1 / 60

Hydrologic Modeling in 2011

Hydrologic Modeling in 2011. David R. Maidment Center for Research in Water Resources University of Texas at Austin Leader of the CUAHSI Hydrologic Information System Project. With acknowledgements to Rick Hooper, David Tarboton & Barbara Minsker. Hydrologic Modeling in 2011.

mimi
Télécharger la présentation

Hydrologic Modeling in 2011

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Hydrologic Modeling in 2011 David R. Maidment Center for Research in Water Resources University of Texas at Austin Leader of the CUAHSI Hydrologic Information System Project With acknowledgements to Rick Hooper, David Tarboton & Barbara Minsker

  2. Hydrologic Modeling in 2011 • The charge and challenges • Hydrologic information system – web services • Integrating models and data using scientific workflows • Hydrologic Observing System

  3. Hydrologic Modeling in 2011 • The charge and challenges • Hydrologic information system – web services • Integrating models and data using scientific workflows • Hydrologic Observing System

  4. Workshop Charge • What new technologies for observing, simulating, and tele-communicating will emerge over the next 5-10 years? • how will they change the grand challenges for modeling, what will those challenges be? • Challenge for this session: • How all the new devices/opportunities emerging in the realm of “cyber-infrastructure”— including, perhaps especially, visualization schemes — might change the way models are developed and applied, including the new kinds of scientific questions to be asked in association with modeling.

  5. We want to trace the movement of water, chemical and biological constituents through atmospheric, surface and subsurface water We want to do water, mass and energy balances Hydrologic Modeling

  6. Hydrologic Information System • A system is a connected set of components e.g. University of Texas System • A web-based system is a set of components connected using the internet • A hydrologic information system (HIS) is a web-based system linking hydrologic databases, tools and models CUAHSI HIS partner institutions

  7. USGS Water Watch System A national hydrologic observing system already exists – CUAHSI adds to it

  8. Real-time Water Quality Estimates Estimated total nitrogen cfs mg/L Stream discharge

  9. CUAHSI Member Institutions 105 Universities as of May 2006

  10. Challenges • How to use test-beds to design real WATERS Observatories? • How to share data from the test-beds with the whole community? • How to include CUAHSI/CLEANER data not collected in the test-beds? • How to empower individual scientists? • How to make use of petascale computing?

  11. Hydrologic Modeling in 2011 • The charge and challenges • Hydrologic information system – web services • Integrating models and data using scientific workflows • Hydrologic Observing System

  12. CUAHSI Web Services • Your application • Excel, ArcGIS, Matlab • Fortran, C/C++, Visual Basic • Hydrologic model • ……………. • Your operating system • Windows, Unix, Linux, Mac Internet Web Application: Data Portal Simple Object Access Protocol CUAHSI Web Services Library http://www.cuahsi.org/HIS/

  13. Water Data Water quantity and quality Soil water Rainfall & Snow Modeling Meteorology Remote sensing

  14. Water Data Web Sites

  15. NWISWeb site output # agency_cd Agency Code # site_no USGS station number # dv_dt date of daily mean streamflow # dv_va daily mean streamflow value, in cubic-feet per-second # dv_cd daily mean streamflow value qualification code # # Sites in this file include: # USGS 02087500 NEUSE RIVER NEAR CLAYTON, NC # agency_cd site_no dv_dt dv_va dv_cd USGS 02087500 2003-09-01 1190 USGS 02087500 2003-09-02 649 USGS 02087500 2003-09-03 525 USGS 02087500 2003-09-04 486 USGS 02087500 2003-09-05 733 USGS 02087500 2003-09-06 585 USGS 02087500 2003-09-07 485 USGS 02087500 2003-09-08 463 USGS 02087500 2003-09-09 673 USGS 02087500 2003-09-10 517 USGS 02087500 2003-09-11 454 Time series of streamflow at a gaging station

  16. CUAHSI Hydrologic Data Access System http://river.sdsc.edu/HDAS NCDC NASA EPA NWS USGS Observatories A common data window for accessing, viewing and downloading hydrologic information

  17. Observation Stations Map for the US Ameriflux Towers (NASA & DOE) NOAA Automated Surface Observing System USGS National Water Information System NOAA Climate Reference Network

  18. NWIS Station Observation Metadata Describe what has been measured at this station

  19. Web Page Scraping http://nwis.waterdata.usgs.gov/nwis/discharge?site_no=02087500&agency_cd=USGS&.... Programmatically construct a URL string as produced by manual use of the web page Parse the resulting ASCII file

  20. Data Sources NASA Storet Ameriflux Unidata CUAHSI Extract NCAR NWIS Transform CUAHSI Web Services Excel Visual Basic ArcGIS C/C++ Load Matlab Fortran Access SAS Applications http://www.cuahsi.org/his/ Some operational services

  21. Core Web Methods

  22. Operational Services

  23. XML Output from GetValues NWIS DayMet MODIS

  24. What is a Data Model Lets see what Wikipedia says • A data model is a model that describes in an abstract way how data is represented • Data models describe structured data for storage in data management systems such as relational databases. • Early phases of many software development projects emphasize the design of a conceptual data model.

