About

Nicoleta Cristea is an Associate Professor in the Department of Civil & Environmental Engineering at the University of Washington. Her research focuses on hydrology and hydrodynamics, contributing to the understanding of water systems and their interactions with environmental processes. She is involved in advancing knowledge in these areas through her academic and research activities, supporting the department's broader goals of addressing complex engineering challenges related to water and environmental systems.

Research topics

• Artificial Intelligence
• Computer Science
• Geography
• Geology
• Data science
• Remote sensing
• Climatology
• Engineering
• Mathematics
• Meteorology
• Environmental science
• Ecology
• Physical geography

Selected publications

• How farming practices reshape soil hydrodynamics

  2026-03-14

  articleOpen access

  Farming practices reshape soil hydrodynamics by altering near-surface structure, mechanical stiffness, and water transport pathways, yet their impacts remain difficult to observe at field scale and high temporal resolution. Here we combine distributed acoustic sensing with physics-based hydromechanical modeling to quantify how tillage systems and soil compaction influences minute-scale, meter-scale seismic and hydrological responses in agricultural soils. We show that dynamic capillary effects govern transient soil stiffness and moisture redistribution following rainfall, with disturbed soils exhibiting sharp post-rain seismic velocity reductions associated with near-surface saturation. These responses are followed by pronounced hysteretic velocity recoveries driven by evapotranspiration, revealing strong memory effects in soil–water dynamics. Seismically inverted estimates of soil saturation demonstrate how farming-induced disturbance reshapes water flux partitioning and subsurface storage. Our results provide direct observational evidence that farming practices fundamentally reorganize soil hydrodynamics and establish distributed seismic sensing as a scalable, non-invasive approach for observing soil processes relevant to land–atmosphere exchange, Earth system modeling, and resilience to hydrological extremes.

  PublisherDOI

• Open-source models for development of data and metadata standards

  Patterns · 2025-07-01 · 1 citations

  reviewOpen access

  Machine learning and artificial intelligence promise to accelerate research and understanding across many scientific disciplines. Harnessing the power of these techniques requires aggregating scientific data. In tandem, the importance of open data for reproducibility and scientific transparency is gaining recognition, and data are increasingly available through digital repositories. Leveraging efforts from disparate data collection sources, however, requires interoperable and adaptable standards for data description and storage. Through the synthesis of experiences in astronomy, high-energy physics, earth science, and neuroscience, we contend that the open-source software (OSS) model provides significant benefits for standard creation and adaptation. We highlight resultant issues, such as balancing flexibility vs. stability and utilizing new computing paradigms and technologies, that must be considered from both the user and developer perspectives to ensure pathways for recognition and sustainability. We recommend supporting and recognizing the development and maintenance of OSS data standards and software consistent with widely adopted scientific tools.

  PublisherOA PDFDOI

• Could Climate Change Decrease Landslide Hazard in Snow‐Dominated Mountainous Regions? Insights From a Distributed Hydrology‐Shallow Landslide Model of the North Cascades, USA

  Water Resources Research · 2025-12-01 · 3 citations

  articleOpen accessSenior author

  Abstract In snow‐dominated mountain belts, understanding how runoff hydrology and landslide hazard will respond to climate change requires the integration of climate science, hydrology, and geomorphology. In this study, we use the DHSVM distributed hydrology model coupled with a Landlab shallow landslide probability model (LandslideProbability) to assess future shallow landslide hazard in the North Cascades Mountains of Washington, United States where high‐relief terrain leads to distinct rain‐dominated, transient (rain and snow) and snow‐dominated precipitation zones. Three future climate scenarios that represent median, low and high warming scenarios from a collection of Coupled Model Intercomparison Project 5 models of future climate are used to force the hydrology model. We find that, for all scenarios, landslide hazard increases in the transient and rain‐dominated zones but counter to our expectations, decreases in the snow dominated zone. In the rain‐dominated and transient zones, peak saturation is driven by rainfall inputs during the fall‐winter period and increased future precipitation rates directly translate to increased recharge. In contrast, in the snow‐dominated zone, future peak saturation is driven by snowmelt water inputs, which historically occurred during the early summer months, when temperature and total incoming radiation are high. In the future, warmer temperatures cause the snowpack to melt before reaching the high‐melt‐energy summer months which results in a decrease in melt that exceeds the increase in precipitation and overall lower recharge rates.

