Comparison of first‐order error analysis and Monte Carlo Simulation in time‐dependent lake eutrophication models

Comparison of first‐order error analysis and Monte Carlo Simulation in time‐dependent lake... Estimates of variance for a nonlinear, seasonal food chain, nutrient cycle eutrophication model of Saginaw Bay, Lake Huron, calculated by first‐order variance propagation and Monte Carlo analyses, do not always agree. A comparison of estimates of state variables indicates that Monte Carlo means are most like the measurements, whereas Monte Carlo medians are most like the deterministic model output. Best agreement between Monte Carlo and first‐order estimates of both state variable values and their variances occurs when Monte Carlo output distributions are symmetric. Under these conditions, both estimates are measures of variance associated with total populations (i.e., all algae). Those distributions, however, change dramatically in time for most state variables. For asymmetric distributions, first‐order variance estimates measure variability about the typical component of the total population (i.e., the typical algal species) and Monte Carlo variance estimates measure variability of the mean component (which is more reflective of the total). One must be cognizant of these differences when estimating variance associated with model projections. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Water Resources Research Wiley

Comparison of first‐order error analysis and Monte Carlo Simulation in time‐dependent lake eutrophication models

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Publisher
Wiley
Copyright
This paper is not subject to U.S.Copyright © 1981 by the American Geophysical Union.
ISSN
0043-1397
eISSN
1944-7973
D.O.I.
10.1029/WR017i004p01051
Publisher site
See Article on Publisher Site

Abstract

Estimates of variance for a nonlinear, seasonal food chain, nutrient cycle eutrophication model of Saginaw Bay, Lake Huron, calculated by first‐order variance propagation and Monte Carlo analyses, do not always agree. A comparison of estimates of state variables indicates that Monte Carlo means are most like the measurements, whereas Monte Carlo medians are most like the deterministic model output. Best agreement between Monte Carlo and first‐order estimates of both state variable values and their variances occurs when Monte Carlo output distributions are symmetric. Under these conditions, both estimates are measures of variance associated with total populations (i.e., all algae). Those distributions, however, change dramatically in time for most state variables. For asymmetric distributions, first‐order variance estimates measure variability about the typical component of the total population (i.e., the typical algal species) and Monte Carlo variance estimates measure variability of the mean component (which is more reflective of the total). One must be cognizant of these differences when estimating variance associated with model projections.

Journal

Water Resources ResearchWiley

Published: Aug 1, 1981

References

  • Probabilistic methods in stream quality management
    Burges, Burges; Lettenmaier, Lettenmaier
  • Describing variance with a simple water quality model and hypothetical sampling programs
    Moore, Moore; Dandy, Dandy; Delucia, Delucia

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