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Estimation of soil water content at permanent wilting point using hygroscopic water content
Author(s) -
Chen Chong,
Zhou Hu,
Shang Jianying,
Hu Kelin,
Ren Tusheng
Publication year - 2020
Publication title -
european journal of soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.244
H-Index - 111
eISSN - 1365-2389
pISSN - 1351-0754
DOI - 10.1111/ejss.12887
Subject(s) - permanent wilting point , soil water , wilting , water content , mathematics , soil science , mean squared error , field capacity , environmental science , statistics , geotechnical engineering , agronomy , engineering , biology
Traditional methods for determining soil water content at permanent wilting point (θ PWP ) are time consuming. The objectives of this study are to develop a model for predicting θ PWP based on easily measured hygroscopic water content and to compare the performance of the proposed model with three previous models: the Aina & Periaswamy model (AP model), the van den Berg et al. model (vdB model) and the Seybold & Harms model (SH model), which estimate θ PWP from particle size distribution and/or organic matter (OM) content. By assuming that the dry end of the soil water retention curve could be described with the linear Campbell & Shiozawa model, a simple linear model, θ PWP = 3θ RH50 , was developed to predict θ PWP using θ RH50 . The θ RH50 of the soils ranged from 0.002 to 0.157 g g −1 and the θ PWP ranged from 0.014 to 0.279 g g −1 . The validation results on 48 soils showed that the root mean squared error (RMSE) and mean absolute error (MAE) of the proposed model were 0.007 g g −1 and 0.005 g g −1 , respectively. Model comparison showed that the prediction accuracy of four models for θ PWP was in the order of proposed model > AP model > SH model > vdB model. Thus, θ RH50 is a viable predictor for θ PWP , which eliminates the need for soil particle size distribution and OM content. Highlights Water content at permanent wilting point (θ PWP ) relates to hygroscopic water content. A model was developed to predict θ PWP using water content at relative humidity of 50%. The proposed model performed better than existing models.