About

Dr. Jennifer Irish specializes in coastal engineering, focusing on storm surge characterization, surge hazard assessment, and nature-based coastal infrastructure. Her research aims to enhance community resilience against climate change impacts through coastal hazard mitigation efforts.

Research topics

• Computer Science
• Political Science
• Geography
• Environmental resource management
• Engineering
• Environmental science
• Data science
• Public relations
• Environmental planning
• Psychology
• Ecology
• Business
• Meteorology
• Geology
• Climatology
• Geomorphology
• Oceanography
• Engineering management
• Systems engineering

Selected publications

• Quantifying Future Effects of Low-frequency Tropical Cyclones and Sea Level Rise Scenarios on Nonlinear Interactions in Total Water Levels

  2026-01-18

  articleOpen accessSenior author

  Total water levels during tropical cyclones (TCs) are triggered by multiple synergistic drivers that nonlinearly interact in space and time, namely, astronomical tides, storm surge, waves, and river discharge. It is also known that TC-characteristics, sea level rise (SLR), and hydrodynamic and morphologic features influence such nonlinear interactions (NIs), thereby contributing to total water level variability in coastal regions worldwide. However, low-probability (intense) TCs in coastal regions are rare, and reliance on sparse historical records hinder our understanding of system response to such extreme events. Here, we force a coastal hydrodynamic model of the Chesapeake Bay, the largest estuarine system of the U.S., with a globally consistent suite of synthetic TC tracks (STORM). Then, we analyze how NIs respond to shifts in total water levels driven by SLR, and to what extent NIs are sensitive to the TC’s intensity and translational speed under different SLR scenarios by 2050. Results of this study show that SLR will significantly alter NIs in total water levels by reducing damping response (5-9%) or increasing amplification (1-5%) across Chesapeake Bay. TC-intensity modulates the magnitude of NIs in the bay especially in the lower-bay region, whereas the translational speed effect is confined to slow-moving TCs that generate localized damping near to the ocean boundary. Lower-bay NI peaks exhibit instantaneous responses tightly synchronized with peak surge timing near TC landfall. Understanding NIs in future total water levels due to TC-characteristics and SLR scenarios is key to bolster resilient infrastructure designs, flood adaptation, and wetland resilience strategies.

  PublisherOA PDFDOI

• External Hazard Probabilistic Risk Assessment of Advanced Nuclear Power Technologies under Evolving Climate Conditions

  2025-01-01

  article
  PublisherDOI

• Advances in Morphodynamic Modeling of Coastal Barriers: A Review

  WORLD SCIENTIFIC eBooks · 2025-11-01

  book-chapter
  PublisherDOI

• PROTECTING DIGITAL DEMOCRACY: THREATS AND RESPONSES

  The Journal of Intelligence Conflict and Warfare · 2025-01-31

  articleOpen access1st authorCorresponding

  On November 18th, 2024, Ms. Jennifer Irish presented Protecting Digital Democracy: Threats and Responses for this year’s West Coast Security Conference. The presentation was followed by a question-and-answer period with questions from the audience and CASIS Vancouver executives. The key points discussed were threats to digital democracy and the core response imperatives: intervention and inoculation. Received: 12-16-2024 Revised: 01-28-2025

  PublisherOA PDFDOI

• Predicting the Evolution of Extreme Water Levels With Long Short‐Term Memory Station‐Based Approximated Models and Transfer Learning Techniques

