This is a presentation of the JGrass-newAGE system held in Potenza on February 24 20117. It contains an overview of concepts, ideas, behing JGrass-NewAGE ans shows some achievements in a critical manner.
Complexity Neural Networks for Estimating Flood Process in Internet-of-Things...Dr. Amarjeet Singh
With the advancement of the Internet of Things (IoT)-based water conservation computerization, hydrological data is increasingly enriched. Considering the ability of deep learning on complex features extraction, we proposed a flood process forecastin gmodel based on Convolution Neural Network(CNN) with two-dimension(2D) convolutional operation. At first, we imported the spatial-temporal rainfall features of the Xixian basin. Subsequently, extensive experiments were carried out to determine the optimal hyperparameters of the proposed CNN flood forecasting model.
IEEE CASE 2016 On Avoiding Moving Objects for Indoor Autonomous QuadrotorsPeter SHIN
Abstract - A mini quadrotor can be used in many applica- tions, such as indoor airborne surveillance, payload delivery, and warehouse monitoring. In these applications, vision-based autonomous navigation is one of the most interesting research topics because precise navigation can be implemented based on vision analysis. However, pixel-based vision analysis approaches require a high-powered computer, which is inappropriate to be attached to a small indoor quadrotor. This paper proposes a method called the Motion-vector-based Moving Objects Detec- tion. This method detects and avoids obstacles using stereo motion vectors instead of individual pixels, thereby substan- tially reducing the data processing requirement. Although this method can also be used in the avoidance of stationary obstacles by taking into account the ego-motion of the quadrotor, this paper primarily focuses on providing our empirical verification on the real-time avoidance of moving objects.
Applying Photonics to User Needs: The Application ChallengeLarry Smarr
05.02.28
Invited Talk to the 4th Annual On*VECTOR International Photonics Workshop
Sponsored by NTT Network Innovation Laboratories
Title: Applying Photonics to User Needs: The Application Challenge
University of California, San Diego
An overview of the scientific, technological and engineering achievements of Lawrence Livermore National Laboratory researchers from January to March 2014. For more Science and Technology Updates, visit https://st.llnl.gov/showcase/st-update.
Complexity Neural Networks for Estimating Flood Process in Internet-of-Things...Dr. Amarjeet Singh
With the advancement of the Internet of Things (IoT)-based water conservation computerization, hydrological data is increasingly enriched. Considering the ability of deep learning on complex features extraction, we proposed a flood process forecastin gmodel based on Convolution Neural Network(CNN) with two-dimension(2D) convolutional operation. At first, we imported the spatial-temporal rainfall features of the Xixian basin. Subsequently, extensive experiments were carried out to determine the optimal hyperparameters of the proposed CNN flood forecasting model.
IEEE CASE 2016 On Avoiding Moving Objects for Indoor Autonomous QuadrotorsPeter SHIN
Abstract - A mini quadrotor can be used in many applica- tions, such as indoor airborne surveillance, payload delivery, and warehouse monitoring. In these applications, vision-based autonomous navigation is one of the most interesting research topics because precise navigation can be implemented based on vision analysis. However, pixel-based vision analysis approaches require a high-powered computer, which is inappropriate to be attached to a small indoor quadrotor. This paper proposes a method called the Motion-vector-based Moving Objects Detec- tion. This method detects and avoids obstacles using stereo motion vectors instead of individual pixels, thereby substan- tially reducing the data processing requirement. Although this method can also be used in the avoidance of stationary obstacles by taking into account the ego-motion of the quadrotor, this paper primarily focuses on providing our empirical verification on the real-time avoidance of moving objects.
Applying Photonics to User Needs: The Application ChallengeLarry Smarr
05.02.28
Invited Talk to the 4th Annual On*VECTOR International Photonics Workshop
Sponsored by NTT Network Innovation Laboratories
Title: Applying Photonics to User Needs: The Application Challenge
University of California, San Diego
An overview of the scientific, technological and engineering achievements of Lawrence Livermore National Laboratory researchers from January to March 2014. For more Science and Technology Updates, visit https://st.llnl.gov/showcase/st-update.
Introduzione alla geomorfologia. Dati digitali del terreno. Grandezze primarie: quote, pendenze, curvature. La classificazione del paesaggio in funzione delle curvature.
This contains the description of the class of Hydrology at the Dipartimento di Ingegneria Civile Ambientale e Meccanica dell'Università di Trento. For the year 2017.
Della parte introduttiva delle mie lezioni di idrologia, gli elementi del bilancio idrologico e di energia, il bilancio globale di energia, il bilancio globale di massa
Almost the same as the talk given to Ph.D. students one year ago. It covers the problem of research reproducibility and the tools for doing it. First comes some "theoretical" arguments, then the enumeration of some tools.
