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Naresh Devineni

Faculty and Staff Profiles

Naresh Devineni

Assistant Professor

School/Division
Department

Additional Departments/Affiliated Programs::

Office
T-106
Building: 
Steinman Hall
Phone Number: 
(212) 650-8440
Email: 
ndevineni@ccny.cuny.edu
Personal Website: 
http://www.nareshdevineni.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: 
Recent Publications
Description: 

2017

Classifying urban rainfall extremes using weather radar data: An application to the Greater New York Area 

We present the first of its kind classification analysis for urban rainfall extremes using machine learning techniques on high-resolution radar rainfall data. Clear spatial variability is found for the Greater New York City Area. We also investigated the relationships of the classified events with synoptic circulation patterns and validated them with the storm event database. This article is published in Journal of Hydrometeorology.

CitationHamidi, A., N. Devineni, J. Booth, A. Hosten, R. Ferraro, and R. Khanbilvardi. (2017). Classifying Urban Rainfall Extremes Using Weather Radar Data: An Application to the Greater New York Area. Journal of  Hydrometeorology, 18611–623, doi: 10.1175/JHM-D-16-0193.1.

The future role of dams in the United States of America 

A commentary on the future role of dams in the United States in presented. We propose a comprehensive reassessment of dam impacts and the design, operation and need for new dams considering paleo and future climate information along with the changing societal values. This article is published in Water Resources Research.

CitationHo, M.U. LallM. AllaireN. DevineniH. H. KwonI. PalD. Raff, & D. Wegner. (2017). The future role of dams in the United States of America. Water Resources Research53, doi:10.1002/2016WR019905.

Hydroclimate drivers and atmospheric teleconnections of long duration floods: An application to large reservoirs in the Missouri River Basin 

A comprehensive framework is presented to assess the flood types, their spatiotemporal characteristics and causes based on the rainfall statistics, antecedent flow conditions, and atmospheric teleconnections. The Missouri River Basin (MRB) is used as a case study for the application of the framework. We identify the synoptic scale atmospheric processes that cause long duration floods. Long duration floods are triggered by high antecedent flow conditions which are in turn caused by high moisture release from the repeated storm tracks. Atmospheric teleconnections are distinctively persistent and well developed for long duration flood events.  For short duration floods, these are insignificant and appear to occur random across the MRB in the recent half-century. The implication of analyzing the duration and volume of the floods in the context of flood frequency analysis for dams is presented. This article is published in Advances in Water Resources Journal.

Citation: 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:http://dx.doi.org/10.1016/j.advwatres.2016.12.004.

2016

An environmental perspective on the water management policies of the upper Delaware River basin 

A comprehensive history of New York City water supply and the Delaware River Basin Compacts is provided in this article. New York, New Jersey, Pennsylvania and Delaware have claims on the waters. This has lead to competing interests, conflicts, and disputes over the years. This article explores important changes in the allocation rules, key implementation issues surrounding drinking water supply and environmental impacts on the downstream ecosystem, wildlife, and fisheries, and provides context for social value changes. Understanding the dynamics of human actions and its intersection with natural systems is the key for future water sustainability.  This article is published in Water Policy Journal.

Citation: Ravindranath, A., Devineni, N., & Kolesar, P. (2016). An environmental perspective on the water management policies of the Upper Delaware River Basin. Water Policy, .

A hierarchical Bayesian GEV model for improving local and regional flood quantile estimates 

The local and regional Generalized Extreme Value (GEV) distribution parameters for flood frequency analysis are estimated in a multilevel, hierarchical Bayesian framework, to explicitly model and reduce uncertainties. The GEV location and scale parameters for each site come from independent log-normal distributions, whose mean parameter scales with the drainage area. From empirical and theoretical arguments, the shape parameter for each site is shrunk towards a common mean. The proposed Bayesian method is able to produce adequate credible intervals for flood quantiles that are in accordance with empirical estimates. This article is published in Journal of Hydrology.

Citation: 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.

America’s water: Agricultural water demands and the response of groundwater 

The Demand-Sensitive Drought Index (developed by Elius Etiennne) is used to examine the impacts of agricultural water needs, driven by low precipitation, high agricultural water demand, or a combination of both, on the temporal variability of depth to groundwater across the CONUS. The relationship between changes in groundwater levels, agricultural water deficits relative to precipitation during the growing season, and winter precipitation are characterized. Declines in groundwater levels in the High Plains aquifer and around the Mississippi River Valley are driven by groundwater withdrawals used to supplement agricultural water demands. Reductions in agricultural water demands for crops do not, however, lead to immediate recovery of groundwater levels due to the demand for groundwater in other sectors in regions such as Utah, Maryland, and Texas. This article is published in Geophysical Research Letters.

Citation: 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.

Ensuring water and environmental sustainability through modelling

An interview on how I got into this field and a summary of what we do in the Water Analytics Research group at City College of New York for managing water resources for the future. You can download the article here.

Citation: Devineni, N. (2016). Ensuring water and environmental sustainability through modelling. International Innovation, feature article.

Development of a Demand Sensitive Drought Index and its application for agriculture over the conterminous United States

A new drought index is introduced that explicitly considers both water supply and demand. It can be applied to aggregate demand over a geographical region, or for disaggregated demand related to a particular crop or use. It is more directly related than existing indices, to potential drought impacts on different segments of society, and is also suitable to use as an index for drought insurance programs targeted at farmers growing specific crops. An application of the index is presented for the drought characterization at the county level for the aggregate demand of eight major field crops in the conterminous United States. This article is published in Journal of Hydrology. A five-minute audio description by Eilus Etienne can be found on the journal’s website.

