arXiv subjects

Udit Bhatia

Publications and source records attributed to Udit Bhatia.

23 records · Page 2Linked to original sources

arXiv2020

Bias-corrected climate projections from Coupled Model Intercomparison Project-6 (CMIP6) for South Asia

Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951-2014) and projected (2015-2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 CMIP6-GCMs. The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3-5°C) and wetter (13-30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate change impact assessment in South Asia and hydrologic impact assessment in the sub-continental river basins.

physics.ao-ph↗

arXiv2019

Precipitation extremes and depth-duration-frequency under internal climate variability

Natural climate variability, captured through multiple initial condition ensembles, may be comparable to the variability caused by knowledge gaps in future emissions trajectories and in the physical science basis, especially at adaptation-relevant scales and projection horizons. The relations to chaos theory, including sensitivity to initial conditions, have caused the resulting variability in projections to be viewed as the irreducible uncertainty component of climate. The multiplier effect of ensembles from emissions-trajectories, multiple-models and initial-conditions contribute to the challenge. We show that ignoring this variability results in underestimation of precipitation extremes return periods leading to maladaptation. However, we show that concatenating initial-condition ensembles results in reduction of hydroclimate uncertainty. We show how this reduced uncertainty in precipitation extremes percolates to adaptation-relevant-Depth-Duration Frequency curves. Hence, generation of additional initial condition ensembles therefore no longer needs to be viewed as an uncertainty explosion problem but as a solution that can lead to uncertainty reduction in assessment of extremes.

stat.AP↗

arXiv2018

Generalized network recovery based on topology and optimization for real-world systems

Designing effective recovery strategies for damaged networked systems is critical to the resilience of built, human and natural systems. However, progress has been limited by the inability to bring together distinct philosophies, such as complex network topology through centrality measures and network flow optimization through entropy measures. Network centrality-based metrics are relatively more intuitive and computationally efficient while optimization-based approaches are more amenable to dynamic adjustments. Here we show, with case studies in real-world transportation systems, that the two distinct network philosophies can be blended to form a hybrid recovery strategy that is more effective than either, with the relative performance depending on aggregate network attributes. Direct applications include disaster management and climate adaptation sciences, where recovery of lifeline networks can save lives and economies.

physics.soc-ph↗

arXiv2018

Universal and generalizable restoration strategies for degraded ecological networks

Humans are increasingly stressing ecosystems via habitat destruction, climate change and global population movements leading to the widespread loss of biodiversity and the disruption of key ecological services. Ecosystems characterized primarily by mutualistic relationships between species such as plant-pollinator interactions may be particularly vulnerable to such perturbations because the loss of biodiversity can cause extinction cascades that can compromise the entire network. Here, we develop a general restoration strategy based on network-science for degraded ecosystems. Specifically, we show that network topology can be used to identify the optimal sequence of species reintroductions needed to maximize biodiversity gains following partial and full ecosystem collapse. This restoration strategy generalizes across topologically-disparate and geographically-distributed ecosystems. Additionally, we find that although higher connectance and diversity promote persistence in pristine ecosystems, these attributes reduce the effectiveness of restoration efforts in degraded networks. Hence, focusing on restoring the factors that promote persistence in pristine ecosystems may yield suboptimal recovery strategies for degraded ecosystems. Overall, our results have important insights for designing effective ecosystem restoration strategies to preserve biodiversity and ensure the delivery of critical natural services that fuel economic development, food security and human health around the globe

q-bio.PE↗

arXiv2015

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.

physics.soc-ph↗