Multi‐Scale Hydrologic Evaluation of the National Water Model Streamflow Data Assimilation
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Multi‐Scale Hydrologic Evaluation of the National Water Model Streamflow Data Assimilation

Filetype[PDF-336.90 KB]



Details:

  • Journal Title:
    JAWRA Journal of the American Water Resources Association
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    Streamflow predictions derived from a hydrologic model are subjected to many sources of errors, including uncertainties in meteorological inputs, representation of physical processes, and model parameters. To reduce the effects of these uncertainties and thus improve the accuracy of model prediction, the United States (U.S.) National Water Model (NWM) incorporates streamflow observations in the modeling framework and updates model‐simulated values using the observed ones. This updating procedure is called streamflow data assimilation (DA). This study evaluates the prediction performance of streamflow DA realized in the NWM. We implemented the model using WRF‐Hydro® with the NWM modeling elements and assimilated 15‐min streamflow data into the model, observed during 2016–2018 at 140 U.S. Geological Survey stream gauge stations in Iowa. In its current DA scheme, known as “nudging,” the assimilation effect is propagated downstream only, which allows us to assess the performance of streamflow predictions generated at 70 downstream stations in the study domain. These 70 locations cover basins of a range of scales, thus enabling a multi‐scale hydrologic evaluation by inspecting annual total volume, peak discharge magnitude and timing, and an overall performance indicator represented by the Kling–Gupta efficiency. The evaluation results show that DA improves the prediction skill significantly, compared to open‐loop simulation, and the improvements increase with areal coverage of upstream assimilation points.
  • Keywords:
  • Source:
    JAWRA Journal of the American Water Resources Association, 57(6), 875-884
  • DOI:
  • ISSN:
    1093-474X;1752-1688;
  • Format:
  • Publisher:
  • Document Type:
  • Funding:
  • Rights Information:
    Accepted Manuscript
  • Compliance:
    Library
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

  • No Additional Files
More +

You May Also Like

Checkout today's featured content at repository.library.noaa.gov

Version 3.27.1