World's Best Scientists 2026 revealed!

D-Index & Metrics

Environmental Sciences

D-Index
50
Citations
11195
World Ranking
4911
National Ranking
205

Alex J. Cannon publication distribution in Environmental Sciences in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Environmental Sciences in 2026. The highlighted bar marks where Alex J. Cannon sits on this spectrum.

41–50 publications: 21 scientists 51–60 publications: 62 scientists 61–70 publications: 133 scientists 71–80 publications: 257 scientists 81–90 publications: 361 scientists 91–100 publications: 440 scientists 101–110 publications: 492 scientists 111–120 publications: 541 scientists 121–130 publications: 617 scientists 131–140 publications: 544 scientists 141–150 publications: 541 scientists 151–160 publications: 539 scientists 161–170 publications: 444 scientists 171–180 publications: 444 scientists 181–190 publications: 400 scientists 191–200 publications: 377 scientists 201–210 publications: 318 scientists 211–220 publications: 283 scientists 221–230 publications: 263 scientists 231–240 publications: 220 scientists 241–250 publications: 217 scientists 251–260 publications: 180 scientists 261–270 publications: 181 scientists 271–280 publications: 155 scientists 281–290 publications: 130 scientists 291–300 publications: 127 scientists 301–310 publications: 130 scientists 311–320 publications: 85 scientists 321–330 publications: 106 scientists 331–340 publications: 80 scientists 341–350 publications: 83 scientists 351–360 publications: 75 scientists 361–370 publications: 69 scientists 371–380 publications: 52 scientists 381–390 publications: 54 scientists 391–400 publications: 56 scientists 401–410 publications: 44 scientists 411–420 publications: 40 scientists 421–430 publications: 36 scientists 431–440 publications: 25 scientists 441–450 publications: 25 scientists 451–460 publications: 32 scientists 461–470 publications: 29 scientists 471–480 publications: 21 scientists 481–490 publications: 26 scientists 491–500 publications: 25 scientists 501–510 publications: 17 scientists 511–520 publications: 18 scientists 521–530 publications: 15 scientists 531–540 publications: 22 scientists 541–550 publications: 12 scientists 551–560 publications: 15 scientists 561–570 publications: 11 scientists 571–580 publications: 19 scientists 581–590 publications: 9 scientists 591–600 publications: 9 scientists 601–610 publications: 7 scientists 611–620 publications: 11 scientists 621–630 publications: 5 scientists 631–640 publications: 5 scientists 641–650 publications: 6 scientists 651–660 publications: 3 scientists 661–670 publications: 3 scientists 671–680 publications: 4 scientists 681–686 publications: 3 scientists 687+ publications: 100 scientists
41 publications 687+

This scientist: 172 publications — 52nd percentile

52% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 687 publications or more.

Alex J. Cannon D-index placement in Environmental Sciences in 2026

The chart shows the D-index (discipline H-index) distribution of Environmental Sciences scientists ranked by Research.com in 2026. The highlighted bar marks where Alex J. Cannon sits on this spectrum.

