Alex J. Cannon mainly investigates Climatology, Climate model, Precipitation, Downscaling and Climate change. His Climatology study incorporates themes from Streamflow and Meteorology. His Meteorology study combines topics from a wide range of disciplines, such as Bootstrap aggregating, Linear regression, Artificial neural network, Regression and Quantile regression.
His Climate model research includes elements of Statistics, Quantile and Range. His work carried out in the field of Precipitation brings together such families of science as Atmospheric sciences, Hydrology, Hydrology, Hydrograph and General Circulation Model. Alex J. Cannon has included themes like Cluster analysis and Surface water in his Climate change study.
His main research concerns Climatology, Precipitation, Climate model, Climate change and Downscaling. His Climatology research is multidisciplinary, relying on both Linear regression, Artificial neural network, Streamflow, Meteorology and Hydrology. As a part of the same scientific study, Alex J. Cannon usually deals with the Precipitation, concentrating on Global warming and frequently concerns with Mean radiant temperature.
As part of the same scientific family, Alex J. Cannon usually focuses on Climate model, concentrating on Quantile and intersecting with Quantile regression and Regression. His study in Climate change is interdisciplinary in nature, drawing from both Hydrology, Watershed, Hydrological modelling and Forcing. The Downscaling study combines topics in areas such as General Circulation Model, Atmospheric circulation and Range.
The scientist’s investigation covers issues in Climatology, Climate model, Precipitation, Climate change and Global warming. His Climatology research includes themes of Extreme events, Streamflow and Downscaling. The various areas that Alex J. Cannon examines in his Downscaling study include General Circulation Model, Coupled model intercomparison project and Atmospheric circulation.
His Climate model research focuses on Multivariate statistics and how it connects with Projection and Bias correction. Alex J. Cannon combines subjects such as Snow, Water balance and Evapotranspiration with his study of Precipitation. Alex J. Cannon has researched Climate change in several fields, including Fire season, Watershed and Spatial dependence.
His main research concerns Climatology, Climate change, Climate model, Precipitation and Global warming. His Climatology research is multidisciplinary, incorporating elements of Streamflow, Downscaling and Statistical dispersion. His studies deal with areas such as Fire season and Canola as well as Climate change.
His Climate model study combines topics in areas such as Agriculture and Environmental planning. The concepts of his Precipitation study are interwoven with issues in Wind speed, Water balance and Evapotranspiration. His Global warming research incorporates themes from Matching, Generalized extreme value distribution and Mean radiant temperature.
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Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?
Alex J. Cannon;Stephen R. Sobie;Trevor Q. Murdock.
Journal of Climate (2015)
Quantile regression neural networks: Implementation in R and application to precipitation downscaling
Alex J. Cannon.
Computers & Geosciences (2011)
Coupled modelling of glacier and streamflow response to future climate scenarios
K. Stahl;K. Stahl;R. D. Moore;J. M. Shea;D. Hutchinson.
Water Resources Research (2008)
Groundwater–surface water interaction under scenarios of climate change using a high-resolution transient groundwater model
Jacek Scibek;Diana M. Allen;Alex J. Cannon;Paul H. Whitfield.
Journal of Hydrology (2007)
Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models
Alex J Cannon;Paul H Whitfield.
Journal of Hydrology (2002)
Recent Variations in Climate and Hydrology in Canada
Paul H. Whitfield;Alex J. Cannon.
Canadian Water Resources Journal (2000)
Complexity in estimating past and future extreme short-duration rainfall
Xuebin Zhang;Francis W. Zwiers;Guilong Li;Hui Wan.
Nature Geoscience (2017)
Downscaling Extremes—An Intercomparison of Multiple Statistical Methods for Present Climate
G. Bürger;T. Q. Murdock;A. T. Werner;S. R. Sobie.
Journal of Climate (2012)
Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables
Alex J. Cannon.
Climate Dynamics (2018)
Daily streamflow forecasting by machine learning methods with weather and climate inputs
Kabir Rasouli;William W. Hsieh;Alex J. Cannon.
Journal of Hydrology (2012)
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