PRMS/MMS: A Model and Modeling Framework for

Use in Water- and Environmental-Resources

ManagementGeorge Leavesley

USGS, Denver, Colorado USA

Overview

PRMS MMS Focus Areas in Model Development

Model Processes Toolbox

Summary

PRMS

PRMS Features

Modular Design Deterministic Distributed Parameter Daily and Storm Mode User Modifiable

Distributed Parameter Approach

Hydrologic Response Units - HRUs

HRU Delineation Based on:

- Slope - Aspect

- Elevation - Vegetation

- Soil - Precip Distribution

HRU DELINEATION AND CHARACTERIZATION

Polygon Hydrologic Response Units (HRUs)

Grid Cell Hydrologic Response Units (HRUs)

TopographicTopographic PixelatedPixelated

PRMS -- HRU PRMS -- HRU DelineationDelineation

CASCADING FLOW PLANES

3

Overland Flow Path

Channel Segment

Overland Flow Plane2

1

3

2

7

4

5

6

8

9 10

1112

Grass/Agriculture

Bare Ground/Rock

Trees

Shrubslength

width

1

3

1

2

4 Channel Junction

The Modular Modeling System (MMS)

An integrated system of computer software developed

to: Provide a common framework for model

development and operational applications Facilitate the integration of multi-disciplinary

modeling approaches for use in addressing complex, multi-objective, resource-management problems

Provide a toolbox-approach to the development of resource-management, decision-support systems

Model Builder

MMS Interface

MODULAR MODELING

SYSTEM (MMS)

Object User Interface

LEVELS OF MODULAR DESIGN

PROCESS MODEL FULLY COUPLED MODELS LOOSELY COUPLED MODELS RESOURCE MANAGEMENT

DECISION SUPPORT SYSTEMS ANALYSIS AND SUPPORT TOOLS

PRMS

WEBMOD

PRMS

National Weather Service - Hydro17

TOPMODEL

Next step: Use tracers to constrain the hydrologic solutions

Use Chemical and Use Chemical and Isotopic Tracers to Isotopic Tracers to Evaluate Simulated Evaluate Simulated

Flow Paths and Flow Paths and Residence TimesResidence Times

Coupled WEBMOD and PHREEQC

LEVELS OF MODULAR DESIGN

PROCESS MODEL FULLY COUPLED MODELS LOOSELY COUPLED MODELS RESOURCE MANAGEMENT

DECISION SUPPORT SYSTEMS ANALYSIS AND SUPPORT TOOLS

GSFLOW -- Coupled PRMS, MODFLOW, SFR, and Unsaturated Zone

Models

Streamflow

Unsaturated Zone Model:

PRMS to UNSAT UNSAT to MODFLOW

PRMS to SFR

PRMS to MODFLOW

MODFLOW to SFR

Sagehen Creek Model

Springs Stream gage

2x vertical exaggeration

Ground-Water Discharge & Saturation Excess

LEVELS OF MODULAR DESIGN

PROCESS MODEL FULLY COUPLED MODELS LOOSELY COUPLED MODELS RESOURCE MANAGEMENT

DECISION SUPPORT SYSTEMS ANALYSIS AND SUPPORT TOOLS

Aquatic Habitat Models

Watershed Model

Hydraulics Model

Fish Model

Velocity HSI - Brown Trout

00.20.40.6

0.81

1.2

0 1 2 3 4

velocity m/s

HS

I

Adult

Juvenile

Fingerling

Spawning

Depth HSI - Brown Trout

00.20.40.6

0.81

1.2

0 1 2 3

Depth m

HS

I

Adult

Juvenile

Fingerling

Spawning

Aquatic Habitat Models Results

ftft2

ftft3/s

LEVELS OF MODULAR DESIGN

PROCESS MODEL FULLY COUPLED MODELS LOOSELY COUPLED MODELS RESOURCE MANAGEMENT

DECISION SUPPORT SYSTEMS ANALYSIS AND SUPPORT TOOLS

Watershed and River Systems

Management Program

Recreation

Municipal & Industrial

Riparian Habitat

Hydropower

Irrigation

Endangered Species

Research and development of decision support systems and their application to achieve an equitable balance among water resource issues.

