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The Modular Modeling System (MMS): A Toolbox for Water- and Environmental-Resources Modeling and Management PowerPoint Presentation
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The Modular Modeling System (MMS): A Toolbox for Water- and Environmental-Resources Modeling and Management. G.H. Leavesley, S.L. Markstrom, R.J. Viger, and L.E. Hay. U.S. Geological Survey, Denver, CO USA. STARTING POINTS. There are no universal models

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


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    1. The Modular Modeling System (MMS): A Toolbox for Water- and Environmental-Resources Modeling and Management G.H. Leavesley, S.L. Markstrom, R.J. Viger, and L.E. Hay U.S. Geological Survey, Denver, CO USA

    2. STARTING POINTS • There are no universal models • Models for different purposes require different levels of detail and comprehensiveness • Appropriate model process conceptualizations are a function of problem objectives, data constraints, and spatial and temporal scales of application

    3. Modeling Conundrum • Experimental science builds on hypothesis testing and interpretation based on earlier published hypotheses and results • Modelers tend to build from the ground up because existing models are not well designed for incremental improvement by others

    4. TOOL BOX MODELING VIEWS • Research Model Developer Complex Detail • Application Model Developer • Model User • Resource Manager • Policy Maker Condensed Analysis

    5. LEVELS OF MODULAR DESIGN • PROCESS • MODEL • FULLY COUPLED MODELS • LOOSELY COUPLED MODELS • RESOURCE MANAGEMENT DECISION SUPPORT SYSTEMS • ANALYSIS AND SUPPORT TOOLS Single Purpose Multi-objective, Complex

    6. LEVELS OF MODULAR DESIGN • PROCESS • MODEL • FULLY COUPLED MODELS • LOOSELY COUPLED MODELS • RESOURCE MANAGEMENT DECISION SUPPORT SYSTEMS • ANALYSIS AND SUPPORT TOOLS

    7. CRITERIA AND RULES FOR GOOD MODULE DESIGN • relate directly to real world components or processes • have input and output variables that are measurable values • communicate solely via these input and output variables Modules should Reynolds J.F., and Acock, B., 1997, Modularity and genericness in plant and ecosystem models: Ecological Modeling 94, p 7-16

    8. Model Building Tool - XMBUILD

    9. USGS PRMS GSFLOW WEBMOD OTHER TOPMODEL Hydro-17 (NWS snowmelt) Sacramento Model (NWS) Snowmelt Runoff Model (SRM) (ARS) ENNS Model (modified HBV, Austria) SELECTED MODELS AND MODULES IN MMS

    10. LEVELS OF MODULAR DESIGN • PROCESS • MODEL • FULLY COUPLED MODELS • LOOSELY COUPLED MODELS • RESOURCE MANAGEMENT DECISION SUPPORT SYSTEMS • ANALYSIS AND SUPPORT TOOLS

    11. Ground Water - Surface Water Coupling DAFLOW PRMS MODFLOW

    12. GSFLOW -- Coupled PRMS, MODFLOW, SFR, and Unsaturated Zone Models PRMS to SFR Unsaturated Zone Model: PRMS to UNSAT UNSAT to MODFLOW PRMS to MODFLOW MODFLOW to SFR Streamflow

    13. LEVELS OF MODULAR DESIGN • PROCESS • MODEL • FULLY COUPLED MODELS • LOOSELY COUPLED MODELS • RESOURCE MANAGEMENT DECISION SUPPORT SYSTEMS • ANALYSIS AND SUPPORT TOOLS

    14. Watershed Model Hydraulics Model Fish Model LOOSELEY COUPLED MODELS MMS Model Off-the-shelf Model Database Data Management Interface (DMI)

    15. Watershed Model Fish Model LOOSELEY COUPLED MODELS Hydraulics Model

    16. Watershed Model Fish Model LOOSELEY COUPLED MODELS PRMS Channel Geometry Analysis Program (CGAP) Hydraulics Model Habitat Suitability Index

    17. Coupled Model Results

    18. Watershed Model Hydraulics Model Fish Model LOOSELEY COUPLED MODELS MMS Model Off-the-shelf Model Database Data Management Interface (DMI) Model Management Interface (MMI) [XML]

    19. OBJECT USER INTEFACE (OUI)

    20. LEVELS OF MODULAR DESIGN • PROCESS • MODEL • FULLY COUPLED MODELS • LOOSELY COUPLED MODELS • RESOURCE MANAGEMENT DECISION SUPPORT SYSTEMS • ANALYSIS AND SUPPORT TOOLS

