prms/mms: a model and modeling framework for use in water- and environmental- resources management...
TRANSCRIPT
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PRMS/MMS: A Model and Modeling Framework for
Use in Water- and Environmental-Resources
ManagementGeorge Leavesley
USGS, Denver, Colorado USA
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Overview
PRMS MMS Focus Areas in Model Development
Model Processes Toolbox
Summary
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PRMS
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PRMS Features
Modular Design Deterministic Distributed Parameter Daily and Storm Mode User Modifiable
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Distributed Parameter Approach
Hydrologic Response Units - HRUs
HRU Delineation Based on:
- Slope - Aspect
- Elevation - Vegetation
- Soil - Precip Distribution
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HRU DELINEATION AND CHARACTERIZATION
Polygon Hydrologic Response Units (HRUs)
Grid Cell Hydrologic Response Units (HRUs)
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TopographicTopographic PixelatedPixelated
PRMS -- HRU PRMS -- HRU DelineationDelineation
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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
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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
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Model Builder
MMS Interface
MODULAR MODELING
SYSTEM (MMS)
Object User Interface
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LEVELS OF MODULAR DESIGN
PROCESS MODEL FULLY COUPLED MODELS LOOSELY COUPLED MODELS RESOURCE MANAGEMENT
DECISION SUPPORT SYSTEMS ANALYSIS AND SUPPORT TOOLS
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PRMS
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WEBMOD
PRMS
National Weather Service - Hydro17
TOPMODEL
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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
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Coupled WEBMOD and PHREEQC
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LEVELS OF MODULAR DESIGN
PROCESS MODEL FULLY COUPLED MODELS LOOSELY COUPLED MODELS RESOURCE MANAGEMENT
DECISION SUPPORT SYSTEMS ANALYSIS AND SUPPORT TOOLS
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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
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Sagehen Creek Model
Springs Stream gage
2x vertical exaggeration
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Ground-Water Discharge & Saturation Excess
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LEVELS OF MODULAR DESIGN
PROCESS MODEL FULLY COUPLED MODELS LOOSELY COUPLED MODELS RESOURCE MANAGEMENT
DECISION SUPPORT SYSTEMS ANALYSIS AND SUPPORT TOOLS
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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
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Aquatic Habitat Models Results
ftft2
ftft3/s
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LEVELS OF MODULAR DESIGN
PROCESS MODEL FULLY COUPLED MODELS LOOSELY COUPLED MODELS RESOURCE MANAGEMENT
DECISION SUPPORT SYSTEMS ANALYSIS AND SUPPORT TOOLS
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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.
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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
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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
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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
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OBJECT USER INTEFACE (OUI)
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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)
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Focus Area
Model Processes
Precipitation Distribution Snow accumulation and melt proceses Parameter estimation in ungauged
basins
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Precipitation Interpolation Methods
Inverse distance weighting Kriging Multiple linear regression Climatological multiple linear
regression Locally weighted polynomial k nearest neighbor …
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PRMS Snowpack Energy Balance Components
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Evaluating MM5 Output Using PRMS
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Nested Domains for MM5
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HRU ConfigurationsPolygoPolygo
nnGridGrid
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HRU HRU ConfigurationsConfigurations
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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
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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
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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
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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
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NOHRSC Snow Model Physics
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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
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Animas Animas River @ River @ DurangoDurango
1805 1805 kmkm2
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Animas River Basin, Animas River Basin, COCO
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Predicted and Predicted and Measured StreamflowMeasured Streamflow
Animas Basin, Animas Basin, CO 1990 - 2005CO 1990 - 2005
PREDICTEPREDICTEDDMEASUREMEASUREDD
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Animas Basin SWE - Animas Basin SWE - 2004 2004
SNODASSNODAS PRMSPRMS
April April 11
May May 11
(in.(in.))
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Animas Basin SWE Animas Basin SWE - 2005- 2005
SNODASSNODAS PRMSPRMS(in.(in.
))
April April 11
May May 11
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SWE_diff = SNODAS - SWE_diff = SNODAS - PRMSPRMS
SWE Difference on SWE Difference on Selected HRUsSelected HRUs
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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
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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
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Gauged Subbasins in the Upper Gunnison Basin, CO
East River
Taylor River
Lake Fork
Cochetopa Creek
Tomichi Creek
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Hunter Creek nr Aspen, Colorado
Hunter
Midway
No Name
Gage Trans-mountain Diversion
PointsHRUs
Forecasting at Internal Nodes
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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•……
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Vegetation Type (USFS)
Vegetation Density (USFS)
Land Use-Land Cover (USGS)
DIGITAL DATABASESDIGITAL DATABASES STATSGO Soils (USDA)
Satellite SW Radiation (U Md)
Monthly PET
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AUTOMATED PARAMETER ESTIMATION USING THE GIS WEASEL
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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
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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
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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
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Ensemble Modeling
10 Model Ensemble
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Dill Dill Basin, Basin,
GermanyGermany750 km750 km2
Land UseLand Use
Sub-basinsSub-basins
TopograpTopographyhy
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Bias & Efficiency of Model Bias & Efficiency of Model SimulationsSimulations
Original
Precip Data Set
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Focus Area
Toolbox
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TOOL PITCH Parameterizer (GIS Weasel)
TOOLBOX
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TOOL PITCH Parameterizer (GIS Weasel) Optimizer (Luca)
TOOLBOX
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TOOL PITCH Parameterizer (GIS Weasel) Optimizer (Luca) Analyzer
Statistical and graphical sensitivity and uncertainty analysis tools
TOOLBOX
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OMSOMS
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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
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Working with the
NRCS and NWS to
develop a Modular Modeling System
forecasting toolbox using
MMS/OMS and PRMS
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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.
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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