Evaluation of precipitation forecasts for five-day streamflow forecasting in Narmada River basin

Singh, Ankit; Mondal, Soubhik; Samal, Nibedita; Jha, Sanjeev

doi:10.1080/02626667.2022.2151913

Cited by 6 publications

(5 citation statements)

References 55 publications

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“…The percentile range of the forecast also steadily decreased as the forecast horizon increased, with Day 1 having the largest range. Similar trends in steadily declining performance skills have been observed by other researchers-in India [48], Canada [7], China [22,49], and the USA [17]. Furthermore, when assessing rainfall forecast performance using KGE and PCC, we found relative increases, but there was an overall decrease as the forecast horizon extended.…”

Section: Performance Of Ensemble Meansupporting

confidence: 88%

“…formance skills have been observed by other researchers-in India [48], Canada [7], China [22,49], and the USA [17]. Furthermore, when assessing rainfall forecast performance using KGE and PCC, we found relative increases, but there was an overall decrease as the forecast horizon extended.…”

Section: Performance Of Ensemble Meansupporting

confidence: 82%

“…Only about 25% of catchments had an NSE greater than 0.6 and 0.4, respectively, for lead times of 3-7 days. 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].…”

Section: Performance Of Ensemble Meanmentioning

confidence: 86%

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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.

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Section: Performance Of Ensemble Meansupporting

confidence: 88%

Section: Performance Of Ensemble Meansupporting

confidence: 82%

Section: Performance Of Ensemble Meanmentioning

confidence: 86%

See 1 more Smart Citation

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

“…The percentile range of forecast also steadily decreased as the forecast horizon increased, with Day 1 having the largest range. Similar trends in steadily declining performance skills have been observed by other researchers -in India [48], Canada [7], China [22,49] and USA [17], just to name a few. Furthermore, when assessing rainfall forecast performance using KGE and PCC, we found relative increases, but an overall decrease as the forecast horizon extended.…”

Section: Performance Of Ensemble Meansupporting

confidence: 85%

“…Only about 25% of catchments had NSE greater than 0.6 and 0.4, respectively, for lead times 3 to 7 days. 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].…”

Section: Performance Of Ensemble Meanmentioning

confidence: 84%

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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Assessing Fourier and Latin hypercube sampling methods as new multi-model methods for hydrological simulations

Singh,

Mondal,

Pandey

et al. 2023

Stoch Environ Res Risk Assess

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Evaluation of precipitation forecasts for five-day streamflow forecasting in Narmada River basin

Cited by 6 publications

References 55 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

Assessing Fourier and Latin hypercube sampling methods as new multi-model methods for hydrological simulations

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