=Paper=
{{Paper
|id=Vol-1871/paper5
|storemode=property
|title=A Science Gateway for Biodiversity and Climate Change Research
|pdfUrl=https://ceur-ws.org/Vol-1871/paper5.pdf
|volume=Vol-1871
|authors=Donatello Elia,Alessandra Nuzzo,Paola Nassisi,Sandro Fiore,Ignacio Blanquer,Francisco V. Brasileiro,Iana A. A. Rufino,Arie C. Seijmonsbergen,Niels S. Anders,Carlos de O. Galvao,John E. de B. L. Cunha,Mariane de Sousa-Baena,Vanderlei P. Canhos,Giovanni Aloisio
|dblpUrl=https://dblp.org/rec/conf/iwsg/EliaNNFBBRSAGCS16
}}
==A Science Gateway for Biodiversity and Climate Change Research==
https://ceur-ws.org/Vol-1871/paper5.pdf
8th International Workshop on Science Gateways (IWSG 2016), 8-10 June 2016
A Science Gateway for Biodiversity and Climate
Change Research
Donatello Elia∗ , Alessandra Nuzzo∗ , Paola Nassisi∗ , Sandro Fiore∗ , Ignacio Blanquer† , Francisco V. Brasileiro‡ ,
Iana A. A. Rufino‡ , Arie C. Seijmonsbergen§ , Niels S. Anders§ , Carlos de O. Galvão‡ ,
John E. de B. L. Cunha‡ , Mariane de Sousa-Baena¶ , Vanderlei P. Canhos¶ and Giovanni Aloisio∗k
∗ Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce, Italy
† Universitat Politecnica de Valencia, Valencia, Spain
‡ Universidade Federal de Campina Grande, Campina Grande, PB, Brasil
§ IBED, University of Amsterdam, Amsterdam, Netherlands
¶ Centro de Referência em Informação Ambiental, Campinas, SP, Brasil
k University of Salento, Lecce, Italy
Abstract—Climate and biodiversity systems are closely in- dresses the scientific challenges of three multidisciplinary and
terlaced across a wide range of scales. To better understand highly complementary scenarios, among which the one on
the mutual interaction between climate change and biodiversity biodiversity, natural resources and climate change represents
there is a strong need for multidisciplinary skills, tools and
a large variety of heterogeneous, distributed data sources. In the most challenging one from the scientific data management
this regard, the EUBrazilCloudConnect project provides a user- standpoint. The proposed scientific scenarios require access to
centric research environment built on top of a federated cloud the project e-infrastructure to run complex workflow pipelines
infrastructure across Europe and Brazil to serve scientific needs. as well as access to heterogeneous and large datasets for data
One of the test cases implemented in this project focuses on analysis and visualisation.
climate change and biodiversity research. The BioClimate is the
Science Gateway of the use case. It aims at providing end-users The Biodiversity and Climate Change use case (BioClimate)
with a highly integrated environment, addressing mainly data
analytics requirements. This paper presents a complete overview involves multiple heterogeneous data sources (e.g. SEBAL,
about BioClimate and the scientific environment delivered to the LiDAR, CRU, CMIP5, speciesLink, GBIF, etc.) and several
user community at the end of the project. processing pipelines, integrated through the BioClimate Sci-
Keywords—Science Gateways, Scientific Data Management and entific Gateway. The gateway sits on top of the databases and
Analytics, Environmental Sciences. enables near-real-time analysis of large volume datasets (from
multi-GBs to multi-TBs scale depending on the specific data
I. I NTRODUCTION source) through the Parallel Data Analysis Service (PDAS).
Climate and biodiversity systems are closely interlaced PDAS clusters are deployed on the site where the databases
across a wide range of scales. In order to predict the effects of are stored providing the end-user with a high-level, parallel,
climate change on the biodiversity system, which is essential and server-side interface for scientific data analysis.
