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.

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. 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. 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. 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. - 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
ext
Introduction: Grand challenge
Wuletawu Abera Worku
Modern day societal demand of
hydrological information
Friday, 29 April 16

7. 7
ext
Wuletawu Abera Worku
Modern day societal demand of
hydrological information
Introduction: Grand challenge
Friday, 29 April 16

8. 8
ext
Wuletawu Abera Worku
Modern day societal demand of
hydrological information
Introduction: Grand challenge
Friday, 29 April 16

9. 9
ext
Wuletawu Abera Worku
Modern day societal demand of
hydrological information
Introduction: Grand challenge
Friday, 29 April 16

10. Not just discharge:
➡Precipitation
➡Evapotranspiration
➡Storage
Wuletawu Abera Worku
Introduction: Grand challenge
Friday, 29 April 16

11. 11
At each HRU and
links
Wuletawu Abera Worku
Introduction: Grand challenge
Friday, 29 April 16

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. The Modelling Framwork: JGrass-NewAge system
Luca
Wuletawu Abera Worku
Formettal et al, 2014
Friday, 29 April 16

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. 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
HRU-channel partition
42 HRUs
402 HRU
Water budget closure for each HRUs
Wuletawu Abera Worku
Friday, 29 April 16

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
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
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
Results: Space-time Precipitation of each HRU + errors
Wuletawu Abera Worku
Friday, 29 April 16

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
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
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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
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
Ecdf Matching
Panofsky and Brier, 1986; Michelangeli et al. 2009
BIAS correction
Wuletawu Abera Worku
Friday, 29 April 16

26. 26
Results: SREs GOF comparison
Wuletawu Abera Worku
Friday, 29 April 16

27. 27
Results: Spatial distribution of GOF
Wuletawu Abera Worku
Friday, 29 April 16

28. 28
Results: Annual rainfall volume difference
~2700
~1600
~600
0
Wuletawu Abera Worku
Friday, 29 April 16

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
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
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
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
Results: ET component for Posina basin.....
Alpha-estimation
Wuletawu Abera Worku
Friday, 29 April 16

34. Results: ET component for Posina basin.....
ET-hourly
Wuletawu Abera Worku
Friday, 29 April 16

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
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
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
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
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
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
Results: Output component (ET)
GOF between MOD16 and NewAge ET
Wuletawu Abera Worku
Friday, 29 April 16

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
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
Results:Water budget for UBN basin
Wuletawu Abera Worku
Friday, 29 April 16

45. 45
Results: Mean monthly basin scale Water budget closure
Wuletawu Abera Worku
Friday, 29 April 16

46. 46
Results:Water budget long term annual mean
Spatially distributed annual water budget
Wuletawu Abera Worku
Friday, 29 April 16

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
- 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
Finally, but most importantly, I am grateful for ....
Wuletawu Abera Worku
Friday, 29 April 16

50. 50
TextTextT
Thank you for your attention
wuletawu979@gmail.com
Wuletawu Abera Worku
Friday, 29 April 16