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Jrc jan 2012b
 

Jrc jan 2012b

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    Jrc jan 2012b Jrc jan 2012b Presentation Transcript

    • !The Role of Critical Zone Observatories in Upscaling: A Strategy for Virtual Model-Data Sharing (current Critical Zone Observatories) C.  Duffy,  L.  Leonard,  G.  Bha3,  X.  Yu,  D.  Wu,  M.  Kumar      
    • 2,268 USGS HUC 8 watersheds The Watershed as Basis For Model-Data Sharing103,444 USGS HUC 12 watersheds
    • CZO’s as Testbeds for Scaling Up Processes CZO scale
    • Issues, Questions, Goals Data  Issues:    What  &  Where  are  the  Essen=al  Terrestrial  Variables?     Simula=on  Framework:  What  scales,  processes,  computa=on  resources?         The  Role  of  Testbeds  &  Observatories  to  Scaling  Up  to  Con=nents   Unique  Cyberinfrastructure  Needs  for  Catchment  Data  and  Models?   Towards  a  prototype  for    sharing  geospa=al/temporal  data  and  models   Goal:    Scien=fic  discovery  and  improved  decision  making      
    • What are the Essential Terrestrial Variables?•  Atmospheric Forcing (precipitation, snow cover, wind, relative humidity, temperature, net radiation, albedo, photosynthestic atmospheric radiation, leaf area index)•  Digital elevation models•  River/Stream Discharge•  Soil (class, hydrologic properties)•  Groundwater (levels, extent, hydro-geologic properties)•  Lake/Reservoir (levels, extent)•  Land Cover/Use (biomass, human infrastructure, demography, ecosystem disturbance)•  Water Use Most data reside on federal servers ….many petabytes
    • A-­‐Priori  Data  Sources   Feature/ Property SourceTime Series Porosity; CONUS, SSURGO and STATSGO Sand, Silt, http://www.soilinfo.psu.edu/index.cgi?soil_data&conus Soil Clay http://datagateway.nrcs.usda.gov/NextPage.asp Fractions; http://www.ncgc.nrcs.usda.gov/products/datasets/statsgo/ Bulk Density Bed Rock Depth; http://www.dcnr.state.pa.us/topogeo/, Horizontal Geology http://www.lias.psu.edu/emsl/guides/X.html and Vertical Hydraulic Conductivity http://glcf.umiacs.umd.edu/data/landcover/data.shtml, http://ldas.gsfc.nasa.gov/LDAS8th/MAPPED.VEG/LDASmapveg.shtml; LAILand Cover Manning’s Hernandez et. al., 2000 Roughtness Topology: From Node – To Node, Derived using PIHMgis (Bhatt et. al., 2008) Neighboring Elements; Manning’s Dingman (2002) Roughness; River Coefficient of ModHms Manual (Panday and Huyakorn, 2004) Discharge Shape and Derived from regression using depth, width and discharge data from Dimensions; http://nwis.waterdata.usgs.gov/usa/nwis/measurements Prec, Temp. Forcing RH, Wind, National Land Data Assimilation System : NLDAS-2 Rad. DEM http://seamless.usgs.gov/Streamflow http://nwis.waterdata.usgs.gov/nwis/swGroundwater http://nwis.waterdata.usgs.gov/nwis/gw
    • PIHM GIS Desktop:Manipulating Geospatial Data and Models
    • Irregular Mesh & Stream Network ElevationsNeed to register geospatial Soil Classes & geotemporal fields!!! Land Cover
    • Concept: Penn State Integrated Hydrologic Model (PIHM) Qu and Duffy 2007 Kumar, Bhatt, Duffy 2009
    • Semi-Discrete Approach: PIHM
    • Systems of Fully Coupled ODE’s Source code: www.pihm.psu.edu  o  PIHM Control Volume Kernel: Semi-Discrete Finite Volume formulation of conservation equations. Finite Volume Method ensures mass balance locally (in each control volume) and globally. Interception Unsaturated Vegetation dψ 0 = G 3 − G 4 − G5 Zone Surface dt dψ 3 = G0 − G1 − G8 − G9 Unsaturated Zone Snow Melt dt dψ 1 dt = G 3 − G6 Saturated Saturated Zone Zone Overland Flow dψ 4 = G1 + F2 + F3 + F4 dt dψ 2 = G3 + G5 + G6 + G7 − G0 + F0 + F1 dt Channel dψ 5 = G3 − G2 − G7 + F + F5 + F3 1 dt Sub-Channel dψ 6 = G2 + F4 River Zone Aquifer dt Saturated Zone   The  system  of  ODEs  is  solved  using  state-­‐of-­‐the-­‐art  solver  with  adap:ve  :me  steps   12  
    • Towards a New Cyberinfrastructure For Models and Data Shared Discovery & Shared Decision Making
    • PIHM Model-Data Workflow
    • Model-Data Integration Framework
    • Cloud Services Prototype Monitoring   &   Modeling  
    • Everything As a Service
    • Benefits  •  Scalable  compu=ng  power  in  real  fast  •  Can  run  simula=ons  and  other  tasks  any   where  in  the  world  with  internet  •  No  need  to  concern  about  machines  and   backups    
    • Web Services for data access & rapid Model prototyping Example of PIHM Web Services
    • Towards  Services  for  the  Virtual  Observatory…  
    • Watershed Reanalysis for Assessing the Impact of Climate and Landuse Change Re-analyze and assimilate past observations with current modeling context Reanalysis products produce space-time fields valuable to scientists, stakeholders and resource managers
