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Alberto Bellin

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The value of aggregated measurements of state variables in hydrological modeling

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Alberto Bellin

  1. 1. 1/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   The value of aggregated measurements of state variables in hydrological modeling     Alberto Bellin Department of Civil, Environmental and Mechanical Engineering
  2. 2. 2/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   RESEARCH ARTICLE 10.1002/2015WR016994 On the use of spatially distributed, time-lapse microgravity surveys to inform hydrological modeling Sebastiano Piccolroaz1, Bruno Majone1, Francesco Palmieri2, Giorgio Cassiani3, and Alberto Bellin1 1 Department of Civil, Environmental, and Mechanical Engineering, University of Trento, Trento, Italy, 2 Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Trieste, Italy, 3 Department of Geosciences, University of Padova, Padova, Italy Abstract In the last decades significant technological advances together with improved modeling capa- bilities fostered a rapid development of geophysical monitoring techniques in support of hydrological mod- eling. Geophysical monitoring offers the attractive possibility to acquire spatially distributed information on state variables. These provide complementary information about the functioning of the hydrological system to that provided by standard hydrological measurements, which are either intrinsically local or the result of a complex spatial averaging process. Soil water content is an example of state variable, which is relatively simple to measure pointwise (locally) but with a vanishing constraining effect on catchment-scale modeling, while streamflow data, the typical hydrological measurement, offer limited possibility to disentangle the controlling processes. The objective of this work is to analyze the advantages offered by coupling traditional hydrological data with unconventional geophysical information in inverse modeling of hydrological sys- tems. In particular, we explored how the use of time-lapse, spatially distributed microgravity measurements may improve the conceptual model identification of a topographically complex Alpine catchment (the Ver- migliana catchment, South-Eastern Alps, Italy). The inclusion of microgravity data resulted in a better con- straint of the inversion procedure and an improved capability to identify limitations of concurring conceptual models to a level that would be impossible relying only on streamflow data. This allowed for a better identification of model parameters and a more reliable description of the controlling hydrological processes, with a significant reduction of uncertainty in water storage dynamics with respect to the case when only streamflow data are used. Key Points: Microgravity data constrain subsurface water storage in hydrological models Combining streamflow and microgravity data rules out improper conceptual models Indirect measures of state variables constrain inversion of hydrological data Supporting Information: Supporting Information S1 Correspondence to: S. Piccolroaz, s.piccolroaz@unitn.it Citation: Piccolroaz, S., B. Majone, F. Palmieri, G. Cassiani, and A. Bellin (2015), On the use of spatially distributed, time-lapse microgravity surveys to inform hydrological modeling, Water Resour. Res., 51, doi:10.1002/2015WR016994. Received 30 JAN 2015 Accepted 6 AUG 2015 Accepted article online 11 AUG 2015 Water Resources Research PUBLICATIONS
  3. 3. 3/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Two  concurring  hydrological  models  (M1  and  M2)  having  the  same  high  efficiency  when   calibrated  using  only  streamflow  data  (single  objecve)…   Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul 0 5 10 15 20 25 30 Time Discharge[m3 /s] Observed Sim. M1 (KGE hydro =0.92 Sim. M2 (KGE hydro =0.93
  4. 4. 4/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   …  show  remarkable  differences  when  adding  geophysical  informaon  (mul  objecve):  the   shape  of  the  Pareto  fronts  provide  useful  hints  to  idenfy  model  limitaons,  and  indicate  the   value  of  including  geophysical  data  to  beGer  constraint  of  the  inversion  procedure.  
  5. 5. 5/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Introduc3on Microgravimetry  may  provide  useful  data  on  water  storage  in  the  earth’s  subsurface     • relevant  in  the  evalua3on/quan3fica3on  of  groundwater  resources  (e.g.  GRACE  at  large   scale);   • a  promising  support  in  hydrological  modeling.       Are  microgravimetry  data  useful  to  inform  hydrological  models?  We  are  interested  in   coupling  tradi3onal  hydrological  data  (inherently  global)  and  geophysical  informa3on   (spaally  distributed).   Time-­‐lapse,  rela3ve  microgravimetry  is  a  geophysical   technique  which  allows  to  obtain  an  indirect  esmate  of   surface  and  subsurface  water  storage  varia3ons  (i.e.,  soil   water  content,  groundwater  and  snowpack)     Basic  concept:   the  temporal  variaon  of  the  gravitaonal  aGracon  is   proporonal  to  the  hydrological  storage  changes     (once  all  other  contribuons  are  corrected).  
