JGrass-NewAGE system essentials

Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin

Potenza, 24 Febbraio 2017

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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

Qual è il modello migliore ?

Rigon & Al.

!5

Qual è il modello migliore ?

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

Qual è il modello migliore ?

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

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Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin

Potenza, 24 Febbraio 2017

!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”

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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

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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

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http://geoframe.blogspot.com

Rigon et al.

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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

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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: https://alm.engr.colostate.edu/cb/wiki/17107

https://alm.engr.colostate.edu/cb/wiki/17025

JGrass-NewAGE usa OMS 3

R. Rigon

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OMS console

R. Rigon

OMS Object Modelling System version 3

http://oms.colostate.edu/

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Dettagli su:

http://abouthydrology.blogspot.it/2017/02/hydrology-2017.html

OMS console usage

The JGrass-NewAGE system essentials Components

Giu

sep

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Pen

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e

Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin

Potenza, 24 Febbraio 2017

!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 of VGM model is NOT efficient (so far).

What is there

Rigon et al.

Formetta, 2013, Bancheri et al., 2017 (in preparation)

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• 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

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• 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

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• 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

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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

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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

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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

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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

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A - AGEs

To be checked

B- JGrass-NewAGE (https://github.com/geoframecomponents)

[Adige] BP- Backward probabilitiesClearness IndexETFP -Forward probabilities[Kriging] NetRadiationLWRB -RainSnowSWB (Simple Water Budget) SWRBSnow

C - JGrassTools (http://moovida.github.io/jgrasstools/)

More than 50 components

An index

Rigon et al.

!26

D - OMS (https://alm.engr.colostate.edu)

LUCAParticle Swarm

And the whole infrastructure for running them all

An index

Rigon et al.

The JGrass-NewAGE system essentials Posina

Mau

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Bak

er

Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin

Potenza, 24 Febbraio 2017

!28

CHAPTER 4. ESTIMATING BASIN WATER BUDGETS INPUTS WITHJGRASS-NEWAGE

water budget equation over an appropriate control volume, k:

(4.1)@Sk(t)@t

= Jk(t)+X

iQki(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 fromadjacent control volumes.

ab

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 partitionsused 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, anddischarges, 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 andradiation Rn. Another input of the equation is the definition of the domain of integrationand its“granularity", i.e. its partition into elements for which a singe value of the statevariables is produced.

In this paper we discuss the estimation of all of these input quantities, with thescope to obtain a methodology that is generally applicable, following and expanding

56

Posina

A small (114 km2) basin in Vicenza province, flowing into the Brenta river

Abera et al.

A small basin

Abera, 2017

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CHAPTER 4. ESTIMATING BASIN WATER BUDGETS INPUTS WITHJGRASS-NEWAGE

method; Isaaks et al., 1989), based on removing one data point at a time and performingthe 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 forcingsfor the centroid of each HRU. These forcings, for the purposes of this paper, are keptconstant 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 theform of an experimental variogram. The particle swarm optimization algorithm usesthe experimental variogram to identify the best theoretical semivariogram and optimalparameter sets for each time step. Lastly, Kriging uses the best semivariogram modeland optimal model parameters to estimate the meteorological data at the interpolationpoint or as a raster for a given basin.

In order to understand the effects of the theoretical semivariogram model on krigingand to compare the different kriging methods performances, we applied the following pro-cedures. Firstly, we select a single kriging type (for instance OK) and fit the experimentalsemivariogram with a single theoretical semivariogram (for instance, exponential) andestimate the best semivariogram parameters. Secondly, we perform a cross-validation foreach station, computing estimated time-series forcing values for each (removed) station.Thirdly, measured and estimated time-series forcing values are compared with GOFindices (appendix ??). Lastly, the GOF indices values calculated from 18 years of hourly

64

Calibration of Kriging parameters

Abera et al.

Schemes of work

Abera, 2017

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4.3. METHODOLOGY OF INPUTS ANALYSIS

(4.5) Ωrank = 1°6.

Pnk=1 D2

kn(n2 °1)

where D is the difference between the rank of the MODIS data (FSC or snow albedo)and snowfall, Js, data at the K th pair, and n is the number of observations. The higher thevalue of Ωrank, the higher the correlation between Js and snow albedo. Those parametersproducing the highest Ωrank are used to model the hourly time steps of snowfall for eachHRU.

The derivation of snow separation parameters for each HRU is possible, however, asis pertinent to the overall analysis of other components of the study, single, global andoptimized values of Eq.(4.3) parameters are derived.

