Naresh Devineni

Is an Epic Pluvial Masking the Water Insecurity of the Greater New York City Region

AbstractSix water emergencies have occurred since 1981 for the New York City (NYC) region despite the following: 1) its perhumid climate, 2) substantial conservation of water since 1979, and 3) meteorological data showing little severe or extreme drought since 1970. This study reconstructs 472 years of moisture availability for the NYC watershed to place these emergencies in long-term hydroclimatic context. Using nested reconstruction techniques, 32 tree-ring chronologies comprised of 12 species account for up to 66.2% of the average May–August Palmer drought severity index. Verification statistics indicate good statistical skill from 1531 to 2003. The use of multiple tree species, including rarely used species that can sometimes occur on mesic sites like Liriodendron tulipifera, Betula lenta, and Carya spp., seems to aid reconstruction skill. Importantly, the reconstruction captures pluvial events in the instrumental record nearly as well as drought events and is significantly correlated to precipitation...

The Role of Monthly Updated Climate Forecasts in Improving Intraseasonal Water Allocation

Seasonal streamflow forecasts contingent on climate information are essential for short-term planning (e.g., water allocation) and for setting up contingency measures during extreme years. However, the water allocated based on the climate forecasts issued at the beginning of the season needs to be revised using the updated climate forecasts throughout the season. In this study, 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. The forecastbased allocation is combined with the observed inflows during the season to estimate storages, spill, and generated hydropower from the system. The performance of the reservoir is compared under three scenarios: forecasts issued at the beginning of the season, monthly updated forecasts during the season, and use of climatological values. 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. These analyses suggest the importance of performing experimental reservoir analyses to understand the potential challenges and opportunities in improving seasonal and intraseasonal water allocation by using real-time climate forecasts.

A Tree-Ring-Based Reconstruction of Delaware River Basin Streamflow Using Hierarchical Bayesian Regression

A hierarchical Bayesian regression model is presented for reconstructing the average summer streamflow at five gauges in the Delaware River basin using eight regional tree-ring chronologies. The model provides estimates of the posterior probability distribution of each reconstructed streamflow series considering parameter uncertainty. The vectors ofregressioncoefficientsaremodeled asdraws froma commonmultivariate normal distribution with unknown parameters estimated as part of the analysis. This leads to a multilevel structure.Thecovariancestructureofthestreamflowresidualsacross sitesis explicitlymodeled. Theresulting partial pooling of information across multiple stations leads to a reduction in parameter uncertainty. The effect of no pooling and full pooling of station information, as end points of the method, is explored. The nopooling modelconsiders independent estimationof the regressioncoefficientsfor each streamflow gaugewith respect to each tree-ring chronology. The full-pooling model considers that the same regression coefficients apply across all streamflow sites for a particular tree-ring chronology. The cross-site correlation of residuals is modeled in all cases. Performance on metrics typically used by tree-ring reconstruction experts, such as reduction of error, coefficient of efficiency, and coverage rates under credible intervals is comparable to, or better, for the partial-pooling model relative to the no-pooling model, and streamflow estimation uncertainty is reduced. Long record simulations from reconstructions are used to develop estimates of the probability of duration and severity of droughts in the region. Analysis of monotonic trends in the reconstructed drought events do not reject the null hypothesis of no trend at the 90% significance over 1754‐2000.

Improving the Prediction of Winter Precipitation and Temperature over the Continental United States: Role of the ENSO State in Developing Multimodel Combinations

Abstract Recent research into seasonal climate prediction has focused on combining multiple atmospheric general circulation models (GCMs) to develop multimodel ensembles. 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 Nino-3.4) conditions. The MSE estimates are converted into weights for each GCM for developing multimodel tercile probabilities. 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. To ensure the improved skill exhibited by the multimodel scheme is statistically significant, rigorous hypothesis tests were performed comparing the skill of multimodels with...

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. Unlike most existing drought metrics, we directly consider current daily water demands and renewable daily water supply to estimate the potential stress. Water stress indices developed include the Normalized Deficit Cumulated to represent multiyear droughts by computing the maximum cumulative deficit between demand and supply over the study period (1949–2009) and the Normalized Deficit Index representing drought associated with maximum cumulative deficit each year. These water stress indices map directly to storage requirements needed to buffer multiyear and within-year climate variability and can reveal the dependence on exogenous water transferred by rivers/canals to the area. Future climate change and variability can be also incorporated into this framework to inform climate-driven drought for additional storage development and potential applications of water trading across counties.

