GroundWater Markup Language (GWML) – enabling groundwater data interoperability in spatial data infrastructures

Abstract

Increasing stress on global groundwater resources is leading to new approaches to the management and delivery of groundwater data. These approaches include the deployment of a Spatial Data Infrastructure (SDI) to enable online data interoperability amongst numerous and heterogeneous data sources. Often an important component of an SDI is a global domain schema, which serves as a central structure for the query and transport of data, but at present there does not exist a schema for groundwater data that is strongly compliant with SDI concepts, standards, and technologies. In this paper we present GroundWater Markup Language (GWML), a groundwater application of the Geography Markup Language (GML). GWML can be used in conjunction with a variety of web services to facilitate data interoperability in a SDI. We describe three common usage scenarios that motivate the design of GWML and a three-stage design methodology involving conceptual, logical and physical schemas. The resultant GWML has broad scope as demonstrated by its implementation in the Canadian Groundwater Information Network. Example uses include decision support in resource management, a scientific application for aquifer mapping, and a commercial application for drill site selection. These demonstrated uses suggest GWML can play a key role in emerging groundwater SDI.

Integrated Groundwater Data Management

“…Solutions to data interoperability typically require alignment of the data at five levels: systems, syntax, structure, semantics and pragmatics (Brodaric 2007). Ideally, SDI standards are used at each level, and in the water domain these are being developed in coordination with the Open Geospatial Consortium (OGC), the International Organization for Standardization (ISO), and professional bodies such as the World Meteorological Organization (WMO) and Measurements (O&M), WaterML2 (WML2), and GroundwaterML (GWML), which are built with GML and constitute a common structure for observations, water time series, and groundwater features, respectively (Boisvert and Brodaric 2012;Cox 2011;Taylor et al 2013). Standard schemas are typically diagrammed using well-constrained methods, such as UML, and can be expressed in a variety of formats, such as XML.…”

Spatiotemporal enabled Content-based Image Retrieval

“…However, they are fragmented in that they describe only disconnected subareas of the hydro domain, such as groundwater storage and flow (e.g., GWML2 (Boisvert & Brodaric 2012;Brodaric 2015), INSPIRE Geology (INSPIRE 2013)), surface hydrography and connectivity (e.g., USGS's NHDPlusV2, INSPIRE Hydrography (INSPIRE 2009), HY_Features (Dornblut & Atkinson 2013)), water quality (e.g, WaterML2) or stream geometry (e.g., RiverML). Moreover, the meaning of classes in the existing data models are described only via subclass relationships, via generic UML associations, and via free-text descriptions, which are insufficient for machine-interpretability and incompatible across standards.…”

“…Research and operations in these domains are heavily dependent on digital representations of water features, but the inherent conceptualizations can vary widely. Examples of heterogeneity abound, and can be found when comparing international water data standards (Boisvert & Brodaric 2012;Dornblut & Atkinson 2013;INSPIRE 2013;, national catalogs of hydrographic features (Duce & Janowicz 2010), ontological considerations (Galton & Mizoguchi 2009;Santos et al 2005;Sinha et al 2014;, and database structures (Maidment, 2002;Strassberg, et al, 2011). This is problematic as it inhibits some uses, especially their integration, which is typically an important precursor to regional scientific analysis such as water availability, or complex societal decision-making such as water allotment.…”

“…To prepare for automated integration of geoscience knowledge across these levels, we explore the use of a reference ontology (Noy 2004) as a tool for increasing semantic precision and coherence in geoscience data models. We specifically test this idea within the hydro domain by using the Hydro Foundational Ontology (HyFO), a reference ontology for the hydro domain developed since 2011 (see e.g., Hahmann & Brodaric 2012Brodaric & Hahmann 2014), to semantically analyze the Groundwater Markup Language (GWML2) (Boisvert & Brodaric 2012;Brodaric 2015) as one example of a hydro data model developed by domain scientists. The result is an improved version of GWML2 with (1) increased semantic precision through axiomatic constraints (i.e., constraints expressed in a logical language such as first-order logic) and definitions based on well-defined reference terms from HyFO, (2) a stratified formalization that separates concepts based on how broadly they apply (across geosciences, to the entire water domain, or only to groundwater), and (3) a completed taxonomy that fills gaps and renames classes to better reflect their position within the stratification.…”

An Ontological Analysis of Water Features

“…Research and operations in these domains are heavily dependent on digital representations of water features, but the inherent conceptualizations can vary widely. Examples of heterogeneity abound, and can be found when comparing international water data standards (Boisvert & Brodaric 2012;Dornblut & Atkinson 2013;INSPIRE 2013;2014), national catalogs of hydrographic features (Duce & Janowicz 2010), ontological considerations (Galton & Mizoguchi 2009;Santos et al 2005;Sinha et al 2014;Wellen & Sieber 2013), and database structures (Maidment, 2002;Strassberg, et al, 2011). This is problematic as it inhibits some uses, especially their integration, which is typically an important precursor to regional scientific analysis such as water availability, or complex societal decision-making such as water allotment.…”