Figure 4.4: The Snow separation component, outlining how the MODIS snow productsare used to calibrate the spatial snow accumulation ( Eq. 4.3). The dashed line shows theiterative (calibration) process to optimize the equation. Due to the time step differencesbetween MODIS and the separation model output, the manual calibration is preferredin this case.

4.3.4 Net Radiation

Net radiation is necessary for evapotranspiration estimation and for snow modelling. Itderives from the local difference between downwelling radiation and upwelling radiation,

67

Calibration of snow-rainfall separation

Abera et al.

Schemes of work

Abera, 2017

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CHAPTER 5. ESTIMATING WATER BUDGET MODELLING OUTPUTS ANDSTORAGE COMPONENT

parameters of the model can be interpreted as the mean travel time in each of the surfaceand subsurface compartments of the hydrological cycle.

The NewAge Hymod component is applied to any HRU, in which the basin is subdi-vided and the total watershed discharge is the sum of the contribution of the HRUs. Thissum can include (or not include) the delay due to routing from the HRUs outlet to thebasin outlet, but in this application we excluded it because at these scales (of around tenkilometers) travel time in channels is irrelevant (D’Odorico and Rigon, 2003). Eventuallythe Hymod component provides an estimate of the discharge at each link of the rivernetwork 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, andcalibration component connected with Adige to solve the runoff at high spatial andtemporal resolution. The detail discussion about each component can be referred at itsrespective section.

The first part of the simulation analysis is to evaluate the effects of four precipitationdata set generated using four krigings on the runoff calibration and modelling results.So HYMOD parameters are calibrated for all the four precipitation data sets for fiveyears (1994-1999), using LUCA as optimization tool. The simulation from 2000-2012 is

90

Calibration of the overall system

Abera et al.

Schemes of work

Abera, 2017

!32

CHAPTER 5. ESTIMATING WATER BUDGET MODELLING OUTPUTS ANDSTORAGE COMPONENT

0

1000

2000

3000

Prainfall

Psnow

Pre

cipi, J (

mm

)

0

1000

2000

94

/5

95

/6

96

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97

/8

98

/9

99

/00

00

/01

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05

/06

06

/07

07

/08

08

/09

09

/10

10

/11

11

/12

Q

AET

S

Wate

r co

mponents

, A

ET, S (

mm

)

Hydrological years

Figure 5.11: Water budget components of the basin and its annual variabilities from1994/95 to 2011/2012. It shows the relative share (the size of the bars) of the threecomponents (Q, ET and S) of the total available water J.

104

Annual budget

Abera et al.

The idea is that JGrass-NewAGE obtain water budgets

Abera, 2017

!33

CHAPTER 5. ESTIMATING WATER BUDGET MODELLING OUTPUTS ANDSTORAGE COMPONENT

This could have been deduced from the data alone, However, seeing it with the otherbudget components enlighten the complexity of the interactions actually in place.

0

100

200

300

400

500

01

-201

2

02

-201

2

03

-201

2

04

-201

2

05

-201

2

06

-201

2

07

-201

2

08

-201

2

09

-201

2

10

-201

2

11

-201

2

12

-201

2

Date(month)

Q,E

T,S

(mm

/mon

th)

Q

ET

S

0

100

200

300

J (m

m/m

onth

)

Figure 5.12: The same as figure 5.11, but monthly variability for the year 2012.

106

Monthly budget (temporal)

Abera et al.

The idea is that JGrass-NewAGE obtain water budgets

Abera, 2017

!34

5.4. RESULTS AND DISCUSSIONS

J

80 120 160 200

Q

40 80 160

ET

20 40 60

S

Jan

Apr

Jul

Oct

−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 componentseparately.

107

Monthly budget (spatial)

Abera et al.

The idea is that JGrass-NewAGE obtain water budgets

Abera, 2017

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Events

Abera et al.

But events are equally likely well reproduced

Abera, 2017

The JGrass-NewAGE system essentials Complicarsi la vita

Mar

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Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin

Potenza, 24 Febbraio 2017

!37

Decine di HRU

!38

Centinaia di HRU

!39

Migliaia di HRU

!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

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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

http://abouthydrology.blogspot.it/2016/11/reservoirology-3.html

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

http://abouthydrology.blogspot.it/2016/11/reservoirology-3.html

I cinque elementi nei rettangoli rossi possono funzionare in parallelo, caricare un buffer.

!44

Rigon et al.

