Excluding Organic Matter Content from Pedotransfer Predictors of Soil Water Retention

ffective soil management requires a considerable under-standing of the soil water balance. The use of models to answer soil hydrological questions plays a particularly impor-tant role. A substantial problem with this kind of modeling is the lack of suitable parameters, in particular data on the soil water retention or hydraulic conductivity. Using PTFs to pre-dict water retention is a practical way of obtaining the required parameters. At a regional scale, up to the scale of the catch-ment, this is often the only way to apply hydrological models.In the last few years and decades, many PTFs have been developed that allow water retention properties to be estimated. Generally, three different approaches can be distinguished for deriving the WRC from more easily available parameters:1. Point-based estimation methods: estimating the water content of selected matric potentials from predictors such as the percentage of sand, silt, or clay, the amount of organic matter, or the bulk density (e.g., Gupta and Larson, 1979; Rawls and Brakensiek, 1982)2. Semiphysical approach: deriving the WRC from information on the cumulative particle size distribution (Arya and Paris, 1981); theoretically, this approach is based on the similarity between cumulative particle size distribution and water retention curves. The water contents are derived from the soil’s predicted pore volume and the hydraulic potentials are derived from capillarity relationships.3. Parameter estimation methods: using multiple regression to derive the parameters of an analytical closed-form equation for describing the WRC, using predictors such as the percentage of sand, silt, and clay, the amount of organic matter, or the bulk density (e.g., Vereecken et al., 1989; Wosten et al., 1999). In the last few years, WRC optimization by artiﬁ cial neural network modeling has been successfully used by several researchers (Pachepsky et al., 1996; Schaap et al., 1998; Minasny et al., 1999). Extensive overviews of the recent development and use of PTFs have been given by Timlin et al. (1996), Wosten et al. (2001), Cornelis et al. (2001), Gijsman et al. (2002), and Nemes et al. (2003). Approach 1 has the disadvantage that it uses a large num-ber of regression parameters depending on the number of WRC sampling points, which makes its use in the mathemati-cal modeling more difﬁ cult. For Approach 2, very detailed information about the particle size distribution is required. Furthermore, for modeling continuous scenarios of the soil water regime, these methods require interpolations between the WRC sampling points. The adequacy of the selected interpola-tion method cannot be easily guaranteed. The use of param-eter-based estimations often is the preferred approach.The majority of published PTFs use soil texture, bulk den-sity, and OM as predictors. For hydrologic modeling on a larger scale, the determination of these input parameters often presents a problem. Normally, available soil mapping data is used. While the OM is often measured in soil surveys, such data are often missing in regional soil maps. On the other hand, in many regions data on soil texture or bulk density can often be derived or estimated using soil maps and information about land use. In Germany, for exam-