3 j grass-new-age

JGrass-NewAGE system essentials
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin
Potenza, 24 Febbraio 2017
MakedaBizuneh,Ethiopiandream

JGrass-NewAGE è un sistema modellistico per l’idrologia. Non è un modello perchè è costruito
attraverso elementi (“atomici”) dette “componenti” che possono essere combinati in vario modo per
costruire un modello, o meglio, una “soluzione modellistica”. Queste modalità di lavoro sono rese
possibili da un’infrastruttura informatica detta Object Modelling System (OMS) versione 3. Il codice e il
linguaggio di programmazione di questo sistema è Java, ma moduli software scritti in FORTRAN o C/C+
+ possono essere interfacciati com OMS senza eccessive difficoltà. Essendo il sistema (e l’infrastruttura)
in Java, i moduli possono essere girati su ogni computer o sistema di computer che abbia una Java
Virtual Machine.
OMS è una infrastruttura “light weight” che non impone particolari vincoli alla programmazione e
supporta la modellazione includendo due sistemi di calibrazione (LUCA e Particle Swarm) e fornendo
un sistema di parallelizzazione implicito delle componenti. Ovvero, quando due componenti possono
essere eseguite in parallelo perchè non hanno dipendenze, OMS si incarica di eseguirle in parallelo sui
processori disponibili, senza nessun intervento del programmatore per la gestione dei “threads”.
JGrass-NewAGE consiste in varie componenti che possono essere connesse tra loro e che eseguono vari
“task” necessari alla modellazione idrologica. Qui modellazione idrologica e’ intesa in senso largo, non
riferendosi solo alla costruzione della risposta idrologica (cioe’ il calcolo delle portate in uno o più
punti di un bacino idrografico) ma anche del calcolo della radiazione, dell’evapotraspirazione,
dell’evoluzione del manto nevoso, della propagazione delle onde di piena. Il sistema supporta anche
dei metodi di stima dei tempi di residenza dell’acqua e consente al calcolo delle concentrazioni di
traccianti e isotopi.
In questo seminario descrivo gli elementi essenziali del sistema e mostrero’ alcuni casi di studio,
cercando di illustrare le varie possibilita’ offerte da JGrass-NewAGE ed alcuni risultati che abbiamo
ottenuto usandolo.

!3
Rigon & Al.
Qual è il modello migliore ?
Il modello “Putto”: detto anche modello “angioletto”
E’ quel modello che esiste solo in
formulazioni teoriche, descritto in quale
articolo, ma del cui codice non esistono
che congetture.
Magari bello a vedersi ma non è il
modello migliore
http://abouthydrology.blogspot.it/2012/02/which-hydrological-model-is-better-q.html

!4
Il modello “Zombie”: bello “di fuori” ma contenente un’ idrologia
sorpassata. Not up-to-date.
Death became her, 1993
Magari bello a vedersi, facile da
usarsi, contenente
(apparentemente) tutte le
risposte giuste: ma non è il
modello migliore
Rigon & Al.

!5
Rigon & Al.
Dasterly, Muttley e le macchine volanti
Il modello “Macchine Volanti”: ha tutto quello che serve. Ma
rappresenta un’implementazione
non ragionata di concetti idrologici
presi alla rinfusa ed assemblati
senza ordine.
Magari funziona, ma che
sofferenza ! Non è il
modello migliore
Klemes, Dilettantism in hydrology: Transition or destiny ?, 1986

!6
Rigon & Al.
Bruno Munari, by Enrico Cattaneo
Il modello migliore è quello che: • ha una implementazione solida;
• è disponibile almeno come eseguibile,
ma possibilmente come “open
source”;
• è documentato;
• implementa un’idrologia
ragionevolmente moderna, che da le
risposte giuste per i motivi corretti;
• ha complessità adeguata al problema
affrontato;
• è estensibile;
• può essere inserito in sistemi di
supporto alle decisioni
• Implementa appropriata integrazione
con sistemi GIS
• ha una comunità di sviluppatori
Kirchner, J. W. (2006), Getting the right answers for the right reasons, Water Resour. Res., 42, W03S04, doi:10.1029/2005WR004362.

