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Mauro Sulis

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Assessment of the catchment-scale energy and water balance using fully coupled simulations and observations

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Mauro Sulis

  1. 1. A  synthesis  of  modeling  and   observa4onal  data  for  an  integrated   assessment  of  the  catchment-­‐scale   energy  and  water  cycle        Mauro  Sulis     Meteorological  Ins4tute,  University  of  Bonn   Workshop  on  Coupled  Hydrological  Modeling   Padova,  September  23-­‐24  2015  
  2. 2.  Collaborators   Prabhakar  Shrestha  (MIUB)   Sandra  Steinke  (Uni-­‐Köln)   Susanne  Crewell  (Uni-­‐Köln)   Clemens  Simmer  (MIUB)   Stefan  Kollet  (IBG3)  
  3. 3.  Introduc4on   The   hydrological   and   meteorological   community   have   recently   converged  toward  a  new  integrated  simula5on  paradigm.   Holis5c  and  physically-­‐based  view  of  the  energy,  water,  and  ma=er   cycle  across  a  range  of  spa5al  and  temporal  scales.     New  opportuni5es  and  grand  challenges:   Integrated  diagnosis  of  the  catchment-­‐scale  energy  and  water  cycle   using  fully-­‐coupled  simula5ons  and  observa5ons.   Mo#va#ons  of  the  work:   •  Powerful  tools  to  test  scien5fic  hypothesis.   •  Integrated  assessment  of  the  water  cycle  for  long-­‐term  climate   projec5ons  and  short-­‐  and  medium-­‐term  weather  forecasts.   •  Improved   monitoring   networks   (e.g.,   mul5ple   co-­‐located   measurements)  that  cover  the  SVA  con5nuum.  
  4. 4.  Outline   •  Study  area   •  Observa4onal  dataset   •  TerrSysMP   •  Model  setup   •  Results   •  Conclusions  
  5. 5.  Study  area   North-­‐Rhine  Westphalia  (NRW)  domain   Land  use  classes:   Topography:   Al4tude  range:  15  –  700  m     •  Cropland  (~34  %)   •  Evergreen  forest  (~14  %)     •  Deciduous  forest  (~17%)   •  Grassland  (~25  %)  
  6. 6.     •  Study  area   •  Observa4onal  dataset   •  TerrSysMP   •  Model  setup   •  Results   •  Conclusions  
  7. 7.  Observa4onal  dataset  –  descrip4on     1HD(CP)2  Observa4onal  Prototype  Experiment  (HOPE);2TERrestrial  ENvironmental  Observatories  (TERENO)   3Jülich  ObservatorY  for  Cloud  Evolu4on  (JOYCE);4Transregional  Collabora4ve  Research  Centre  –  32  (TR32)   Data  sources:   TERENO2,  JOYCE3,  Er`  Verband,  and  TR324     Time  period:   April  –  May  2013   HOPE1  campaign   Variables:   States,  fluxes,  and  diagnos5cs  across  the  subsurface,  land  surface,   and  atmosphere  compartments  of  the  terrestrial  system.   •  Radia4on  balance  composites  (radiometers)   •  Energy  fluxes  (eddy  covariance  measurements)   •  Soil  moisture  (cosmic-­‐ray  probes)   •  Precipita4on  (X-­‐band  composites)   •  Boundary  layer  height   •  Water  table  depth   •  Humidity  and  temperature  profiles  (mul4ple  meas.)  
  8. 8.  Observa4onal  dataset  –  temporal  distribu4on     Average  data  coverage:  70%   56%   64%   70%   67%   67%   66%   67%   86%   76%   76%   Latent  heat   Sensible  heat   2m  humidty   Incoming  longwave   Emiged  longwave   Incoming  shortwave   Reflected  shortwave   2m  temperature   10m  u-­‐wind   10m  v-­‐wind  
  9. 9.  Observa4onal  dataset  –  spa4al  distribu4on  
  10. 10.       •  Study  area   •  Observa4onal  dataset   •  TerrSysMP   •  Model  setup   •  Results   •  Conclusions  
