Systematic Comparison of ILWAS, MAGIC, and ETD Watershed Acidification Models: 1. Mapping Among Model Inputs and Deterministic Results
Systematic Comparison of ILWAS, MAGIC, and ETD Watershed Acidification Models: 1. Mapping Among Model Inputs and Deterministic Results
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ILWAS、MAGIC 和 ETD 流域酸化模型的系统比较： 1. 模型输入和确定性结果之间的映射
DOI：
10.1029/91wr01718
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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;R. Cook;A. Brenkert;R. Gardner;J. Hettelingh
当没有足够的模型试验数据时，很难评估依赖于涡轮机的配置和校准程序对模型预测的影响。我们得出了一套规则和算法（称为输入映射），提供一致的输入为综合湖泊流域酸化研究（ILWAS），地下水酸化模型在集水区（MAGIC），和增强滴流（ETD）流域酸化模型没有校准。湖泊化学模型预测的基础上输入映射是相似的两个不同的美国东北部流域，并在独立校准的三个模型和自然流域的两项研究中观察到的年际变化所获得的变异。在一篇配套论文（Rose等人，本期），蒙特卡罗分析与输入映射结合使用，以比较不同输入下的模型预测。
The effects of investigator-dependent configuration and calibration procedures on model predictions are difficult to evaluate when sufficient data for model testing are not available. We derived a set of rules and algorithms (referred to as input mapping) to provide consistent inputs for the Integrated Lake Watershed Acidification Study (ILWAS), Model of Acidification of Groundwater in Catchments (MAGIC), and Enhanced Trickle Down (ETD) watershed acidification models without calibration. Model predictions of lake chemistry based on input mapping were similar for two dissimilar northeast U.S. watersheds, and were within the variability obtained with independent calibration of the three models and the interannual variability observed in two studies of natural watersheds. In a companion paper (Rose et al., this issue), Monte Carlo analysis is used, in conjunction with input mapping, to compare model predictions under varying inputs.