Model Configuration And Data Management In The Short-Term Water Information Forecasting Tools

The Short-term Water Information and Forecasting Tools (SWIFT) is a suite of tools for flood and short-term streamflow forecasting, consisting of a collection of hydrologic model components and utilities. Catchments are modeled using conceptual subareas and a node-link structure for channel routing. The tools comprise modules for calibration, model state updating, output error correction, ensemble runs and data assimilation. Given the combinatorial nature of the modelling experiments and the sub-daily time steps typically used for simulations, the volume of model configurations and time series data is substantial and its management is not trivial. SWIFT is currently used mostly for research purposes but has also been used operationally, with intersecting but significantly different requirements. Early versions of SWIFT used mostly ad-hoc text files handled via Fortran code, with limited use of netCDF for time series data. The configuration and data handling modules have since been redesigned. The model configuration now follows a design where the data model is decoupled from the on-disk persistence mechanism. For research purposes the preferred on-disk format is JSON, to leverage numerous software libraries in a variety of languages, while retaining the legacy option of custom tab-separated text formats when it is a preferred access arrangement for the researcher. By decoupling data model and data persistence, it is much easier to interchangeably use for instance relational databases to provide stricter provenance and audit trail capabilities in an operational flood forecasting context. For the time series data, given the volume and required throughput, text based formats are usually inadequate. A schema derived from CF conventions has been designed to efficiently handle time series for SWIFT

Enhancing Water Quality Data Service Discovery And Access Using Standard Vocabularies

There is a growing need for consistency across the publishing, discovering, integrating and access to scientific datasets, such as water quality data. Such datasets may have varying formats and service interfaces. The Network Common Data Form (NetCDF) is both a software package and a data format for producing array-oriented scientific data, which is commonly used to exchange data, including water quality data. NetCDF datasets are also published through service interfaces using the THREDDS data server. Alternatively water quality datasets can be encoded with standard XML formats such as WaterML 2.0, which can be published with services such as the Open Geospatial Consortium (OGC) community\u27s Web Feature Service interface standard (WFS). However, appropriate interpretation of the content, discovery and interoperability of data depends on common models, schemas and vocabularies, though these may not always be available. Using the water quality vocabulary we have developed, formalized using the Resource Description Framework (RDF) language, and published as Linked Data, we demonstrate the use of such standard vocabularies in existing data services for providing service capability metadata. We also present methods for augmenting existing metadata fields for water quality data specifically in formats such as NetCDF, WaterML 2.0 using standard vocabularies. We show how using standard vocabularies that are encoded and published using semantic technologies can enhance discovery, integration and access to existing data services delivering water quality datasets