About

James A. Smith is the Henry L. Kinnier Professor of Civil Engineering at the University of Virginia's Department of Civil and Environmental Engineering. His research focuses on sustainable point-of-use water treatment technologies for the developing world, including the development of household-level water-treatment devices that improve water quality and human health. He has made significant contributions to the field through the invention of the MadiDrop+, a silver-embedded porous ceramic tablet for household water purification, and is the founder of Silivhere Technologies, Inc., which produces and sells MadiDrops+ globally. Dr. Smith holds a B.S. and M.S. in Civil Engineering from Virginia Tech and a Ph.D. in Civil Engineering from Princeton University. His professional background includes work as a research hydrologist with the U.S. Geological Survey. He has held visiting professorships at Stanford University and Princeton University and has received numerous awards for his teaching and research, including the Fulbright Research Fellowship, the AEESP/McGraw Hill Outstanding Teaching Award, and the Wesley W. Horner Award. He is a Fellow of the American Society of Civil Engineers and the founder of PureMadi, a nonprofit organization dedicated to addressing global water and health issues. His research interests encompass nanomaterials, environmental engineering, water resources, and low-impact stormwater management, with a particular emphasis on the impact of water treatment technologies on human health and environmental sustainability.

Research topics

• Environmental science
• Geology
• Climatology
• Geography
• Atmospheric sciences
• Meteorology
• Physical geography
• Chemistry
• Botany
• Oceanography
• Physics
• Mathematics
• Ecology
• Statistics

Selected publications

• Cloudbursts of the Mid‐Atlantic

  Water Resources Research · 2025-09-01

  articleOpen access1st authorCorresponding

  Abstract Extreme short‐duration rainfall in the Mid‐Atlantic region of the US is examined through polarimetric radar analyses of storms that produced rainfall accumulations exceeding 1,000‐year values for time scales less than 3 hr. Polarimetric radar analyses of Mid‐Atlantic cloudbursts focus on dynamical processes associated with updrafts and downdrafts, microphysical processes associated with extreme rainfall rates and mesoscale processes associated with structure, motion and evolution of convective systems over short time scales and small spatial scales. Dynamical processes associated with updrafts and downdrafts play a key role in determining the spatial and temporal distribution of extreme rainfall and in dictating errors in radar rainfall estimates through the effects of vertical motion. The microphysics of extreme short‐duration rainfall exhibit a mix of cold and warm rain processes, with cold rain processes contributing to cycles of growth and decay in raindrop size distributions. Analyses are designed to address critical research problems linked to modernizing methods for estimating Probable Maximum Precipitation (PMP). Polarimetric radar provides an important path for estimating rainfall for PMP‐magnitude storms. We compare rainfall analyses from recent storms in the Mid‐Atlantic with cloudburst rainfall from the pre‐radar era, including storms that produced record or near‐record rainfall accumulations for the US and the world. Rainfall accumulations at time scales shorter than 3 hr for polarimetric era storms are large relative to rainfall frequency results, but modest in comparison with rainfall maxima from historical cloudbursts in the Mid‐Atlantic.

  PublisherOA PDFDOI

• Evaluation of Radar-Derived Polarimetric Precipitation Estimates for Extreme Rain Events Using a Dense Network of Rain Gauges

