Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Wuletawu Abera Ph.D. defense

537 views

Published on

This illustrate the long and detailed work of Wuletawu during his Ph.D. His topic was modelling the whole hydrological cycle, meaning, all the components together with JGrass-NewAGE. In order to do this, he had to line up several tools, partition the basins, interpolate meteorological data, to go crazy when the data were not available. Calibrate the submodels, each one by each one with available data; doing educated guesses, when any other option was inexistent. He introduces the use of satellite data in JGrass-NewAGE, and, I think, he did it well. He never gave up when I bother him. And I think he did a god job.

Published in: Education
  • Be the first to comment

Wuletawu Abera Ph.D. defense

  1. 1. Modelling Water Budget at basin scale Using JGrass- NewAge system April 28, 2016, Trento University PhD candidate: Wuletawu Abera Worku FarmerPrayforrain,inafarregion Supervisor: Prof. Riccardo Rigon Friday, 29 April 16
  2. 2. Global Water scarcity Introduction: water emergency - Water for food (agriculture) - Water for Energy - Ecosystem 4 billion people faces water scarcity Mekonnen and Hoekstra Sci. Adv. 2016 Wuletawu Abera Friday, 29 April 16
  3. 3. curse of hydroclimatic variability intraannual (e.g. seasonal and monthly) interannual (year-to-year) unpredictable timing and intensity of extremes Introduction: Hydroclimatic variability +/-+/- - - Climate change Aggravate: Wuletawu Abera Worku Friday, 29 April 16
  4. 4. Coping strategies 2. Infrastructure e.g. - desalination - Transfer canal and pipes - Storage dams 3. Hydrological Information 1. Institutions (legal and policy) Hall et al, 2015, Pedro-Monzonís et al, 201 3I Introduction: coping strategy California Aqueduct Wuletawu Abera Worku Friday, 29 April 16
  5. 5. - Hydrometeorological data Hydrological information - Hydrological models Introduction: the challenge Hydrological systems is highly variable in space and time Wuletawu Abera Worku Friday, 29 April 16
  6. 6. 6 ext Introduction: Grand challenge Wuletawu Abera Worku Modern day societal demand of hydrological information Friday, 29 April 16
  7. 7. 7 ext Wuletawu Abera Worku Modern day societal demand of hydrological information Introduction: Grand challenge Friday, 29 April 16
  8. 8. 8 ext Wuletawu Abera Worku Modern day societal demand of hydrological information Introduction: Grand challenge Friday, 29 April 16
  9. 9. 9 ext Wuletawu Abera Worku Modern day societal demand of hydrological information Introduction: Grand challenge Friday, 29 April 16
  10. 10. Not just discharge: ➡Precipitation ➡Evapotranspiration ➡Storage Wuletawu Abera Worku Introduction: Grand challenge Friday, 29 April 16
  11. 11. 11 At each HRU and links Wuletawu Abera Worku Introduction: Grand challenge Friday, 29 April 16
  12. 12. - Spatially and temporally continues WB closure - Develop methods and procedures Te Precipi = rainfall + snow melting Te Runoff contribution from upstream Te Evapotranspiration Te Runoff loss to downstream Introduction: Water budget modelling Jk(t) + m(k) X i Qki(t) ETk(t) Qk(t) = @Sk(t) @t Te Storage Wuletawu Abera Worku Friday, 29 April 16
  13. 13. The Modelling Framwork: JGrass-NewAge system Luca Wuletawu Abera Worku Formettal et al, 2014 Friday, 29 April 16
  14. 14. -small basin (116km2) -snow dominated, alpine -topographically complex -gauged (12 meteo, 3 hydrometers) -1994-2012 simulation Posina basin Upper Blue Nile (UBN) basin -176000km2 -Topographically, sociopolitically complex -Data scarce 1994-2009 The Study basins Wuletawu Abera Worku Friday, 29 April 16