    • Shale Hills CZO: 1979-2010 Watershed Reanalysis
    • CZO Data ->lidar, Soil, Regolith,Veg
    • 1979-2010 Watershed Reanalysis pihm.psu.edu/applications. 0.09 0.206 0.08 Soil Moisture Storage (m) 0.204Groundwater Level (m) 0.07 0.202 0.06 0.2 0.05 0.198 0.04 0.196 0.03 0.02 0.194 0.01 0.192 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350 Time (months) Time (months) 3 x 10 0.07 2 Interception Loss Transpiration Evaporation 0.06 Evapotranspiration (m) 0.05 Recharge (m) 1 0.04 0.03 0 0.02 0.01 0 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350 Time (months) Time (months)
    • CZO Reanalysis:Shale Hills Storm Library1979-2011
    • T=2009/10/23 T=2009/10/2400:00:00 00:00:00 Groundwater     Groundwater     depth(m)   depth(m)   stream bank stream bank 0.1679 - 1.509 0.1679 - 1.509 1.51 - 1.631 1.51 - 1.631 1.632 - 1.657 1.632 - 1.657 1.658 - 1.665 1.658 - 1.665 1.666 - 1.674 1.666 - 1.674 1.675 - 1.683 1.675 - 1.683 1.684 - 1.692 1.684 - 1.692 1.693 - 1.7 1.693 - 1.7 1.701 - 1.718 1.701 - 1.718 1.719 - 1.753 1.719 - 1.753 Plan     1.754 - 1.805 1.806 - 2.388 Plan   1.754 - 1.805 1.806 - 2.388 280 270 7  Sec=on     265 260 255 a) Dry watershed: Pre-event groundwater table depth and HEF path. 250 b) Wet watershed: During-event groundwater table depth and HEF path. Horizontally, HEF vaies (gaining/loosing) with stream reaches . 245 Horizontally, the stream is mainly recharging the aquifer; Vertically, some reaches have no exchange with the river beds. Vertically, all the reaches have dynamic HEF with the river beds. Stream-Groundwater Interaction
    • Nested Grid to resolve wetland1986  TINs  
    • Comparing predicted wetlands basedon 30 cm depth rule with National WetlandInventory (NWI) map
    • Compare Historical & IPCC ForcingPrecipitation 2046-2065 1979-1998Temperature 2046-2065 1979-1998
    • Evap-Transp-Recharge Historical-IPCC Evapotranspiration 2046-2065 1979-1998 Recharge 2046-2065 1979-1998
    • Shaver’s Creek
    • Shaver’s Creek
    • Upscaling Model-Data-Process: Chesapeake Bay•  Groundwater-Stream-Land-Surface model•  Fully coupled surface water, soil water, groundwater, and land surface components•  C-N-Sediments•  Vegetation Growth•  Environmental Tracers
    • Data :: Climate :: NLDAS v2 (1979 – recent) 8km Hourly time-series 1. Precipitation 2. Temperature 3. Solar Radiation 4. Vapor Pressure 5. Relative Humidity 6. Wind Speed We have developed tools that extracts forcing variables (from NLDAS-2 grib2 data) and formats all above mentioned variables according to PIHM data structure     ~3300  Climate  Grids  or  Time-­‐series  Data  for  each  climate  variables!   38  
    • Data :: Land Cover :: NLCD 2006 + Veg Parameters Vegetation Parameters 1. Leaf Area Index (TS) 2. Roughness Length (TS) 3. Min. Stomata Resist. 4. Albedo 5. Vegetation Fraction 6. Rooting Depth     Vegeta:on  parameters  are  mapped  to  each  LC-­‐type  using  custom  rou:nes   39  
    • Data :: Soils :: SSURGO-STATSGO Soil Hydraulic Properties 1. Sat. Hydraulic Cond. 2. Porosity 3. Residual porosity 4. van-Genuchten α 5. van-Genuchten β 6. Macropore fraction 6. Macropore K   for CB watersheds: 598 STATSGO classes vs. 28,611 SSURGO classes    Pedo-­‐transfer  func:ons  are  used  to  obtain  hydraulic  proper:es  from  texture  informa:on   40  
    • Test Bed :: Juniata River Basin The test-bed consists of: 97 HUC-12 units  Large  scale  applica:on  strategy  development  and  tes:ng   41  
    • Scaling up to River Basins: Juniata River DD statistics: Number of Mesh: 85817 Smallest Area: 0.00096 sq. km Largest Area: 0.02 sq. km Shortest Edge: 0.027 km Longest Edge: 1.360 km     Unit  Watershed  and  Parameteriza:on   42  
    • HPC: Juniata River Domain Partitioning Auto  execu=on  sequence  of  the  model  par==ons  would  be:   #1:  1,  2,  4,  5,  7,  9,10,  11,  13,  14,  17,  18,  21,  23,  25,  26  28   #2:  3,  6,  12,  19,  22,  24   #3:  8,  15   #4:  16   #5:  20   #6:  27     Headwaters  >>  Downstream  ::  6  Step  Execu:on   43  
    • Issues, Questions, Goals Data  Issues:    What  &  Where  are  the  Essen=al  Terrestrial  Variables?     Simula=on  Framework:  What  scales,  processes,  computa=on  resources?         The  Role  of  Testbeds  &  Observatories  to  Scaling  Up  to  Con=nents   Unique  Cyberinfrastructure  Needs  for  Catchment  Data  and  Models?   Towards  a  prototype  for    sharing  geospa=al/temporal  data  and  models   Goal:    Scien=fic  discovery  and  improved  decision  making