JGrass-NewAGE
&
MarialauraBancheri,GEOframe

!8
JGrass-NewAGE - GEOframe
Rigon & Al.
JGrass-NewAGE si ispira ai concetti appena elencati
For more details on the philosophy:
http://abouthydrology.blogspot.it/2016/05/geoframe-system-for-doing-hydrology-by.html
Nell’idea che non esista “Un vero e proprio modello migliore” è basato
sul concetto di “componenti”

!9
Unità discrete di software che sono riusabili,
anche esternamente al framework.
Tanti strumenti (per la simulazione, calibratione, etc.) che
l’utente è libero di usare e di comporre in varie
soluzioni modellistiche.
Un repository dove preservare i modelli e (le
simulazioni) da condividere con gli altri.
GEOframe
R. Rigon
Beneﬁts

!10
Beneﬁci per la gestione dei progetti
Facile tracciamento della proprietà intellettuale del software.
Lo sviluppatore si concentra sulla componente, non su tutto l’insieme.
Sono le componenti ad essere mantenute. Non i modelli.
Questo rende più facile l’aggiornamento del software
Le componenti sono debugged e testate più
facilmente dei modelli. L’incapsulamento aiuta!
R. Rigon
Beneﬁts

!11
http://geoframe.blogspot.com
Rigon et al.

!12
JGrass-NewAGE usa OMS 3
OMS3 è un software framework per la modellazione ambientale:
• Fornisce alcune facilities che aiutano il lavoro del modellista (visualizzazione
dei dati, analisi di incertezza, strumenti di calibrazione);
• Aiuta l’integrazione dei modelli (attraverso l’uso delle componenti);
• Supporta il multithreading e la parallelizzazione dei processi;
• C’è una community di supporto.
• David, O., Ascough, J. C., II, Lloyd, W., Green, T. R., Rojas, K. W., Leavesley, G. H., & Ahuja, L. R. (2012). A
software engineering perspective on environmental modeling framework design: The Object Modeling
System. Environmental Modelling and Software, 1–13. http://doi.org/10.1016/j.envsoft.2012.03.006
R. Rigon

!13
Maggiori informazioni su ed esempi sono disponibili qui:
https://alm.engr.colostate.edu/cb/wiki/17108
L’ultima versione della console (v 3.5.2) è scaricabile da qui:
https://alm.engr.colostate.edu/cb/proj/doc.do?page=2&doc_id=17899
Le istruzioni per l’istallazione della console sono disponibili qui:
JGrass-NewAGE usa OMS 3
R. Rigon

!14
OMS console
R. Rigon
OMS
Object Modelling System version 3
http://oms.colostate.edu/

!15
Dettagli su:
http://abouthydrology.blogspot.it/2017/02/hydrology-2017.html
OMS console usage

The JGrass-NewAGE system essentials
Components
GiuseppePenone

!17
Kriging
• Ordinary Kriging and detrended kriging and
their local versions: results are in form of raster maps
or shapeﬁles for selected points
Based on the in situ data, it selects the best variogram
(VGM) model, without any human decision, and optimises
VGM parameters automatically at each time steps.
Selection ofVGM model is NOT efﬁcient (so far).
What is there
Rigon et al.
Formetta, 2013, Bancheri et al., 2017 (in preparation)

!18
• Separate rain from snow based on temperature:
results are in form of raster maps or shapeﬁles for selected
points
It can be used conjointly with calibrators and satellite (e.g.
MODIS) data to obtain local estimates of the parameters.
RainSnow
What is there
Rigon et al.
Formetta et al. 2014

!19
• Implements degree-day, Casorzi-Dalla Fontana
and Hocks methods: needs radiation components.
Results are in form of raster maps or shapeﬁles for selected
points
Snow
What is there
Rigon et al.
Formetta et al. 2014

!20
• Priestley Taylor, FAO and Penman-Monteith
versions.
Various strategies were adopted to calibrate parameters.
Only PT has been throughly tested and applied.
ET
What is there
Rigon et al.
Formetta, 2013

!21
Adige
• Implements Hymod and separation of basin
area in sub-catchments numbered according to
a modiﬁcation of the Pfastetter algorithm.
Probably next version needs to be split apart into two or
three components.
What is there
Rigon et al.
Formetta et al., 2011

