PEDOTRANSFER FUNCTIONS TO PREDICT SPATIALLY VARIABLE POTENTIAL WATER-UNSTABLE FRACTIONS

Kutatásunk célja a víztartóképesség-függvény (VKF) paramétereit az átnézetes térképeink adattartalmával becslő módszerek megbízhatóságának összehasonlítása és továbbfejlesztésük vizsgálata a Magyarországi Részletes Talajfizikai és Hidrológiai Adatbázison (MARTHA).Az irodalomban fellehető módszerek közül VKF-becslő módszert hazai átnézetes talajtérképi információkra eddig egyedül a Kreybig térképekre alkalmazták (Bakacsi et al., 2012). Ők a talaj higroszkópossága (hy) alapján becsülték adott talaj FAO (1995) fizikai féleség kategóriába tartozását. Wösten és munkatársai (1999) és Nemes (2003) pedotranszfer-függvényei alapján rendelték továbbá a talajhoz a fizikai féleség kategóriára meghatározott víztartóképesség-függvény (VKF) van Genuchten paramétereit (HYPRES_hy és HUNSODA_hy módszerek).Bakacsi és munkatársai (2012) eljárását követve, a MARTHA adatbázison vizsgáltuk a hy és az ötkategóriás FAO fizikai féleség kapcsolatát. A fizikai féleség becslését az Arany-féle kötöttség (KA) alapján is kidolgoztuk.Wösten és munkatársai (1999) módszerével meghatároztuk a MARTHA adatbázis talajainak a FAO fizikai féleség kategóriákra jellemző víztartóképességfüggvényeinek van Genuchten paramétereit. A meghatározást a pF6,2 értéken felül a legalább három, majd a legalább öt mért víztartóképesség-értékű talajmintákon végeztük.Megállapítottuk, hogy a KA alapján hatékonyabb a talajminták FAO fizikai féleség kategóriába sorolása, mint a hy alapján.Abban az esetben, amikor nem áll rendelkezésre mechanikai összetétel és a fizikai féleség kategóriába történő besorolást a talaj higroszkópossága alapján végezzük, akkor a VKF-becslés megbízhatósága szignifikánsan rosszabb. Hazai talajmintákon vizsgálva a MARTHA adatbázison pontosított VKF-becslő módszerek szignifikánsan megbízhatóbbak a HYPRES és HUNSODA VKF-becslő módszereinél. A hy-ból kiinduló MARTHA VKF-becslések annak ellenére szignifikánsan megbízhatóbbak a WÖSTEN és munkatársai (1999) módszerénél (HYPRES), hogy utóbbit nem rontja a fizikai féleségbe sorolás hibája.A dolgozat az EU FP7/2007-2013 (Nr. 263188) MyWater és a TÁMOP-4.2.2.A-11/1/KONV-2012-0064 projekt keretében készült. A TÁMOP projekt az Európai Unió támogatásával, az Európai Szociális Alap társfinanszírozásával valósul meg.

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A framework for quantifying hydrologic effects of soil structure across scales

Communications Earth & Environment ◽

10.1038/s43247-021-00180-0 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Sara Bonetti ◽

Zhongwang Wei ◽

Dani Or

Keyword(s):

Soil Texture ◽

Land Surface ◽

Soil Structure ◽

Pedotransfer Functions ◽

Small Scale ◽

Spatial Correlations ◽

Surface Parameterization ◽

Exchange Processes ◽

Local Soil ◽

Spatially Distributed

AbstractEarth system models use soil information to parameterize hard-to-measure soil hydraulic properties based on pedotransfer functions. However, current parameterizations rely on sample-scale information which often does not account for biologically-promoted soil structure and heterogeneities in natural landscapes, which may significantly alter infiltration-runoff and other exchange processes at larger scales. Here we propose a systematic framework to incorporate soil structure corrections into pedotransfer functions, informed by remote-sensing vegetation metrics and local soil texture, and use numerical simulations to investigate their effects on spatially distributed and areal averaged infiltration-runoff partitioning. We demonstrate that small scale soil structure features prominently alter the hydrologic response emerging at larger scales and that upscaled parameterizations must consider spatial correlations between vegetation and soil texture. The proposed framework allows the incorporation of hydrological effects of soil structure with appropriate scale considerations into contemporary pedotransfer functions used for land surface parameterization.

