Advancing Water Resources Systems Modeling Cyberinfrastructure to Enable Systematic Data Analysis, Modeling, and Comparisons

Water resources systems models aid in managing water resources holistically considering water, economic, energy, and environmental needs, among others. Developing such models require data that represent a water system’s physical and operational characteristics such as inflows, demands, reservoir storage, and release rules. However, such data is stored and described in different formats, metadata, and terminology. Therefore, Existing tools to store, query, and visualize modeling data are model, location, and dataset-specific, and developing such tools is time-consuming and requires programming experience. This dissertation presents an architecture and three software tools to enable researchers to more readily and consistently prepare and reuse data to develop, compare, and synthesize results from multiple models in a study area: (1) a generalized database design for consistent organization and storage of water resources datasets independent of study area or model, (2) software to extract data out of and populate data for any study area into the Water Evaluation and Planning system, and (3) software tools to visualize online, compare, and publish water management networks and their data for many models and study areas. The software tools are demonstrated using dozens of example and diverse local, regional, and national datasets from three watersheds for four models; the Bear and Weber Rivers in the USA and the Monterrey River in Mexico

A Data Model to Manage Data for Water Resources Systems Modeling

Current practices to identify, organize, analyze, and serve data to water resources systems models are typically model and dataset-specific. Data are stored in different formats, described with different vocabularies, and require manual, model-specific, and time-intensive manipulations to find, organize, compare, and then serve to models. This paper presents the Water Management Data Model (WaMDaM) implemented in a relational database. WaMDaM uses contextual metadata, controlled vocabularies, and supporting software tools to organize and store water management data from multiple sources and models and allow users to more easily interact with its database. Five use cases use thirteen datasets and models focused in the Bear River Watershed, United States to show how a user can identify, compare, and choose from multiple types of data, networks, and scenario elements then serve data to models. The database design is flexible and scalable to accommodate new datasets, models, and associated components, attributes, scenarios, and metadata

Enabling collaborative numerical modeling in earth sciences using knowledge infrastructure

Knowledge Infrastructure is an intellectual framework for creating, sharing, and distributing knowledge. In this paper, we use Knowledge Infrastructure to address common barriers to entry to numerical modeling in Earth sciences: computational modeling education, replicating published model results, and reusing published models to extend research. We outline six critical functional requirements: 1) workflows designed for new users; 2) a community-supported collaborative web platform; 3) distributed data storage; 4) a software environment; 5) a personalized cloud-based high-performance computing platform; and 6) a standardized open source modeling framework. Our methods meet these functional requirements by providing three interactive computational narratives for hands-on, problem-based research demonstrating how to use Landlab on HydroShare. Landlab is an open-source toolkit for building, coupling, and exploring two-dimensional numerical models. HydroShare is an online collaborative environment for the sharing of data and models. We describe the methods we are using to accelerate knowledge development by providing a suite of modular and interoperable process components that allows students, domain experts, collaborators, researchers, and sponsors to learn by exploring shared data and modeling resources. The system is designed to support uses on the continuum from fully-developed modeling applications to prototyping research software tools

Numerical modeling of the pollutant spread and a web application for environmental monitoring to support mine reclamation activities

Heavy metal pollution is a serious and urgent issue of integrated watershed management in Sardinia and worldwide. To determine the effective management strategies for pollution control, it is essential to quantify the input and output fluxes of metal by analyzing the environmental system in all its main components. Numerical physically-based models can simulate the behavior of a system, such as a river’s watershed, starting from the knowledges of the physical processes that occur in the studied area. In this work, the Rio San Giorgio basin was studied, due to the mining activities in the Iglesias Mine District, that caused severe pollution in the area. This area is in fact characterized by several mining areas, wastes and tailings, abandoned after centuries of intense mining activity, that mainly exploited minerals of Zn and Pb. SWAT hydrological model (Arnold et al., 1998) and SWAT Heavy Metal (HM) module (Meng et al., 2018) were used to simulate the fate and the transport of the Zn and Pb in the different mediums of the watershed system. Moreover, future simulations were run using Regional Climate Models of the Euro-CORDEX experiment (Giorgi et al., 2009) as climate forcing for the watershed models. This toolset allowed to simulate transport of Zn and Pb in the surface waters of Rio San Giorgio for a historical and a future period, with evaluation of some waste management scenarios. The general decrease of mean rainfall and the increase of extreme precipitation events projected by the RCMs for the future is well reflected in the results of the SWAT and SWAT-HM modellization. In fact, the mean loads of Zn and Pb in the river tend to a decrease, but extreme loads are projected for the future in occurrence of intense precipitations predicted by the RCMs models. A web-application (CESApp) Decision Support System (DSS) was also developed to share the results of the modeling, such as models’ inputs, outputs and the different scenarios of the metals spread with researchers and stakeholders

