The influence of dependence in characterizing multi-variable uncertainty for climate change impact assessments

ABSTRACT Few approaches exist that explicitly use the uncertainty associated with the spread of climate model simulations in assessing climate change impacts. An approach that does so is second-order approximation (SOA). This incorporates quantification of uncertainty to ascertain its impact on the derived response using a Taylor series expansion of the model. This study uses SOA in a statistical downscaling model of monthly streamflow, with a focus on the influence of dependence in the uncertainty of multiple atmospheric variables. Uncertainty is quantified using the square root error variance concept with a new extension that allows the inter-dependence terms among the atmospheric variable uncertainty to be specified. Applying the model to selected point locations in Australia, it is noted that the downscaling results differ considerably from downscaling that ignores uncertainty. However, when the effects of dependence in uncertainty are incorporated, the results differ according to the regional variations in dependence structure.

[1]  Fiona M. Johnson,et al.  A software toolkit for correcting systematic biases in climate model simulations , 2018, Environ. Model. Softw..

[2]  Ashish Sharma,et al.  Using second-order approximation to incorporate GCM uncertainty in climate change impact assessments , 2017, Climatic Change.

[3]  L. Band,et al.  On the non-stationarity of hydrological response in anthropogenically unaffected catchments: an Australian perspective , 2016 .

[4]  Ashish Sharma,et al.  Quantifying the sources of uncertainty in upper air climate variables , 2016 .

[5]  Murray C. Peel,et al.  Simulating runoff under changing climatic conditions: Revisiting an apparent deficiency of conceptual rainfall‐runoff models , 2016 .

[6]  Ashish Sharma,et al.  Quantification of precipitation and temperature uncertainties simulated by CMIP3 and CMIP5 models , 2016 .

[7]  J. Gregory,et al.  Irreducible uncertainty in near-term climate projections , 2016, Climate Dynamics.

[8]  David R. Easterling,et al.  CHALLENGES IN QUANTIFYING CHANGES IN THE GLOBAL WATER CYCLE , 2015 .

[9]  E. Fischer,et al.  Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes , 2015 .

[10]  Yee Leung,et al.  High-order Taylor series expansion methods for error propagation in geographic information systems , 2015, J. Geogr. Syst..

[11]  Rajeshwar Mehrotra,et al.  Correcting for systematic biases in multiple raw GCM variables across a range of timescales , 2015 .

[12]  E. Hawkins,et al.  Wetter then drier in some tropical areas , 2014 .

[13]  K. Bollmann,et al.  Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change , 2013 .

[14]  T. Ouarda,et al.  Databased comparison of Sparse Bayesian Learning and Multiple Linear Regression for statistical downscaling of low flow indices. , 2013 .

[15]  D. A. Sachindra,et al.  Least square support vector and multi‐linear regression for statistically downscaling general circulation model outputs to catchment streamflows , 2013 .

[16]  Ashish Sharma,et al.  An error estimation method for precipitation and temperature projections for future climates , 2012 .

[17]  Ashish Sharma,et al.  A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations , 2012 .

[18]  E. Hawkins,et al.  A Simple, Coherent Framework for Partitioning Uncertainty in Climate Predictions , 2011 .

[19]  H. Moradkhani,et al.  Assessing the uncertainties of hydrologic model selection in climate change impact studies , 2011 .

[20]  A. Thomson,et al.  The representative concentration pathways: an overview , 2011 .

[21]  E. Hawkins,et al.  The potential to narrow uncertainty in projections of regional precipitation change , 2011 .

[22]  Stefano Alvisi,et al.  Pipe roughness calibration in water distribution systems using grey numbers , 2010 .

[23]  B. De Baets,et al.  Error assessment of nitrogen and oxygen isotope ratios of nitrate as determined via the bacterial denitrification method. , 2010, Rapid communications in mass spectrometry : RCM.

[24]  E. Hawkins,et al.  The Potential to Narrow Uncertainty in Regional Climate Predictions , 2009 .

[25]  Kuo‐Chin Hsu,et al.  The application of the first-order second-moment method to analyze poroelastic problems in heterogeneous porous media , 2009 .

[26]  Ashwani Kumar,et al.  Improved Estimation of Soil Organic Carbon Storage Uncertainty Using First-Order Taylor Series Approximation , 2008 .

[27]  Subimal Ghosh,et al.  Statistical downscaling of GCM simulations to streamflow using relevance vector machine , 2008 .

[28]  Hayley J. Fowler,et al.  Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling , 2007 .

[29]  Reto Knutti,et al.  The use of the multi-model ensemble in probabilistic climate projections , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[30]  Adebayo Adeloye,et al.  Monte Carlo Assessment of Sampling Uncertainty of Climate Change Impacts on Water Resources Yield in Yorkshire, England , 2006 .

[31]  Fernando Pérez-Cruz,et al.  Support Vector Regression for the simultaneous learning of a multivariate function and its derivatives , 2005, Neurocomputing.

[32]  L. Kajfez-Bogataj,et al.  N–PLS regression as empirical downscaling tool in climate change studies , 2005 .

[33]  Carl Gold,et al.  Model selection for support vector machine classification , 2002, Neurocomputing.

[34]  Michael D. Dettinger,et al.  First order analysis of uncertainty in numerical models of groundwater flow part: 1. Mathematical development , 1981 .

[35]  Lawrence L. Kupper,et al.  Probability, statistics, and decision for civil engineers , 1970 .