Hierarchical Stochastic Modelling of Large River Ecosystems and Fish

Geological and Atmospheric Sciences Publications Geological and Atmospheric Sciences 10-12-2015 Hierarchical stochastic modelling of large river ecosystems and fish growth across spatio-temporal scales and climate models: the Missouri River endangered pallid sturgeon example Mark L. Wildhaber United States Geological Survey Christopher K. Wikle University of Missouri Edward H. Moran United States Geological Survey Christopher J. Anderson Iowa State University Kristie J. Franz Iowa State University, [email protected] See next page for additional authors Follow this and additional works at: https://lib.dr.iastate.edu/ge_at_pubs Part of the Climate Commons, and the Spatial Science Commons The complete bibliographic information for this item can be found at https://lib.dr.iastate.edu/ ge_at_pubs/290. For information on how to cite this item, please visit http://lib.dr.iastate.edu/ howtocite.html. This Article is brought to you for free and open access by the Geological and Atmospheric Sciences at Iowa State University Digital Repository. It has been accepted for inclusion in Geological and Atmospheric Sciences Publications by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Hierarchical stochastic modelling of large river ecosystems and fish growth across spatio-temporal scales and climate models: the Missouri River endangered pallid sturgeon example 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. Disciplines Climate | Oceanography and Atmospheric Sciences and Meteorology | Spatial Science Comments This article is published as Mark L. Wildhaber, Christopher K. Wikle, Edward H. Moran, Christopher J. Anderson, Kristie J. Franz and Rima Dey Geological Society, London, Special Publications, 408, 119-145, 12 October 2015, doi: 10.1144/SP408.11. Authors Mark L. Wildhaber, Christopher K. Wikle, Edward H. Moran, Christopher J. Anderson, Kristie J. Franz, and Rima Dey This article is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/ge_at_pubs/290 Downloaded from http://sp.lyellcollection.org/ at Iowa State University on February 11, 2020 Hierarchical stochastic modelling of large river ecosystems and fish growth across spatio-temporal scales and climate models: the Missouri River endangered pallid sturgeon example MARK L. WILDHABER1*, CHRISTOPHER K. WIKLE2, EDWARD H. MORAN1, CHRISTOPHER J. ANDERSON3, KRISTIE J. FRANZ4 & RIMA DEY2 1United States Geological Survey, Columbia Environmental Research Center, 4200 New Haven Road, Columbia, MO 65201-8709, USA 2Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO 65211-6100, USA 3Climate Science Initiative, Iowa State University, 2021 Agronomy Hall, Ames, IA 50011, USA 4Geological and Atmospheric Sciences, Iowa State University, 3023 Agronomy Hall, Ames, IA 50011, USA *Correspondence: [email protected] 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. Recent decades have brought substantive changes in When modelling ecosystem response to climate, land use and climate across the Earth, prompting a the resolution of AOGCMs is not typically sufficient need to think of population and community ecology to draw inferences at the scales of variability neces- not as a static entity, but as a dynamic process sary for understanding ecological processes (e.g. (United States Climate Change Science Program: Tabor & Williams 2010). Rather, the AOGCM CCSP 2003). Increasingly, there is evidence of eco- physical variables must be ‘downscaled’ to local logical changes due to climate change (e.g. Walther ecological/biological response scales that can be et al. 2005; Bergengren et al. 2011). Although much used in vulnerability and risk assessments of cli- of this evidence comes from ground-truth observa- mate change (CCSP 2003). Traditionally, one either tions of biogeographical data, there is increasing accomplishes the downscaling by linking the reliance on models that relate climate variables to AOGCM to a smaller scale through the use of deter- biological systems (CCSP 2003). Such models are ministic models (i.e. ‘dynamical downscaling’) or used to explore potential changes to population and through the use of statistical models (e.g. Grotch community-level ecological systems in response to & MacCracken 1991; Fowler et al. 2007). In the climate scenarios as obtained from atmosphere – case of evaluating individual organism response to ocean global climate models (AOGCMs) (Nakicen- potential climate variability, one must project across voic et al. 2000; CCSP 2003). multiple scales of spatial and temporal variability, From:Riddick, A. T., Kessler,H.&Giles, J. R. A. (eds) 2017. Integrated Environmental Modelling to Solve Real World Problems: Methods, Vision and Challenges. Geological Society, London, Special Publications, 408, 119–145. First published online October 12, 2015, https://doi.org/10.1144/SP408.11 # 2017 The Author(s). Published by The Geological Society of London. All rights reserved. For permissions: http://www.geolsoc.org.uk/permissions. Publishing disclaimer: www.geolsoc.org.uk/pub_ethics Downloaded from http://sp.lyellcollection.org/ at Iowa State University on February 11, 2020 120 M. L. WILDHABER ET AL. and it is not likely that the ‘transfer function’ that greater than the variation that would occur under converts large-scale climate simulation results to different climate scenarios, thereby limiting the very small spatial and temporal scales can be re- ability to draw inference concerning potential im- presented well by a single statistical downscaling pacts to the ecological system. This leads to the fun- model, especially given the non-linear nature of damental question that this paper seeks to address: the transfer of information across scale. Rather, a when the uncertainty in the ecological process is more realistic transfer function would attempt taken into account through a stochastic parameter to accommodate the various scales of variability, ensemble, are there still significant differences in such as through a series of deterministic process the ecological response forced from various climate models. models in a MME framework? If the answer is yes, Consideration of the ‘cascade of uncertainty’ is the difference in ecological response associated that arises in the application of multiple models is with a future climate scenario and a current climate a necessary step (e.g. Henderson-Sellers 1993; scenario also significant relative to the uncertainty Jones 2000; Wilby & Harris 2006). Several studies associated with the ecological process? If the exploring the potential hydrological

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