Uncertainty in soil hydraulic parameters: A review of basics and methods

2021 ◽  
pp. 418-439
Author(s):  
Sahil Sharma ◽  
Deepak Swami ◽  
Chandni Thakur
Keyword(s):  
Monte Carlo ◽  
Parametric Uncertainty ◽  
Uncertainty Estimation ◽  
Pedotransfer Functions ◽  
Hydraulic Parameters ◽  
Empirical Methods ◽  
Soil Hydraulic Parameters ◽  
Modelling Techniques ◽  
Soil Hydraulic ◽  
Deterministic And Probabilistic Approaches

The paper is a review article on the basics of uncertainty, necessity of its quantification and a comparative study of various methods of uncertainty estimation. The paper primarily focusses on uncertainty estimation of soil hydraulic parameters as of their pivotal importance in groundwater flow and transport simulations, soil moisture modelling techniques etc. The deterministic and probabilistic approaches of uncertainty quantification are studied and an understanding of uncertainty based on field scale measurements, empirical methods and pedotransfer functions is established. A comparative analysis of the basic methods of uncertainty analysis Monte Carlo, Bayesian, FORM/SORM and GLUE is done and the preferential use based on the importance is suggested. Bayesian approach was most suitable for evaluating parametric uncertainty, Monte Carlo was one of the most powerful tools but computationally expensive, FORM was applicable to both numerical and analytical solutions but didn’t guarantee a global convergence and GLUE was conceptually simple but gave only a statistical measure.

A general BEST method predicting soil hydraulic parameters for any type of water retention and hydraulic conductivity curves

2020 ◽  
Author(s):  
Jesús Fernández-Gálvez ◽  
Joseph Pollacco ◽  
Laurent Lassabatere ◽  
Rafael Angulo-Jaramillo ◽  
Sam Carrick
Keyword(s):  
Hydraulic Conductivity ◽  
Water Retention ◽  
Pedotransfer Functions ◽  
Distribution Data ◽  
Hydraulic Parameters ◽  
Pore Size Distributions ◽  
Soil Transfer ◽  
Soil Hydraulic Parameters ◽  
Transfer Parameters ◽  
Soil Hydraulic

<p>Soil hydraulic characterization is crucial to describe the retention and transport of water in soil, but current methodologies limit its spatial applicability. This work presents a cost-effective general Beerkan Estimation of Soil Transfer parameters (BEST) methodology using single ring infiltration experiments to derive soil hydraulic parameters for any type of unimodal water retention and hydraulic conductivity functions. The proposed method relies on the BEST approach. The novelty lies in the use of Kosugi hydraulic parameters without need for textural information. Kosugi functions were chosen because they are based on physical principles (log-normal distribution for pore size distributions). A link between the Kosugi parameters (i.e., relationship between <em>σ</em> and <em>h</em><sub>kg</sub>) was introduced to reduce the number of parameters estimated and to avoid the need for information on the soil texture. This simplifies the procedures and avoids sources of errors related to the use of pedotransfer functions as for the previous BEST methods. Lastly, the method uses a quasi-exact formulation that is valid for all times, instead of the approximate expansions previously used, avoiding related inaccuracy and allowing the use of any infiltration data encompassing or not both transient and steady states. The new BEST methods were tested against numerically generated data for several contrasting synthetic soils, and the results show that these methods provide consistent hydraulic functions close to the target functions. The new BEST method is accurate and can use any type of water retention and hydraulic conductivity functions (Fernández-Gálvez et al., 2019).</p><p> </p><p> </p><p><strong>Reference</strong></p><p>Fernández-Gálvez, J., Pollacco, J.A.P., Lassabatere, L., Angulo-Jaramillo, R., Carrick, S., 2019. A general Beerkan Estimation of Soil Transfer parameters method predicting hydraulic parameters of any unimodal water retention and hydraulic conductivity curves: Application to the Kosugi soil hydraulic model without using particle size distribution data. Adv. Water Resour. 129, 118–130. https://doi.org/10.1016/j.advwatres.2019.05.005</p>