The future role of dams in the United States of America

Storage and controlled distribution of water have been key elements of a human strategy to overcome the space and time variability of water, which have been marked by catastrophic droughts and floods throughout the course of civilization. In the United States, the peak of dam building occurred in the mid-20th century with knowledge limited to the scientific understanding and hydrologic records of the time. Ecological impacts were considered differently than current legislative and regulatory controls would potentially dictate. Additionally, future costs such as maintenance or removal beyond the economic design life were not fully considered. The converging risks associated with aging water storage infrastructure and uncertainty in climate in addition to the continuing need for water storage, flood protection, and hydropower result in a pressing need to address the state of dam infrastructure across the nation. Decisions regarding the future of dams in the United States may, in turn, influence regional water futures through groundwater outcomes, economic productivity, migration, and urban growth. We advocate for a comprehensive national water assessment and a formal analysis of the role dams play in our water future. We emphasize the urgent need for environmentally and economically sound strategies to integrate surface and groundwater storage infrastructure in local, regional, and national water planning considerations. A research agenda is proposed to assess dam failure impacts and the design, operation, and need for dams considering both paleo and future climate, utilization of groundwater resources, and the changing societal values toward the environment.

An Empirical, Nonparametric Simulator for Multivariate Random Variables with Differing Marginal Densities and Nonlinear Dependence with Hydroclimatic Applications

Multivariate simulations of a set of random variables are often needed for risk analysis. Given a historical data set, the goal is to develop simulations that reproduce the dependence structure in that data set so that the risk of potentially correlated factors can be evaluated. A nonparametric, copula-based simulation approach is developed and exemplified. It can be applied to multiple variables or to spatial fields with arbitrary dependence structures and marginal densities. The nonparametric simulator uses logspline density estimation in the univariate setting, together with a sampling strategy to reproduce dependence across variables or spatial instances, through a nonparametric numerical approximation of the underlying copula function. The multivariate data vectors are assumed to be independent and identically distributed. A synthetic example is provided to illustrate the method, followed by an application to the risk of livestock losses in Mongolia.

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 the country’s hydropower and protecting downstream ecology. The dam serves as the primary storage reservoir, controlling streamflow through a series of downstream hydroelectric reservoirs. The Masinga dam’s operation is crucial in meeting power demands and thus contributing significantly to the country’s economy. La Ni~ prolonged droughts of 1999‐2001 resulted in severe power shortages in Kenya. Therefore, seasonal streamflow forecasts contingent on climate information are essential to estimate preseason water allocation. Here, the authors utilize reservoir inflow forecasts downscaled from monthly updated precipitation forecasts from ECHAM4.5 forced with constructed analog SSTs and multimodel precipitation forecastsdevelopedfromtheEnsemble-BasedPredictionsofClimateChangesandtheirImpacts(ENSEMBLES) project to improve water allocation during the April‐June and October‐December seasons for the Masinga Reservoir. Three-month-ahead inflow forecasts developed from ECHAM4.5, multiple GCMs, and climatological ensembles are used in a reservoir model to allocate water for power generation by ensuring climatological probability of meeting the end-of-season target storage required to meet seasonal water demands. Retrospective reservoir analysis shows that inflow forecasts developed from single GCM and multiple GCMs perform better than use of climatological values by reducing the spill and increasing the allocation for hydropower during above-normal inflow years. Similarly, during below-normal inflow years, both of these forecasts could be effectively utilized to meet the end-of-season target storage by restricting releases for power generation. The multimodel forecasts preserve the end-of-season target storage better than the singlemodel inflow forecasts by reducing uncertainty and the overconfidence of individual model forecasts.

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

Agricultural, industrial, and urban water use in the conterminous United States (CONUS) is highly dependent on groundwater that is largely drawn from nonsurficial wells (>30 m). We use a Demand-Sensitive Drought Index 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. We characterize the relationship between changes in groundwater levels, agricultural water deficits relative to precipitation during the growing season, and winter precipitation. We find that 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.

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. Punjab, a major agricultural supplier for the rest of India, supports irrigation with a canal system and groundwater, which is vastly overexploited. The detailed data required to estimate future impacts on water supplies or develop sustainable water management practices is not readily available for this region. Therefore, we use Bayesian methods to estimate hydrologic properties and irrigation requirements for an under-constrained mass balance model. Using the known values of precipitation, total canal water delivery, crop yield, and water table elevation, we present a method using a Markov chain Monte Carlo (MCMC) algorithm to solve for a distribution of values for each unknown parameter in a conceptual mass balance model. Model results are used to test three water management strategies, which show that replacement of rice with pulses may be sufficient to stop water table decline. 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.