=Paper=
{{Paper
|id=Vol-2065/paper02
|storemode=property
|title=Capturing Scientific Knowledge for Water Resources Sustainability in the Rio Grande Area
|pdfUrl=https://ceur-ws.org/Vol-2065/paper02.pdf
|volume=Vol-2065
|authors=Natalia Villanueva-Rosales,Luis Garnica Chavira,Smriti Rajkarnikar Tamrakar,Deana Pennington,Raul Alejandro Vargas-Acosta,Frank Ward ,Alex S. Mayer
|dblpUrl=https://dblp.org/rec/conf/kcap/Villanueva-Rosales17
}}
==Capturing Scientific Knowledge for Water Resources Sustainability in the Rio Grande Area==
https://ceur-ws.org/Vol-2065/paper02.pdf
Capturing Scientific Knowledge for Water Resources
Sustainability in the Rio Grande Area
Natalia Villanueva-Rosales Luis Garnica Chavira1 Smriti Rajkarnikar Tamrakar1
Cyber-ShARE Center of Excellence, Center for Environmental Resources & Cyber-ShARE Center of Excellence,
Department of Computer Science Management Department of Computer Science
University of Texas at El Paso, US University of Texas at El Paso, US University of Texas at El Paso, US
nvillanuevarosales@utep.edu luis@gitgudconsulting.com smritirtamrakar@gmail.com
Deana Pennington Raul Alejandro Vargas-Acosta Frank Ward
Cyber-ShARE Center of Excellence, Cyber-ShARE Center of Excellence, Department of Agricultural Economics
Geology Department Department of Computer Science and Agricultural Business
University of Texas at El Paso, US University of Texas at El Paso, US New Mexico State University, US
ddpennington@utep.edu ravargasaco@miners.utep.edu fward@nmsu.edu
Alex S. Mayer
Department of Civil and Environmental
Engineering
Michigan Technological University, US
asmayer@mtu.edu
ABSTRACT KEYWORDS
This paper presents our experience in capturing scientific Knowledge representation, provenance, workflow visualization,
knowledge for enabling the creation of user-defined modeling interdisciplinary research.
scenarios that combine availability and use of water resources with
potential climate in the middle Rio Grande region. The knowledge 1 INTRODUCTION
representation models in this project were created and validated by
The Middle Rio Grande watershed is comprised of parts of southern
an international, interdisciplinary team of scientists and engineers.
New Mexico and far west Texas in the U.S. and northern
These models enable the automated generation of water
Chihuahua in Mexico. Figure 1 contains a map illustrating the
optimization models and visualization of output data and
study area of this project modified from [24] using Google My
provenance traces that support the reuse of scientific knowledge.
Maps. Over the past 100 years, the Middle Rio Grande has been the
Our efforts include an educational and outreach component to
primary source of water in this desert region, providing water for
enable students and a wide variety of stakeholders (e.g., farmers,
substantive irrigated agriculture and to three municipalities with a
city planners, and general public) to access and run water models.
combined population of over 2 million people. The surface water
Our approach, the Integrated Water Sustainability Modeling
in the region is highly managed in accordance with national
Framework, uses ontologies and light-weight standards such as
treaties, state compacts, and water rights that date back well over a
JSON-LD to enable the exchange of data across the different
century [25]. However, due to recent periods of severe drought and
components of the system and third-party tools, including modeling
growing demand, the river alone no longer meets regional water
and visualization tools. Future work includes the ability to
needs, leading to increased groundwater use and dropping water
automatically integrate further models (i.e., model integration).
tables [22]. Sustainable water management in this region faces a
CCS CONCEPTS number of drivers of change, including: 1) climate change that is
impacting both water supply and demand [11]; 2) agricultural
• Computer methodologies → Artificial intelligence →
practices and trends, including high water demand crops and
Knowledge representation and reasoning
greater reliance on groundwater for irrigation [22]; 3) urban growth
[16]; and 4) growing demand for environmental services such as
riverside habitat and environmental flows [9]. A core question is
K-CAP2017 Workshops and Tutorials Proceedings, how can water be managed so that the three competing sectors—
© Copyright held by the owner/author(s)
1
Affiliated with the University of Texas at El Paso when producing this work.