  PublisherOA PDFDOI

• Downscaling 3m resolution snow cover from MODIS, VIIRS, and HLS using commercial satellite imagery and terrain information

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Enhancing fine-scale snow cover mapping in forested mountain basins using high-resolution PlanetScope imagery

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Substantial Cold Bias During Wintertime Cold Extremes in the Southern Cascadia Region in Historical CMIP6 Simulations

  Journal of Geophysical Research Atmospheres · 2024-09-28 · 1 citations

  articleOpen accessSenior author

  Abstract Global climate models often simulate atmospheric conditions incorrectly due to their coarse grid resolution, flaws in their dynamics, and biases resulting from parameterization schemes. Here we document a bias in the magnitude and extent of minimum temperature extremes in the CMIP6 model ensemble, relative to ERA5. The bias is present in the southern Cascadia region (i.e., Pacific Northwestern United States and southwestern British Columbia, Canada, spanning from the coast to the Rocky Mountains), with some models showing a bias magnitude in excess of −10°C in the first percentile of daily winter minimum temperature. The sea level pressure pattern for these events is similar in CMIP6 models and ERA5, showing high anomalies in the Northeast Pacific that are indicative of an atmospheric blocking pattern and consequently more northerly flow. Though this atmospheric blocking pattern is typically concurrent with cold winter temperatures across much of North America, Rocky and Cascade mountain ranges prevent the cold air from reaching the southern Cascadia region as confirmed by the observations and reanalysis. Our results suggest that the bias in CMIP6 minimum temperatures is a result of unresolved topography in the Rockies and Cascade mountain ranges, such that the terrain does not adequately block cold air advection from reaching the southern Cascadia region.

  PublisherOA PDFDOI

• Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent

  Water Resources Research · 2024-10-30 · 5 citations

  articleOpen access

  Abstract Snow water equivalent (SWE) distribution at fine spatial scales (≤10 m) is difficult to estimate due to modeling and observational constraints. However, the distribution of SWE throughout the spring snowmelt season is often correlated to the timing of snow disappearance. Here, we show that snow cover maps generated from PlanetScope's constellation of Dove Satellites can resolve the 3 m date of snow disappearance across seven alpine domains in California and Colorado. Across a 5‐year period (2019–2023), the average uncertainty in the date of snow disappearance, or the period of time between the last date of observed snow cover and the first date of observed snow absence, was 3 days. Using a simple shortwave‐based snowmelt model calibrated at nearby snow pillows, the PlanetScope date of snow disappearance could be used to reconstruct spring SWE. Relative to lidar SWE estimates, the SWE reconstruction had a spatial coefficient of correlation of 0.75, and SWE spatial variability that was biased by 9%, on average. SWE reconstruction biases were then improved to within 0.04 m, on average, by calibrating snowmelt rates to track the spring temporal evolution of fractional snow cover observed by PlanetScope, including fractional snow cover over the full modeling domain, and across domain subsections where snowmelt rates may differ. This study demonstrates the utility of fine‐scale and high‐frequency optical observations of snow cover, and the simple and annually repeatable connections between snow cover and spring snow water resources in regions with seasonal snowpack.

  PublisherOA PDFDOI

• Towards an open-source model for data and metadata standards

  2024-10-23

  preprintOpen access

  Progress in machine learning and artificial intelligence promises to advance research and understanding across a wide range of fields and activities. In tandem, increased awareness of the importance of open data for reproducibility and scientific transparency is making inroads in fields that have not traditionally produced large publicly available datasets. Data sharing requirements from publishers and funders, as well as from other stakeholders, have also created pressure to make datasets with research and/or public interest value available through digital repositories. However, to make the best use of existing data, and facilitate the creation of useful future datasets, robust, interoperable and usable standards need to evolve and adapt over time. The open-source development model provides significant potential benefits to the process of standard creation and adaptation. In particular, data and meta-data standards can use long-standing technical and socio-technical processes that have been key to managing the development of software, and which allow incorporating broad community input into the formulation of these standards. On the other hand, open-source models carry unique risks that need to be considered. This report surveys existing open-source standards development, addressing these benefits and risks. It outlines recommendations for standards developers, funders and other stakeholders on the path to robust, interoperable and usable open-source data and metadata standards.