  15. 15. Basin partitioning procedures and connection Formetta et al. 2015 The uDig Spatial Toolbox for hydro-geomorphic analysis Wuletawu Abera, Andrea Antonello, Silvia Franceschi, Giuseppe Formetta, Riccardo Rigon Book chapter, British Society for Geomorphology, 2014 Jk(t) + m(k) X i Qki(t) ETk(t) Qk(t) = @Sk(t) @t Wuletawu Abera Worku Friday, 29 April 16
  16. 16. 16 HRU-channel partition 42 HRUs 402 HRU Water budget closure for each HRUs Wuletawu Abera Worku Friday, 29 April 16
  17. 17. 17 Geostatistics (Kriging): •Experimental semivariogram •Theoretical semivariogram: Exp, Gau, Sph, Lin •Kriging estimates: OK, LOK, DK, LDK Optimal VGM model and parameters are fitted to each time steps 4 kriging X 4 VGM model = 16 data sets Improving spatial field of input from meteo data: Posina basin Jk(t) ETk(t) Qk(t) = @Sk(t) @t Wuletawu Abera Worku Friday, 29 April 16
  18. 18. 18 based on discharge Formetta et al. 2014; Hall et al.,2006; Li et al., 2012; Mou et al., 2008; He et al., 2014 MOD10A1 and MYD10A1 Modelling the input component: Posina Basin Of the total J(t), how much are rainfall and snowfall ? Wuletawu Abera Worku Friday, 29 April 16
  19. 19. 19 The effect of semivariogram model is minimal The difference is between Local & universal Cross validation analysis Wuletawu Abera Worku Friday, 29 April 16
  20. 20. 20 Results: Space-time Precipitation of each HRU + errors Wuletawu Abera Worku Friday, 29 April 16
  21. 21. 21 Results: Snowfall and rainfall separation r=0.6 AI=60% Wuletawu Abera Worku Jk(t) ETk(t) Qk(t) = @Sk(t) @t Friday, 29 April 16
  22. 22. 22 Wuletawu Abera Worku Results: Snowfall and rainfall separation Estimating water budgets at the basin scale with JGrass-NewAge system, Part I: water inputs, their variability and uncertainty Wuletawu Abera, Giuseppe Formetta, Marco Borga, Riccardo Rigon submitted, HESS, 2016 Friday, 29 April 16
  23. 23. 23 ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 8 9 10 11 12 13 36 38 40 Long Lat 0 1000 2000 3000 4000 Altitude (m) Gauge stations are very scarce • 1 station/5000km^2 • Poor maintenance Option: Satellite Rainfall Estimation products Spatial J(t) info using Kriging is elusive t Improving rainfall spatial field when in-situ observation is absent: UBN basin Jk(t) ETk(t) Qk(t) = @Sk(t) @t Wuletawu Abera Worku Friday, 29 April 16
  24. 24. 24 GOF between SREs and in-situ data - correlation -RMSE -BIAS Five High temporal and spatial SREs 1. CMORPH 2.TRMM 3.TAMSAT 4.SM2R-CCI 5.CFSR Which satellite product ? Wuletawu Abera Worku Knoche et al. 2014 Friday, 29 April 16
  25. 25. 25 Ecdf Matching Panofsky and Brier, 1986; Michelangeli et al. 2009 BIAS correction Wuletawu Abera Worku Friday, 29 April 16
  26. 26. 26 Results: SREs GOF comparison Wuletawu Abera Worku Friday, 29 April 16
  27. 27. 27 Results: Spatial distribution of GOF Wuletawu Abera Worku Friday, 29 April 16
  28. 28. 28 Results: Annual rainfall volume difference ~2700 ~1600 ~600 0 Wuletawu Abera Worku Friday, 29 April 16
  29. 29. 29 Comparative evaluation of different satellite rainfall estimation products and bias correction in Upper Blue Nile (UBN) basin Wuletawu Abera, Luca Brocca, Riccardo Rigon Journal of atmospheric Research, 2016 Results: BIAS correction procedure improvement Wuletawu Abera Worku Friday, 29 April 16
  30. 30. 30 HYMOD T Van Delft et al. 2009 ext Improving output components: Rainfall-runoff Model Jk(t) ETk(t) Qk(t) = @Sk(t) @t Calibration of model parameters: Particle swarm; Luca Wuletawu Abera Worku Friday, 29 April 16
  31. 31. 31 Storage information from HYMOD Text Rn is modulated using SWRB and LWRB components (Formetta et al.2013) Improving output components: ET Jk(t) ETk(t) Qk(t) = @Sk(t) @t P r i e s t l e y - T a y l o r ( P T ) formulation ET(t) = ↵ C(t) Cmax (t) (t) + Rn The problem is how to estimate/calibrate ext Wuletawu Abera Worku Friday, 29 April 16
