1. Introduction
To simulate soil water content, 
We selected the most frequently used empirical functions for 
Direct measurement of parameters associated with 
Land surface models for numerical weather prediction, climate modeling, and hydrological modeling usually use lookup tables of mean values of hydraulic parameters based on soil textural classes or continuous PTFs provided by Clapp and Hornberger (1978) and Cosby et al. (1984). Table 1 provides an overview of the PFTs and global soil datasets of the seven most widely used LSMs in the hydrometeorology community. Most of them use the taxonomy-based PTFs and expert rules based on soil parameter estimation. During the past decades, many new PTFs and soil datasets have been developed. However, these PTFs and soil datasets have not yet been incorporated into LSMs.
Land surface models and their soil hydraulic parameters: datasets are the FAO Soil Map of the World (SMW) (FAO 1971–1981, 1996); World Inventory of Soil Emission Potentials (WISE); U.S. State Soil Geographic Database (STATSGO); International Geosphere–Biosphere Programme Soil Dataset (IGBP-SOIL); and Harmonized World Soil Database (HWSD) (FAO/IIASA/ISRIC/ISSCAS/JRC 2012).
A large number of studies have been performed recently to develop PTFs (Vereecken et al. 2010). Most published PTFs provided a very limited description of the functions and where they can potentially be used (McBratney et al. 2011). The accuracy of a PTF outside of its development dataset is generally unknown. A PTF may perform well in one region where it was developed and tested but perform relatively poorly in other regions. In past decades, there were many evaluations of the PTFs using different datasets, including those of PTFs for the soil moisture retention curve (Cornelis et al. 2001; Givi et al. 2004; Nemes et al. 2003; Rajkai et al. 2004; Rubio 2008; Stumpp et al. 2009) and those for the saturated or unsaturated hydraulic conductivity (e.g., Abdelbaki et al. 2009; Julia et al. 2004; Lee 2005; Minasny and McBratney 2000; Sobieraj et al. 2001; Stolte et al. 1994; Tietje and Hennings 1996; Wagner et al. 2001). However, it is not clear that a best set of PTFs for both the soil moisture retention curve and hydraulic conductivity exists.
The inability and uncertainty of PTFs in the estimation of soil hydraulic parameters can be attributed to factors as follows: 1) the intrinsic inability (i.e., PTF structural error) to accurately describe complex physical relationships (Tomasella et al. 2003), 2) the intrinsic inability or uncertainty caused by limited data used for PTF training, and 3) the uncertainty in PTF inputs to reflect soil spatial heterogeneity at different scales. Chirico et al. (2010) evaluated the effect of PTF prediction uncertainty on the components of the soil water balance at the hill slope scale. They found the simulated evaporation to be much more affected by the PTF intrinsic inability than by errors due to uncertainty in the input data (Vereecken et al. 2010). Therefore, using ensembles of PTFs to estimate soil hydraulic properties may be a practical approach in land surface modeling (Guber et al. 2006).
Global or regional datasets of hydraulic parameters have recently been compiled using soil profile attributes or PTFs. However, most of the datasets of the hydraulic parameters are only usable for simple bucket models with a specified water-holding capacity, such as available water capacity (AWC) and saturated volumetric water content (Table 2). So far, no dataset is available for the FCH and FGM for applications at regional or global scales.
Recent datasets of the soil hydraulic parameters for use in regional and global hydrological and climate modeling: European Soil Database (ESDB); the 1:1 million Soil Map of China (SMC); various regional maps from Soil and Terrain Digital Database (SOTWIS); available water storage capacity (AWC); and Ks, saturated hydraulic conductivity; θs, saturated volumetric water content; θ33, field capacity; and θ1500, volumetric water content at 1500-kPa potential. Other abbreviations as in Table 1.
Another limitation of soil datasets listed in Table 2 is a dearth of information on vertical variability of soil profiles. In most datasets, the properties of the A horizon (or surface–0.3 m layer) are assumed to be representative of the entire soil profile including the root zone; then soil hydraulic parameters are assigned through PTFs. However, the attributes of the underlying horizons in most soils are significantly different from those of the surface layer (Williams et al. 2006).
Besides uncertainty in meteorological forcing data, the inaccuracy in land hydrometeorological modeling can be attributed in part to inadequate land surface hydrology parameterizations, including poor estimates of soil hydraulic parameters. So far, there is not a soil hydraulic parameter dataset available for hydrometeorological modeling from catchments to global scales because soil hydraulic parameters have not been derived from soil profile databases to adequately reflect spatial heterogeneity of the soil.