!22
LWRB
SWRB
• Shortwave and longwave radiation estimation.
Contains algorithms for estimating shadows
according to the geometry of complex terrain.
They also have parameterisation for cloud
cover.
What is there
Rigon et al.
Formetta et al., 2013 Formetta et al., 2016

!23
LUCA
Particle Swarm
• Calibration tools. The ﬁrst implements classic
shufﬂe-complex evolution tools. They are part
of OMS core.
What is there
Rigon et al.
David et al., 2012

!24
deSaintVenant
• Integration of de Saint-Venant 1D equation
(part of Jgrasstools)
What is there
Rigon et al.
http://abouthydrology.blogspot.it/search/label/de%20Saint-Venant%20equation

!25
A - AGEs
To be checked
B- JGrass-NewAGE (https://github.com/geoframecomponents)
[Adige]
BP- Backward probabilities
Clearness Index
ET
FP -Forward probabilities
[Kriging]
NetRadiation
LWRB -
RainSnow
SWB (Simple Water Budget)
SWRB
Snow
C - JGrassTools (http://moovida.github.io/jgrasstools/)
More than 50 components
An index
Rigon et al.

!26
D - OMS (https://alm.engr.colostate.edu)
LUCA
Particle Swarm
And the whole infrastructure for running them all
An index
Rigon et al.

Posina
MaureenBaker

!28
(4.1)
@t
= Jk(t)+
i
Qki(t)° ETk(t)°Qk(t)
for an appropriate set of elementary control volumes connected together. In Eq.(5.1),
S [L3
] represents the total water storage of the basin, J [L3
T°1
], ET [L3
T°1
], and Q
[L3
T°1
] are precipitation, evapotranspiration, and runoff (surface and groundwater)
respectively. The Qis represent input fluxes, of the same nature of Q, coming from
adjacent control volumes.
a
b
Figure 4.1: The location of the Posina basin in the Northeast of Italy (a) and DEM elava-
tion, location of rain gauges and hydrometer stations, subbasin-channel link partitions
used for this modelling (b).
It is clear that Eq.(5.1) is governed by two types of terms, which can be easily identi-
fied as “inputs" and “outputs". The outputs are certainly evapotranspiration, ET, and
discharges, Q, including the Qis, because they come from the assembly of control volumes.
The inputs are J(t), but this term has to be split into rainfall and snowfall. Moreover,
other inputs are ancillary to the estimation of outputs, in particular temperature, T and
radiation Rn. Another input of the equation is the definition of the domain of integration
and its“granularity", i.e. its partition into elements for which a singe value of the state
variables is produced.
In this paper we discuss the estimation of all of these input quantities, with the
Posina
A small (114 km2) basin in Vicenza province,
flowing into the Brenta river
Abera et al.
A small basin
Abera, 2017

!29
method; Isaaks et al., 1989), based on removing one data point at a time and performing
the interpolation for the location of the removed point using the remaining meteo-stations.
Finally, for this paper, kriging is used to generate time series of meterological forcings
for the centroid of each HRU. These forcings, for the purposes of this paper, are kept
constant over the whole HRU area.
Figure 4.3: The Spatial interpolation component of the NewAge system (SI-NewAge).
The ﬁgure shows how different components are connected together, here the variogram
(semivariogram) component solves for the spatial structure of measured data in the
form of an experimental variogram. The particle swarm optimization algorithm uses
the experimental variogram to identify the best theoretical semivariogram and optimal
parameter sets for each time step. Lastly, Kriging uses the best semivariogram model
Calibration of Kriging parameters
Abera et al.
Schemes of work
Abera, 2017

!30
value of Ωrank, the higher the correlation between Js and snow albedo. Those parameters
producing the highest Ωrank are used to model the hourly time steps of snowfall for each
HRU.
The derivation of snow separation parameters for each HRU is possible, however, as
is pertinent to the overall analysis of other components of the study, single, global and
optimized values of Eq.(4.3) parameters are derived.
Figure 4.4: The Snow separation component, outlining how the MODIS snow products
are used to calibrate the spatial snow accumulation ( Eq. 4.3). The dashed line shows the
iterative (calibration) process to optimize the equation. Due to the time step differences
between MODIS and the separation model output, the manual calibration is preferred
in this case.
Calibration of snow-rainfall separation
Abera et al.
Schemes of work
Abera, 2017

