Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting
The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/8/4/188 |
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| author | Rodrigo Valdés-Pineda Juan B. Valdés Sungwook Wi Aleix Serrat-Capdevila Tirthankar Roy |
| author_facet | Rodrigo Valdés-Pineda Juan B. Valdés Sungwook Wi Aleix Serrat-Capdevila Tirthankar Roy |
| author_sort | Rodrigo Valdés-Pineda |
| collection | DOAJ |
| description | The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed. |
| first_indexed | 2024-03-10T03:58:09Z |
| format | Article |
| id | doaj.art-08d6b6fc11e14a35a7e922e9b19c458f |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-10T03:58:09Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-08d6b6fc11e14a35a7e922e9b19c458f2023-11-23T08:39:50ZengMDPI AGHydrology2306-53382021-12-018418810.3390/hydrology8040188Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble ForecastingRodrigo Valdés-Pineda0Juan B. Valdés1Sungwook Wi2Aleix Serrat-Capdevila3Tirthankar Roy4Department of Hydrology and Atmospheric Sciences, University of Arizona, J W Harshbarger Bldg., 1133 James E. Rogers Way #122, Tucson, AZ 85721, USADepartment of Hydrology and Atmospheric Sciences, University of Arizona, J W Harshbarger Bldg., 1133 James E. Rogers Way #122, Tucson, AZ 85721, USADepartment of Civil and Environmental Engineering, University of Massachusetts, Amherst, MA 01003, USAGlobal Water Practice, The World Bank, Washington, DC 20433, USADepartment of Civil and Environmental Engineering, College of Engineering, University of Nebraska, 900 N. 16th St., Lincoln, NE 68588, USAThe combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.https://www.mdpi.com/2306-5338/8/4/188variational ensemble forecastinghydrologic processing strategies or hypothesesSR2MR streamflow forecastingreal-time hydrologic forecasting systemsatellite precipitation productsmulti models |
| spellingShingle | Rodrigo Valdés-Pineda Juan B. Valdés Sungwook Wi Aleix Serrat-Capdevila Tirthankar Roy Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting Hydrology variational ensemble forecasting hydrologic processing strategies or hypotheses SR2MR streamflow forecasting real-time hydrologic forecasting system satellite precipitation products multi models |
| title | Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting |
| title_full | Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting |
| title_fullStr | Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting |
| title_full_unstemmed | Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting |
| title_short | Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting |
| title_sort | improving operational short to medium range sr2mr streamflow forecasts in the upper zambezi basin and its sub basins using variational ensemble forecasting |
| topic | variational ensemble forecasting hydrologic processing strategies or hypotheses SR2MR streamflow forecasting real-time hydrologic forecasting system satellite precipitation products multi models |
| url | https://www.mdpi.com/2306-5338/8/4/188 |
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