World's Best Scientists 2026 revealed!

D-Index & Metrics

Environmental Sciences

D-Index
78
Citations
16165
World Ranking
1095
National Ranking
476

Hamid Moradkhani 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 Hamid Moradkhani 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: 273 publications — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Hamid Moradkhani 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 Hamid Moradkhani 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: 78 D-Index — 89th percentile

89% of scientists in this discipline score the same or lower.

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

Overview

Hamid Moradkhani is affiliated with the University of Alabama in the United States and specializes in Environmental Science and Earth and Planetary Sciences. Their work spans a range of subfields including Global and Planetary Change, Atmospheric Science, Water Science and Technology, Environmental Engineering, and Sociology and Political Science.

Their research covers multiple topics such as Flood Risk Assessment and Management, Tropical and Extratropical Cyclones Research, Hydrology and Watershed Management Studies, Meteorological Phenomena and Simulations, Hydrology and Drought Analysis, Climate Variability and Models, and Disaster Management and Resilience.

Frequent co-authors in their publications include Hamed Moftakhari, Peyman Abbaszadeh, Keighobad Jafarzadegan, David F. Muñoz, and Keyhan Gavahi.

Hamid Moradkhani publishes regularly in several scientific journals. The most frequent venues for their work are:

  • Water Resources Research
  • SSRN Electronic Journal
  • Earth's Future
  • Advances in Water Resources
  • Journal of Hydrology

Some of their recent papers include:

  • In-situ and triple-collocation based evaluations of eight global root zone soil moisture products, 2020, Remote Sensing of Environment
  • Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index, 2020, Remote Sensing of Environment
  • MXenes: The two-dimensional influencers, 2021, Materials Today Advances
  • From local to regional compound flood mapping with deep learning and data fusion techniques, 2021, The Science of The Total Environment
  • Causes of the Widespread 2019-2020 Australian Bushfire Season, 2020, Earth's Future

Best Publications

  • Dual state-parameter estimation of hydrological models using ensemble Kalman filter

    Hamid Moradkhani;Soroosh Sorooshian;Hoshin Vijai Gupta;Paul R. Houser

  • Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter

    Hamid Moradkhani;Kuo-Lin Hsu;Hoshin V. Gupta;Soroosh Sorooshian

  • Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

    Yuqiong Liu;Yuqiong Liu;A. Weerts;M. Clark;H.-J Hendricks Franssen

  • Future drought risk in Africa: Integrating vulnerability, climate change, and population growth

    Ali Ahmadalipour;Hamid Moradkhani;Andrea Castelletti;Nicholas Magliocca

  • Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter

    Carsten Montzka;Hamid Moradkhani;Lutz Weihermüller;Harrie-Jan Hendricks Franssen

  • Assessing the uncertainties of hydrologic model selection in climate change impact studies

    M. R. Najafi;H. Moradkhani;I. W. Jung

  • Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response

    Yang Hong;Yang Hong;Kuo-lin Hsu;Hamid Moradkhani;Soroosh Sorooshian

  • Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method

    Hamid Moradkhani;Caleb Matthew DeChant;Soroosh Sorooshian

  • Improved streamflow forecasting using self-organizing radial basis function artificial neural networks

    Hamid Moradkhani;Kuo Lin Hsu;Hoshin V. Gupta;Soroosh Sorooshian

  • General Review of Rainfall-Runoff Modeling: Model Calibration, Data Assimilation, and Uncertainty Analysis

    Hamid Moradkhani;Soroosh Sorooshian

  • Hydrologic Remote Sensing and Land Surface Data Assimilation.

    Hamid Moradkhani

  • Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting

    Caleb Matthew DeChant;Hamid Moradkhani

  • Downscaling SMAP Radiometer Soil Moisture Over the CONUS Using an Ensemble Learning Method

    Peyman Abbaszadeh;Hamid Moradkhani;Xiwu Zhan

  • Drought Analysis under Climate Change Using Copula

    Shahrbanou Madadgar;Hamid Moradkhani

  • Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation

    Mark A. Parrish;Hamid Moradkhani;Caleb Matthew DeChant

  • Improved Bayesian multimodeling: Integration of copulas and Bayesian model averaging

    Shahrbanou Madadgar;Hamid Moradkhani

  • Radiance data assimilation for operational snow and streamflow forecasting

    Caleb Dechant;Hamid Moradkhani

  • A Bayesian Framework for Probabilistic Seasonal Drought Forecasting

    Shahrbanou Madadgar;Hamid Moradkhani

  • In-situ and triple-collocation based evaluations of eight global root zone soil moisture products

    Lei Xu;Nengcheng Chen;Xiang Zhang;Hamid Moradkhani

  • Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index

    Lei Xu;Lei Xu;Peyman Abbaszadeh;Hamid Moradkhani;Nengcheng Chen

  • Snow water equivalent prediction using Bayesian data assimilation methods

    Marc Leisenring;Hamid Moradkhani

  • Uncertainty Quantification of Satellite Precipitation Estimation and Monte Carlo Assessment of the Error Propagation into Hydrologic Response

    Y. Hong;H. Moradkhani;K. Hsu;S. Sorooshian

Frequent Co-Authors

Soroosh Sorooshian
Soroosh Sorooshian University of California, Irvine
Ashish Sharma
Ashish Sharma University of New South Wales
Kuolin Hsu
Kuolin Hsu University of California, Irvine
Carsten Montzka
Carsten Montzka Forschungszentrum Jülich
Heejun Chang
Heejun Chang Portland State University
Harrie-Jan Hendricks Franssen
Harrie-Jan Hendricks Franssen Forschungszentrum Jülich
Bjørn Kløve
Bjørn Kløve University of Oulu
Harry Vereecken
Harry Vereecken Forschungszentrum Jülich
Hoshin V. Gupta
Hoshin V. Gupta University of Arizona
Xiang Zhang
Xiang Zhang University of Hong Kong

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