Sciences and Exploration Directorate

Goutam Konapala

(Associate Scientist, Earth Sciences)

Org Code: 618

Mail Code: 618
Greenbelt, MD 20771



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


Associate Scientist

USRA/NASA-GFSC - Greenbelt

May 2020 - Present

Post doctoral Associate

Oak Ridge National Laboratory - Oak Ridge

October 2017 - May 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

Selected Publications


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]