A Streamflow Bias Correction and Performance Evaluation Web Application for GEOGloWS ECMWF Streamflow Services
Abstract
:1. Introduction
1.1. Background and Need
1.2. Global Streamflow Modeling and Prediction
1.3. Global Modeling Challenges
- Big data management, having solid infrastructure where the model can be automatically computed, stored and retrieved;
- Communication, using web apps services, standards for producing and sharing hydrological data;
- Adoption in different places through the use of hydrologic modeling as a service (HMaaS) web applications and representational state transfer (REST) application programming interfaces (API’s); and
- Validation and verification of results so that confidence in using the model output can be established.
1.4. Global Model Calibraton and Validation
1.5. Bias Correction
2. Data and Methods
2.1. Overview
2.2. Requirements
2.3. Data
2.4. Spatial Data Pre-processing
- The location of the gauging stations is often not exact or does not match the river reach segments in the shapefile. Therefore, the locations should be visually examined and adjusted to match the right reach ID and location.
- The areas of the simulated basins are not the same as the area that corresponds to the basin defined by a gauging station point.
2.5. Web Applicaation Design
- retrieve data from mapping services for the stations and stream network in a region and present these data using a JavaScript map interface;
- retrieve, process, and visualize the simulated data (i.e., historical simulation and forecast) from the GEOGloWS or GESS servers;
- perform bias corrections on the historic simulation data using observed data;
- construct a bias-corrected forecast using the observed data and the historic simulated data; and
- compute and present comparisons and error metric reports of the historical simulation with and without the bias correction.
2.6. Bias Correction
3. Results
3.1. Software Implementation Results
- Data and plots for the observed, simulated, and bias-corrected historic streamflow data;
- Visual analysis of daily seasonality, monthly seasonality, and scatter plots in normal and log scale;
- Computations and plots of volume analysis;
- Statistical error metrics validation for observed versus simulated and observed versus bias-corrected simulated data to evaluate the improvement from bias corrections; and
- Access to and visualizations of both the GESS hydrologic forecast and the bias-corrected forecast that can be used by local hydrological managers.
3.1.1. User Interface: Hydrographs
3.1.2. User Interface: Visual Analysis
3.1.3. User Interface: Metrics Report
3.1.4. User Interface: Forecast Visualization
3.2. Experimental Case Study Results
- The Australia HVT application retrieved data using the web interface to the Bureau of Meteorology to access the daily streamflow data for the 122 gauging stations. For some of the stations, these data include real-time observed streamflow. HVT accesses these real-time data and uses them in the Forecast tab.
- The Brazil HVT application uses the open data portal of the National Water Agency to access the daily streamflow data for the 1008 gauging stations.
- The Colombia HVT application uses comma-separated-value (CSV) files for the observed data at each of the 412 stations. These data were provided by the Colombian Institute of Hydrology, Meteorology and Environmental Studies (IDEAM). To customize HVT, we grouped and added the observed streamflow CSV files for the 412 gauging stations in a HydroShare resource. The Colombia HVT application also uses the IDEAM Flood Early Warning System (FEWS) web service to retrieve the real time observed and sensor streamflow data that we added to the app in the Forecast section.
- The Dominican Republic HVT application uses an existing HydroServer to retrieve the observed data for each of the 84 gauging stations. The Dominican National Hydrologic service (INDRHI) provided the data on the HydroServer.
- The Peru HVT application uses CSV files for the observed data at each of the 303 stations. These data were provided by the Peruvian National Meteorology and Hydrology Service (SENAMHI). To customize HVT, we grouped and added the CSV files with the observed streamflow for the 303 gauging stations in a HydroShare resource.
4. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
- http://www.bom.gov.au/waterdata/ (accessed on 16 March 2021)
- http://telemetriaws1.ana.gov.br/ServiceANA.asmx (accessed on 16 March 2021)
- https://www.hydroshare.org/resource/d222676fbd984a81911761ca1ba936bf/ (accessed on 16 March 2021)
- http://128.187.106.131/app/index.php/dr (accessed on 16 March 2021)
- https://www.hydroshare.org/resource/b7efdb43bf59470fb5baa4426931d8fe/ (accessed on 16 March 2021)
Acknowledgments
Conflicts of Interest
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Sanchez Lozano, J.; Romero Bustamante, G.; Hales, R.C.; Nelson, E.J.; Williams, G.P.; Ames, D.P.; Jones, N.L. A Streamflow Bias Correction and Performance Evaluation Web Application for GEOGloWS ECMWF Streamflow Services. Hydrology 2021, 8, 71. https://doi.org/10.3390/hydrology8020071
Sanchez Lozano J, Romero Bustamante G, Hales RC, Nelson EJ, Williams GP, Ames DP, Jones NL. A Streamflow Bias Correction and Performance Evaluation Web Application for GEOGloWS ECMWF Streamflow Services. Hydrology. 2021; 8(2):71. https://doi.org/10.3390/hydrology8020071
Chicago/Turabian StyleSanchez Lozano, Jorge, Giovanni Romero Bustamante, Riley Chad Hales, E. James Nelson, Gustavious P. Williams, Daniel P. Ames, and Norman L. Jones. 2021. "A Streamflow Bias Correction and Performance Evaluation Web Application for GEOGloWS ECMWF Streamflow Services" Hydrology 8, no. 2: 71. https://doi.org/10.3390/hydrology8020071