Updates on the statistics and citations
All articles in chronological order with brief synopsis and citation
Statistical filtering of river survey and streamflow data for improving at-a-station hydraulic geometry relations
A recursive data filtering procedure to find both random and systematic errors in streamflow and river survey data that can be used to produce robust and informative station hydraulic geometry relations is presented. This article is published in Journal of Hydrology.
Citation: Afshari, S., B. M. Fekete, L. S. Dingman, N. Devineni, D. M. Bjerklie, and R. Khanbilvardi. (2017). Statistical filtering of river survey and streamflow data for improving At-A-Station Hydraulic Geometry Relations. Journal of Hydrology, doi:http://dx.doi.org/10.1016/j.jhydrol.2017.01.038.
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.
Citation: 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.(
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.
Citation: 2017). The future role of dams in the United States of America. Water Resources Research, 53, 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.
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
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 Letters, 10(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 Science, 25(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 Hydrology, 522, 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 Letters, 10(2), 024014. doi:10.1088/1748-9326/10/2/024014
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 Sciences, 18(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 Sciences, 18(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.
The role of multimodel climate forecasts in improving water and energy management over the Tana River basin, Kenya
The Masinga Reservoir located in the upper Tana River Basin, Kenya, is extremely important in supplying country’s hydropower and protecting downstream ecology. Seasonal streamflow forecasts contingent on climate information are essential to estimate pre-season water allocation. We utilize reservoir inflow forecasts downscaled from monthly updated precipitation forecasts from ECHAM4.5 forced with constructed analogue SSTs and multimodel precipitation forecasts developed from ENSEMBLES project to improve water allocation during April-June and October-December seasons. The streamflow forecasts are ingested into a reservoir model to allocate water for power generation by ensuring climatological probability of meeting the end of the season target storage required to meet seasonal water demands. The multimodel forecasts preserve the end of the season target storage better than the single model inflow forecasts by reducing uncertainty and the overconfidence of individual model forecasts. This article is published in Journal of Applied Meteorology and Climatology.
Citation: 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
A tree ring based reconstruction of Delaware River basin streamflows using hierarchical Bayesian regression
Using hierarchical Bayesian statistical techniques for understanding and modeling the hydrologic systems is one of the emerging areas of research. Given their ability to explicitly quantify the process model and parameter uncertainty through each estimation stage, Bayesian methods can be employed to better represent model and estimation uncertainties and indeed to find ways to reduce them by appropriate shrinking across spatial instances. In this article, we developed various hierarchical Bayesian statistical techniques for reconstructing Delaware River flows using paleo climatic information such as tree rings. This analysis will serve as the necessary building block for simulating water system operation and to provide a more objective evaluation of operating rules for reservoir systems consider changing conditions. This article is published in Journal of Climate.
Citation: 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
Assessing chronic and climate-induced water risk through spatially distributed cumulative deficit measures: A new picture of water sustainability in India
Measures of water scarcity need to reflect temporal imbalances even for a fixed location. We introduce spatially distributed indices of water stress that integrate over time variations in water supply and demand. The indices reflect the maximum cumulative deficit in a regional water balance within year and across years. This can be interpreted as the amount that needs to be drawn from external storage (either aquifers or surface reservoirs or interarea transfers) to meet the current demand pattern given a variable climate and renewable water supply. The index is useful for indicating whether small or large surface storage will suffice, or whether the extent of groundwater storage or external transfers, or changes in demand are needed to achieve a sustainable solution. This article is published in Water Resources Research. The article also received coverage on National Geographic News.
Citation: 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
Is an epic pluvial masking the water insecurity of the greater New York City region?
This study reconstructs 472 years of moisture availability for the NYC watershed to place the droughts in long-term hydroclimatic context. While the mid-1960s drought is a severe drought in the context of the new reconstruction, the region experienced repeated droughts of similar intensity, but greater duration during the sixteenth and seventeenth centuries. In the context of the current pluvial, decreasing water usage, but increasing extra-urban pressures, it appears that the water supply system for the greater NYC region could be severely stressed if the current water boom shifts toward hydroclimatic regimes like the sixteenth and seventeenth centuries. This article is published in Journal of Climate.
Citation: 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
Towards hedging climate risk in corporate value chains
Description of a prototype corporate water risk and sustainability framework for quantifying and analysis climate induced water risks is presented. The climate risk tool is based on (a) developing specific indicators for assessment of climate induced water risk as aggregate seasonal water deficits; (b) investigating the sources of predictability for these indicators; and (c) developing statistically verifiable models for issuing season ahead probabilistic forecasts for potential water deficits that imply regional production shortfalls. This article is published as a joint white paper from Columbia Water Center and PepsiCo.
