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CBWM Phase 5 Status Report. Modeling Subcommittee Meeting October 2, 2002. Overview. Model structure and responsibilities Model segmentation Precipitation modeling. Model Responsibilities. Source data /p50X/pp/data/ CBP External transfer module (scripts) /p50X/pp/etm/ CBP

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CBWM Phase 5 Status Report

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Cbwm phase 5 status report

CBWMPhase 5 Status Report

Modeling Subcommittee Meeting

October 2, 2002



  • Model structure and responsibilities

  • Model segmentation

  • Precipitation modeling

Model responsibilities

Model Responsibilities

  • Source data


  • External transfer module (scripts)


  • Model parameters


  • Watershed-reach connection files




  • Downstream ID has been added to all reaches in the Chesapeake Bay Watershed except for those draining directly to the estuary. (Their ID is zero.)

  • Working on attributing the southern Virginia watersheds with DSID.



  • Reaches and watersheds have been reconnected so that we know what streams are in what watersheds.

  • Working on transferring the mean annual discharge attributes to the watersheds so we have the flow attribute in the names.

Precipitation modeling

Precipitation Modeling

  • Objectives:

    • Spatially distribute recorded point-precipitation data to provide high-resolution, low-bias areal precipitation time-series data at an hourly time step for use as input to hydrologic models.

    • Provide areal precipitation time-series data that can be used in a consistent manner over a range of spatial scales.

Precipitation modeling scope

Precipitation Modeling: Scope

  • Data used to achieve these objectives are daily and hourly recorded precipitation totals from climate stations within the study area, including a 25-km buffer around the area boundary.

  • Precipitation data are from the period 1985 through 2000.

Precipitation modeling scope1

Precipitation Modeling: Scope

  • 487 daily-data stations

  • 192 hourly-data stations

Precipitation modeling scope2

Precipitation Modeling: Scope

  • Daily precipitation data are simulated for 10-km square grid cells throughout the study area.

  • Daily precipitation for each FIPS2 area within the study area is computed based on time-series data from grid cells within each FIPS2 area.

Xyz methodology

xyz Methodology

  • Multiple linear regression (MLR) equations are developed for daily precipitation (p) in each of six sub-regions using independent variables x, y, and z from climate stations in each sub-region.

  • The general form of the MLR equation is:

Hourly disaggregation

Hourly Disaggregation

  • Simulated daily precipitation data are disaggregated into hourly data using:

    • recorded hourly precipitation from the nearest climate station, or

    • Mean-hourly precipitation by month and weather type.

  • Simulated daily p totals are preserved during disaggregation.

Mean hourly rainfall feb

Mean-hourly rainfall (Feb)

Example data

Example Data

  • 10x10km daily data

  • January, 1990

  • mm/day

Future plans

Future Plans

  • Complete trial Patuxent run

  • Complete F-tables

  • Begin PEST implementation

  • Continue investigating inclusion of estimates of ground-water residence times

On the graph paper

“on the graph paper”

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