Publications

Increase in compound natural and man-made extreme events, and growing interconnectedness of critical societal functions emphasize further understanding of interactions between elements of risk and resilience. While conceptual frameworks have mapped quantified risks to the failure of critical societal functions, feedback from failure and recovery pathways to management of risk has not been systematically considered yet. In this brief perspective, we revisit the risk-resilience feedback paradigm in the context of connected critical societal functions and discuss its role in advancing science-informed disaster policy development. Specifically, we focus on connecting robustness and recovery elements of resilience to the management of risk elements, including threat, vulnerability, and consequence. Furthermore, we briefly describe a notional networked supply chain system example to illustrate how robustness and recovery measures can inform elements of risk. Understanding interactions between elements of risk and resilience is not a fully solved problem. Extending these concepts in the context of connected natural-built-human systems subject to compound and cascading risks is an open challenge. This discussion represents steps toward addressing these challenges.

Samrat Chatterjee, Auroop Ganguly, Dennis Thomas, Lauren Wind

Risk Analysis, 2026

The US $4.2 trillion US municipal debt market finances over 70% of essential infrastructure, but escalating physical climate risks, such as flooding and wildfires, are exposing the market’s emerging vulnerabilities. Rising disaster costs and insurance retreat threaten property values, and hence municipal tax bases that secure debt repayment. Despite these signals, municipal bond prices have been slow to reflect climate risk adequately. Well-resourced municipalities may use bonds for adaptation, but those facing constrained credit access may struggle to access capital. This US-focused Review identifies three challenges: (1) climate risk is underpriced in municipal bonds; (2) abrupt repricing could affect high-risk and under-resourced cities most by increasing borrowing costs and limiting capital access; (3) misalignment between adaptation planning and municipal finance weakens long-term resilience and affects creditworthiness. Together, these challenges contribute to a climate-debt doom loop that can be triggered by climate shocks. This synthesis offers actionable strategies for cities’ adaptation plans and governance frameworks to disrupt this loop and strengthen municipalities’ financial resilience.

Aayushi Mishra, Advait Arun, Evan Kodra, Erika Smull, Owen Woolcock, Tom Doe, Justin Marlowe, Auroop R. Ganguly

Nature Cities, 2026

Modeling spreading dynamics on spatial networks is crucial to addressing challenges related to traffic congestion, epidemic outbreaks, efficient information dissemination, and technology adoption. Existing approaches include domain-specific agent-based simulations, which offer detailed dynamics but often involve extensive parameterization, and simplified differential equation models, which provide analytical tractability but may abstract away spatial heterogeneity in propagation patterns. As a step toward addressing this trade-off, this work presents a hierarchical multiscale framework that approximates spreading dynamics across different spatial scales under certain simplifying assumptions. Applied to the Susceptible-Infected-Recovered (SIR) model, the approach ensures consistency in dynamics across scales through multiscale regularization, linking parameters at finer scales to those obtained at coarser scales. This approach constrains the parameter search space, and enables faster convergence of the model fitting process compared to the non-regularized model. Using hierarchical modeling, the spatial dependencies critical for understanding system-level behavior are captured while mitigating the computational challenges posed by parameter proliferation at finer scales. Considering traffic congestion and COVID-19 spread as case studies, the calibrated fine-scale model is employed to analyze the effects of perturbations and to identify critical regions and connections that disproportionately influence system dynamics. This facilitates targeted intervention strategies and provides a tool for studying and managing spreading processes in spatially distributed sociotechnical systems.

Aparimit Kasliwal, Abdullah Alhadlaq, Ariel Salgado, Auroop R. Ganguly, Marta C. González

Computer-Aided Civil and Infrastructure Engineering, 2025

A comprehensive characterization of internal climate variability (ICV) in initial-condition (IC) large ensembles of Earth system models (ESMs) remains a significant challenge in climate science. In this study, we leverage the spatial connectivity structures of temperature networks to characterize ICV, observing substantial differences across ensemble members, particularly in the prevalence of long-range connections. Based on this feature, we introduce the ‘Connectivity Ratio’ (CR), a new quantifier that captures long-range spatial connectivity within climate networks. CR is applied to two ESMs, EC-Earth3 and MPI-ESM1-2-LR, to evaluate structural variability across IC ensemble members, models, and climate time horizons. CR reveals systematic differences in long-range connectivity between forced and unforced simulations, as well as across future climate periods. As such, CR provides an interpretable measure for capturing ICV across ensemble members and models. It has the potential to support the quantification of irreducible uncertainty and contributes to a robust evaluation of climate models.

