Measurement and estimation of the soil water retention curve using the evaporation method and the pseudo continuous pedotransfer function

Haghverdi A., ÖZTÜRK H. S., Durner W.

JOURNAL OF HYDROLOGY, cilt.563, ss.251-259, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 563
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1016/j.jhydrol.2018.06.007
  • Dergi Adı: JOURNAL OF HYDROLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.251-259
  • Anahtar Kelimeler: Data mining, Evaporation method, Neural network, Pseudo continuous pedotranfer function, Soil water retention curve, HYDRAULIC-PROPERTIES, MEASUREMENT RANGE, UNSATURATED SOIL, NEURAL-NETWORKS, CONDUCTIVITY, PRESSURE, PREDICTION, PARAMETERS
  • Ankara Üniversitesi Adresli: Evet

Özet

HYPROP (Hydraulic Property Analyzer) system works based on the simplified evaporation method, which determines soil hydraulic properties in the laboratory. This system simultaneously monitors the changes in weight of soil samples as well as the soil matric potential at two depths throughout a drying process governed by evaporation from the soil surface. In this study, we examined the performance of the pseudo continuous pedotranfer function (PC-PTF) to estimate the soil water retention curve (SWRC) using high resolution data measured by HYPROP system. The dataset consisted of 7963 measured water retention data points obtained from 81 Turkish soil samples from which 60%, 20% and 20% were randomly selected for training, cross-validation and test subsets, respectively. The best PC-PTF developed in this study with a mean absolute error of 0.023 m(3) m(-3) (a root mean square error of 0.033 m(3) m(-3)) and a correlation coefficient of 0.96 showed promising performance considering the typical performance range (RMSE = 0.034-0.085 m(3) m(-3)) for parametric PTFs estimating SWRC. The best PC-PTF used soil textural information, soil bulk density, the percentage of stable aggregates, soil organic matter content and the initial water content as the input attributes. PTFs developed in this study also ranked high among previously developed PC-PTFs (with RMSE ranging from 0.027 to 0.056 m(3) m(-3)) using sparse datasets collected via the traditional equilibrium approach (i.e. sandbox apparatus/pressure plates extractor). We, therefore, recommend further application of the PC-PTF approach for development of PTFs using high resolution data obtained by HYPROP system.