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.

Volunteered Geographic Information For Water Management: A Prototype Architecture

Driven by Web 2.0 technology and the almost ubiquitous presence of mobile devices, Volunteered Geographic Information (VGI) is knowing an unprecedented growth. These notable technological advancements have opened fruitful perspectives also in the field of water management and protection, raising the demand for a reconsideration of policies which also takes into account the emerging trend of VGI. This research investigates the opportunity of leveraging such technology to involve citizens equipped with common mobile devices (e.g. tablets and smartphones) in a campaign of report of water-related phenomena. The work is carried out in collaboration with ADBPO - Autorità di bacino del fiume Po (Po river basin Authority), i.e. the entity responsible for the environmental planning and protection of the basin of river Po. This is the longest Italian river, spreading over eight among the twenty Italian Regions and characterized by complex environmental issues. To enrich ADBPO official database with user-generated contents, a FOSS (Free and Open Source Software) architecture was designed which allows not only user field-data collection, but also data Web publication through standard protocols. Open Data Kit suite allows users to collect georeferenced multimedia information using mobile devices equipped with location sensors (e.g. the GPS). Users can report a number of environmental emergencies, problems or simple points of interest related to the Po river basin, taking pictures of them and providing other contextual information. Field-registered data is sent to a server and stored into a PostgreSQL database with PostGIS spatial extension. GeoServer provides then data dissemination on the Web, while specific OpenLayers-based viewers were built to optimize data access on both desktop computers and mobile devices. Besides proving the suitability of FOSS in the frame of VGI, the system represents a successful prototype for the exploitation of user local, real-time information aimed at managing and protecting water resources.

Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for large-scale systems. Nonetheless, a critical obstacle, which needs to be overcome in MPC, is the large computational burden when a large-scale system is considered or a long prediction horizon is involved. In order to solve this problem, we use an adaptive prediction accuracy (APA) approach that can reduce the computational burden almost by half. The proposed MPC scheme with this scheme is tested on the northern Dutch water system, which comprises Lake IJssel, Lake Marker, the River IJssel and the North Sea Canal. The simulation results show that by using the MPC-APA scheme, the computational time can be reduced to a large extent and a flood protection problem over longer prediction horizons can be well solved.