  32. 32. 32 3. Use of GRACE 2. Budyko assumption s(t) s(0) = Z T 0 J(t) Q(t) ↵AET (t)ds s(TB) s(0) = 0 This becomes measured ds(t) dt = J(t) Q(t) ↵ET(t) The only unknown 1.Literature e.g Cristea et al. 2012, (0.6 to 2.4); Problem? Three options ↵(TB) = R T B 0 J(t) R T B 0 Q(t)dt R T B 0 ET(t)dt Wuletawu Abera Worku Friday, 29 April 16
  33. 33. 33 Results: ET component for Posina basin..... Alpha-estimation Wuletawu Abera Worku Friday, 29 April 16
  34. 34. Results: ET component for Posina basin..... ET-hourly Wuletawu Abera Worku Friday, 29 April 16
  35. 35. 35 Results: Q component for Posina basin..... Cal KGE=0.71 Val KGE=0.63 KGE=0.73 KGE=0.62 Wuletawu Abera Worku Friday, 29 April 16
  36. 36. 36 Results: Spatial and temporal dynamics of water balance for Posina basin Jk(t) ETk(t) Qk(t) = @Sk(t) @t Wuletawu Abera Worku Friday, 29 April 16
  37. 37. 37 Results: Basin scale monthly water budget for Posina basin Water budget forecast based on only Precip for 2012 Wuletawu Abera Worku Friday, 29 April 16
  38. 38. 38 Results: Basin scale annual water budget for Posina basin Annual WB (1994-2011) Annual variability of J Q follows J ET is less variable J: 1730 +/- 344 Q 76.5% ET 30% ds/dt -4.5% Estimating water budgets at the basin scale with JGrass-NewAge system, Part II: water outputs, and Storage components Wuletawu Abera, Giuseppe Formetta, Marco Borga, Riccardo Rigon submitted, HESS, 2016 Wuletawu Abera Worku Friday, 29 April 16
  39. 39. 39 Water budget when data is scarce: UBN basin Independent data/estimation/satellite are used to verify the results: Q: split time series data/internal sites ET: MODIS MOD16 satellite data ds/dt: GRACE satellite Wuletawu Abera Worku Friday, 29 April 16
  40. 40. 40 Results: Output component (ET) MOD16 underestimate Jk(t) ETk(t) Qk(t) = @Sk(t) @t Yilmaz et al., 2014; Knipper et al., 2016; Ramoelo et al., 2014 Wuletawu Abera Worku Friday, 29 April 16
  41. 41. 41 Results: Output component (ET) GOF between MOD16 and NewAge ET Wuletawu Abera Worku Friday, 29 April 16
  42. 42. 42 Results: Output component (Q) KGE=0.93 KGE=0.91 KGE=0.55 KGE=0.81 KGE=0.38 KGE=0.58KGE=0.72KGE=0.55 Wuletawu Abera Worku Friday, 29 April 16
  43. 43. 43 Results: Output component (ds/dt) Jk(t) ETk(t) Qk(t) = @Sk(t) @t -seasonality + -amplitude Wuletawu Abera Worku Friday, 29 April 16
  44. 44. 44 Results:Water budget for UBN basin Wuletawu Abera Worku Friday, 29 April 16
  45. 45. 45 Results: Mean monthly basin scale Water budget closure Wuletawu Abera Worku Friday, 29 April 16
  46. 46. 46 Results:Water budget long term annual mean Spatially distributed annual water budget Wuletawu Abera Worku Friday, 29 April 16
  47. 47. 47 Results: Mean annual basin scale Water budget closure Water budget modelling of Upper Blue Nile basin using JGrass-NewAge model system and Satellite data Wuletawu Abera, Giuseppe Formetta, Luca Brocca, Riccardo Rigon To be submitted soon, H Wuletawu Abera Worku Friday, 29 April 16
  48. 48. 48 - New simplified methods of separating rainfall from snowfall using MODIS data - Evaluation of different SREs data is very crucial, and the difference between could be as high as 2700 mm per year. Procedures are presented. - The Adige (HYMOD) component is effectively calibrated in different basin (Posina and UBN), and the performance ranges from very high to acceptable. -The Budyko assumption used to reformulate PT ET model, with less uncertainty - Effectively employed different Remote sensing data for water budget closure Conclusions and contributions... Wuletawu Abera Worku Friday, 29 April 16
  49. 49. 49 Finally, but most importantly, I am grateful for .... Wuletawu Abera Worku Friday, 29 April 16
  50. 50. 50 TextTextT Thank you for your attention wuletawu979@gmail.com Wuletawu Abera Worku Friday, 29 April 16

×