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AIP-6 Water SBA GEOSS Water Services (GWS) Introduction. AIP-6 Kickoff, Silver Spring MD, USA 28-29 March 2013 David Arctur David Maidment University of Texas at Austin. AIP-6 GEOSS Water Services Team. Globally Distributed Team. Weather. PYXIS. Disaster Mgmt. JRC-ECMWF GloFAS. GIWS.
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AIP-6 Water SBAGEOSS Water Services (GWS)Introduction AIP-6 Kickoff, Silver Spring MD, USA 28-29 March 2013 David Arctur David Maidment University of Texas at Austin
Globally Distributed Team Weather PYXIS Disaster Mgmt JRC-ECMWF GloFAS GIWS CUAHSI Suitability Analysis Agriculture BYU ISPRA ARPA NASA LDAS NOAA NWS UT Health GCI/DAB TBD? KISTERS NIWA HRC
University of Texas at Austin CIESS – Center for Integrated Earth System Science David Maidment, David Arctur • Overall project coordination for GEOSS Water Services • Relations among standards orgs (OGC, WMO) and stakeholders
Brigham Young University (BYU) Department of Civil & Environmental Engineering Jim Nelson, Dan Ames • WaterML development • Outreach and capacity building in Latin America • Web hosting services & toolkit for developing countries
University of Saskatchewan, CanadaGlobal Institute for Water Security (GIWS) Howard Wheater, BrankoZdravkovic • Datasets for precipitation, stream flow, and non-standard hydro-meteorological variables, encoded in WaterML 2 • Development and testing of the software related to publishing, querying, and retrieving of the current and historical measurement data • Contribute to web portal user interface development for improved usability
CUAHSI – Consortium of Universities for Advancement of Hydrologic Science Alva L. Couch, Rick P. Hooper • CUAHSI Hydrologic Information System: A five-year project to create a data discovery platform for water data • A catalog of water data time series (HIS Central). • A standard for water data publication (WaterML). • Community-moderated controlled vocabularies for time series metadata, including: • Measured variables, units, sample medium, etc. • An ontology of concepts, together with a novel ontologically-driven data discovery client.
CUAHSI goals for GEOSS and AIP-6: Long term: CUAHSI catalog in GEOSS format. Short term (AIP-6): • Factor CUAHSI catalog into separate reusable subsystems for: • Publication and discovery • Controlled vocabulary curation, reusability, and internationalization. • Enable federated CUAHSI catalogs in Italy, South Korea, and New Zealand.
US NASAHydrological Sciences Laboratory Matt Rodell, Bill Teng • Satellite data products, modeling, and observations from TRMM (precipitation), LDAS (soil moisture, evapo-transpiration) • WaterML 2 time series outputs from these sources
US NOAAData Management and Integration Team Matt Austin, Michelle Hertzfeld • Satellite data products, modeling, and observations for global precipitation and other products • Coordination with Integrated Global Water Observations (IGWCO) Community of Practice • OGC standard services, GCI and GEO Portal support • Exploring implementation of WaterML; not operational for AIP-6
Joint Research Centre (JRC)Institute for Environment and Sustainability Peter Salamon, Milan Kalas • JRC Climate Risk Management Unit within the Institute for Environment and Sustainability – focus on observation, evaluation, anticipation and communication of impacts of weather extremes and future climate change • Development of the Global Flood Awareness System (GloFAS) initiated in 2011, to study WaterML-based architecture and web services support
European Centre for Mid-range Weather Forecasting (ECMWF) Florian Pappenberger, Fredrik Wetterhall • Carries out scientific and technical research for development and operation of global models and data-assimilation systems for the Earth system, and for improving quality of weather forecasts • Will provide test data of the TIGGE archive and a combination of the ECMWF land surface scheme HTessel and CamaFlood (a river routing algorithm) • Exploring use of WaterML for probabilistic discharge forecasts within GEOWOW
Italian National Institute for Environmental Protection and Research (ISPRA) Martina Bussettini, Michele Munafò • Environmental research institute and national environmental agency to coordinate federation of regional systems • Has recently implemented WaterML service-based architecture to federate, standardize and publish hydrological time series • Will host a hydro-catalog of all Italian Regions, in which one can search and find water data in a standard format
Regional Agency for Environmental Protection in Emilia-Romagna (ARPA ER) SilvanoPecora, Bordini Fabio • Gives technical support to regional, district and local authorities on environmental policy • Hydro-Meteo-Climate Service (SIMC) is a thematic operational node of ARPA ER; implementing WaterML-based architecture for real-time flood and other water management modeling for European Flood Awareness System (EFAS)
New Zealand National Institute for Water and Atmospheric Research (NIWA) JochenSchmidt, Brent Wood • Manages about 20% of New Zealand water monitoring stations; coordinates NZ regional water agencies • Hosting OGC WMS, WFS, CSW, SOS services and WaterML time series data
