About

Upmanu Lall is the director of the Water Institute at the Julie Ann Wrigley Global Futures Laboratory at Arizona State University and a professor in the School of Complex Adaptive Systems within the College of Global Futures. Prior to joining ASU in January 2024, he was the Alan and Carol Silberstein Professor of Engineering at Columbia University and served as Director of the Columbia Water Center. His work encompasses hydrology, climate dynamics, statistics, machine learning, and risk/insurance analysis, with a focus on developing and implementing financial, technical, and policy solutions for water and climate challenges across various scales from village to country on all major continents. Lall has led initiatives such as the Global Water Sustainability Initiative, the Global Flood Initiative, and the America’s Water Initiative, contributing significantly to the field through research and collaboration with development banks, governments, and the private sector.

Research topics

• Environmental science
• Geography
• Meteorology
• Ecology
• Engineering
• Geology
• Environmental planning
• Natural resource economics
• Water resource management
• Atmospheric sciences
• Environmental resource management
• Economics
• Physics
• Computer Science
• Electrical engineering
• Statistical physics
• Climatology
• Environmental health
• Economic growth
• Business
• Civil engineering
• Risk analysis (engineering)
• Physical geography
• Cartography

Selected publications

• Instability-Aware Perturbations of Extreme Events in an AI Weather Foundation Model

  2026-03-14

  articleOpen accessSenior author

  Extreme weather events are intensifying under climate change, yet recent advances in weather prediction operate within a forecast-only paradigm that does not directly mitigate impacts once an extreme event is anticipated. Motivated by chaos control theory, we explore whether small, instability-aware perturbations can leverage intrinsic atmospheric sensitivity to influence extreme weather evolution within an AI-based forecasting framework. We use the Aurora foundation model and identify dynamically sensitive perturbation locations using Finite-Time Lyapunov Exponent (FTLE) diagnostics. To implement a physically interpretable intervention compatible with foundation models, we introduce an idealized cloud seeding based perturbation operator that mimics condensation-driven latent heat release applied in the lower–mid troposphere. In a case study, these upstream perturbations induce coherent downstream changes in integrated vapor transport, leading to reduced peak landfall intensity and slower precipitation accumulation. These results demonstrate that instability-aware perturbations within an AI foundation model can induce dynamically meaningful downstream impacts, providing a first step toward bridging chaos control concepts and data-driven weather prediction.

  PublisherDOI

• Instability-Aware Steering of an Extreme Atmospheric River in an AI Weather Foundation Model

  arXiv (Cornell University) · 2026-04-20

  preprintOpen accessSenior author

  Advances in deep learning methods for weather forecasting are creating opportunities to computationally explore the potential for steering or control of extreme weather trajectories for societal risk reduction. We present initial investigations into the feasibility of redirecting extreme atmospheric rivers (ARs) through small, instability-aware perturbations. Using the Aurora AI weather foundation model, we identify sensitive upstream locations using finite-time Lyapunov exponents and jet-eddy interaction criteria. We apply an idealized cloud-seeding operator that mimics latent heat release to assess whether these Lyapunov-guided interventions can influence downstream evolution. In a case study of a severe California AR, perturbations induce coherent downstream shifts in moisture transport, reducing intensity at landfall under favorable kinematic conditions. The response is nonlinear and contingent on the local flow geometry. These initial results suggest that the atmosphere's intrinsic chaotic sensitivity could be leveraged for dynamical control, offering a new research direction for extreme event risk mitigation.

  PublisherDOI

• A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)

  arXiv (Cornell University) · 2026-05-19

  preprintOpen accessSenior author

  Reliable assessment of tropical cyclone (TC) risk is limited by the brevity and spatial sparsity of the historical record, particularly for the rare, high-intensity landfalls that dominate insured loss. We present WHITS (Wind-focused Hurricane Interactive Track Simulator), a non-parametric semi-Markov track generator that extends the HITS framework of Nakamura et al. (2015) in three ways: transitions between historical track segments are conditioned on local wind speed in addition to position, age, and forward vector; the kernel selection on the comparative-vector term is sharpened to suppress dynamically inconsistent jumps; and a short smoothing window is applied across each transition to remove the position and wind discontinuities reported by downstream surge users. WHITS is fit to the full available best-track record in each of six basins in IBTrACS, extending in the North Atlantic to 1851 and in other basins to the earliest year of reliable best-track data. The resulting 10,000-yr global synthetic catalog reproduces observed track density and the annual hurricane/typhoon-force wind-hit probability across all basins. The catalog is intended for catastrophe-risk applications where a large, low-bias sample of physically plausible tracks is more useful than a small, statistically corrected one.