As land modelers, we expect that the dataset of the hydraulic parameters associated with FCH and FGM developed in this study could 1) describe spatial variability of the soil hydraulic characteristics in both vertical and horizontal directions, 2) provide multiple choices for users to use either a single set or an ensemble of hydraulic parameters associated with different PTFs, and 3) provide a benchmark dataset (the median values) from multiple PTFs or as a reference dataset.
We estimate the hydraulic parameters associated with 
In this paper, we describe the development of a China dataset with multiple PTF-derived soil hydraulic parameters of the FCH and FGM. Our effort is unique in that the science community will have access to a dataset of soil hydraulic parameters specifically designed for land modeling applications.
2. Data and methods
The generation of soil hydraulic parameters requires a soil property dataset and appropriate PTFs. The soil property dataset should include the percentages of sand, silt, and clayin addition to bulk density, and soil organic matter in the profiles. The PTFs provide algorithms to derive the hydraulic parameters of the functions of Clapp and Hornberger (FCH) and van Genuchten and Mualem (FGM).
a. Soil datasets
1) Soil map of China
The 1:1 000 000 soil map of China was compiled from the Second National Soil Survey (1979–85; Shi et al. 2004). At present, it is the most detailed digitized national soil map of China. This spatial dataset is based on the Genetic Soil Classification of China (GSCC), consisting of 12 orders, 61 great groups, 235 subgroups, 909 families, and 11 nonsoil map units (i.e., glacier, river, lake and man-made reservoir, rock debris/detritus, coral reef and islet, salt desert and crust, coastal salt marsh, in-river sand bar and islet, urban and builtup lands, coastal aquatic farm, and coastal ocean) in the soil map. However, there are only 925 soil map units in the soil map, and each map unit has only one soil type at family, subgroup, and great group levels. Not all soil types appear in the soil map, which is delineated into 94 303 map polygons.
2) Soil profile database
We collected 8595 representative soil profiles with 33 039 soil horizons from the literature of the Second National Soil Survey of China. The soil profiles were digitized from published books, including soil books at the national and provincial level and at prefectural and county levels of Tibet (Shangguan et al. 2012, 2013). The information collected for each profile includes classification under the GSCC; physical and chemical properties of each horizon including soil particle size distribution, SOM, and BD, which are used as inputs to the PTFs in this study.
3) The derived spatial distribution of soil properties
The spatial distribution of soil properties are derived using a polygon linkage method from the 1:1 000 000 soil map of China and soil profile database (Shangguan et al. 2012). The polygon linkage method links soil polygons and soil profiles taking the distance between soil profiles and soil polygons into account to preserve spatial variations of a soil type. In the Chinese soil profile database, soil particle size distribution was measured under the International Society of Soil Science (ISSS) and Katschinski schemes (Katschinski 1956). However, a PTF usually requires soil texture data of the Food and Agriculture Organization–U. S. Department of Agriculture (FAO-USDA) System. The limits of sand, silt, and clay fractions are between 2 and 0.05, 0.05 and 0.002, and <0.002 mm, respectively. For PTF and LSM applications, the particle size distribution data are converted to the FAO-USDA System using several particle size distribution models (Moeys and Shangguan 2010). The soil characteristics of soil profiles are standardized into seven layers (0–0.045, 0.045–0.091, 0.091–0.166, 0.166–0.289, 0.289–0.493, 0.493–0.829, and 0.829–1.383 m) to match the soil column discretization in the Community Land Model (CLM) (Dai et al. 2003; Oleson et al. 2004). However, in the deepest layer, there is little information of the needed input properties, so it was not included in our study. Since LSMs are usually grid based, the original vector soil map was rasterized at a resolution of 30 × 30 arcseconds.
