Space-time Trends in U.S. Meteorological Droughts

Understanding droughts in a climate context remains a major challenge. Over the United States, different choices of observations and metrics have often produced diametrically opposite insights. This paper focuses on understanding and characterizing meteorological droughts from station measurements of precipitation. The Standardized Precipitation Index is computed and analyzed to obtain drought severity, duration and frequency. Average drought severity trends are found to be uncertain and data-dependent. Furthermore, the mean and spatial variance do not show any discernible non-stationary behavior. However, the spatial coverage of extreme meteorological droughts in the United States exhibits an increasing trend over nearly all of the last century. Furthermore, the coverage over the last half decade exceeds that of the dust bowl era. Previous literature suggests that climate extremes do not necessarily follow the trends or uncertainties exhibited by the averages. While this possibility has been suggested for droughts, this paper for the first time clearly delineates and differentiates the trends in the mean, variability and extremes of meteorological droughts in the United States, and uncovers the trends in the spatial coverage of extremes. Multiple data sets, as well as years exhibiting large, and possibly anomalous, droughts are carefully examined to characterize trends and uncertainties. Nonlinear dependence among meteorological drought attributes necessitates the use of copula-based tools from probability theory. Severity-duration-frequency curves are generated to demonstrate how these insights may be translated to design 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 the 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 methods framework for measuring, comparing and interpreting hazard responses and as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. The methods are demonstrated through the resilience of the network to natural or human-induced hazards and electric grid failure. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario. 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 recovery strategies suggests that faster and more resource-effective recovery is possible through network centrality measures. Case studies based on two historical events, specifically the 2004 Indian Ocean tsunami and the 2012 North Indian blackout, and a simulated cyber-physical attack scenario, provides means for interpreting the relative performance of various recovery strategies. Quantitative evaluation of recovery strategies suggests that faster and more resource-effective restoration is possible through network centrality measures, even though the specific strategy may be different for sub-networks or for the partial recovery

Explainable deep learning for insights in El Ni\~no and river flows

The El Ni\~no Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections

‘Bayesian source detection and parameter estimation of a plume model based on sensor network measurements’ by C. Huang et al .: Rejoinder

No AbstractPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78083/1/858_ftp.pd