N Villanueva-Rosales et al.
agricultural, urban, and environmental—can realize a project is further described in sections 2 and 3. One example of
sustainable future in this challenged water system? reusing provenance trace is the visualization of provenance through
Investigating potential ways to achieve long term water a third-party visualization suite with minimal effort. We envision
sustainability requires the use of simulation models that integrate that other tools that can ingest data in standard Web-based
the biophysical workings of the natural system with human choices languages such as JSON-LD [3] and the Web Ontology Language
that impact the system. Such modeling approaches enable the - OWL [19] will further demonstrate the ability to share and reuse
computational testing of alternative climate, population, and water scientific knowledge and resources using knowledge representation
use scenarios that can improve understanding of the coupled languages.
human-natural system and facilitate discussion among researchers,
water managers, and other stakeholders [28]. A wide range of water
models exist – typically focusing on one aspect of the system (e.g.
groundwater, surface water, or water economics). Exploring
potential solutions to water sustainability requires integration
across these aspects, addressed by researchers from different
disciplines using different modeling approaches [1]. Yet the
resulting infrastructure must be lightweight, usable, and useful for
people with a wide range of technical skills – including
stakeholders who may have limited modeling and technical
experience [13].
This paper discusses the efforts of a large, interdisciplinary
group to create a water modeling framework to address this
problem. Our solution, the Integrated Water Sustainability
Modeling Framework or IWASM for short, combines hydrologic
biophysical models [15] with an economic optimization model [26]
into a “bucket model” implemented in the General Algebraic
Modeling System (GAMS) [8]. Bucket model is a longstanding Figure 1: Map of the study area extending from Elephant Butte
phrase used by hydrologic modelers for models that consider water Reservoir in Southern New Mexico through the El Paso/Ciudad
Juarez region in Texas and Chihuahua, Mexico to the entrance
storage as a set of buckets that have inflows (increasing storage)
of the Rio Conchos from Mexico modified from [24].
and outflows (decreasing storage). The IWASM bucket model
simulates major water sources, uses and losses and water supply
constraints to improve our understanding of hydrology, agronomy, 2 IDENTIFIYING DATA AND KNOWLEDGE
institutions, and economics that guide analysis of policy and FOR WATER SUSTAINABILITY
management and answer questions important to stakeholders. A MODELING IN THE RIO GRANDE AREA
key challenge in this collaborative project was developing a shared Due to the interdisciplinary nature of this project, the modeling
understanding of team members’ expertise and how their research team was exposed in the early stages to artifacts such as concept
could contribute to a more comprehensive whole. Integration of maps that allowed them to represent and negotiate the minimal
deep knowledge has been identified as one of seven key challenges information needed to communicate with members from
confronting interdisciplinary teams [4]. One approach to disciplines including Computer Science, Civil Engineering,
overcoming this challenge is to facilitate structured team Hydrology and Agriculture. Concept maps, diagrams, and Excel
interactions that expose team members to vocabulary, concepts, files were generated to create a shared understanding of the bucket
and methods with which they may be unfamiliar [20]. The team model, its inputs, output, and parameters as well as the semantics
must evolve their understanding of the problem from initially ill- of these data. Through several workshops and meetings, the
structured, vague, and incomplete to well-structured, explicitly modeling and the development team identified the importance of
represented, and integrated across disciplines. keeping track of data sources, user-defined parameters, and
Our approach uses knowledge representation languages and workflow steps every time an instance of the model was generated.
tools to automate the exchange of data between IWASM modules The need of tracking provenance information was also identified
and third-party tools. IWASM Web-based interfaces support the by potential end-users of IWASM through a survey [21]. This
use of the bucket model by stakeholders. A provenance trace survey was taken by 36 scientists and students working on water
describes the people, institutions, entities, and activities involved in resources modeling in the El Paso – Juarez border area during the
producing, influencing, or delivering some of data or thing [18]. Regional Water Symposium in January 2017 at the University of
Capturing provenance for the execution model, including Texas at El Paso. Respondents came from a diverse pool of
information about the model, input parameters, and output disciplines, including: Water Sustainability, Hydrology, Geology,
variables aims to support the understanding and reusability of the Environmental Science, Economics, and Computer Science. After
bucket model. The representation of data and provenance in this a short demo of IWASM, the respondents answered a list of
2
Capturing Scientific Knowledge for
Water Resources Sustainability in the Rio Grande Area
questions using a five-point from “strongly disagree” to “strongly Figure 2: Excerpt of IWAMS output composed by a variable,
agree” and open-ended questions. Survey results showed that most corresponding value and annotations.
of the respondents considered it important to know the source of
the data (88% of respondents responded agree or strongly agree). Figure 2 provides an example of the output variable farm
Moreover, 88% of the respondents indicated that knowing the income represented as an array of JSON objects. The object context
source of the parameters used in the model would instill trust in the enables the semantic annotation of fields with linked-data
model and 81% of respondents indicated that data and model vocabulary, e.g., the SIO Ontology [7].
