Ph.D., Civil Engineering, 2018, Clemson University
M.Tech., Civil Engineering , 2014, Indian Institute of Technology, Bombay, India
B.Tech., Civil Engineering, 2012, Indian Institute of Technology, Bhubaneswar, India
Goutam Konapala
(Associate Scientist, Earth Sciences)
Org Code: 618
NASA/GSFCMail Code: 618
Greenbelt, MD 20771
Employer: UNIVERSITY OF MARYLAND BALTIMORE CO
Education
Positions/Employment
Associate Scientist
USRA/NASA-GFSC - Greenbelt
June 2020 - Present
Post doctoral Associate
Oak Ridge National Laboratory - Oak Ridge
November 2017 - June 2020
Current Projects
Flood Inundation through Deep learning approaches
Hydrology / Water Cycle
Rapid and accurate detection of flood inundation from satellite imagery can aid emergency planning and damage assessment. We develop deep learning frameworks to automate flood inundation mapping from remote sensing imagery
Improving streamflow simulations through deep learning approaches
Hydrology / Water Cycle
Incomplete representations of physical processes often lead to structural errors in process-based hydrologic models. Machine learning (ML) algorithms can reduce streamflow modeling errors but do not enforce physical consistency. Therefore, we build hybrid models by integrating process-based hydrologic model outputs with ML algorithm to streamflow simulations across the world
Publications
Refereed
Konapala, G., S. Mondal, and A. Mishra. 2022. Quantifying Spatial Drought Propagation Potential in North America Using Complex Network Theory Water Resources Research 58 (3): [10.1029/2021wr030914]
Konapala, G., S. V. Kumar, and S. Khalique Ahmad. 2021. Exploring Sentinel-1 and Sentinel-2 diversity for flood inundation mapping using deep learning ISPRS Journal of Photogrammetry and Remote Sensing 180 163-173 [10.1016/j.isprsjprs.2021.08.016]
Konapala, G., S. Kao, and N. Addor. 2020. Exploring Hydrologic Model Process Connectivity at the Continental Scale Through an Information Theory Approach Water Resources Research 56 (10): [10.1029/2020wr027340]
Konapala, G., and A. Mishra. 2020. Dynamics of virtual water networks: Role of national socio-economic indicators across the world Journal of Hydrology 589 125171 [10.1016/j.jhydrol.2020.125171]
Konapala, G., S.-C. Kao, S. L. Painter, and D. Lu. 2020. Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US Environmental Research Letters 15 (10): 104022 [10.1088/1748-9326/aba927]
Konapala, G., A. K. Mishra, Y. Wada, and M. E. Mann. 2020. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation Nature Communications 11 (1): 3044 [10.1038/s41467-020-16757-w]
Konapala, G., and A. Mishra. 2020. Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States Water Resources Research 56 (1): [10.1029/2018wr024620]
Konapala, G., and A. Mishra. 2017. Review of complex networks application in hydroclimatic extremes with an implementation to characterize spatio-temporal drought propagation in continental USA Journal of Hydrology 555 600-620 [10.1016/j.jhydrol.2017.10.033]
Konapala, G., A. Valiya Veettil, and A. K. Mishra. 2017. Teleconnection between low flows and large-scale climate indices in Texas River basins Stochastic Environmental Research and Risk Assessment 32 (8): 2337-2350 [10.1007/s00477-017-1460-6]
Konapala, G., A. Mishra, and L. R. Leung. 2017. Changes in temporal variability of precipitation over land due to anthropogenic forcings Environmental Research Letters 12 (2): 024009 [10.1088/1748-9326/aa568a]
Konapala, G., and A. K. Mishra. 2016. Three-parameter-based streamflow elasticity model: application to MOPEXbasins in the USA at annual and seasonal scales Hydrology and Earth System Sciences 20 (6): 2545-2556 [10.5194/hess-20-2545-2016]