wuletawu abera ph.d. defense
TRANSCRIPT
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Modelling Water Budget at basin scale Using JGrass-
NewAge system
April 28, 2016, Trento University
PhD candidate: Wuletawu Abera Worku
Farm
er P
ray
for
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Supervisor: Prof. Riccardo Rigon
Friday, 29 April 16
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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 AberaFriday, 29 April 16
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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:
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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
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- Hydrometeorological data
Hydrological information
- Hydrological models
Introduction: the challenge
Hydrological systems is highly variable in space and time
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Introduction: Grand challenge
Wuletawu Abera Worku
Modern day societal demand of hydrological information
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Wuletawu Abera Worku
Modern day societal demand of hydrological information
Introduction: Grand challenge
Friday, 29 April 16
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ext
Wuletawu Abera Worku
Modern day societal demand of hydrological information
Introduction: Grand challenge
Friday, 29 April 16
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ext
Wuletawu Abera Worku
Modern day societal demand of hydrological information
Introduction: Grand challenge
Friday, 29 April 16
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Not just discharge:➡Precipitation➡Evapotranspiration➡Storage
Wuletawu Abera Worku
Introduction: Grand challenge
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At each HRU and links
Wuletawu Abera Worku
Introduction: Grand challenge
Friday, 29 April 16
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Water Budget!
- 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)� ET k(t)�Qk(t) =@Sk(t)
@t
Te Storage
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The Modelling Framwork: JGrass-NewAge system
Luca
Wuletawu Abera Worku
Formettal et al, 2014
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-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
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Water Budget! 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)� ET k(t)�Qk(t) =@Sk(t)
@t
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HRU-channel partition
42 HRUs402 HRU
Water budget closure for each HRUs
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Water Budget!
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)� ET k(t)�Qk(t) =@Sk(t)
@t
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Water Budget!
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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 ?
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The effect of semivariogram model is minimal
The difference is between Local & universal
Cross validation analysis
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Results: Space-time Precipitation of each HRU + errors
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Water Budget!
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Results: Snowfall and rainfall separation
r=0.6AI=60%
Wuletawu Abera Worku
Jk(t)� ET k(t)�Qk(t) =@Sk(t)
@t
Friday, 29 April 16
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Water Budget!
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
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Water Budget!
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36 38 40Long
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0
1000
2000
3000
4000Altitude (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)� ET k(t)�Qk(t) =@Sk(t)
@t
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Water Budget!
24
GOF between SREs and in-situ data
- correlation -RMSE-BIAS
Five High temporal and spatial SREs1. CMORPH2.TRMM3.TAMSAT4.SM2R-CCI5.CFSR
Which satellite product ?
Wuletawu Abera Worku
Knoche et al. 2014
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Water Budget!
25
Ecdf Matching
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Panofsky and Brier, 1986; Michelangeli et al. 2009
BIAS correction
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Water Budget!
26
Results: SREs GOF comparison
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Water Budget!
27
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Results: Spatial distribution of GOF
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Water Budget!
28
Results: Annual rainfall volume difference
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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
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Water Budget!
30
HYMOD
T Van Delft et al. 2009 ext
Improving output components: Rainfall-runoff Model
Jk(t)� ET k(t)�Qk(t) =@Sk(t)
@t
Calibration of model parameters: Particle swarm; Luca
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Water Budget!
31
Storage information from HYMOD Text
Rn is modulated using SWRB and LWRB components (Formetta et al.2013)
Improving output components: ET
Jk(t)� ET k(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) + �R
n
The problem is how to estimate/calibrate ext
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Water Budget!
32
3. Use of GRACE
2. Budyko assumption
s(t)� s(0) =
Z T
0J(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 TB0 J(t)�
R TB0 Q(t)dt
R TB0 ET (t)dt
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Water Budget!
33
Results: ET component for Posina basin.....
Alpha-estimation
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Water Budget! Results: ET component for Posina basin.....
ET-hourly
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Water Budget!
35
Results: Q component for Posina basin.....
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CalKGE=0.71
ValKGE=0.63
KGE=0.73
KGE=0.62
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Results: Spatial and temporal dynamics of water balance for Posina basin
Jk(t)� ET k(t)�Qk(t) =@Sk(t)
@t
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Results: Basin scale monthly water budget for Posina basin
Water budget forecast based on only Precip
for 2012
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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 WorkuFriday, 29 April 16
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Water Budget!
39
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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
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Water Budget!
40
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Results: Output component (ET)
MOD16 underestimate
Jk(t)� ET k(t)�Qk(t) =@Sk(t)
@t
Yilmaz et al., 2014; Knipper et al., 2016; Ramoelo et al., 2014
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Water Budget!
41
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Results: Output component (ET)
GOF between MOD16 and NewAge ET
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Water Budget!
42
Results: Output component (Q)
KGE=0.93 KGE=0.91KGE=0.55 KGE=0.81
KGE=0.38
KGE=0.58KGE=0.72KGE=0.55
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Water Budget!
43
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Results: Output component (ds/dt)
Jk(t)� ET k(t)�Qk(t) =@Sk(t)
@t
-seasonality+
-amplitude
Wuletawu Abera WorkuFriday, 29 April 16
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Water Budget!
44
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Results: Water budget for UBN basin
Wuletawu Abera WorkuFriday, 29 April 16
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Water Budget!
45
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Results: Mean monthly basin scale Water budget closure
Wuletawu Abera WorkuFriday, 29 April 16
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Water Budget!
46
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Results: Water budget long term annual mean
Spatially distributed annual water budget
Wuletawu Abera WorkuFriday, 29 April 16
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Water Budget!
47
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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, HESS
Wuletawu Abera WorkuFriday, 29 April 16
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Water Budget!
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- 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 WorkuFriday, 29 April 16
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Water Budget!
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Finally, but most importantly, I am grateful for ....
Wuletawu Abera WorkuFriday, 29 April 16
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Water Budget!
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Thank you for your attention
Wuletawu Abera WorkuFriday, 29 April 16