The Modular Modeling System (MMS): A Toolbox for Water ...web.sahra.arizona.edu/unesco/shortcourses/Leavesley_George.pdf · ... A Toolbox for Water-and Environmental-Resources Management

The Modular Modeling System The Modular Modeling System (MMS): A Toolbox for Water(MMS): A Toolbox for Water--and Environmentaland Environmental--Resources Resources

ManagementManagement

G.H. Leavesley, S.L. Markstrom, R.J. Viger, and L.E. Hay

U.S. Geological Survey, Denver, CO USA

STARTING POINTSSTARTING POINTS

There are no universal modelsThere are no universal modelsModels for different purposes require different Models for different purposes require different levels of detail and comprehensivenesslevels of detail and comprehensivenessAppropriate model process conceptualizations Appropriate model process conceptualizations are a function of problem objectives, data are a function of problem objectives, data constraints, and spatial and temporal scales of constraints, and spatial and temporal scales of applicationapplication

Modeling ConundrumModeling Conundrum

Experimental science builds on hypothesis Experimental science builds on hypothesis testing and interpretation based on earlier testing and interpretation based on earlier published hypotheses and resultspublished hypotheses and results

Modelers tend to build from the ground up Modelers tend to build from the ground up because existing models are not well designed because existing models are not well designed for for incremental improvement by othersincremental improvement by others

TOOL BOX MODELING TOOL BOX MODELING VIEWSVIEWS

Research Model Developer Research Model Developer Complex DetailComplex DetailApplication Model DeveloperApplication Model DeveloperModel UserModel UserResource ManagerResource ManagerPolicy Maker Policy Maker Condensed AnalysisCondensed Analysis

LEVELS OF MODULAR LEVELS OF MODULAR DESIGNDESIGN

PROCESSPROCESSMODELMODELFULLY COUPLED MODELSFULLY COUPLED MODELSLOOSELY COUPLED MODELSLOOSELY COUPLED MODELSRESOURCE MANAGEMENT RESOURCE MANAGEMENT DECISION SUPPORT SYSTEMSDECISION SUPPORT SYSTEMSANALYSIS AND SUPPORT ANALYSIS AND SUPPORT TOOLSTOOLS

Single Purpose

Multi-objective, Complex


PROCESSPROCESSMODELMODELFULLY COUPLED MODELSFULLY COUPLED MODELSLOOSELY COUPLED MODELSLOOSELY COUPLED MODELSRESOURCE MANAGEMENT DECISION RESOURCE MANAGEMENT DECISION SUPPORT SYSTEMSSUPPORT SYSTEMSANALYSIS AND SUPPORT TOOLSANALYSIS AND SUPPORT TOOLS

Model Building Tool - XMBUILD

PRMS

SELECTED MODELS AND SELECTED MODELS AND MODULES IN MMSMODULES IN MMS

USGSUSGSPRMSPRMSDAFLOWDAFLOWMODFLOWMODFLOWWEBMODWEBMOD

OTHEROTHERTOPMODELTOPMODELHydroHydro--17 (NWS snowmelt)17 (NWS snowmelt)Sacramento Model (NWS)Sacramento Model (NWS)Snowmelt Runoff Model Snowmelt Runoff Model (SRM) (ARS)(SRM) (ARS)ENNS Model (modified ENNS Model (modified HBV, Austria)HBV, Austria)

• IN DEVELOPMENT– PHREEQC, OTIS, OTEQ, AHM (QW models)– RZWQM, RUSLE, SWAT, Generic Crop (ARS Ag models

via OMS)



Ground Water - Surface Water Coupling

MODFLOW

PRMS

DAFLOW



LOOSELEY COUPLED MODELSLOOSELEY COUPLED MODELS

Watershed Model

Hydraulics Model

Database

Fish ModelData Management

Interface (DMI)

MMS Model

Off-the-shelf Model

LOOSELEY COUPLED MODELSLOOSELEY COUPLED MODELS

Watershed Model

Hydraulics Model

Database

Fish ModelData Management

Interface (DMI)

MMS Model

Off-the-shelf Model

Model Management Interface (MMI) [XML]

OBJECT USER INTEFACE (OUI)


PROCESSPROCESSMODELMODELFULLY COUPLED MODELSFULLY COUPLED MODELSLOOSELY COUPLED MODELSLOOSELY COUPLED MODELSRESOURCE MANAGEMENT RESOURCE MANAGEMENT DECISION SUPPORT SYSTEMSDECISION SUPPORT SYSTEMSANALYSIS AND SUPPORT TOOLSANALYSIS 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.

