Reliable large-scale characterization of soil water retention (WRF) and hydraulic conductivity (HCF) functions requires a large amount of direct measurements for hydrological modeling applications. Although direct measurements of WRF are time consuming, they tend to be far less time and labor-intensive than direct measurements of HCF. Therefore, WRF experimental data are often exploited to predict the HCF through available existing models. Current popular approaches include the well known physically-based Mualem’s model. We hereby propose a novel, simple and physically sound model to predict the relative HCF from Kosugi’s WRF expression [14]. The proposed model is based on the introduction of a new conductivity parameter, , that is related to the coefficient of variation of the WRF. The model is calibrated on 20 soil samples and subsequently tested on an independent data set of 57 samples. The predictions show relatively high accuracy and reliability when compared to those derived from Mualem’s model.
Prediction of unsaturated relative hydraulic conductivity from Kosugi's water retention function / Nasta, Paolo; Assouline, S.; Gates, J. B.; Hopmans, J. W.; Romano, Nunzio. - In: PROCEDIA ENVIRONMENTAL SCIENCES. - ISSN 1878-0296. - 19:(2013), pp. 609-617. (Intervento presentato al convegno Four Decades of Progress in Monitoring and Modeling of Processes in the Soil-Plant-Atmosphere System: Applications and Challenges tenutosi a Naples nel 19-21 June 2013) [10.1016/j.proenv.2013.06.069].
Prediction of unsaturated relative hydraulic conductivity from Kosugi's water retention function.
NASTA, PAOLO;ROMANO, NUNZIO
2013
Abstract
Reliable large-scale characterization of soil water retention (WRF) and hydraulic conductivity (HCF) functions requires a large amount of direct measurements for hydrological modeling applications. Although direct measurements of WRF are time consuming, they tend to be far less time and labor-intensive than direct measurements of HCF. Therefore, WRF experimental data are often exploited to predict the HCF through available existing models. Current popular approaches include the well known physically-based Mualem’s model. We hereby propose a novel, simple and physically sound model to predict the relative HCF from Kosugi’s WRF expression [14]. The proposed model is based on the introduction of a new conductivity parameter, , that is related to the coefficient of variation of the WRF. The model is calibrated on 20 soil samples and subsequently tested on an independent data set of 57 samples. The predictions show relatively high accuracy and reliability when compared to those derived from Mualem’s model.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
