PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
July 1, 2003Soil Science Society of America Journal184 citations

Functional Evaluation of Pedotransfer Functions Derived from Different Scales of Data Collection

View Full Paper
ANAttila NemesMSMarcel G. SchaapJWJ.H.M. Wösten

Key Points

Key points are not available for this paper at this time.

Abstract

Estimation of soil hydraulic properties by pedotransfer functions (PTFs) can be an alternative to troublesome and expensive measurements. New approaches to develop PTFs are continuously being introduced, however, PTF applicability in locations other than those of data collection has been rarely reported. We used three databases were used to develop PTFs using artificial neural networks (NNs). Data from Hungary were used to derive national scale soil hydraulic PTFs. The HYPRES database was used to develop continental scale PTFs. Finally, a database containing mostly American and European data was used to develop intercontinental scale PTFs. For each database, 11 PTFs were developed that differed in detail of input data. Accuracy of the estimations was tested using independent Hungarian data. First, soil water retention at nine values of matric potential were estimated. Root mean squared residuals (RMSRs) using different inputs ranged from 0.02 to 0.06 m 3 m −3 for national scale PTFs, while international scale PTFs had RMSRs from 0.025 to 0.088 m 3 m −3 Estimated water retention curves (WRCs) were then used to simulate soil moisture time series of seven Hungarian soils. Root mean squared residuals during a growing season ranged from 0.065 to 0.07 m 3 m −3 , using different PTF estimates. Simulations using laboratory‐measured WRCs had RMSR of 0.061 m 3 m −3 Such small differences in the accuracy of simulations make international PTFs an alternative to national PTFs and measurements. However, testing of the international PTFs with a specific model for specific soil and land use remains desirable because of uncertainty in soil representation in such databases.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Nemes et al. (2003) studied this question.

synapsesocial.com/papers/6a20714f67f86f1baaf4865ahttps://doi.org/10.2136/sssaj2003.1093
Ask AI
Helpful
Bookmark
Share
View Full Paper

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1rosetta : a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions2001 · 2,699 citations
  2. 2DATABASE-RELATED ACCURACY AND UNCERTAINTY OF PEDOTRANSFER FUNCTIONS1998 · 466 citations
  3. 3Neural Networks: A Comprehensive Foundation1998 · 29,834 citations
  4. 4A new model for predicting the hydraulic conductivity of unsaturated porous media1976 · 8,348 citations
  5. 5Evaluation of Soil Water Retention Models Based on Basic Soil Physical Properties1995 · 173 citations