b. The PTFs of soil water retention and hydraulic conductivity
1) The functions of Clapp and Hornberger
2) The functions of van Genuchten–Mualem
4) Field capacity and permanent wilting point
Field capacity is the amount of water content retained in soil after excessive water has drained away under gravity. Gravitational drainage usually lasts for 2–3 days after a rain or irrigation in pervious soils of uniform structure and texture, and the drainage rate decreases substantially. Permanent wilting point, or wilting point, is defined as the minimal point of soil moisture the plant requires not to wilt. In this study, field capacity and permanent wilting point is regarded as the water content at about −33 (
c. The selected PTFs
FCH and FGM have shown great values in the widespread applications of water flow models at field and larger scales. The PTFs have been developed from easily measured and widely available soil properties such as sand, silt, and clay percentages; bulk density; or organic matter content. Our rules to select PTFs are as follows:
PTFs of
PTFs developed based on large training samples (>200) are preferred (Guber et al. 2006). However, the databases with fewer samples typically have provided better PTFs, since additional samples may create additional variability, and smaller databases have typically used the same measurement methodology for all water retention curves (Vereecken et al. 2010).
PTFs should have more positive evaluations. In comparison evaluations, the PTFs that perform best and have high rankings include the PTFs of Wösten et al. (1999), ranked by Wagner et al. (2001), and the PTFs of Cosby et al. (1984), ranked by Tietje and Hennings (1996). The PTFs developed by Cosby et al. (1984) were adopted in the CLM (Dai et al. 2003; Oleson et al. 2004).
From the literature, we selected five PTFs for estimating the FCH parameters (
3. Results
The data products developed in this study include 1) the resulting hydraulic parameters from individual PTFs and 2) statistics of the parameters from multiple PTFs, that is, median and coefficient of variation (CV). Median values were taken as the best estimations as these can avoid excessive influence of extreme values. The CV was used to show the variation of different estimates from various PTFs. In this section, we present an overview of the spatial variations of the estimated hydraulic parameters and a comparison of the lookup tables of parameters with previous estimations from U.S. soil samples (Clapp and Hornberger 1978; Cosby et al. 1984; van Genuchten et al. 1991; Meyer et al. 1997).
a. Horizontal variation of the estimated soil hydraulic parameters
As an example, we only show the horizontal distribution of the median and CV of the soil hydraulic parameters of the second land model standardized soil layer (0.045–0.091 m) (Figs. 1 and 2). The 
The median and coefficient of variation (CV) of the FCH parameters for layer 2 (0.045–0.091 m): saturated water content (θs, cm3 cm−3), pore size distribution index (λ, dimensionless), saturated capillary potential (
Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0149.1
The median and CV of the FGM water retention parameters for layer 2 (0.045–0.091 m): saturated water content (θs, cm3 cm−3), residual water content (θr, cm3 cm−3), parameter corresponding approximately to the inverse of the air-entry value (α, cm−1), shape parameter (n, dimensionless), saturated hydraulic conductivity (
Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0149.1
The spatial distributions of 
The median and CV of field capacity (θ33, cm3 cm−3) and permanent wilting point (θ1500, cm3 cm−3) for layer 2 (0.045–0.091 m).
Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0149.1
b. Vertical variation of the estimated soil hydraulic parameters
In this subsection, we take the soil hydraulic parameters of the functions of Clapp and Hornberger as an example to show the vertical variation of soil hydraulic parameters (Fig. 4). In almost all areas, 
The vertical variation of soil hydraulic parameters of FCH (layer 2 divided by layer 6): (a) saturated water content (
Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0149.1
c. Difference between FCH and FGM parameters
Figure 5 shows the difference of 
The difference of saturated water content (
Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0149.1
d. Comparison of the lookup tables in soil textural class with previous studies
Table 3 shows the median and standard deviation of the median of hydraulic parameters estimated by PTFs of FCH for each texture class using the China database. These values are quite different from the popular lookup table developed by Cosby et al. (1984), which was based on 1448 samples from 23 states of the United States. The values of 
Median and standard deviation of the parameters of the water retention and hydraulic conductivity of Clapp and Hornberger (1978) in each USDA soil textural class (std dev in parentheses).
Lookup tables were available for the hydraulic parameters of FGM, 
Median and standard deviation of the water retention and hydraulic conductivity of van Genuchten (1980) and Mualem (1976) in each USDA soil textural class (std dev in parentheses).
Median and standard deviation for field capacity (θ33) and permanent wilting point (θ1500) in each USDA soil textural class (std dev in parentheses).
