World's Best Scientists 2026 revealed!

D-Index & Metrics

Environmental Sciences

D-Index
54
Citations
9364
World Ranking
4087
National Ranking
141

Quan J. Wang 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 Quan J. Wang 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: 203 publications — 65th percentile

65% of scientists in this discipline score the same or lower.

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

Quan J. Wang 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 Quan J. Wang 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: 54 D-Index — 59th percentile

59% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2019 - Member of the European Academy of Sciences
  • 2013 - Fellow of the American Society of Mechanical Engineers
  • Member of the European Academy of Sciences and Arts
  • The Canadian Academy of Engineering
  • Member of the European Academy of Sciences and Arts
  • The Canadian Academy of Engineering

Overview

Quan J. Wang is affiliated with the University of Melbourne in Australia. Their professional career reflects involvement in the academic research community with a focus that can be inferred from their recognized memberships and honors.

Throughout their career, Quan J. Wang has been acknowledged by various prestigious scientific and engineering organizations. They were named Member of the European Academy of Sciences in 2019. In 2013, they were honored as a Fellow of the American Society of Mechanical Engineers. Additionally, they hold membership in the European Academy of Sciences and Arts and the Canadian Academy of Engineering.

Their profile does not include specific details regarding research papers, co-authorship, or publication venues. Likewise, there is no information provided on particular fields or subfields of study, book publications, or main topics of work.

Available data centers primarily around academic recognition and professional association memberships, indicating active engagement and acknowledgment within the scientific and engineering communities at an international level.

Best Publications

  • The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models

    Q. J. Wang

  • A review of advances in flash flood forecasting

    H. A. P. Hapuarachchi;Q. J. Wang;T. C. Pagano

  • A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites.

    Q. J. Wang;D. E. Robertson;F. H. S. Chiew

  • A Review of Quantitative Precipitation Forecasts and Their Use in Short- to Medium-Range Streamflow Forecasting

    Lan Cuo;Thomas C. Pagano;Q. J. Wang

  • Using genetic algorithms to optimise model parameters

    Q.J. Wang

  • LH moments for statistical analysis of extreme events

    Q. J. Wang

  • How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts

    Tongtiegang Zhao;James C. Bennett;Q. J. Wang;Andrew Schepen

  • Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences

    Q. J. Wang;D. E. Robertson

  • The POT model described by the generalized Pareto distribution with Poisson arrival rate

    Q.J. Wang

  • A log-sinh transformation for data normalization and variance stabilization

    Q. J. Wang;D. L. Shrestha;D. E. Robertson;P. Pokhrel

  • The utility of L-moment ratio diagrams for selecting a regional probability distribution

    Murray C. Peel;Q. J. Wang;Richard M. Vogel;Thomas A. McMAHON

  • Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting

    D. E. Robertson;D. L. Shrestha;Q. J. Wang

  • An ANN-based emulation modelling framework for flood inundation modelling: Application, challenges and future directions

    Haibo Chu;Haibo Chu;Wenyan Wu;Quan J. Wang;Rory Nathan

  • Evidence for Using Lagged Climate Indices to Forecast Australian Seasonal Rainfall

    Andrew Schepen;Q. J. Wang;David Robertson

  • Monthly versus daily water balance models in simulating monthly runoff

    Q.J. Wang;T.C. Pagano;S.L. Zhou;H.A.P. Hapuarachchi

  • Estimation of the GEV distribution from censored samples by method of partial probability weighted moments

    Q.J. Wang

  • Merging Seasonal Rainfall Forecasts from Multiple Statistical Models through Bayesian Model Averaging

    Q. J. Wang;Andrew Schepen;David E. Robertson

  • Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose

    D. L. Shrestha;D. E. Robertson;Q. J. Wang;T. C. Pagano

  • DIRECT SAMPLE ESTIMATORS OF L MOMENTS

    Q. J. Wang

  • Artificial neural network based hybrid modeling approach for flood inundation modeling

    Shuai Xie;Wenyan Wu;Sebastian Mooser;Q.J. Wang

  • Unbiased estimation of probability weighted moments and partial probability weighted moments from systematic and historical flood information and their application to estimating the GEV distribution

    Q.J. Wang

Frequent Co-Authors

Thomas A. McMahon
Thomas A. McMahon University of Melbourne
Andrew W. Western
Andrew W. Western University of Melbourne
Murray C. Peel
Murray C. Peel University of Melbourne
Florian Pappenberger
Florian Pappenberger European Centre for Medium-Range Weather Forecasts
Quanxi Shao
Quanxi Shao Commonwealth Scientific and Industrial Research Organisation
Enli Wang
Enli Wang Commonwealth Scientific and Industrial Research Organisation
Francis H. S. Chiew
Francis H. S. Chiew Commonwealth Scientific and Industrial Research Organisation
Jeffrey P. Walker
Jeffrey P. Walker Monash University
Richard M. Vogel
Richard M. Vogel Tufts University
Deli Chen
Deli Chen University of Melbourne

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