Moradkhani's research team has aimed to make significant contributions to hydrologic science/water resources system analysis and computational modeling with emphasis on harnessing data revolution, predictive science, uncertainty analysis, deep learning and high performance computing. Our research has focused on advancing our understanding of hydrologic science through modeling climate-water-human interactions as a complex system to result in sustainable management. Our group contributes to the interactions between climate, hydrology and water resources using the data sets and methods including climate model downscaling, remote sensing, state-of-the art data assimilation, distributed hydrologic modeling, ensemble inference, post-processing and multi-modeling. We are interested in characterizing, quantifying, reducing and communicating uncertainties and risks in all layers of simulation and forecasting while providing reliable hydroclimate extremes analyses under nonstationarity across spatial and temporal scales to allow understanding the impact of climate variability and change on water resources and environment. These include developing drought early warning system for monitoring and predictions and flood forecasting and inundation likelihood mapping. Our everlasting desire is to continue contributing to hydroclimate and remote sensing research not only by applied research but also by developing new insights by means of Bayesian inference, inverse modeling and cyberinovations.
Research Examples:
Land Data Assimilation
- Pathiraja, S., H. Moradkhani, L. Marshall, A. Sharma, G, Geenens (2018), Data Driven Model Uncertainty Estimation in Data Assimilation, Water Resources Research, 10.1002/2018WR022627.
- Abbaszadeh, P., H. Moradkhani, and H. Yan (2018), Enhancing Hydrologic Data Assimilation by Evolutionary Particle Filter and Markov Chain Monte Carlo , Advances in Water Resources, 111, 192-204, doi:10.1016/j.advwatres.2017.11.011.
- Pathiraja, S., Anghileri, D., Burlando, P., Sharma, A., Marshall, L., and Moradkhani, H. (2018), Insights on the impact of systematic model errors on data assimilation performance in changing catchments, Advances in Water Resources, 113,202-222.
- Pathiraja, S., Anghileri, D., Burlando, P., Sharma, A., Marshall, L., and Moradkhani, H. (2017), Time varying parameter models for catchments with land use change: the importance of model structure, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-382.
- Yan, H., and H. Moradkhani (2016), Combined Assimilation of Streamflow and Satellite Soil Moisture with the Particle Filter and Geostatistical Modeling, Advances in Water Resources, 94 364–378, doi:10.1016/j.advwatres.2016.06.002.
- Pathiraja, S., L. Marshall, A. Sharma, and H. Moradkhani (2016), Detecting non-stationary hydrologic model parameters in a paired catchment system using Data Assimilation, Advances in Water Resources, 94, 103–119, doi:10.1016/j.advwatres.2016.04.021.
- Pathiraja, S., L. Marshall, A. Sharma, and H. Moradkhani (2016), Hydrologic Modeling in Dynamic catchments: A Data Assimilation Approach, Water Resources Research, DOI: 10.1002/2015WR017192.
- Moradkhani, H., C.M. DeChant and S. Sorooshian (2012), Evolution of Ensemble Data Assimilation for Uncertainty Quantification using the Particle Filter-Markov Chain Monte Carlo Method, Water Resources Research,48,W12520, doi:10.1029/2012WR012144.
- Parrish, M., H. Moradkhani, and C.M. DeChant (2012), Towards Reduction of Model Uncertainty: Integration of Bayesian Model Averaging and Data Assimilation, Water Resources Research, 48, W03519, doi:10.1029/2011WR011116.
Probabilistic Drought Monitoring and Forecasting
- Yan, H., M Zarekarizi, and H. Moradkhani (2018), Toward Improving Drought Monitoring using the Remotely Sensed Soil Moisture Assimilation: A Parallel Particle Filtering Framework, Remote Sensing of Environment, 216:456-471, doi: 10.1016/j.rse.2018.07.017
- Yan, H., H. Moradkhani, and M. Zarekarizi (2017), A Probabilistic Drought Forecasting Framework: A Combined Dynamical and Statistical Approach, Journal of Hydrology, 548, 291–304, doi: 0.1016/j.jhydrol.2017.03.004
- Ahmadalipour, A. and H. Moradkhani and M. Svoboda (2016), Centennial drought outlook over the CONUS using NASA-NEX downscaled climate ensemble, International Journal of Climatology, doi: 10.1002/joc.4859.
- Ahmadalipour, A., H. Moradkhani, H. Yan, M. Zarekarizi (2016), Remote Sensing of Drought: Vegetation, Soil Moisture and Data Assimilation, Remote Sensing of Hydrological Extremes, Springer International Publishing Switzerland 2017, DOI 10.1007/978-3-319-43744-6_7.
- Madadgar, S. and H. Moradkhani (2016), Copula Function and Drought, Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Francis and Taylor.
- DeChant C.M., and H. Moradkhani (2015), Analyzing the Sensitivity of Drought Recovery Forecasts to Land Surface Initial Conditions, Journal of Hydrology, special issue on Drought, 526, 89–100, DOI:10.1016/j.jhydrol.2014.10.021.