30 D-Index: 12 scientists 31 D-Index: 26 scientists 32 D-Index: 51 scientists 33 D-Index: 88 scientists 34 D-Index: 123 scientists 35 D-Index: 163 scientists 36 D-Index: 206 scientists 37 D-Index: 267 scientists 38 D-Index: 265 scientists 39 D-Index: 275 scientists 40 D-Index: 321 scientists 41 D-Index: 343 scientists 42 D-Index: 305 scientists 43 D-Index: 336 scientists 44 D-Index: 330 scientists 45 D-Index: 348 scientists 46 D-Index: 291 scientists 47 D-Index: 275 scientists 48 D-Index: 272 scientists 49 D-Index: 273 scientists 50 D-Index: 263 scientists 51 D-Index: 232 scientists 52 D-Index: 266 scientists 53 D-Index: 217 scientists 54 D-Index: 199 scientists 55 D-Index: 177 scientists 56 D-Index: 202 scientists 57 D-Index: 204 scientists 58 D-Index: 166 scientists 59 D-Index: 177 scientists 60 D-Index: 166 scientists 61 D-Index: 152 scientists 62 D-Index: 143 scientists 63 D-Index: 150 scientists 64 D-Index: 124 scientists 65 D-Index: 119 scientists 66 D-Index: 120 scientists 67 D-Index: 118 scientists 68 D-Index: 82 scientists 69 D-Index: 98 scientists 70 D-Index: 94 scientists 71 D-Index: 105 scientists 72 D-Index: 74 scientists 73 D-Index: 84 scientists 74 D-Index: 70 scientists 75 D-Index: 67 scientists 76 D-Index: 78 scientists 77 D-Index: 60 scientists 78 D-Index: 59 scientists 79 D-Index: 52 scientists 80 D-Index: 47 scientists 81 D-Index: 38 scientists 82 D-Index: 48 scientists 83 D-Index: 42 scientists 84 D-Index: 42 scientists 85 D-Index: 43 scientists 86 D-Index: 29 scientists 87 D-Index: 37 scientists 88 D-Index: 29 scientists 89 D-Index: 30 scientists 90 D-Index: 34 scientists 91 D-Index: 20 scientists 92 D-Index: 22 scientists 93 D-Index: 17 scientists 94 D-Index: 19 scientists 95 D-Index: 24 scientists 96 D-Index: 21 scientists 97 D-Index: 20 scientists 98 D-Index: 24 scientists 99 D-Index: 17 scientists 100 D-Index: 17 scientists 101 D-Index: 21 scientists 102 D-Index: 25 scientists 103 D-Index: 18 scientists 104 D-Index: 26 scientists 105 D-Index: 19 scientists 106 D-Index: 15 scientists 107 D-Index: 10 scientists 108 D-Index: 13 scientists 109 D-Index: 15 scientists 110 D-Index: 12 scientists 111 D-Index: 8 scientists 112 D-Index: 7 scientists 113 D-Index: 9 scientists 114 D-Index: 6 scientists 115 D-Index: 12 scientists 116 D-Index: 7 scientists 117 D-Index: 8 scientists 118 D-Index: 3 scientists 119 D-Index: 5 scientists 120 D-Index: 7 scientists 121 D-Index: 2 scientists 122 D-Index: 4 scientists 123 D-Index: 8 scientists 124 D-Index: 7 scientists 125+ D-Index: 99 scientists
30 D-Index 125+

This scientist: 50 D-Index — 50th percentile

50% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 125 D-Index or more.

Overview

Alex J. Cannon is affiliated with the University of Victoria in Canada and specializes in research within Environmental Science and Earth and Planetary Sciences. Their work encompasses a broad range of topics primarily centered on climate variability, meteorological phenomena, and atmospheric science.

The scientist's recent publications include studies on climate model biases, downscaled climate normals, and atmospheric circulation. Notable papers are:

  • "Multivariate bias corrections of climate simulations: which benefits for which losses?" (2020) in Earth System Dynamics
  • "A global climate model ensemble for downscaled monthly climate normals over North America" (2022) in International Journal of Climatology
  • "Reductions in daily continental-scale atmospheric circulation biases between generations of global climate models: CMIP5 to CMIP6" (2020) in Environmental Research Letters
  • "Human influence on the 2021 British Columbia floods" (2022) in Weather and Climate Extremes
  • "Multivariate Bias-Correction of High-Resolution Regional Climate Change Simulations for West Africa: Performance and Climate Change Implications" (2022) in Journal of Geophysical Research Atmospheres

Their research contributions have been published frequently in venues such as:

  • Zenodo (CERN European Organization for Nuclear Research)
  • Climate Dynamics
  • Environmental Research Letters
  • Journal of Climate
  • Climatic Change

Frequent collaborators include:

