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.

土壤有效水容量（SAWC）是关键的土壤指标，在许多生态系统服务（例如粮食生产，灌溉管理，土壤干旱，防洪，气候和气体调节）中起着重要作用。数字土壤测绘（DSM）可用于获取所需的SAWC地图。但是，SAWC与DSM中考虑的常规土壤特性不同，因为它涉及在多个土壤层确定的几种土壤特性。因此，需要一种特定的方法来获得SAWC图和相关的不确定性预测。

这项研究的目的是建立一种SAWC映射方法，可以预测三个最大生根深度（60、100和200 cm）的SAWC值及其相关的预测不确定性。

该方法在朗格多克－鲁西永地区（法国南部）进行了测试。每层的基本可用水容量（cm.cm ^-1）和土层厚度首先分别绘制在0–30、30–60、60–100和100–200 cm处，然后合计以估计上述三个最大生根深度的SAWC。SAWC不确定性通过使用一阶泰勒分析的误差传播模型估算。该分析考虑了每个相关属性的映射误差，这些误差是由分位数回归林算法估计的。我们测试了不同的误差传播模型，这些模型以不同的方式考虑了这些映射误差之间的相关性：未考虑相关性，仅在每层土壤层厚度和基本水容量之间的相关性，仅在土壤层之间的相关性或已考虑的所有相关性。

SAWC预测的性能及其不确定性通过重复20次的10倍交叉验证进行评估。SAWC的预测显示出较差的准确性（解释方差的百分比范围为0.12至0.13）。当在误差传播模型中考虑土壤层误差之间的相关性时，可以最好地估计SAWC预测的不确定性，而当忽略这些相关性时，则严重低估了SAWC预测的不确定性。

尽管由于土壤观测值低（1/19 km ²）而无法在准时水平上预测SAWC ，但SAWC方法还是很有希望的，因为它产生的地图与可用的土壤学知识相吻合并精确地估计了不确定性。