Oceanic-Atmospheric and Hydrologic Variability in Long Lead-Time Forecasting

Water managers all over the world are challenged with managing a scarce resource.  They rely heavily on forecasts to allocate and meet various water demands. The need for improved streamflow and precipitation forecast models is of utmost importance. My research in water resources including the use of stochastic and physical modeling to understand and improve forecasting.

Part of my research efforts include the identification of large scale climate signals such as ENSO and their use in long lead-time (3 to 9 months) forecasting of streamflow and snowpack. This involves analyzing large spatial-temporal datasets (e.g., gridded sea surface temperatures and 500mb pressures, streamflow gage and SNOTEL data) and applying advanced multivariate statistical techniques to identify teleconnection between climate and continental hydrology and incorporate the resulting relationships to hydrological forecasting. 

 My experience also includes the development and calibration of physical hydrologic models. At Oak Ridge National Laboratory (ORNL) where I was a post-doctoral research associate, I was in charge of building a physical hydrological model of all conterminous U.S. HUC 08 sub-basins at a high resolution of 4km which accounted for about half a million grids. This included the processing of large soil, vegetation and land use datasets and the calibration of the hydrological model at each U.S.G.S HUC08.  Precipitation and Temperature data from DayMet, Soil data from LDAS, vegetation classification data from the University of Maryland Land Cover Classification, and Leaf Area Index data from MODIS data were processed for each of those grids and the simulation results were aggregated for more than 2100 HUC08 and compared with WaterWatch runoff data aggregated as well by HUC08 in a parallelization scheme using ORNL supercomputing resources. This model is used in evaluating the hydrologic response to climate change for the continental U.S. and the resulting impact on water availability.

 At the Environment Institute of the University of Alabama where I am currently a research engineer, I have been working on the development of decision support tools for water manager for the implementation of SCADA system and for decision making in the event of water network contamination. Information acquired from publications, books and other sources were distilled into various knowledge “bits” and a set of rules that were used in conjunction with an Inference Engine developed in Python (PyKe) to develop tools for the android and PC platforms.

I have also been working on the extension of my previous effort at ORNL to include a GIS platform that would allow users and stakeholders to get flow information at virtually any location and also couple the hydrological and the routing models with a meteorological model and a  hydraulic models such has HEC-RAS to evaluate flood areas.  So far, this has been limited to the Mobile-Alabama River System (MARS) and it has shown great results in simulating gage flow in that area.

I have also been involved in the investigation of the impact of land use change and deforestation on climate as well as on hydrology. In addition, I have taken part in a pilot project on weather modification as a climate change mitigation strategy.  One objective is to evaluate how much increase in streamflow is due to the weather modification operations in Wyoming. This is a research that receives positive reviews despite some credibility issues that stem from the fact that there has yet to be a definitive experiment that settles the debate on whether weather modification technologies using cloud seeding work or not.  The state of Wyoming is running a promising experiment, spending about U.S. $9 million on a five-year series of cloud-seeding experiments that is evaluated by the National Center for Atmospheric Research (NCAR). 


Surface water hydrology, hydroclimatology, climate change impacts and adaptation, statistical modeling, stochastic hydrology, water resources availability, water resources planning, stormwater, water distribution systems, water resources engineering, groundwater modeling, hydrologic and hydraulics modeling