About

Amir Aghakouchak is a Chancellor’s Professor of Civil and Environmental Engineering at the University of California, Irvine, where he also serves as the director of the Center for Hydrometeorology and Remote Sensing (CHRS). His research focuses on natural hazards and climate extremes, bridging the disciplines of hydrology, climatology, and remote sensing. A central theme of his work is the study of interactions among climatic and nonclimatic hazards, particularly compound and cascading events. He has developed innovative tools for monitoring and modeling hydrologic extremes and has organized international meetings and training workshops in various regions for multiple agencies, including the U.S. State Department, NOAA, UNESCO, and others.

Research topics

• Computer Science
• Environmental science
• Geography
• Ecology
• Political Science
• Sociology
• Environmental resource management
• Business
• Psychology
• Economic growth
• Risk analysis (engineering)
• Machine Learning
• Cartography
• Biology
• Computer Security
• Geology
• Meteorology
• Socioeconomics
• Atmospheric sciences
• Agroforestry
• World Wide Web
• Chemistry
• Agronomy
• Database

Selected publications

• Hazards Beyond Belief: Storylines for the most extreme disasters

  2026-03-14

  articleOpen access

  Multiple recent weather and climate disasters have shattered assumptions about the nature of regional climate risks. This power of surprise comes not just from the unprecedented severity of the disasters’ component hazards, but from the intricate system interactions that have led to their devastating impacts. The usual tools for risk characterization are particularly challenged by events that, like these, stretch the limits of experience, observation, imagination, and/or modeling capability. Here, we draw from several recent projects to describe the development and application of ‘complex-risk’ storylines that arc from blue-sky discussion of fundamental uncertainties and event conceptualization through to hazard-impact-response cascades and potential state changes in natural and human systems. Such storylines entrain diverse types of knowledge to flexibly envision and strategize for yet-unrealized risks spanning a range of timescales and socioenvironmental conditions. We use our narrative set to identify 12 major emergent themes across physical, social, and institutional domains, and discuss how these themes can contribute crucial guidance for anticipating what the next unprecedented disaster might look like — and thus how to design basic and applied research that speaks to it. The themes also provide a framework that helps highlight historically overlooked geographies, hazard combinations, and event dynamics. We conclude by enumerating several stubborn cross-disciplinary challenges that we see complicating extreme-weather risk calculations, and discuss the potential for this storyline approach, among other techniques, to foster productive insights.

  PublisherDOI

• Transboundary water conflicts, cooperation, and pathways forward

  Proceedings of the National Academy of Sciences · 2026-02-04

  articleOpen access1st author
  PublisherOA PDFDOI

• Increasing global human exposure to wildland fires despite declining burned area

  2026-03-13

  articleOpen accessSenior author

  Although half of Earth’s population resides in the wildland-urban interface, human exposure to wildland fires remains unquantified. We show that the population directly exposed to wildland fires increased 40% globally from 2002 to 2021 despite a 26% decline in burned area. Increased exposure was mainly driven by enhanced colocation of wildland fires and human settlements, doubling the exposure per unit burned area. We show that population dynamics accounted for 25% of the 440 million human exposures to wildland fires. Although wildfire disasters in North America, Europe, and Oceania have garnered the most attention, 85% of global exposures occurred in Africa. The top 0.01% of fires by intensity accounted for 0.6 and 5% of global exposures and burned area, respectively, warranting enhanced efforts to increase fire resilience in disaster-prone regions.

