Systematic Comparison of ILWAS, MAGIC, and ETD Watershed Acidification Models: 2. Monte Carlo Analysis Under Regional Variability
Systematic Comparison of ILWAS, MAGIC, and ETD Watershed Acidification Models: 2. Monte Carlo Analysis Under Regional Variability
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ILWAS、MAGIC和ETD流域酸化模型的系统比较：2.区域变异下的蒙特卡罗分析
DOI：
10.1029/91wr01717
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发表时间：
1991
影响因子：
5.4
通讯作者：
J. Hettelingh
中科院分区：
文献类型：
--
作者：
K. Rose;A. Brenkert;R. Cook;R. Gardner;J. Hettelingh
输入映射的组合（Rose等人，本期）和蒙特卡罗模拟用于定量比较综合湖泊流域酸化研究（ILWAS）、流域地下水酸化模型（MAGIC）和增强滴流（ETD）流域酸化模型的预测。蒙特卡洛模拟被用来施加区域变异性的两个不同的流域，类似于在美国东北部的四个不同的SO42−沉积情景中发现的选择模型输入。模型预测被认为是在聚合（分布，中位数）对应于人口水平的预测，并在迭代迭代的基础上对应于特定流域的预测。对于这些中的每一个，酸中和能力（ANC）的预测值被视为相对规模（ANC的变化，以响应沉积的变化）和绝对规模（ANC浓度，酸性湖泊的数量）的预测。在SO42−沉积的小到中等变化下，三个模型在相对尺度上观察到的聚合和迭代特定预测相似。当从绝对尺度上看时，模型之间的聚合和迭代特定预测不同。绝对规模的预测是最相似的条件下，高变异性的沉积。正确解释多模型预测需要客观量化模型差异对预测的影响。
A combination of input mapping (Rose et al., this issue) and Monte Carlo simulation was used to quantitatively compare the predictions of the Integrated Lake Watershed Acidification Study (ILWAS), Model of Acidification of Groundwater in Catchments (MAGIC), and Enhanced Trickle Down (ETD) watershed acidification models. Monte Carlo simulation was used to impose regional variability on selected model inputs of two dissimilar watersheds resembling those found in the northeastern United States for four different SO42− deposition scenarios. Model predictions were viewed in aggregate (distributions, medians) corresponding to population-level predictions, and on an iteration-by-iteration basis corresponding to watershed-specific predictions. For each of these, predicted values of acid-neutralizing capacity (ANC) were treated as relative scale (changes in ANC in response to changes in deposition) and absolute scale (ANC concentrations, number of acidic lakes) predictions. Aggregate and iteration-specific predictions viewed on a relative scale were similar for three models under small to moderate changes in SO42− deposition. Aggregate and iteration-specific predictions differed among models when viewed on an absolute scale. Absolute scale predictions were most similar under conditions of high variability in deposition. Proper interpretation of multiple model forecasts requires objective quantification of the effects of model differences on predictions.