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Hierarchical stochastic modelling of large river ecosystems and fish growth across spatio-temporal scales and climate models: the Missouri River endangered pallid sturgeon example

By
Mark L. Wildhaber
Mark L. Wildhaber
1
United States Geological Survey, Columbia Environmental Research Center, 4200 New Haven Road, Columbia, MO 65201-8709, USA
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Christopher K. Wikle
Christopher K. Wikle
2
Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO 65211-6100, USA
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Edward H. Moran
Edward H. Moran
1
United States Geological Survey, Columbia Environmental Research Center, 4200 New Haven Road, Columbia, MO 65201-8709, USA
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Christopher J. Anderson
Christopher J. Anderson
3
Climate Science Initiative, Iowa State University, 2021 Agronomy Hall, Ames, IA 50011, USA
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Kristie J. Franz
Kristie J. Franz
4
Geological and Atmospheric Sciences, Iowa State University, 3023 Agronomy Hall, Ames, IA 50011, USA
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Rima Dey
Rima Dey
2
Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO 65211-6100, USA
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Published:
January 01, 2017

Abstract

We present a hierarchical series of spatially decreasing and temporally increasing models to evaluate the uncertainty in the atmosphere – ocean global climate model (AOGCM) and the regional climate model (RCM) relative to the uncertainty in the somatic growth of the endangered pallid sturgeon (Scaphirhynchus albus). For effects on fish populations of riverine ecosystems, climate output simulated by coarse-resolution AOGCMs and RCMs must be downscaled to basins to river hydrology to population response. One needs to transfer the information from these climate simulations down to the individual scale in a way that minimizes extrapolation and can account for spatio-temporal variability in the intervening stages. The goal is a framework to determine whether, given uncertainties in the climate models and the biological response, meaningful inference can still be made. The non-linear downscaling of climate information to the river scale requires that one realistically account for spatial and temporal variability across scale. Our downscaling procedure includes the use of fixed/calibrated hydrological flow and temperature models coupled with a stochastically parameterized sturgeon bioenergetics model. We show that, although there is a large amount of uncertainty associated with both the climate model output and the fish growth process, one can establish significant differences in fish growth distributions between models, and between future and current climates for a given model.

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Contents

Geological Society, London, Special Publications

Integrated Environmental Modelling to Solve Real World Problems: Methods, Vision and Challenges

A. T. Riddick
A. T. Riddick
British Geological Survey, UK
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H. Kessler
H. Kessler
British Geological Survey, UK
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J. R. A. Giles
J. R. A. Giles
British Geological Survey, UK
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Geological Society of London
Volume
408
ISBN electronic:
9781862396968
Publication date:
January 01, 2017

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