!31
basin outlet, but in this application we excluded it because at these scales (of around ten
kilometers) travel time in channels is irrelevant (D’Odorico and Rigon, 2003). Eventually
the Hymod component provides an estimate of the discharge at each link of the river
network of the watershed, downstream to the HRUs.
ADIGE
Figure 5.2: The HYmod component of NewAge system and its input providing compo-
nents. It shows how different components are connected, here kriging, SWE, ETP, and
calibration component connected with Adige to solve the runoff at high spatial and
temporal resolution. The detail discussion about each component can be referred at its
respective section.
Calibration of the overall system
Abera et al.
Schemes of work
Abera, 2017

!32
CHAPTER 5. ESTIMATING WATER BUDGET MODELLING OUTPUTS AND
STORAGE COMPONENT
0
1000
2000
3000
Prainfall
Psnow
Precipi,J(mm)
0
1000
2000
94/5
95/6
96/7
97/8
98/9
99/00
00/01
01/02
02/03
03/04
04/05
05/06
06/07
07/08
08/09
09/10
10/11
11/12
Q
AET
S
Watercomponents,AET,S(mm)
Hydrological years
Figure 5.11: Water budget components of the basin and its annual variabilities from
1994/95 to 2011/2012. It shows the relative share (the size of the bars) of the three
components (Q, ET and S) of the total available water J.
Annual budget
Abera et al.
The idea is that JGrass-NewAGE obtain water budgets
Abera, 2017

!33
CHAPTER 5. ESTIMATING WATER BUDGET MODELLING OUTPUTS AND
STORAGE COMPONENT
This could have been deduced from the data alone, However, seeing it with the other
budget components enlighten the complexity of the interactions actually in place.
0
100
200
300
400
500
01-2012
02-2012
03-2012
04-2012
05-2012
06-2012
07-2012
08-2012
09-2012
10-2012
11-2012
12-2012
Date(month)
Q,ET,S(mm/month)
Q
ET
S
0
100
200
300
J(mm/month)
Figure 5.12: The same as ﬁgure 5.11, but monthly variability for the year 2012.
Monthly budget (temporal)
Abera et al.
Abera, 2017

!34
J
80 120 160 200
Q
40 80 160
ET
20 40 60
S
JanAprJulOct
−150 −100 −50 0 50
Figure 5.13: The spatial variability of the long term mean monthly water budget com-
ponents (J, ET, Q, S). For reason of visibility, the color scale is for each component
separately.
Monthly budget (spatial)
Abera et al.
Abera, 2017

!35
Events
Abera et al.
But events are equally likely well reproduced
Abera, 2017

Complicarsi la vita
MarkRydens,Selfportraitasadodecahedron

!40
Se la maggior parte dei processi avviene
indipendentemente nelle HRU
HRU := “Hydrologic Response unit”
è possibile eseguirli in parallelo ?Node - A very ﬁrst idea
NODE
Connectionbinary
. . .
Entity
basin
drainArea
. . .
Traverser
binary
. . .
17 / 68
Organizzate in una rete di interazioni

!41
L’intero sistema di interazioni della rete in figura può essere
rappresentato come un grafo. Qui sotto (nel quadrato il modulo
elementare)
Rigon et al.
River Networks
http://abouthydrology.blogspot.it/2016/11/reservoirology-3.html
In questa rappresentazione, ad ogni cerchio corrisponde

!42
Rigon et al.
River Networks
In questa rappresentazione, ad ogni cerchio corrisponde un serbatoio (o, se
si preferisce, una equazione differenziale ordinaria). Ad ogni quadrato un
flusso (entrante o uscente)

!43
Rigon et al.
River Networks
I cinque elementi nei rettangoli rossi possono funzionare in parallelo,
caricare un buffer.

