One of the most relevant inputs for hydrological modeling is the soil map. The soil sources and scales for the soil properties are diverse, and the quality of soil mapping is increasing, but soil surveying is time-consuming and large area campaigns are expensive. The taxonomic unit approach for soil mapping is common and limited to one layer of data. This limitation causes errors in simulated water fluxes through the soil when taxonomic units approach is implemented during hydrological modeling analysis. Some strategies using geostatistics and machine learning algorithms such as Kriging and Self-Organizing maps (SOM) are improving the taxonomic units’ approach and could serve as an alternative for soil mapping for hydrological purposes. The aim of this work is to study the influence of different soil maps and resolutions on the main hydrological components of a sub-arid watershed in central Spain. For this, the Soil Water and Assessment Tool (SWAT) was parameterized with three different soil maps. A first one was based on Harmonized World Soil database from FAO, at scale 1:1,000,000 (HWSD). The other two were based on a Kriging interpolation at 100 × 100 m from soil samples. To obtain soil properties map from it, two strategies were applied: one was to average the soil properties following the official taxonomic soil units at 1:400,000 scale (Agricultural Technological Institute of Castilla and Leon - ITACyL) and the other was to applied Self-organizing map (SOM) to create the soil units (SOMM). The results suggest that scale and soil properties mapping influence HRU definition, which in turn affects water flow through the soils. Statistical metrics of model performance were improved from R2 =0.62 and NSE=0.46 with HWSD soil map to R2 =0.86 and NSE=0.84 with SOM and similar values were achieved during validation. Thus, the SOM is presented as an innovative algorithm applied for hydrological modeling with SWAT, significantly increasing the level of model accuracy to stream flow in sub-arid watersheds.

中文翻译：

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水文建模背景下的土壤特性自组织图

水文建模最相关的输入之一是土壤图。土壤来源和土壤性质的尺度多样，土壤测绘质量不断提高，但土壤测量耗时且大面积运动成本高。土壤制图的分类单位方法很常见，并且仅限于一层数据。当在水文建模分析期间实施分类单位方法时，此限制会导致通过土壤的模拟水通量出现错误。一些使用地质统计学和机器学习算法的策略，例如克里金法和自组织地图 (SOM)，正在改进分类单位的方法，并可作为水文目的的土壤制图的替代方案。这项工作的目的是研究不同土壤图和分辨率对西班牙中部亚干旱流域主要水文组成部分的影响。为此，土壤水和评估工具 (SWAT) 使用三种不同的土壤图进行参数化。第一个基于来自粮农组织的协调世界土壤数据库，比例为 1:1,000,000 (HWSD)。另外两个基于来自土壤样品的 100 × 100 m 的克里金插值法。为了从中获得土壤特性图，应用了两种策略：一种是按照 1:400,000 比例的官方分类土壤单元（卡斯蒂利亚和莱昂农业技术研究所 - ITACyL）对土壤特性进行平均，另一种是应用 Self-组织图 (SOM) 以创建土壤单元 (SOMM)。结果表明，尺度和土壤特性映射影响 HRU 定义，这反过来又会影响流经土壤的水流。模型性能的统计指标从 HWSD 土壤图的 R2 =0.62 和 NSE=0.46 提高到 SOM 的 R2 =0.86 和 NSE=0.84，并且在验证过程中获得了类似的值。因此，SOM 被作为一种创新算法应用于 SWAT 水文建模，显着提高了亚干旱流域水流模型的精度水平。