  PublisherDOI

• Hydroclimatic and anthropogenic fingerprints drive the game between surface-water and sediment microplastics

  Communications Earth & Environment · 2026-04-28

  articleOpen access

  Microplastics are pervasive in inland waters, yet large-scale association patterns of abundance and traits remain unclear. Here we compiled a global lake database integrating surface-water and sediment observations with 56 hydroclimatic and anthropogenic indicators. Most hydroclimatic variables show positive associations with abundance in surface waters but act oppositely in sediments. Leading contribution patterns differ: leaf area index and 2-m dew-point temperature rank highest in surface waters, whereas solar radiation and wind speed lead in sediments. To enable finer-scale evaluation, we conducted dense sampling in China, which hosts one of the world’s largest and most intensively studied lake systems. Hydroclimatic and anthropogenic fingerprints show clear cross-compartment contrasts: surface-water traits associate strongly with aquaculture production and rural income, while sediment traits more link to population aged 0–14 and chemical oxygen demand. Our findings imply a hydroclimate–human game across compartments, highlighting fingerprints structuring microplastic abundance and traits. Dominant hydroclimatic and anthropogenic factors and their association with microplastic abundance and traits differ markedly between surface waters and sediments, based on a meta-analysis of 56 hydroclimatic and anthropogenic indicators.

  PublisherOA PDFDOI

• A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)

  ArXiv.org · 2026-05-19

  articleOpen accessSenior author

  Reliable assessment of tropical cyclone (TC) risk is limited by the brevity and spatial sparsity of the historical record, particularly for the rare, high-intensity landfalls that dominate insured loss. We present WHITS (Wind-focused Hurricane Interactive Track Simulator), a non-parametric semi-Markov track generator that extends the HITS framework of Nakamura et al. (2015) in three ways: transitions between historical track segments are conditioned on local wind speed in addition to position, age, and forward vector; the kernel selection on the comparative-vector term is sharpened to suppress dynamically inconsistent jumps; and a short smoothing window is applied across each transition to remove the position and wind discontinuities reported by downstream surge users. WHITS is fit to the full available best-track record in each of six basins in IBTrACS, extending in the North Atlantic to 1851 and in other basins to the earliest year of reliable best-track data. The resulting 10,000-yr global synthetic catalog reproduces observed track density and the annual hurricane/typhoon-force wind-hit probability across all basins. The catalog is intended for catastrophe-risk applications where a large, low-bias sample of physically plausible tracks is more useful than a small, statistically corrected one.

  PublisherOA PDF

• A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)

  2026-05-21

  articleOpen accessSenior author

  Reliable assessment of tropical cyclone (TC) risk is limited by the brevity and spatial sparsity of the historical record, particularly for the rare, high-intensity landfalls that dominate insured loss. We present WHITS (Wind-focused Hurricane Interactive Track Simulator), a non-parametric semi-Markov track generator that extends the HITS framework of Nakamura et al. (2015) in three ways: transitions between historical track segments are conditioned on local wind speed in addition to position, age, and forward vector; the kernel selection on the comparative-vector term is sharpened to suppress dynamically inconsistent jumps; and a short smoothing window is applied across each transition to remove the position and wind discontinuities reported by downstream surge users. WHITS is fit to the full available best-track record in each of six basins in IBTrACS, extending in the North Atlantic to 1851 and in other basins to the earliest year of reliable best-track data. The resulting 10,000-yr global synthetic catalog reproduces observed track density and the annual hurricane/typhoon-force wind-hit probability across all basins. The catalog is intended for catastrophe-risk applications where a large, low-bias sample of physically plausible tracks is more useful than a small, statistically corrected one.

  PublisherOA PDFDOI

• Evaluating the Predictability of Selected Weather Extremes with Aurora, an AI Weather Forecast Model

  Open MIND · 2026-03-06

  preprintSenior author

  AI weather foundation models now achieve forecast skill comparable to numerical weather prediction at far lower computational cost, yet their predictability for high-impact extremes across dynamical regimes remains uncertain. We evaluate Aurora using an event-based framework spanning tropical cyclones, freezes, heatwaves, atmospheric rivers, and extreme precipitation at lead times from 1 to 21 days. Aurora demonstrates strong short-range (1-7 day) skill across event types, including competitive tropical cyclone track accuracy and high spatial agreement for temperature and moisture extremes. However, a consistent subseasonal failure mode emerges: while large-scale circulation patterns remain moderately skillful at 14-21 day leads, threshold-based extreme intensity collapses as fields regress toward climatology. This divergence indicates that Aurora retains synoptic-scale dynamical structure but loses surface-impact amplitude beyond 7-10 days. The practical predictability horizon for deterministic AI extreme-event forecasting therefore remains constrained by intrinsic atmospheric dynamics.