  Journal of Hydrometeorology · 2025-05-16

  preprintOpen access

  Abstract The study evaluates radar-based quantitative precipitation estimations (QPEs) for 10 extreme rain events that occurred between 2013 and 2019 in the Kansas City metropolitan area, United States. These precipitation estimates were derived at hourly and approximately 0.5-km scales using two polarimetric QPE algorithms—one based on specific attenuation ( A ) and the other on specific differential phase ( K DP )—for the study area covered by two overlapping radars in Topeka, Kansas, and Kansas City, Missouri. The polarimetric QPE assessment for extreme rain events was motivated by improved flood forecasting and precipitation frequency analysis. The analysis utilizes ground reference observations from a dense network of about 170 rain gauges over the study area to quantitatively assess the accuracy of these polarimetric rainfall ( R ) estimates. The comparison of R ( A ) and R ( K DP ) with the conventional algorithm based on radar reflectivity observations reveals that the two polarimetric algorithms outperform the reflectivity-based approach. While R ( K DP ) shows a systematic conditional feature (i.e., underestimation at high rain rates) with reduced scatter, R ( A ) appears to be less biased but with relatively large scatter. The significanct overestimation of R ( A ) for one of the extreme events was attributed to the misestimation of its key parameter ( α ), which resulted from hail contaminated data samples. To examine the observed underestimation tendency of R ( K DP ), we characterized the magnitude of underestimation (bias) with rainfall spatial variability as this variability may account for different rainfall regimes or the smoothing effect of K DP to reduce its inherent noisiness. Our result demonstrates that the underestimation tendency of R ( K DP ) becomes more pronounced as rainfall spatial variability increases.

  PublisherOA PDFDOI

• A nonstationary probabilistic approach for probable maximum precipitation estimation based on global climate model large ensembles

  2025-03-01 · 1 citations

  preprintOpen access

  Conventional Probable Maximum Precipitation (PMP) methods face several limitations, including the lack of statistical uncertainty characterization, subjectivity in storm maximization, and the assumption of a stationary climate. To address these limitations, we propose a nonstationary PMP approach that combines the novel stochastic spatiotemporal rainfall generation model StormLab with a nonstationary Generalized Extreme Value (GEV) model. We applied the new approach to the Upper Red River Basin in the south-central United States. StormLab provided 10,000 years of high-resolution (6-hour, 0.03°) precipitation fields from 1901 to 2100, based on 50 ensembles of a global climate model (GCM). A nonstationary GEV model was fitted to the simulated precipitation annual maxima, providing PMP estimates under different climate periods with an associated annual exceedance probability (AEP). The simulated precipitation was then integrated with a hydrologic model to generate annual peak discharge in major tributaries and to estimate the probable maximum flood (PMF). Our approach produces PMP estimates for areas ranging from 10-20,000 mi2 and durations from 6 to 360 hours. Results show a 15-25% increase in PMP with an AEP of 10-4 from 2020 to 2100 at different spatial and temporal scales. Higher increases of 35% and 36% are projected in PMF with the same AEP in two major tributaries. The PMP and PMF results were further compared with previous PMP/PMF estimates. This study demonstrates the value of utilizing stochastic rainfall models and GCM large ensembles to inform PMP and PMF analysis in a changing climate.

  PublisherOA PDFDOI

• A Nonstationary Probabilistic Approach for Probable Maximum Precipitation Estimation Based on Global Climate Model Large Ensembles

  Journal of Hydrometeorology · 2025-11-26 · 1 citations

  article

  Abstract Conventional probable maximum precipitation (PMP) methods face several limitations, including uncertainty in estimating the upper bound, subjectivity in storm maximization, and the assumption of a stationary climate. To address these limitations, we propose a new approach that estimates PMP with specific annual exceedance probabilities (AEPs) across varying climate periods. Our approach integrates a stochastic rainfall generator (StormLab) with a novel nonstationary generalized extreme value (GEV) model and is applied to the upper Red River basin in the south-central United States. StormLab simulated 10 000 years of high-resolution (6 h, 0.03°) precipitation fields from 1901 to 2100, driven by 50 ensembles of a global climate model (GCM). We then fitted the GEV model to the simulated precipitation annual maxima to derive nonstationary PMP estimates. StormLab was further coupled with a hydrologic model estimate probable maximum flood (PMF) in major tributaries. Our approach estimates PMP for areas ranging from 10 to 20 000 mi 2 and durations from 6 to 360 h. Results show a 15%–25% increase in PMP (with an AEP of 10 −4 ) from 2020 to 2100 across various spatial and temporal scales. Higher increases of 35% and 36% are projected for PMF in two major tributaries with the same AEP. This study underscores the value of using stochastic rainfall models and GCM large ensembles to inform PMP and PMF analysis in a changing climate.

