Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia

Tedla, Hailay Zeray; Taye, Estefanos Fikadu; Walker, David W.; Haile, Alemseged Tamiru

doi:10.1016/j.ejrh.2022.101273

Cited by 5 publications

(5 citation statements)

References 39 publications

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“…In an operational context, this performance skill is better than that found in a similar study in India using an ECMWF dataset as the forcing variable [48]. Categorical metrics are widely used to assess the performance of the streamflow and flood predictions [52,53,[55][56][57]. Similarly to rainfall, we classified the streamflow of different percentiles and calculated the CSI, POD, and FAR for all catchments (an example is The NSE results for all catchments and forecasting locations are presented in Figure 9.…”

Section: Performance Of Ensemble Meanmentioning

confidence: 90%

“…Categorical metrics are widely used to assess the performance of the streamflow and flood predictions [52,53,[55][56][57]. Similarly to rainfall, we classified the streamflow of different percentiles and calculated the CSI, POD, and FAR for all catchments (an example is Categorical metrics are widely used to assess the performance of the streamflow and flood predictions [52,53,[55][56][57]. Similarly to rainfall, we classified the streamflow of different percentiles and calculated the CSI, POD, and FAR for all catchments (an example is shown in Figure A2) for different flow volumes to further understand the model's predictive capabilities.…”

Section: Performance Of Ensemble Meanmentioning

confidence: 99%

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Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia

Bari,

Hasan,

Amirthanathan

et al. 2024

Water

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The Australian Bureau of Meteorology offers a national operational 7-day ensemble streamflow forecast service covering regions of high environmental, economic, and social significance. This semi-automated service generates streamflow forecasts every morning and is seamlessly integrated into the Bureau’s Hydrologic Forecasting System (HyFS). Ensemble rainfall forecasts, European Centre for Medium-Range Weather Forecasts (ECMWF), and Poor Man’s Ensemble (PME), available in the Numerical Weather Prediction (NWP) suite, are used to generate these streamflow forecasts. The NWP rainfall undergoes pre-processing using the Catchment Hydrologic Pre-Processer (CHyPP) before being fed into the GR4H rainfall–runoff model, which is embedded in the Short-term Water Information Forecasting Tools (SWIFT) hydrological modelling package. The simulated streamflow is then post-processed using Error Representation and Reduction In Stages (ERRIS). We evaluated the performance of the operational rainfall and streamflow forecasts for 96 catchments using four years of operational data between January 2020 and December 2023. Performance evaluation metrics included the following: CRPS, relative CRPS, CRPSS, and PIT-Alpha for ensemble forecasts; NSE, PCC, MAE, KGE, PBias, and RMSE; and three categorical metrics, CSI, FAR, and POD, for deterministic forecasts. The skill scores, CRPS, relative CRPS, CRPSS, and PIT-Alpha, gradually decreased for both rainfall and streamflow as the forecast horizon increased from Day 1 to Day 7. A similar pattern emerged for NSE, KGE, PCC, MAE, and RMSE as well as for the categorical metrics. Forecast performance also progressively decreased with higher streamflow volumes. Most catchments showed positive performance skills, meaning the ensemble forecast outperformed climatology. Both streamflow and rainfall forecast skills varied spatially across the country—they were generally better in the high-runoff-generating catchments, and poorer in the drier catchments situated in the western part of the Great Dividing Range, South Australia, and the mid-west of Western Australia. We did not find any association between the model forecast skill and the catchment area. Our findings demonstrate that the 7-day ensemble streamflow forecasting service is robust and draws great confidence from agencies that use these forecasts to support decisions around water resource management.

show abstract

Section: Performance Of Ensemble Meanmentioning

confidence: 90%

Section: Performance Of Ensemble Meanmentioning

confidence: 99%

Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia

Bari,

Hasan,

Amirthanathan

et al. 2024

Water

View full text Add to dashboard Cite

show abstract

“…In an operational context, this performance skill is better than that found in a similar study in India using ECMWF data set as the forcing variable [48]. Categorical metrics are widely used to assess performance of streamflow and flood predictions [52,53,[55][56][57]. Similar to rainfall, we classified the streamflow of different percentiles and calculated CSI, POD and FAR for all catchments (an example shown in Figure A2) for different flow regimes to further understand the model's predictive capabilities.…”