Arnob Ray, Abhirup Banerjee, Rachindra Mawalagedara & Auroop R. GangulyArnob Ray, Abhirup Banerjee, Rachindra Mawalagedara, Auroop R. Ganguly

npj Complexity, 2025

Mitigating the deleterious effects of warming is critical to ensure the persistence of plant-pollinator systems. Previous research relying on single-species frameworks has highlighted the conservation implications of universal increases in extinction risk due to warming, yet effective management approaches that incorporate the role of complex species interaction networks remain poorly understood. Here, we integrate monthly temperature projections with region-specific thermal-performance parameters to simulate population dynamics in 11 plant-pollinator networks across tropical, temperate, and Mediterranean ecosystems. Our results show that tropical networks that are near their thermal limits face pronounced pollinator declines (~50%) but that multi-species management targeting keystone plants offsets these effects by boosting both pollinator abundance and evenness. In contrast, temperate networks remain below critical temperature thresholds, with minimal pollinator declines (~5%) and marginal gains from management. These findings highlight the importance of considering the environmental and ecological contexts when attempting to manage ecosystems under climate change.

Adrija Datta, Sarth Dubey, Tarik C. Gouhier, Auroop R. Ganguly, Udit Bhatia

Communications Earth and Environment, 2025

Internal climate variability (ICV) remains a major source of uncertainty in climate projections, complicating impact assessments across critical sectors, especially at stakeholder-relevant scales. Given that ICV emerges from the nonlinear interactions of the climate system, we argue that nonlinear dynamical (NLD) approaches can improve its characterization, providing physically interpretable insights that strengthen adaptation strategies and support multisector decision-making. However, despite their suitability for such problems, NLD approaches remain largely underutilized in the analysis of initial condition large ensembles (LEs). We argue that a diverse suite of NLD approaches offers a promising pathway for systematically extracting robust insights from LEs. If effectively applied and systematically integrated, these methods could fully harness the potential of LEs, uncovering underlying patterns and variability across ensemble members to refine fundamental insights from climate projections. This will help bridge the gap between complex climate dynamics and practical resilience strategies, ensuring that decision-makers, resource managers, and infrastructure planners have a more reliable foundation for navigating irreducible uncertainty.

Rachindra Mawalagedara, Arnob Ray, Puja Das, Jack Watson, Ashis Kumar Pal, Kate Duffy, Udit Bhatia, Daniel P. Aldrich, Auroop R. Ganguly

npj Climate and Atmospheric Science, 2025

Causal and attribution studies are essential for earth scientific discoveries and critical for informing climate, ecology, and water policies. However, the current generation of methods needs to keep pace with the complexity of scientific, data availability, and stakeholder challenges, combined with the adequacy of data-driven methods. Unless carefully informed by physics, they run the risk of conflating correlation with causation or getting overwhelmed by estimation inaccuracies. In particular, information-theoretic approaches such as transfer entropy (TE), despite their recent popularity and widespread use, can yield spurious causal links, even when statistical significance testing is applied. To address this, we introduce CAST (Causal Analysis Spuriousness Test), a subsample-based ensemble framework that quantifies the robustness of inferred links via the CAST index. Through extensive simulations across systems with varying dynamics as well as applications to real-world climate datasets, we demonstrate that CAST effectively filters unreliable connections while preserving true causal relationships. Our findings underscore the need for consistency-based evaluation in causal discovery and provide a generalizable strategy to enhance the reliability of TE-based and other data-driven causal methods in Earth sciences.