RiverWare – Lower Colorado River

RulebasedRulebasedSimulationSimulation

Simulation is underSimulation is under--determineddeterminedOperating policies are prioritized rulesOperating policies are prioritized rules

IF (state of system)IF (state of system)THEN (set slots)THEN (set slots)

Rules drive simulationRules drive simulationPolicy groups, rules can be toggled Policy groups, rules can be toggled

ON/OFFON/OFF

OptimizationOptimizationPrePre--emptive Linear Goal Programmingemptive Linear Goal Programming

MultiMulti--objectives without userobjectives without user--defined penaltiesdefined penaltiesPolicies (Goals) are PrioritizedPolicies (Goals) are PrioritizedMinimized infeasibilitiesMinimized infeasibilities

Goals/constraints formulated in EditorGoals/constraints formulated in EditorVariables automatically Variables automatically linearizedlinearized

user controls user controls linearizationslinearizations methodsmethodsEconomic (hydropower) objective Thermal Economic (hydropower) objective Thermal

ObjectObjectPhysical constraints generatedPhysical constraints generated

by objectsby objectsCPLEX solverCPLEX solver

Can “tune” parametersCan “tune” parametersInformational diagnosticsInformational diagnostics

PostPost--optimization Simulationoptimization Simulation

Upper Gunnison DSSUpper Gunnison DSS

HydrologicDatabase

HydrometReal-time climate

data feed

RiverWareReservoir and River System

Operations Model

DMI

DMI

DMI

Object User InterfaceInterface for data visualization

and modeling Modular Modeling SystemPrecipitation/Runoff Model (PRMS)

DMI

Generic DSS Framework for a Wide Generic DSS Framework for a Wide Range of Management IssuesRange of Management Issues

AnyDatabase

Climate Data Source


and modeling

Modular Modeling SystemPhysical Process Models

DMI

DMI

DMI

MMI

Any Resource Management

Model

HYDROLOGIC RESPONSE UNITS

MODELED SUBBASINS AND FORECAST NODES

WARSMP WARSMP BasinsBasins

Currently Active

Under Development

Gunnison, Truckee, Upper Rio Grande, Yakima

San Juan, Umatilla, Upper Columbia

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


PROCESSPROCESSMODEL MODEL FULLY COUPLED MODELSFULLY COUPLED MODELSLOOSELY COUPLED MODELSLOOSELY COUPLED MODELSRESOURCE MANAGEMENT DECISION RESOURCE MANAGEMENT DECISION SUPPORT SYSTEMSSUPPORT SYSTEMSANALYSIS AND SUPPORT TOOLSANALYSIS AND SUPPORT TOOLS

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 DATABASESSTATSGO Soils (USDA)

Satellite SW Radiation (U Md)

Monthly PET

AUTOMATED PARAMETER ESTIMATION USING THE GIS WEASEL

GIS WEASELParameterization:•200+ methods available•Easily add custom methods

•Configure recipes•Apply to feature maps

•Exploit many types of data•Produce maps and ASCII

files of parameters

PARAMETER

PARAMETER ESTIMATION METHOD

MODEL RECIPE FILE

MultiMulti--step Calibrationstep Calibration

24

68

1012

200300400500600700anm

as, So

lar Ra

diation

Month

Solar Radiation

OBSca

l

SIMcal

SIMver

24

68

1012

20406080100140

anmas,

PET

Month

PET

OBSca

l

SIMcal

SIMver

1988

1990

1992

1994

1996

1998

60080010001200

anmas,

Water

Balanc

e

Water

Year

Annual WB

1988

1990

1992

1994

1996

1998

0.50.60.70.80.9

anmas,

Nash−

Sutcliff

e Good

ness o

f Fit

Nash

Sutcli

ffe

Annual NSMultiMulti--step calibration visualizationstep calibration visualization

ANALYSIS and SUPPORT ANALYSIS and SUPPORT TOOLSTOOLS

Currently AvailableStatistical and Graphical Analyses

Parameter Optimization

Parameter Sensitivity Analysis

Beta TestingShuffle Complex Evolution Optimization

Multi-Objective COMplex Evolution Algorithm (MOCOM)

Generalized Likelihood Uncertainty Estimation (GLUE)