Citation: Devineni, N., Perveen, S., & Lall, U. (2013). Towards hedging climate risk in corporate value chains. Columbia Water Center White Paper, (April).
Americas water risk: water stress and climate variability
A clear understanding of shortages induced by droughts, in terms of the magnitude, duration and recurrence frequency will better inform the water businesses and water related sectors. To properly diagnose water risk, one needs to examine both existing demand and variations in renewable water supply at an appropriate spatial resolution and unit. Here, we provide ways to estimate this risk and map it for the USA at a county level. This article is published as a joint white paper from Columbia Water Center and Growing Blue.
Citation: Shi, Daniel; Devineni, Naresh; Lall, U. P. E. (2013). America’s water risk: Water stress and climate variability. Columbia Water Center White Paper, (February).
Seasonality of monthly runoff over the continental United States: causality and relations to mean annual and mean monthly distributions of moisture and energy
An overview of the seasonality of streamflow over the continental US and a comprehension of the processes that control the streamflow seasonality is presented. While the distribution of mean monthly precipitation is uniform throughout the year over most of the eastern United States (except peninsular Florida), mean monthly streamflow exhibits pronounced seasonality with peak runoff occurring during the winter (early spring) over the Southeast (Mid-Atlantic and Northeast) regions. In contrast, over the western US, both precipitation and streamflow exhibit strong seasonality with respective monthly peaks occurring in early and late winter months. For catchments over the Midwest and peninsular Florida, mean monthly runoff peaks occur in the spring and early summer season. This article is published in Journal of Hydrology.
Citation: 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
Securing the future of India’s “water, energy and food”
The work provides an initial, formal analysis for the re-design of the Indian food procurement system that considers climate driven variations in renewable water supply, the sustainability of groundwater pumping, varying regional productivity of crops and farm level economics. Assuming that the food security goals are to be met while keeping current procurement prices fixed for each crop, the scheme attempts to maximize net aggregate farm income from the procurement system. The results suggest that net farm revenue could be doubled while minimizing or eliminating the need for irrigation to meet the food requirements. This article is published as a discussion paper in UNESCO’s Global Water Forum. This article is also selected as one of the 10 finalists for the Emerging Scholars Award.
Citation: Devineni, N., & Perveen, S. (2012). Securing the future of India ’ s “ Water , energy and food .” Global Water Forum, Discussion Series.
Climate variability and water stress in India. How much storage in needed and where?
The analysis presented in this paper is posited on the presumption that the appropriate choice for water development and management in India needs to be made on the basis of clear scientific criteria of the spatial locations (and the magnitude) of water deficits in terms of variability in supply and demands. Along with this, the impact of cumulative deficits over time (e.g., multiyear drought impacts) on available surface and groundwater resources is also discussed. This article is published as a white paper from Columbia Water Center.
Citation: Devineni, N., Perveen, S., & Lall, U. (2011). Climate Variability and Water Stress in India : How much storage is needed and where. Columbia Water Center White Paper, (December).
Shifting crops, saving water
A platform for exploring systematic solutions to water – food – energy security challenge for India at the national scale (while minimizing local water stress) in the face of climate variability is developed. We present here a brief overview of the crop diversification analysis tools and several strategic spatial optimization scenarios. The model identifies feasible regions and the crop choices that can support the area expansion and productivity enhancement in a sustainable manner. This article is published as a white paper from Columbia Water Center.
Citation: Devineni, Naresh; Perveen, Shama; Lall, U. (2011). Shfitng Crops , Saving Water. Columbia Water Center White Paper, (December).
Improving U.S winter forecasts using multimodel combinations and ENSO
This is a paper of note published in Bulletin of American Meteorological Society’s Nowcast magazine.
Citation: Devineni, N., & Sankarasubramanian, b. (2010). Improving U.S. winter forecasts using multimodel combinations and ENSO. Bulletin of American Meteorological Society, Nowcast, Papers of Note, October 2010.
