Global Change Fellow Alumnus | Department of Civil and Environmental Engineering | North Carolina State University
2015 – 2016 Global Change Fellow
Where are they now?
Amir is now working at the National Center for Atmospheric Research as an Associate Scientist in the Research Applications Lab.
Statement of Purpose:
I am a Ph.D. student in the field of Water Resources Management in the Department of Civil and Environmental Engineering working with Dr. Sankar Arumugam. I received my Masters degree from NCSU in 2014 that was focused on utilizing short-term climate forecasts in developing seasonal streamflow forecasts over the US Sunbelt. My Ph.D. research is focused on developing probabilistic statistical downscaling methods that utilize the entire ensemble of climate forecasts from Global Climate Models (GCM) to develop 1-month ahead streamflow forecasts, as well as, developing data assimilation algorithms for including Remotely-Sensed satellite data in Land Surface Models (LSM) in order to improve the streamflow forecasts. In general, my research interests include: Remote Sensing Applications in Hydrology and Hydro-climatology, Uncertainty Quantification, Hydrological Data Assimilation, and Spatial/Temporal Downscaling methods.
Description of Research:
In general, my research is focused on reducing various uncertainties in streamflow forecasts in order to improve them. We all know that there are a lot of uncertainties in climate models projections of temperature and precipitation variables. Consequently, the uncertainty in both of the mentioned variables significantly arise the uncertainty in projected streamflows, since they are main drivers of streamflow in hydrological models. My previous research was about developing an approach in order to systematically decompose various sources of errors in seasonal streamflow forecast products from multiple Land Surface Models (LSMs) forced with spatially and temporally downscaled climate forecasts. One of my proposed research topics which is an extension of my previous work, is to quantify and reduce errors/uncertainties in streamflow projections by utilizing climate projections for one or two decades ahead, along with making use of various optimization methods (GA, LP, NLP) in order to develop the best managing plans for water resources managers, which is related to Theme 3 of the SECSC Science Plan. My current research is on analyzing and comparing different statistical methods using probabilistic climate forecast ensembles to issue categorical streamflow forecasts over couple of river basins across the US under different hydro-climatic regimes. In addition, I am examining the advantage of using observed streamflow from gauge measurements in better updating Initial Hydrologic Conditions (IHCs) (e.g. Soil moisture) for LSMs using data assimilation techniques.