About

Haruko Murakami Wainwright is the Mitsui Career Development Professor in Contemporary Technology and an assistant professor in the Department of Nuclear Science and Engineering, as well as Civil and Environmental Engineering at the Massachusetts Institute of Technology. She received her MA in statistics and PhD in nuclear engineering from the University of California, Berkeley. Prior to joining MIT, she was a staff scientist in the Earth and Environmental Sciences Area at Lawrence Berkeley National Laboratory. Her research focuses on environmental modeling and monitoring technologies, with particular emphasis on nuclear waste and nuclear-related contamination. Her work involves developing new paradigms of environmental monitoring by integrating in situ sensors, geophysics, remote sensing, contaminant and radionuclide transport simulations, and machine learning. She also works on nuclear waste disposal, addressing large uncertainties in performance assessment models related to geological heterogeneity and climate change, and developing uncertainty quantification methods such as global sensitivity analysis, Bayesian parameter estimation, and surrogate modeling. Her contributions aim to improve long-term environmental safety and understanding of nuclear contamination, with a focus on ensuring public safety and combating misinformation at contaminated sites.

Research topics

• Computer Science
• Machine Learning
• Environmental resource management
• Environmental science
• Data Mining
• Artificial Intelligence
• Data science
• Geography
• Engineering
• Biology
• Remote sensing
• Ecology
• Programming language

Selected publications

• A physics-guided sensor-to-model framework for real-time estimation and near-future forecasting of soil moisture

  Advances in Water Resources · 2026-01-22

  articleSenior author
  PublisherDOI

• Effective risk communication strategies for nuclear energy: emphasizing monitoring and control over risk comparisons

  Frontiers in Energy Research · 2026-04-15

  articleOpen access1st authorCorresponding

  Nuclear energy is considered an important carbon-free and reliable energy source. However, ionizing radiation associated with nuclear power plants is a long-standing public concern. National surveys have been conducted over the last 30 years—in 1991, 2001, 2008, 2011, and 2022—to assess the trends in public perceptions about radiation associated with nuclear energy, as well as to investigate which messages could be effective in correcting misperceptions. This study examines the 2022 survey data in greater depth to understand the efficacy of messages. Results confirm the findings from the earlier research by Bisconti (2011): that (1) providing key facts and well-designed messages to the public can change perceptions, and (2) informing the public about how radiation is controlled and monitored at nuclear power plants is more effective than comparing radiation levels from different sources. In addition, our results show that this pattern is true across all demographic groups, even among those who began the survey with the most negative perceptions. A message about the many beneficial societal uses of radiation also is effective, particularly for those with advanced education. Our results offer a framework by which to improve public perception and create pathways for greater public understanding of radiation associated with nuclear power plants. The findings provide a framework for developing communications on a range of technical topics and for siting energy facilities.

  PublisherOA PDFDOI

• The Role of Snow and Subsurface Heterogeneity in a Mountainous Headwater Catchment in Colorado: A Model-Data Integration Approach

  2025-04-07

  preprintOpen access

  Mountainous headwater streams are sustained by both snowmelt-driven streamflow and groundwater discharge in the Upper Colorado River Basin. However, predicting headwater stream discharge magnitude and peak flow timing is challenging in mountainous terrains, where snowmelt rates vary substantially across different vegetation types and elevations, and heterogeneous subsurface physical properties influence groundwater storage and its release. To determine the roles of snowmelt, topography and subsurface structure on surface and subsurface water flow, we used a model-data integration approach: we combined observations of snow depth measurements using distributed temperature probes, stream discharge, and groundwater levels with integrated surface-subsurface hydrologic modeling at a mountainous headwater catchment (1.5 km$^2$) near Crested Butte, Colorado. Our models simulated varying snowmelt dynamics across different vegetation types and subsurface structures with distinct physical properties and layers. We also calibrated hydrologic models using field measurements through machine learning-based model calibration. Results indicated slower snowmelt rates in evergreen forests delayed the peak flow and baseflow onset. In upstream areas with lower subsurface permeability, water was stored within the subsurface but was not released as interflow or shallow groundwater flow, and thereby not contributing to downstream streamflow during recession limb periods. This improved understanding of groundwater and snowmelt dynamics in mountainous headwaters enhances our ability to better predict headwater stream discharge and assess headwater resilience in changing climates.

  PublisherOA PDFDOI

• Multiscale model coupling for watershed-scale contaminant transport modeling from point sources in Savannah River Site

  2025-03-14

  preprintOpen accessCorresponding

  Soil and groundwater contamination at some sites impacts downstream populations when contaminants migrate from groundwater to rivers. Predictive modeling is challenging since it is required to include detailed subsurface structure and groundwater flow models within the site, as well as watershed-scale models for large-scale transport. Now that climate change impacts are major concerns at many sites, it is important to have the capability to represent the water balance change and its impact on contaminant transport both at the site and watershed scale in a consistent manner. This study introduces a new simulation framework to couple a detailed 2D site/hillslope-scale groundwater model to the 3D watershed-scale model to describe contaminant transport from groundwater to river water within the catchment. Within the site, we estimate the contaminant discharges to the river from contaminant sources based on the Richards equation and advection-dispersion equation. The discharges are then applied as the boundary conditions to the watershed-scale model considering the width of the 2D site/hillslope-scale groundwater model and recharge rates for both models.We demonstrate and validate our framework based on the tritium concentration datasets in surface water and groundwater collected at the Savannah River Site F-Area. Results show that the method can successfully reproduce the contaminant concentration time series in river water.