Improved categorical winter precipitation forecasts through multimodel combinations of coupled GCMs
A new approach to combine precipitation forecasts from multiple models is evaluated by analyzing the skill of the candidate models contingent on the forecasted predictor(s) state. Using five leading coupled GCMs (CGCMs) from the ENSEMBLES project, we develop multimodel precipitation forecasts over the continental United States (U.S) by considering the forecasted Nino3.4 from each CGCM as the conditioning variable. The main advantage in using this algorithm for multimodel combination is that it assigns higher weights for climatology and lower weights for CGCM if the skill of a CGCM is poor under ENSO conditions. Combining multiple models based on their skill in predicting under a given predictor state(s) provides an attractive strategy to develop improved climate forecasts. This article is published in Geophysical Research Letters.
Citation: Devineni, N., & Sankarasubramanian, b. (2010). Improved categorical winter precipitation forecasts through multimodel combinations of coupled GCMs. Geophysical Research Letters, 37(24), n/a–n/a. doi:10.1029/2010GL044989
Improved prediction of winter precipitation and temperature over the continental United States: Role of ENSO state in developing multimodel combinations
A new approach to combining multiple GCMs is proposed by analyzing the skill levels of candidate models contingent on the relevant predictor(s) state. To demonstrate this approach, historical simulations of winter (December–February, DJF) precipitation and temperature from seven GCMs were combined by evaluating their skill—represented by mean square error (MSE)—over similar predictor (DJF Nin ̃ o-3.4) conditions. A total of six multimodel schemes are considered that include combinations based on pooling of ensembles as well as on the long-term skill of the models. This article is published in Monthly Weather Review. The article also received coverage in international (Canadian newspapers and radio shows), national (NSF – Science 360 and other science blogs), academic (Bulletin of American Meteorological Society) and local (NPR – WUNC and WRAL) media.
Citation: 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
Seasonal hydroclimatology of the Continental United States: Forecasting and its relevance to water management
Citation: Devineni, N. (2010). Seasonal hydroclimatology of the Continental United States: Forecasting and its relevance to water management. NCSU Libraries.
Improved drought management of Falls Lake reservoir: Role of multimodel streamflow forecasts in setting up restrictions
We utilized 3-month ahead probabilistic multimodel streamflow forecasts developed using climatic information to invoke restrictions for Falls Lake Reservoir in the Neuse River Basin, N.C. Multimodel streamflow forecasts developed from two single models, a parametric regression approach and semiparametric resampling approach, are forced with a reservoir management model that takes ensembles to estimate the reliability of meeting the water quality and water supply releases and the end of the season target storage. The study suggests that, by constraining the end of the season target storage conditions being met with high probability, the climate information based streamflow forecasts could be utilized for invoking restrictions during below- normal inflow years. Further, multimodel forecasts perform better in detecting the below-normal inflow conditions in comparison to single model forecasts by reducing false alarms and missed targets, which could improve public confidence in utilizing climate forecasts for developing proactive water management strategies. This article is published in Journal of Water Resources Planning and Management.
Citation: 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.
The role of monthly updated climate forecasts in improving intraseasonal water allocation
Reservoir inflow forecasts downscaled from monthly updated precipitation forecasts from ECHAM4.5 forced with ‘‘persisted’’ SSTs were used to improve both seasonal and intraseasonal water allocation during the October–February season for the Angat reservoir, a multipurpose system, in the Philippines. Monthly updated reservoir inflow forecasts are ingested into a reservoir simulation model to allocate water for multiple uses by ensuring a high probability of meeting the end-of-season target storage that is required to meet the summer (March–May) demand. Retrospective reservoir analysis shows that the operation of a reservoir by using monthly updated inflow forecasts reduces the spill considerably by increasing the allocation for hydropower during above-normal-inflow years. During below-normal-inflow years, monthly updated streamflow forecasts could be effectively used for ensuring enough water for the summer season by meeting the end-of-season target storage. This article is published in Journal of Applied Meteorology and Climatology.
Citation: 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
Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations
A new approach for developing multimodel streamflow forecasts is presented. The methodology combines streamflow forecasts from individual models by evaluating their skill, represented by rank probability score (RPS), contingent on the predictor state. Using average RPS estimated over the chosen neighbors in the predictor state space, the methodology assigns higher weights for a model that has better predictability under similar predictor conditions. We assess the performance of the proposed algorithm by developing multimodel streamflow forecasts for Falls Lake Reservoir in the Neuse River Basin, North Carolina (NC), by combining streamflow forecasts developed from two low- dimensional statistical models that use sea-surface temperature conditions as underlying predictors. This article is published in Water Resources Research.
Citation: 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
Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combination
Citation: Devineni, N. (2007). Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combination. NCSU Libraries, (Thesis).