Share This

Naresh Devineni

Faculty and Staff Profiles

Naresh Devineni

Associate Professor

School/Division
  • Grove School of Engineering
Department
  • Civil Engineering

Additional Departments/Affiliated Programs::

  • Earth System Science and Environmental Engineering
  • NOAA CREST
Office
T-106
Building: 
Steinman Hall
Phone Number: 
(212) 650-8440
Email: 
ndevineni@ccny.cuny.edu
Personal Website: 
http://www.nareshdevineni.com
Heading: 
Follow Me
Description: 

twitter.com/@realDevineni

medium.com/@Devineni

www.dataanalysisclassroom.com

 
Heading: 
Education
Description: 

PhD, North Carolina State University, Raleigh, NC, 2010

MS, North Carolina State University, Raleigh, NC, 2007           

BE, Osmania University, Hyderabad, India, 2005

Heading: 
Courses Taught
Description: 

Civil Engineering Data Analysis (CE 26400)

This class provides an introduction to applied probability and statistics to develop capacity to analyze and model key data frequently encountered in engineering. Key techniques, their underlying ideas and applicability for solving Civil Engineering problems are taught.

 

Civil Engineering Decision and Systems Analysis (CE 31600)

This class provides fundamentals of engineering economic analysis and project evaluation, the general standard principles of systems analysis and optimization. It also provides the fundamentals of mathematical modeling to formulate typical CE design and decision problems.

 

Water Systems Analysis (G 9100)

This course includes modules on integrated water management and water systems analysis including water supply/demand imbalances, the modeling and design of a regulatory system for water allocation and tools for conservation incentives and insurance system design; and a multi-scale view of operation and planning from weekly to seasonal to decadal planning for multiple, competing objectives.  

 

Advanced Data Analysis (G 1101)

This class provides advanced exploratory data analysis methods to detect trends and distributional properties, spatio-temporal variability, and causality in data. Generalized linear and non-linear predictive model building is taught. The class also provides an introduction to hierarchical Bayesian modeling.

Heading: 
Research
Description: 

Water Analytics

Modeling of Hydrologic Systems and Developing Integrated Risk Hedging Methods using Multi-scale Climate Information

wateranalytics

In the Water Analytics Group, we are focused on developing an integrated science based approach to finding solutions for societal problems. We work on a domain of issues related to Hydrology and Water Resources Management that require a rigorous systems based inquiry, and involve methodical uncertainties quantification. Large scale issues in hydroclimatology and their relations to oceanic, atmospheric, and land surface conditions and issues of global and regional water sustainability form the crux of our research.

Current initiatives in the group are

Water Risk and Drought Initiative: This initiative is designed towards understanding the exposure of a region to climate and demand induced stress and developing supervised scenarios to increase its resilience to periodic shocks.

Water Sustainability and Economy Initiative: This initiative is under-way to explore how climate variability and changing water demands manifest as water deficits and how economics and public-private management decisions determine regional water availability and drought resilience.

Remote Sensing Floods Initiative: This is motivated towards developing space-time stochastic models for flood risk estimation using high resolution radar data.

Continental Paleo-hydrologic Initiative: This initiative will focus on continental scale modeling of low frequency climate variability, particularly at decadal and longer time scales through paleo-reconstruction of continental streamflow.

 

Heading: 
Publications
Description: 

Published and In Press

* Graduate Students

  1. Najibi, N*., and Devineni, N. (2018). Recent trends in the frequency and duration of global floods. Earth System Dynamics, 9, 757-783, https://doi.org/10.5194/esd-9-757-2018.

  2. Najafi, E*., Devineni, N., Khanbilvardi, R. M., & Kogan, F. (2018). Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology. Earth's Future, 6, 410–427. https://doi.org/10.1002/2017EF000690.

  3. Peterson, T*., Devineni, N., & Sankarasubramanian, A. (2018). Monthly Hydroclimatology of the continental United States. Advances in Water Resources, Volume 114, Pages 180-195, ISSN 0309-1708, https://doi.org/10.1016/j.advwatres.2018.02.010.

  4. Vollmer, D, D., K. Shaad, N. J. Souter, T. Farrell, D. Dudgeon, C. A. Sullivan, I. Fauconnier, G. M. MacDonald, M. P. McCartney, A. G. Power, A. McNally, S. J. Andelman, T. Capon, Devineni, N, C. Apirumanekul, C. N. Ng, M. R. Shaw, R. Y. Wang, C. Lai, Z. Wang, & H. M. Regan. (2018). Integrating the social, hydrological and ecological dimensions of freshwater health: The Freshwater Health Index. Science of The Total Environment, Volume 627, 15 June 2018, Pages 304-313, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2018.01.040.