    21. Recreation Watershed and River Systems Management Program Municipal & Industrial Irrigation Hydropower Research and development of decision support systems and their application to achieve an equitable balance among water resource issues. Riparian Habitat Endangered Species

    22. RiverWare – Lower Colorado River

    23. DMI Object User Interface Interface for data visualization and modeling Modular Modeling System Precipitation/Runoff Model (PRMS) Upper Gunnison River DSS Hydromet Real-time climate data feed Hydrologic Database DMI DMI RiverWare Reservoir and River System Operations Model DMI

    24. Generic DSS Framework for a Wide Range of Management Issues Any Resource Management Model Climate Data Source Any Database DMI DMI DMI MMI Object User Interface Interface for data visualization and modeling Modular Modeling System Physical Process Models

    25. WARSMP Basins Currently Active Gunnison, Truckee, Upper Rio Grande, Yakima Under Development San Juan, Umatilla, Upper Columbia Future Bitterroot, Carson, Central Platte, Lower Rio Grande, Salmon

    26. LEVELS OF MODULAR DESIGN • PROCESS • MODEL • FULLY COUPLED MODELS • LOOSELY COUPLED MODELS • RESOURCE MANAGEMENT DECISION SUPPORT SYSTEMS • ANALYSIS AND SUPPORT TOOLS

    27. GIS WEASEL • Delineation: • Only requires elevation Grid as input • Interactively delineate • Area of Interest • Many kinds of features • Streams • Elevation bands • Landuse • Contributing areas • Topographic index • ……

    28. DIGITAL DATABASES STATSGO Soils (USDA) Vegetation Type (USFS) Satellite SW Radiation (U Md) Vegetation Density (USFS) Land Use-Land Cover (USGS) Monthly PET

    29. AUTOMATED PARAMETER ESTIMATION USING THE GIS WEASEL

    30. GIS WEASEL • Parameterization: • 200+ methods available • Easily add custom methods • Exploit many types of data • Produce maps and ASCII files of parameters • Configure recipes • Apply to feature maps +

    31. MODEL RECIPE FILE PARAMETER ESTIMATION METHOD PARAMETER

    32. Multi-step Calibration

    33. Multi-step calibration visualization

    34. ANALYSIS and SUPPORT TOOLS Currently Available Statistical and Graphical Analyses Parameter Optimization Parameter Sensitivity Analysis Beta Testing Shuffle Complex Evolution Optimization Multi-Objective COMplex Evolution Algorithm (MOCOM) Generalized Likelihood Uncertainty Estimation (GLUE) Visualization

    35. Forecast Methodologies • - Historic data as analog for the future • Ensemble Streamflow Prediction (ESP) • Synthetic time-series • Weather Generator • - Atmospheric model output • Dynamical Downscaling • Statistical Downscaling

    36. MMS Development is Accomplished Through Collaborative Multi-disciplinary Efforts

    37. Sleepers River, Vermont Trout Lake, Wisconsin Panola Mountain, Georgia Loch Vale, Colorado Luquillo, Puerto Rico USGS WATER, ENERGY, AND BIOGEOCHEMICAL BUDGETS (WEBB) PROGRAM

    38. WEBMOD PRMS National Weather Service - Hydro17 TOPMODEL

    39. Use Chemical and Isotopic Tracers to Evaluate Simulated Flow Paths and Residence Times Next step: Use tracers to constrain the hydrologic solutions

    40. Coupled WEBMOD and PHREEQC

    41. Effects of Vegetation and Vegetation Change on Water and Energy Partitioning Natural Managed Satellite image of the Rodeo and Chediski fires before they merged into one fire, eventually burning some 470,000 acres Fool Creek Watershed, Fraser Experimental Forest, Colorado (714 acres) Issues: Process, Scale, Parameter Estimation

    42. Vegetation Management Model US Forest Service, Missoula, Mt • Simulates vegetation patterns and processes emphasizing the dynamics of landscape level change. • Produces ensemble output

    43. Land Use Climate CO2 Human population Invasive species NMD BRD • Flood & drought impacts • Vegetation change • Wildland fire • Grazing impacts • Landslides • Water quality Physical models • Resource availability • Public landmanagement • Restoration plans • … BLM USFS GD WRD SIMPPLLE NPS CESU Integrating Science with Resource Management through Collaborative Approaches and Adaptive Modeling Systems Resource Management Science Synthesis Modeling Framework Development +