  6. 6. 6/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Study  site:  the  Vermigliana  catchment,  located  in  the  South-­‐Eastern  Alps  (Italy).   Main  characteris3cs:   •  contribung  area:  104  km2;   •  the  main  creek  has  a  length  of  4  km  and  an   average  slope  of  1%;   •  elevaon  ranging  from  950  to  3558  m  a.s.l.;   •  very  steep  slopes  and  a  narrow  valley   boGom  (U-­‐shaped  profile);   •  nivo-­‐glacial  regime;   •  almost  prisne  condions.   Microgravity  campaign
  7. 7. 7/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Fieldwork  and  data  acquisi3on1:   •  6  field  campaigns  between  June  2009  and  May  2011;   •  extensive  point  gravity  measurements  on  a  network  of  53  sta3ons;   •  the  Vermigliana  catchment  has  been  monitored  through  8  sta3ons;   •  streamflow  data  are  available  at  the  Vermiglio  stream  gauging  staon.   1  Equipment:  LaCoste-­‐Romberg  mod  D-­‐018  equipped  with  a  Zero  Length  Spring  feedback.   Microgravity  campaign Reference     measurement
  8. 8. 8/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Modeling  approach Hydrological  model:  a  modified  version  of  GEOTRANSF  (Majone  et  al.,  2012,  WATER  RESOUR   RES),  a  distributed  hydrological  model  characterized  by   1.  subdividing  the  catchment  into  slope  and  valley  boGom  areas  (assuming  they  are  governed   by  inherently  different  processes);     2.  explicitly  coupling  vadose-­‐zone  and  groundwater  dynamics.     The  valley  boGom  is  idenfied  considering  thresholds  on  slope  and  elevaon     (≈  1.8  km2,  2%  of  the  catchment  area)  
  9. 9. 9/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Valley bottom
  10. 10. 10/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Valley bottom Sub-basin entirely confined in the hillslope unit
  11. 11. 11/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Valley bottom Sub-basin partitioned between hillslope and valley bottom
  12. 12. 12/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Modeling  approach Calibra3on  procedure:  parcle  swarm   algorithm  to  solve  a  mul-­‐objecve   opmizaon  problem     (see  e.g.,  Robinson  and  Rahmat-­‐Samii,  2004,  IEEE  T   ANTENN  PROPAG)   Objec3ve  func3on:  the  following  expression  has  been  used  as  model  performance  criterion   KGE  =  Kling–Gupta  efficiency  index  (Gupta  et  al.,  2009,  J  HYDROL)   = µs µ0 = s 0 r = covSO ( S 0) KGE = 1 q ( 1) 2 + ( 1) 2 + (r 1) 2 KGEtot = (1 ↵)KGEhydro + ↵KGEgrav, ↵ = 0 ÷ 1
  13. 13. 13/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Modeling  approach Gravity  model:    the  domain  is  divided  into  n  prisms  for   which  the  individual  contribuon  to  the  hydrology-­‐ induced  gravity  variaons  is  evaluated  according  to   The  following  approximated  soluon  has  been   applied  for  distances  larger  than  a  given   threshold  from  the  gravimeter
  14. 14. 14/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Domain  discre3za3on:  carried  out  at  two  levels       • Horizontal  plane  based  on  the  10x10  m  resoluon  DEM  available  for  the  catchment       • Ver3cal  direc3on  considering  the  same  layers  of  the  hydrological  model,  including   snowpack   Modeling  approach Main  water  storage  components:  soil   moisture  in  the  rizosphere  and  in  the   vadose  zone,  groundwater  and  snowpack.  
  15. 15. 15/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Model  M1 Model  M2
  16. 16. 16/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Two  concurring  hydrological  models  (M1  and  M2)  having  the  same  high  efficiency  when   calibrated  using  only  streamflow  data  (single  objecve)…   Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul 0 5 10 15 20 25 30 Time Discharge[m3 /s] Observed Sim. M1 (KGE hydro =0.92 Sim. M2 (KGE hydro =0.93
  17. 17. 17/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   …  show  remarkable  differences  when  adding  geophysical  informaon  (mul  objecve):  the   shape  of  the  Pareto  fronts  provide  useful  hints  to  idenfy  model  limitaons,  and  indicate  the   value  of  including  geophysical  data  to  beGer  constraint  of  the  inversion  procedure.  
