1.7k citations · 2.1k across the 41 of their papers we have counts for
43 papers
Nonuniform and inequitable healthcare accessibility losses during flooding-amplified congestion across US cities
Raviraj Dave, Danish Mansoor Tantary, Dongqin Zhou +2
Cities in the United States and worldwide concentrate populations with diverse incomes, demographics and health risks, making access to healthcare facilities and emergency services…
Domain-Adaptive Climate Downscaling Under Temporal Distribution Shift
Shuochen Wang, Nishant Yadav, Auroop R. Ganguly
Deep-learning-based climate downscaling aims to learn relationships from historical low-resolution (LR) and high-resolution (HR) climate data to generate HR climate projections. Ho…
Intercomparison of Machine Learning Algorithms for Remote Sensing-based In-season Crop Mapping
August Posch, Jitendra Kumar, Forrest M. Hoffman +1
In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops. Currently, the USDA Cropland Data Layer provides cro…
Learning Transferable Predictability Representations
Diyali Goswami, Auroop R. Ganguly
We study the problem of assigning a scalar score to a short trajectory window that reflects its position on an ordered continuum of predictability regimes, spanning structured dete…
climt-paraformer: Stable Emulation of Convective Parameterization using a Temporal Memory-aware Transformer
Shuochen Wang, Nishant Yadav, Joy Merwin Monteiro +1
Accurate representation of moist convective sub-grid-scale processes remains a major challenge in global climate models, as traditional parameterization schemes are both computatio…
MAUNet-Light: A Concise MAUNet Architecture for Bias Correction and Downscaling of Precipitation Estimates
Sumanta Chandra Mishra Sharma, Adway Mitra, Auroop Ratan Ganguly
Satellite-derived data products and climate model simulations of geophysical variables like precipitation, often exhibit systematic biases compared to in-situ measurements. Bias co…
An Ecologically-Informed Deep Learning Framework for Interpretable and Validatable Habitat Mapping
Iván Felipe Benavides-Martínez, Cristiam Victoriano Portilla-Cabrera, Katherine E. Mills +4
Benthic habitat is challenging due to the environmental complexity of the seafloor, technological limitations, and elevated operational costs, especially in under-explored regions.…
Land Cover Changes Cause Increased Losses during Photosynthetic Extremes
Bharat Sharma, Jitendra Kumar, Nathan Collier +2
Human-induced carbon dioxide (CO2) emissions, primarily from fossil fuel combustion and changes in land use and land cover (LULCC), are a key contributor to climate change. As the…
Carbon Cycle Extremes Accelerate Weakening of the Land Carbon Sink in the Late 21st Century
Bharat Sharma, Jitendra Kumar, Auroop R. Ganguly +1
Increasing surface temperature could lead to enhanced evaporation, reduced soil moisture availability, and more frequent droughts and heat waves. The spatiotemporal co-occurrence o…
Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes
Bharat Sharma, Jitendra Kumar, Auroop R. Ganguly +1
Rising atmospheric carbon dioxide due to human activities through fossil fuel emissions and land use changes have increased climate extremes such as heat waves and droughts that ha…
Warming demands extensive tropical but minimal temperate management in plant-pollinator networks
Adrija Datta, Sarth Dubey, Tarik C. Gouhier +2
Anthropogenic warming impacts ecological communities and disturbs species interactions, particularly in temperature sensitive plant pollinator networks. While previous assessments…
Network science disentangles internal climate variability in global spatial dependence structures
Arnob Ray, Abhirup Banerjee, Rachindra Mawalagedara +1
A comprehensive characterization of internal climate variability and irreducible uncertainty through initial-condition large ensembles of Earth system models across different spati…
Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers
Shuochen Wang, Nishant Yadav, Auroop R. Ganguly
One of the major sources of uncertainty in the current generation of Global Climate Models (GCMs) is the representation of sub-grid scale physical processes. Over the years, a seri…
Finer resolutions and targeted process representations in earth systems models improve hydrologic projections and hydroclimate impacts
Puja Das, Auroop R. Ganguly
Earth system models inform water policy and interventions, but knowledge gaps in hydrologic representations limit the credibility of projections and impacts assessments. The litera…
Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcasting
Puja Das, August Posch, Nathan Barber +6
Precipitation nowcasting, critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) su…
Topological Determinants of Resilience in Urban Rail Networks Facing Multi-Hazard Disruptions
Ashis Kumar Pal, Auroop R. Ganguly
This study examines the failure and recovery, two key components of resilience of nine major urban rail networks - Washington DC, Boston, Chicago, Delhi, Tokyo, Paris, Shanghai, Lo…
Assessing the Impact of Distribution Shift on Reinforcement Learning Performance
Ted Fujimoto, Joshua Suetterlein, Samrat Chatterjee +1
Research in machine learning is making progress in fixing its own reproducibility crisis. Reinforcement learning (RL), in particular, faces its own set of unique challenges. Compar…
A Tri-Level Optimization Model for Interdependent Infrastructure Network Resilience Against Compound Hazard Events
Matthew R. Oster, Ilya Amburg, Samrat Chatterjee +4
Resilient operation of interdependent infrastructures against compound hazard events is essential for maintaining societal well-being. To address consequence assessment challenges…
CDA: Contrastive-adversarial Domain Adaptation
Nishant Yadav, Mahbubul Alam, Ahmed Farahat +3
Recent advances in domain adaptation reveal that adversarial learning on deep neural networks can learn domain invariant features to reduce the shift between source and target doma…
Robust Causality and False Attribution in Data-Driven Earth Science Discoveries
Elizabeth Eldhose, Tejasvi Chauhan, Vikram Chandel +2
Causal and attribution studies are essential for earth scientific discoveries and critical for informing climate, ecology, and water policies. However, the current generation of me…
Ad Hoc Teamwork in the Presence of Adversaries
Ted Fujimoto, Samrat Chatterjee, Auroop Ganguly
Advances in ad hoc teamwork have the potential to create agents that collaborate robustly in real-world applications. Agents deployed in the real world, however, are vulnerable to…
Predictions of damages from Atlantic tropical cyclones: a hierarchical Bayesian study on extremes
Lindsey Dietz, Sakshi Arya, Vishal Subedi +2
Bayesian hierarchical models are proposed for modeling tropical cyclone characteristics and their damage potential in the Atlantic basin. We model the joint probability distributio…
Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations
Nishant Yadav, Meytar Sorek-Hamer, Michael Von Pohle +9
Urban air pollution is a public health challenge in low- and middle-income countries (LMICs). However, LMICs lack adequate air quality (AQ) monitoring infrastructure. A persistent…
Climate-mediated shifts in temperature fluctuations promote extinction risk
Kate Duffy, Tarik C. Gouhier, Auroop R. Ganguly
Climate-mediated changes in the spatiotemporal distribution of thermal stress can destabilize animal populations and promote extinction risk. Using quantile, spectral, and wavelet…