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Alex J. Cannon
Alex J. Cannon
Research Scientist, Climate Research Division, Environment and Climate Change Canada
Verified email at ec.gc.ca - Homepage
Title
Cited by
Cited by
Year
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
11052015
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
4552018
Quantile regression neural networks: Implementation in R and application to precipitation downscaling
AJ Cannon
Computers & Geosciences, 2011
3962011
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
3152007
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
3102008
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
3002016
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
2822012
Recent variations in climate and hydrology in Canada
PH Whitfield, AJ Cannon
Canadian Water Resources Journal 25 (1), 19-65, 2000
2602000
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
2562002
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
234*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
2302019
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
2142012
Multivariate bias correction of climate model output: Matching marginal distributions and intervariable dependence structure
AJ Cannon
Journal of Climate 29 (19), 7045-7064, 2016
1892016
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
1662015
Hydrologic extremes–an intercomparison of multiple gridded statistical downscaling methods
AT Werner, AJ Cannon
Hydrology and Earth System Sciences 20 (4), 1483-1508, 2016
1632016
A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology
AJ Cannon
Hydrological Processes 24 (6), 673-685, 2010
1612010
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
1472018
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
1362012
Probabilistic multisite precipitation downscaling by an expanded Bernoulli-gamma density network
AJ Cannon
Journal of Hydrometeorology 9 (6), 1284-1300, 2008
1362008
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
1222017
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