River Networks

http://abouthydrology.blogspot.it/2016/11/reservoirology-3.html

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 S

Ssnow

M

SCanopy

E

Tr

SRootzone

TRZ

SRunoff

TR

Re

SGroundwater

QR

QG

U

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)S

canopy

S

Canopy

max

(1� SCF )ETp

U(t) pSRootzone

TRZ

(t) S

Rootzone

S

Rootzone

max

ETp

Re

(t) Pmax

S

Rootzone

S

Rootzone

max

QR

(t) ARt

0 uW (ut� ⌧)↵(⌧)Tr

(⌧)d⌧

TR

(t)S

Runoff

S

Runoff

max

ETp

QG

(t) aSGroundwater

E dove vogliamo avere più interazioni

Bancheri et al., in preparazione, 2017Per 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 15Time [h]

Q [m

3 /s]

MeasuredHymodModel

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

tTime

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

JGrass-NewAGE system essentials Blue Nile

Potenza, 24 Febbraio 2017

Ab

rah

am A

beb

e

Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin

!59

6.1. INTRODUCTION

10

20

30 40 50Long

Lat

a

8

9

10

11

12

13

36 38 40Long

Lat

1000

2000

3000

4000Elevation(m)

Lat

Station

Lake Tana

b

Figure 6.1: The geographic location of Upper Blue Nile basin in the Nile basin (a) anddigitale elevation model of the basin (b). The points in figure b are the meteorologicalstations 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). Forinstance, two comparative studies by Dinku et al. (2007) and Dinku et al. (2008) on hightemporal (less than and equal to 10 days) and spatial (less than or equal to 10) resolutionproducts shows that CMORPH, TAMSAT (Grimes et al., 1999) and TRMM 3B42 (thegauge-corrected version of TMPA products, Huffman et al. (2007)) are three SREs withgood accuracy and potentially useful for hydrological applications in the region. Dinkuet al. (2008) reported that CMORPH works better in Ethiopia than other regions ofAfrica, while Haile et al. (2013), studying the accuracy of CMORPH over a subbasin ofUBN basin for three months, found poor accuracy with respect to other regions. Morerecently, Gebremichael et al. (2014), by designing experimental rain gauges for twosummer seasons in two experimental locations (one in the lowlands and one in thehighlands) of the UBN basin, examined the accuracy of three high-resolution satelliterainfall products (CMORPH, TRMM 3B42RT - the real-time version of TMPA - andTRMM 3B42). Regarding the relationships between SREs goodness-of-fit values andtopography (particularly elevation) of the experimental sites, SREs overestimate themean rainfall rate in the lowlands and, vice versa, underestimate at the highland site.

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CHAPTER 6. EVALUATION OF DIFFERENT SRES AND BIAS CORRECTION

In general, the effect of data length on BIAS is very small, and it is valid for all theSREs (figure 6.3, third row). For instance, the BIAS for 3B42V7 decreases from 4% for 1year evaluation to -4% in 10 years, the same level of BIAS but opposite sign. A similarslight decline in BIAS is shown for CMORPH (frm -66% to -72%) when the number ofyears in the analysis increases. The comparison of the five products using BIAS is notconsistent with the products comparison using r and RMSE (figure 6.3, the third row).For instance, SM2R-CCI (0.001) has the lowest BIAS, followed by 3B42V7 (-0.042) andCFSR (-0.06). The low BIAS of SM2R-CCI has to be attributed to the use of 3B42V7for the calibration of the parameter values of the SM2RAIN algorithm. Note that whileCMORPH 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 ofthe five SREs. This could be because CMORPH is only based on satellite products, andnot corrected using ground data as 3B42V7. TAMSAT, on average, is underestimatingrainfall by 30%.

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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 presentedin figure 6.4. Overall the distribution of the statistics can depict a spatial pattern, i.e., thecorrelations in the eastern and northeastern part of the basin are higher than westernand southwestern part. Similar pattern can be inferred from the RMSE and BIASstatistics that are smaller in the eastern part (the highlands), while they are higher in

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A.Mehal Meda B.Debre Markos C.Assosa

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Figure 6.6: Annual mean cumulative rainfall estimations based on five SREs and gaugesdata.

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 highestrecorded is for the 0.1-2 mm observed rainfall class (54%), while the systematic biasfor all the classes is relatively low (figure 6.5e). The SREs detection capacity is furtherevaluated by the overall accuracy capacity, and the comparison is shown in figure 6.5f.The result confirms the confusion matrix analysis.

