Applying Different Levels of Soil Information to Estimate Available Water Capacity

Abstract

Abstract. Soil available water capacity (AWC) plays an important role in soil and water resource management, and is time-consuming and expensive to measure over large areas. AWC is often predicted with pedotransfer functions (PTFs) using easily measured soil properties (e.g., texture, bulk density and organic matter content). This study investigated the effectiveness of using neural network based-ROSETTA PTF to estimate AWC from four hierarchical levels of soil information: textural class (R1), texture (R2), texture and bulk density (R3), texture, bulk density, and water content at -33 kPa (θ -33 ) and -1500 kPa (θ -1500 ) (θ -33 and θ -1500 derived from Gupta-Larson PTF) (R4). A comprehensive dataset containing 58 soil samples collected from eastern China were used for evaluation. Results show that all the ROSETTA models except R1 estimate reliably the AWC. R2 and R3 produce similar estimates, both of which have only slightly poorer performance than R4. In this case soil texture is considered to be the most important soil property influencing AWC, making negligible the effects of the bulk density and organic matter content. It can be concluded that measuring soil texture as inputs for ROSETTA is the most affordable and reasonable approach to predict AWC in the study area.