Naresh Devineni

Associate Professor

Main Affiliation

Civil Engineering

Additional Departments/Affiliated Programs

Earth System Science and Environmental Engineering

NOAA-CREST

CUNY-CREST Institute

Building

Steinman Hall

Office

106

Phone

212-650-8440

Email

ndevineni@ccny.cuny.edu

Website

http://www.nareshdevineni.com

Naresh Devineni

Follow Me

twitter.com/@realDevineni

medium.com/@Devineni

www.dataanalysisclassroom.com

 
 

Education

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

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

BE, Osmania University, Hyderabad, India, 2005

Courses Taught

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 and Environmental Systems Analysis (CE H6100)

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 (CE H1100)

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.

Research

Risky Waters

Understanding Water Systems

In the Risky Waters Lab, we are focused on modeling of water systems and developing integrated risk hedging methods using multi-scale climate information. Our research team works on a domain of issues related to water resources and hydrology that require rigorous systems based inquiry and involve methodical quantification of uncertainties. Large-scale dynamics of hydroclimatology and their relations to oceanic, atmospheric, and land surface conditions and how they map to global and regional water sustainability form the crux of our research.

The current initiatives in the Risky Waters Lab are:

 

Water Sustainability

Water scarcity is a concern for the sustainability of life and human societies. Today many countries are facing severe and persistent water resources crisis owing to a growing imbalance of supply and demand. The simultaneous effects of agricultural growth, industrialization, and urbanization coupled with declining surface and groundwater quantity, regional water disputes, and inefficiencies in water use practices are some of the crucial problems facing the water sectors globally. The effects of climate variability and change, including increasing frequency of extreme phenomena (e.g., droughts and floods) are also creating pressures on scarce water supplies. As water bodies go dry, there is an increasing threat to food self-sufficiency and sustainability across the world. High rainfall variability and increasing consumptive use across the globe exacerbates the situation further and is a constraint on the reliable supply of water for various needs and future development. 

Under the Water Sustainability Initiative, we focus on developing fundamental knowledge in risk assessment and management for water and environmental sustainability, considering linkages between climate, water supply, competing demands, and institutional constraints. We are developing strategies for water and food security for nations in the face of climate constraints and competing demands so they can serve as a basis for targeting policy interventions.

 

Hydrometeorological Extremes

Multi-timescale climate variations due to natural processes such as ocean-atmosphere interactions and anthropogenic forcing lead to spatiotemporal correlations in hydrometeorological extreme events. From a risk management perspective, one has to focus on the prediction of the timing and co-location of these events, their interrelationships, as well as how their statistics may change with time. There is also a need to understand the various flood types, their spatiotemporal characteristics and causes based on rainfall statistics, antecedent flow conditions, and atmospheric teleconnections. 

Under the Hydrometeorological Extremes Initiative, we focus on strategies for the prediction, and integrated management of climate and weather extremes using physics informed predictors. Our work includes an investigation into the causality of hydrometeorological extremes using Information Theory. We are developing a unified prediction and adaptive risk management framework using Bayesian multi-scale modeling. 

 

Dynamic Risk Management

Dynamic Risk Management is the overarching framework for our research. Dynamic Risk is the risk that changes with time due to natural or anthropogenic factors, and evolving social priorities. Dynamic Risk Management (DRM) framework is a formal approach to adaptation to such changes. DRM framework will assess the risk exposure of water systems in a time-varying manner conditional on factors that lead to either cyclical or monotonic change. It also factors in the changes in exposure to risk given societal adaptation and mitigation actions. 

We are creating integrated strategies for estimation, prediction, and decision support at different lead times to support adaptive risk management. A probabilistic framework using Bayesian methods is used to address modeling and informational uncertainties, and to facilitate quantitative decision-making. Currently, we are focusing on understanding decadal to multi-decadal hydroclimatic variability using observed and paleo streamflow records and future climate projections. We combine them with adaptation actions to inform infrastructure investments and periodically update sectoral and interstate allocation rules. DRM framework provides a capacity to assess the implications of climate, policy, water demand, water conservation, and infrastructure development scenarios on socio-economic and water outcomes.

