soil also work to reduce the diurnal variation of surface temperature.

Effects of Gravel on Energy and Water Transport in the Soil of Qinghai-Tibet Plateau

2020 ◽  
Author(s):  
shihua lyu
Keyword(s):  
Soil Temperature ◽  
Soil Water ◽  
Land Surface ◽  
Land Surface Model ◽  
Thermal Inertia ◽  
Tibet Plateau ◽  
Surface Model ◽  
Gravel Content ◽  
Water Move ◽  
The Mean

<p>According to the shortcomings of the land surface model, the new scheme is developed and applied to the simulating soil process at Madoi and Nagqu. Simulations show that gravel tend to reduce soil water holding capacity and enhance soil hydraulic conductivity, surface infiltration and drainage. As a result, the upper layer of soil mixed with gravel tends to drier due to the soil water move to deeper layer. The mean biases of soil moisture between the simulation and observation reduced by 25- 48% at two sites. Soil thermal conductivity is increased with gravel content and the soil thermal inertia was decreased with gravel content increasing. Therefore the deeper layer temperature of soil containing gravel is rapid response to air temperature change. The mean biases of soil temperature between the simulation and observation reduced by 9.1-25% at two sites. From the simulation results at Madoi and Nagqu, we find that the new scheme performed better than the original scheme in simulating soil temperature and water content and the land model implemented the new scheme is suitable for simulating land process in the QTP.</p>

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