provenance increased their trust to use or reproduce a water model
generated from IWASM. Similarly, 88% of the respondents 4 AUTOMATING THE DATA INTEGRATION
considered important to know how the data was manipulated to AND EXCHANGE OF DATA IN THE
generate a water model. In addition, 85% of respondents considered WATER SUSTAINABILITY MODEL
that it would be easier for them to replicate a water model if the
Figure 3 shows an excerpt of a JSON-LD file containing the
provenance of data and workflow is provided to them along with
provenance trace of a sample user-scenario execution on IWASM.
the model outputs. A slightly smaller percentage of respondents
The terms used to annotate the JSON-LD are mapped to the PROV-
(69%) indicated that they were willing to spend additional time
O ontology [14] and schema.org vocabulary [10]. This figure
annotating data sources and workflows so that other people could
illustrates how the JSON-LD describes that the model-outputs were
reuse them. In general, respondents indicated that a provenance
generated by the previous task in the user-scenario execution called
trace is important for them. This survey, along with input of the
review-and-run and it was derived from a list of variables. Note
research team influenced the design decisions for modelling
that terms wasGeneratedBy and wasAttributedTo are mapped to
metadata, including provenance, in IWASM.
PROV-O by using the JSON-LD context containing the namespace
prov, and terms hasName and hasURL from schema.org to extend
3 CAPTURING DATA AND KNOWLEDGE the description of the modeling agent.
FOR WATER SUSTAINABILITY
The bucket model requires a variety of data inputs that originate {"@id": "Step5: model-outputs",
from multiple decoupled sources and heterogeneous formats, e.g., "@type": "prov:Entity",
spreadsheets, database records or full text documents. To integrate "wasGeneratedBy": "review-and-run",
these data and formats, JSON-LD was chosen due to its lightweight "wasAttributedTo": "Modeling Agent",
characteristic of serializing Linked Data. Most of the data retrieved "wasDerivedFrom": "List of Variables",
to execute the bucket model in IWASM is transformed semi- "Modeling Agent": [{
automatically by using third-party transformation, e.g., CSV-to- "@id": "prov:SoftwareAgent",
JSON [5]. Data is manually curated and annotated with vocabulary "@type": "@id",
describing modeling or provenance concepts e.g., agriculture, thus "hasName": "The General Algebraic Modeling System
IWASM extends JSON-LD standards. (GAMS)",
"hasURL": "https://www.gams.com/" }],
{ "modelOutputs" : [{ "@context": {
"varLabel" : "Discounted Net Regional Farm Income", "prov" : "http://www.w3.org/ns/prov#",
"varCategory" : "Summary", "sch" : "http://schema.org/",
"varName" : "T_ag_ben_v", "wasGeneratedBy" : "prov:wasGeneratedBy",
"varValue" : [{ "wasAttributedTo" : "prov:wasAttributedTo",
"p" : "1-policy_hist", "wasDerivedFrom" : "prov:wasDerivedFrom",
"w" : "1-w_supl_base", "hasName" : "sch:name",
"value" : 1884324.28 }], "hasURL" : "sch:url"
"varDescription" : "Discounted net present value of regional }}
farm income",
"varUnit" : "1000 USD" }], Figure 3: Excerpt of JSON-LD file containing provenance data
"@context": { of a user-scenario execution in IWASM.
"modelOutputs": "http://purl.org/wf4ever/wfdesc#Output",
"rdfs" : "http://www.w3.org/2000/01/rdf-schema/", 5 CAPTURING PROVENANCE IN IWASM
"sio" : "http://semanticscience.org/resource/"
The bucket model requires a large number of data sources, fixed
"varLabel": { "@id": "rdfs:label", "@type": "xsd:string"},
parameters, and customizable parameters. In this project, we used
"varCategory": { "@id": "sio:SIO_000137",
a design pattern for workflow execution described in the wprov
"@type": "xsd:string"
namespace which has also been used by the research team in the
}}}
context of biodiversity modeling [21]. A design pattern in the
context of this project is a generic, yet customizable, solution that
3
N Villanueva-Rosales et al.