  11. 11.  TerrSysMP   COSMO   Convec4on  permihng  configura4on  (COSMO-­‐DE)   (Baldauf  et  al.  2011).   CLM   Land  surface  scheme  (Oleson  et  al.  2008).   ParFlow   Integrated   surface-­‐subsurface   flow   model   with   terrain   following  coordinates  (Kollet  and  Maxwell,  2006;  Maxwell,   2012).   OASIS3  –  OASIS-­‐MCT   External   coupler   with   mul4ple   executable   approach   (Valcke  2013).   Model  developments,  improvements,  and  applicaLons:   Shrestha  et  al.,  2014  MWR;  Gasper  et  al.,  2014  GMD;  Sulis  et  al.,  2015  JHM;  Rahman  et  al.,  2015  AWR     Shrestha  et  al.,  2014  MWR  
  12. 12.       •  Study  area   •  Observa4onal  dataset   •  TerrSysMP   •  Model  setup   •  Results   •  Conclusions  
  13. 13.  Model  setup   SpaLal  resoluLon:   •  COSMO:  ΔX  =  ΔY  =  1000  m       •  ParFlow-­‐CLM:  ΔX  =  ΔY  =  500m     Temporal  resoluLon:   •  COSMO:  Δt  =  10  sec       •  ParFlow-­‐CLM:  Δt  =  900  sec     Coupling  frequencies:   •  COSMO-­‐CLM:  CPL1  =  900  sec       •  CLM-­‐ParFlow:  CPL2  =  900  sec     Boundary  condiLons:   •  COSMO:  Hourly  reanalysis  COSMO-­‐DE  forcing       •  ParFlow:  No-­‐flux  condi4ons    
  14. 14.       •  Study  area   •  Observa4onal  dataset   •  TerrSysMP   •  Model  setup   •  Results   •  Conclusions  
  15. 15.  Results  –  Radia4on  balance   *bias    =  (Xsim  —    Xobs)  /  Xobs   Systema4c  overes4ma4on  of  the  net  shortwave  radia4on  by  TerrSysMP.       Beger  match  of  the  net  longwave,with  the  excep4on  of  Wuestbach.    
  16. 16.  Results  –  Radia4on  balance   Analysis  of  the  shortwave  radia5on  composites:   screening  for  “clear-­‐sky”  days     Overes4ma4on  of  incoming  shortwave:  cloudiness  effect.        Underes4ma4on  of  reflected  shortwave:  albedo  parameterizaLon.        
  17. 17.  Results  –  Radia4on  balance   Analysis  of  the  longwave  radia5on  composites:   screening  for  “clear-­‐sky”  days     Underes4ma4on  of  incoming  longwave:  liquid  water  path.        Good  agreement  in  the  emiged  longwave:  land  surface  temperature.        
  18. 18.  Results  –  Atmospheric  states     Analysis  of  the  integrated  water  vapor  (IWV):   Slight   underes4ma4on   of   the   simulated   IWV,   especially   with   respect   to   MWR,   and   late   in   the   a`ernoon.   TerrSysMP   response   is   consistent   with   COSMO-­‐DE   lateral  BCs.        
  19. 19.  Results  –  Energy  fluxes   TerrSysMP  overesLmates  H,  larger  Bowen  ra4os  for  most  of  the  sta4ons.      
  20. 20.  Results  –  Land  surface  states   Soil  moisture  dynamics  :   Soil  porosity   Underes5ma5on  of  precipita5on  
  21. 21.  Results  –  Land  surface  states   Soil  moisture  dynamics  :  
  22. 22.       •  Study  area   •  Observa4onal  dataset   •  TerrSysMP   •  Model  setup   •  Results   •  Conclusions  
  23. 23.  Conclusions   •  Need  of  an  accurate  assessment  of  the  radia4on  balance.     •  Dras4c  influence  of  local  features  in  the  soil  moisture   dynamics  and  par44oning  of  land  surface  energy  fluxes.   •  Soil  moisture  dynamics  generally  well  reproduced.   •  Es4mate  the  integrated  water  balance.   •  Perform  ensemble  simula4ons  (e.g.,  COSMO-­‐DE-­‐EPS).     •  Extend  the  simula4on  to  longer  4me  periods.   Preliminary  results:   Next  steps:   •  Coherence  in  observa4ons  and  modeling  results.  
  24. 24.  Acknowledgments   Alexander  Graf  and  Marius  Schmidt  (IBG3-­‐FZJ)   Roland  Baatz  and  Heye  Bogena  (IBG3-­‐FZJ)   Malte  Diederich  (MIUB)   Stefan  Simon  (Er`  Verband)   Jan  Schween  (Uni-­‐Köln)   Sidney  Marschollek  (MIUB)  

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