  Water Resources Research · 2025-03-01 · 12 citations

  articleOpen accessSenior author

  Abstract Extreme water levels (EWLs) resulting from cyclones pose significant flood hazards and risks to coastal communities and interconnected ecosystems. To date, physically based models have enabled accurate prediction of EWLs despite their inherent high computational cost. However, the applicability of these models is limited to data‐rich sites with diverse characteristics. The dependence on high quality spatiotemporal data, which is often computationally expensive, hinders the applicability of these models to regions of either limited or data‐scarce conditions. To address this challenge, we present a Long Short‐Term Memory (LSTM) network framework to predict the evolution of EWLs beyond site‐specific training stations. The framework, named LSTM‐Station Approximated Models (LSTM‐SAM), consists of a collection of bidirectional LSTM models enhanced with a custom attention mechanism layer embedded in the architecture. LSTM‐SAM incorporates a transfer learning approach applicable to target (tide‐gage) stations along the U.S. Atlantic Coast. Importantly, LSTM‐SAM helps analyze: (a) the underlying limitations associated with transfer learning, (b) evaluate EWL predictions beyond training domains, and (c) capture the evolution of EWL caused by tropical and extratropical cyclones. The framework demonstrates satisfactory performance with “transferable” models achieving Kling‐Gupta Efficiency (KGE), Nash‐Sutcliffe Efficiency (NSE), and Root‐Mean Square Error (RMSE) ranging from 0.78 to 0.92, 0.90 to 0.97, and 0.09–0.18 m at the target stations, respectively. We show that LSTM‐SAM can accurately predict not only EWLs but also their evolution over time, that is, onset, peak, and dissipation, which could assist in operational flood forecasting in regions with limited resources to set up physically based models.

  PublisherOA PDFDOI

• Predicting the Evolution of Extreme Water Levels with Long Short-Term Memory Station-based Approximated Models and Transfer Learning Techniques

  2025-02-18

  preprintOpen accessSenior author

  • We present a deep learning framework that accurately predicts the evolution of cycloneinduced water levels across multiple domains. • An attention mechanism enhances the framework's recognition of extreme water level patterns within and beyond training locations. • It effectively identifies unseen water level patterns, different from those in training, thus enhancing model's transfer learning capability.

  PublisherOA PDFDOI

• COMPUTATIONAL SIMULATION SET SELECTION FOR STORM SURGE SURROGATE MODELING

  Coastal Engineering Proceedings · 2025-05-29

  articleOpen access1st authorCorresponding

  Flooding by tropical cyclones poses a significant threat to life and livelihood in coastal communities worldwide. Recent advances in machine learning have enabled efficient estimation of tropical cyclone surge using surrogate models (e.g., Kajbaf and Bensi 2020 and refs therein). In turn, the application of these surrogate models supports the probabilistic hazard characterization and flood warning essential for prosperity in the coastal zone. Typically, storm surge surrogate models are developed from discrete sets of high-fidelity storm surge simulations with physics-based models such as ADCIRC (e.g., Westerink et al., 2008). Herein, we explore the significance of storms represented in these physics-based simulation sets—hereafter training sets—as they relate to the performance of storm surge surrogate models.

  PublisherDOI

• Impacts of barrier-island breaching on mainland flooding during storm events applied to Moriches, New York

  Natural hazards and earth system sciences · 2025-09-08

  articleOpen access

  Abstract. Barrier islands can protect the mainland from flooding during storms through reduction of storm surge and dissipation of storm-generated wave energy. However, the protective capability is reduced when barrier islands breach and a direct hydrodynamic connection between the water bodies on both sides of the barrier island is established. Breaching of barrier islands during large storm events is complicated, involving nonlinear processes that connect water, sediment transport, dune height, and island width, among other factors. In order to assess how barrier-island breaching impacts flooding on the mainland, we used a statistical approach to analyze the sensitivity of mainland storm surge to barrier-island breaching by randomizing the location, time, and extent of a breach event. We created a framework that allows breaching to develop during the course of a simulation and imposes a breach in an approximation of a Gaussian bell curve that deepens over time. We show that simulating a storm event and varying the size, location, and number of breaches in the barrier island that mainland storm surge and horizontal inundation is affected by breaching; total inundation has a logarithmic relationship with total breach area which tapers off after the entire island is removed. Breach location is also an important predictor of inundation and bay surge. The insights we have gleaned from this study can help prepare shoreline communities for the differing ways that breaching affects the mainland coastline. Understanding which mainland locations are vulnerable to breaching, planners and coastal engineers can design interventions to reduce the likelihood of a breach occurring in areas adjacent to high flood risk.