  18. 18. 18/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Uncertainty  assessment   Results Comparison   among   parameters   and   model   output   uncertainty   (model   M1)   for   two   significant  cases:   • only  streamflow  data  are  available  for  model  calibraon  (α=0)   • both   streamflow   and   microgravity   data   are   used   for   parameters   inference   with   a   balanced  aggregated  objecve  funcon  (α=0.5)   Among   the   soluons   obtained   during   the   exploraon   of   the   parameters   space,   only   those   with   KGEtot   differing   less  the  1%  from  the  maximum  value  (colored  squares  in   the  previous  slide)  have  been  retained  for  the  uncertainty   analysis.     The   analysis   has   been   focused   on   the   100%   confidence   bands  resulng  from  the  retained  soluons.   To  address  the   c l u s t e r i n g   effect  of  PSO.  
  19. 19. 19/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Model  parameters   Results The  values  of  the  parameters  are  normalized  with  respect  to  their  range   Confidence  bands  are:   • 45%  smaller  for  α=0.5  than   for  α=0   • well  distributed  between  0   and  1  (proper  choice  of  their   prior  range  of  variaon)  
  20. 20. 20/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Results Water  storage   When  including   microgravity  data,   confidence  bands   reduce  by   • 26%  in  the  hillslope   • 95%   in   the   valley   boGom   The  accumulaon  of   water  in  the  valley   boGom  (due  to  a   parcularly  wet   summer  in  2010)  is   observed  only  for   α=0.5    
  21. 21. 21/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Results Water  table  fluctua3on   When  including   microgravity  data,   confidence  bands   reduce  by   •  39%  in  the  hillslope   •  93%   in   the   valley   boGom  
  22. 22. 22/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Results Streamflow   The  averaged  confidence   band  reduces  from  0.95  m3/ s  to  0.78  m3/s  when   microgravity  data  are   included   microgravity  data  not  only  allow  for  a  beGer   representaon  of  subsurface  storage,  but  they   also  reduce  uncertainty  of  streamflow.  
  23. 23. 23/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Results Gravity  change  (for  α=0.5)     Valley  boGom  staons    (green)   show  a  good  agreement  between   simulated  and  observed  data,   while  hillslope  staons  (blue)  show   larger  deviaons.     In  general,  seasonal  variaons  are   well  reproduced.   Errors  are  likely  due  to  the  simplificaon,   dictated  by  the  lack  of  detailed  stragraphic   data,  of  assuming  the  water   table  moving  parallel  to  the  ground  surface.  
  24. 24. 24/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Conclusions Conclusions   Coupled  approach   tradi3onal  streamflow  data  +  me-­‐lapse,  spaally  distributed  microgravity  measurements       Main  results   •  significant  reduc3on  of  the  uncertainty  associated  with  the  esmaon  of  the  me   variaons  of  the  main  hydrological  state  variables  (e.g.,  total  water  volume,  water  table  elevaon)   •  the  major  effects  can  be  observed  in  the  valley  boaom  area     → higher  density  of  gravimetric  staons •  uncertainty  on  simulated  streamflow  decreases  as  well   •  gravimetric  data  allowed  for  a  beaer  idenficaon  of  the  hydrological  conceptual  model   including  the  definion  of  the  valley  boGom  region   Thank  you
  25. 25. 25/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it  
  26. 26. 26/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Pareto  front    
  27. 27. 27/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   HydroSCAPE: a multi-scale framework for streamflow routing in large-scale hydrological models Leno River at Rovereto
  28. 28. 28/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Open  un3l  30  October  2015
  29. 29. 29/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Large  scale  hydrological  model   •  Rainfall-­‐runoff  model  aimed  at  simulang  the  occurrence  of  floods  at  the  large  scale   (i.e.,  catchments  with  an  extension  ranging  from  1000  km2  up  to  the  connental  scale).   Parcular  emphasis  is  given  to  extreme  events.   •  Possible  extensions  include  the  applicaon  to  several  water  resources  issues:  aquifer   recharge,  storage  dynamics,  water  resources  availability,  interacons  with  river   ecosystems  etc.   •  Specific  aGenon  is  given  to  the  flow  rou3ng  algorithm,  which  has  been  indicated  as  an   aspect  of  LSM  and  global  hydrological  models  needing  some  improvement  (Clark  et  al,   WRR  2015)   REVIEW ARTICLE 10.1002/2015WR017096 Improving the representation of hydrologic processes in Earth System Models Martyn P. Clark1, Ying Fan2, David M. Lawrence1, Jennifer C. Adam3, Diogo Bolster4, David J. Gochis1, Richard P. Hooper5, Mukesh Kumar6, L. Ruby Leung7, D. Scott Mackay8, Reed M. Maxwell9, Chaopeng Shen10, Sean C. Swenson1, and Xubin Zeng11 1 National Center for Atmospheric Research, Boulder, Colorado, USA, 2 Department of Earth and Planetary Sciences, Rutgers University, New Brunswick, New Jersey, USA, 3 Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, USA, 4 Department of Civil Environmental Engineering and Earth Sciences, University of Notre Dame, South Bend, Indiana, USA, 5 The Consortium of Universities for the Advancement of Hydrologic Science, Inc., 6 Nichols Schools of Environment, Duke University, Durham, North Carolina, USA, 7 Pacific Northwest National Laboratory, Richland, Washington, USA, 8 Department of Geography, University at Buffalo, State University of New York, Special Section: The 50th Anniversary of Water Resources Research Key Points: Land model development can benefit from recent advances in hydrology Accelerating modeling advances requires comprehensive benchmarking activities Water Resources Research PUBLICATIONS
  30. 30. 30/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   •  Some  key  scien3fic  ques3ons:   •  How  climate  change  may  modify  the  stascs  of  floods  occurring  IN  large  river   basins?   •  What  are  the  consequences  on  the  management  of  the  hydraulic  structures?   •  Can  we  determine  the  uncertainty  associated  with  the  esmate  of  extreme   hydrological  events? Large  scale  hydrological  model   LETTERS PUBLISHED ONLINE: 21 OCTOBER 2012 | DOI: 10.1038/NCLIMATE1719 Hydroclimatic shifts driven by human water use for food and energy production Georgia Destouni*, Fernando Jaramillo and Carmen Prieto Hydrological change is a central part of global change1–3 . Its drivers in the past need to be understood and quantified for ac- curate projection of disruptive future changes4 . Here we anal- yse past hydro-climatic, agricultural and hydropower changes from twentieth century data for nine major Swedish drainage basins, and synthesize and compare these results with other regional5–7 and global2 assessments of hydrological change by irrigation and deforestation. Cross-regional comparison shows similar increases of evapotranspiration by non-irrigated agri- culture and hydropower as for irrigated agriculture. In the Swedish basins, non-irrigated agriculture has also increased, whereas hydropower has decreased temporal runoff variability. A global indication of the regional results is a net total increase of evapotranspiration that is larger than a proposed associated planetary boundary8 . This emphasizes the need for climate and Earth system models to account for different human uses of water as anthropogenic drivers of hydro-climatic change. The present study shows how these drivers and their effects can be distinguished and quantified for hydrological basins on different scales and in different world regions. This should en- courage further exploration of greater basin variety for better understanding of anthropogenic hydro-climatic change. Drivers of freshwater changes are multiple and difficult to distinguish and quantify9–12 . Global increase of evapotranspiration while instrumental hydro-climatic data have become available for direct analysis of their hydrological effects. In this study, we investigate twentieth century land–water use and hydro-climatic data, and interpret them by the fundamental water balance quantification P ET R DS = 0 (where P is precipitation, R is runoff and DS is water storage change) for nine major Swedish drainage basins (Fig. 1a). The investigation has three goals. First, a basin-scale distinction of hydrological changes and their drivers in the Swedish basins, which among them represent different land–water uses (Fig. 1a, green: dominant non-irrigated agriculture, red: dominant hydropower, blue: with essentially unregulated rivers and little agriculture) and hydro- climatic conditions in terms of surface temperature (T; Fig. 1b) and P (Fig. 1c). Second, cross-regional result comparison across the complementary Swedish, Aral and Mahanadi basins (Fig. 1a), representing an even wider range of different hydro-climatic conditions (Fig. 1b,c) and land–water uses (irrigated and non- irrigated agriculture, hydropower, unregulated). Third, cross- scale/method comparison between the extrapolation implications of the regional basin results and previous spatial estimates of global hydrological