Parma 2016-05-17 - JGrass-NewAGE - Some About The State of ArtRiccardo Rigon
This describes the motivation behind the JGrass-NewAGE infrastructure. It also shows the main components that were implemented. Finally it shows and comments some case studies and some use cases
Introduzione alla geomorfologia. Dati digitali del terreno. Grandezze primarie: quote, pendenze, curvature. La classificazione del paesaggio in funzione delle curvature.
This contains the description of the class of Hydrology at the Dipartimento di Ingegneria Civile Ambientale e Meccanica dell'Università di Trento. For the year 2017.
Della parte introduttiva delle mie lezioni di idrologia, gli elementi del bilancio idrologico e di energia, il bilancio globale di energia, il bilancio globale di massa
Almost the same as the talk given to Ph.D. students one year ago. It covers the problem of research reproducibility and the tools for doing it. First comes some "theoretical" arguments, then the enumeration of some tools.
Parma 2016-05-17 - JGrass-NewAGE - Some About The State of ArtRiccardo Rigon
This describes the motivation behind the JGrass-NewAGE infrastructure. It also shows the main components that were implemented. Finally it shows and comments some case studies and some use cases
A consistent and efficient graphical User Interface Design and Querying Organ...CSCJournals
We propose a software layer called GUEDOS-DB upon Object-Relational Database Management System ORDMS. In this work we apply it in Molecular Biology, more precisely Organelle complete genome. We aim to offer biologists the possibility to access in a unified way information spread among heterogeneous genome databanks. In this paper, the goal is firstly, to provide a visual schema graph through a number of illustrative examples. The adopted, human-computer interaction technique in this visual designing and querying makes very easy for biologists to formulate database queries compared with linear textual query representation.
Software aging prediction – a new approach IJECEIAES
To meet the users’ requirements which are very diverse in recent days, computing infrastructure has become complex. An example of one such infrastructure is a cloud-based system. These systems suffer from resource exhaustion in the long run which leads to performance degradation. This phenomenon is called software aging. There is a need to predict software aging to carry out pre-emptive rejuvenation that enhances service availability. Software rejuvenation is the technique that refreshes the system and brings it back to a healthy state. Hence, software aging should be predicted in advance to trigger the rejuvenation process to improve service availability. In this work, the k-nearest neighbor (k-NN) algorithm-based new approach has been used to identify the virtual machine's status, and a prediction of resource exhaustion time has been made. The proposed prediction model uses static thresholding and adaptive thresholding methods. The performance of the algorithms is compared, and it is found that for classification, the k-NN performs comparatively better, i.e., k-NN showed an accuracy of 97.6. In contrast, its counterparts performed with an accuracy of 96.0 (naïve Bayes) and 92.8 (decision tree). The comparison of the proposed work with previous similar works has also been discussed.
Implementation of reducing features to improve code change based bug predicti...eSAT Journals
Abstract Today, we are getting plenty of bugs in the software because of variations in the software and hardware technologies. Bugs are nothing but Software faults, existing a severe challenge for system reliability and dependability. To identify the bugs from the software bug prediction is convenient approach. To visualize the presence of a bug in a source code file, recently, Machine learning classifiers approach is developed. Because of a huge number of machine learned features current classifier-based bug prediction have two major problems i) inadequate precision for practical usage ii) measured prediction time. In this paper we used two techniques first, cos-triage algorithm which have a go to enhance the accuracy and also lower the price of bug prediction and second, feature selection methods which eliminate less significant features. Reducing features get better the quality of knowledge extracted and also boost the speed of computation. Keywords: Efficiency, Bug Prediction, Classification, Feature Selection, Accuracy
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...ijcsa
The work is about using Simulated Annealing Algorithm for the effort estimation model parameter
optimization which can lead to the reduction in the difference in actual and estimated effort used in model
development.
The model has been tested using OOP’s dataset, obtained from NASA for research purpose.The data set
based model equation parameters have been found that consists of two independent variables, viz. Lines of
Code (LOC) along with one more attribute as a dependent variable related to software development effort
(DE). The results have been compared with the earlier work done by the author on Artificial Neural
Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) and it has been observed that the
developed SA based model is more capable to provide better estimation of software development effort than
ANN and ANFIS
Survey on evolutionary computation tech techniques and its application in dif...ijitjournal
In computer science, 'evolutionary computation' is an algorithmic tool based on evolution. It implements
random variation, reproduction and selection by altering and moving data within a computer. It helps in
building, applying and studying algorithms based on the Darwinian principles of natural selection. In this
paper, studies about different evolutionary computation techniques used in some applications specifically
image processing, cloud computing and grid computing is carried out briefly. This work is an effort to help
researchers from different fields to have knowledge on the techniques of evolutionary computation
applicable in the above mentioned areas.