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

Soil Science Society of America Journal ◽

10.2136/sssaj2011.0090 ◽

2012 ◽

Vol 76 (3) ◽

pp. 829-844 ◽

Cited By ~ 17

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

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Numerical solution of the Dirac equation for an armchair graphene nanoribbon in the presence of a transversally variable potential

2010 14th International Workshop on Computational Electronics ◽

10.1109/iwce.2010.5677938 ◽

2010 ◽

Cited By ~ 3

Author(s):

P. Marconcini ◽

D. Logoteta ◽

M. Fagotti ◽

M. Macucci

Keyword(s):

Dirac Equation ◽

Numerical Solution ◽

Graphene Nanoribbon ◽

Armchair Graphene Nanoribbon ◽

Variable Potential ◽

Armchair Graphene

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Evaluation of pedotransfer functions for predicting soil hydraulic properties: A voyage from regional to field scales across Europe

Journal of Hydrology Regional Studies ◽

10.1016/j.ejrh.2021.100903 ◽

2021 ◽

Vol 37 ◽

pp. 100903

Author(s):

P. Nasta ◽

B. Szabó ◽

N. Romano

Keyword(s):

Hydraulic Properties ◽

Pedotransfer Functions ◽

Soil Hydraulic Properties ◽

Soil Hydraulic

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Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks

Water ◽

10.3390/w13050705 ◽

2021 ◽

Vol 13 (5) ◽

pp. 705

Author(s):

Josué Trejo-Alonso ◽

Carlos Fuentes ◽

Carlos Chávez ◽

Antonio Quevedo ◽

Alfonso Gutierrez-Lopez ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Hydraulic Conductivity ◽

Input Data ◽

Logistic Function ◽

Saturated Hydraulic Conductivity ◽

Irrigation District ◽

Pedotransfer Functions ◽

Back Propagation Algorithm ◽

Artificial Neural

In the present work, we construct several artificial neural networks (varying the input data) to calculate the saturated hydraulic conductivity (KS) using a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. All of them were constructed using two hidden layers, a back-propagation algorithm for the learning process, and a logistic function as a nonlinear transfer function. In order to explore different arrays for neurons into hidden layers, we performed the bootstrap technique for each neural network and selected the one with the least Root Mean Square Error (RMSE) value. We also compared these results with pedotransfer functions and another neural networks from the literature. The results show that our artificial neural networks obtained from 0.0459 to 0.0413 in the RMSE measurement, and 0.9725 to 0.9780 for R2, which are in good agreement with other works. We also found that reducing the amount of the input data offered us better results.

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A generally applicable pedotransfer function that estimates field capacity and permanent wilting point from soil texture and bulk density

Canadian Journal of Soil Science ◽

10.4141/cjss07120 ◽

2008 ◽

Vol 88 (5) ◽

pp. 761-774 ◽

Cited By ~ 23

Author(s):

J. A. P. Pollacco

Keyword(s):