Visualization

CURRENT FOCUS ISSUES IN CURRENT FOCUS ISSUES IN MMS DEVELOPMENT AND MMS DEVELOPMENT AND

APPLICATIONAPPLICATIONCoupling of SW and GW ModelsCoupling of SW and GW Modelsa prioria priori Parameter EstimationParameter EstimationIncorporation of Remotely Sensed Data Incorporation of Remotely Sensed Data Coupling of Atmospheric and Hydrologic ModelsCoupling of Atmospheric and Hydrologic ModelsImproved Hydrologic and Ecosystem Process Improved Hydrologic and Ecosystem Process SimulationSimulationIntegrated Analysis and Support ToolsIntegrated Analysis and Support Tools

Coupled PRMS, MODFLOW, Coupled PRMS, MODFLOW, DAFLOW, Unsaturated Zone DAFLOW, Unsaturated Zone

ModelsModels

Streamflow

Unsaturated Zone Model:

PRMS to UNSAT UNSAT to MODFLOW

PRMS to DAFLOW

PRMS to MODFLOW

MODFLOW to DAFLOW

a prioria priori Parameter EstimationParameter EstimationIAHS Predictions in Ungauged Basins (IAHS Predictions in Ungauged Basins (PUBsPUBs))

predict the fluxes of water and associated constituents predict the fluxes of water and associated constituents from ungauged basins, along with estimates of the from ungauged basins, along with estimates of the uncertainty of predictionsuncertainty of predictions

Model Parameter Estimation Experiment Model Parameter Estimation Experiment (MOPEX)(MOPEX)

develop techniques for the develop techniques for the a prioria priori estimation of the estimation of the parameters used in land surface parameterization schemes parameters used in land surface parameterization schemes of atmospheric models and in hydrologic modelsof atmospheric models and in hydrologic models

MOPEX US Basin LocationsMOPEX US Basin Locations

-1 0 0 -9 5 -9 0 -8 5 -8 0 -7 52 8

3 0

3 2

3 4

3 6

3 8

4 0

4 2L o c a tio n o f 1 2 B a s in s fo r 2 n d M O P E X w o rk s h o p in T u c s o n

Workshop IWorkshop I

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

Workshop ?Workshop ?

438 basins438 basins

Participants MOPEX Workshop IParticipants MOPEX Workshop I

NWS NWS (SWB & SAC)(SWB & SAC)MeteoMeteo France France (ISBA)(ISBA)Russian Academy of Russian Academy of Science Science (SWAP)(SWAP)UC Berkeley / UC Berkeley / Princeton Princeton (VIC)(VIC)CemagrefCemagref, France , France (GR4J)(GR4J)NCEP NCEP (NOAH)(NOAH)USGS USGS (PRMS)(PRMS)

Yamanashi Yamanashi University University (BTOPMC)(BTOPMC)Swedish Meteor. Swedish Meteor. and Hydro. and Hydro. Institute, Sweden Institute, Sweden (HBV)(HBV)University of University of Alberta, Canada Alberta, Canada (SAC)(SAC)

University of University of Newcastle, AustraliaNewcastle, AustraliaUniversity of ArizonaUniversity of ArizonaCentre for Ecology and Centre for Ecology and

Hydrology, UKHydrology, UKOregon State UniversityOregon State UniversityWageningenWageningen University, University,

The NetherlandsThe NetherlandsNational Institute of National Institute of

Hydrology, CanadaHydrology, CanadaUniversity of New University of New

HampshireHampshire

MOPEX Workshop I ResultsMOPEX Workshop I Results

Average Daily Nash-Sutcliffe EfficiencyA Priori - 1960-1998

00.1

0.20.3

0.40.50.6

0.70.8

0.91

A B C D E F G H

Model

Effic

iency

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

Stan

dard

Dev

iation

of Ef

ficien

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

Effic

iency

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

Stan

dard

Dev

iation

of Ef

ficien

cy

UncalibratedUncalibrated CalibratedCalibratedAverage daily NashAverage daily Nash--Sutcliffe CE for all 12 basinsSutcliffe CE for all 12 basins

RADAR DATARADAR DATA

NEXRAD vs S-POL, Buffalo Creek, CO

Buffalo Creek

Estimated Peak

Coupling SCA remote sensing products with point measures and modeled SWE to improve models and predictions

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

1996

Perc

ent B

asin

Sno

wco

ver

MODELSATELLITE

Integrating Remotely Sensed Data

Forecast MethodologiesForecast Methodologies- Historic data as analog for the future

Ensemble Streamflow Prediction (ESP)

