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首页> 外文期刊>Ecosystems >Quantifying the Uncertainty in Modeled Water Drainage and Nutrient Leaching Fluxes in Forest Ecosystems
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Quantifying the Uncertainty in Modeled Water Drainage and Nutrient Leaching Fluxes in Forest Ecosystems

机译：量化森林生态系统中建模水排水和营养浸出通量的不确定性

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In terrestrial ecosystem studies, water drainage and nutrient leaching in the soil profile are estimated with hydrological models. Comparing modeled results to empirical data or comparing data from different models is, however, difficult because the uncertainty of input-output budget predictions is often unknown. In this study, we developed a procedure combining a Generalized Likelihood Uncertainty Estimation and a Monte-Carlo modeling approach to estimate uncertainty in model parameter estimates and model outputs water drainage and nutrient leaching fluxes for the WatFor water balance model. This procedure was then applied to compare different model optimization strategies (daily soil moisture measurements, monthly measurements of chloride concentrations in soil solution, and the elution of a concentrated chloride) at the same experimental site in a 90-year-old European beech (Fagus sylvatica L.) forest in Brittany (France). We show that the monitoring data of natural variations of chloride concentrations in soil solution were the most efficient dataset to calibrate the WatFor model compared to the soil moisture and chloride tracing experimental data. We also show that water tracing experimental data are the most efficient data to estimate the preferential flow generation model parameters. The optimization strategy had little influence on the predicted water drainage flux and nutrient leaching flux at the root zone boundary on a yearly time scale but influenced water and nutrient fluxes in the topsoil layers.
机译：在陆地生态系统研究中，用水文模型估算土壤剖面中的排水和养分浸出。然而，将建模结果与来自不同模型的数据进行比较，因为输入输出预算预测的不确定性通常是未知的。在这项研究中，我们开发了一种组合广义似然性不确定性估计和蒙特卡罗建模方法来估计模型参数估计中的不确定性的蒙特卡罗建模方法，以及用于Watfor水平衡模型的水流输出排水和养分浸出通量。然后应用该程序以比较不同的模型优化策略（日常土壤湿度测量，土壤溶液中的每月测量，以及浓缩的氯化物的洗脱）在90岁的欧洲山毛榉（Fagus布列塔尼（法国）的Sylvatica L.）森林。我们表明，与土壤水分和氯化物追踪实验数据相比，土壤溶液中氯化物浓度的自然变化的监测数据是最有效的数据集，用于校准WAT模型。我们还表明，水追踪实验数据是最有效的数据，以估算优先流量产生模型参数。优化策略对每年时间尺度的根区边界处的预测排水助焊剂和营养浸出通量几乎没有影响，但在表土层中影响了水和营养通量。

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