Citation: 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: http://dx.doi.org/10.1016/j.jhydrol.2015.12.060

2015

Assessment of agricultural water management in Punjab, India using Bayesian methods

The success of the Green Revolution in Punjab, India, is threatened by a significant decline in water resources. The detailed data required to estimate future impacts on water supplies or develop sustainable water management practices is not readily available for this region. Bayesian methods are used to estimate hydrologic properties and irrigation requirements for an under-constrained mass balance model. This computational method can be applied in data-scarce regions across the world, where integrated water resource management is required to resolve competition between food security and available resources. This invited book chapter is published in Sustainability of Integrated Water Resources Management: Water Governance, Climate and Ecohydrology by Springer International.

Citation: 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

Can improved agricultural water use efficiency save India’s groundwater?

The potential impact of technology adoption on aquifers in India is investigated. We find substantial technical potential for reversing water table declines. However, we show that these impacts are highly sensitive to assumptions about farmers’ water use decisions. The analysis provides quantitative input to the debate of incentives versus technology based water policies in India. This article is published in Environmental Research Letters.

Citation: Fishman, R., Devineni, N., & Raman, S. (2015). Can improved agricultural water use efficiency save India’s groundwater? Environmental Research Letters10(8), 084022. doi:10.1088/1748-9326/10/8/084022

An empirical, nonparametric simulator for multivariate random variables with differing marginal densities and nonlinear dependence with hydroclimatic applications

 A new nonparametric simulation approach is developed that reproduces the dependence structure in the data set. It can be applied to multiple variables or to spatial fields with arbitrary dependence structure and marginal densities. The risk of potentially correlated factors can be evaluated. An example that simulates the livestock mortality rate for Mongolia to assess the spatial risk is presented. This article is published in Risk Analysis.

Citation: 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, n/a–n/a. doi:10.1111/risa.12432

Scaling of extreme rainfall areas at a planetary scale

 A global analysis of the scaling characteristics of extreme rainfall areas for durations ranging from 1 to 30 days is presented. We find that the power law scaling may also apply to planetary scale phenomenon, such as frontal and monsoonal systems, and their interaction with local moisture recycling. This article is published in Chaos.

Citation: Devineni, N., Lall, U., Xi, C., & Ward, P. (2015). Scaling of extreme rainfall areas at a planetary scale. Chaos: An Interdisciplinary Journal of Nonlinear Science25(7), 075407. doi:10.1063/1.4921719

America’s water risk, current demand and climate variability

A new indicator of drought induced water stress is introduced and applied at the county level in the USA. Potential water stress for each county is estimated using current daily water demand and daily renewable water supply. The indicator directly informs the county’s dependence on exogenous water transfers to meet demands and to buffer multi-year and within year climate variability. This article is published in Geophysical Research Letters. The article also received coverage on Bloomberg News.

 Citation: 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.

A climate informed model for nonstationary flood risk prediction: Application to Negro river at Manaus, Amazonia

A flood risk model that is based on the knowledge of the operating climate regime (e.g. El Niño Southern Oscillation) is presented to predict the probability of flood each year. For the Negro River at Manaus, Amazonia, the annual peak flood (occurring in summer) can be predicted using the river stage at the beginning of the year and the previous December’s sea surface temperature in the tropical Pacific. The model provides an early flood alert system for the city of Manaus by quantifying the changing flood hazard several months in advance. This article is published in Journal of Hydrology.

Citation: 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 Hydrology522, 594–602. doi:10.1016/j.jhydrol.2015.01.009

Up-to-date probabilistic temperature climatologies

Climatologies based on average past temperatures are increasingly recognized as imperfect guides for current conditions. We present several alternatives to derive updated climatologies as probability distributions for monthly temperatures. The exponentially weighted moving average with a time scale of 15 years has good overall performance in hindcasting temperature over the last 30 years. This article is published in Environmental Research Letters.

Citation: Krakauer, N. Y., & Devineni, N. (2015). Up-to-date probabilistic temperature climatologies. Environmental Research Letters10(2), 024014. doi:10.1088/1748-9326/10/2/024014

2014

China’s water sustainability in the 21st century: A climate informed water risk assessment covering multi-sector water demands

China is facing a water resources crisis with growing concerns of reliable supply of water for agricultural, industrial and domestic needs. In this article, we modeled the differences in water demand and supply to quantify the dimensions of the water risk. The work provides a detailed quantitative assessment of water risk as measured by the cumulated deficits for China. The risk measures highlight North China Plain counties as highly water stressed. These regions now have depleted groundwater aquifers. This article is published in Hydrology and Earth System Sciences.

Citation: Chen, X., Naresh, D., Upmanu, L., 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 Sciences18(5), 1653–1662. doi:10.5194/hess-18-1653-2014

Climate information based streamflow and rainfall forecasts for Huai River basin using hierarchical Bayesian modeling

In this work, we developed a statistical model that will forecast the amount of total summer rainfall for the Huai River Basin, China. The probable rainfall for the months of June, July and August every year is predicted at the beginning of May. This one month lead time will enable water managers to make decisions on whether to release more water during the season (if there is a forecast of good rainfall) or to store more water in the dams (if there is a forecast of drought). Farmers can use this forecast information and the lead time to make choices on what type of crop to grow and secure the sources of irrigation. We used a Hierarchical Bayesian Model to explicitly quantify the parameter uncertainty through each estimation stage using appropriate conditional and prior distributions. This article is published in Hydrology and Earth System Sciences.

Citation: 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 Sciences18(4), 1539–1548. doi:10.5194/hess-18-1539-2014

India’s water: A reflection of a nation’s soul?

A short description of the current water issues in India is presented. This article is published as an opinion piece in Center for International Project’s Trust newsletter (CIPT Sandesh).

Citation: Lall, Upmanu ; Devineni, N. (2014). India’s water: A reflection of a nation’s soul? CIPT Sandesh, 1–12.

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