Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed
Accurate and reliable flow forecasting in complex Canadian prairie watersheds has been one of the major challenges faced by hydrologists. In an attempt to improve the accuracy and reliability of a reservoir inflow forecast, this study investigates structurally different hydrological models along wit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/11/11/2201 |
| _version_ | 1811220258001780736 |
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| author | Frezer Seid Awol Paulin Coulibaly Ioannis Tsanis Fisaha Unduche |
| author_facet | Frezer Seid Awol Paulin Coulibaly Ioannis Tsanis Fisaha Unduche |
| author_sort | Frezer Seid Awol |
| collection | DOAJ |
| description | Accurate and reliable flow forecasting in complex Canadian prairie watersheds has been one of the major challenges faced by hydrologists. In an attempt to improve the accuracy and reliability of a reservoir inflow forecast, this study investigates structurally different hydrological models along with ensemble precipitation forecasts to identify the most skillful and reliable model. The key goal is to assess whether short- and medium-range ensemble flood forecasting in large complex basins can be accurately achieved by simple conceptual lumped models (e.g., SACSMA with SNOW17 and MACHBV with SNOW17) or it requires a medium level distributed model (e.g., WATFLOOD) or an advanced macroscale land-surface based model (VIC coupled with routing module (RVIC)). Eleven (11)-member precipitation forecasts from second-generation Global Ensemble Forecast System reforecast (GEFSv2) were used as inputs. Each of the ensemble members was bias-corrected by Empirical Quantile Mapping method using the Canadian Precipitation Analysis (CaPA) as a training/verification dataset. Forecast evaluation is performed for 1-day up to 8-days forecast lead times in a 6-month hindcast period. Results indicate that bias-correcting precipitation forecasts using verifying datasets (such as CaPA) for a training period of at least two years before the forecast time, produces skillful ensemble hydrological forecasts. A comparison of models in forecast mode shows that the two lumped models (SACSMA and MACHBV) can provide better overall forecast performance than the benchmark WATFLOOD and the macroscale Variable Infiltration Capacity (VIC) model. However, for shorter lead-times, particularly up to day 3, the benchmark distributed model provides competitive reliability, as compared to the lumped models. In general, the SACSMA model provided better forecast quality, reliability and differentiation skill than other considered models at all lead times. |
| first_indexed | 2024-04-12T07:38:38Z |
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| id | doaj.art-2c4d8e90b15648378933f6c95296b2da |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-04-12T07:38:38Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-2c4d8e90b15648378933f6c95296b2da2022-12-22T03:41:52ZengMDPI AGWater2073-44412019-10-011111220110.3390/w11112201w11112201Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie WatershedFrezer Seid Awol0Paulin Coulibaly1Ioannis Tsanis2Fisaha Unduche3Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S4L7, CanadaJointly in School of Geography and Earth Sciences and Department of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S4K1, CanadaDepartment of Civil Engineering, McMaster University, 1280 Main Street West, Hamilton, ON L8S4L7, CanadaHydrologic Forecasting & Coordination, Manitoba Infrastructure, 280 Broadway, Winnipeg, MB R3C 0R8, CanadaAccurate and reliable flow forecasting in complex Canadian prairie watersheds has been one of the major challenges faced by hydrologists. In an attempt to improve the accuracy and reliability of a reservoir inflow forecast, this study investigates structurally different hydrological models along with ensemble precipitation forecasts to identify the most skillful and reliable model. The key goal is to assess whether short- and medium-range ensemble flood forecasting in large complex basins can be accurately achieved by simple conceptual lumped models (e.g., SACSMA with SNOW17 and MACHBV with SNOW17) or it requires a medium level distributed model (e.g., WATFLOOD) or an advanced macroscale land-surface based model (VIC coupled with routing module (RVIC)). Eleven (11)-member precipitation forecasts from second-generation Global Ensemble Forecast System reforecast (GEFSv2) were used as inputs. Each of the ensemble members was bias-corrected by Empirical Quantile Mapping method using the Canadian Precipitation Analysis (CaPA) as a training/verification dataset. Forecast evaluation is performed for 1-day up to 8-days forecast lead times in a 6-month hindcast period. Results indicate that bias-correcting precipitation forecasts using verifying datasets (such as CaPA) for a training period of at least two years before the forecast time, produces skillful ensemble hydrological forecasts. A comparison of models in forecast mode shows that the two lumped models (SACSMA and MACHBV) can provide better overall forecast performance than the benchmark WATFLOOD and the macroscale Variable Infiltration Capacity (VIC) model. However, for shorter lead-times, particularly up to day 3, the benchmark distributed model provides competitive reliability, as compared to the lumped models. In general, the SACSMA model provided better forecast quality, reliability and differentiation skill than other considered models at all lead times.https://www.mdpi.com/2073-4441/11/11/2201hydrological modelsensemble hydrological forecastingbias-correctionsacsmacomplex watershedsreservoir inflow |
| spellingShingle | Frezer Seid Awol Paulin Coulibaly Ioannis Tsanis Fisaha Unduche Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed Water hydrological models ensemble hydrological forecasting bias-correction sacsma complex watersheds reservoir inflow |
| title | Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed |
| title_full | Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed |
| title_fullStr | Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed |
| title_full_unstemmed | Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed |
| title_short | Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed |
| title_sort | identification of hydrological models for enhanced ensemble reservoir inflow forecasting in a large complex prairie watershed |
| topic | hydrological models ensemble hydrological forecasting bias-correction sacsma complex watersheds reservoir inflow |
| url | https://www.mdpi.com/2073-4441/11/11/2201 |
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