 

Publications

Summary: 43 peer-reviewed journal publications; 1 book chapter; 2 databases; 9 op-eds/proceedings

Peer-Reviewed Journal Publications

*Graduate Students

  1. Ravindranath, A*., & Devineni, N. (2020). Quantifying streamflow regime behavior and its sensitivity to demand. Journal of Hydrology, 582, 124423. https://doi.org/10.1016/j.jhydrol.2019.124423

  2. Zhu, W., Jia, S., Devineni, N., Lv, A., & Lall, U. (2019). Evaluating China’s water security for food production: The role of rainfall and irrigation. Geophysical Research Letters, 46 (20), 11155 - 11166. https://doi.org/10.1029/2019GL083226

  3. Ravindranath, A*., Devineni, N., Lall, U., Cook, E. R., Pederson, G., Martin, J., & Woodhouse, C. (2019). Streamflow reconstruction in the upper Missouri River basin using a novel Bayesian network model. Water Resources Research, 55 (9), 7694 - 7716. https://doi.org/10.1029/2019WR024901

  4. Najibi, N*., Devineni, N., Lu, M., & Perdigao, R. A. (2019). Coupled flow accumulation and atmo- spheric blocking govern flood duration. Nature partner journal (npj) Climate and Atmosphere, 2, 19. https://doi.org/10.1038/s41612-019-0076-6

  5. Zhu, X*., Troy, T., & Devineni, N. (2019). Stochastically modeling the projected impacts of climate change on rainfed and irrigated US crop yields. Environmental Research Letters, 14 (7), 074021. https://doi.org/10.1088/1748-9326/ab25a1

  6. González, J. E., Ortiz, L., Smith, B. K., Devineni, N., Colle, B., Booth, J. F., Ravindranath, A*., Rivera, L*., Horton, R., Towey, K., Kushnir, Y., Manley, D., Bader, D., & Rosenzweig, C. (2019), New York City panel on climate change 2019 report Chapter 2: New methods for assessing extreme temperatures, heavy downpours, and drought. Annals of the New York Academy of Sciences, 1446(1), 172 - 172. https://doi.org/10.1111/nyas.14007

  7. Najafabadi, S*., Hamidi, A*., Allahviranloo, M., & Devineni, N. (2019). Does demand for subway ridership in Manhattan depend on the rainfall events? Transportation Policy, 74, 201 - 213. https://doi.org/10.1016/j.tranpol.2018.11.019

  8. Kim, S., Devineni, N., Lall, U., & Kim, H. S. (2018). Sustainable development of water resources: Spatio-temporal analysis of water stress in South Korea. Sustainability, 10, 3795, 1 - 17. https://doi.org/10.3390/su10103795

  9. Ravindranath, A*., Devineni, N., Lall, U., & Larrauri, P. C. (2018). Season-ahead forecasting of water storage and irrigation requirements–an application to the southwest monsoon in India. Hydrology and Earth System Sciences, 22, 5125 - 5141. https://doi.org/10.5194/hess-22-5125-2018

  10. Rao, M. P*., Cook, E. R., Cook, B. I., Palmer, J. G., Uriarte, M., Devineni, N., Lall, U., D’Arrigo, R. D., Woodhouse, C, A., Ahmed, M., Zafar, M. U., Khan, N., Khan, A., & Wahab, M. (2018). Six centuries of Upper Indus Basin streamflow variability and its climatic drivers. Water Resources Research, 54, 5687 - 5701. https://doi.org/10.1029/2018WR023080

  11. Najibi, N*., & 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

  12. 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

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

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

  15. Vatta, K., 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, 43(2), 305 - 321. https://doi.org/10.1080/02508060.2017.1416443

  16. 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

  17. 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, 547, 443 - 454. https://doi.org/10.1016/j.jhydrol.2017.01.038

  18. 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. https://doi.org/10.1175/JHM-D-16-0193.1

  19. 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, 982 - 998. https://doi.org/10.1002/2016WR019905

  20. 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. https://doi.org/10.1016/j.advwatres.2016.12.004

  21. Ravindranath, A*., Devineni, N., & Kolesar, P. (2016). An environmental perspective on the water management policies of the Upper Delaware River Basin. Water Policy, 18(6), 1399 - 1419. https://doi.org/10.2166/wp.2016.166

  22. 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, 541, 816 - 823. https://doi.org/10.1016/j.jhydrol.2016.07.042

  23. 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. https://doi.org/10.1002/2016GL069797

  24. 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. https://doi.org/10.1016/j.jhydrol.2015.12.060

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

  26. 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, 36, 57 - 73. https://doi.org/10.1111/risa.12432

  27. 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. https://doi.org/10.1063/1.4921719

  28. Devineni, N., Lall, U., Etienne, E*., Shi, D*., & Xi, C*. (2015). America’s water risk: Current demand and climate variability. Geophysical Research Letters, 42, 2285 - 2293. https://doi.org/10.1002/2015GL063487