Horizons Regional Council (HRC) of New Zealand Jeff Watson, Sean Hodges • Regional water agency • Plans to implement WaterML-based architecture for time series data and services • Interested to use NASA LDAS to define severity and spatial variability of current drought conditions
KISTERS Water Solutions Michael Natschke, Stefan Fuest • Provides environmental data management solutions in many countries, and helped develop WaterML 2 • WISKI and Hydstra products used to collect, process and disseminate hydrological information • Interoperability Server KIWIS serves hydrological data and derived data products from various sources through WaterOneFlow/WaterML1, SOS1/O&M and SOS2/WaterML2 • Will contribute to the architectural design and infrastructure of GEOSS Water Services, and offer their professional server and service components KiTSM and KIWIS
the PYXIS innovation, Inc. Perry Peterson, IdanShatz • Software company that develops geospatial-intelligence technologies • Provides WorldView client application based on use of an optimized hexagonal discrete global grid (DGG); this will be used for analysis and modeling of hydrologic processes on disparate data discovered through GEOSS resources and populated into the DGG • Directly accesses GEOSS registries via CSW; supports OGC WMS, WFS, WCS, SOS, WPS
AIP-6 GEOSS Water Services • Purpose: Provide additional operational, federated water information resources in GEOSS • Scope: A global registry of water data, map and modeling services catalogued using the standards and procedures of the OGC and the WMO • In collaboration with WMO CHyTheme 2 “Data Operations and Management”, 2012-16 • In support of GEO Water Task and Integrated Global Water Cycle Observations (IGWCO) Community of Practice
AIP-6 GEOSS Water Services • Application Services • Community info services, for serving: • Observation Services • Water time series • Model Services • Computed water time series • Map Services • Web maps, geoprocessing Based on OGC WaterML, WFS, WMS, SOS, CSW
AIP-5 Architecture and Interaction WFS GRDC Metadata (UnivTx host) GEOSS Search WFS Dominican Republic Metadata (UnivTx host) WWO Map Viewer ArcGIS Online / PYXIS WorldView WFS USGS Metadata (UnivTx host)
AIP-5 Architecture and Interaction SOS GRDC WaterML2 (Kisters host) WWO Map Viewer WOF Dominican Republic WaterML1 (BYU host) ArcGIS Online / PYXIS WorldView REST USGS WaterML1 (USGS host)
AIP-6 Thematic Goals – 1 • Provide a systematic way for publishing map services of water observations that index locations of accessible WaterML time series services. • Produce web services of the results of global models for flood and drought analysis, including: • EC Global Flood Awareness System (GloFAS) • NASA/NOAA Global Land Data Assimilation System for drought assessments • Link web services for observations and modeling, so observations can inform model calibration, and so models can furnish larger context for observations
AIP-6 Thematic Goals – 2 • Form federated national water data services for common variables • Italy: ISPRA national, ARPA regional agencies • New Zealand: NIWA national, HRC regional • Synthesize data, map, and modeling services to support specific useful applications, eg, linkage of watershed delineation with precipitation mapping over watershed + flow observations + model results at outlet • Advance the utilization of water services in developing countries, through access to global web services, and lightweight methods of publishing local data
AIP-6 Connections • GEOSS Water Services can connect to • Other Water SBA threads (Esri, GLOS, MEDINA) • Technical threads (Esri, GEOWOW) • Other related threads (GeoViQua (Ag), FCU1 (DM), FCU2 (Ag)) • Capacity Building, End-User focus
Project Output: enabling Thematic Mapsof water information anyone can find & use • Water data & model output providers follow consistent practices in applying the OGC O&M model and WaterML 2 standard • This model description becomes part of the WMO Glossary of Terms • GEOSS provides rules for publishing these maps • Easy registration and quick, consistent discovery How we’ll know we’re done: • We can search GEOSS for time series of soil moisture, precipitation, stream flow and get a federated map of the results • Other results…?
World Soil Moisture from the NASA Global Land Data Assimilation System (GLDAS) http://www.arcgis.com/home/webmap/viewer.html?webmap=7d6cefdf3f324b55b08c136654e91612 Charts show 3-hourly variation (37,000 values) of soil water content of the top 1m of soil from 2000 to 2012 Popup on point links to data and chart New Zealand A very wet region with little seasonality A dryer region with significant seasonality
Global Water Maps Describe a water property over a domain of space and time • History • Current conditions • Forecasts Water Property ModelWeb SensorWeb Precipitation Evaporation Transpiration Soil Moisture Streamflow Groundwater Reservoirs Future Past Present
Water SBA Breakout Discussion • Mature AIP-5 • User Requirements • Use of GCI • New elements, alterations • Ministerial Summit demo proposals • Data Sharing • More in-situ observations • Involvement of Developing Countries / Capacity Building