towards sustainable landscape and eco-services management, The design of the software infrastructure and the BioCli-
there is a need to further investigate the interaction between mate Scientific Gateway for end-users facilitates joint research
the climate system and biodiversity. using data that is otherwise difficult to access or for which
Direct measurements of climate and biodiversity are often availability is fragmented and/or too large to process using
difficult and time-consuming to obtain, instead it is common traditional computational means. With regard to existing ap-
practice to use climate and biodiversity indicators. These proaches and tools that are mainly client-side/desktop based,
interactions can be studied at various scales, ranging from the use case delivers a well-integrated environment for climate
microscopic scales, and at (genomic, taxonomic, ecosystem) change and biodiversity research with cloud-based infrastruc-
scales of individual plant and animal species. A multi-scale ture and server-side capabilities.
and integrated approach is required to investigate the climate-
biodiversity system as a whole. Presently, in this scenario, This work presents the BioClimate Scientific Gateway, the
researchers and professionals are burdened by scattered data scientific challenges addressed and the implementation details.
sources, wealth of analysis tools to master and implement, and The remainder of this work is organised as it follows. Section
computational limitations to upscale their analysis. II provides an overview of the BioClimate use case and its
EUBrazilCloudConnect [1] is a project from the third main goals. Section III provides a general description of the
coordinated EU-Brazil call. It is a preliminary step towards BioClimate Scientific Gateway architecture, whereas Section
providing a user-centric environment for the scientific research IV and Section V give, respectively, a detailed description of
communities to test the execution of challenging applications the graphic interface and the back-end. Finally, Section VI
exploiting a federated cloud infrastructure. The project ad- draws the main conclusions and describes the future activities.
8th International Workshop on Science Gateways (IWSG 2016), 8-10 June 2016
II. A B IODIVERSITY & C LIMATE C HANGE U SE C ASE
The EUBrazilCloudConnect (EUBrazilCC) use case on
climate change and biodiversity is a data-driven use case,
aiming at better understanding the interactions between the
biodiversity system and the climate system. This use case
focuses on bringing together a wide variety of climate and
biodiversity data and analysis tools into a user-friendly and
web-based Science Gateway to provide an integrated approach
of investigating climate and biodiversity across different tem-
poral and spatial scales.
To address all these scientific challenges, the use case
joins together heterogeneous data sources, on-premises cloud
infrastructures, multiple data services, and a Science Gateway
into a single, federated trans-Atlantic environment.
The Science Gateway provides access to historical tempe-
rature and precipitation records, different climate model scena-
rios with predictions of future temperature and precipitation,
Landsat [2] satellite imagery for climate and biodiversity indi-
cators, LiDAR 3D forest metrics and biodiversity indicators at Fig. 1. BioClimate high-level use case architecture
a very high resolution, and plant occurrences data for ecologi-
cal niche models for the prediction of future plant distribution
• Usability. The interface is designed to: (i) facilitate the
based on different climate scenarios. The proposed pipelines/
end-user to select the target data source, an area of
workflows combine the analysis of data acquired from these
interest and the temporal scale; (ii) submit an experiment
different technologies to study the impact of climate change in
computation; (iii) visualise the processed results in terms
regions with high interest for biodiversity conservation, such
of maps, graphs, tables and comparative charts; and (iv)
as the Brazilian Amazon and the semi-arid Caatinga regions
download the aggregated results and products regarding
in Brazil. The analysis of remote sensing images provides 3D
satellite images and 3D vegetation products (CSV, Raster,
information concerning the structure of the vegetation, which
GeoTIFF and PNG formats).
improves biodiversity indicators such as the energy balance
and evapotranspiration. III. G ATEWAY A RCHITECTURE
The EUBrazilCC infrastructure provides the computing The software architecture of the use case is shown in Figure
power needed to support data processing and analysis, the 1. The BioClimate Scientific Gateway represents the high-level
management of metadata to enable search and discovery user interface provided by the use case. It allows data access,
as well as provenance management to address re-usability analysis and visualisation over multiple, heterogeneous data
and reproducibility, both strongly relevant for scientific data sources, by exposing an integrated view of the data level. It
environments. The BioClimate Scientific Gateway integrates in supports several features, such as time-series and statistical
a web-based environment the data sources and the processing analysis, data inspection, intercomparison and subsetting.