  PublisherOA PDFDOI

• Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent

  2024-05-30

  preprintOpen access

  Snow water equivalent (SWE) distribution at fine spatial scales (≤ 10 m) is difficult to estimate due to modeling and observational constraints. However, the distribution of SWE throughout the spring snowmelt season is often correlated to the timing of snow disappearance. Here, we show that snow cover maps generated from PlanetScope’s constellation of Dove Satellites can resolve the 3 m date of snow disappearance across seven alpine domains in California and Colorado. Across a 5-year period (2019 – 2023), the average uncertainty in the date of snow disappearance, or the period of time between the last date of observed snow cover and the first date of observed snow absence, was 3 days. Using a simple shortwave-based snowmelt model calibrated at nearby snow pillows, the PlanetScope date of snow disappearance could be used to reconstruct spring snow water equivalent (SWE). Relative to lidar SWE estimates, the SWE reconstruction had a spatial coefficient of correlation of 0.75, and SWE spatial variability that was biased by 9%, on average. SWE reconstruction biases were then improved to within 0.04 m, on average, by calibrating snowmelt rates to track the spring temporal evolution of fractional snow cover observed by PlanetScope, including fractional snow cover over the full modeling domain, and across domain subsections where snowmelt rates may differ. This study demonstrates the utility of fine-scale and high-frequency optical observations of snow cover, and the simple and annually repeatable connections between snow cover and spring snow water resources in regions with seasonal snowpack.

  PublisherOA PDFDOI

• Using photographs and deep neural networks to understand flowering phenology and diversity in mountain meadows

  Remote Sensing in Ecology and Conservation · 2024-02-13 · 10 citations

  articleOpen access

  Abstract Mountain meadows are an essential part of the alpine–subalpine ecosystem; they provide ecosystem services like pollination and are home to diverse plant communities. Changes in climate affect meadow ecology on multiple levels, for example, by altering growing season dynamics. Tracking the effects of climate change on meadow diversity through the impacts on individual species and overall growing season dynamics is critical to conservation efforts. Here, we explore how to combine crowd‐sourced camera images with machine learning to quantify flowering species richness across a range of elevations in alpine meadows located in Mt. Rainier National Park, Washington, USA. We employed three machine‐learning techniques (Mask R‐CNN, RetinaNet and YOLOv5) to detect wildflower species in images taken during two flowering seasons. We demonstrate that deep learning techniques can detect multiple species, providing information on flowering richness in photographed meadows. The results indicate higher richness just above the tree line for most of the species, which is comparable with patterns found using field studies. We found that the two‐stage detector Mask R‐CNN was more accurate than single‐stage detectors like RetinaNet and YOLO, with the Mask R‐CNN network performing best overall with mean average precision (mAP) of 0.67 followed by RetinaNet (0.5) and YOLO (0.4). We found that across the methods using anchor box variations in multiples of 16 led to enhanced accuracy. We also show that detection is possible even when pictures are interspersed with complex backgrounds and are not in focus. We found differential detection rates depending on species abundance, with additional challenges related to similarity in flower characteristics, labeling errors and occlusion issues. Despite these potential biases and limitations in capturing flowering abundance and location‐specific quantification, accuracy was notable considering the complexity of flower types and picture angles in this dataset. We, therefore, expect that this approach can be used to address many ecological questions that benefit from automated flower detection, including studies of flowering phenology and floral resources, and that this approach can, therefore, complement a wide range of ecological approaches (e.g., field observations, experiments, community science, etc.). In all, our study suggests that ecological metrics like floral richness can be efficiently monitored by combining machine learning with easily accessible publicly curated datasets (e.g., Flickr, iNaturalist).

  PublisherOA PDFDOI

Recent grants

• CyberTraining: Implementation: Medium: Machine Learning Training and Curriculum Development for Earth Science Studies

  NSF · $996k · 2021–2026

• COLLABORATIVE RESEARCH: GI CATALYTIC TRACK: Cyberinfrastructure for Intelligent High-Resolution Snow Cover Inference from Cubesat Imagery

  NSF · $553k · 2020–2024

Frequent coauthors

• Jessica D. Lundquist

  24 shared

• Justin M. Pflug

  University of Maryland, College Park

  22 shared

• Kehan Yang

  University of Washington

  15 shared

• Carrie Vuyovich

  Goddard Space Flight Center

  13 shared

• Stephen J. Burges

  University of Washington

  12 shared

• Ziheng Sun

  Nantong University

  11 shared

• William Ryan Currier

  11 shared

• Melissa L. Wrzesien

  Goddard Space Flight Center

  9 shared