Urban Water Use Farm Income
water-model
Water Stocks prov:hadMember
prov:hadMember
prov:used
prov:hadMember
prov:wasInformedBy wprov:user-
workflow list-of-variables
wprov:human-
prov:wasInformedBy
intervention prov:wasInformedBy
wprov:hadNextStep
prov:wasInformedBy prov:wasDerivedFrom
wprov:climate- wprov:review-and- prov:wasGeneratedBy
model-outputs
selection run
wprov:hadNextStep wprov:customize-
parameters wprov:hadNextStep prov:wasAssociatedWith prov:wasAttributedTo
wprov:hadParameter
Water Price Elasticity prov:hadMember wprov:Modeling
list-of-parameters Agent
of Demand
prov:hadMember
Urban Average Cost Namespaces
prov: https://www.w3.org/ns/prov-o
wprov: http://ontology.cybershare.utep.edu/wprov
Figure 4: Graphical representation of a user-scenario workflow execution provenance trace in the Integrated Water
Modeling Platform. Provenance concepts and their relations are aligned to PROV-O concepts.
provides a template to represent generic elements and their output variables and their values through the property
relationships. The provenance captured in IWASM is mapped to prov:hadMember.
PROV-O and other widely-used controlled vocabularies including The automated generation of provenance in IWASM uses
the Workflow Description (wfdesc) [23] and Dublin Core metadata from the bucket model and the workflow provenance
Metadata Initiative (dcterms) [27]. The provenance trace captured pattern currently stored in an instance of the MongoDB [17]
in IWASM captures the main components of the user-scenario database. The wprov workflow provenance pattern, also
execution including: workflow information, user-scenario represented in JSON, is used to automatically generate the
execution steps, inputs, parameter collection, and output (variable) provenance trace of a user-scenario execution. The user-scenario
results. execution provenance is merged with additional model metadata
Figure 4 shows a graphical representation of a user-scenario into a single provenance JSON-LD file illustrated in Figure 5. The
execution provenance trace in IWASM. The wprov:user-workflow integrated JSON-LD file can be directly downloaded or shared as a
represents the overall user-scenario execution composed by a series link with other users and can be consumed by third-party tools such
of steps and uses the water-model (bucket model), as a guideline to as the JSON visualization tool used in IWASM - described in the
execute a series of steps. The PROV-O property following section.
prov:wasInformedBy links the wprov:user-workflow with specific
steps executed, e.g., wprov:human-intervention. Each workflow 6 VISUALIZING PROVENANCE TO INSTILL
step is connected to the previous step by the wprov:hadNextStep TRUST AND PROMOTE REUSABILITY
relation.
The JSON-LD generated by IWASM can be reused by third-party
The wprov:list-of-parameters, an extension of prov:Collection,
applications due to the use of standard languages. A module to
is linked to each parameter wprov:Parameter sent to the bucket
visualize metadata and provenance trace of user-scenario execution
model implementation in GAMS through the property
is provided by IWASM using the third-party tool jsonld-vis [12]
prov:hadMember. Steps in the user-scenario execution, e.g.,
(Figure 5). This open-source visualization tool constructs a
wprov:review-and-run, are linked to the wprov:ModelingAgent that
visualization graph of JSON-LD files. A few modifications to the
is an extension of prov:Agent, using the property
services provided by jsonld-vis were performed in order to generate
prov:wasAssociatedWith relation. The outputs of the
a workflow-like visualization. Figure 5 shows the provenance for
wprov:review-and-run step are annotated as wprov:model-outputs
the outputs of the model including the modeling agent.
and linked to this step with the prov:wasGeneratedBy property.
The wprov:model-outputs are linked to a wprov:list-of-variables,
an extension of prov:Collection, through the property
prov:wasDerivedFrom. The wprov:list-of-variables is linked to
4
Capturing Scientific Knowledge for
Water Resources Sustainability in the Rio Grande Area
Figure 5: Visualization of provenance trace generated for a user-scenario execution using the third-party tool jsonld-vis.
scenarios include alternative climate, population, and water usage
7 PRELIMINARY EVALUATION that can improve understanding of the coupled human-natural
system and facilitate discussions and policy making among a wide
From the scientific perspective, a standard model evaluation
range of stakeholders. This highly-interdisciplinary endeavor used
approach was used to verify that the model works as intended and
proven techniques for knowledge negotiation, including the
produces believable results. This approach relies on selecting a time
creation of concept models, and the development of common
period to simulate for which observational data exists - in this case
vocabularies through ontologies and knowledge representation
reservoir capacity, streamflow at two gauges, and groundwater
languages that enable the integration and exchange of data through
depth in specific wells were used. The data are subdivided into two
the Web. The requirements elicitation process as well as the
parts [2]. The first part is used to calibrate the model (training
development of IWASM was driven by the interdisciplinary
dataset) and the second part is used to test how well results match
research team of this project along with input from potential end-
observations. A twenty-year period from 1994 to 2013 was used.