Elizabeth Eldhose, Auroop R. Ganguly, Snigdhansu Chatterjee, Tejasvi Chauhan, Vikram Chandel6, Subimal Ghosh

Physical Review E, 2025

Earth system models inform water policy and interventions, but knowledge gaps in hydrologic representations limit the credibility of projections and impacts assessments. The literature does not provide conclusive evidence that incorporating higher resolutions, comprehensive process models, and latest parameterization schemes, will result in improvements. We compare hydroclimate representations and runoff projections across two generations of Coupled Modeling Intercomparison Project (CMIP) models, specifically, CMIP5 and CMIP6, with gridded runoff from Global Runoff Reconstruction (GRUN) and ECMWF Reanalysis V5 (ERA5) as benchmarks. Our results show that systematic embedding of the best available process models and parameterizations, together with finer resolutions, improve runoff projections with uncertainty characterizations in 30 of the largest rivers worldwide in a mechanistically explainable manner. The more skillful CMIP6 models suggest that, following the mid-range SSP370 emissions scenario, 40% of the rivers will exhibit decreased runoff by 2100, impacting 850 million people.

Puja Das, Auroop R. Ganguly

npj Climate and Atmospheric Science, 2024

Resilient operation of interdependent infrastructures against compound hazard events is essential for maintaining societal well-being. To address consequence assessment challenges in this problem space, we propose a novel tri-level optimization model applied to a proof-of-concept case study with fuel distribution and transportation networks – encompassing one realistic network; one fictitious, yet realistic network; as well as networks drawn from three synthetic distributions. Mathematically, our approach takes the form of a defender-attacker-defender (DAD) model—a multi-agent tri-level optimization, comprised of a defender, attacker, and an operator acting in sequence. Here, our notional operator may choose proxy actions to operate an interdependent system comprised of fuel terminals and gas stations (functioning as supplies) and a transportation network with traffic flow (functioning as demand) to minimize unmet demand at gas stations. A notional attacker aims to hypothetically disrupt normal operations by reducing supply at the supply terminals, and the notional defender aims to identify best proxy defense policy options which include hardening supply terminals or allowing alternative distribution methods such as trucking reserve supplies. We solve our DAD formulation at a metropolitan scale and present practical defense policy insights against hypothetical compound hazards. We demonstrate the generalizability of our framework by presenting results for a realistic network; a fictitious, yet realistic network; as well as for three networks drawn from synthetic distributions. We also analyze the sensitivity of outputs on budget constraints through a detailed case study. Additionally, we demonstrate the scalability of the framework by investigating runtime performance as a function of the network size. Steps for future research are also discussed.

Matthew R. Oster, Ilya Amburg, Samrat Chatterjee, Daniel A. Eisenberg, Dennis G. Thomas, Feng Pan, Auroop R. Ganguly

International Journal of Critical Infrastructure Protection, 2024

Precipitation nowcasting, which is critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) suggest the possibility of improvements. River management centers, such as the Tennessee Valley Authority, have been using Numerical Weather Prediction (NWP) models for nowcasting, but they have been struggling with missed detections even from best-in-class NWP models. While decades of prior research achieved limited improvements beyond advection and localized evolution, recent attempts have shown progress from so-called physics-free machine learning (ML) methods, and even greater improvements from physics-embedded ML approaches. Developers of DGM for nowcasting have compared their approaches with optical flow (a variant of advection) and meteorologists’ judgment, but not with NWP models. Further, they have not conducted independent co-evaluations with water resources and river managers. Here we show that the state-of-the-art physics-embedded deep generative model, specifically NowcastNet, outperforms the High Resolution Rapid Refresh (HRRR) model, which is the latest generation of NWP, along with advection and persistence, especially for heavy precipitation events. Thus, for grid-cell extremes over 16 mm/h, NowcastNet demonstrated a median critical success index (CSI) of 0.30, compared with median CSI of 0.04 for HRRR. However, despite hydrologically-relevant improvements in point-by-point forecasts from NowcastNet, caveats include overestimation of spatially aggregate precipitation over longer lead times. Our co-evaluation with ML developers, hydrologists and river managers suggest the possibility of improved flood emergency response and hydropower management.

