Abstract
We present the development and testing of a web application called the historical validation tool (HVT) that processes and visualizes observed and simulated historical stream discharge data from the global GEOGloWS ECMWF streamflow services (GESS), performs seasonally adjusted bias correction, computes goodness-of-fit metrics, and performs forward bias correction on subsequent forecasts. The HVT corrects GESS output at a local scale using a technique that identifies and corrects model bias using observed hydrological data that are accessed using web services. HVT evaluates the performance of the GESS historic simulation data and provides more accurate historic simulation and bias-corrected forecast data. The HVT also allows users of the GEOGloWS historical streamflow data to use local observed data to both validate and improve the accuracy of local streamflow predictions. We developed the HVT using Tethys Platform, an open-source web application development framework. HVT presents data visualization using web mapping services and data plotting in the web map interface while functions related to bias correction, metrics reporting, and data generation for statistical analysis are computed by the back end. We present five case studies using the HVT in Australia, Brazil, Colombia, the Dominican Republic, and Peru. In these case studies, in addition to presenting the application, we evaluate the accuracy of the method we implemented in the HVT for bias correction. These case studies show that the HVT bias correction in Brazil, Colombia, and Peru results in significant improvement in historic simulation across the countries, while bias correction only resulted in marginal historic simulation improvements in Australia and the Dominican Republic. The HVT web application allows users to use local data to adjust global historical simulation and forecasts and validate the results, making the GESS modeling results more useful at a local scale.
Highlights
historical validation tool (HVT) needs to be customized for different regions to be able to access local observed data sources, or users can upload their own data to a HydroShare server if other services are not available
We present the results of our experimental test cases in Australia, Brazil, Colombia, the Dominican Republic, and Peru
This presentation includes key error metrics displayed throughout the different stations across each of these study areas that show the improvement provided by the bias correction
Summary
Flooding is one of the most frequent and expensive natural disasters worldwide, both in terms of loss of human life and property damage. While some developing countries do have systems for hydrologic monitoring and forecasting [14], their hydrologic services are often ineffective or only functional in limited regions. This can be due to a number of issues, including a lack of financial resources, challenges in maintaining the needed technological infrastructure, difficulties in attracting, training, and retaining a qualified workforce, no perceived benefit to the provider for making their observations available, issues related to water rights, policy related to transnational river basins, poorly gauged river basins, failure to adopt a single international standard for data and metadata sharing, and limited or poor connections with local users [15,16]
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