Data Assimilation with Soil Water Content Sensors and Pedotransfer Functions in Soil Water Flow Modeling

2012 ◽  
Vol 76 (3) ◽  
pp. 829-844 ◽  
Author(s):  
Feng Pan ◽  
Yakov Pachepsky ◽  
Diederik Jacques ◽  
Andrey Guber ◽  
Robert L. Hill
Keyword(s):  
Data Assimilation ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Flow ◽  
Pedotransfer Functions ◽  
Flow Modeling ◽  
Soil Water Flow

HYDRUS (2d/3d) simulation of water flow through sandy loam soil under potato cultivation in Southern Manitoba

2017 ◽  
pp. 1.9-1.19 ◽  
Author(s):  
Afua Mante ◽  
Ramanathan Sri Ranjan
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Flow ◽  
Sandy Loam ◽  
Crop Evapotranspiration ◽  
Reference Crop Evapotranspiration ◽  
Loam Soil ◽  
Flow Through ◽  
Reference Crop

The HYDRUS (2D/3D) modeling tool was used to simulate water flow through subsurface-drained sandy loam soil under potato (Solanum tuberosum) cultivation in Southern Manitoba. The model was used to simulate water flow through a 2-D model domain of dimensions, 15 m width × 2.5 m depth. The model was calibrated and validated with field data measured during the growing season of year 2011 at the Hespler Farms, Winkler, Manitoba. Field measurements, including soil water content and watertable depth, for two test plots under subsurface free drainage were used for the calibration and validation. Weather data were also obtained to estimate reference crop evapotranspiration, which was used as input data in the model. Based on the reference crop evapotranspiration, and crop coefficient of the potato crop, the actual crop evapotranspiration was estimated and compared to the simulated actual crop evapotranspiration results. The results showed that the model was able to account for 50% to 78% of the variation in the estimated actual crop evapotranspiration. With respect to water flow through the soil, the observed soil water content and the simulated soil water content were compared using graphical and quantitative analysis. Based on the coefficient of determination (R2), the model accounted for 68% to 89% variation in the observed data. The intercept of the regression line varied from 0.01 to 0.08, and the slope, 0.75 to 0.99. The Nash–Sutcliffe modeling efficiency coefficient (NSE) varied from 0.62-0.89, the Percent bias (PBIAS) values varied from -1.99% to 1.16%. The root mean square error-observations standard deviation ratio (RSR) values varied from 0.33 to 0.61. The values for the evaluation parameters show that the model was able to simulate the water flow through the soil profile reasonably well.

Modeling of a forested study site with the Community Land Model version 5 using climate projections for the 21st century.

2020 ◽  
Author(s):  
Lukas Strebel ◽  
Klaus Goergen ◽  
Bibi S. Naz ◽  
Heye Bogena ◽  
Harry Vereecken ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Model Parameters ◽  
Climate Projections ◽  
Modeling Framework ◽  
Area Index ◽  
Community Land Model

<p>Modeling forest ecosystems is important to facilitate adaptations in forest management approaches necessary to address the challenges of climate change, particularly of interest are ecohydrological states and fluxes such as soil water content, biomass, leaf area index, and evapotranspiration.</p><p>The community land model in its current version 5 (CLM5) simulates a broad collection of important land-surface processes; from moisture and energy partitioning, through biogeophysical processes, to surface and subsurface runoff. Additionally, CLM5 contains a biogeochemistry model (CLM5-BGC) which includes prognostic computation of vegetation states and carbon and nitrogen pools. However, CLM5 predictions are affected by uncertainty related to uncertain model forcings and parameters. Here, we use data assimilation methods to improve model performance by assimilating soil water content observations into CLM5 using the parallel data assimilation framework (PDAF).</p><p> </p><p>The coupled modeling framework was applied to the small (38.5 ha) forested catchment Wüstebach located in the Eifel National Park near the German-Belgian border. As part of the terrestrial environmental observatories (TERENO) network, the SoilNet sensors at the study site provide soil water content and soil temperature measurements since 2009.</p><p>CLM5 simulations for the period 2009-2100 were made, using local atmospheric observations for the period of 2009-2018 and an ensemble of regional climate model projections for 2019-2100. Simulations illustrate that data assimilation of soil water content improves the characterization of past model states, and that estimated model parameters and default model parameters result in different trajectories of ecohydrological states for 2019-2100. The simulations also illustrate that this site is hardly affected by increased water stress in the future.</p><p>The developed framework will be extended and applied for both ecosystem reanalysis as well as further simulations using climate projections across forested sites over Europe.</p>

scholarly journals Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: Description and applications

2021 ◽  
Author(s):  
Lukas Strebel ◽  
Heye Bogena ◽  
Harry Vereecken ◽  
Harrie-Jan Hendricks Franssen
Keyword(s):  
Data Assimilation ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Land Surface Models ◽  
Community Land Model ◽  
Model Version ◽  
Parallel Data ◽  
Surface Models