Puja Das, August Posch, Nathan Barber, Michael Hicks, Kate Duffy, Thomas Vandal, Debjani Singh, Katie van Werkhoven, Auroop R. Ganguly

npj Climate and Atmospheric Science, 2024

Quantile mapping based bias correction and spatial disaggregation (BCSD) have emerged as the de facto standard for rectifying bias and scale-mismatch in global climate models (GCMs) leading to novel climate science insights and new information for impacts and adaptation. Focusing on critical variables crucial for understanding climate dynamics in India and the United States, our evaluation challenges the premise of BCSD approach. We find that BCSD overcorrects GCM simulations to observed patterns while minimizing or even nullifying science-informed projections generated by GCMs. Furthermore, we show that BCSD incorrectly captures extremes and complex climate signals. Our evaluation in the context of the Walker circulation suggests that this inability to adequately capture multivariate and spatial-temporal dependence patterns may at least partially explain the challenges with BCSD.

Vikram Singh Chandel, Udit Bhatia, Auroop R Ganguly, Subimal Ghosh

Environmental Research Letters, 2024

Networked dynamics are essential for assessing the resilience of lifeline infrastructures. The dimension-reduction approach was designed as an efficient way to map the high-dimensional dynamics to a low-dimensional representation capturing system-level behavior while taking into consideration network structure. However, its application to socio-technical systems has not been considered yet. Here, we extend the dimension-reduction approach to resource-flow dynamics in multiplex networks. We apply it to the San Francisco fuel transportation network, considering the flow between refineries, terminals and gas stations. We capture the aggregated dynamics between the facilities of each type and identify macroscopic conditions for the system to supply a given demand of fuel. By considering multiple sea level rise scenarios between 2020 and 2100, we address the impact of coastal flooding due to climate change on the maximum suppliable demand. Finally, we analyze the system’s transient response to production failures, investigating the temporary interruption in production and the duration it takes for complete demand satisfaction to become unachievable after the interruption.

Ariel Salgado, Yiyi He, John Radke, Auroop Ratan Ganguly, Marta C. Gonzalez

Communications Physics, 2024

Recent major investments in infrastructure in the United States and globally present a crucial opportunity to embed equity within the heart of resilient infrastructure decision-making. Yet there is a notable absence of frameworks within the engineering and scientific fields for integrating equity into planning, design, and maintenance of infrastructure. Additionally, whole-of-government approaches to infrastructure, including the Justice40 Initiative, mimic elements of process management that support exploitative rather than exploratory innovation. These and other policies risk creating innovation traps that limit analytical and engineering advances necessary to prioritize equity in decision-making, identification and disruption of mechanisms that cause or contribute to inequities, and remediation of historic harms. Here, we propose a three-tiered framework toward equitable and resilient infrastructure through restorative justice, incremental policy innovation, and exploratory research innovation. This framework aims to ensure equitable access and benefits of infrastructure, minimize risk disparities, and embrace restorative justice to repair historical and systemic inequities. We outline incremental policy innovation and exploratory research action items to address and mitigate risk disparities, emphasizing the need for community-engaged research and the development of equity metrics. Among other action items, we recommend a certification system—referred to as Social, Environmental, and Economic Development (SEED)—to train infrastructure engineers and planners and ensure attentiveness to gaps that exist within and dynamically interact across each tier of the proposed framework. Through the framework and proposed actions, we advocate for a transformative vision for equitable infrastructure that emphasizes the interconnectedness of social, environmental, and technical dimensions in infrastructure planning, design, and maintenance.