and analysis capabilities exploiting the project infrastructure. The elastic-job engine takes care of the execution of the
More specifically, the gateway has been designed to fulfil some requests submitted through the gateway interface by translating
key requirements: the requests in PDAS tasks and then properly scheduling
• Integration of heterogenous data sources. The gateway the jobs on the available resources. To guarantee scalability,
provides a unified interface to access and process satellite it elastically adapts to the analytics workload exploiting the
images (from Landsat), environmental data, future cli- underlying cloud resources. The engine interacts with the
mate scenarios, biodiversity data like species distributions Infrastructure Manager (IM) [4] to deploy and un-deploy
and LiDAR datasets related to some target areas. Further- PDAS cluster instances on-demand. A detailed description of
more, the gateway provides also metadata information the implementation and the main features of both the Science
describing these data sources. Gateway interface and the engine is provided in the next
• Implementation of processing tools. To support data sections.
analysis, several tools are integrated in the gateway to A system catalog is used by both the front-end and the back-
allow: computation of 3D vegetation products based on end to store useful information regarding user management,
LiDAR data [3] (e.g. Digital Surface Model (DSM), Digi- experiment execution requests and results, PDAS cluster usage
tal Terrain Model (DTM), Canopy Height Model (CHM), history and it also serves as a centralised data repository.
Relative Height at 50% (RH50)), execution of Ecological The PDAS, a core component of the Ophidia project [5], [6],
Niche Modeling over species data and processing of provides support in terms of data analytics applied to large sci-
datasets from climate models and the SEBAL algorithm. entific datasets. It includes functionalities to deal with different
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8th International Workshop on Science Gateways (IWSG 2016), 8-10 June 2016
scientific data formats, such as NetCDF (Network Common available under the Open Database License by Climatic
Data Form) [7] and satellite data, and allow mathematical and Research Unit, University of East Anglia.
statistical operations on this data. Python scripts, integrated in Finally, security cuts across the whole architecture and
the PDAS, provide additional functionalities to process LiDAR is taken into account at several levels. With regard to the
products and interact with external tools (e.g GDAL [8]) and front-end, the security is implemented in terms of user au-
services (e.g OpenModeller [9]). thentication. In order to avoid potential attacks that aim at
The gateway also provides access to the BioClimate Clear- stealing passwords, the system employs a technique based on
ing House, a database where the user can persistently store salted password hashing, based on a Java implementation of a
the results of the experiment run during a session and retrieve Cryptographically Secure Pseudo-Random Number Generator,
them through the search functionalities. called Password-Based Key Derivation Function 2 (PBKDF2)
The lowest layer of the diagram comprises the several [16]. Additionally, HTTPS is used to provide encryption for
private clouds, running OpenNebula or OpenStack at the the communications between client and server.
Infrastructure as a Service (IaaS) level, and the data sources, At the elastic-job engine level, the PDAS terminal is used
made available by the project partners or already available to send requests to a PDAS server interface. It can exploit
from national and international agencies, which are part of the X509v3 digital certificates-based authentication and the
the infrastructure with a more static setup. VOMS-based authorisation. Different levels of privileges are
The data sources integrated through the gateway are re- defined to distinguish user roles locally at each PDAS server or
ported in the following: globally at the VOMS server. For this purpose, a GSI/VOMS
enabled interface, supporting both X.509 certificates and
• SEBAL datasets. These are an output of satellite images VOMS-based authorisation and addressing the interoperability
series (Landsat) processed by the SEBAL [10], [11] with the EGI Fed Cloud environment [17], has been defined.
algorithm to produce estimates of energy balance and
evapotranspiration of water to the atmosphere. Remote IV. U SER INTERFACE INSIGHTS
sensing data are provided by the United States Geological In order to address portability of the system and the
Survey (USGS) and the National Aeronautics and Space separation of concerns between the presentation layer and the
Administration (NASA). In particular, the infrastructure business logic, the gateway has been implemented according
allows processing of Landsat data coming from the to the Model-View-Controller pattern.
