Use of inverse modelling and Bayesian optimization for investigating the effect of biochar on soil hydrological properties

Abstract

Physical properties of biochar such as small particle size and high porosity can modify soil properties and help to improve soil water dynamics. However, there has been no consistent long-term measurements of change in soil physical properties due to biochar application under real field conditions. In this study, we use a unique dataset of soil water content measurements in a corn-soybean cropping system (with biochar and no-biochar) for two years. Soil water content was measured every 30 min at 4 different depths and with 3 replications in corn plots. The effect of biochar was expected to be the difference between the physical soil properties of the two treatments. The APSIM model, a process-oriented crop model, was employed in order to find the physical properties of biochar and no-biochar treatments by using inverse modeling. First, a global sensitivity analysis was carried out to find the most sensitive inputs for the APSIM model for soil water simulation. Then the Metropolis-Hasting algorithm was used to inversely estimate the APSIM soil input properties using the soil moisture measurements. Results of the sensitivity analysis showed that the drainage upper limit (DUL) was the most sensitive soil property followed by saturated hydraulic conductivity (KS), saturated water content (SAT), maximum rate of plant water uptake (KL), maximum depth of surface storage (MAXPOND), lower limit volumetric water content (LL15) and lower limit for plant water uptake (LL). The difference between the posterior distributions (with and without biochar) showed an increase in DUL by approximately 10%. No considerable change was noted in LL15, MAXPOND and KS whereas SAT and LL showed a slight increase and decrease in biochar treatment respectively compared to no-biochar.