Section: Performance Of Ensemble Meanmentioning

confidence: 90%

Performance Evaluation of a National 7-Day Ensemble Streamflow Forecast Service for Australia

Bari,

Hasan,

Amirthanathan

et al. 2024

Preprint

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The Australian Bureau of Meteorology offers a national operational 7-day ensemble streamflow forecast service covering regions of high environmental, economic and social significance. This semi-automated service generates streamflow forecasts every morning and is seamlessly integrated into the Bureau's Hydrologic Forecasting System (HyFS). Ensemble rainfall forecasts, European Centre for Medium-Range Weather Forecasts (ECMWF) and Poor Man's Ensemble (PME), available in the Numerical Weather Prediction (NWP) suite, are used to generate these streamflow forecasts. The NWP rainfall undergoes pre-processing using the Catchment Hydrologic Pre-Processer (CHyPP) before being fed into the GR4H rainfall-runoff model, which is embedded in the Short-term Water Information Forecasting Tools (SWIFT) hydrological modelling package. The simulated streamflow is then post-processed using the Error Representation and Reduction In Stages (ERRIS). We evaluated the performance of the operational rainfall and streamflow forecasts for 96 catchments using four years of operational data between January 2020 and December 2023. Performance evaluation metrics included CRPS, relative CRPS, CRPSS, and PIT-Alpha for ensemble forecasts and NSE, PCC, MAE, KGE, PBias and RMSE and three categorical metrics CSI, FAR and POD for deterministic forecasts. The skill scores CRPS, relative CRPS, CRPSS and PIT-Alpha, gradually decreased for both rainfall and streamflow as the forecast horizon increased from Day 1 to Day 7. A similar pattern emerged for NSE, KGE, PCC, MAE and RMSE as well as the categorical metrics. Forecast performance also progressively decreased with the higher streamflow regime. Most catchments showed positive performance skills, meaning the ensemble forecast outperformed climatology. Both streamflow and rainfall forecast skills varied spatially across the country – and were generally better in the high runoff generating catchments, and poorer in the drier catchments situated in the western part of the Great Dividing Range, South Australia and mid-west of Western Australia. We did not find any association between model forecast skill and the catchment area. Our findings demonstrate that the 7-day ensemble streamflow forecasting service is robust and draws great confidence from agencies that use these forecasts to support decisions around water resources management.

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“…In Ethiopia, Tedla et al (2022) examined the WRF model's rainfall forecasting accuracy in a catchment, with results indicating a high forecasting accuracy for 1-3-day rainfall forecasting periods while the accuracy would drop for periods of 4-5 days. The model was also revealed to be more accurate in simulating light rains (less than 6mm daily) than medium and heavy rains (over 6mm daily) (Tedla et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a weather forecasting model and HEC-HMS for flood forecasting: case study of Talesh catchment

Goodarzi,

Poorattar,

Vazirian

et al. 2024

Appl Water Sci

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Reports demonstrate that floods are among the most prevalent and deadliest natural disasters affecting 520 million people annually. The present study seeks to evaluate flood forecasting using the weather research and forecasting (WRF) model and the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model. To this end, WRF and HEC-HMS were calibrated by comparing their results with the data observed at measuring stations. Then, the output rainfall data of the WRF model were implemented by the calibrated HEC-HMS model and were examined using the statistical indices, which were revealed to be 4.13, 3.42, and 2.67 for the flow volume and 6.2, 2.46, and 5.11 for the peak flow, suggesting the accurate performance of WRF model alongside HEC-HMS in the Talesh catchment.

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Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia

Cited by 5 publications

References 39 publications

Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia

Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia

Performance Evaluation of a National 7-Day Ensemble Streamflow Forecast Service for Australia

Evaluation of a weather forecasting model and HEC-HMS for flood forecasting: case study of Talesh catchment

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