  PublisherDOI

• Flooding from Hurricane Helene and associated impacts: A historical perspective

  Journal of Hydrology X · 2025-05-01 · 11 citations

  articleOpen accessSenior author

  During September 2024, Hurricane Helene devasted large areas of western North Carolina and eastern Tennessee, causing extensive loss of life and widespread damage due to heavy rainfall and extreme flooding. Despite the impacts of this storm, Helene’s heavy rainfall and resulting floods were not entirely unprecedented, as the region experienced several floods linked to tropical cyclones in the past, including multiple storms during the 2004 hurricane season. To make matters worse, this is an area with historically low market penetration by the National Flood Insurance Program, highlighting a strong asymmetry with respect to the coastal areas: while roughly 14% of buildings in the eastern third of North Carolina were insured against floods, inland areas had less than a tenth of that coverage. Therefore, to improve resiliency and reduce the residual flood losses, it is critical to reconcile perceived versus actual flood risk and expand insurance coverage in hurricane-prone areas.

  PublisherDOI

• Polarimetry Based Radar Estimation of Extreme Rainfall: Case Studies

  2025-10-04

  articleSenior author

  The study evaluated radar-derived polarimetric rainfall estimates for extreme rain events that occurred in the Kansas City Metropolitan area in the United States. To derive quantitative precipitation estimates (QPE), we implemented two polarimetric algorithms based on specific attenuation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(A)$</tex> and specific differential phase (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K_{D P}$</tex>), along with the reflectivity (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{Z}$</tex>) based one using data from two radars in the study area. The analysis to assess radar-rainfall estimates (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R$</tex>) utilizes ground observations from a dense network of about 170 rain gauges. Based on our analysis results, the two polarimetric estimates from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R(A)$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R\left(K_{D P}\right)$</tex> outperform the conventional estimation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R(Z)$</tex>. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R(A)$</tex> appeared to be less biased with relatively large scatter while <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R\left(K_{D P}\right)$</tex> underestimates at high rainfall rate with less scatter compared to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R(A)$</tex>. To generate robust rainfall estimates by accounting for the error structure of the individual algorithms, we decomposed the errors into systematic and random components, conditioned on the magnitude of radar estimates. These conditional features were then used to generate compositeweighted rainfall estimates. The composite estimates derived from two polarimetric algorithms, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R(A)$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R\left(K_{D P}\right)$</tex>, showed significant improvement, particularly for reduction in bias and variability. The spatial averaging of these composite estimates over an experimental domain demonstrates their potential for streamflow prediction.

  PublisherDOI

• Extreme Rainfall Risk in Hurricane Ida’s Extratropical Stage: An Analysis with Convection-Permitting Ensemble Hindcasts

