D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Environmental Sciences D-index 42 Citations 5,154 105 World Ranking 2944 National Ranking 1319

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Climate change
  • Hydrology

His primary areas of investigation include Data assimilation, Econometrics, Hydrological modelling, Meteorology and Particle filter. His studies in Data assimilation integrate themes in fields like Ensemble Kalman filter and Operations research. His study on Econometrics also encompasses disciplines like

  • Calibration that connect with fields like Algorithm,
  • Probability distribution that intertwine with fields like Bayesian probability and Bayesian inference.

His biological study deals with issues like Surface runoff, which deal with fields such as Model selection, GCM transcription factors, Albedo and Remote sensing. His study in Meteorology is interdisciplinary in nature, drawing from both Uncertainty analysis and Streamflow. In Streamflow, he works on issues like Climatology, which are connected to General Circulation Model and Extreme events.

His most cited work include:

  • Dual state-parameter estimation of hydrological models using ensemble Kalman filter (645 citations)
  • Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter (537 citations)
  • Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities (272 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Climatology, Data assimilation, Streamflow, Precipitation and Meteorology. The various areas that Hamid Moradkhani examines in his Climatology study include Drainage basin, Climate change, Surface runoff and Hydrological modelling. His Data assimilation research includes elements of Remote sensing, Econometrics, Ensemble Kalman filter and Water content.

Hamid Moradkhani interconnects Calibration, Bayesian inference, Statistics, Probabilistic logic and Bayesian probability in the investigation of issues within Econometrics. His Streamflow study incorporates themes from Snowpack and Snowmelt. His Precipitation study combines topics in areas such as Spatial variability, Evapotranspiration, Flood forecasting and Temporal scales.

He most often published in these fields:

  • Climatology (28.77%)
  • Data assimilation (20.28%)
  • Streamflow (17.92%)

What were the highlights of his more recent work (between 2019-2021)?

  • Remote sensing (7.55%)
  • Flash flood (5.19%)
  • Hazard (5.66%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Remote sensing, Flash flood, Hazard, Water content and Precipitation. His Remote sensing research includes themes of Vegetation and Sensor fusion. His work focuses on many connections between Water content and other disciplines, such as Evapotranspiration, that overlap with his field of interest in Multivariate statistics, Assimilation, Moderate-resolution imaging spectroradiometer, Topsoil and Hydrological modelling.

His work deals with themes such as Remote sensing, Climatology and Groundwater storage, which intersect with Precipitation. The Climatology study combines topics in areas such as Global warming, Data assimilation, Ensemble Kalman filter and Scale. His Data assimilation research is included under the broader classification of Meteorology.

Between 2019 and 2021, his most popular works were:

  • A Place-based Assessment of Flash Flood Hazard and Vulnerability in the Contiguous United States. (14 citations)
  • Leveraging machine learning for predicting flash flood damage in the Southeast US (13 citations)
  • Drought and food security in the middle east: An analytical framework (10 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Climate change
  • Artificial intelligence

Hamid Moradkhani mainly focuses on Flash flood, Remote sensing, Precipitation, Hazard and Tropical cyclone. His Remote sensing research integrates issues from Topsoil and Hydrological modelling. His Precipitation research incorporates themes from Water content, Evapotranspiration, Ensemble Kalman filter and Moderate-resolution imaging spectroradiometer.

His Tropical cyclone study combines topics from a wide range of disciplines, such as Hydrometeorology, Weather Research and Forecasting Model, Flood myth, Streamflow and Discharge. His studies deal with areas such as Climatology and Data assimilation as well as Discharge. Many of his studies on Data assimilation involve topics that are commonly interrelated, such as Watershed.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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

Hamid Moradkhani;Soroosh Sorooshian;Hoshin Vijai Gupta;Paul R. Houser.
Advances in Water Resources (2005)

914 Citations

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.
Water Resources Research (2005)

680 Citations

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

Yuqiong Liu;Yuqiong Liu;A. Weerts;M. Clark;H.-J Hendricks Franssen.
Hydrology and Earth System Sciences (2012)

355 Citations

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

M. R. Najafi;H. Moradkhani;I. W. Jung.
Hydrological Processes (2011)

241 Citations

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

Carsten Montzka;Hamid Moradkhani;Lutz Weihermüller;Harrie-Jan Hendricks Franssen.
Journal of Hydrology (2011)

232 Citations

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

Hamid Moradkhani;Kuo Lin Hsu;Hoshin V. Gupta;Soroosh Sorooshian.
Journal of Hydrology (2004)

215 Citations

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.
Water Resources Research (2006)

207 Citations

Hydrologic Remote Sensing and Land Surface Data Assimilation.

Hamid Moradkhani.
Sensors (2008)

195 Citations

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

Hamid Moradkhani;Caleb Matthew DeChant;Soroosh Sorooshian.
Water Resources Research (2012)

195 Citations

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

Caleb Matthew DeChant;Hamid Moradkhani.
Water Resources Research (2012)

160 Citations

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