Global Dataset of Ecohydrological Parameters Inferred from Satellite Observations

This dataset contains global maps of (1) ecohydrological parameters for a theoretical model of the probability distribution of soil saturation;<br> (2) convergence, uncertainty and goodness-of-fit diagnostics; and<br> (3) soil water stress and uptake indexes, associated with analysis in: Bassiouni, M., S.P. Good, C.J. Still, and C.W. Higgins (2020), Plant water uptake thresholds inferred from satellite soil moisture. Geophysical Research Letters. https://doi.org/10.1029/2020GL087077 All variable descriptions and units are included in the .nc metadata. Code associated with this dataset are publicly available:<br> Probabilistic Inference of Ecohydrological Parameters (PIEP): http://doi.org/10.5281/zenodo.1257718.<br> Data Management for Global PIEP: http://doi.org/10.5281/zenodo.3235820 <strong>Abstract</strong><br> Empirical functions are widely used in hydrological, agricultural, and earth system models to parameterize plant water uptake. We infer soil water potentials at which uptake is downregulated from its maximum rate and at which uptake is zero, in biomes with &lt; 60% woody vegetation at 36-km grid resolution. We estimate thresholds through Bayesian inference using a stochastic water balance framework to construct theoretical soil moisture probability distributions consistent with satellite surface soil moisture. The global median Nash–Sutcliffe efficiency between empirical soil moisture distributions derived from satellite soil moisture observations and best-fit theoretical distributions using inferred parameters is 0.8. Spatially variable thresholds capture location-specific vegetation and climate characteristics and can be connected to biome-level water uptake strategies.本数据集包含三类全球地图：(1) 用于土壤饱和度概率分布理论模型的生态水文参数；(2) 收敛性、不确定性与拟合优度诊断结果；(3) 与下述研究分析相关的土壤水分胁迫与吸收指数：Bassiouni, M.、S.P. Good、C.J. Still 与 C.W. Higgins（2020），《从卫星土壤湿度推求植物水分吸收阈值》，《地球物理研究快报》，https://doi.org/10.1029/2020GL087077。所有变量说明与单位均包含在.nc 元数据中。 本数据集相关代码已公开发布：生态水文参数概率推断（Probabilistic Inference of Ecohydrological Parameters，缩写PIEP）：http://doi.org/10.5281/zenodo.1257718；全球PIEP数据集管理工具：http://doi.org/10.5281/zenodo.3235820。 **摘要** 经验函数被广泛应用于水文、农业及地球系统模型中，用于参数化植物水分吸收过程。我们以36千米的网格分辨率，在木本植被占比低于60%的生物群区内，推求了植物水分吸收速率从最大值下调，以及吸收速率降为零时对应的土壤水势阈值。研究采用随机水量平衡框架，通过贝叶斯推断估算阈值，以构建与卫星表层土壤湿度观测结果一致的理论土壤湿度概率分布。基于推求参数得到的经验土壤湿度分布，与卫星观测土壤湿度得到的最优拟合理论分布之间，全球中位数纳什-萨克利夫效率（Nash–Sutcliffe efficiency）为0.8。空间可变的阈值能够反映特定区域的植被与气候特征，并可与生物群区尺度的水分吸收策略建立关联。