Water Data Science: Data Driven Techniques, Training, and Tools for Improved Management of High Frequency Water Resources Data

Electronic sensors can measure water and climate conditions at high frequency and generate large quantities of observed data. This work addresses data management challenges associated with the volume and complexity of high frequency water data. We developed techniques for automatically reviewing data, created materials for training water data managers, and explored existing and emerging technologies for sensor data management. Data collected by sensors often include errors due to sensor failure or environmental conditions that need to be removed, labeled, or corrected before the data can be used for analysis. Manual review and correction of these data can be tedious and time consuming. To help automate these tasks, we developed a computer program that automatically checks the data for mistakes and attempts to fix them. This tool has the potential to save time and effort and is available to scientists and practitioners who use sensors to monitor water. Scientists may lack skillsets for working with sensor data because traditional engineering or science courses do not address how work with complex data with modern technology. We surveyed and interviewed instructors who teach courses related to “hydroinformatics” or “water data science” to understand challenges in incorporating data science techniques and tools into water resources teaching. Based on their feedback, we created educational materials that demonstrate how the articulated challenges can be effectively addressed to provide high-quality instruction. These materials are available online for students and teachers. In addition to skills for working with sensor data, scientists and engineers need tools for storing, managing, and sharing these data. Hydrologic information systems (HIS) help manage the data collected using sensors. HIS make sure that data can be effectively used by providing the computer infrastructure to get data from sensors in the field to secure data storage and then into the hands of scientists and others who use them. This work describes the evolution of software and standards that comprise HIS. We present the main components of HIS, describe currently available systems and gaps in technology or functionality, and then discuss opportunities for improved infrastructure that would make sensor data easier to collect, manage, and use. In short, we are trying to make sure that sensor data are good and useful; we’re helping instructors teach prospective data collectors and users about water and data; and we are making sure that the systems that enable collection, storage, management, and use of the data work smoothly

Modelo de gestión de seguridad de la información con enfoque de riesgos para apoyar en los servicios de modelado numérico ambiental

La “Gestión de la Seguridad de la Información”, cada día asume mayor importancia en las actividades empresariales; aportando valor a los negocios, principalmente cuando confluye la gestión de la información y la infraestructura tecnológica digital, dando lugar a emprendimientos innovadores de alta rentabilidad en diversas actividades económicas, tal es el caso de empresas como UBER, AirB&B, entre otras empresas de servicios altamente tecnológicas. En torno a los Estudios de Impacto Ambiental, se desarrollan servicios complementarios, relacionado a la evaluación se posibles escenarios de Impacto Ambiental ante la instalación de diversos proyectos de inversión, para lo cual se utilizan herramientas de modelado, las mismas que se basan en cálculos numéricos computacionales, que requieren de información ambiental para los fines de evaluación. El servicios de modelado numéricos ambiental, demandan de equipos de cómputo especializado, software, lenguajes de programación y técnicas de procesamiento de información muy específicas, todas ellas necesitan mantener la disponibilidad, integridad y confidencialidad de la información en todas las etapas de los procesos. En este sentido, se propone un modelo de Gestión de Seguridad de la Información basado en normas internacionales de “Gestión de Riesgos”. Llegándose a analizar estándares de Gestión de Seguridad de la Información basado en la Gestión del Riesgos y proponer un modelo de Gestión de Seguridad de la Información, se aplicó; propuesta que fue implementada parcialmente en una empresa, la misma que fue sometida a evaluación de juicio de expertos

Modelo de gestión de seguridad de la información con enfoque de riesgos para apoyar en los servicios de modelado numérico ambiental

