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 | 1108 | 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 | 456 | 2018 |
Quantile regression neural networks: Implementation in R and application to precipitation downscaling AJ Cannon Computers & Geosciences, 2011 | 396 | 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 | 315 | 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 | 311 | 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 | 300 | 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 | 282 | 2012 |
Recent variations in climate and hydrology in Canada PH Whitfield, AJ Cannon Canadian Water Resources Journal 25 (1), 19-65, 2000 | 260 | 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 | 256 | 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 | 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 | 232 | 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 | 214 | 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 | 189 | 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 | 166 | 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 | 164 | 2016 |
A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology AJ Cannon Hydrological Processes 24 (6), 673-685, 2010 | 161 | 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 | 147 | 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 | 136 | 2012 |
Probabilistic multisite precipitation downscaling by an expanded Bernoulli-gamma density network AJ Cannon Journal of Hydrometeorology 9 (6), 1284-1300, 2008 | 136 | 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 | 122 | 2017 |