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Systematic Comparison of ILWAS, MAGIC, and ETD Watershed Acidification Models: 1. Mapping Among Model Inputs and Deterministic Results
Author(s) -
Rose K. A.,
Cook R. B.,
Brenkert A. L.,
Gardner R. H.,
Hettelingh J. P.
Publication year - 1991
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/91wr01718
Subject(s) - watershed , environmental science , calibration , monte carlo method , hydrology (agriculture) , set (abstract data type) , computer science , statistics , mathematics , geology , machine learning , geotechnical engineering , programming language
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.