changes and drivers2 . Hydrological, and other physical and ecological science communities11,17,18 have identified fundamental needs for such synthesis and comparisons along climatic and other gradients. Resilience of river flow regimes Gianluca Bottera , Stefano Bassoa,b , Ignacio Rodriguez-Iturbec , and Andrea Rinaldoa,d,1 a Department of Civil, Architectural, and Environmental Engineering, University of Padua, I-35100 Padua, Italy; b Department of Water Resources and Drinking Water, Swiss Federal Institute of Aquatic Science and Technology, CH-8600 Dübendorf, Switzerland; c Department of Civil and Environmental Engineering, Princeton University, Princeton 08540, NJ; and d Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland Contributed by Andrea Rinaldo, June 25, 2013 (sent for review March 3, 2013) Landscape and climate alterations foreshadow global-scale shifts of river flow regimes. However, a theory that identifies the range of foreseen impacts on streamflows resulting from inhomogeneous forcings and sensitivity gradients across diverse regimes is lacking. Here, we derive a measurable index embedding climate and landscape attributes (the ratio of the mean interarrival of stream- flow-producing rainfall events and the mean catchment response time) that discriminates erratic regimes with enhanced intraseaso- nal streamflow variability from persistent regimes endowed with regular flow patterns. Theoretical and empirical data show that from the censoring operated by catchment soils on daily rainfall, and they are modeled as a spatially uniform marked Poisson process with mean depth α [L] and mean frequency λ ½T−1 Š. Al- though α quantifies the average daily intensity of rainfall events, λ is smaller than the underlying precipitation frequency because the soil–water deficit created by plant transpiration in the root zone may hinder the routing of some inputs to streams (Eq. S3). Therefore, λ crucially embeds rainfall attributes; soil/vegetation properties; and other climate variables, such as temperature, hu- midity, and wind speed.
  31. 31. 31/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Characteristics of the model Macro-­‐regional/connental  hydrological  model  for  flood  assessment.   Possible   applicaon   to   water   resources   problems:   aquifer   recharge,   storage   dynamics,   water  resources  availability,  interacons  with  river  ecosystems  etc. Discrezaon   into   large   macro-­‐cells:   computaonal   requirements,   direct   coupling   with   climate  models,  focus  on  large  scale  dynamics.   Simplicity:   a   few   parameters,   possibly   linked   to   physical   properes   for   which   distributed   informaon  is  easily  available.   Linearity   of   the   processes:   in   order   to   ensure   a   simple   and   efficient   numerical   parallelizaon.   Rigorous  upscaling  of  the  spaal  distribuon  of  water  fluxes.   Physically  based  (WFIUH  approach).  
  32. 32. 32/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Delineaon  of  the  catchment  area.   Pre-processing
  33. 33. 33/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Pre-processing Discre3za3on   of   the   domain   into   a   number  of  macrocells.   Delineaon  of  the  catchment  area.  
  34. 34. 34/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Pre-processing Discre3za3on   of   the   domain   into   a   number  of  macrocells.   Extracon  of  the  river  network  from  the   DEM.     Delineaon  of  the  catchment  area.  
  35. 35. 35/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Discre3za3on   of   the   domain   into   a   number  of  macrocells.   Extracon  of  the  river  network  from  the   DEM.     Iden3fica3on   of   target   points   (hereater   referred   to   as   nodes):   gauged   secons,   residenal   areas,   hydraulic   structures,   reservoir  etc.   Definion  of  the  width  func3on  for  each   node-­‐macrocell  pair.     Delineaon  of  the  catchment  area.   Pre-processing
  36. 36. 36/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Structure of the model Water   balance   dynamics   at   the   hillslope   scale  are  simulated  using  a  simple  rainfall-­‐ runoff  model  (e.g.,  SCS)      Hillslope   drains   into   the   river   network,   where   water   is   transferred   towards   nodes,  with  a  given  channel  velocity.   WFIUH   approach   (e.g.   Rinaldo,   Marani,   Rigon,  1991)     The  streamflow  simulated  at  each  node  is   given  by  the  sum  of  the  contribuons  of   all  macrocells  contribung  to  that  node.   The   model   is   linear   →   the   streamflow   generated  at  each  macrocell  is  evaluated   independently  from  the  others.    
