Devashish Kumar

Regional and seasonal intercomparison of CMIP3 and CMIP5 climate model ensembles for temperature and precipitation

Abstract Regional and seasonal temperature and precipitation over land are compared across two generations of global climate model ensembles, specifically, CMIP5 and CMIP3, through historical twentieth century skills and multi-model agreement, and twenty first century projections. A suite of diagnostic and performance metrics, ranging from spatial bias or model-consensus maps and aggregate time series plots, to measures of equivalence between probability density functions and Taylor diagrams, are used for the intercomparisons. Pairwise and multi-model ensemble comparisons were performed for 11 models, which were selected based on data availability and resolutions. Results suggest little change in the central tendency or variability or uncertainty of historical skills or consensus across the two generations of models. However, there are regions and seasons, at different levels of aggregation, where significant changes, performance improvements, and even degradation in skills, are suggested. The insights may provide directions for further improvements in next generations of climate models, and in the meantime, help inform adaptation and policy.

Network science based quantification of resilience demonstrated on the Indian Railways Network

The structure, interdependence, and fragility of systems ranging from power-grids and transportation to ecology, climate, biology and even human communities and the Internet have been examined through network science. While response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science based quantitative framework for measuring, comparing and interpreting hazard responses as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario illustrate hazard responses and effectiveness of proposed recovery strategies. Multiple metrics are used to generate various recovery strategies, which are simply sequences in which system components should be recovered after a disruption. Quantitative evaluation of these strategies suggests that faster and more efficient recovery is possible through network centrality measures. Optimal recovery strategies may be different per hazard, per community within a network, and for different measures of partial recovery. In addition, topological characterization provides a means for interpreting the comparative performance of proposed recovery strategies. The methods can be directly extended to other Large-Scale Critical Lifeline Infrastructure Networks including transportation, water, energy and communications systems that are threatened by natural or human-induced hazards, including cascading failures. Furthermore, the quantitative framework developed here can generalize across natural, engineered and human systems, offering an actionable and generalizable approach for emergency management in particular as well as for network resilience in general.

Climate Adaptation Informatics: Water Stress on Power Production

Resilience to nonstationarity and deep uncertainty is a prerequisite to water security. Stakeholder planning horizons typically extend to about 30 years in water quantity or quality management, flood or drought hazard resilience, or the water-energy-food-ecosystems nexus. Projections of stressors, such as population, land use, stability assumptions of technologies, infrastructures, and organizations, are relatively more credible at the nearer term. However, compared to longer lead times of mid- to end-century and beyond, climate adaptation challenges are more acute. Over 30-year horizons, the degree of nonstationarity is comparable to the overall uncertainty, which in turn is dominated by natural variability, especially at higher space-time resolutions. A case study with power production at risk in the US suggests that informed decisions could be possible despite nonstationarity and deep uncertainty.

US Power Production at Risk from Water Stress in a Changing Climate

Thermoelectric power production in the United States primarily relies on wet-cooled plants, which in turn require water below prescribed design temperatures, both for cooling and operational efficiency. Thus, power production in US remains particularly vulnerable to water scarcity and rising stream temperatures under climate change and variability. Previous studies on the climate-water-energy nexus have primarily focused on mid- to end-century horizons and have not considered the full range of uncertainty in climate projections. Technology managers and energy policy makers are increasingly interested in the decadal time scales to understand adaptation challenges and investment strategies. Here we develop a new approach that relies on a novel multivariate water stress index, which considers the joint probability of warmer and scarcer water, and computes uncertainties arising from climate model imperfections and intrinsic variability. Our assessments over contiguous US suggest consistent increase in water stress for power production with about 27% of the production severely impacted by 2030s.

Analyzing big spatial and big spatiotemporal data: A case study of methods and applications

Abstract Spatial and spatiotemporal data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from the data collected over time and space. However, explosive growth in the spatial and spatiotemporal data, and the emergence of social media and location sensing technologies, emphasizes the need for developing new and computationally efficient methods tailored for analyzing big data. In this chapter, we study approaches to handle big spatial and spatiotemporal data by closely looking at the computational and I/O requirements of several analysis algorithms for such data. We also study applications of such methods in domains where data is encountered at a massive scale.

Author Correction: US Power Production at Risk from Water Stress in a Changing Climate

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Water Stress on U.S. Power Production at Decadal Time Horizons

Thermoelectric power production at risk, owing to current and projected water scarcity and rising stream temperatures, is assessed for the contiguous United States at decadal scales. Regional water scarcity is driven by climate variability and change, as well as by multi-sector water demand. While a planning horizon of zero to about thirty years is occasionally prescribed by stakeholders, the challenges to risk assessment at these scales include the difficulty in delineating decadal climate trends from intrinsic natural or multiple model variability. Current generation global climate or earth system models are not credible at the spatial resolutions of power plants, especially for surface water quantity and stream temperatures, which further exacerbates the assessment challenge. Population changes, which are difficult to project, cannot serve as adequate proxies for changes in the water demand across sectors. The hypothesis that robust assessments of power production at risk are possible, despite the uncertainties, has been examined as a proof of concept. An approach is presented for delineating water scarcity and temperature from climate models, observations and population storylines, as well as for assessing power production at risk by examining geospatial correlations of power plant locations within regions where the usable water supply for energy production happens to be scarcer and warmer. Our analyses showed that in the near term, more than 200 counties are likely to be exposed to water scarcity in the next three decades. Further, we noticed that stream gauges in more than five counties in the 2030s and ten counties in the 2040s showed a significant increase in water temperature, which exceeded the power plant effluent temperature threshold set by the EPA. Power plants in South Carolina, Louisiana, and Texas are likely to be vulnerable owing to climate-driven water stresses.