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Modern Applied Statistics With S

Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

https://pubag.nal.usda.gov/download/10780/pdf

rosetta: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions

Hydrologic analyses often involve the evaluation of soil water infiltration, conductivity, storage, and plant-water relationships. To define the hydrologic soil water effects requires estimating soil water characteristics for water potential and hydraulic conductivity using soil variables such as texture, organic matter (OM), and structure. Field or laboratory measurements are difficult, costly, and often impractical for many hydrologic analyses. Statistical correlations between soil texture, soil water potential, and hydraulic conductivity can provide estimates sufficiently accurate for many analyses and decisions. This study developed new soil water characteristic equations from the currently available USDA soil database using only the readily available variables of soil texture and OM. These equations are similar to those previously reported by Saxton et al. but include more variables and application range. They were combined with previously reported relationships for tensions and conductivities and the effects of density, gravel, and salinity to form a comprehensive predictive system of soil water characteristics for agricultural water management and hydrologic analyses. Verification was performed using independent data sets for a wide range of soil textures. The predictive system was programmed for a graphical computerized model to provide easy application and rapid solutions and is available at http://hydrolab.arsusda. gov/soilwater/Index.htm.

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Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions

Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.

Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009

The solution of many field-scale flow and transport problems requires estimates of unsaturated soil hydraulic properties. The objective of this study was to calibrate neural network models for prediction of water retention parameters and saturated hydraulic conductivity, K s , from basic soil properties. Twelve neural network models were developed to predict water retention parameters using a data set of 1209 samples containing sand, silt, and clay contents, bulk density, porosity, gravel content, and soil horizon as well as water retention data. A subset of 620 samples was used to develop 19 neural network models to predict K s . Prediction of water retention parameters and K s generally improved if more input data were used. In a more detailed investigation, four models with the following levels of input data were selected: (i) soil textural class, (ii) sand, silt, and clay contents, (iii) sand, silt, and clay contents and bulk density, and (iv) the previous variables and water content at a pressure head of 33 kPa. For water retention, the root mean square residuals decreased from 0.107 for the first to 0.060 m 3 m -3 for the fourth model while the root mean square residual K s decreased from 0.627 to 0.451 log(cm d -1 ). The neural network models performed better on our data set than four published pedotransfer functions for water retention (by 0.01-0.05 m 3 m -3 ) and better than six published functions for K s (by 0.1-0.9 order of magnitude). Use of the developed hierarchical neural network models is attractive because of improved accuracy and because it permits a considerable degree of flexibility toward available input data.

Neural network analysis for hierarchical prediction of soil hydraulic properties

Pedotransfer functions (PTFs) are becoming a more common way to predict soil hydraulic properties from soil texture, bulk density, and organic matter content. Thus far, the calibration and validation of PTFs has been hampered by a lack of suitable databases. In this paper we employed three databases

Database-related accuracy and uncertainty of pedotransfer functions

In many vadose zone hydrological studies, it is imperative that the soil's unsaturated hydraulic conductivity is known. Frequently, the Mualem-van Genuchten model (MVG) is used for this purpose because it allows prediction of unsaturated hydraulic conductivity from water retention parameters. For this and similar equations, it is often assumed that a measured saturated hydraulic conductivity (K s ) can be used as a matching point (K o ) while a factor S L e is used to account for pore connectivity and tortuosity (where S e is the relative saturation and L = 0.5). We used a data set of 235 soil samples with retention and unsaturated hydraulic conductivity data to test and improve predictions with the MVG equation. The standard practice of using K o = K, and L = 0.5 resulted in a root mean square error for log(K) (RMSE K ) of 1.31. Optimization of the matching point (K o ) and L to the hydraulic conductivity data yielded a RMSE K of 0.41. The fitted K 0 were, on average, about one order of magnitude smaller than measured K s . Furthermore, L was predominantly negative, casting doubt that the MVG can be interpreted in a physical way, Spearman rank correlations showed that both K 0 and L were related to van Genuchten water retention parameters and neural network analyses confirmed that K 0 and L could indeed be predicted in this way. The corresponding RMSE K was 0.84, which was half an order of magnitude better than the traditional MVG model. Bulk density and textural parameters were poor predictors while addition of K s improved the RMSE K only marginally. Bootstrap analysis showed that the uncertainty in predicted unsaturated hydraulic conductivity was about one order of magnitude near saturation and larger at lower water contents.

Improved Prediction of Unsaturated Hydraulic Conductivity with the Mualem-van Genuchten Model

We reviewed the use of the van Genuchten–Mualem (VGM) model to parameterize soil hydraulic properties and for developing pedotransfer functions (PTFs). Analysis of literature data showed that the moisture retention characteristic (MRC) parameterization by setting shape parameters m = 1 − 1/ n produced the largest deviations between fitted and measured water contents for pressure head values between 330 (log10 pressure head [pF] 2.5) and 2500 cm (pF 3.4). The Schaap–van Genuchten model performed best in describing the unsaturated hydraulic conductivity, K . The classical VGM model using fixed parameters produced increasingly higher root mean squared residual, RMSR, values when the soil became drier. The most accurate PTFs for estimating the MRC were obtained when using textural properties, bulk density, soil organic matter, and soil moisture content. The RMSR values for these PTFs approached those of the direct fit, thus suggesting a need to improve both PTFs and the MRC parameterization. Inclusion of the soil water content in the PTFs for K only marginally improved their prediction compared with the PTFs that used only textural properties and bulk density. Including soil organic matter to predict K had more effect on the prediction than including soil moisture. To advance the development of PTFs, we advocate the establishment of databases of soil hydraulic properties that (i) are derived from standardized and harmonized measurement procedures, (ii) contain new predictors such as soil structural properties, and (iii) allow the development of time-dependent PTFs. Successful use of structural properties in PTFs will require parameterizations that account for the effect of structural properties on the soil hydraulic functions.

Marcel G. Schaap

Papers

rosetta: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions

Neural network analysis for hierarchical prediction of soil hydraulic properties

Database-related accuracy and uncertainty of pedotransfer functions

Improved Prediction of Unsaturated Hydraulic Conductivity with the Mualem-van Genuchten Model

Using pedotransfer functions to estimate the van Genuchten-Mualem Soil Hydraulic Properties: a review.