Pedotransfer Functions for Estimating Soil Hydraulic Properties: A Review

Patil, N. G.; Singh, Surendra

doi:10.1016/s1002-0160(15)60054-6

Cited by 126 publications

(94 citation statements)

References 65 publications

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“…Analysis has shown that the Van Genuchten () pedotransfer functions are more accurate than Clapp and Hornberger () pedotransfer functions (Shao & Irannejad ). More recently, some researchers (Nemes et al, ; Patil & Chaturvedi, ) have advocated the use of non‐parametric nearest‐neighbor models as an alternative to conventional parametric formulations such as Clapp and Hornberger () and Van Genuchten (), and the development of an improved set of pedotransfer functions (Patil & Singh, ). It may also be necessary to incorporate region‐specific values for soil properties, which can be determined from advanced statistical or intelligence techniques (Merdun, ), rather than applying a universal parametric pedotransfer function.…”

Section: Discussionmentioning

confidence: 99%

The Sensitivity of North American Terrestrial Carbon Fluxes to Spatial and Temporal Variation in Soil Moisture: An Analysis Using Radar‐Derived Estimates of Root‐Zone Soil Moisture

et al. 2019

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This study examines the impact of variation in root-zone soil moisture (RZSM), a key component of the Earth's hydrologic cycle and climate system, on regional carbon fluxes across seven North American ecosystems. P-band synthetic aperture radar-derived RZSM estimates were incorporated into the ecosystem demography (ED2) terrestrial biosphere model through a model-data blending approach. Analysis shows that the model qualitatively captures inter-daily and seasonal variability of observed RZSM at seven flux tower sites (r = 0.59 ± 0.26 and r = 0.70 ± 0.22 for 0-10 and 10-40 cm of soil layers, respectively; P < 0.001). Incorporating the remotely sensed RSZM estimates increases the accuracy (root-mean-square deviations decrease from 0.10 ± 0.07 and 0.09 ± 0.06 m 3 ·m −3 to 0.08 ± 0.05 and 0.07 ± 0.03 m 3 ·m −3 for 0-10 and 10-40 cm of soil layers, respectively) of the model's RZSM predictions. The regional carbon fluxes predicted by the native and RZSM-constrained model were used to quantify sensitivities of gross primary productivity, autotrophic respiration (R a ), heterotrophic respiration (R h ), and net ecosystem exchange to variation in RZSM. Gross primary productivity exhibited the largest sensitivity (6.6 ± 10.7 kg·cm −2 ·year·θ −1 ) followed by R a (2.9 ± 7.3 kg·cm −2 ·year −1 ·θ −1 ), R h (2.6 ± 3.1 kg·cm −2 ·year −1 ·θ −1 ), and net ecosystem exchange (−1.7 ± 7.8 kg·cm −2 ·year −1 ·θ −1 ). Analysis shows that these carbon flux sensitivities varied considerably across regions, reflecting influences of canopy structure, soil properties, and the ecophysiological properties of different plant functional types. This study highlights (1) the importance of improved terrestrial biosphere model predictions of RZSM to improve predictions of terrestrial carbon fluxes, (2) a need for improved pedotransfer functions, and (3) improved understanding of how soil characteristics, climate, and vegetation composition interact to govern the responses of different ecosystems to changing hydrological conditions.

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Section: Discussionmentioning

confidence: 99%

The Sensitivity of North American Terrestrial Carbon Fluxes to Spatial and Temporal Variation in Soil Moisture: An Analysis Using Radar‐Derived Estimates of Root‐Zone Soil Moisture

et al. 2019

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“…Soil texture perturbation considers that in CLM a single set of pedotransfer functions is assumed to be valid throughout the globe while pedotransfer functions are usually specific to regions (e.g. Patil and Singh, 2016). In other words, the perturbation of soil texture also covers the uncertainty in the pedotransfer function itself.…”

Section: Model Ensemblementioning

confidence: 99%

Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

Baatz¹,

Franssen²,

Han³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km 2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm 3 cm −3 for the assimilation period and 0.05 cm 3 cm −3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm 3 cm −3 ). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

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“…Soil PSD, or texture, is a complex distribution usually reported in terms of the mass percentage of clay, silt and sand, which is known to be a leading property affecting soil hydraulic properties. Consequently, soil texture fractions have been widely used as predictor variables in most of the pedotransfer functions proposed to estimate the soil water characteristics (Saxton et al, 1986;Pachepsky and Rawls, 2004;Patil and Singh, 2016). This work tries to bridge the conceptual distance between the geological material of the Earth's surface and the soil hydraulic properties of the overlying soils by studying the relationships between the parent material characteristics and PSD parameters which have been shown to be useful in estimating soil water retention properties.…”

Section: Introductionmentioning

confidence: 99%