  PublisherDOI

• Global drought extremes in 2025

  Nature Reviews Earth & Environment · 2026-04-20

  articleSenior author
  PublisherDOI

• Impacts of Meteorological, Hydrological, and Compound Droughts on the Precipitation–Runoff Relationship Across Timescales

  Earth s Future · 2026-04-01

  articleOpen accessSenior author

  Abstract Droughts alter the precipitation‐runoff (PR) relationship, thereby influencing water resources planning and management. Previous studies have mainly focused on the influence of multi‐year droughts on PR relationship. The influence of different drought types across various timescales (monthly, seasonal, and annual) remains understudied. In this study, we applied a Cumulative Distribution Function‐Linear Regression‐Random Forest (CDF‐LR‐RF) algorithm to evaluate, bias correct, and integrate multi‐source precipitation data sets for drought assessment in a data‐poor basin. Then, meteorological drought (MD) and hydrological drought (HD) were identified using the Standardized Precipitation‐Evapotranspiration Index (SPEI) and Standardized Runoff Index (SRI), respectively, with compound drought (CD) events defined where MD and HD overlapped. The PR relationship was characterized using Slope (conversion speed) and R 2 (relationship strength). The approach was tested in the source region of the Yellow River basin (SRYB) in northwest China. The key findings include: (a) The CDF‐LR‐RF method efficiently integrates multi‐source precipitation data, significantly improving accuracy for drought assessment. (b) In the SRYB, MD, HD, and CD exhibit clear time‐scale effects, with CD being more severe and longer‐lasting than MD, but less so than HD. (c) Different drought types impact the PR relationship differently across timescales, with short‐term droughts (identified through monthly and seasonal SPEI/SRI) showing a stronger effect than long‐term droughts (e.g., annual scale). (d) CD influences both the strength and conversion speed of the PR relationship, with its impact generally stronger than MD but weaker than HD. These insights help managers predict water availability and target drought responses for specific drought types.

  PublisherDOI

• Iran’s policy priorities intensify water crisis

  Nature Sustainability · 2026-03-13

  articleSenior author
  PublisherDOI

• Accounting for Extremes in Modeling the Size and Likelihood of Large Fires in the United States

  Earth s Future · 2026-05-01

  articleOpen access

  Abstract Wildfires pose growing threats to ecosystems, infrastructure, and communities, yet fire size distributions are often modeled without sufficient attention to rare, high‐impact events. Most statistical approaches emphasize the central body of the distribution, which can obscure the behavior of extreme wildfires (i.e., tail events) that have the most significant impact. Here, we introduce a framework that explicitly distinguishes between the statistical characteristics of body and tail fire size distributions. We analyzed 30,331 large fire perimeters from the Monitoring Trends in Burn Severity (MTBS) data set (1984–2024), disaggregated across 105 Level III ecoregions and 10 Geographic Area Coordination Centers (GACCs) across the United States. For each region, we fit three candidate distributions (Pareto Type II, lognormal, Weibull) to both the full data set and an exceedance‐based tail sample. Results show that models calibrated only to the body can substantially misestimate the exceedance probability (by up to three orders of magnitude) and burned area (by hundreds of thousands of acres) for large fires. For the body of the distribution, the lognormal and Pareto Type II distributions generally outperform the Weibull. For the tail, the picture is more nuanced: the Pareto Type II and lognormal perform comparably at the ecoregion level, while the Weibull, despite being the weakest body model, provides the best tail fit at the GACC scale. These findings highlight the need to model body and tail behavior separately and show that optimal distributions vary across scales, underscoring the importance of region‐specific tail models for suppression budgeting, fuel treatment, and resilience planning.

  PublisherDOI

• Flash droughts exacerbate global vegetation loss and delay recovery

  Nature Communications · 2025-12-08 · 27 citations

  articleOpen access

  The increasing incidence of flash droughts globally presents a great challenge to the agriculture sector, ecosystem resilience and water resource systems. Here we introduce a methodology that improves the accuracy of quantifying drought-induced global vegetation loss (using Normalized Difference Vegetation Index (NDVI)-derived metric). Our results reveal that NDVI loss during flash droughts (9.0%) is approximately 1.5 times higher than that during conventional droughts (5.3%), highlighting the increasing role of flash droughts as the key driver of drought-induced NDVI loss worldwide. Furthermore, we identify a significant upward trend (1.8% per decade) in global NDVI loss due to flash droughts, primarily driven by the increasing frequency of such events, which account for 81.2% of the overall trend. Although NDVI typically recovers within 36 pentads across more than 9256.3 × 104 km2 of the global land surface after flash droughts, there is a notable increase (0.4 pentads per year) in NDVI recovery time from 1982 to 2020, particularly in tropical rainforests and temperate forests. These findings highlight the alarming ecological consequences of increasingly frequent and intense flash droughts, with impacts expected to intensify in the future. Climate change is increasing the frequency of flash droughts worldwide, posing threats to global ecosystems. This study suggests that flash droughts cause 1.5 times greater vegetation loss than conventional droughts and delay ecosystem recovery, with impacts intensifying over recent decades.