-Synthetic time-series Weather Generator

- Atmospheric model outputDynamical Downscaling

Statistical Downscaling

MMS ESP ToolMMS ESP ToolAll Computed Traces using 1975 - 1998

MMS ESP ToolMMS ESP ToolManager selected 10, 50, and 90 % probability of exceedance

Weather Generator

Develop synthetic timeDevelop synthetic time--series of precipitation series of precipitation and temperatureand temperature

LongLong--term planning or policy analysisterm planning or policy analysisClimate change scenariosClimate change scenarios

Collaboratively with Dr. Balaji Rajagopalan, University of Colorado, Boulder

Dynamical DownscalingDynamical DownscalingRegCM2RegCM2 ((GiorgiGiorgi et al., 1993, 1996) et al., 1993, 1996)

Period: 1979Period: 1979--19881988Boundary conditions: NCEP ReanalysisBoundary conditions: NCEP Reanalysis52 km grid (Lambert conformal projection)52 km grid (Lambert conformal projection)

Nested Domains for MM5

Statistical Downscaling AtmosphericStatistical Downscaling Atmospheric ModelsModelsMultiple linear regression Multiple linear regression equations developed for equations developed for selected climate stations selected climate stations

Predictors chosen from Predictors chosen from over 300 NCEP variables over 300 NCEP variables (< 8 chosen for given (< 8 chosen for given equation) equation)

PredictandsPredictands are maximum are maximum and minimum temperature, and minimum temperature, precipitation occurrence, precipitation occurrence, and precipitation amountsand precipitation amounts

Stochastic modeling of the Stochastic modeling of the residuals in the regression residuals in the regression equations to provide equations to provide ensemble time seriesensemble time series

11,000 Climate Station Locations

NCEP Model Nodes

Collaboratively with U. of Colorado

Representative Representative Elevation of Elevation of Atmospheric Atmospheric

ModelModelOutput based on Output based on Regional StationRegional Station

ObservationsObservations

NashNash--Sutcliff Coefficient of Efficiency Scores Sutcliff Coefficient of Efficiency Scores Simulated Simulated vsvs Observed Daily StreamflowObserved Daily Streamflow

Animas River at Durango, ColoradoAnimas River at Durango, Colorado

Ranked Probability Skill Score (RPSS) for each Ranked Probability Skill Score (RPSS) for each forecast day and month using measured runoff forecast day and month using measured runoff

and simulated runoff (Animas River, CO) produced and simulated runoff (Animas River, CO) produced using: (1)using: (1) SDSSDS output and (2)output and (2) ESPESP techniquetechnique

Fore

c as t

Day

Month MonthJ F M A M J J A S O N D J F M A M J J A S O N D

8

6

4

2

0

8

6

4

2

0

0.1 0.3 0.5 0.7 0.9RPSSRPSS

ESPSDS

Perfect Forecast: RPSS=1

Given current uncertainty in long-term atmospheric-model forecasts, seasonal to annual forecasts may be better with ESP

UncertaintyError Sources

•Data

•Model Structure

•Parameter

•Forecasts

Robust uncertainty estimation procedures need to be developed to 1) assess the sources and magnitudes of uncertainty in individual and coupled models, and 2) provide measures of confidence in simulation results.

Integrated ModelsIntegrated Models

Sleepers River, Vermont

Trout Lake, Wisconsin

Panola Mountain, Georgia

Luquillo, Puerto Rico

Loch Vale, Colorado

WATER, ENERGY, AND BIOGEOCHEMICAL BUDGETS (WEBB) PROGRAM

USGS

WEBMODWEBMOD

PRMS

National Weather Service - Hydro17

TOPMODEL

Next step: Use tracers to constrain the hydrologic solutions

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

Simulated Flow Paths and Simulated Flow Paths and Residence TimesResidence Times

Coupled WEBMOD and PHREEQC

WEBMOD and SWAT are being applied at 5 NAWQA agricultural study areas with

intensive data collection

Satellite image of the Rodeo and Chediski fires before they merged into one fire, eventually burning some 470,000 acres

Effects of Vegetation and Vegetation Effects of Vegetation and Vegetation Change on Water and Energy Change on Water and Energy

PartitioningPartitioning

Fool Creek Watershed, Fraser Experimental Forest, Colorado (714 acres)

Managed Natural

Issues: Process, Scale, Parameter Estimation

Vegetation Management ModelUS Forest Service, Missoula, Mt

•Simulates vegetation patterns and processes emphasizing the dynamics of landscape level change.

