Management of Natural and Environmental Resources for Sustainable Agricultural Development

Use of the Object Modeling System for Operational Water Supply Forecasting

ByTom Perkins (NRCS) & Tom Pagano (NRCS)

14 February 2006 Portland, OR

World Meteorological Organization (WMO)

The path we’ve trod…

•Early Years

•State-based Operations

•Centralized in Portland

•New Technologies

The first forecast of the Lake Tahoe “rise” was in 1911. This is the earliest known water supply forecast in the United States.

The snow water content on Mt. Rose, Nevada on April of 1910 was 597 mm. In 1911, it was 1128 mm. Based on those snow course measurements, Dr. James Church, predicted that the Lake Tahoe “rise” would be 189 percent of the previous year.

A bit short of water with such a short calculator!

• Modernize water supply forecasting environment

• Desire for new hydrograph- based products

• We’ve got all of this daily SNOTEL data

• Modeling dream

• 1983 reorganization

Simple linear regression

Stepwise linear regression

Principal components statistical techniques

Statistical analysis with jackknifing

Non-linear statistical analysis

Z-score statistical techniques

110 baud communications to Fort Collins, Colorado

Water resource managers and users want/need forecasts of any parameter you can derive from a hydrograph:

Flow on a dateDate of a threshold

Magnitude and date of peak

Daily scenarios for use in reservoir optimization programs

Weekly, monthly volumes

Some forecast methodologies are decades to centuries old

MODELS

Leavesley 1985Marron 1986(PRMS)

Jones 1986Perkins 1988(SSARR)

Cooley 1986 (NWSRFS)

Shafer/Marron 1987 (SRM)

Garen/Marks 1996-98(spatial snow model)

NRCS Operational Simulation Modeling History

Competition and increasing demands on our finite water resources are causing water managers to modify their management strategies

Water resource managers want to know more than just seasonal volumes

1991 NRCS Simulation Modeling Plan

• Compared options: SRM, SSARR, PRMS, NWSRFS

• Calibrate 200 basins in 5 years with 3 staff hydrologists

Several recent technological advances make simulation modeling do-able with fewer human resources

So…what’s changed?

Current Modeling Activities

•Models can be tailored to forecaster’s situation/need•There is no need to reinvent modeling infrastructure•System is flexible and up-gradable•Partnership and technology sharing with other agencies•Widely used and documented

Precipitation-Runoff Modeling SystemPRMS

Modular Modeling SystemMMS

Current modeling components• ArcGis to reproject downloaded basin digital elevation model data

• GIS Weasel to distribute hydrologic response unit (HRU) parameters

• Precipitation-Runoff Modeling System (PRMS) model - (includes HRU parameter distribution and ensemble streamflow prediction (ESP) modules)

• Microsoft Office Excel-based automatic data downloading routines and output viewer

Components missing on NWCC system• In-house application to develop relationships to distribute precipitation and temperature

over the basin hydrologic response units

• Automatic, step-wise, multiple-objective calibration procedure.

USGSModularModelingSystem

Currently using“off the shelf”PRMS package developed by

USGS

Calibration of PRMS Spatial Parameters

The GIS “Weasel”

Uses slope, aspect, elev, soils, veg type, veg density to define “Hydrologic Response Units” andassociated model parameters

“1-button” Weaselrecently developed and is being tested

Estimating non-spatial parameters

Traditional Approach

Manually tweak parameters to minimize bias,visually fit hydrograph to observed flow.

Not without its problems

Estimating non-spatial parameters

Traditional Approach

Manually tweak parameters to minimize bias,visually fit hydrograph to observed flow.

Not without its problems

Multi-step Automatic Calibration Scheme (Hay/USGS)

Iteratively and automatically calibratemodel internal states.

Not without its problems

Solar Radiation

PotentialEvapotrans

Water Balance

Peak Flows

Adjust radiation-related parameters. Check if model seasonal cycle matches “observed” radiation data.

Solar Radiation

PotentialEvapotrans

Water Balance

Peak Flows

Adjust radiation-related parameters. Check if model seasonal cycle matches “observed” radiation data.

Adjust evaporation parameters. Check seasonal cycle against “observed” potential ET data.

Solar Radiation

PotentialEvapotrans

Water Balance

Peak Flows

Adjust radiation-related parameters. Check if model seasonal cycle matches “observed” radiation data.

Adjust evaporation parameters. Check seasonal cycle against “observed” potential ET data.

Adjust water balance parameters.Check annual flow volume vs obs.

Solar Radiation

PotentialEvapotrans

Water Balance

Peak Flows

Adjust radiation-related parameters. Check if model seasonal cycle matches “observed” radiation data.

Adjust evaporation parameters. Check seasonal cycle against “observed” potential ET data.

Adjust water balance parameters.Check annual flow volume vs obs.

Adjust flow timing parameters.Evaluate flow on peak flow days.