  Journal of the Atmospheric Sciences · 2024-05-14 · 3 citations

  article

  Abstract The extratropical stage of Hurricane Ida (2021) brought extreme subdaily rainfall and devastating flooding to parts of eastern Pennsylvania, New Jersey, and New York. We investigate the predictability and character of this event using 31-member ensembles of perturbed initial condition hindcasts with the Tropical Atlantic version of GFDL’s System for High-resolution prediction on Earth-to-Local Domains (T-SHiELD), a ∼13-km global weather forecast model with a ∼3-km nested grid. At lead times of up to 4 days, the ensembles are able to capture the most extreme observed hourly and daily rainfall accumulations but are negatively biased in the spatial extent of heavy precipitation. Large intraensemble differences in the magnitudes and locations of simulated extremes suggest that although impacts were highly localized, risks were widespread. In Ida’s tropical stage, interensemble spread in extreme hourly rainfall is well predicted by large-scale moisture convergence; by contrast, in Ida’s extratropical stage, the most extreme rainfall is governed by mesoscale processes that exhibit chaotic and diverse forms across the ensembles. Our results are relevant to forecasting and communication in advance of extratropical transition and imply that flood preparedness efforts should account for the widespread possibility of severe localized impacts. Significance Statement After making landfall in Louisiana, Hurricane Ida (2021) transitioned to an extratropical storm which brought extreme rainfall and unprecedented flooding to parts of the northeastern United States. To what extent were these impacts knowable in advance? We use a numerical weather model with very high resolution to produce ensemble hindcasts—simulations of a past weather event initialized with tiny perturbations to the initial conditions, representing dozens of equally plausible versions of Ida’s extratropical stage. We find that the observed hourly and daily rainfall maxima fall within the simulated outcomes of ensembles initialized with lead times of about 4 days or less. The location and intensity of the heaviest rainfall vary widely across these ensembles, suggesting that many locations across the Northeast were exposed to some likelihood of extreme rainfall.

  PublisherDOI

• A nonstationary probabilistic approach for probable maximum precipitation estimation based on global climate model large ensembles

  2024-12-19

  preprintOpen accessSenior author

  Conventional Probable Maximum Precipitation (PMP) methods face several limitations, including the lack of statistical uncertainty characterization, subjectivity in storm maximization, and the assumption of a stationary climate. To address these limitations, we propose a nonstationary PMP approach that combines the novel stochastic spatiotemporal rainfall generation model StormLab with a nonstationary Generalized Extreme Value (GEV) model. We applied the new approach to the Upper Red River Basin in the south-central United States. StormLab provided 10,000 years of high-resolution (6-hour, 0.03°) precipitation fields from 1901 to 2100, based on 50 ensembles of a global climate model (GCM). A nonstationary GEV model was fitted to the simulated precipitation annual maxima, providing PMP estimates under different climate periods with an associated annual exceedance probability (AEP). The simulated precipitation was then integrated with a hydrologic model to generate annual peak discharge in major tributaries and to estimate the probable maximum flood (PMF). Our approach produces PMP estimates for areas ranging from 10-20,000 mi2 and durations from 6 to 360 hours. Results show a 16-25% increase in PMP with an AEP of 10-4 from 2020 to 2100 at different spatial and temporal scales. Higher increases of 35% and 37% are projected in PMF with the same AEP in two major tributaries. The PMP and PMF results were further compared with previous PMP/PMF estimates. This study demonstrates the value of utilizing stochastic rainfall models and GCM large ensembles to inform PMP and PMF analysis in a changing climate.

  PublisherOA PDFDOI

• Rainfall Frequency Analysis Based on Long‐Term High‐Resolution Radar Rainfall Fields: Spatial Heterogeneities and Temporal Nonstationarities

  Water Resources Research · 2024-03-01 · 13 citations

  articleOpen access1st authorCorresponding

  Abstract Rainfall frequency analysis methods are developed and implemented based on high‐resolution radar rainfall data sets, with the Baltimore metropolitan area serving as the principal study region. Analyses focus on spatial heterogeneities and time trends in sub‐daily rainfall extremes. The 22‐year radar rainfall data set for the Baltimore study region combines reflectivity‐based rainfall fields during the period from 2000 to 2011 and polarimetric rainfall fields for the period from 2012 to 2021. Rainfall frequency analyses are based on non‐stationary formulations of peaks‐over‐threshold and annual peak methods. Increasing trends in short‐duration rainfall extremes are inferred from both peaks‐over‐threshold and annual peak analyses for the period from 2000 to 2021. There are pronounced spatial gradients in short‐duration rainfall extremes over the study region, with peak values of rainfall between Baltimore City and Chesapeake Bay. Spatial gradients in 100‐year, 1 hr rainfall over 20 km length scale are comparable to time trends over 20 years. Rainfall analyses address the broad challenge of assessing changing properties of short‐duration rainfall in urban regions. Analyses of high‐resolution rainfall fields show that sub‐daily rainfall extremes are only weakly related to daily extremes, pointing to difficulties in inferring climatological properties of sub‐daily rainfall from daily rainfall analyses. Changing measurement properties are a key challenge for application of radar rainfall data sets to detection of time trends. Mean field bias correction of radar rainfall fields using rain gauge observations is an important tool for improving radar rainfall fields and provides a useful tool for addressing problems associated with changing radar measurement properties.