  5. Vatta, K., R. S. Sidhu, R.S., Lall, U., Birthal, P. S., Taneja, G., Kaur, B., Devineni, N., & MacAlister, C. (2018). Assessing the economic impact of a lowcost water-saving irrigation technology in Indian Punjab: the tensiometer. Water International, DOI:10.1080/02508060.2017.1416443.

  6. Armal, S*., Devineni, N., & Khanbilvardi, R. (2018). Trends in extreme rainfall frequency in the contiguous United States: Attribution to climate change and climate variability modes. Journal of Climate, 31, 369–385, https://doi.org/10.1175/JCLI-D-17-0106.1.

  7. Afshari, S*., Fekete, B. M., Dingman, L. S., Devineni, N., Bjerklie, D. M., & Khanbilvardi, R. (2017). Statistical filtering of river survey and streamflow data for improving At-A-Station Hydraulic Geometry Relations. Journal of Hydrology, doi:10.1016/j.jhydrol.2017.01.038.

  8. Hamidi, A*., Devineni, N., Booth, J., Hosten, A., Ferraro, R., & Khanbilvardi, R. (2017). Classifying urban rainfall extremes using weather radar data: An application to the Greater New York Area. Journal of Hydrometeorology, 18, 611–623, doi: 10.1175/JHM-D-16-0193.1.

  9. Ho, M., Lall, U., Allaire, M., Devineni, N., Kwon, H. H., Pal, I., Raff, D., & Wegner, D. (2017). The future role of dams in the United States of America. Water Resources Research, 53, doi:10.1002/2016WR019905.

  10. Najibi, N*., Devineni, N., & Lu, M. (2017). Hydroclimate drivers and atmospheric teleconnections of long duration floods: An application to large reservoirs in the Missouri River Basin. Advances in Water Resources, 100, 153–167. doi:10.1016/j.advwatres.2016.12.004.

  11. Ravindranath, A*., Devineni, N., & Kolesar, P. (2016). An environmental perspective on the water management policies of the Upper Delaware River Basin. Water Policy, doi: 10.2166/wp.2016.166.

  12. Lima, C. H. R., Lall, U., Troy, T., & Devineni, N. (2016). A Hierarchical Bayesian GEV Model for Improving Local and Regional Flood Quantile Estimates. Journal of Hydrology, doi:10.1016/j.jhydrol.2016.07.042.

  13. Ho, M., Parthasarathy, V*., Etienne*, E., Russo, T. A., Devineni, N., & Lall, U. (2016). America’s water: Agricultural water demands and the response of groundwater. Geophysical Research Letters, 43(14), 7546–7555. doi:10.1002/2016GL069797.

  14. Etienne, E*., Devineni, N., Khanbilvardi, R., & Lall, U., (2016). Development of a Demand Sensitive Drought Index and its Application for Agriculture over the Conterminous United States. Journal of Hydrology, 534, 219–229. doi:10.1016/j.jhydrol.2015.12.060.

  15. Fishman, R., Devineni, N., & Raman, S. (2015). Can improved agricultural water use efficiency save India’s groundwater? Environmental Research Letters, 10(8), 084022. doi:10.1088/1748-9326/10/8/084022.

  16. Lall, U., Devineni, N., & Kaheil, Y. (2015). An Empirical, Nonparametric Simulator for Multivariate Random Variables with Differing Marginal Densities and Nonlinear Dependence with Hydroclimatic Applications. Risk Analysis, doi:10.1111/risa.12432.

  17. Devineni, N., Lall, U., Xi, C*., & Ward, P. (2015). Scaling of extreme rainfall areas at a planetary scale. Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(7), 075407. doi:10.1063/1.4921719.

  18. Devineni, N., Lall, U., Etienne, E*., Shi, D*., & Xi, C. (2015). America’s water risk: Current demand and climate variability. Geophysical Research Letters, 1–9. doi:10.1002/2015GL063487. 

  19. Lima, C. H. R., Lall, U., Troy, T. J., & Devineni, N. (2015). A climate informed model for nonstationary flood risk prediction: Application to Negro River at Manaus, Amazonia. Journal of Hydrology, 522, 594–602. doi:10.1016/j.jhydrol.2015.01.009.