Jason P Giovannettone , Gregg P Macey , Amir AghaKouchak , Michele Barbato , William J Capehart , Auroop R Ganguly , Mital Hall , Jennifer F Helgeson , Si Han Li , Teng Wu , Guirong Yan , Farshid Vahedifard

PNAS Nexus, 2024

Urban air pollution is a critical public health challenge in low-and-middle-income countries (LMICs). At the same time, LMICs tend to be data-poor, lacking adequate infrastructure to monitor air quality (AQ). As LMICs undergo rapid urbanization, the socio-economic burden of poor AQ will be immense. Here we present a globally scalable two-step deep learning (DL) based approach for AQ estimation in LMIC cities that mitigates the need for extensive AQ infrastructure on the ground. We train a DL model that can map satellite imagery to AQ in high-income countries (HICs) with sufficient ground data, and then adapt the model to learn meaningful AQ estimates in LMIC cities using transfer learning. The trained model can explain up to 54% of the variation in the AQ distribution of the target LMIC city without the need for target labels. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned and adapted from two HIC cities, specifically Los Angeles and New York.

Nishant Yadav, Meytar Sorek-Hamer, Michael Von Pohle, Ata Akbari Asanjan, Adwait Sahasrabhojanee, Esra Suel, Raphael E Arku, Violet Lingenfelter, Michael Brauer, Majid Ezzati, Nikunj Oza, Auroop R. Ganguly

ScienceDirect, 2023

Redressing global patterns of biodiversity loss requires quantitative frameworks that can predict ecosystem collapse and inform restoration strategies. By applying a network-based dynamical approach to synthetic and real-world mutualistic ecosystems, we show that biodiversity recovery following collapse is maximized when extirpated species are reintroduced based solely on their total number of connections in the original interaction network. More complex network-based strategies that prioritize the reintroduction of species that improve ‘higher order’ topological features such as compartmentalization do not provide meaningful performance improvements. These results suggest that it is possible to design nearly optimal restoration strategies that maximize biodiversity recovery for data-poor ecosystems in order to ensure the delivery of critical natural services that fuel economic development, food security, and human health around the globe.

Udit Bhatia, Sarth Dubey, Tarik C. Gouhier & Auroop R. Ganguly

Nature, 2023

The timing and strength of wind-driven coastal upwelling along the eastern margins of major ocean basins regulate the productivity of critical fisheries and marine ecosystems by bringing deep and nutrient-rich waters to the sunlit surface, where photosynthesis can occur. How coastal upwelling regimes might change in a warming climate is therefore a question of vital importance. Although enhanced land–ocean differential heating due to greenhouse warming has been proposed to intensify coastal upwelling by strengthening alongshore winds, analyses of observations and previous climate models have provided little consensus on historical and projected trends in coastal upwelling. Here we show that there are strong and consistent changes in the timing, intensity and spatial heterogeneity of coastal upwelling in response to future warming in most Eastern Boundary Upwelling Systems (EBUSs). An ensemble of climate models shows that by the end of the twenty-first century the upwelling season will start earlier, end later and become more intense at high but not low latitudes. This projected increase in upwelling intensity and duration at high latitudes will result in a substantial reduction of the existing latitudinal variation in coastal upwelling. These patterns are consistent across three of the four EBUSs (Canary, Benguela and Humboldt, but not California). The lack of upwelling intensification and greater uncertainty associated with the California EBUS may reflect regional controls associated with the atmospheric response to climate change. Given the strong linkages between upwelling and marine ecosystems the projected changes in the intensity, timing and spatial structure of coastal upwelling may influence the geographical distribution of marine biodiversity.

Daiwei Wang, Tarik C. Gouhier, Bruce A. Menge & Auroop R. Ganguly

Nature, 2015

The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However,contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. Depending on statistical modeling choices, downscaled projections have been shown to vary significantly terms of accuracy and reliability. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework for statistical downscaling of climate variables. DeepSD augments SRCNN with multi-scale input channels to maximize predictability in statistical downscaling. We provide a comparison with Bias Correction Spatial Disaggregation as well as three Automated-Statistical Downscaling approaches in downscaling daily precipitation from 1 degree ( 100km) to 1/8 degrees ( 12.5km) over the Continental United States.Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios.