Assessment of Empirical and Semi-Empirical Models for Estimating a Soil Infiltration Function

2020 ◽  
Vol 63 (4) ◽  
pp. 833-845
Author(s):  
Mohamed Khaled Salahou ◽  
Xiyun Jiao ◽  
Haishen Lü
Keyword(s):  
Pedotransfer Functions ◽  
Observation Data ◽  
Hydraulic Parameters ◽  
Soil Infiltration ◽  
Infiltration Model ◽  
Infiltration Models ◽  
Soil Hydraulic Parameters ◽  
Soil Hydraulic ◽  
Semi Empirical ◽  
Kostiakov Model

HighlightsThe hydraulic performance computed with the KE or GA model is nearly the same, as long the models are calibrated using the same observation data.The GA model with the soil hydraulic parameters obtained from the pedotransfer functions adequately represented the soil infiltration function.The particle size distribution or the soil texture are recommended to estimate soil hydraulic parameters with the VG-ROSETTA model. Abstract. Field-scale estimation of a soil infiltration function is important for the design, simulation, and/or evaluation of surface irrigation systems. Semi-empirical and empirical infiltration models are used to estimate the infiltration function. Semi-empirical infiltration models have substantial computational and parameterization complexities, e.g., soil hydraulic parameters are needed to estimate the infiltration function. In contrast, empirical infiltration models are generally not considered to have specific initial and boundary conditions. The objectives of this study were to compare a semi-empirical infiltration model and an empirical infiltration model. The Green-Ampt model (GA) and the Kostiakov model (KE) were used as semi-empirical and empirical infiltration models, respectively. The soil hydraulic parameters for the GA model were estimated using various pedotransfer functions (PTFs), and in an additional assessment, the measured water content data were used to calibrate and validate the soil hydraulic parameters using the HYDRUS-1D model. The results show that the hydraulic performance computed with the KE or GA model is nearly the same, as long as they are calibrated using the same observation data. Additionally, the results indicate that the GA model with the soil hydraulic parameters obtained from the PTFs adequately represented the soil infiltration function in the borders. Keywords: Empirical infiltration model, Green-Ampt model, Infiltration model, Kostiakov model, Semi-empirical infiltration model, Soil hydraulic properties.

Development of a China Dataset of Soil Hydraulic Parameters Using Pedotransfer Functions for Land Surface Modeling

2013 ◽  
Vol 14 (3) ◽  
pp. 869-887 ◽  
Author(s):  
Yongjiu Dai ◽  
Wei Shangguan ◽  
Qingyun Duan ◽  
Baoyuan Liu ◽  
Suhua Fu ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Soil Characteristics ◽  
Surface Modeling ◽  
Pedotransfer Functions ◽  
Water Retention Curve ◽  
Hydraulic Parameters ◽  
Land Surface Modeling ◽  
Soil Hydraulic Parameters ◽  
Soil Hydraulic

Abstract The objective of this study is to develop a dataset of the soil hydraulic parameters associated with two empirical soil functions (i.e., a water retention curve and hydraulic conductivity) using multiple pedotransfer functions (PTFs). The dataset is designed specifically for regional land surface modeling for China. The authors selected 5 PTFs to derive the parameters in the Clapp and Hornberger functions and the van Genuchten and Mualem functions and 10 PTFs for soil water contents at capillary pressures of 33 and 1500 kPa. The inputs into the PTFs include soil particle size distribution, bulk density, and soil organic matter. The dataset provides 12 estimated parameters and their associated statistical values. The dataset is available at a 30 × 30 arc second geographical spatial resolution and with seven vertical layers to the depth of 1.38 m. The dataset has several distinct advantages even though the accuracy is unknown for lack of in situ and regional measurements. First, this dataset utilizes the best available soil characteristics dataset for China. The Chinese soil characteristics dataset was derived by using the 1:1 000 000 Soil Map of China and 8595 representative soil profiles. Second, this dataset represents the first attempt to estimate soil hydraulic parameters using PTFs directly for continental China at a high spatial resolution. Therefore, this dataset should capture spatial heterogeneity better than existing estimates based on lookup tables according to soil texture classes. Third, the authors derived soil hydraulic parameters using multiple PTFs to allow flexibility for data users to use the soil hydraulic parameters most preferable to or suitable for their applications.

