Drainage is a crucial soil hydrological process that governs the partitioning of rainfall into runoff, groundwater recharge, soil water storage and evapotranspiration. Despite its significance, the drainage process is poorly understood due to the difficulty in direct measurements and insufficient understanding of its underlying physical mechanisms. To address these challenges, we present an innovative, physically-based, data-driven approach, SM2D (Soil Moisture to Drainage), to estimate drainage. SM2D was applied and examined using soil moisture data from a large-scale observation network over mountainous areas during 2014–2020. The soil moisture threshold governing drainage initiation proves to be significantly lower than the commonly employed field capacity metric in hydrological models. This threshold is influenced by factors such as mean soil moisture, bulk density, residual soil moisture, soil organic carbon, and parameters n and α of soil retention curve. Notably, field capacity has minimal impact on this threshold. Additionally, our analysis reveals that the drainage process is more influenced by the Soil Water Storage Increment (SWSI) than by mean soil moisture (MSM) that has traditionally been recognized as a key factor in drainage control. In comparison to commonly used exponential equations and those in models such as the Soil & Water Assessment Tool (SWAT), SM2D demonstrates superior performance in estimating drainage. The exponential equation derived from the SWSI outperforms those derived from other soil moisture metrics, including the commonly utilized MSM, challenging prevailing norms in drainage equations. SM2D holds the potential to generate extensive drainage datasets from satellite or large-scale soil moisture observations, advancing large-scale hydrological studies.

中文翻译：

---

从大规模土壤水分观测中对山区的排水量进行估算


排水是一个重要的土壤水文过程，它控制着将降雨划分为径流、地下水补给、土壤储水和蒸散。尽管引流过程很重要，但由于难以直接测量和对其潜在物理机制的了解不足，人们对引流过程知之甚少。为了应对这些挑战，我们提出了一种创新的、基于物理的、数据驱动的方法 SM2D（土壤湿度到排水量），来估算排水量。SM2D 使用 2014-2020 年山区大规模观测网络的土壤水分数据进行应用和检查。事实证明，控制排水开始的土壤水分阈值明显低于水文模型中常用的田间容量指标。该阈值受平均土壤水分、容重、土壤残余水分、土壤有机碳以及土壤保持曲线参数 n 和 α 等因素的影响。值得注意的是，字段容量对此阈值的影响最小。此外，我们的分析表明，排水过程受土壤储水增量 （SWSI） 的影响更大，而不是受传统上被认为是排水控制关键因素的平均土壤水分 （MSM） 的影响。与常用的指数方程以及土壤和水评估工具（SWAT）等模型中的指数方程相比，SM2D在估计排水方面表现出卓越的性能。从 SWSI 得出的指数方程优于从其他土壤水分指标（包括常用的 MSM）得出的指数方程，这挑战了排水方程中的普遍规范。 SM2D 有可能从卫星或大规模土壤水分观测中生成广泛的排水数据集，从而推进大规模水文研究。