The time series rainfall summary analysis is useful for comparative evaluation, butdoes not provide insight into the aggregate effects of using different SREs on waterresource modelling. Figure 6.6 shows the comparison of long term (2003-2012, 10 years),mean cumulative rainfall for different SREs and measured data. A sample of threestations systematically selected to represent different ranges of elevation and spatiallocation is used in the analysis. These are Mehal Meda, Debre Markos, and Assosawhich are located at high (3084 meters), medium (2446 meters) and low (1600 meters)elevations, respectively. The spatial location of the three stations is shown in the mapsplotted in figure 6.6. Four comments can be drawn:

1. Based on the three stations, the observed long term annual rainfall shows that theeffect of elevation is masked by the rainfall climatological regime difference (Mel-

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7.4. CALIBRATION AND VALIDATION APPROACH

GRACE is a mission based on two twin satellites that measures spatiotemporal variationsof water storage that is derived from a continuous observation of the gravity field. Atthis scale, however, GRACE can still be used for constraining and validating data to themodelling solutions. Here, the performance of our modelling approach to close the waterbudget i.e. estimating storage following the characterization of all the terms, is assessedusing the GRACE estimation at the basin scale. Since the other fluxes are modeled asfunction of basin water storage, for instance Q and ET, good estimation of water storageof 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 ofthe simulation, at least at the basin scale.

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Figure 7.4: The spatial distribution of daily mean (a) and annual mean rainfall estimatedfrom long term data (1994-2009).

7.4 Calibration and validation approach

The precipitation data is error corrected based on the in situ observation. The Adigerainfall-runoff component, i.e HYMOD model parameter, are calibrated to fit the observeddischarge during the six years of calibration period (1994-1999) at daily time step. Basedon the approach described ET estimation, the ADIGE component is also used to calibratethe PT Æ. The simulation for each hydrological component is verified using the availablein-situ or remote sensing data as follows:

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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 thesize of the basin, it can be considered an acceptable compromise to capture the waterbudget spatial variability.

ADIGE: Rainfall-runoff

Figure 7.3: Workflow with a list of NewAge components (in white), and remote sensingdata processing parts (gray shaded, not yet included in JGrass-NewAGE but performedwith R tools) used to derive the water budget of UBN. It does not include the componentsused for the validation and verification processes.

7.3.2.1 Precipitation J(t)

Regards to the input term of Eq. 7.1 (J(t)), the spatio-temporal precipitation, it is quan-tified based on RS-based approaches (chapter 6). Different satellite rainfall estimates(SREs) available for varied accuracy and purposes. The use of SREs and lists productsthat can be used in hydrological applications can be found elsewhere in literature (Honget al., 2006; Bellerby, 2007; Huffman et al., 2007; Kummerow et al., 1998; Joyce et al.,2004; Sorooshian et al., 2000; Brocca et al., 2014). Regardless of the recent advancementof rainfall retrieval algorithm, SREs are still subjected to significant uncertainty due tovarious factors including sensor problem, infrequent satellite overpasses, large spatio-temporal scale, and retrieval algorithm (Hong et al., 2006; AghaKouchak et al., 2009;Hossain et al., 2006).

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CHAPTER 7. WATER BUDGET MODELLING OF UPPER BLUE NILE BASIN USINGJGRASS-NEWAGE MODEL SYSTEM AND SATELLITE DATA

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Figure 7.16: Basin scale long term monthly mean Water budget components based onestimates from 1994 to 2009. It shows the relative share of the three components (Q, ETand S) of the total available water J.

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capability to reproduce other components well,as it is the residual terms to balance theflux dynamics.

The spatial distribution of NewAge ds/dt and GRACE based TWSC for four months(January, April, July, and October) of 2005 is shown at figure 7.12. The comparison isbased on the NewAge modelling at subbasin scale, and GRACE grid resolution of 10. Dueto the possible high leakage error introduced at high spatial resolution (Swenson andWahr, 2006), statistical comparison at subbasin level is not performed. However, focusingon maps of the sample months, some level of similar spatial and temporal pattern isrevealed (figure 7.12).

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Figure 7.11: Comparison between basin scale NewAge ds/dt and GRACE TWSC from2004-2009 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 forduration of 1994-2009 at daily time series. Figure 7.13 is long term monthly mean waterbudget closure derived from 1994-2009. The four months (January, April, July, and Octo-

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JGrass-NewAGE system essentials

Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin

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JGrass-NewAGE system essentials

Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin

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Source code OMS projects

Community blog Documentation

Manca Mailing list

To sum up

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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

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Find this presentation at

http://abouthydrology.blogspot.com

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Other material at

Domande

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http://abouthydrology.blogspot.it/2017/02/jgrass-newage-potenza-lecture.html