Gabriele Villarini

Princeton University

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Gabriel A. Vecchi

Princeton University

James A. Smith

Princeton University

Witold F. Krajewski

University of Iowa

Wei Zhang

Chinese Academy of Fishery Sciences

Mary Lynn Baeck

Princeton University

Hiroyuki Murakami

Pharmaceuticals and Medical Devices Agency

Enrico Scoccimarro

CMCC Foundation - Euro-Mediterranean Center on Climate Change

Louise Slater

University of Oxford

Thomas L. Delworth

NOAA Geophysical Fluid Dynamics Laboratory

Thomas R. Knutson

NOAA Geophysical Fluid Dynamics Laboratory

Cooperative Institutions

Princeton University

University of Iowa

National Oceanic and Atmospheric Administration

NOAA Geophysical Fluid Dynamics Laboratory

NSF National Center for Atmospheric Research

University of Reading

University Corporation for Atmospheric Research

Centre National de la Recherche Scientifique

Columbia University

Met Office

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Estimation of radar-rainfall error spatial correlation

Advances in Water Resources (2008)

Pradeep V. Mandapaka Witold F. Krajewski Grzegorz J. Ciach Gabriele Villarini James A. Smith

Spatial correlation

Spatial Dependence

Weather radar

10.1016/j.advwatres.2008.08.014

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Citations (61)

Riverine Flooding and Landfalling Tropical Cyclones Over China

Earth s Future (2020)

Long Yang Gabriele Villarini Zhenzhong Zeng James A. Smith Maofeng Liu

Abstract Riverine flooding associated with landfalling tropical cyclones (TCs) in the western North Pacific basin is responsible for some of the most severe socioeconomic losses in East Asian countries. However, little is known about the spatial and temporal patterns of TC flooding and its climate controls, which constrain the predictive understandings of flood risk in this highly populated region. We provide a climatological characterization of TC flooding over China based on an exceptional network of stream gauging stations across the entire country. The most extreme floods in central and northeastern China are associated with TCs despite infrequent TC visits in these regions. Temporal variations in TC flooding demonstrate a mixture of controls tied to surface temperature anomalies in the northern hemisphere. The established links between TC flooding and climate controls present a potentially predictive tool of TC flood risk over China and other East Asian countries under future climate conditions.

Typhoon

Pacific basin

10.1029/2019ef001451

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Citations (18)

Climate change projected to impact structural hillslope connectivity at the global scale

Nature Communications (2023)

Alexander Michalek Gabriele Villarini Admin Husic

Abstract Structural connectivity describes how landscapes facilitate the transfer of matter and plays a critical role in the flux of water, solutes, and sediment across the Earth’s surface. The strength of a landscape’s connectivity is a function of climatic and tectonic processes, but the importance of these drivers is poorly understood, particularly in the context of climate change. Here, we provide global estimates of structural connectivity at the hillslope level and develop a model to describe connectivity accounting for tectonic and climate processes. We find that connectivity is primarily controlled by tectonics, with climate as a second order control. However, we show climate change is projected to alter global-scale connectivity at the end of the century (2070 to 2100) by up to 4% for increasing greenhouse gas emission scenarios. Notably, the Ganges River, the world’s most populated basin, is projected to experience a large increase in connectivity. Conversely, the Amazon River and the Pacific coast of Patagonia are projected to experience the largest decreases in connectivity. Modeling suggests that, as the climate warms, it could lead to increased erosion in source areas, while decreased rainfall may hinder sediment flow downstream, affecting landscape connectivity with implications for human and environmental health.