  PublisherOA PDFDOI

• Simulation of Flood-Induced Human Migration at the Municipal Scale: A Stochastic Agent-Based Model of Relocation Response to Coastal Flooding

  Water · 2024-01-11 · 11 citations

  articleOpen accessSenior author

  Human migration triggered by flooding will create sociodemographic, economic, and cultural challenges in coastal communities, and adaptation to these challenges will primarily occur at the municipal level. However, existing migration models at larger spatial scales do not necessarily capture relevant social responses to flooding at the local and municipal levels. Furthermore, projecting migration dynamics into the future becomes difficult due to uncertainties in human–environment interactions, particularly when historic observations are used for model calibration. This study proposes a stochastic agent-based model (ABM) designed for the long-term projection of municipal-scale migration due to repeated flood events. A baseline model is demonstrated initially, capable of using stochastic bottom-up decision rules to replicate county-level population. This approach is then combined with physical flood-exposure data to simulate how population projections diverge under different flooding assumptions. The methodology is applied to a study area comprising 16 counties in coastal Virginia and Maryland, U.S., and include rural areas which are often overlooked in adaptation research. The results show that incorporating flood impacts results in divergent population growth patterns in both urban and rural locations, demonstrating potential municipal-level migration response to coastal flooding.

  PublisherOA PDFDOI

• Storm surges and extreme sea levels: Review, establishment of model intercomparison and coordination of surge climate projection efforts (SurgeMIP).

  Weather and Climate Extremes · 2024-05-14 · 40 citations

  articleOpen accessCorresponding

  Coastal flood damage is primarily the result of extreme sea levels. Climate change is expected to drive an increase in these extremes. While proper estimation of changes in storm surges is essential to estimate changes in extreme sea levels, there remains low confidence in future trends of surge contribution to extreme sea levels. Alerting local populations of imminent extreme sea levels is also critical to protecting coastal populations. Both predicting and projecting extreme sea levels and require reliable numerical prediction systems. The SurgeMIP (surge model intercomparison) community has been established to tackle such challenges. Efforts to intercompare storm surge numerical systems and coordinate the community’s prediction and projection efforts are introduced. An overview of past and recent advances in storm surge science such as physical processes to consider and the recent development of global forecasting systems are briefly introduced. Selected historical events and drivers behind fast increasing service and knowledge requirements for emergency response to adaptation considerations are also discussed. The community’s initial plans and recent progress are introduced. These include the establishment of an intercomparison project, the identification of research and development gaps, and the introduction of efforts to coordinate projections that span multiple climate scenarios.

  PublisherDOI

Recent grants

• NEESR: Tsunami Run-up and Withdrawal Dynamics on a Sloping Beach with Discontinuous Macro-Roughness

  NSF · $686k · 2012–2018

Frequent coauthors

• Robert Weiss

  73 shared

• Donald T. Resio

  27 shared

• Troy Tanner

  University of Florida

  23 shared

• Francisco Olivera

  21 shared

• Kurtis R. Gurley

  20 shared

• Scott B. Miles

  20 shared

• Ann Bostrom

  University of Washington

  20 shared

• Laura N. Lowes

  University of Washington

  20 shared

Education

• Ph.D., Civil Engineering

  University of Delaware

  2005

• M.S., Civil Engineering

  Lehigh University

  1994

• B.S., Civil Engineering

  Lehigh University

  1992

Awards & honors

• Fellow, American Society of Civil Engineers 2015 - 2017
• Civil Engineering Excellence in Research Award, Texas A&M Un…
• Zachry Department of Civil Engineering Award for Excellence,…
• Department of the Army Superior Civilian Service Award, U.S.…
• Department of the Army Achievement Medal, U.S. Army Corps of…