WARSMP Basins

Currently Active

Under Development

Gunnison, Truckee, Upper Rio Grande, Yakima

San Juan, Umatilla, Upper Columbia

Future

Bitterroot, Carson, Central Platte, Lower Rio Grande, Salmon

Generic DSS Framework for a Wide Range of Management

Issues

AnyDatabase

Climate Data Source

Object User InterfaceInterface for data visualization

and modeling

Modular Modeling SystemPhysical Process Models

DMI

DMI

DMI

MMI

Any Resource Management Model

Database-Centered Decision Support System

HydrologicDatabase

Hydromet

Real-time climate data feed

USBR RiverWareReservoir and River System

Operations Model

DMI

DMI

DMI

USGS Object User InterfaceInterface for data visualization

and modelingUSGS Modular Modeling System

Precipitation/Runoff Model (PRMS)

DMI

OBJECT USER INTEFACE (OUI)

ResourceDatabase

Resource Management Alternatives

SIMPPLLEVegetation Dynamics Models (USFS)

Object User InterfaceInterface for data visualization

and modeling

Modular Modeling System

Physical Process Models

DMI

DMI

DMI

DMI

DMI

Environmental Database-Centered Decision Support System (DSS)

Focus Area

Model Processes

Precipitation Distribution Snow accumulation and melt proceses Parameter estimation in ungauged

basins

Precipitation Interpolation Methods

Inverse distance weighting Kriging Multiple linear regression Climatological multiple linear

regression Locally weighted polynomial k nearest neighbor …

PRMS Snowpack Energy Balance Components

Evaluating MM5 Output Using PRMS

Nested Domains for MM5

HRU ConfigurationsPolygoPolygo

nnGridGrid

HRU HRU ConfigurationsConfigurations

GridGrid PolygonPolygon

Effects of HRU Configuration and Data Effects of HRU Configuration and Data SourceSource (Yampa River @Steamboat Springs)(Yampa River @Steamboat Springs)

(Clima(Climate te

StatioStation n

Data)Data)

20 5 20 5 1.71.7

20 5 20 5 1.71.7

HRU HRU Type/ Type/ Data Data SizeSize

eastEast River

0

0.2

0.4

0.6

0.8

1

1/0 1/20 2/9 2/29 3/20 4/9 4/29 5/19 6/8 6/28 7/18

1996

Per

cen

t B

asin

Sn

ow

cove

r

MODELSATELLITE

East River

0

0.2

0.4

0.6

0.8

1

1/0 1/20 2/9 2/29 3/20 4/9 4/29 5/19 6/8 6/28 7/18

1992

Per

cen

t B

asin

Sn

ow

cove

r

MODEL

SATELLITE

Satellite vs Modeled Snow Covered Area

Animas Animas Basin Basin Snow-Snow-

covered covered Area Area Year 2000Year 2000SimulateSimulate

dd

MeasurMeasured ed

(MODI(MODIS S

SatellitSatellite)e)

Error Range <= Error Range <= 0.10.1

NSA ProductGeneration

Interactive MapsDigital DataDiscussions

NSA ProductGeneration

Interactive MapsDigital DataDiscussions

TemperatureRelative Humidity

Wind SpeedSolar Radiation

Atmos. RadiationPrecipitation

Precipitation Type

Hourly InputGridded Data (1 km)

Hourly InputGridded Data (1 km)

Soils PropertiesLand Use/Cover

Forest Properties

Static GriddedData (1 km)

Static GriddedData (1 km)