  37. 37. 37/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Characteris3cs:   •  Few   reservoir   and   hydraulic   structures.   •  Snow  precipitaon  and  melng  is   negligible.   •  Historical   series   (a   few   decades)   of   streamflow,   rainfall   and   air   temperature  are  available.   •  Surface   area   of   the   catchment   equal  to  4116  km2.   •  5   nodes,   in   correspondence   to   gauging  secons.         Example application: Upper Tiber
  38. 38. 38/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Example application: Upper Tiber
  39. 39. 39/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   3  grid  sizes:   5,  10  and  50  km Example application: Upper Tiber
  40. 40. 40/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Width  funcons  at  Ponte  Nuovo   obtained  aggregang  the  original   20  m  DEM  to  5,  10  and  50  km:   Models  in  which  the   geomorphological  descripon  of   the  drainage  network  has  the  same   scale  of  the  computaonal  grid,   suffer  from  a  deterioraon  of  the   geomorphological  response  of  the   watershed.     This  is  not  the  case  of  the  proposed   formulaon,  in  which  the  width   funcon  maintains  all  the   informaon  derived  from  the   original  DEM         Example application: Upper Tiber Figure 5. Width functions of the Upper Tiber river basin at Ponte Nuovo (PN) outlet (4116 km2 ) derived from DEMs obtained aggregating the original 20 m DEM to 5, 10 and 50 km (lower panels). The width function obtained with the original 20 m DEM is also shown with a red line. to represent the travel time distribution at the hillslope scale. In this case, rescaling may be obtained by using a hillslope specific velocity V` Vc.
  41. 41. 41/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Rainfall-­‐runoff  models   Model parameters v λ cS kH SCSLR H ✓ ✓ ✓ ✓ Data requirements •  SCSLRH:  SCS  +  linear  reservoir  for  the  hillslope   QUESTO  E’  IL  MODELLO  DI  PRODUZIONE  USATO   IN  HESS,  LE  ALTRE  SLIDE  LE  HO  NASCOSTE
  42. 42. 42/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Rainfall-­‐runoff  models   •  none:  runoff=rainfall   Model parameters v λ cS k kH none ✓ - - - - SCS ✓ ✓ ✓ - - SCSLR I ✓ ✓ ✓ ✓ - SCSLR H ✓ ✓ ✓ - ✓ SCSLR HI ✓ ✓ ✓ ✓ ✓ Data requirements                                                                                                    
  43. 43. 43/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Rainfall-­‐runoff  models   •  none:  runoff=rainfall   Model parameters v λ cS k kH none ✓ - - - - SCS ✓ ✓ ✓ - - SCSLR I ✓ ✓ ✓ ✓ - SCSLR H ✓ ✓ ✓ - ✓ SCSLR HI ✓ ✓ ✓ ✓ ✓ Data requirements •  SCS:  Soil  Conservaon  Service  –  Curve  Number  Methodology                                                                                                      
  44. 44. 44/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Rainfall-­‐runoff  models   •  none:  runoff=rainfall   Model parameters v λ cS k kH none ✓ - - - - SCS ✓ ✓ ✓ - - SCSLR I ✓ ✓ ✓ ✓ - SCSLR H ✓ ✓ ✓ - ✓ SCSLR HI ✓ ✓ ✓ ✓ ✓ Data requirements •  SCS:  Soil  Conservaon  Service  –  Curve  Number  Methodology                                                                                                       •  SCSLRI:  SCS  +  linear  reservoir  for  infiltraon  
  45. 45. 45/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Rainfall-­‐runoff  models   •  none:  runoff=rainfall   Model parameters v λ cS k kH none ✓ - - - - SCS ✓ ✓ ✓ - - SCSLR I ✓ ✓ ✓ ✓ - SCSLR H ✓ ✓ ✓ - ✓ SCSLR HI ✓ ✓ ✓ ✓ ✓ Data requirements •  SCS:  Soil  Conservaon  Service  –  Curve  Number  Methodology   •  SCSLRH:  SCS  +  linear  reservoir  for  the  hillslope   •  SCSLRHI:  SCS  +  linear  reservoir  for  the  hillslope  +  linear  reservoir  for  infiltraon   •  SCSLRI:  SCS  +  linear  reservoir  for  infiltraon  
  46. 46. 46/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Calibraon  at  Ponte  Nuovo Example application: Upper Tiber
  47. 47. 47/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Mul  site  validaon Example application: Upper Tiber
  48. 48. 48/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Mul  event  validaonExample application: Upper Tiber
  49. 49. 49/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   Conclusions •  We proposed a routing scheme independent on the gridding of the LSM; •  “perfect” upscaling with geomorphological dispersion preserved; •  The scheme is computational efficient (major computational burden in the preprocessing); •  Routing dependent on 2 parameters.
  50. 50. 50/50     Microgravity  observa3ons  and  hydrological  modelling   e-­‐mail:  alberto.bellin@unitn.it   THANK YOU

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