!44
Rigon et al.
River Networks
Gli elementi nel rettangolo verde possono funzionare “in piping”, anch’essi
in parallelo. La situazione potrebbe essere più complicata se vi fossero, tra i
vari elementi dei feedback.

!45
Nelle simulazioni fatte con Adige-Hymod, il modulo elementare delle HRU è
un po’ più complicato e sono presenti più HRU (42)
Rigon et al.
The Adige-Hymod Case

!46
Bancheri M. , A travel time model for the water budgets of complex catchments
Getting the right answers for the right reasons: toward many “embedded” reservoirs.
R
R S
Ssnow
M
SCanopy
E
Tr
SRootzone
TRZ
SRunoﬀ
TR
Re
SGroundwater
QR
QG
U
The entire model is based on the assumption that the water budget has been solved
and the ﬂuxes are known.
Flux Expression
Tr(t) H(Scanopy(t) Imax)ac Scanopy(t)
E(t)
Scanopy
SCanopymax
(1 SCF) ETp
U(t) p SRootzone
TRZ(t) SRootzone
SRootzonemax
ETp
Re(t) Pmax
SRootzone
SRootzonemax
QR(t) A
R t
0
uW(ut ⌧)↵(⌧)Tr(⌧)d⌧
TR(t)
SRunoff
SRunoffmax
ETp
QG(t) a SGroundwater
E dove vogliamo avere più interazioni
Bancheri et al., in preparazione, 2017
Per capire il linguaggio grafico: http://abouthydrology.blogspot.it/2016/10/reservoirology-2.html
Ma lo vogliamo ancora più complicato, per rendere conto della varietà di processi
Rigon et al.

!47
Alcuni contronti tra i modelli
Rigon et al.

!48
0
50
100
Oct 01 Oct 15 Nov 01 Nov 15 Dec 01 Dec 15
Time [h]
Q[m3
/s]
Measured
Hymod
Model
Discharge peak
Rigon et al.

!49
Energy budget
Rigon et al.
A
Rigon et al.

!50
Montaldo-Alberson-DellaChiesa-Bertoldi model
A
Rigon et al.
This model represents a lumped model where
some just some relevant aspects are faced.
Chiesa, Della, S., Bertoldi, G., Niedrist, G., Obojes, N., Endrizzi, S., Albertson, J. D., et al. (2014). Modelling changes in grassland hydrological cycling along an elevational
gradient in the Alps. Ecohydrology, 7(6), 1453–1473. http://doi.org/10.1002/eco.1471
Rigon et al.

!51
La descrizione può diventare ben più complicata
Rigon et al.

!52
Altri punti di vista sono possibili
Rigon et al.
Changing perspective

!53
Travel time T
Residence time Tr
Life expectancy Le
Injection
time tin
Exit
time tex
t
Time
Travel time: the time a water particle takes to travel across a catchment
T = (t tin)
| {z }
Tr
+ (tex t)
| {z }
Le
Bancheri M., A travel time model for the water budgets of complex catchments
Travel times as random variables
Rigon R., Bancheri M., Green T., Age-ranked hydrological budgets and a travel time description of catchment hydrology, in
publication, Hydrol. Earth Syst. Sci., 20, 4929-4947, 2016 http://www.hydrol-earth-syst-sci.net/20/4929/2016/
doi:10.5194/hess-20-4929-2016}
Tempi di residenza, tempi di risposta etc
Rigon et al.
http://abouthydrology.blogspot.it/2016/12/this-is-presentation-given-by.html

!54
L’età dell’acqua può variare … ed è misurabile …
Tempi di residenza, tempi di risposta etc
Rigon et al.

!55
All the budgets together
Rigon et al.