This is a short introduction to understand just a little how hydrological models and some hydraulics works. Much relies on the oral presentation. Unfortunately this is is Italian
A short introduction to some hydrological extreme phenomenaRiccardo Rigon
For high School teachers. Kept at MUSE on October 20th 2017. It covers the typology of some phenomena giving a little of explanation of the diverse dynamics. Is a product of LIFE FRANCA EU project
This is the presentation given for the admission to his second year of Ph.D. studies by Michele Bottazzi. Besides sumamrizing the work done during the first year, Michele traces his pathways into the second year with an abrupt change of direction towards simulating and discussion transpiration from plants.
This is the presentation for his admission to the third year of his Ph.D.. It talks about the several direction his work had taken and look forward to the conclusion of some task in form of code release and published papers.
This contains a summary of the data available for torrente Meledrio. We are using it for the project SteepsStreams, and we want to estimate its water and sediment budgets.
This contains the talk given at the 2017 meeting of the SteepStream ERANET project. It is assumed to talk about the hydrological cycle of the Noce river in Val di Sole valley (Trentino, Italy). It is a preliminary view of what we are going to do in the project.
This contains some hints and discussions about how to implement Grids in a Object Oriented language. Specifically the discussion is made with Java in mind, but obviosly, not limited to it.
How to implement unstructured grids in Java (or BTW in another OO language). First start from understanding what grids are and how they are described in algebraic topology. Mathematics first, can be a good idea. No explicit implementation here, but concept and literature to study and start from..
This is the outstanding lecture given by Dani Or when receiving his Dalton Prize at 2017 Wien EGU General Assembly. It is a must-read for who deals with ET and good material also for teaching to students.
Projecting Climate Change Impacts on Water Resources in Regions of Complex To...Riccardo Rigon
The title describes it all. Jeremy Pal's student Brianna Pagàn and coworkers put an impressive set of tools to estimate the impacts of land use and climate change on water resources of south California.
This is the English translation, with some relevant corrections, of the talk I gave at University of Calabria, about the contemporary and post-contemporary flood forecasting.
Hydrological Extremes and Human societies Riccardo Rigon
This is the talk given by Giuliano di Baldassarre at the Summer School on Hydrological Modeling kept in Cagliari this here. The topic is very up-to-date and important. He presented an analysis of a few case studies and suggested some literature.
The Science of Water Transport and Floods from Theory to Relevant Application...Riccardo Rigon
This is the presentation given by Ricardo Mantilla at University of Iowa in 2017. It talks about the system implemented in Iowa for flood forecasting in real time
These are the slides presented at EGU 2017 General Meeting, the Pico session was entlited: Monitoring and modelling flow paths, supply and quality in a changing mountain cryosphere
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
1. JGrass-NewAGE system essentials
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin
Potenza, 24 Febbraio 2017
MakedaBizuneh,Ethiopiandream
2. 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. !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. !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. !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. !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.
8. !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. !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. !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
12. !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. !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:
https://alm.engr.colostate.edu/cb/wiki/17107
https://alm.engr.colostate.edu/cb/wiki/17025
JGrass-NewAGE usa OMS 3
R. Rigon
16. The JGrass-NewAGE system essentials
Components
GiuseppePenone
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin
Potenza, 24 Febbraio 2017
17. !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. !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. !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. !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. !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. !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. !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. !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. !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. !26
D - OMS (https://alm.engr.colostate.edu)
LUCA
Particle Swarm
And the whole infrastructure for running them all
An index
Rigon et al.
27. The JGrass-NewAGE system essentials
Posina
MaureenBaker
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin
Potenza, 24 Febbraio 2017
28. !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. !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. !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. !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. !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. !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.
The idea is that JGrass-NewAGE obtain water budgets
Abera, 2017
34. !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.
The idea is that JGrass-NewAGE obtain water budgets
Abera, 2017
36. The JGrass-NewAGE system essentials
Complicarsi la vita
MarkRydens,Selfportraitasadodecahedron
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin
Potenza, 24 Febbraio 2017
40. !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. !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. !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. !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. !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. !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. !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.
50. !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.
52. !52
Altri punti di vista sono possibili
Rigon et al.
Changing perspective
53. !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. !54
L’età dell’acqua può variare … ed è misurabile …
Tempi di residenza, tempi di risposta etc
Rigon et al.
56. !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. !57
Partizione tra evaporazione e deflusso superficiale
Rigon et al.
Senza arrivare a tutta questa complessità
alcuni risultati si sono già raggiunti
58. JGrass-NewAGE system essentials
Blue Nile
Potenza, 24 Febbraio 2017
AbrahamAbebe
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin
59. !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. !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. !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. !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. !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
67. !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)
Aberaetal,inreview,2016c
68. !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
Aberaetal,inreview,2016c
69. JGrass-NewAGE system essentials
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin
Potenza, 24 Febbraio 2017
GinoCastelli
L’Adige
72. JGrass-NewAGE system essentials
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Wuletawu Abera, Francesco Serafin
Potenza, 24 Febbraio 2017
KenojuakAshevak
Epilogo
73. !73
Source code OMS projects
Community blog Documentation
Manca Mailing list
To sum up
Rigon et al.
74. !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. !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