  PublisherDOI

• A global analysis of the influence of shallow and deep groundwater tables on relationships between environmental parameters and heatwaves

  Environmental Research · 2025-11-20 · 1 citations

  articleOpen access

  Heatwaves increasingly impact ecosystems, human health, and economic activities worldwide. As their frequency and intensity rise, understanding the mechanisms driving heatwave dynamics and interactions with land surface processes becomes crucial. While numerous studies have examined atmospheric and land surface variables, the role of groundwater, through its effects on soil moisture and surface evaporative fluxes, remains less understood. Although modeling approaches at various scales have enhanced our understanding of groundwater-atmosphere coupling, machine learning (ML) enables capturing complex, nonlinear interactions and evaluating the relative importance of key drivers globally. We developed pixel-based ML models to estimate global summer heatwave frequency over the past 21 years. For each pixel, we considered data within a 1.5° radius (149 neighboring pixels), identified as the optimal scale through a saturation radius analysis. We used feature importance metrics to identify the dominant drivers among surface fluxes, land characteristics, atmospheric and hydrological variables, and interpreted these results in relation to contrasting groundwater depths (<10 m and >100 m). We ensured robustness using 10-fold cross-validation and confirmed that results were not driven by randomness with two additional validation runs on a subset of the data, with shuffled targets and randomized covariates. Our findings suggest that geopotential height showed the highest relative importance among predictors in regions with deep groundwater tables, while in areas with shallow groundwater, surface fluxes emerge as the key contributor. Incorporating groundwater-related processes may therefore improve understanding of land-atmosphere interactions and support more robust assessments of future heatwave risks.

  PublisherDOI

• Global hydroclimatic risks and strategic decommissioning pathways for thermal power units

  Nature Sustainability · 2025-12-09 · 1 citations

  article
  PublisherDOI

Recent grants

• I-Corps: Global Integrated Drought Monitoring and Prediction System

  NSF · $50k · 2013–2015

• Collaborative Research: Resilience of Geotechnical Infrastructure under a Changing Climate: Quantitative Assessment for Extreme Events

  NSF · $240k · 2016–2019

• A Nested Multi-Scale Hydrological Modeling Framework: Assessing Resilience and Vulnerability to Climate Change

  NSF · $225k · 2013–2017

• CLIMA/Collaborative Research: Equitable Adaptive Strategies for Flood Protection Infrastructure under Current and Future Compound Hazards

  NSF · $325k · 2023–2026

• Frameworks: Collaborative Proposal: Software Infrastructure for Transformative Urban Sustainability Research

  NSF · $260k · 2019–2025

Frequent coauthors

• Mojtaba Sadegh

  Boise State University

  137 shared

• Hamed Moftakhari

  125 shared

• Farshid Vahedifard

  Tufts University

  105 shared

• Laurie S. Huning

  University of California, Irvine

  97 shared

• Omid Mazdiyasni

  University of California, Irvine

  94 shared

• Iman Mallakpour

  Irvine University

  82 shared

• Brett F. Sanders

  Irvine University

  80 shared

• Soroosh Sorooshian

  University of California, Irvine

  75 shared

Awards & honors

• Plinius Medal, European Geosciences Union (EGU) (2026)
• Robert E. Horton Lecturer in Hydrology, American Meteorologi…
• Clarivate Highly Cited Researcher Award Recipient (2020, 202…
• ASCE Norman Medal (2022)
• ASCE Huber Prize (2020)