Solar Radiation

PotentialEvapotrans

Water Balance

Peak Flows

Adjust radiation-related parameters. Check if model seasonal cycle matches “observed” radiation data.

Adjust evaporation parameters. Check seasonal cycle against “observed” potential ET data.

Adjust water balance parameters.Check annual flow volume vs obs.

Adjust flow timing parameters.Evaluate flow on peak flow days.

Rinse and repeat 4-8 times.

Gathering Data

Gathering DataWanted:A cheap, clean, reliable supply

Applied climate information system (ACIS)National Water Information System (NWIS)SNOw TELemetry System (SNOTEL)HYDROlogic and METeorologic Monitoring System (Hydromet)Others….

Automated data networks…

Snotel (NRCS) Network ACIS (NWS) Network

Mar 31, 2005

Blue: Reporting

Red: Missing

Data gathering and screening for MMSReal-time data automatically downloaded and

reformatted daily

• Real-time data quality “a concern”• Martyn Clark (University of Colorado) has created a

real-time temperature and precipitation quality control module that can be used stand-alone or as part of Modular Modeling System (MMS).

• Martyn also provided an initial cleaned up historical NWS/NRCS dataset

• United States Geological Survey (USGS)• National Weather Service (NWS)• Regional Climate Centers (RCC-ACIS)• Natural Resources Conservation Service (NRCS)

Results to date……..

PRMS-MMS Calibration and Operations

Missouri BasinColumbia BasinColorado/Rio Grande

16 headwater basins in diverse climates

NWCC personnel calibratedspatial parameters (Oct 2004)

USGS has automatic procedure to calibrate remaining parameters (Nov 2004)

USGS/USBR also running model in Gunnison, San Juan, Rio Grande, Carson, Yakima, Klamath, etc., so additional basins could be provided to us.

PRMS-MMS Calibration and Operations

Missouri BasinColumbia BasinColorado/Rio GrandePlanned

16 headwater basins in diverse climates

NWCC personnel calibratedspatial parameters (Oct 2004)

USGS has automatic procedure to calibrate remaining parameters (Nov 2004)

USGS/USBR also running model in Gunnison, San Juan, Rio Grande, Carson, Yakima, Klamath, etc., so additional basins could be provided to us.

Specific plans to increase roster to ~35

Animas River at Durango, Colorado

East River at Almont, Colorado

Yampa River at Maybell, Colorado

Black = observed

Red = simulated

NRCS Spreadsheet-based output interface

Little Wood Reservoir, Idaho – May 2005

Slide for fatheadPRMS

NOHRSC-SatelliteHistoricalSimulated

Snow Covered Area SimulationFr

actio

n co

vere

d

ESP conditional forecast using calibration #2Salmon Falls Creek nr San Jacinto, Nevada (USGS gage 13105000)

Initialized January 6, 2005

Mar-Jun Mar-Jul Mar-Sept Peak Peak Date(106m3) (106m3) (106m3) (m3/s)

Minimum 9.87 14.80 19.74 3.34 11-Mar90% exc 22.20 29.60 34.54 6.91 20-Apr70% exc 37.00 48.11 53.04 11.33 30-Apr50% exc 60.44 76.48 83.88 16.68 4-May30% exc 74.01 93.77 99.91 26.22 15-May10% exc 118.41 140.62 148.02 33.05 1-JunMaximum 151.72 185.02 194.89 44.32 10-Jun

Observed 109.16 115.08 120.63 39.64 18-May

Basin Area = 3755 km2

Probability distribution based on 23 historical years

PRMS Ensemble Streamflow Prediction (ESP) results:

Future Modeling Activities

OMS-PRMS

OMSObject-oriented Modeling System

•Library of science and database components

•Facilitates assembly of modeling packages

•Supported by graphical user interface modules

•Data retrieval, statistical, visualization utilities

•EXtensible Markup Language mechanisms

•Web based sharing of modeling resources

Pre-ProcessorsAccess & prepare data

Pre-ProcessorsAccess & prepare data

ModelsSimulate hydrologic & ecosystem processes

Pre-ProcessorsAccess & prepare data

ModelsSimulate hydrologic & ecosystem processes

Post-ProcessorsDisplay & analyze model

results

• OMS will be the modeling platform for NRCS• TR20, TR55, WEPP, PRMS will be included

initially• Other models will be considered in the future• The NWCC Water Supply Forecasting models

will be the initial program prototypes

Plots of individual parameters vs time

Plots of combined parameters vs time

Zoom feature

Data Plots

Probability Distributions

XY Plots

Flow Durations

Observed/Predicted Statistics

Etc.

Current OMS Modeling Components•PRMS model with all MMS modules

•Graphical User Interface

•Output module

Soon to be completed components•Hydrologic response unit parameter distribution modules•Hydrologic response unit delineation module•Automatic calibration module•Conditioned ensemble streamflow prediction scenarios•10-day quantitative climatic forecast interface•Data acquisition modules•Data quality control routines•Report analysis