2024 Abstracts

Advancing Watershed Science Via Multiscale Data and Model-Experiment (ModEx) Integration


Dipankar Dwivedi1* ([email protected]), Yuxin Wu1, Charuleka Varadharajan1, Kenneth H. Williams1, Chunwei Chou1, Lucien Stolze1, Chuyang Liu1, Carl Steefel1, Baptiste Dafflon1, Sebastian Uhlemann1, Robin Thibaut1, Sergi Molins1, Jinyun Tang1, Bhavna Arora1, Nicola Falco1, Ulas Karaoz1, Vincent Noel2, Madison Burrus1, Stijn Wielandt1, Danielle Christianson1, Hesham Elbashandy1, Boris Faybishenko1, Doug Jones3, Dylan O’Ryan1, Roelof Versteeg3, Eoin Brodie1 ([email protected])


1Lawrence Berkeley National Laboratory, Berkeley, CA; 2SLAC National Laboratory, Stanford, CA; 3Subsurface Insights, Hanover, NH



Implementing an integrated model-experiment (ModEx) approach in watershed science necessitates a robust framework for multi-scale data collection, management, and data-model integration. This comprehensive approach is pivotal in fostering a synergistic relationship between experimental observations and model simulations, enhancing the understanding of watershed dynamics and functions. At East River, researchers collect and utilize multiple observations, including remote sensing, in-field sensing and sampling, and laboratory analysis. A framework that integrates these data for calibrating and validating watershed models is central to the ModEx approach. Specifically, researchers have leveraged decadal-scale remote sensing datasets to unravel complex vegetation dynamics within the watershed. This analysis provides insights into the correlations and potential impacts on vegetation from key watershed traits, including topography, bedrock composition, and their interactions with changing climate. Additionally, the extensive network of in-field sensors, crucial for real-time data collection, monitors parameters like soil moisture, temperature, and wind speed, providing detailed datasets that capture the dynamic nature of the watershed environment. For the next phase, researchers aim to enhance ModEx framework integration by acquiring new observations through a model-guided approach for sample collection and sensor deployment. The data-gathering campaigns will feed into developing and applying ATS-EcoSIM, the core hydrobiogeochemical simulation capability. ATS-EcoSIM development will feature a dynamic, trait-based model of plant-soil-microbial interactions to understand emergent behaviors in response to interacting press and pulse disturbances. The Science Focus Area (SFA) data management and integration component offers infrastructure and services for the project’s data lifecycle, including data publication, wireless sensor connectivity, automated quality checks of sensor datasets, and integration of diverse time-series data for analysis and modeling. The ModEx approach, developed in the SFA, offers transferable, validated methodologies to the broader scientific community, enhancing collaboration, data sharing, and cross-disciplinary integration, and ensures that the data management, observation, and modeling insights are applicable in wider research contexts.