users. As a result, IWASM provides a friendly interface that
Simulated results for this time period were strongly correlated with
enables user-scenario executions of the bucket model as well as
observations, indicating the model has acceptable validity.
outputs of the system with a provenance trace serialized as a JSON-
To verify that the infrastructure created was generating the same
LD file. The provenance visualization module illustrates the reuse
results as if the modeling tool GAMS was executed directly we
of JSON-LD files by third-party tools and fosters the understanding
used a black box approach - a model with the same inputs was
and reusability of models by end-users, including stakeholders that
generated both using GAMS directly and using the Web interface.
may not be familiar with modeling systems.
The outputs of the two models were compared to make sure they
were the same and thus verify that the Web-based graphical user
interface, web service executions, and the infrastructure created 9 FUTURE WORK
was generating the expected results. The bucket model is constantly evolving to support additional
From the end-user perspective, we evaluated the usability of the features such as the dynamic generation of parameters. IWASM is
graphical user interface in a number of ways. Initially we asked also being updated to support these changes. We are in the process
team members and others affiliated with the project to step through of incorporating additional models of water including simulation
a series of tasks and provide feedback through a survey as described models of water consumption using different modeling tools. Our
in section 2. Then, we asked other participants in two workshops to ultimate goal is to enable users to ask English-like scientific
step through the same tasks and provide feedback, both through a questions that will trigger the automatic selection and execution of
survey and facilitated discussion. Lastly, we recruited five students a modeling algorithm exposed as a Semantic Web Service based on
with agricultural backgrounds to test the interface, assuming they our previous work on workflow orchestration for biodiversity
would more closely represent our agricultural stakeholders. sciences [6]. This new feature will also assist end-users in the
We are in the process of incorporating suggestions from end- selection of parameters using context provided by ontologies.
users into current versions of the bucket model and graphical user Additional data will be needed for new versions of the data model,
interface. including data provided by members of the research team in
Mexico. These data introduces the challenge of integrating data
8 CONCLUSIONS collected through different survey protocols, different unit scales
(e.g., Metric instead of English) and languages (e.g., Spanish). We
This paper reports in our efforts towards providing a Web-based
will pursue the use of further ontologies and ontology mappings to
platform – IWASM that enables the generation of user-scenario
automate the integration of these data that ultimately represents
executions of the bucket model that integrates biophysical
different perspectives in studying water sustainability.
workings of nature with human choices that impact IWASM. User
5
N Villanueva-Rosales et al.
ACKNOWLEDGMENTS and Sciences. 6, 2 (Jun. 2016), 278–286. DOI:https://doi.org/10.1007/s13412-
015-0335-8.
This material is based upon work that is supported by the National [21] Rajkarnikar Tamrakar, S. 2017. Describing Data and Workflow Provenance
Institute of Food and Agriculture, U.S. Department of Agriculture, Using Design Patterns and Controlled Vocabularies. ETD Collection for
University of Texas, El Paso. (Jan. 2017), 1–72.
under award number 2015-68007-23130 “Sustainable water [22] Sheng, Z. 2013. Impacts of groundwater pumping and climate variability on
resources for irrigated agriculture in a desert river basin facing groundwater availability in the Rio Grande Basin. Ecosphere. 4, 1 (Jan. 2013),
1–25. DOI:https://doi.org/10.1890/ES12-00270.1.
climate change and competing demands: From characterization to [23] The Wfdesc ontology (wfdesc): 2015.
solutions”. Authors would like to thank the valuable contributions http://lov.okfn.org/dataset/lov/vocabs/wfdesc. Accessed: 2017-10-26.
of the research team (scientists and students) participating in this [24] USDA Project CAP Study Area: 2015. http://purl.org/iwasm/basemapmeta.
Accessed: 2017-11-22.
project and the GAMS developers. Special thanks to Bill Hargrove, [25] Walsh, C. 2013. Water infrastructures in the U.S./Mexico borderlands.
Joe Heyman, Dave Gutzler, Alfredo Granados, Zhuping Sheng, Ecosphere. 4, 1 (Jan. 2013), 1–20. DOI:https://doi.org/10.1890/ES12-00268.1.