Landscape connectivity

10.1038/s41467-023-42384-2

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Citations (6)

Tropical cyclone precipitation in the HighResMIP atmosphere-only experiments of the PRIMAVERA Project

Climate Dynamics (2021)

Wei Zhang Gabriele Villarini Enrico Scoccimarro Malcolm Roberts Pier Luigi Vidale

Atmospheric models

10.1007/s00382-021-05707-x

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Citations (40)

Projected changes in monthly baseflow across the U.S. Midwest

International Journal of Climatology (2021)

Jessica R. Ayers Gabriele Villarini Keith E. Schilling Christopher S. Jones

Abstract Baseflow is an essential water resource because it is the groundwater discharged to streams and represents long‐term storage. Understanding its future changes is a major concern for water supply and ecosystem health. This study examines the impacts of climate and agriculture on monthly baseflow in the U.S. Midwest through the end of the 21st century. We use a statistical approach to evaluate three scenarios. The first scenario is based on downscaled and bias corrected global climate model (GCM) outputs and the representative concentration pathway (RCP) 8.5, and agriculture is held constant (and equal to the mean from 2013 to 2019). In the next two scenarios, climate is held constant (2010–2019) to isolate the impact of agriculture on baseflow. In terms of agricultural changes, we consider scenarios representative of either increases or decreases with respect to the production of corn and soybeans. Changes in the climate system point to increases in baseflow that are likely a result of increased precipitation and antecedent wetness. Seasonally, warmer temperature in the winter and spring (i.e., February to July) is expected to cause increasing trends in baseflow. Changes in land use showed that agriculture would either mitigate the impact of climate change or possibly amplify it. Expanding corn and soybean areas would increase baseflow in the Corn Belt region. On the other hand, converting land back to perennial vegetation would decrease baseflow throughout the entire year. Despite its simplicity, this study can provide basic information to understand where to expect adverse effects on baseflow and thus improve land management practices in those areas.

Base flow

10.1002/joc.7140

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Citations (3)

Assessing Current and Future Freshwater Flood Risk from North Atlantic Tropical Cyclones via Insurance Claims

Scientific Reports (2017)

Jeffrey Czajkowski Gabriele Villarini Marilyn Montgomery Erwann Michel‐Kerjan Radosław Goska

Abstract The most recent decades have witnessed record breaking losses associated with U.S. landfalling tropical cyclones (TCs). Flood-related damages represent a large portion of these losses, and although storm surge is typically the main focus in the media and of warnings, much of the TC flood losses are instead freshwater-driven, often extending far inland from the landfall locations. Despite this actuality, knowledge of TC freshwater flood risk is still limited. Here we provide for the first time a comprehensive assessment of the TC freshwater flood risk from the full set of all significant flood events associated with U.S. landfalling TCs from 2001 to 2014. We find that the areas impacted by freshwater flooding are nearly equally divided between coastal and inland areas. We determine the statistical relationship between physical hazard and residential economic impact at a community level for the entire country. These results allow us to assess the potential future changes in TC freshwater flood risk due to changing climate pattern and urbanization in a more heavily populated U.S. Findings have important implications for flood risk management, insurance and resilience.

Storm Surge

Coastal flood

Flood insurance

Landfall

Resilience

10.1038/srep41609

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Citations (68)

Disentangling the Sources of Uncertainties in the Projection of Flood Risk Across the Central United States (Iowa)

Geophysical Research Letters (2023)

Alexander Michalek Gabriele Villarini Taereem Kim Felipe Quintero Witold F. Krajewski

Abstract We explore the projected changes in flood impacts across Iowa (central United States) and the associated uncertainties by forcing a hydrologic model with downscaled global climate model outputs and four Shared Socioeconomic Pathways. Our results point to projected increasing magnitude and variability in flooding across the state, especially for high‐emission scenarios. Next, we partition the flood impacts' projections into: (a) the response of the global climate models to anthropogenic forcing, (b) scenario uncertainty due to emissions, and (c) internal climate variability. We find scenario uncertainty plays a small role, while climate model uncertainty and internal climate variability dominate the flood impacts' projections, with the contribution of model uncertainty increasing toward the end of this century. Insights from our work can be utilized by stakeholders to understand the current limitations of flood impact projections and provide suggestions about where modelers should focus efforts to reduce uncertainty.