Brazilian Semiarid region. The presentation layer, running on the client side (i.e. a
• LiDAR data. For the areas near Manaus in Brazil, where browser), provides a rich user interface to submit the data
hyper-spectral imagery is apparently absent, EUBrazil analysis tasks and visualise their results. It is implemented as
Cloud Connect will leverage of the available LiDAR data a JavaScript web application based on the ExtJS library [18],
provided by EMBRAPA [12] (Brazilian Agricultural and which offers a number of gadgets such as panels, charts and
Livestock Research Corporation). Vegetation and terrain grids, and Google Maps API [19] for the visualisation of geo-
metrics represent the key indicators that can be inferred referenced data.
from these datasets. The server side of the Science Gateway implements the
• Biodiversity data sources. The speciesLink datasets [13], business logic to manage users, handle the requests and the
provided by CRIA, the Reference Center on Environmen- post-processing of the results and is based on Java and Apache
tal Information, are an output of networking activities Struts2 framework [20].
to provide free and open access to 7.3 million primary To increase the performance and make the output visuali-
research-grade data, derived from the federation of 350 sation faster, it has been decided to perform the heavier tasks,
Brazilian biodiversity datasets, gathered from 150 insti- related to the post-processing of the outputs, on the server side
tutions in Brazil and abroad. They represent valuable and to present the ready-to-use result to the JavaScript library
biodiversity data sources. on the presentation layer.
• Climate data from the CMIP5 Federated Data Archive Usability has been addressed by defining and implement-
(ESGF) [14]. The Coupled Model Intercomparison ing a set of pre-defined experiments regarding the different
Project (CMIP) provides a community-based infrastruc- data sources and type of analysis. Each experiment defines
ture in support of climate model diagnosis, validation, a customisable template to perform data analytics tasks on
intercomparison, documentation and data access. CMCC climate and biodiversity data and requires a specific pipeline
provides about 100TB of data related to three different of operations, including subsetting, data reduction and mathe-
models, NetCDF format, CF conventions. Starting from matical/statistical functions.
these datasets, multiple climate indicators can be com- The following subsections provide a description of the main
puted. views and interfaces made available by the gateway.
• Climate data from observed data. These high-resolution
gridded datasets (CRU TS v.3.23 [15]) provide monthly A. Interactive analysis
values for several variables, such as temperature and The ”Interactive analysis” panel allows a real-time, ex-
precipitation, for an historical time period and are made ploratory analysis of time series from the climate data available
3
8th International Workshop on Science Gateways (IWSG 2016), 8-10 June 2016
Fig. 2. Interactive analysis Fig. 3. SEBAL Interannual analysis compute interface
in the use case. In particular, it provides access to CRU
historical data (temperature and precipitation variables) and
future simulated data from the CMIP5 experiment (maximum
and minimum temperatures from different climate models and
scenarios).
As shown in Figure 2, the interface allows the selection
of a dataset and a variable from the list of datasets/variables
available and a point from the map. The bottom section of the
Science Gateway displays the result of the analysis in terms
of: (i) a chart with the time series and its trend line and (ii) a
table with a comprehensive set of aggregated statistics.
B. Batch analysis
Fig. 4. SEBAL Interannual analysis details interface
The ”Compute” panel provides the features to define and
submit complex experiments regarding the available data
sources. For each experiment, a map for spatial selection and based on maximum and minimum temperature are availa-
a form to set the input parameters is provided. The following ble for comparison (i.e. TXx, TNx, TXn, TNn [22]).
experiments are defined: • Ecological Niche Modelling (ENM) experiment integrates
• Interannual analysis of SEBAL output (see Figure 3) the functionalities available through the OpenModeller
provides information about interannual trends and sta- Web Service API to create and project models defined
tistical information of a specific SEBAL variable. The over occurrences of biodiversity data. This experiment
Science Gateway integrates data processed by the SEBAL allows the comparison of the projections of models into
algorithm and provides functionalities to analyse several three different environmental scenarios (present, future
variables produced by this algorithm (e.g. Enhanced optimistic and future pessimistic). The models are created
Vegetation Index, Leaf Area Index, Normalized Diffe- with the maximum entropy algorithm [23] and are based
rence Vegetation index, etc.). The interface allows both on the species occurrences selected by the user.
spatial and temporal selection. • LiDAR products intercomparison allows comparison and
• Climate and SEBAL variables intercomparison allows evaluation of the statistical relationship between LiDAR
the comparison of the behaviour of climate and SEBAL products available through the gateway (e.g. DSM, DTM,
variables. In particular it supports analysis over the vari- CHM). In this case, a LiDAR tile can be selected from
ables produced by the SEBAL algorithm and variables the map.