  29. 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. https://doi.org/10.1016/j.jhydrol.2015.01.009

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

  31. 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. http://doi.org/10.5194/hess-18-1653-2014

  32. 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. http://doi.org/10.5194/hess-18-1539-2014

  33. Oludhe, C., Sankarasubramanian, A., Sinha, T., Devineni, N., & Lall, U. (2013). The role of mul- timodel climate forecasts in improving water and energy management over the Tana River Basin, Kenya. Journal of Applied Meteorology and Climatology, 52(11), 2460 - 2475. http://doi.org/10.1175/JAMC-D-12-0300.1

  34. 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. http://doi.org/10.1175/JCLI-D-11-00675.1

  35. 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. http://doi.org/10.1002/wrcr.20184

  36. 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. http://doi.org/10.1175/JCLI-D-11-00723.1

  37. 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. https://doi.org/10.1016/j.jhydrol.2012.08.028

  38. Devineni, N., & Sankarasubramanian, A. (2010). Improving U.S. winter forecasts using multimodel combinations and ENSO. Nowcast, Bulletin of American Meteorological Society, 91, 1343 - 1356. https://doi.org/10.1175/1520-0477-91.10.1343

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

  40. 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 multi- model combinations. Monthly Weather Review, 138(6), 2447 - 2468. https://doi.org/10.1175/2009MWR3112.1

  41. Golembesky, K., Sankarasubramanian, A., & Devineni, N. (2009). Improved drought management of Falls Lake reservoir: Role of multimodel streamflow forecasts in setting up restrictions. Journal of Water Resources Planning and Management, 135(3), 188 - 197. https://doi.org/10.1061/(ASCE)0733-9496(2009)135:3(188)

  42. 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 Cli- matology, 48(7), 1464 - 1482. https://doi.org/10.1175/2009JAMC2122.1

  43. 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. https://doi.org/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: Springer. https://doi.org/10.1007/978-3-319-12194-9_9

Published Datasets and Software

  1. Contribution to water stress index as part of the sustainable development metrics of the US sus- tainable development goals project that reports on how states in the US do on the United Nations’ Sustainable Development Goals, 2018. https://sdgdata.sdgusa.org/#/SDG/6#indicator-3

  2. Contribution to water stress indices (Normalized Deficit Index (NDI) and Normalized Deficit Cu- mulated (NDC)) for India Water Tool Version 2 (IWT 2.0) developed by the World Business Council for Sustainable Development (WBCSD) for companies and users to understand their water risks and prioritize actions toward sustainable water management, 2015. http://www.indiawatertool.in

Op-eds, Proceedings and White Papers

  1. Nary a drop to drink? Article with Rosemarie Wesson in Discovery magazine, American Society for Engineering Education, March 2017. http://www.asee-prism.org/discovery-mar-apr-2/

  2. India’s water: A reflection of a nation’s soul? Op-ed with Upmanu Lall in Center for International Projects Trust (CIPT) Sandesh, Issue 3, September 2014. http://water.columbia.edu/files/2014/12/CIPT-Sandesh-3.pdf

  3. Delaware reservoir’s drought risk assessment, a paleo view, Proceedings in 11th International Hydro- informatics Conference, August 2014. https://academicworks.cuny.edu/cc_conf_hic/340/

  4. Towards hedging climate risk in corporate value chains. White Paper with Columbia Water Center and PepsiCo, April 2013. http://water.columbia.edu/files/2013/04/Columbia-PepsiCo-Forecast-White…

  5. Americas water risk: water stress and climate variability, White Paper with Columbia Water Center and Growing Blue, February 2013. http://water.columbia.edu/files/2013/09/GB_CWC_whitepaper_climate-water…

  6. Securing the future of India’s "water, energy and food." Global Water Forum Discussion Series 1240, Global Water Forum, UNESCO, October 2012. http://www.globalwaterforum.org/2012/10/08/securing-the-future-of-india…

  7. Climate variability and water stress in India. How much storage in needed and where? White Paper with Columbia Water Center, December 2011. http://cipt.in/publications/pdf/Climate_Variability_and_Water_Stress_in…

  8. Shifting crops, saving water. White Paper with Columbia Water Center, December 2011. http://water.columbia.edu/files/2012/03/Shifting-Crops-Saving-Water.pdf

  9. Climatology of monthly runoff: Causality and relations to seasonality in precipitation and temperature. Proceedings in ASCE’s World Environmental and Water Resources Congress, May 2010. https://doi.org/10.1061/41114(371)457