Organic Matter ◽

Soil Moisture ◽

Bulk Density ◽

Soil Texture ◽

The United States ◽

Pedotransfer Functions ◽

Pedotransfer Function ◽

Data Set ◽

Wide Range ◽

Fitting Parameters

Hydrological models require the determination of fitting parameters that are tedious and time consuming to acquire. A rapid alternative method of estimating the fitting parameters is to use pedotransfer functions. This paper proposes a reliable method to estimate soil moisture at -33 and -1500 kPa from soil texture and bulk density. This method reduces the saturated moisture content by multiplying it with two non-linear functions depending on sand and clay contents. The novel pedotransfer function has no restrictions on the range of the texture predictors and gives reasonable predictions for soils with bulk density that varies from 0.25 to 2.16 g cm-3. These pedotransfer functions require only five parameters for each pressure head. It is generally accepted that the introduction of organic matter as a predictor improves the outcomes; however it was found by using a porosity based pedotransfer model, using organic matter as a predictor only modestly improves the accuracy. The model was developed employing 18 559 samples from the IGBP-DIS soil data set for pedotransfer function development (Data and Information System of the International Geosphere Biosphere Programme) database that embodies all major soils across the United States of America. The function is reliable and performs well for a wide range of soils occurring in very dry to very wet climates. Climatical grouping of the IGBP-DIS soils was proposed (aquic, tropical, cryic, aridic), but the results show that only tropical soils require specific grouping. Among many other different non-climatical soil groups tested, only humic and vitric soils were found to require specific grouping. The reliability of the pedotransfer function was further demonstrated with an independent database from Northern Italy having heterogeneous soils, and was found to be comparable or better than the accuracy of other pedotransfer functions found in the literature. Key words: Pedotransfer functions, soil moisture, soil texture, bulk density, organic matter, grouping

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Evaluation of Pedotransfer Functions for Predicting Saturated Hydraulic Conductivity for U.S. Soils

10.13031/2013.27309 ◽

2009 ◽

Cited By ~ 1

Author(s):

Ahmed M Abdelbaki ◽

Mohamed A Youssef ◽

Esmail M. F Naguib ◽

Mohamed E Kiwan ◽

Emad I El-giddawy

Keyword(s):

Hydraulic Conductivity ◽

Saturated Hydraulic Conductivity ◽

Pedotransfer Functions

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Assessment of pedotransfer functions for estimating soil water retention curves for the amazon region

Revista Brasileira de Ciência do Solo ◽

10.1590/s0100-06832014000300005 ◽

2014 ◽

Vol 38 (3) ◽

pp. 730-743 ◽

Cited By ~ 12

Author(s):

João Carlos Medeiros ◽

Miguel Cooper ◽

Jaqueline Dalla Rosa ◽

Michel Grimaldi ◽

Yves Coquet

Keyword(s):

Soil Water ◽

Water Retention ◽

Soil Layer ◽

Direct Determination ◽

Exchange Capacity ◽

Pedotransfer Functions ◽

Water Retention Curve ◽

Spatial And Temporal Variability ◽

Soil Water Retention Curve ◽

Soil Water Retention

Knowledge of the soil water retention curve (SWRC) is essential for understanding and modeling hydraulic processes in the soil. However, direct determination of the SWRC is time consuming and costly. In addition, it requires a large number of samples, due to the high spatial and temporal variability of soil hydraulic properties. An alternative is the use of models, called pedotransfer functions (PTFs), which estimate the SWRC from easy-to-measure properties. The aim of this paper was to test the accuracy of 16 point or parametric PTFs reported in the literature on different soils from the south and southeast of the State of Pará, Brazil. The PTFs tested were proposed by Pidgeon (1972), Lal (1979), Aina & Periaswamy (1985), Arruda et al. (1987), Dijkerman (1988), Vereecken et al. (1989), Batjes (1996), van den Berg et al. (1997), Tomasella et al. (2000), Hodnett & Tomasella (2002), Oliveira et al. (2002), and Barros (2010). We used a database that includes soil texture (sand, silt, and clay), bulk density, soil organic carbon, soil pH, cation exchange capacity, and the SWRC. Most of the PTFs tested did not show good performance in estimating the SWRC. The parametric PTFs, however, performed better than the point PTFs in assessing the SWRC in the tested region. Among the parametric PTFs, those proposed by Tomasella et al. (2000) achieved the best accuracy in estimating the empirical parameters of the van Genuchten (1980) model, especially when tested in the top soil layer.

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