La “Gestión de la Seguridad de la Información”, cada día asume mayor importancia en las actividades empresariales; aportando valor a los negocios, principalmente cuando confluye la gestión de la información y la infraestructura tecnológica digital, dando lugar a emprendimientos innovadores de alta rentabilidad en diversas actividades económicas, tal es el caso de empresas como UBER, AirB&B, entre otras empresas de servicios altamente tecnológicas. En torno a los Estudios de Impacto Ambiental, se desarrollan servicios complementarios, relacionado a la evaluación se posibles escenarios de Impacto Ambiental ante la instalación de diversos proyectos de inversión, para lo cual se utilizan herramientas de modelado, las mismas que se basan en cálculos numéricos computacionales, que requieren de información ambiental para los fines de evaluación. El servicios de modelado numéricos ambiental, demandan de equipos de cómputo especializado, software, lenguajes de programación y técnicas de procesamiento de información muy específicas, todas ellas necesitan mantener la disponibilidad, integridad y confidencialidad de la información en todas las etapas de los procesos. En este sentido, se propone un modelo de Gestión de Seguridad de la Información basado en normas internacionales de “Gestión de Riesgos”. Llegándose a analizar estándares de Gestión de Seguridad de la Información basado en la Gestión del Riesgos y proponer un modelo de Gestión de Seguridad de la Información, se aplicó; propuesta que fue implementada parcialmente en una empresa, la misma que fue sometida a evaluación de juicio de expertos

A Two-Level Information Modelling Translation Methodology and Framework to Achieve Semantic Interoperability in Constrained GeoObservational Sensor Systems

As geographical observational data capture, storage and sharing technologies such as in situ remote monitoring systems and spatial data infrastructures evolve, the vision of a Digital Earth, first articulated by Al Gore in 1998 is getting ever closer. However, there are still many challenges and open research questions. For example, data quality, provenance and heterogeneity remain an issue due to the complexity of geo-spatial data and information representation. Observational data are often inadequately semantically enriched by geo-observational information systems or spatial data infrastructures and so they often do not fully capture the true meaning of the associated datasets. Furthermore, data models underpinning these information systems are typically too rigid in their data representation to allow for the ever-changing and evolving nature of geo-spatial domain concepts. This impoverished approach to observational data representation reduces the ability of multi-disciplinary practitioners to share information in an interoperable and computable way. The health domain experiences similar challenges with representing complex and evolving domain information concepts. Within any complex domain (such as Earth system science or health) two categories or levels of domain concepts exist. Those concepts that remain stable over a long period of time, and those concepts that are prone to change, as the domain knowledge evolves, and new discoveries are made. Health informaticians have developed a sophisticated two-level modelling systems design approach for electronic health documentation over many years, and with the use of archetypes, have shown how data, information, and knowledge interoperability among heterogenous systems can be achieved. This research investigates whether two-level modelling can be translated from the health domain to the geo-spatial domain and applied to observing scenarios to achieve semantic interoperability within and between spatial data infrastructures, beyond what is possible with current state-of-the-art approaches. A detailed review of state-of-the-art SDIs, geo-spatial standards and the two-level modelling methodology was performed. A cross-domain translation methodology was developed, and a proof-of-concept geo-spatial two-level modelling framework was defined and implemented. The Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard was re-profiled to aid investigation of the two-level information modelling approach. An evaluation of the method was undertaken using II specific use-case scenarios. Information modelling was performed using the two-level modelling method to show how existing historical ocean observing datasets can be expressed semantically and harmonized using two-level modelling. Also, the flexibility of the approach was investigated by applying the method to an air quality monitoring scenario using a technologically constrained monitoring sensor system. This work has demonstrated that two-level modelling can be translated to the geospatial domain and then further developed to be used within a constrained technological sensor system; using traditional wireless sensor networks, semantic web technologies and Internet of Things based technologies. Domain specific evaluation results show that twolevel modelling presents a viable approach to achieve semantic interoperability between constrained geo-observational sensor systems and spatial data infrastructures for ocean observing and city based air quality observing scenarios. This has been demonstrated through the re-purposing of selected, existing geospatial data models and standards. However, it was found that re-using existing standards requires careful ontological analysis per domain concept and so caution is recommended in assuming the wider applicability of the approach. While the benefits of adopting a two-level information modelling approach to geospatial information modelling are potentially great, it was found that translation to a new domain is complex. The complexity of the approach was found to be a barrier to adoption, especially in commercial based projects where standards implementation is low on implementation road maps and the perceived benefits of standards adherence are low. Arising from this work, a novel set of base software components, methods and fundamental geo-archetypes have been developed. However, during this work it was not possible to form the required rich community of supporters to fully validate geoarchetypes. Therefore, the findings of this work are not exhaustive, and the archetype models produced are only indicative. The findings of this work can be used as the basis to encourage further investigation and uptake of two-level modelling within the Earth system science and geo-spatial domain. Ultimately, the outcomes of this work are to recommend further development and evaluation of the approach, building on the positive results thus far, and the base software artefacts developed to support the approach