!56
In totale, questo sistema di grafi contiene 13 ODEs che sono connesse in vari
modi. u/na volta che le 5 equazioni che regolano i bilanci di massa, le
distribuzioni dei tempi di residenza dell’acqua nei diversi comparti può
essere derivata come mostrato nell’articolo RGB.
Certamente, c’è molto da fare per arrivare a questo risultato.
Volendo semplificare, il bilancio di energia delle chiome e della root zone
potrebbero essere fuse in un unico bilancio.
Ma, nelle semplificazioni, non andrei oltre.
La complessità delle interazioni rimanda alla ricerca di metodi oggettivi per
la semplificazione del sistema di equazioni, la riduzione dei parametri.
Ma esiste una letteratura consistente sul tema (mutuata dalla biologia
matematica).
All the budgets together
Rigon et al.
e.g. Huang, Z. J., Chu, Y., & Hahn, J. (2010). Model simplification procedure for signal transduction
pathway models An application to IL-6 signaling. Chemical Engineering Science, 65(6), 1964–1975.
http://doi.org/10.1016/j.ces.2009.11.035

!57
Partizione tra evaporazione e deﬂusso superﬁciale
Rigon et al.
Senza arrivare a tutta questa complessità
alcuni risultati si sono già raggiunti

Blue Nile
AbrahamAbebe

!59
6.1. INTRODUCTION
10
20
30 40 50
Long
Lat
a
8
9
10
11
12
13
36 38 40
Long
Lat
1000
2000
3000
4000
Elevation(m)
Lat
Station
Lake Tana
b
Figure 6.1: The geographic location of Upper Blue Nile basin in the Nile basin (a) and
digitale elevation model of the basin (b). The points in ﬁgure b are the meteorological
stations used for this study.
Several validation studies of SREs have been conducted in the Ethiopian UBN basin
(Dinku et al., 2007, 2008; Haile et al., 2013; Gebremichael et al., 2014; Worqlul et al.,
2014; Romilly and Gebremichael, 2011; Hirpa et al., 2010; Habib et al., 2012). For
instance, two comparative studies by Dinku et al. (2007) and Dinku et al. (2008) on high
Blue Nile
(175000 Km2)
Abera et al.
Larger rivers
Aberaetal,2016

!60
CMORPH is better in estimating ground-gauge rainfall using the two previous statistics
(i.e., r and RMSE), it is underestimating by 72%, thus being the most biased product of
the five SREs. This could be because CMORPH is only based on satellite products, and
not corrected using ground data as 3B42V7. TAMSAT, on average, is underestimating
rainfall by 30%.
CorrelationRMSEBIAS 3B42V7 CMORPH CFSR SM2R-CCI TAMSAT
8
9
10
11
12
13Lat
Correlation
<0.2
(0.2,0.3]
(0.3,0.4]
(0.4,0.5]
(0.5,0.6]
(0.6,0.7]
8
9
10
11
12
13
Lat
RMSE(mm/day)
[4, 6]
(6, 8]
(8, 10]
(10, 12]
(12, 14]
>14
8
9
10
11
12
13
36 38 40 36 38 40 36 38 40 36 38 40 36 38 40
Long
Lat
BIAS
(-0.9,-0.6]
(-0.6,-0.3]
(-0.3,-0.1]
(-0.1,0.1]
(0.1,0.3]
(0.3,0.6]
(0.6,1.4]
Figure 6.4: The spatial distribution of GOF values for different SREs: correlation coeffi-
cient (first row), RMSE (second row) and Bias (third row).
The spatial distribution of the the three GOF values (r, RMSE, BIAS) are presented
in figure 6.4. Overall the distribution of the statistics can depict a spatial pattern, i.e., the
correlations in the eastern and northeastern part of the basin are higher than western
and southwestern part. Similar pattern can be inferred from the RMSE and BIAS
Satellites products comparison
Abera et al.
Approached with satellite data
Aberaetal,2016

!61
6.5. RESULTS AND DISCUSSIONS
A.Mehal Meda B.Debre Markos C.Assosa
0
1000
2000
3000
0 100 200 300 0 100 200 300 0 100 200 300
SREs
Gauge observations
CFSR
CMORPH
SM2R-CCI
TAMSAT
3B42V7
MeanCumulativerainfall(mm)
Days of year
Mehal_Meda
Debre_Markos Assosa
Figure 6.6: Annual mean cumulative rainfall estimations based on ﬁve SREs and gauges
data.
these two kinds of SREs (e.g., SM2R-CCI and CMORPH or 3B42V7 or TAMSAT).
Among the ﬁve SREs, TAMSAT has the highest detection capacity for lowest rainfall
intensities (91%). For all classes, TAMSAT has the highest missing rate and the highest
recorded is for the 0.1-2 mm observed rainfall class (54%), while the systematic bias
Big Bias
Abera et al.
Which are not always good
Aberaetal,2016