•Produces ensemble output

Integrating Science with Resource Management through Collaborative Approaches and

Adaptive Modeling SystemsLand Use Climate CO2 Human population Invasive species

GD WRD

BRDNMD

Physicalmodels

•Resource availability

•Public landmanagement

•Restoration plans

•…

•Flood & drought impacts

•Vegetation change

•Wildland fire

•Grazing impacts

•Landslides

•Water quality

Science Synthesis Modeling Framework Development

NPS CESU

USFSBLM

SIMPPLLE +

Resource Management

Initial DSS Development Area

USGS, NPS, USFS, BLM, CO State University, N AZ University

Integrating WaterIntegrating Water--Resource Resource and Socioand Socio--Economic ModelsEconomic Models

Population and Demand ForecastsPopulation and Demand ForecastsWater MarketsWater Markets

(change in ownership in perpetuity)(change in ownership in perpetuity)Water BankingWater Banking

(lease options over some period of time)(lease options over some period of time)

Issue: The better the prediction of the spatial and temporal distribution of water, the better the markets can perform.

This requires the full integration of physical-process and socio-economic models to optimize economic and institutional analyses.

Adding Water Banking to a Adding Water Banking to a Water Resources DecisionWater Resources Decision--

Support SystemSupport SystemHydrologicDatabase

HydrometReal-time climate

data feed

RiverWareReservoir and River System

Operations Model


and modeling Modular Modeling SystemPrecipitation/Runoff Model (PRMS)

DMI

DMI

DMI

DMIWater

Banking

DMIs

Integrated Framework DevelopmentIntegrated Framework DevelopmentCollaborative effort to integrate the Object Collaborative effort to integrate the Object

Modeling System (OMS) and the Modular Modeling System (OMS) and the Modular Modeling System (MMS)Modeling System (MMS)

OMS PlatformM

odel

Core

Mod

elBu

ilder

RZW

QM

PRM

S

Dat

abas

e

Netbeans IDE, Sun Forte,…

Java

Com

pile

r

XML

Test

ing

C++

Oth

er…

- ARS - USGS - NRCS

-Friedrich Schiller University, Germany

Branding

U.S. MULTIU.S. MULTI--AGENCY AGENCY MEMORANDUM OF MEMORANDUM OF UNDERSTANDINGUNDERSTANDING

Nuclear Regulatory Nuclear Regulatory Commission (NRC)Commission (NRC)Army Corp of Engineers Army Corp of Engineers (COE)(COE)US Geological Survey US Geological Survey (USGS)(USGS)NOAANOAADepartment of Department of Homeland SecurityHomeland Security

Facilitates cooperation in R&D of multi-media environmental models, frameworks, and databases for use

in human and environmental health risk assessment

Environmental Protection Agency (EPA)

Department of Energy (DOE)

US Agricultural Research Service (ARS)

Natural Resources Conservation Service (NRCS)

Bureau of Reclamation

Website Information Website Information http://http://www.iscmem.orgwww.iscmem.org

MOU and addendumsMOU and addendumsWorkshop proceedingsWorkshop proceedingsISCMEM meeting minutesISCMEM meeting minutesWorking group Working group proposals/plansproposals/plansAnnouncements of Announcements of upcoming meetingsupcoming meetingsContact infoContact info

GeoSpatialGeoSpatial Library for Library for Environmental Modeling Environmental Modeling

((GeoLEMGeoLEM))

GIS Data:

GIS Processing:

GEOLEM Geographic Feature Library

API

API

Environmental Model Framework

APIVisualization

Uncertainty Analysis, Sensitivity Analysis,

Parameter Estimation/Optimization

Toolbox

Interagency MOU Modeling Tool Development

MMS Development is MMS Development is Accomplished Through Accomplished Through

Collaborative MultiCollaborative Multi--disciplinary disciplinary EffortsEfforts

MMS UNIVERSITY MMS UNIVERSITY COLLABORATIONCOLLABORATION

University of ArizonaUniversity of ArizonaNSF fundedNSF funded ---- Sustainability of semiSustainability of semi--Arid Hydrology and Arid Hydrology and Riparian Areas (SAHRA)Riparian Areas (SAHRA)

University of ColoradoUniversity of ColoradoNOAA fundedNOAA funded ---- Coupling of hydrologic and Coupling of hydrologic and atmospheric models to provide shortatmospheric models to provide short-- to longto long--term term forecastsforecasts