Snow Energy and Mass Balance Model

Snow Energy and Mass Balance Model

Blowing Snow ModelBlowing Snow Model

Radiative Transfer ModelRadiative Transfer Model

State Variables forMultiple Vertical

Snow & Soil LayersSnow Water Equivalent

Snow DepthSnow Temperature

Liquid Water ContentSnow Sublimation

Snow Melt

State Variables forMultiple Vertical

Snow & Soil LayersSnow Water Equivalent

Snow DepthSnow Temperature

Liquid Water ContentSnow Sublimation

Snow Melt

NOHRSC Snow Modeling Framework

1

1

Data Assimilation2

3

Snow Observations

Snow Water Equivalent

Snow Depth

Snow Cover

Snow Observations

Snow Water Equivalent

Snow Depth

Snow Cover

NOHRSC Snow Model Physics

National Snow Analyses (NSA)

High-resolution Daily and Hourly Gridded Snow Data Sets of Fused Model and Observations

• Snow Water Equivalent

• Snow Density

• Snow Sfc. Temperature

• Snow Avg. Temperature

• Snow Melt

• Sublimation

• Snow Wetness

Local Information (1 km2)

Continental U.S. Information

• Snow Depth

• Archived at NCDC, NSIDC, and NDFD (soon)

Data Products

Interactive Maps

Time Series Plots

Text Discussions

Snow Information Products

Animas Animas River @ River @ DurangoDurango

1805 1805 kmkm2

Animas River Basin, Animas River Basin, COCO

Predicted and Predicted and Measured StreamflowMeasured Streamflow

Animas Basin, Animas Basin, CO 1990 - 2005CO 1990 - 2005

PREDICTEPREDICTEDDMEASUREMEASUREDD

Animas Basin SWE - Animas Basin SWE - 2004 2004

SNODASSNODAS PRMSPRMS

April April 11

May May 11

(in.(in.))

Animas Basin SWE Animas Basin SWE - 2005- 2005

SNODASSNODAS PRMSPRMS(in.(in.

))

April April 11

May May 11

SWE_diff = SNODAS - SWE_diff = SNODAS - PRMSPRMS

SWE Difference on SWE Difference on Selected HRUsSelected HRUs

PRMSPRMS

OBSOBS

Q Q (cfs)(cfs) AnimasAnimas PRMSPRMS

OBSOBS

PRMSPRMSSNODASNODA

SS

melt melt (in)(in)

PRMSPRMSSNODASNODA

SS

PRMSPRMSSNODASNODA

SS

swe swe (in)(in) PRMSPRMS

SNODASNODASS

a priori Parameter Estimation

IAHS Predictions in Ungauged Basins (PUBs) predict the fluxes of water and associated

constituents from ungauged basins, along with estimates of the uncertainty of predictions

Model Parameter Estimation Experiment (MOPEX) develop techniques for the a priori estimation

of the parameters used in land surface parameterization schemes of atmospheric models and in hydrologic models

Gauged Subbasins in the Upper Gunnison Basin, CO

East River

Taylor River

Lake Fork

Cochetopa Creek

Tomichi Creek

Hunter Creek nr Aspen, Colorado

Hunter

Midway

No Name

Gage Trans-mountain Diversion

PointsHRUs

Forecasting at Internal Nodes

GIS WEASELDelineation:•Only requires elevation Grid as input

•Interactively delineate •Area of Interest•Many kinds of features

•Streams•Elevation bands•Landuse•Contributing areas•Topographic index•……

Vegetation Type (USFS)

Vegetation Density (USFS)

Land Use-Land Cover (USGS)

DIGITAL DATABASESDIGITAL DATABASES STATSGO Soils (USDA)

Satellite SW Radiation (U Md)

Monthly PET

AUTOMATED PARAMETER ESTIMATION USING THE GIS WEASEL

MOPEX US Basin Locations

-100 -95 -90 -85 -80 -7528

30

32

34

36

38

40

42Location of 12 Basins for 2nd MOPEX workshop in Tucson

Workshop IWorkshop I

Workshop II: 20 Workshop II: 20 basins in Francebasins in France

Workshop ?Workshop ?