scholarly journals Evaluation and improvement of the default soil hydraulic parameters for the Noah Land Surface Model

Geoderma ◽  
2017 ◽  
Vol 285 ◽  
pp. 247-259 ◽  
Author(s):  
Andrea Sz. Kishné ◽  
Yohannes Tadesse Yimam ◽  
Cristine L.S. Morgan ◽  
Bright C. Dornblaser
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Hydraulic Parameters ◽  
Soil Hydraulic Parameters ◽  
Noah Land Surface Model ◽  
Soil Hydraulic

Exploring hydrological processes at a small agricultural catchment in the Czech Republic

2021 ◽  
Author(s):  
Tailin Li ◽  
Nina Noreika ◽  
Jakub Jeřábek ◽  
Tomáš Dostál ◽  
David Zumr
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Hydrological Processes ◽  
Hydraulic Parameters ◽  
Rainfall Runoff ◽  
The Czech Republic ◽  
Mike She ◽  
Stream Discharge ◽  
Soil Hydraulic Parameters ◽  
Soil Hydraulic

<p>A better understanding of hydrological processes in agricultural catchments is not only crucial to hydrologists but also helpful for local farmers. Therefore, we have built the freely-available web-based WALNUD dataset (Water in Agricultural Landscape – NUčice Database) for our experimental catchment Nučice (0.53 km<sup>2</sup>), the Czech Republic. We have included observed precipitation, air temperature, stream discharge, and soil moisture in the dataset. Furthermore, we have applied numerical modelling techniques to investigate the hydrological processes (e.g. soil moisture variability, water balance) at the experimental catchment using the dataset.</p><p>The Nučice catchment, established in 2011, serves for the observation of rainfall-runoff processes, soil erosion and water balance of the cultivated landscape. The average altitude is 401 m a.s.l., the mean land slope is 3.9 %, and the climate is humid continental (mean annual temperature 7.9 °C, average annual precipitation 630 mm). The catchment consists of three fields covering over 95 % of the area. There is a narrow stream which begins as a subsurface drainage pipe in the uppermost field draining the water at catchment. The typical crops are winter wheat, rapeseed, mustard and alfalfa. The installed equipment includes a standard meteorological station, several rain gauges distributed in the area of the basin, and an H flume to monitor the stream discharge, water turbidity and basic water quality indicators. The soil water content (at point scale) and groundwater level are also recorded. Recently, we have installed two cosmic-ray soil moisture sensors (StyX Neutronica) to estimate large-scale topsoil water content at the catchment.</p><p>Even though the soil management and soil properties in the fields of Nučice seem to be nearly homogeneous, we have observed variability in the topsoil moisture pattern. The method for the explanation of the soil water regime was the combination of the connectivity indices and numerical modelling. The soil moisture profiles from the point-scale sensors were processed in a 1-D physically-based soil water model (HYDRUS-1D) to optimize the soil hydraulic parameters. Further, the soil hydraulic parameters were used as input into a 3D spatially-distributed model, MIKE-SHE. The MIKE-SHE simulation has been mainly calibrated with rainfall-runoff observations. Meanwhile, the spatial patterns of the soil moisture were assessed from the simulation for both dry and wet catchment conditions. From the MIKE-SHE simulation, the optimized soil hydraulic parameters have improved the estimation of soil moisture dynamics and runoff generation. Also, the correlation between the observed and simulated soil moisture spatial patterns showed different behaviors during the dry and wet catchment conditions.</p><p>This study has been supported by the Grant Agency of the Czech Technical University in Prague, grant No. SGS20/156/OHK1/3T/11 and the Project SHui which is co-funded by the European Union Project: 773903 and the Chinese MOST.</p>

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