4. Discussion and conclusions
The land modeling community has struggled for years with the lack of adequate soil information at scales that will support regional modeling of climatic and hydrologic processes. The development of this dataset is a first step in providing realistic and useful data about the soil hydraulic properties that can then be used with a range of empirical approaches to determine the subsequent hydraulic nature of the soil environment. The dataset represents a “best available” Chinese regional digital soil properties dataset for use in land modeling applications. Users could run the land model to choose outputs of their preferred PTFs, statistical analysis values, multiparameter ensembles, or lookup tables.
Compared to previous datasets used in LSMs (Tables 1 and 2), we used multiple PTFs and more input data [i.e., bulk density (BD) and soil organic matter (SOM)] than soil texture only. Extensive research in the past has focused on improving the estimates of hydraulic properties using PTFs. An important aspect of the research is the identification of additional soil information that may improve accuracy of the PTFs, besides classical PTF predictors such as sand, silt, and clay content in addition to BD and SOM. Additional information may involve more detailed terrain attributes, vegetation, soil structure, water content at selected pressure heads, morphological properties, or taxonomic information. On the other hand, multimodeling approaches have been developed recently that combine predictions with different PTFs to either derive a single set of hydraulic parameters or aggregate the output of model runs that were obtained for each individual PTF (Guber et al. 2006, 2009).
A number of important limitations in our study remain. First, it is still difficult to quantify the precision of the hydraulic parameter data. Second, the information on the spatial distribution of the China soils needs to be updated. The accuracy of the dataset to depict soil physical features of the real world cannot go beyond the source map, the measured attributes of soil profiles, and the way they are linked, all of which are the sources of uncertainty in the soil basic properties (Shangguan et al. 2012). This paper has not factored in the uncertainty associated with soil basic properties. Users of this dataset should exercise considerable care and be aware of the limitations of the source data. It is always worth bearing in mind that a very large proportion of soil within a region varies over short distances and cannot be resolved by coarse-scale maps.
Even though the uncertainty of the soil basic properties, which are inputs to PTFs, are hard to quantify, it can be partly represented by the probability distribution functions (PDFs) of the properties at a location. However, these PDFs cannot take all aspects of the uncertainty into account because they were based on observations of available soil profiles linked to a map polygon through the polygon linkage method. In computing the PTFs, we used averaged values of the basic soil properties instead of whole probability distributions. Using the PDFs directly would raise several questions: 1) How do we sample from the PDFs of soil basic properties to calculate the PTFs when the relationships between the input data and soil hydraulic parameters are nonlinear? 2) How do we integrate PDFs of the outputs of different PTFs into an ensemble PDF prediction of the hydraulic parameters? 3) How do we make use of uncertainty information associated with hydraulic parameters when current LSMs are not designed to do so? These questions are not unanswerable, but, even if there are answers, the uncertainty estimates will not be realistic because they do not account for full uncertainty.
The soil hydraulic parameter dataset produced in this study is available online (at http://globalchange.bnu.edu.cn) for free download.
Acknowledgments
This work was supported by the Natural Science Foundation of China (under Grants 41205037, 40875062, and 40225013), the R&D Special Fund for Nonprofit Industry (Meteorology, GYHY201206013, GYHY200706025), and the Key International S and T Cooperation Project (2008DFA22180). We want to thank Ya. A. Pachepsky and Andrey K. Guber for sharing their FORTRAN code of the PTF calculator. We would like to acknowledge the helpful discussions and English revision from Robert E. Dickinson. We thank the two reviewers for their time and effort to thoroughly review the manuscript. Their suggestions have greatly improved the paper.
APPENDIX
Soil Water Retention and Hydraulic Conductivity Relationships
In the following equations, the symbols are defined as they appeared in section 2. The sand, silt, and clay denote percentages (%) of textural fractions according to the USDA textural classification. SOM is the organic matter content (%), SOC is the organic C content (%), BD is the bulk density (g cm−3), and 
a. Equations to estimate the Clapp–Hornberger parameters
b. Equations to estimate van Genuchten parameters
- Wösten et al. (1999):where topsoil is an ordinal variable having the value of 1 (depth 0–30 cm) or 0 (depth >30 cm).
The estimated average van Genuchten parameters for the FAO textural classes were given in Table A1.
Schaap et al. (2001) used the soil dataset of North America and Europe to develop five hierarchical pedotransfer functions (H1–H5) for the estimation of the soil hydraulic properties using limited to more extended input data. The H3 model is very practical and recommended.
Van Genuchten parameters by FAO textural classes.
c. Equations for water contents at capillary pressures of 33 and 1500 kPa
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