Harnessing the Environmental Data Flood: A Comparative Analysis of Hydrologic, Oceanographic, and Meteorological Informatics Platforms

Abstract

Researchers studying large-scale questions in hydrology, oceanography, and meteorology can work with existing data through myriad platforms that provide access to remote datasets and render said information in various graphical outputs for interpretation and analysis. A survey of 30 publicly available hydrometeorological data platforms reviews the current state of the art in water-data discovery and visualization. Such platforms best meet the needs of a diverse user community by providing valuable data content, facilitating data exchange, and supporting visual analysis. To provide datasets of value to wider audiences, data providers can emphasize building datasets that are not only voluminous but also proportionally content rich in geographic, temporal, and measurement breadth, through integration of complementary datasets and coordinated data collection programs. In support of efficient data interchange, including software-driven integration of complementary content, data providers should increase adoption of web services to share data, along with machine-readable formats. In addition, this work surveys best practices in visualization and advocates for graphical user interface features that provide the flexibility required to integrate heterogeneous datasets by both novice and expert end users. Features that stand out as particularly useful include comprehensive content filters for customizable data queries, multiparameter plotting, statistical analysis tools, and predictive visualization. Effective combination of comprehensive—as well as voluminous—content, widely implemented standards for efficient data interchange, and appropriate visualization flexibility in the next generation of tools provided as software services will empower a wide user community, spanning laypersons, students, and researchers.

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

“…Water scientists need streamflow, evaporation, groundwater level, and water quality measurements (Beran, Cox, Valentine, Zaslavsky, & McGee, 2009). Although they do not exist for every condition, continuously collected data that can be analyzed by location and time are expected (Dow et al, 2015). This need for data at different geographic and temporal scales connects the disciplines.…”

“…Integrating diverse data is problematic across the environmental sciences. Data collected at different scales and using different nomenclatures are difficult to merge (Dow et al, 2015;Maier, et al, 2014;Bowker, 2000b). Natural variances in systems and populations further complicate fitting biodiversity data together (Bowker, 2000b;Zimmerman, 2007).…”

“…Compounding this problem, data are distributed across numerous repositories (Dow et al, 2015). Users must first discover the repository, and then invest significant time and energy becoming familiar with each search environment (Ames et al, 2012;Beran et al, 2009).…”

“…When evaluating data for reuse, researchers use contextual information about data provenance (Dow et al, 2015;Murillo, 2014), technical instrumentation , and original research questions (Zimmerman, 2008). Researchers reuse data they understand, seeking data collected via practices they have used themselves (Zimmerman, 2007;Zimmerman, 2008) and with familiar data types (Murillo, 2014).…”

“…Researchers may instead base decisions on the word-of-mouth reputation of the dataset (Weber et al, 2012) or rely on more experienced researchers to develop understanding or alternative evaluation strategies (Zimmerman, 2008) Data must have sufficient quality, often defined by community standards, to be reused (Zimmerman, 2007). Water researchers and earth science modelers consider comprehensiveness and continuity over time and space (Dow et al, 2015;Parsons, 2011) as well as uncertainties and error estimates (Larsen, Hamilton, Lucido, Garner, & Young, 2016;Parsons, 2011) when determining data quality. Volcanologists use image resolution as a quality indicator (Weber et al, 2012).…”