University of Nevada RenoUniversity of Nevada RenoDesert Research Institute fundedDesert Research Institute funded –– Watershed modeling Watershed modeling and coupled SW/GW modelingand coupled SW/GW modeling

Colorado State UniversityColorado State UniversityInteragency MOU funded Interagency MOU funded –– GeoLEMGeoLEM and Parameter and Parameter estimation, optimization, and sensitivity analysis toolsestimation, optimization, and sensitivity analysis tools

Working with the NRCS to incorporate the USGS Modular Modeling

System into the NRCS forecasting

toolbox

DSS Draa River Basin Atlas Mountains, Morocco

GLOWA – IMPETUS University of Bonn

USGS

Agence du Bassin SousMassa, (ABSM) Agadir.

Upper Upper DraaDraa

34,600 km34,600 km2

InstrumentationInstrumentation

3850 m3850 m 2960 m2960 m

2400 m2400 m 2300 m2300 m

TariaTaria CatchmentCatchment5.5 km5.5 km2, gauge at 2752 m , gauge at 2752 m

HeiheHeihe River Basin, ChinaRiver Basin, China

from from XinXin Li, CAREERILi, CAREERI

DEM and landscape mapsDEM and landscape maps


ObservationsObservations

Land surface process experiment sitesLand surface process experiment sites

Hydrological experimentsHydrological experiments

Isotopic hydrologyIsotopic hydrology

EcoEco--hydrological observationshydrological observations

Remote sensing observationsRemote sensing observations


Land surface experiment sitesLand surface experiment sites


Modeling activitiesModeling activities

Land surface modelingLand surface modelingDistributed hydrological modelingDistributed hydrological modelingBasinBasin--scale climate modeling scale climate modeling Towards a decision support system Towards a decision support system


Remote sensingRemote sensing

Hydrological modelingHydrological modeling

Visual / Infrared MODIS TM ASTER AVHRR

Visual / Infrared MODIS TM ASTER AVHRR

Passive MicrowaveSSM/I AMSR

AMSR-E

Passive MicrowaveSSM/I AMSR

AMSR-E

SARASAR PALSAR

Vegetation index, LAI, fpar, NPP

Vegetation index, LAI, fpar, NPP

Surface temperature &

fluxes

Surface temperature &

fluxesSnowSnow Soil moistureSoil moisture Volume water

content

Land surface scheme

Land surface scheme

Distributed runoff model

Distributed runoff model

Ground water dynamics

Ground water dynamics

Integrated watershed model(new generation LSS)

Integrated watershed model(new generation LSS)

Meso-scale coupled model

Land surface data assimilation

system

Land surface Land surface data assimilation data assimilation

systemsystem

Mixel problem &data fusion

Ecological condition

Ecological conditionRe-analysis Dataset

Hydrological condition

Hydrological condition

Impacts on water resources

in basin scale

Impacts on water resources

in basin scale

Model integration, the strategy


Integrated 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.

SUMMARYSUMMARYIntegrated toolbox approach to model development and Integrated toolbox approach to model development and application for waterapplication for water-- and environmentaland environmental--resources resources managementmanagementEasily configured and/or enhanced for userEasily configured and/or enhanced for user--specific needsspecific needsSupports multiSupports multi--disciplinary model integration for decision disciplinary model integration for decision support systemssupport systemsFlexible framework approach enables the incorporation of Flexible framework approach enables the incorporation of continuing advances in science, databases, and computer continuing advances in science, databases, and computer technology as well as changes in management objectivestechnology as well as changes in management objectivesOpen source software design allows many to share Open source software design allows many to share resources, expertise, knowledge, and costsresources, expertise, knowledge, and costs

Chicken Soup for the Modeling SoulChicken Soup for the Modeling Soul

“A fool with a tool is still a fool.”

System Development magazine

POINTPOINT

MORE INFORMATIONMORE INFORMATIONhttp://http://wwwbrr.cr.usgs.govwwwbrr.cr.usgs.gov/mms/mms

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

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

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

http://http://www.iscmem.orgwww.iscmem.org

http://http://wwwbrr.cr.usgs.gov/projectswwwbrr.cr.usgs.gov/projects//SW_precip_runoff/papersSW_precip_runoff/papers

Documents

The Modular Modeling System (MMS): A Toolbox for Water ...web.sahra.arizona.edu/unesco/shortcourses/Leavesley_George.pdf · ... A Toolbox for Water-and Environmental-Resources Management