Forcing (mathematics)

10.1029/2023gl105852

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Citations (3)

Influence of Urbanization on Precipitation and Flooding Caused by Landfalling Tropical Cyclones: The Case of Houston

Gabriele Villarini Wei Zhang Gabriel A. Vecchi James Smith

<p>We examine the impact of urbanization on precipitation and flooding caused by tropical cyclones under a dynamical modeling framework, using Hurricane Harvey (2017) and Tropical Storms Allison (2001) and Imelda (2019) as case studies. Hurricane Harvey poured more than a metre of rainfall across the heavily populated Houston area, leading to unprecedented flooding and damage. Although studies have focused on the contribution of anthropogenic climate change to this extreme rainfall event, limited attention has been paid to the potential effects of urbanization on the hydrometeorology associated with this hurricane. Here we find that urbanization exacerbated not only the flood response but also the storm total rainfall. Using the Weather Research and Forecast model&#8212;a numerical model for simulating weather and climate at regional scales&#8212;and statistical models, we quantify the contribution of urbanization to rainfall and flooding. We expand these analyses to examine the impacts of urbanization on Tropical Storms Allison and Imelda, two other storms that affected the Houston area causing widespread heavy rainfall and flooding.</p>

Hydrometeorology

Severe weather

10.5194/egusphere-egu2020-2468

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Statistical–Dynamical Seasonal Forecast of North Atlantic and U.S. Landfalling Tropical Cyclones Using the High-Resolution GFDL FLOR Coupled Model

Monthly Weather Review (2016)

Hiroyuki Murakami Gabriele Villarini Gabriel A. Vecchi Wei Zhang R. Gudgel

Abstract Retrospective seasonal forecasts of North Atlantic tropical cyclone (TC) activity over the period 1980–2014 are conducted using a GFDL high-resolution coupled climate model [Forecast-Oriented Low Ocean Resolution (FLOR)]. The focus is on basin-total TC and U.S. landfall frequency. The correlations between observed and model predicted basin-total TC counts range from 0.4 to 0.6 depending on the month of the initial forecast. The correlation values for U.S. landfalling activity based on individual TCs tracked from the model are smaller and between 0.1 and 0.4. Given the limited skill from the model, statistical methods are used to complement the dynamical seasonal TC prediction from the FLOR model. Observed and predicted TC tracks were classified into four groups using fuzzy c-mean clustering to evaluate the model’s predictability in observed classification of TC tracks. Analyses revealed that the FLOR model has the highest skill in predicting TC frequency for the cluster of TCs that tracks through the Caribbean and the Gulf of Mexico. New hybrid models are developed to improve the prediction of observed basin-total TC and landfall TC frequencies. These models use large-scale climate predictors from the FLOR model as predictors for generalized linear models. The hybrid models show considerable improvements in the skill in predicting the basin-total TC frequencies relative to the dynamical model. The new hybrid model shows correlation coefficients as high as 0.75 for basinwide TC counts from the first two lead months and retains values around 0.50 even at the 6-month lead forecast. The hybrid model also shows comparable or higher skill in forecasting U.S. landfalling TCs relative to the dynamical predictions. The correlation coefficient is about 0.5 for the 2–5-month lead times.

Landfall

Predictability

Oceanic basin

10.1175/mwr-d-15-0308.1

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Citations (62)

Changes in the Seasonality of North Atlantic Tropical Cyclones

Dario Treppiedi Gabriele Villarini Leonardo Noto Enrico Scoccimarro Wenchang Yang

The North Atlantic hurricane season is officially recognized to start on June 1st and end on November 30th. The awareness of this time window is crucial both for federal, state and local agencies as well as the general public to put in place preparation and mitigation efforts to mitigate the impacts of this natural hazard. However, there is an underlying assumption of stationarity in the seasonality of these storms, implying a lack of narrowing or expanding of the hurricane season over the years. Here, we consider the days when tropical cyclones happen (TCdays) to model their seasonality using circular statistics, which is the appropriate modeling framework due to the nature of this quantity. By using mixtures of distributions to model the inter-annual variability of the TCdays, we find an expansion of the tails of the distributions over the period 1966 – 2020 for the North Atlantic basin, leading to a more prolonged hurricane season over the recent decades. These results will be explained in terms of physical drivers, providing insights into the mechanisms that could be responsible for the observed lengthening of the hurricane season.

Seasonality

Tropical Atlantic

Atlantic hurricane

African easterly jet

10.5194/egusphere-egu24-15011

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