Thomas Vandal, Evan Kodra, Sangram Ganguly, Andrew Michaelis, Ramakrishna Nemani, Auroop R Ganguly

ACM, 2017

Recent studies disagree on how rainfall extremes over India have changed in space and time over the past half century as well as on whether the changes observed are due to global warming or regional urbanization7. Although a uniform and consistent decrease in moderate rainfall has been reported a lack of agreement about trends in heavy rainfall may be due in part to differences in the characterization and spatial averaging of extremes. Here we use extreme value theory to examine trends in Indian rainfall over the past half century in the context of long-term, low-frequency variability. We show that when generalized extreme value theory is applied to annual maximum rainfall over India, no statistically significant spatially uniform trends are observed, in agreement with previous studies using different approaches. Furthermore, our space–time regression analysis of the return levels points to increasing spatial variability of rainfall extremes over India. Our findings highlight the need for systematic examination of global versus regional drivers of trends in Indian rainfall extremes, and may help to inform flood hazard preparedness and water resource management in the region.

Subimal Ghosh, Debasish Das, Shih-Chieh Kao , Auroop R. Ganguly

Nature Climate Change, 2011

Natural hazards including floods can trigger catastrophic failures in interdependent urban transport network-of-networks (NoNs). Population growth has enhanced transportation demand while urbanization and climate change have intensified urban floods. However, despite the clear need to develop actionable insights for improving the resilience of critical urban lifelines, the theory and methods remain underdeveloped. Furthermore, as infrastructure systems become more intelligent, security experts point to the growing threat of targeted cyber-physical attacks during natural hazards. Here we develop a hypothesis-driven resilience framework for urban transport NoNs, which we demonstrate on the London Rail Network (LRN). We find that topological attributes designed for maximizing efficiency rather than robustness render the network more vulnerable to compound natural-targeted disruptions including cascading failures. Our results suggest that an organizing principle for post-disruption recovery may be developed with network science principles. Our findings and frameworks can generalize to urban lifelines and more generally to real-world spatial networks.

Nishant Yadav, Samrat Chatterjee , Auroop R. Ganguly

Nature Climate Change, 2011

Timely and accurate knowledge about the geospatial distribution of crops at regional to continental scales is crucial for forecasting crop production and estimating crop water use. The United States (US) is one of the leading food-producing countries, but lacks a nationwide high resolution crop-specific land cover map available publicly during the current growing season. The goal of this study was to map crops across the Continental US (CONUS) before the harvest, and to estimate the earliest date of classification by which crops can be mapped with sufficient accuracy (90% of full-season accuracy). The study employed a scalable cluster-then-label model that was trained on multiple years of MODIS NDVI using ground truth data in the form of US Department of Agriculture (USDA) Cropland Data Layer (CDL) products. The first step in the crop classification was to perform Multivariate Spatio-Temporal Clustering (MSTC) of annual MODIS-derived NDVI trajectories to create phenologically similar regions, or phenoregions. The second step was to assign crop labels to phenoregions based on spatial concordance between phenoregions and crop classes from CDL using Mapcurves. Assigning crop labels to phenoregions was performed within ecoregions to reduce classification errors due to spatial variability in phenology caused by variations in climate, agricultural practices, and growing conditions. The crop classifier was trained and validated on the years 2008–2014, then tested independently on 2015–2018. Ecoregion-level crop classification performed better than state-level and CONUS-level classification. Pixel-wise accuracy of classification for eight major crops by area was around 70% across the major corn-, soybeans- and winter wheatproducing areas, whereas regions characterized by high crop diversity had slightly lower accuracy. Classification accuracy for dominant crops like corn, soybeans, winter wheat, fallow/idle cropland and other hay/non alfalfa improved with time as they grew, reaching 90% of year-end accuracy by the end of August over each of the four unseen years in the test period. For corn and soybeans, the earliest dates of classification were found to be much earlier in the central regions of the Corn Belt (parts of Iowa, Illinois and Indiana) than in peripheral areas. The ability to map growing crops may permit near real-time monitoring of the health status and vigor of agricultural crops nationally.

Venkata Shashank Konduri,Jitendra Kumar, William W. Hargrove, Forrest M. Hoffman, Auroop R.Ganguly

ScienceDirect, 2019