Projection of changes in extreme indices of climate variables such as temperature and precipitation are critical to assess the potential impacts of climate change on human-made and natural systems, ...including critical infrastructures and ecosystems. While impact assessment and adaptation planning rely on high-resolution projections (typically in the order of a few kilometers), state-of-the-art Earth System Models (ESMs) are available at spatial resolutions of few hundreds of kilometers. Current solutions to obtain high-resolution projections of ESMs include downscaling approaches that consider the information at a coarse-scale to make predictions at local scales. Complex and non-linear interdependence among local climate variables (e.g., temperature and precipitation) and large-scale predictors (e.g., pressure fields) motivate the use of neural network-based super-resolution architectures. In this work, we present auxiliary variables informed spatio-temporal neural architecture for statistical downscaling. The current study performs daily downscaling of precipitation variable from an ESM output at 1.15 degrees (~115 km) to 0.25 degrees (25 km) over the world's most climatically diversified country, India. We showcase significant improvement gain against three popular state-of-the-art baselines with a better ability to predict extreme events. To facilitate reproducible research, we make available all the codes, processed datasets, and trained models in the public domain.
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{\deg} 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{\deg}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.
Rising economic instability and continuous evolution in international relations demand a self-reliant trade and commodity flow networks at regional scales to efficiently address the growing human ...needs of a nation. Despite its importance in securing India's food security, the potential advantages of inland trade remain unexplored. Here we perform a comprehensive analysis of agricultural flows and contrast it with non-agricultural commodities flow across Indian states. The spatiotemporal evolution of both the networks for the period 2010 to 2018 was studied and compared using network properties along with the total traded value. Our results show an increase in annual traded volume by nearly 37 % and 87 %, respectively, for agriculture and non-agriculture trade. An increase in total trade volume without a significant increase in connectivity over the analyzed time-period is observed in both networks, reveals the over-reliance and increased dependency on particular export hubs. Our analysis further revealed a more homogeneous distribution between import and export connection nodes for agriculture trade compared to non-agriculture trade, where Indian states with high exports also have high imports. Overall our analysis provide a quantitative description of Indian inland trade as a complex network that could further us design resilient trade networks within the nation.
Aspirations to slow down the spread of Novel Coronavirus (SARS-CoV2) resulted in unprecedented restrictions on personal and work-related travels in various nations across the globe. As a consequence, ...economic activities within and across the countries were almost halted. As restrictions loosen and cities start to resume public and private transport to revamp the economy, it becomes critical to assess the commuters' travel-related risk in light of the ongoing pandemic. We develop a generalizable quantitative framework to evaluate the commute-related risk arising from inter-district and intra-district travels by combining Nonparametric Data Envelopment analysis for vulnerability assessment with transportation network analysis. We demonstrate the application of the proposed model for establishing travel corridors or travel bubbles within and across Gujarat and Maharashtra, two Indian states that have reported many SARS-CoV2 cases since early April 2020. Our findings suggest that establishing the travel bubble between a pair of districts solely based on the health vulnerability indices of origin-destination discards the en-route travel risks due to prevalent pandemic, hence underestimating the threat. For example, while the resultant of social and health vulnerabilities of Narmada and Vadodara's districts is relatively moderate, the en-route travel risk exacerbates the overall travel risk. Our study provides actionable insights to users into choosing the alternate path with the least risk and can inform political decisions to establish low-risk travel corridors within and across the states while accounting for social and health vulnerabilities in addition to transit-time related risks.
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
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.
Current literature suggests that urban heat-islands and their consequences are intensifying under climate change and urbanization. Here we explore the relatively unexplored hypothesis that emerging ...urban corridors (UCs) spawn megaregions of intense heat which are evident from observations. A delineation of the eleven United States UCs relative to their underlying climatological regions (non-UCs) suggest a surprisingly mixed trend. Medians and trends of winter temperatures over the last 60-years are generally higher in the UCs but no such general trends are observed in the summer. Heat wave metrics related to public health, energy demand and relative intensity do not exhibit significantly higher overall trends. Temperature and heat wave indices in the UCs exhibit high correlations with each other including across seasons. Spatiotemporal patterns in population, along with urbanization, agriculture and elevation, exhibit high (positive or negative) rank correlations with warming and heatwave intensification. The findings can inform climate adaptation in megalopolises.
