Incorporation of seasonal climate forecasts in the ensemble streamflow prediction system

Gobena, Adam Kenea; Gan, Thian Yew

doi:10.1016/j.jhydrol.2010.03.002

Cited by 54 publications

(43 citation statements)

References 31 publications

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“…Here, we assessed the overall performance of the forecasts, their capacity of discrimination, their sharpness and reliability. For these last two attributes, we consider the paradigm that better forecasts are those that maximise sharpness while guaranteeing reliability (Gneiting et al, 2007). …”

Section: Forecast Verification Methodsmentioning

confidence: 99%

“…Werner et al (2004) selected and weighted traces based on the ENSO and showed that some of the proposed methods yielded improvements in forecast overall performance. Gobena and Gan (2010) used the PDO in several resampling strategies, including an approach benefiting from monthly precipitation and temperature statistically derived from climate model outputs. Their study showed that the method yielded moderate improvements to overall forecast skill.…”

Section: Selecting Ensembles For Long-range Forecastingmentioning

confidence: 99%

“…It indicates how spread the members of an ensemble forecast are. Here, sharpness was computed as the average difference between the 95th and the 5th percentiles of the forecast distribution over the evaluation period (Gneiting et al, 2007). It thus corresponds to the average 90 % interquantile range (IQR).…”

Section: Evaluation Of Forecast Attributesmentioning

confidence: 99%

“…Reliability was evaluated with the probability integral transform (PIT; Gneiting et al, 2007;Laio and Tamea, 2007) diagram. The PIT diagram represents the cumulative distribution of the positions of the observation within the distribution of forecast values.…”

Section: Evaluation Of Forecast Attributesmentioning

confidence: 99%

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Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Crochemore¹,

Ramos²,

Pappenberger³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in longrange streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

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Section: Forecast Verification Methodsmentioning

confidence: 99%

Section: Selecting Ensembles For Long-range Forecastingmentioning

confidence: 99%

Section: Evaluation Of Forecast Attributesmentioning

confidence: 99%

Section: Evaluation Of Forecast Attributesmentioning

confidence: 99%

See 2 more Smart Citations

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Crochemore¹,

Ramos²,

Pappenberger³

et al. 2017

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Snow cover is especially influential during the spring period as it mostly contributes to mountain river flows and is thus the main source of hydrological predictability for snowmelt dominated headwater basins, such as the South Saskatechwan River Basin in Canada (Gobena et al, 2010). The size of the river basin also has an important impact: for instance, in the Ohio River Basin with a wide range of basin sizes from a few hundred to over a ten thousand square miles, Li et al (2009) found that the larger the basin, the stronger the influence of initial conditions.…”

Section: S Singla Et Al: Predictability Of Soil Moisture and River mentioning

confidence: 99%

Predictability of soil moisture and river flows over France for the spring season

Singla

Céron²,

Martin

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract. Sources of spring predictability of the hydrological system over France were studied on a seasonal time scale over the 1960-2005 period. Two random sampling experiments were set up in order to test the relative importance of the land surface initial state and the atmospheric forcing. The experiments were based on the SAFRAN-ISBA-MODCOU hydrometeorological suite which computed soil moisture and river flow forecasts over a 8-km grid and more than 880 rivergauging stations. Results showed that the predictability of hydrological variables primarily depended on the seasonal atmospheric forcing (mostly temperature and total precipitation) over most plains, whereas it mainly depended on snow cover over high mountains. However, the Seine catchment area was an exception as the skill mainly came from the initial state of its large and complex aquifers. Seasonal meteorological hindcasts with the Météo-France ARPEGE climate model were then used to force the ISBA-MODCOU hydrological model and obtain seasonal hydrological forecasts from 1960 to 2005 for the entire March-April-May period. Scores from this seasonal hydrological forecasting suite could thus be compared with the random atmospheric experiment. Soil moisture and river flow skill scores clearly showed the added value in seasonal meteorological forecasts in the north of France, contrary to the Mediterranean area where values worsened.

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Climate index weighting of ensemble streamflow forecasts using a simple Bayesian approach

Bradley

Habib

Schwartz³

2015

Water Resour. Res.

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Climate state can be an important predictor of future hydrologic conditions. In ensemble streamflow forecasting, where historical weather inputs or streamflow observations are used to generate the ensemble, climate index weighting is one way to represent the influence of climate state. Using a climate index, each forecast variable member of the ensemble is selectively weighted to reflect the climate state at the time of the forecast. A new approach to climate index weighting of ensemble forecasts is presented. The method is based on a sampling-resampling approach for Bayesian updating. The original hydrologic ensemble members define a sample drawn from the prior distribution; the relationship between the climate index and the ensemble member forecast variable is used to estimate a likelihood function. Given an observation of the climate index at the time of the forecast, the estimated likelihood function is then used to assign weights to each ensemble member. The weights define the probability of each ensemble member outcome given the observed climate index. The weighted ensemble forecast is then used to estimate the posterior distribution of the forecast variable conditioned on the climate index. The Bayesian climate index weighting approach is easy to apply to hydrologic ensemble forecasts; its parameters do not require calibration with hindcasts, and it adapts to the strength of the relation between climate and the forecast variable, defaulting to equal weighting of ensemble members when no relationship exists. A hydrologic forecasting application illustrates the approach and contrasts it with traditional climate index weighting approaches.

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Incorporation of seasonal climate forecasts in the ensemble streamflow prediction system

Cited by 54 publications

References 31 publications

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Predictability of soil moisture and river flows over France for the spring season

Climate index weighting of ensemble streamflow forecasts using a simple Bayesian approach

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