  25. A relational database stored in Access, PostgreSQL, SQL/Server, …. Stores observation data made at points Consistent format for storage of observations from many different sources and of many different types. CUAHSI Point Hydrologic Observations Data Model Streamflow Groundwater levels Precipitation & Climate Soil moisture data Water Quality Flux tower data

  26. Hydrologic Observations Data Model (HODM)

  27. Several choices You build CUAHSI compatible services from your database You copy data into the HODM and use CUAHSI services You copy your data to an HODM and it is served from SDSC Serving investigator data Standard CUAHSI services Your implementation of CUAHSI services Your database HODM

  28. Simulation models can be packaged as web services They can be queried and provide responses just like data archives We have an integrated network of data sources and models Modeling Services A big challenge to integrate all the data streams!

  29. request return return request NAWQA request return return request NAM-12 request return NWIS request return request return return request NARR Objective • Search multiple heterogeneous data sources simultaneously regardless of semantic or structural differences between them What we don’t want ….. Michael Piasecki Drexel University

  30. NAWQA NWIS NARR HODM Semantic Mediator What we do want ….. request request request request generic request request request Michael Piasecki Drexel University request request

  31. Hydrologic Modeling in 2011 • The charge and challenges • Hydrologic information system – web services • Integrating models and data using scientific workflows • Hydrologic Observing System

  32. Regional Storm Water Modeling Program and Master Plan for San Antonio City of San Antonio

  33. San Antonio Regional Watershed Modeling System Geospatial Data: City, County SARA, other “Bring the models together” Modeling System Rainfall Data: Rain gages Nexrad Calibration Data: Flows Water Quality Floodplain Management Capital Improvement Planning Water quality planning Integrated Regional Water Resources planning Flood Forecasting

  34. GIS Preprocessors for Hydrologic Models GIS Interface HEC-HMS Geo- HMS Database HEC-RAS Geo- RAS Gflow GIS- Gflow

  35. Connecting Arc Hydro and Hydrologic Models GIS Interfacedata models HMS Geo Database Arc Hydro data model RAS Gflow

  36. Digital Rain Maps from National Weather Service(03/04/2004)

  37. FEMA 100-year flood plain map in Bexar County

  38. Regional Watershed Modeling System Case Study Salado Creek watershed Components: • Arc HydroGeodatabase • for whole watershed • HEC-HMShydrology model • for whole watershed • HEC-RAShydraulic model • for Rosillo Creek Bexar County Rosillo Creekwatershed

  39. Arc Hydro and HEC-HMS HEC-HMS Hydrologic Model Arc Hydro Schematic Network Calculates Flows

  40. Arc Hydro and HEC-RAS HEC-RAS Hydraulic Model Calculates Water Surface Elevations Arc Hydro Channel Cross Sections

  41. Flow Change Points Models communicate with one another through Arc Hydro at designated points

  42. FLOODPLAIN MAP Nexrad Map to Flood Map in Arc 9 Model Builder Flood map as output Model for flood flow HMS Model for flood depth Nexrad rainfall map as input

  43. Web-Accessible Regional Watershed Modeling System Complete storage of simulation models and workflows in geodatabases

  44. Hydrologic Modeling in 2011 • The charge and challenges • Hydrologic information system – web services • Integrating models and data using scientific workflows • Hydrologic Observing System

  45. CUAHSI Hydrologic Observing System A multiscale web portal system for observing and interpreting hydrologic phenomena by integrating data and models for any location or region in the United States North American Scale (e.g. North American Regional Reanalysis of climate) 1:1,000,000 scale Multiscale information delivery Continental US Scale (coast to coast data coverage, HIS-USA) 1:500,000 scale 1:100,000 scale Regional Scale (e.g. Neuse basin) Watershed Scale (e.g. Eno watershed ) 1:24,000 scale Site Scale (experimental site level) Site scale Point Point Observation Scale (gage, sampling location)

  46. GeoTemporal Reference Frame • A defined geospatial coordinate system for (x,y,z) • A defined time coordinate system (UTC, Eastern Standard Time, ….) • A set of variables, V • Data values v(x,y,z,t) Time, t v – data values Space (x,y,z) Data Cube Variables, V

  47. Series and Fields Features Series – ordered sequence of numbers Point, line, area, volume Discrete space representation Time series – indexed by time Frequency series – indexed by frequency Surfaces Fields – multidimensional arrays Continuous space representation Scalar fields – single value at each location Vector fields – magnitude and direction Random fields – probability distribution

  48. North American Regional Reanalysis of Climate Evaporation Precipitation Variation during the day, July 2003 NetCDF format mm / 3 hours

  49. Continuous Space-Time Model – NetCDF (Unidata) Time, T Coordinate dimensions {X} D Space, L Variable dimensions {Y} Variables, V

  50. Discrete Space-Time Data ModelArcHydro Time, TSDateTime TSValue Space, FeatureID Variables, TSTypeID

More Related