(precipitation and temperature) from historical climate • Relative Height analysis of LiDAR data provides infor-
data. From a scientific point of view, this experiment pro- mation about relative height at different percentiles (25%,
vides useful information about the relationship between 50%, 66%, 75% and 90%) of the points in a LiDAR tile.
climate and vegetation indices.
• Climate indices intercomparison allows comparison of C. Experiment visualisation & download
indicators computed on CMIP5 datasets belonging to Once the computation of the experiment is completed,
different climate models and future emission scenarios details about the experiment are available through the ”Expe-
(RCP4.5 and RCP8.5 [21]). Four well-known indicators riment Details” section. Figure 4 displays the output produced
4
8th International Workshop on Science Gateways (IWSG 2016), 8-10 June 2016
Fig. 5. LiDAR intercomparison details interface Fig. 7. Monitoring Dashboard
spatial domain used for the experiment, (ii) experiment type
and (iii) submission date.
E. Infrastructure Monitoring
The BioClimate Scientific Gateway includes two admini-
strative interfaces that (i) allow managing users and their
privileges and (ii) provide some information about the re-
sources exploited dynamically by the gateway (i.e. PDAS
cluster instances) as well as some statistics regarding the
number of experiments executed in terms of their type and
status. Through this dashboard (see Figure 7) it is possible to
get some insights about the use of the system by the end-users.
The charts mainly provide real-time monitoring information
Fig. 6. Climate-SEBAL intercomparison details interface regarding the number of experiments running/pending and the
status of the resources. In particular, a histogram shows the set
of experiments and their distribution across the active PDAS
by a SEBAL interannual experiment, whereas Figure 5 and instances for the last couple of minutes, whereas a pie chart
Figure 6 display the output produced by a LiDAR inter- shows the set of clusters currently running the experiments.
comparison experiment and Climate-SEBAL intercomparison
experiment respectively. V. E LASTIC - JOB ENGINE
In particular, to better suit the experiment peculiarities, a The elastic-job engine is designed to guarantee fast process-
specific detail view is provided for each experiment defined ing of the user requests by exploiting dynamically and elasti-
above. Hence, various gadgets organised in different fashions cally the federated cloud infrastructure. To meet scalability and
are used to display the results, among these are: line charts performance requirements, the engine is implemented as multi-
to display statistical values and trend lines; scatter plots to threaded daemon, based on GNU C libraries, that exploits the
evaluate variable and indicators correlation; tables to show PDAS capabilities to perform pipelines of analytics tasks.
the results and statistical values; maps with the environmental Data-driven processing pipelines, based on PDAS operators,
scenario; images of the LiDAR products; and histograms of have been defined integrating different tools, services and data
the point distribution. formats.
Most of the information provided through the gadgets is Management of the workload is performed exploiting a
also available for download in CSV, raster, GeoTIFF or PNG smart scheduling algorithm, which provides dynamic job
format, depending on the type of experiment run. Furthermore, scheduling over a set of queues. A job queue is associated to
metadata regarding the experiment is available in the same each PDAS cluster running on the infrastructure. To horizon-
view. tally scale on the workload, a new PDAS instance is deployed
automatically on the private cloud resources when the number
D. BioClimate Clearing House of pending jobs on all the queues exceed a configurable
The BioClimate Clearing House system allows users to threshold. A more detailed description of the automated cloud
store a relevant experiment, run during a session, for future deployment (through the elastic-job engine) of the PDAS, as
analysis. A smart search feature is available to filter out the well as of the queue policy adopted and its rationale, are out
experiments saved into the Clearing House, based on: (i) of the scope of this paper and can be found in [24].
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8th International Workshop on Science Gateways (IWSG 2016), 8-10 June 2016
A. PDAS ACKNOWLEDGMENT
This work was supported by the EU FP7 EUBrazilCC
As mentioned before, the PDAS provides the capabilities to Project (Grant Agreement 614048), and CNPq/Brazil (Grant
perform data analytics on large scientific datasets and includes Agreement 490115/2013-6).
a set of libraries able to deal with different data formats. In the
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6