Alex J. Cannon
Alex J. Cannon
Research Scientist, Climate Research Division, Environment and Climate Change Canada
Verified email at - Homepage
Cited by
Cited by
Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes?
AJ Cannon, SR Sobie, TQ Murdock
Journal of Climate 28 (17), 6938-6959, 2015
Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables
AJ Cannon
Climate Dynamics 50 (1-2), 31-49, 2018
Quantile regression neural networks: Implementation in R and application to precipitation downscaling
AJ Cannon
Computers & Geosciences, 2011
Groundwater-surface water interaction under scenarios of climate change using a high-resolution transient groundwater model
J Scibek, DM Allen, AJ Cannon, PH Whitfield
Journal of Hydrology 333 (2-4), 165-181, 2007
Coupled modelling of glacier and streamflow response to future climate scenarios
K Stahl, RD Moore, JM Shea, D Hutchinson, AJ Cannon
Water Resources Research 44 (2), W02422, 2008
Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods
MD Johnson, WW Hsieh, AJ Cannon, A Davidson, F Bédard
Agricultural and Forest Meteorology 218, 74-84, 2016
Daily streamflow forecasting by machine learning methods with weather and climate inputs
K Rasouli, WW Hsieh, AJ Cannon
Journal of Hydrology 414, 284-293, 2012
Recent variations in climate and hydrology in Canada
PH Whitfield, AJ Cannon
Canadian Water Resources Journal 25 (1), 19-65, 2000
Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models
AJ Cannon, PH Whitfield
Journal of Hydrology 259 (1-4), 136-151, 2002
Complexity in estimating past and future extreme short-duration rainfall
X Zhang, FW Zwiers, G Li, H Wan, AJ Cannon
Nature Geoscience 10 (4), 255-259, 2017
Attribution of the influence of human‐induced climate change on an extreme fire season
MC Kirchmeier‐Young, NP Gillett, FW Zwiers, AJ Cannon, FS Anslow
Earth's Future, 2019
Downscaling Extremes: An Intercomparison of Multiple Statistical Methods for Present Climate
G Bürger, TQ Murdock, AT Werner, SR Sobie, AJ Cannon
Journal of Climate 25 (12), 4366-4388, 2012
Multivariate bias correction of climate model output: Matching marginal distributions and intervariable dependence structure
AJ Cannon
Journal of Climate 29 (19), 7045-7064, 2016
Selecting GCM Scenarios that Span the Range of Changes in a Multimodel Ensemble: Application to CMIP5 Climate Extremes Indices
AJ Cannon
Journal of Climate 28 (3), 1260-1267, 2015
Hydrologic extremes–an intercomparison of multiple gridded statistical downscaling methods
AT Werner, AJ Cannon
Hydrology and Earth System Sciences 20 (4), 1483-1508, 2016
A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology
AJ Cannon
Hydrological Processes 24 (6), 673-685, 2010
Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes
AJ Cannon
Stochastic Environmental Research and Risk Assessment, 1-19, 2018
Downscaling extremes: an intercomparison of multiple methods for future climate
G Bürger, SR Sobie, AJ Cannon, AT Werner, TQ Murdock
Journal of Climate 26 (2012), 3429–3449, 2012
Probabilistic multisite precipitation downscaling by an expanded Bernoulli-gamma density network
AJ Cannon
Journal of Hydrometeorology 9 (6), 1284-1300, 2008
Intercomparison of projected changes in climate extremes for South Korea: application of trend preserving statistical downscaling methods to the CMIP5 ensemble
HI Eum, AJ Cannon
International Journal of Climatology 37 (8), 3381-3397, 2017
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