  • Dae Il Jeong
  • Joe R. Melton
  • Bo Qu
  • Bin Yu
  • Mohammad Reza Najafi

Their main fields of study focus on Environmental Science and Earth and Planetary Sciences. Within these fields, their subfields of study cover:

  • Global and Planetary Change
  • Atmospheric Science
  • Environmental Engineering
  • Water Science and Technology
  • Ecology, Evolution, Behavior and Systematics

Key topics addressed in their work include:

  • Climate variability and models
  • Meteorological Phenomena and Simulations
  • Atmospheric and Environmental Gas Dynamics
  • Climate change and permafrost
  • Hydrology and Watershed Management Studies
  • Cryospheric studies and observations
  • Flood Risk Assessment and Management

Best Publications

  • 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

  • Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

    Alex J. Cannon

  • Quantile regression neural networks: Implementation in R and application to precipitation downscaling

    Alex J. Cannon

  • Coupled modelling of glacier and streamflow response to future climate scenarios

    K. Stahl;K. Stahl;R. D. Moore;J. M. Shea;D. Hutchinson

  • Daily streamflow forecasting by machine learning methods with weather and climate inputs

    Kabir Rasouli;William W. Hsieh;Alex J. Cannon

  • Complexity in estimating past and future extreme short-duration rainfall

    Xuebin Zhang;Francis W. Zwiers;Guilong Li;Hui Wan

  • 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

  • Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods

    Michael D. Johnson;William W. Hsieh;Alex J. Cannon;Andrew Davidson

  • Attribution of the Influence of Human-Induced Climate Change on an Extreme Fire Season

    M. C. Kirchmeier-Young;N. P. Gillett;F. W. Zwiers;A. J. Cannon

  • Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models

    Alex J Cannon;Paul H Whitfield

  • Recent Variations in Climate and Hydrology in Canada

    Paul H. Whitfield;Alex J. Cannon

  • Multivariate Bias Correction of Climate Model Output: Matching Marginal Distributions and Intervariable Dependence Structure

    Alex J. Cannon

  • Downscaling Extremes—An Intercomparison of Multiple Statistical Methods for Present Climate

    G. Bürger;T. Q. Murdock;A. T. Werner;S. R. Sobie

  • Hydrologic extremes – an intercomparison of multiple gridded statistical downscaling methods

    Arelia T. Werner;Alex J. Cannon

  • Selecting GCM Scenarios that Span the Range of Changes in a Multimodel Ensemble: Application to CMIP5 Climate Extremes Indices*

    Alex J. Cannon

  • Probabilistic Multisite Precipitation Downscaling by an Expanded Bernoulli–Gamma Density Network

    Alex J. Cannon

  • A closer look at novel climates: new methods and insights at continental to landscape scales

    Colin R. Mahony;Alex J. Cannon;Tongli Wang;Sally N. Aitken

  • Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes

    Alex J. Cannon

  • Downscaling Extremes: An Intercomparison of Multiple Methods for Future Climate

    Gerd Bürger;S. R. Sobie;A. J. Cannon;A. T. Werner

  • A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology

    Alex J. Cannon

  • Multivariate bias corrections of climate simulations: which benefits for which losses?

    Bastien François;Mathieu Vrac;Alex J. Cannon;Yoann Robin

Frequent Co-Authors

William W. Hsieh
William W. Hsieh University of British Columbia
Paul H. Whitfield
Paul H. Whitfield University of Saskatchewan
Francis W. Zwiers
Francis W. Zwiers University of Victoria
Budong Qian
Budong Qian Agriculture and Agriculture-Food Canada
John W. Pomeroy
John W. Pomeroy University of Saskatchewan
Barrie Bonsal
Barrie Bonsal University of Victoria
Howard Wheater
Howard Wheater Imperial College London
Alain Pietroniro
Alain Pietroniro University of Saskatchewan
Qi Jing
Qi Jing Environment and Climate Change Canada
Chong-Yu Xu
Chong-Yu Xu North China University of Water Conservancy and Electric Power

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