  DOI

• Urban Flood Observations (UFO): A hand-labeled training and validation dataset of post-flood inundation

  ArXiv.org · 2026-04-24

  articleOpen access

  Urban flooding affects lives and infrastructure worldwide. Mapping inundation in complex urban environments from satellite imagery remains challenging due to limited spatial resolution, infrequent acquisitions, and cloud cover. We present Urban Flood Observations (UFO), a global, hand-labeled dataset of post-flood inundation in diverse urban settings. UFO comprises 215 image chips (1024 by 1024 pixels) from 14 flood events between 2017 and 2021, derived from 3 m PlanetScope imagery. Each chip is annotated with two classes: 'inundated' (all visible surface water, including floodwater and pre-existing water bodies (permanent or seasonal)) and 'non-inundated'. To demonstrate the dataset's utility, we trained a segmentation model using leave-one-event-out cross-validation, achieving a mean Intersection over Union (IoU) of 77.3. We also used UFO to evaluate two widely used surface water products, the Sentinel-1-based NASA IMPACT model and Google's 10 m Dynamic World water class, which yielded IoUs of 44.1 and 48.1, respectively. UFO is publicly available to support the development and validation of urban inundation mapping methods.

  PublisherOA PDF

• Instability-Aware Steering of an Extreme Atmospheric River in an AI Weather Foundation Model

  ArXiv.org · 2026-04-20

  articleOpen accessSenior author

  Advances in deep learning methods for weather forecasting are creating opportunities to computationally explore the potential for steering or control of extreme weather trajectories for societal risk reduction. We present initial investigations into the feasibility of redirecting extreme atmospheric rivers (ARs) through small, instability-aware perturbations. Using the Aurora AI weather foundation model, we identify sensitive upstream locations using finite-time Lyapunov exponents and jet-eddy interaction criteria. We apply an idealized cloud-seeding operator that mimics latent heat release to assess whether these Lyapunov-guided interventions can influence downstream evolution. In a case study of a severe California AR, perturbations induce coherent downstream shifts in moisture transport, reducing intensity at landfall under favorable kinematic conditions. The response is nonlinear and contingent on the local flow geometry. These initial results suggest that the atmosphere's intrinsic chaotic sensitivity could be leveraged for dynamical control, offering a new research direction for extreme event risk mitigation.

  PublisherOA PDF

• Weather Jiu-Jitsu: Prospects for Atmospheric Nudging to Defuse the Impact of Catastrophic Weather Extremes

  2026-03-14

  articleOpen accessSenior author

  Extreme weather events, e.g., droughts, floods, heatwaves, freezes, increasingly challenge physical, financial, and social infrastructure as population and economic growth increase exposure and vulnerability. We propose supplementing conventional disaster risk management strategies with Weather Jiu-Jitsu, an approach that leverages the chaotic dynamics of weather systems to redirect or dissipate destructive trajectories through targeted, low-energy perturbations. Coupled with deep learning models, this framework could serve as a form of nature-assisted global infrastructure to reduce catastrophic climate-extreme impacts in the 21st century. We demonstrate the potential of this strategy through successful perturbation experiments applied to tropical cyclones, atmospheric rivers, freezes, and other high-impact events.

  PublisherDOI

Recent grants

• WSC-Category 3 Collaborative: America's Water - The Changing Landscape of Risk, Competing Demands and Climate

  NSF · $2.5M · 2014–2019

• Belmont Forum Collaborative Research:Data-driven Disaster Response Systems Dependent on Time of Day, Season and Location for Megacities

  NSF · $300k · 2021–2025

• NSF Convergence Accelerator Track D America's Water Risk: Water System Data Pooling for Climate Vulnerability Assessment and Warning System

  NSF · $1000k · 2020–2023

Frequent coauthors

• Naresh Devineni

  City College of New York

  125 shared

• Balaji Rajagopalan

  86 shared

• Edward R. Cook

  Columbia University

  41 shared

• Francesco Cioffi

  Sapienza University of Rome

  40 shared

• Xun Sun

  39 shared

• Hyun‐Han Kwon

  Sejong University

  36 shared

• Michael R. Petersen

  United States Army Command and General Staff College

  36 shared

• D. Nagesh Kumar

  Indian Institute of Science Bangalore

  36 shared

Education

• Ph.D., Civil and Environmental Engineering

  University of Texas at Austin

  1981

• M.S., Civil and Environmental Engineering

  University of Texas at Austin

  1980

• Other, Civil Engineering

  Indian Institute of Technology Kanpur, U.P., India

  1976

Awards & honors

• American Society of Civil Engineers Arid Lands Hydrology Awa…
• European Geosciences Union Henry Darcy Medal (2014)
• American Geophysical Union President of the Natural Hazards…
• American Geophysical Union Fellow (2017)
• American Association for the Advancement of Science Fellow (…