Management of Natural and Environmental Resources for Sustainable Agricultural Development

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1 World Meteorological Organization (WMO) Management of Natural and Environmental Resources for Sustainable Agricultural Development Use of the Object Modeling System for Operational Water Supply Forecasting By Tom Perkins (NRCS) & Tom Pagano (NRCS) 14 February 2006 Portland, OR

2 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.

3 The first forecast of the Lake Tahoe rise was in 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.

4 A bit short of water with such a short calculator!

5 Modernize water supply forecasting environment Desire for new hydrograph- based products We ve got all of this daily SNOTEL data Modeling dream 1983 reorganization

6 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

8 Some forecast methodologies are decades to centuries old Water resource managers and users want/need forecasts of any parameter you can derive from a hydrograph: Magnitude and date of peak Flow on a date Date of a threshold Weekly, monthly volumes Daily scenarios for use in reservoir optimization programs

9 MODELS Leavesley 1985 Marron 1986 (PRMS) Jones 1986 Perkins 1988 (SSARR) Cooley 1986 (NWSRFS) Shafer/Marron 1987 (SRM) NRCS Operational Simulation Modeling History Garen/Marks (spatial snow model)

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

10 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 So what s changed? Several recent technological advances make simulation modeling do-able with fewer human resources

11 Current Modeling Activities Precipitation-Runoff Modeling System PRMS Modular Modeling System MMS 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

12 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.

13 USGS Modular Modeling System Currently using off the shelf PRMS package developed by USGS

14 Calibration of PRMS Spatial Parameters The GIS Weasel Uses slope, aspect, elev, soils, veg type, veg density to define Hydrologic Response Units and associated model parameters 1-button Weasel recently developed and is being tested

15 Estimating non-spatial parameters Traditional Approach Manually tweak parameters to minimize bias, visually fit hydrograph to observed flow. Not without its problems

16 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 calibrate model internal states. Not without its problems

17 Solar Radiation Adjust radiation-related parameters. Check if model seasonal cycle matches observed radiation data. Peak Flows Potential Evapotrans Water Balance

18 Solar Radiation Adjust radiation-related parameters. Check if model seasonal cycle matches observed radiation data. Peak Flows Potential Evapotrans Adjust evaporation parameters. Check seasonal cycle against observed potential ET data. Water Balance

19 Solar Radiation Adjust radiation-related parameters. Check if model seasonal cycle matches observed radiation data. Peak Flows Potential Evapotrans Adjust evaporation parameters. Check seasonal cycle against observed potential ET data. Adjust water balance parameters. Check annual flow volume vs obs. Water Balance

20 Solar Radiation Adjust radiation-related parameters. Check if model seasonal cycle matches observed radiation data. Peak Flows Potential Evapotrans 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. Water Balance

21 Solar Radiation Adjust radiation-related parameters. Check if model seasonal cycle matches observed radiation data. Peak Flows Potential Evapotrans 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. Water Balance Rinse and repeat 4-8 times.

22 Gathering Data

23 Automated data networks Applied climate information system (ACIS) National Water Information System (NWIS) SNOw TELemetry System (SNOTEL) HYDROlogic and METeorologic Monitoring System (Hydromet) Others. Gathering Data Wanted: A cheap, clean, reliable supply

24 Snotel (NRCS) Network ACIS (NWS) Network Mar 31, 2005 Blue: Reporting Red: Missing

25 Data gathering and screening for MMS Real-time data automatically downloaded and reformatted daily United States Geological Survey (USGS) National Weather Service (NWS) Regional Climate Centers (RCC-ACIS) Natural Resources Conservation Service (NRCS) 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

26 Results to date..

27 PRMS-MMS Calibration and Operations 16 headwater basins in diverse climates NWCC personnel calibrated spatial 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. Missouri Basin Columbia Basin Colorado/Rio Grande

PRMS-MMS Calibration and Operations 16 headwater basins in diverse climates NWCC personnel calibrated spatial parameters (Oct 2004) USGS has automatic procedure to calibrate remaining parameters (Nov

28 PRMS-MMS Calibration and Operations 16 headwater basins in diverse climates NWCC personnel calibrated spatial 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 Missouri Basin Columbia Basin Colorado/Rio Grande Planned

29 Animas River at Durango, Colorado East River at Almont, Colorado Black = observed Red = simulated Yampa River at Maybell, Colorado

30 NRCS Spreadsheet-based output interface

31 Little Wood Reservoir, Idaho May 2005

32 Snow Covered Area Simulation Fraction covered Slide for fathead PRMS NOHRSC-Satellite Historical Simulated

33 PRMS Ensemble Streamflow Prediction (ESP) results: ESP conditional forecast using calibration #2 Salmon Falls Creek nr San Jacinto, Nevada (USGS gage ) Initialized January 6, 2005 Mar-Jun Mar-Jul Mar-Sept Peak Peak Date (10 6 m 3 ) (10 6 m 3 ) (10 6 m 3 ) (m 3 /s) Minimum Mar 90% exc Apr 70% exc Apr 50% exc May 30% exc May 10% exc Jun Maximum Jun Observed May Basin Area = 3755 km 2 Probability distribution based on 23 historical years

34 Future Modeling Activities OMS-PRMS

35 OMS Object-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

36 Pre-Processors Access & prepare data

37 Pre-Processors Access & prepare data Models Simulate hydrologic & ecosystem processes

38 Pre-Processors Access & prepare data Models Simulate hydrologic & ecosystem processes Post-Processors Display & analyze model results

39 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

40 Plots of individual parameters vs time

41 Plots of combined parameters vs time

42 Zoom feature

43 Data Plots Probability Distributions XY Plots Flow Durations Observed/Predicted Statistics Etc.

44 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

A Review of the 2007 Water Year in Colorado

A Review of the 2007 Water Year in Colorado Nolan Doesken Colorado Climate Center, CSU Mike Gillespie Snow Survey Division, USDA, NRCS Presented at the 28 th Annual AGU Hydrology Days, March 26, 2008,