©2018 BY MAHKAMEH.ZAREKARIZI. PROUDLY CREATED WITH WIX.COM

Remote Sensing 

Remote sensing data play an important role in my research. For example, I use remotely sensed nighttime light data to observe the spatial patterns of urbanization and study the consequences of urban flooding. I use remotely sensed precipitation to learn about the spatial variations of precipitation extremes. Most importantly, I use remotely sensed soil moisture to improve land surface models by updating their simulations via data assimilation techniques.

For more information, please refer to our paper in Journal of hydrology and book chapter in Remote Sensing of Hydrological Extremes where we use satellite soil moisture to improve model estimations which results in better drought detection and forecasts. The link is provided here:

Data Assimilation

As more and more satellites become available, we need to increase our capabilities of taking full advantage of their data. This becomes true by data assimilation techniques. I use data assimilation to correct model simulations. Several data assimilation techniques have been developed such as Ensemble Kalman Filtering and Particle Filtering. 

In my most recent research, I used Particle Filter-Markov Chain Monte Carlo to integrate SMAP (Soil Moisture Active Passive) satellite data with VIC (Variable Infiltration Capacity; a land surface model) simulations and obtain improved drought monitoring and forecasting skills. 

For more information, please check our paper in journal of hydrology found below. We have also employed this method for drought detection and forecasting over the united stated and the results are presented in two papers. We are submitting the revisions of one and the other one is under review. Please stay tuned...

High Performance computing

High Performance Computing (HPC) has become one of my major interests in my PhD. I believe HPC will become an integral part of Earth system modeling given the growing number of data sources and supercomputers becoming available. I particularly use HPC for large scale hydrologic modeling and assimilation. In our recent paper, we were able to increase the sample size in data assimilation to 100 members which in turn decreased the uncertainty (to some extent). We have explained more about HPC in our paper which is under review in the Remote Sensing of Environment. Please stay tuned...

Climate Projections

In my research, climate projections help me understand the future of extremes. For example, we studied the spatiotemporal variations of precipitation extremes over the Pacific Northwest in the short-term, medium-term, and long-term future as compared to the historical extremes. For future projections we used 10 dynamically and statistically downscaled climate models. For more information about this research please check Zarekarizi et al., 2017 in Climate Dynamics using the provided link below.

My current research (my PhD dissertation) takes advantage of climate projections by forcing them into the National Water Model (WRF-HYDRO) to predict short-term, medium-term, and long-term extreme floods. I will post more information with this ongoing research soon...

Extremes

Monitoring, detecting, predicting, and analyzing extreme precipitation, flood, and drought have been of my particular interest since I started graduate school. I started with studying addressing uncertainty of extreme flood frequency analysis in my masters and continued with studying extreme precipitation variations in m PhD. Later, I started researching on extreme flood prediction. In that research, we used learnt about the relationship between large scale climate patterns in the history and applied that information to improve extreme flood forecasts. Besides climate variability, we considered climate change and urbanization as factors giving rise to non-stationarity of floods. The results of this research will be submitted for publication soon.

Hydrologic Modeling

Large scale hydrologic modeling is a fundamental part of my research. To simulate soil moisture over the Contiguous United States (CONUS) I run the Variable Infiltration Capacity (VIC). For similar simulations over California, we calibrated and ran the PRMS (Precipitation Runoff Modeling System) model. I also have the experience of using the latest version of the VIC model, VIC-5. Finally, my PhD dissertation takes advantage of high resolution simulations of the WRF-Hydro/National Water Model which couples Noah-MP with surface and subsurface routing models and generates streamflow/water level simulations. More information about the VIC model could be found in our paper linked below. For inquiries about the PRMS or the National Water Model, feel free to contact me.

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