- Madadgar, S., and H. Moradkhani (2014), Spatio-temporal Drought Forecasting within Bayesian Networks, Journal of Hydrology, 512, 134-146, DOI: 10.1016/j.jhydrol.2014.02.039.
- DeChant C.M., and H. Moradkhani (2014), Toward a Reliable Prediction of Seasonal Forecast Uncertainty: Addressing Model and Initial Condition Uncertainty with Ensemble Data Assimilation and Sequential Bayesian Combination, Journal of Hydrology, special issue on Ensemble Forecasting and data assimilation, 519, 2967-2977, DOI: 10.1016/j.jhydrol.2014.05.045.
- Madadgar, S. and H. Moradkhani (2013), A Bayesian Framework for Probabilistic Drought Forecasting, Journal of Hydrometeorology, special issue of Advances in Drought Monitoring, 14, 1685–1705, DOI: 10.1175/JHM-D-13-010.1
- Madadgar, S., and H. Moradkhani, (2013), Drought Analysis under Climate Change using Copula, Journal of Hydrologic Engineering, 18 (7), doi:http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000532.
Extreme Events
- Bracken, C., B. Rajagopalan, and H. Moradkhani (2018), A Bayesian hierarchical approach to multivariate nonstationary hydrologic frequency analysis , Water Resources Research, doi: 10.1002/2017WR020403.
- Yan, H., and H. Moradkhani (2015), Toward more Robust Extreme Flood Prediction by Bayesian Hierarchical and Multimodeling, Natural Hazards, DOI 10.1007/s11069-015-2070-6.
- Najafi, M.R. and H. Moradkhani (2015), Multi-Model Ensemble Analysis of the Runoff Extremes for Climate Change Impact Assessments, Journal of Hydrology, DOI:10.1016/j.jhydrol.2015.03.045.
- Yan, H., and H. Moradkhani (2014), A Regional Bayesian Hierarchical Model for Flood Frequency Analysis, Stochastic Environmental Research and Risk Assessment, DOI: 10.1007/s00477.014.0975.3.
- Najafi, M.R. and H. Moradkhani (2014), A Hierarchical Bayesian Approach for the Analysis of Climate Change Impact on Runoff Extremes, Hydrological Processes, 28, 6292–6308, DOI: 10.1002/hyp.10113.
- Najafi M.R. and H. Moradkhani (2013), Analysis of Runoff Extremes using Spatial Hierarchical Bayesian Modeling, Water Resources Research, 49, 1–15, DOI:10.1002/wrcr.20381.
Climate Change and Hydrology
- Ahmadalipour, H. Moradkhani, M. Demirel (2017), A comparative assessment of projected meteorological and hydrological droughts: Elucidating the role of temperature, J. of Hydrology, 553 (2017) 785–797.
- Ahmadalipour, A., H. Moradkhani, A. Rana (2017), Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin, Climate Dynamics, doi: 10.1007/s00382-017-3639-4.
- Ahmadalipour, A. and H. Moradkhani and M. Svoboda (2016), Centennial drought outlook over the CONUS using NASA-NEX downscaled climate ensemble, International Journal of Climatology, doi: 10.1002/joc.4859.
- Rana, A., H. Moradkhani, Y. Qin (2016), Understanding the Joint Behavior of Temperature and Precipitation for Climate Change Impact Assessment, Theoretical and Applied Climatology, DOI: 10.1007/s00704-016-1774-1.
- Rana, A., and H. Moradkhani (2016), Spatial, temporal and frequency based climate change assessment in Columbia River Basin using multi downscaled-Scenarios, Climate Dynamics, 47:579–600, doi:10.1007/s00382-015-2857-x.
- Najafi, M.R., H. Moradkhani, I. Jung (2011), Assessing the Uncertainties of Hydrologic Model Selection in Climate Change Impact Studies, Hydrologic Processes, 25(18), 2814-2826.
Vulnerability and Risk Assessment
- Ahmadalipour, A., Moradkhani, H., Kumar, M. (2019) Mortality risk from heat-stress expected to hit poorest nations the hardest. Climatic Change. DOI: 10.1007/s10584-018-2348-2
- Ahmadalipour, A., and H. Moradkhani (2018), Escalating heat-stress mortality risk due to global warming in the Middle East and North Africa (MENA), Environment International, 117, 215–225
- Ahmadalipour, A., and H. Moradkhani (2018) Multi-dimensional assessment of drought vulnerability across Africa: 1960-2100. Science of the Total Environment. 664:520-535. doi:/10.1016/j.scitotenv.2018.07.023.
- Ahmadalipour, A., H. Moradkhani, A. Casteletti, N. Magliocca (2019), Future drought risk in Africa: Integrating vulnerability, climate change, and population growth, Science of the Total Environment, 662: 672-686. doi:10.1016/j.scitotenv.2019.01.278.