  PublisherDOI

• Impact of Salinity on Ground Ice Distribution Across an Arctic Coastal Polygonal Tundra Environment

  Permafrost and Periglacial Processes · 2025-09-15

  articleOpen access

  ABSTRACT The heterogeneous distribution of ground ice in the Arctic is a key driver of uneven ground subsidence as permafrost thaws, significantly impacting infrastructure and surface/subsurface hydrology. These topographic and hydrological changes contribute to major uncertainties in energy and carbon fluxes and storage in a warming Arctic. This study aims to improve our understanding of the controls on ground ice and organic matter distribution within the top 3 m of permafrost in coastal polygonal tundra near Utqiagvik, Alaska. To this end, we apply a neural network approach to bulk density distributions derived from nondestructive X‐ray tomography of soil cores, trained with laboratory analyses, to improve the resolution and spatial coverage of estimates of dry bulk density, ice content, and organic matter content. In addition, we use capacitively coupled geophysical imaging to map soil electrical conductivity and salinity variations. The results show that sedimentary deposits from ocean transgressions, along with subsequent ice wedge polygon geomorphological processes, jointly influence the distribution of ice content at various scales. The impact of the latter decreases with depth, whereas the influence of salinity and sedimentary history increases. Although the controls on the distribution of soil organic matter content (g/cm 3 ) remain unclear, the pronounced heterogeneity in bulk density strongly influences its calculation from laboratory mass fraction measurements (g/g). From a methodological perspective, the interdependencies among soil components and the need for increased data coverage underscore the value of high‐resolution density measurements, such as using X‐ray tomography. Overall, this study emphasizes the importance of considering salinity constraints on ice content distribution in coastal permafrost regions. The results are expected to aid in the development of data products and process representations in geomorphological and ecosystem models.

  PublisherOA PDFDOI

• Optimizing radiation monitoring networks to improve emergency response strategies during nuclear power plant accidents

  Scientific Reports · 2025-04-07 · 3 citations

  articleOpen accessSenior author

  This paper presents a new strategy to optimize radiation monitoring networks for effectively predicting contaminated areas and radiation levels during nuclear power plant accidents in order to improve emergency response efforts. Our strategy addresses variable metrological fields by generating ensemble simulations of wind fields and radionuclide migration in the atmosphere using the WSPEEDI (Worldwide version of System for Prediction of Environmental Emergency Dose Information) simulator. GPCAM (Gaussian Process for Continuous-time Acquisition of Measurements) is then used to capture the heterogeneity of radiation levels by sparse monitoring points, and to optimize their locations. We consider three different scenarios: (a) a single static spatial distribution of the radiation levels, (b) the temporal evolution of the distribution within a single release scenario for mobile sensor deployment, and (c) ensemble optimization with variable metrological conditions for assessing risks and emergency responses at a particular site a priori. The results are compared with the homogeneously-distributed network. Our results show that GPCAM is able to identify effective monitoring locations for each of these scenarios, except that a prevailing wind direction is required for the ensemble case. In addition, we compare the effect of different acquisition functions, kernel functions, and hyperparameters in GPCAM on the sensor locations.

  PublisherOA PDFDOI

• Design and Modeling of Cf-252 -Based Neutron Irradiator for Naa: Mcnp6 Simulations of Dose Rate and Neutron Fluxes

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed'

  Open MIND · 2025-01-01

  articleOpen access

  This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

  OA PDFDOI

• Building confidence in models for complex barrier systems for radionuclides

  Proceedings of the National Academy of Sciences · 2025-07-03 · 2 citations

  articleOpen accessSenior author

  The modeling and simulation of the Cement-clay Interaction-Diffusion field (CI-D) experiment at the Mont Terri site in Switzerland presented here demonstrates that it is possible to capture the multiscale physical and chemical features of natural and engineered barrier systems for radionuclides. The simulations are successfully carried out with the newly developed CrunchODiTi high-performance computing software that accounts for multiple continua, including a continuum representing the electrical double layer (EDL) developed along negatively charged clay particles in clay rock. The simulation also accounts for both the complex three-dimensional (3D) geometry, expected as the norm in a geological waste repository, and the anisotropy of the geological formation. In addition, the high resolution of the model makes it possible to include "skin effects" developed at the interface between highly reactive materials, in this case between the high pH cement and the circumneutral but electrostatic Opalinus Clay. The successful history matching with the field experiment demonstrates that the distinct geochemical and physical properties of the cement and the Opalinus Clay in the CI-D experiment can be accounted for. Such analyses are essential for developing a defensible safety case for the underground storage of radioactive waste.

  PublisherOA PDFDOI

• L-SCIE and SUPCRTNE model and database development

  2025-11-02

  reportOpen access
  PublisherOA PDFDOI

Frequent coauthors

• Susan S. Hubbard

  Oak Ridge National Laboratory

  124 shared

• Kenneth H. Williams

  Lawrence Berkeley National Laboratory

  113 shared

• Nicola Falco

  Lawrence Berkeley National Laboratory

  108 shared

• Baptiste Dafflon

  95 shared

• Michelle Newcomer

  62 shared

• Nicholas Bouskill

  Lawrence Berkeley National Laboratory

  54 shared

• Dipankar Dwivedi

  Lawrence Berkeley National Laboratory

  46 shared

• Peter Nico

  Lawrence Berkeley National Laboratory

  41 shared

Labs

• Nuclear Science and Engineering at MITPI

Awards & honors

• Committed to Caring, MIT (2026)
• RemPlex Summit, Best Presentation Award (2021)
• R&D 100 Award, National Risk Assessment Partnership Toolset…
• LBNL Director’s Award for Early Career Scientific Achievemen…