!62
function of basin water storage, for instance Q and ET, good estimation of water storage
of a model has inference to its reasonable computation of other ﬂuxes as well (Döll et al.,
2014). GRACE data is an extraordinary resource to assess the over all performance of
the simulation, at least at the basin scale.
8
9
10
11
12
35 36 37 38 39 40
long
lat
3.0
3.5
4.0
4.5
5.0
Precip(mm/day)
8
9
10
11
12
35 36 37 38 39 40
long
lat
1000
1200
1400
1600
1800
Precip(mm/year)a b
Figure 7.4: The spatial distribution of daily mean (a) and annual mean rainfall estimated
from long term data (1994-2009).
Final rainfall estimates
Abera et al.
but can be corrected
Aberaetal,2016

!63
We divide the UBN basin into 402 subbasins and channel links as shown in figure 7.2.
This spatial partitioning may not be the finest scale possible, however, considering the
size of the basin, it can be considered an acceptable compromise to capture the water
budget spatial variability.
ADIGE: Rainfall-runoff
Figure 7.3: Workflow with a list of NewAge components (in white), and remote sensing
data processing parts (gray shaded, not yet included in JGrass-NewAGE but performed
with R tools) used to derive the water budget of UBN. It does not include the components
used for the validation and verification processes.
The Modelling Solution
calibration phase
Abera et al.
Schemes of work
Aberaetal,inreview,2016c

!64
Discharges
Abera et al.
At daily time scale

!65
Abera et al.
ET (spatial)

!66
Abera et al.
The water budget (spatial)

!67
JGRASS-NEWAGE MODEL SYSTEM AND SATELLITE DATA
0
100
200
Precip[mm/month]
−100
0
100
01 02 03 04 05 06 07 08 09 10 11 12
Months
Fluxes(Q,ET,S)[mm/month]
ET
Q
S
Figure 7.16: Basin scale long term monthly mean Water budget components based on
estimates from 1994 to 2009. It shows the relative share of the three components (Q, ET
and S) of the total available water J.
160
Abera et al.
The water budget (temporal)

!68
based on the NewAge modelling at subbasin scale, and GRACE grid resolution of 10
. Due
to the possible high leakage error introduced at high spatial resolution (Swenson and
Wahr, 2006), statistical comparison at subbasin level is not performed. However, focusing
on maps of the sample months, some level of similar spatial and temporal pattern is
revealed (ﬁgure 7.12).
−100
0
100
200
2004 2005 2006 2007 2008 2009 2010
Date
TWSC(mm/month)
NewAge
GRACE
Correlation = 0.84
Figure 7.11: Comparison between basin scale NewAge ds/dt and GRACE TWSC from
2004-2009 at monthly time step.
7.5.2 Water budget closure
The water budget components (J, ET, Q, ds/dt) of 402 subbasin of UBN is simulated for
duration of 1994-2009 at daily time series. Figure 7.13 is long term monthly mean water
JGrassNewAGE—GRACE comparison
Abera et al.
Storage variations

GinoCastelli
L’Adige

!70
Adige
(12000 Km2)
This is a work in progress
Abera et al.
Ongoing

!71
Ongoing
Forecasting positions
arm
courtesy of Stefano Tasin
Rigon et al.

KenojuakAshevak
Epilogo

!73
Source code OMS projects
Community blog Documentation
Manca Mailing list
To sum up
Rigon et al.

!74
Rigon et al.
Other Infos
Introduction to JGrass-NewAGE
http://abouthydrology.blogspot.it/2015/03/jgrass-newage-essentials.html
Googlegroup for users
https://groups.google.com/forum/#!forum/geoframe-components-developers
Googlegroup for developers
https://groups.google.com/forum/#!forum/geoframe-components-users

!75
Find this presentation at
http://abouthydrology.blogspot.com
Ulrici,2000?
Other material at
Domande
Rigon et al.
http://abouthydrology.blogspot.it/2017/02/jgrass-newage-potenza-lecture.html

3 j grass-new-age

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