[26] Ward, F.A. and Crawford, T.L. 2016. Economic performance of irrigation
Jose Caballero, and Sarah Sayles for their contributions to this capacity development to adapt to climate in the American Southwest. Journal
work, and Ismael Villanueva-Miranda for the generation of Figure of Hydrology. 540, (2016), 757–773.
[27] Weibel, S. et al. 1998. Dublin core metadata for resource discovery.
1. This work used resources from Cyber-ShARE Center of
[28] Zvoleff, A. and An, L. 2014. Analyzing Human–Landscape Interactions: Tools
Excellence, which is supported by National Science Foundation That Integrate. Environmental Management. 53, 1 (Jan. 2014), 94–111.
grant number HRD-0734825. DOI:https://doi.org/10.1007/s00267-012-0009-1.
REFERENCES
[1] Belete, G.F. et al. 2017. An overview of the model integration process: From
pre-integration assessment to testing. Environmental Modelling & Software. 87,
Supplement C (Jan. 2017), 49–63.
DOI:https://doi.org/10.1016/j.envsoft.2016.10.013.
[2] Bennett, N.D. et al. 2013. Characterising performance of environmental
models. Environmental modelling & software. 40, (2013), 1–20.
DOI:https://doi.org/10.1016/j.envsoft.2012.09.011.
[3] Consortium, W.W.W. 2014. JSON-LD 1.0 : a JSON-based serialization for
linked data. (Jan. 2014).
[4] Cooke, N.J. 2015. Enhancing the Effectiveness of Team Science. The National
Academies Press.
[5] CSV to JSON - CSVJSON: 2014. http://www.csvjson.com/csv2json. Accessed:
2017-11-22.
[6] Del Rio, N. et al. 2013. ELSEWeb meets SADI: Supporting Data-to-model
Integration for Biodiversity Forecasting. Discovery Informatics Symposium
(2013).
[7] Dumontier, M. et al. 2014. The Semanticscience Integrated Ontology (SIO) for
biomedical research and knowledge discovery. J. Biomedical Semantics. 5,
(2014), 14.
[8] GAMS - Cutting Edge Modeling: 2017. https://www.gams.com/. Accessed:
2017-10-05.
[9] Green, P. et al. 2015. Freshwater ecosystem services supporting humans:
Pivoting from water crisis to water solutions. Global Environmental Change.
34, (Sep. 2015), 108–118.
DOI:https://doi.org/10.1016/j.gloenvcha.2015.06.007.
[10] Guha, R.V. et al. 2016. Schema.Org: Evolution of Structured Data on the Web.
Commun. ACM. 59, 2 (Jan. 2016), 44–51.
DOI:https://doi.org/10.1145/2844544.
[11] Gutzler, D.S. 2013. Regional climatic considerations for borderlands
sustainability. Ecosphere. 4, 1 (Jan. 2013), 1–12.
DOI:https://doi.org/10.1890/ES12-00283.1.
[12] jsonld-vis: Turn JSON-LD into pretty graphs: 2015.
https://github.com/scienceai/jsonld-vis. Accessed: 2017-11-26.
[13] Kelly (Letcher), R.A. et al. 2013. Selecting Among Five Common Modelling
Approaches for Integrated Environmental Assessment and Management.
Environ. Model. Softw. 47, C (Sep. 2013), 159–181.
DOI:https://doi.org/10.1016/j.envsoft.2013.05.005.
[14] Lebo, T. et al. 2013. Prov-o: The prov ontology. W3C Recommendation, 30th
April. (2013).
[15] Loucks, D.P. and van Beek, E. Water Resource Systems Planning and
Management - An | Daniel P. Loucks | Springer.
[16] McDonald, R.I. et al. 2014. Water on an urban planet: Urbanization and the
reach of urban water infrastructure. Global Environmental Change. 27, (Jul.
2014), 96–105. DOI:https://doi.org/10.1016/j.gloenvcha.2014.04.022.
[17] MongoDB: 2007. https://www.mongodb.com. Accessed: 2017-11-22.
[18] Moreau, L. and Groth, P. 2013. Provenance: An Introduction to PROV.
Synthesis Lectures on the Semantic Web: Theory and Technology. 3, 4 (Sep.
2013), 1–129. DOI:https://doi.org/10.2200/S00528ED1V01Y201308WBE007.
[19] OWL 2 Web Ontology Language Document Overview (Second Edition): 2012.
https://www.w3.org/TR/owl2-overview/. Accessed: 2017-07-10.
[20] Pennington, D. et al. 2016. The EMBeRS project: employing model-based
reasoning in socio-environmental synthesis. Journal of Environmental Studies
6