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Data-Model Integration for Improved Prediction of River Corridor and Watershed Function
  • +16
  • Timothy Scheibe,
  • Xingyuan Chen,
  • James Stegen,
  • Maoyi Huang,
  • Vanessa Garayburu-Caruso,
  • Amy E. Goldman,
  • Emily Graham,
  • Zhangshuan Hou,
  • Tim Johnson,
  • Christopher Murray,
  • William Nelson,
  • William Perkins,
  • Huiying Ren,
  • Patrick Royer,
  • Hyun-Seob Song,
  • Christopher Strickland,
  • Guzel Tartakovsky,
  • Xuesong Zhang,
  • Yue Zhu
Timothy Scheibe
Pacific NW National Lab

Corresponding Author:tim.scheibe@pnnl.gov

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Xingyuan Chen
Pacific Northwest National Laboratory
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James Stegen
Pacific Northwest National Laboratory
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Maoyi Huang
Pacific NW Nat'l Lab-Atmos Sci
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Vanessa Garayburu-Caruso
Pacific Northwest National Laboratory
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Amy E. Goldman
Pacific Northwest National Laboratory
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Emily Graham
Pacific Northwest National Laboratory
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Zhangshuan Hou
Pacific Northwest National Laboratory
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Tim Johnson
Pacific Northwest National Laboratory
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Christopher Murray
Pacific Northwest National Laboratory
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William Nelson
Pacific Northwest National Laboratory
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William Perkins
Pacific Northwest National Laboratory
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Huiying Ren
Pacific Northwest National Laboratory
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Patrick Royer
Pacific Northwest National Laboratory
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Hyun-Seob Song
Pacific Northwest National Laboratory
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Christopher Strickland
Pacific Northwest National Laboratory
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Guzel Tartakovsky
Pacific Northwest National Laboratory
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Xuesong Zhang
Joint Global Change Research Institute
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Yue Zhu
Pacific Northwest National Laboratory
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Abstract

River corridors, the spatial domains around rivers in which river water interacts with surrounding sediment and rock, are important components of watersheds. They comprise extremely complex ecosystems: heterogeneous at all spatial scales with strong temporal dynamics, coupled biological, geochemical, and hydrologic processes, and ubiquitous human impacts. We present several ways that our project, focused around the 75 km Hanford Reach of the Columbia River but with multiple connections to other systems, is addressing this challenge. These include 1) deployment of intensive, automated sensor networks supplemented by data from the Hanford Environmental Information System (HEIS) for hyporheic zone monitoring 2) data assimilation of these and other data into models using joint hydrologic and geophysical inversion, 3) integrating MASS2 model outputs and bathymetry data using machine learning to classify hydromorphologic features, 4) a community-based effort to develop broad understanding of organic carbon biogeochemistry and microbiomes in diverse river systems, and 5) use of multi-‘omics data to develop new biogeochemical reaction networks. These underpin the incorporation of process understanding and diverse data into high-resolution mechanistic models, and employment of those models to develop reduced-order models that can be applied at large scales while retaining the effects of local features and processes. In so doing we are contributing to reduction of uncertainties associated with major Earth system biogeochemical fluxes, thus improving predictions of environmental and human impacts on water quality and riverine ecosystems and supporting environmentally responsible management of linked energy-water systems.