Soil available water capacity (SAWC) is a key soil indicator that plays a major role in many ecosystem services, such as food production, irrigation management, soil drought, flood control, and climate and gas regulation. Digital soil mapping (DSM) can be used to obtain needed SAWC maps. However, SAWC differs from the usual soil properties considered in DSM in that it involves several soil properties determined at several soil layers. Therefore, a specific approach is required to obtain SAWC maps and the associated uncertainty predictions. The objective of this study was to build a SAWC mapping approach that could predict SAWC values at three maximum rooting depths (60, 100 and 200 cm) and their associated prediction uncertainties. The approach was tested in the Languedoc-Roussillon region (southern France). Elementary available water capacities of each layers (in cm.cm−1) and soil layer thicknesses were first mapped separately at 0–30, 30–60, 60–100 and 100–200 cm and then aggregated to estimate the SAWCs at the three mentioned maximum rooting depths. SAWC uncertainty was estimated with an error propagation model that used a first-order Taylor analysis. This analysis considered the mapping errors of each involved property, which were estimated by the quantile regression forest algorithm. We tested different error propagation models that differently considered the correlations between these mapping errors: no correlation considered, correlations between soil layer thicknesses and elementary water capacities per soil layer only, correlations between soil layers only, or all correlations considered. The performances of both SAWC predictions and their uncertainties were assessed with a 10-fold cross validation that was iterated 20 times. The SAWC predictions showed poor accuracies (percentages of explained variance ranged from 0.12 to 0.13). The uncertainties of SAWC predictions were best estimated when the correlations between the soil layer errors were considered in the error propagation model whereas the uncertainties of SAWC predictions were severely underestimated when these correlations were neglected. In spite of the poor performance in predicting SAWC at the punctual level due to the low density of soil observations (1/19 km2), the SAWC approach appeared promising since it produced maps that agreed with the available pedological knowledge and precisely estimated the uncertainties.