  PublisherOA PDFDOI

• The importance of ‘place’ and its influence on rural and remote health and well‐being in Australia

  Australian Journal of Rural Health · 2024-06-25 · 10 citations

  articleOpen access

  AIMS: This article explores the crucial role of 'place' as an ecological, social and cultural determinant of health and well-being, with a focus on the benefits and challenges of living rurally and remotely in Australia. CONTEXT: The health system, including health promotion, can contribute actively to creating supportive environments and places that foster health and well-being among individuals residing in rural and remote locations. For First Nations peoples, living on Country, and caring for Country and its people, are core to Indigenous worldviews, and the promotion of Aboriginal and Torres Strait Islander health and well-being. Their forced removal from ancestral lands has been catastrophic. For all people, living in rural and remote areas can deliver an abundance of the elements that contribute to a 'liveable' community, including access to fresh air, green and blue space, agricultural employment, tight-knit communities, a sense of belonging and identity, and social capital. However, living remotely also can limit access to employment opportunities, clean water, affordable food, reliable transport, social infrastructure, social networks and preventive health services. 'Place' is a critical enabler of maintaining a healthy life. However, current trends have led to a reduction in local services and resources, and increased exposure to the impacts of climate change. APPROACH: This commentary suggests ideas and strategies through which people in rural and remote locations can strengthen the liveability, resilience and identity of their communities, and regain access to essential health care and health promotion services and resources. CONCLUSION: Recommended strategies include online access to education, employment and telehealth; flexible provision of social infrastructure; and meaningful and responsive university-health service partnerships.

  PublisherOA PDFDOI

Recent grants

• NSF/EAR-BSF - Extreme Floods in Arid/Semi-Arid Watersheds: Paired Studies in Israel and the US

  NSF · $296k · 2016–2020

• Collaborative Research: Small-Scale Variability of Rainfall: Experimental Studies

  NSF · $167k · 2004–2007

• SGER: Hydrometeorological Studies of the 2008 Flooding in Iowa

  NSF · $74k · 2008–2010

• WCR: The Hydrology of Extreme Floods in Mountain Watersheds

  NSF · $215k · 2003–2007

• Collaborative Research, WSC-Category 2: Regional Climate Variability and Patterns of Urban Development - Impacts on the Urban Water Cycle and Nutrient Export

  NSF · $1.4M · 2011–2016

Frequent coauthors

• Mary Lynn Baeck

  Princeton University

  93 shared

• Joshua Benton

  Texas A&M University

  67 shared

• Gabriele Villarini

  Princeton University

  58 shared

• Witold F. Krajewski

  University of Iowa

  53 shared

• Long Yang

  Nanjing University

  34 shared

• A. Allen Bradley

  32 shared

• Gabriel A. Vecchi

  Princeton University

  29 shared

• Brian R. Nelson

  NOAA National Environmental Satellite Data and Information Service

  28 shared

Labs

• James A. Smith LabPI

Education

• B.S.

  University of Virginia

• M.S.

  University of Virginia

Awards & honors

• Virginia Academy of Science, Engineering, and Medicine (2022…
• Wesley W. Horner Award for best paper of the year in an ASCE…
• Fulbright Research Fellow (2017)
• Edlich-Henderson Innovator of the Year Award (2015)
• Diplomate, American Academy of Water Resources (2014)