438 basins438 basins

Participants MOPEX Workshop I

NWS (SWB & SAC)

Meteo France (ISBA)

Russian Academy of Science (SWAP)

UC Berkeley / Princeton (VIC)

Cemagref, France (GR4J)

NCEP (NOAH) USGS (PRMS)

Yamanashi University (BTOPMC)

Swedish Meteor. and Hydro. Institute, Sweden (HBV)

University of Alberta, Canada (SAC)

University of University of Newcastle, Newcastle, AustraliaAustraliaUniversity of University of ArizonaArizonaCentre for Centre for Ecology and Ecology and Hydrology, UKHydrology, UKOregon State Oregon State UniversityUniversityWageningen Wageningen University, The University, The NetherlandsNetherlandsNational Institute National Institute of Hydrology, of Hydrology, CanadaCanadaUniversity of New University of New HampshireHampshire

MOPEX Workshop I Results

Average Daily Nash-Sutcliffe EfficiencyA Priori - 1960-1998

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

A B C D E F G H

Model

Eff

icie

ncy

Inter-Basin Standard Deviation of Daily Nash-Sutcliffe Efficiency

A Priori Results 1960-1998

0

0.2

0.4

0.6

0.8

1

1.2

1.4

A B C D E F G H

Model

Sta

nd

ard

Devia

tio

n o

f E

ffic

ien

cy

Average Daily Nash-Sutcliff EfficiencyCalibration - 1960-1998

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

A B C D E F G H

Model

Eff

icie

nc

y

Inter-Basin Standard Deviation of Daily Nash-Sutcliffe EfficiencyCalibration Rsults 1960 - 1988

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

A B C D E F G H

Model

Sta

nd

ard

De

via

tio

n o

f E

ffic

ien

cy

UncalibratUncalibrateded

CalibratedCalibrated

Average daily Nash-Sutcliffe CE for all Average daily Nash-Sutcliffe CE for all 12 basins12 basins

PRMPRMSS

PRMPRMSS

NWSNWS VICVIC

NWSNWSVICVIC

Ensemble Modeling

10 Model Ensemble

Dill Dill Basin, Basin,

GermanyGermany750 km750 km2

Land UseLand Use

Sub-basinsSub-basins

TopograpTopographyhy

Bias & Efficiency of Model Bias & Efficiency of Model SimulationsSimulations

Original

Precip Data Set

Focus Area

Toolbox

TOOL PITCH Parameterizer (GIS Weasel)

TOOLBOX

TOOL PITCH Parameterizer (GIS Weasel) Optimizer (Luca)

TOOLBOX

TOOL PITCH Parameterizer (GIS Weasel) Optimizer (Luca) Analyzer

Statistical and graphical sensitivity and uncertainty analysis tools

TOOLBOX

OMSOMS

Integrated Framework Development

Collaborative effort to integrate the Object Modeling System (OMS) and the

Modular Modeling System (MMS)

OMS PlatformM

odelC

ore

ModelB

uild

er

RZ

WQ

M

PR

MS

Data

base

Netbeans IDE, Sun Forte,…

Java C

om

pile

r

XM

L

Test

ing

C+

+

Oth

er…

- ARS - USGS - NRCS - (NWS)

-Friedrich Schiller University, Germany

Branding

http://oms.ars.usda.govhttp://oms.ars.usda.gov

Working with the

NRCS and NWS to

develop a Modular Modeling System

forecasting toolbox using

MMS/OMS and PRMS

Integrated Adaptive-Modeling and Decision-Support System

Summary•Facilitates multi-disciplinary integration of models and tools to address the issues of water and environmental-resource management.

•Allows rapid evaluation of the effects of decision and management scenarios.

•Allows incorporation of continuing advances in physical, social, and economic sciences.

•Provides an effective means for sharing scientific understanding with stakeholders and decision makers. •Open source software design allows software design allows many to share resources, expertise, many to share resources, expertise, knowledge, and costs.knowledge, and costs.

MORE INFORMATION

http://wwwbrr.cr.usgs.gov/mms

http://wwwbrr.cr.usgs.gov/weasel

http://wwwbrr.cr.usgs.gov/warsmp

http://oms.ars.usda.gov