  20. Krakauer, N. Y., & Devineni, N. (2015). Up-to-date probabilistic temperature climatologies. Environmental Research Letters, 10(2), 024014. doi:10.1088/1748-9326/10/2/024014.

  21. Chen, X*., Devineni, N., Lall, U., Hao, Z., Dong, L., Ju, Q., Wang, J., Wang, S. (2014). China’s water sustainability in the 21st century: a climate-informed water risk assessment covering multi-sector water demands. Hydrology and Earth System Sciences, 18(5), 1653–1662. doi:10.5194/hess-18-1653-2014.

  22. Chen, X*., Hao, Z., Devineni, N., & Lall, U. (2014). Climate information based streamflow and rainfall forecasts for Huai River basin using hierarchical Bayesian modeling. Hydrology and Earth System Sciences, 18(4), 1539–1548. doi:10.5194/hess-18-1539-2014.

  23. Oludhe, C., Sankarasubramanian, A., Sinha, T., Devineni, N., & Lall, U. (2013). The Role of Multimodel Climate Forecasts in Improving Water and Energy Management over the Tana River Basin, Kenya. Journal of Applied Meteorology and Climatology, 52(11), 2460–2475. doi:10.1175/JAMC-D-12-0300.1.

  24. Devineni, N., Lall, U., Pederson, N., & Cook, E. (2013). A Tree-Ring-Based Reconstruction of Delaware River Basin Streamflow Using Hierarchical Bayesian Regression. Journal of Climate, 26(12), 4357–4374. doi:10.1175/JCLI-D-11-00675.1.

  25. Devineni, N., Perveen, S., & Lall, U. (2013). Assessing chronic and climate-induced water risk through spatially distributed cumulative deficit measures: A new picture of water sustainability in India. Water Resources Research, 49(4), 2135–2145. doi:10.1002/wrcr.20184. 

  26. Pederson, N., Bell, A. R., Cook, E. R., Lall, U., Devineni, N., Seager, R., & Vranes, K. P. (2013). Is an Epic Pluvial Masking the Water Insecurity of the Greater New York City Region?. Journal of Climate, 26(4), 1339–1354. doi:10.1175/JCLI-D-11-00723.1.

  27. Petersen, T*., Devineni, N., & Sankarasubramanian, A. (2012). Seasonality of monthly runoff over the continental United States: Causality and relations to mean annual and mean monthly distributions of moisture and energy. Journal of Hydrology, 468-469, 139–150. doi:10.1016/j.jhydrol.2012.08.028.

  28. Devineni, N., & Sankarasubramanian, A. (2010). Improving U.S. winter forecasts using multimodel combinations and ENSO. Bulletin of American Meteorological Society, Nowcast, Papers of Note, October 2010.

  29. Devineni, N., & Sankarasubramanian, A. (2010). Improved categorical winter precipitation forecasts through multimodel combinations of coupled GCMs. Geophysical Research Letters, 37(24), doi:10.1029/2010GL044989.

  30. Devineni, N., & Sankarasubramanian, A. (2010). Improving the Prediction of Winter Precipitation and Temperature over the Continental United States: Role of the ENSO State in Developing Multimodel Combinations. Monthly Weather Review, 138(6), 2447–2468. doi:10.1175/2009MWR3112.1. 

  31. Golembesky, K., Sankarasubramanian, A., & Devineni, N. (2009). Improved Drought Management of Falls Lake Reservoir : Role of Multimodel Streamflow Forecasts in Setting up. Journal of Water Resources Planning and Management, (June), 188–197.

  32. Sankarasubramanian, A., Lall, U., Devineni, N., & Espinueva, S. (2009). The Role of Monthly Updated Climate Forecasts in Improving Intraseasonal Water Allocation. Journal of Applied Meteorology and Climatology, 48(7), 1464–1482. doi:10.1175/2009JAMC2122.1.

  33. Devineni, N., Sankarasubramanian, A, & Ghosh, S. (2008). Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations. Water Resources Research, 44(9), W09404. doi:10.1029/2006WR005855.

BOOKS AND BOOK CHAPTERS

  1. Russo, T. A., Devineni, N., & Lall, U. (2015). Assessment of agricultural water management in Punjab, India, using Bayesian methods. Sustainability of Integrated Water Resources Management: Water Governance, Climate and Ecohydrology. doi:10.1007/978-3-319-12194-9_9.

<< Back To Directory