The evacuation of the population from flood-affected regions is a non-structural measure to mitigate flood hazards. Shelters used for this purpose usually accommodate a large number of flood evacuees ...for a temporary period. Floods during pandemic result in a compound hazard. Evacuations under such situations are difficult to plan as social distancing is nearly impossible in the highly crowded shelters. This results in a multi-objective problem with conflicting objectives of maximizing the number of evacuees from flood-prone regions and minimizing the number of infections at the end of the shelter's stay. To the best of our knowledge, such a problem is yet to be explored in literature. Here we develop a simulation-optimization framework, where multiple objectives are handled with a max-min approach. The simulation model consists of an extended Susceptible Exposed Infectious Recovered Susceptible (SEIRS) model.We apply the proposed model to the flood-prone Jagatsinghpur district in the state of Odisha, India. We find that the proposed approach can provide an estimate of people required to be evacuated from individual flood-prone villages to reduce flood hazards during the pandemic. At the same time, this does not result in an uncontrolled number of new infections. The proposed approach can generalize to different regions and can provide a framework to stakeholders to manage conflicting objectives in disaster management planning and to handle compound hazards.
The dense social contact networks and high mobility in congested urban areas facilitate the rapid transmission of infectious diseases. Typical mechanistic epidemiological models are either based on ...uniform mixing with ad-hoc contact processes or need real-time or archived population mobility data to simulate the social networks. However, the rapid and global transmission of the novel coronavirus (SARS-CoV-2) has led to unprecedented lockdowns at global and regional scales, leaving the archived datasets to limited use. While it is often hypothesized that population density is a significant driver in disease propagation, the disparate disease trajectories and infection rates exhibited by the different cities with comparable densities require a high-resolution description of the disease and its drivers. In this study, we explore the impact of the creation of containment zones on travel patterns within the city. Further, we use a dynamical network-based infectious disease model to understand the key drivers of disease spread at sub-kilometer scales demonstrated in the city of Ahmedabad, India, which has been classified as a SARS-CoV-2 hotspot. We find that in addition to the contact network and population density, road connectivity patterns and ease of transit are strongly correlated with the rate of transmission of the disease. Given the limited access to real-time traffic data during lockdowns, we generate road connectivity networks using open-source imageries and travel patterns from open-source surveys and government reports. Within the proposed framework, we then analyze the relative merits of social distancing, enforced lockdowns, and enhanced testing and quarantining mitigating the disease spread.
Network structures in a wide array of systems such as social networks, transportation, power and water distribution infrastructures, and biological and ecological systems can exhibit critical ...thresholds or tipping points beyond which there are disproportionate losses in the system functionality. There is growing concern over tipping points and failure tolerance of such systems as tipping points can lead to an abrupt loss of intended functionality and possibly non-recoverable states. While attack tolerance of networked systems has been intensively studied for the disruptions originating from a single point of failure, there have been instances where real-world systems are subject to simultaneous or sudden onset of concurrent disruption at multiple locations. Using open-source data from the United States Airspace Airport network and Indian Railways Network, and random networks as prototype class of systems, we study their responses to synthetic attack strategies of varying sizes. For both types of networks, we observe the presence of warning regions, which serve as a precursor to the tipping point. Further, we observe the statistically significant relationships between network robustness and size of simultaneous distribution, which generalizes to the networks with different topological attributes for random failures and targeted attacks. We show that our approach can determine the entire robustness characteristics of networks of disparate architecture subject to disruptions of varying sizes. Our approach can serve as a paradigm to understand the tipping point in real-world systems, and the principle can be extended to other disciplines to address critical issues of risk management and resilience.