Abstract. Land surface models are important for improving our understanding of the earth system. They are continuously improving and becoming more accurate in describing the varied surface processes, e.g. the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more and higher quality data. For the optimal combination of land surface models and observation data, data assimilation techniques have been developed in the past decades that incorporate observations to update modeled states and parameters. The Parallel Data Assimilation Framework (PDAF) is a software environment that enables ensemble data assimilation and simplifies the implementation of data assimilation systems in numerical models. In this paper, we present the further development of the PDAF to enable its application in combination with CLM5. This novel coupling adapts the optional CLM5 ensemble mode to enable integration of PDAF filter routines while keeping changes to the pre-existing parallel communication infrastructure to a minimum. Soil water content observations from an extensive in-situ measurement network in the Wüstebach catchment in Germany are used to illustrate the application of the coupled CLM5+PDAF system. The results show overall reductions in root mean square error of soil water content from 7 % up to 35 % compared to simulations without data assimilation. We expect the coupled CLM5+PDAF system to provide a basis for improved regional to global land surface modelling by enabling the assimilation of globally available observational data.

Global mapping of volumetric water content at 10, 33 and 1500 kPa using the WoSIS global database

2021 ◽  
Author(s):  
Maria Eliza Turek ◽  
Gerard Heuvelink ◽  
Niels Batjes ◽  
Laura Poggio
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Retention ◽  
Spatial Prediction ◽  
Volumetric Water Content ◽  
Data Availability ◽  
Pedotransfer Functions ◽  
Soil Water Retention ◽  
Direct Mapping

<p>Soil water content is a key property for modelling the water balance in hydrological, eco-hydrological and agro-hydrological models. Currently available global maps of soil water retention are mostly based on pedotransfer functions applied to maps of other basic soil properties. We developed global maps of the volumetric water content at 10, 33 and 1500 kPa by direct mapping based on soil water content data derived from the WoSIS Soil Profile Database and covariates describing vegetation, terrain morphology, climate, geology and hydrology using the SoilGrids workflow. The preparation of the input soil data consisted of the verification of available volumetric water content data and conversion of gravimetric to volumetric data using measured and estimated bulk density. In total we had 9609, 41082 and 49224 soil water content observations at 10, 33 and 1500 kPa, respectively, and prepared around 200 covariates as candidate predictors. After covariates selection, model tuning and cross-validation and final model fitting for 3D spatial prediction, results were presented for the globe with uncertainty estimation. The results were also compared to other available global maps of water retention to evaluate differences between direct mapping against other types of approaches. Directly developing global maps of soil water content, with associated uncertainty, is a novel approach for this type of properties, and contributes to improving global soil data availability and quality.</p>

scholarly journals Data assimilation of soil water flow via ensemble Kalman filter: Infusing soil moisture data at different scales

2017 ◽  
Vol 555 ◽  
pp. 912-925 ◽  
Author(s):  
Penghui Zhu ◽  
Liangsheng Shi ◽  
Yan Zhu ◽  
Qiuru Zhang ◽  
Kai Huang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Soil Water ◽  
Water Flow ◽  
Ensemble Kalman Filter ◽  
Soil Moisture Data ◽  
Soil Water Flow

Measurement of Soil Water Content during Three-Dimensional Axial-Symmetric Water Flow

1990 ◽  
Vol 54 (3) ◽  
pp. 645-649 ◽  
Author(s):  
R. G. Kachanoski ◽  
I. J. Van Wesenbeeck ◽  
P. Von Bertoldi ◽  
A. Ward ◽  
C. Hamlen
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Flow ◽  
Three Dimensional

The relations between soil water retention characteristics, particle size distributions, bulk densities and calcium carbonate contents for Danish soils

2005 ◽  
Vol 36 (3) ◽  
pp. 235-244 ◽  
Author(s):  
Niels Henrik Jensen ◽  
Thomas Balstrøm ◽  
Henrik Breuning-Madsen
Keyword(s):  
Particle Size ◽  
Organic Matter ◽  
Particle Size Distribution ◽  
Water Content ◽  
Bulk Density ◽  
Soil Water ◽  
Soil Water Content ◽  
Pressure Head ◽  
Pedotransfer Functions ◽  
Regression Equations

A database containing about 800 soil profiles located in a 7-km grid covering Denmark has been used to develop a set of regression equations of soil water content at pressure heads −1, −10, −100 and −1500 kPa versus particle size distribution, organic matter, CaCO3 and bulk density. One purpose was to elaborate equations based on soil parameters available in the Danish Soil Classification's texture database of particle size distribution and organic matter. It was also tested to see if inclusion of bulk density or CaCO3 content (in CaCO3-containing samples) as predictors or grouping in surface and subsurface horizons or textural classes improved the regression equations. Compared to existing Danish equations based on much fewer observations the accuracies of the new equations were better. The equations also predicted the soil water content at the measured pressure heads more accurately than the pedotransfer functions developed in HYPRES (Hydraulic Properties of European Soils). Introducing bulk density as a predictor improved the equation for the pressure head of −1 kPa but not for the lower ones. The grouping of data sets in surface and subsurface horizons or in textural classes did not improve the equations. Based on the equations a set of van Genuchten parameters for soil types in the Danish Soil Classification was elaborated. The prediction of soil water content, especially at pressure head −1 kPa, is more accurate using these van Genuchten parameters than using the pedotransfer functions developed in relation to the HYPRES database from a broad range of European soils.

Sign in / Sign up

Export Citation Format

Share Document