On the sources of global land surface hydrologic predictability

Shukla, S.; Sheffield, Justin; Wood, Eric F.; Lettenmaier, Dennis P.

doi:10.5194/hess-17-2781-2013

Cited by 105 publications

(89 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The probability of drought onset detection is higher over parts of North America, Europe, West Russia, and East Africa (Figure 1a). An interesting feature is that areas with relatively higher drought detectability are not located over either humid or arid regions in terms of annual mean precipitation, suggesting that the initial condition for drought onset forecast is more important in transition zones, a finding consistent with Shukla et al [2013]. On average, globally, the drought detectability of the ESP approach is about 0.16 (Table 1).…”

Section: Resultssupporting

confidence: 72%

Multimodel seasonal forecasting of global drought onset

Yuan

Wood

2013

Geophysical Research Letters

143

View full text Add to dashboard Cite

[1] The capability of seasonal forecasting of global drought onset at local scales (1°) has been investigated using multiple climate models with 110 realizations. Climate models increase the global mean probability of drought onset detection from the climatology forecast by 31%-81%, but only increase equitable threat score by 21%-50% due to a high false alarm ratio. The multimodel ensemble increases the drought detectability over some tropical areas where individual models have better performance, but cannot help more over most extratropical regions. On average, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with low potential predictability and weak antecedent El Niño-Southern Oscillation signal. Given the high false alarms, the reliability is very important for a skillful probabilistic drought onset forecast. This raises the question of whether seasonal forecasting of global drought onset is essentially a stochastic forecasting problem.

show abstract

Section: Resultssupporting

confidence: 72%

Multimodel seasonal forecasting of global drought onset

Yuan

Wood

2013

Geophysical Research Letters

143

View full text Add to dashboard Cite

show abstract

“…Yossef et al (2013) showed that in western Europe, from July to October, streamflow forecasts are more dependent on meteorolog-ical forcing than they are on initial conditions, even 1 month ahead. The conclusions of Shukla et al (2013) are quite consistent with these findings. They found that the predictability of a forecast issued in July in France lies in the meteorological forcing for horizons longer than 3 months.…”

Section: Approaches To Seasonal Streamflow Forecastingsupporting

confidence: 82%

“…Research has shown that the relative role of each source of predictability mainly depends on the "inertia" or "memory" of the studied basin, the forecast season and the forecast lead time (Wood and Lettenmaier, 2008;Shukla et al, 2013;Yossef et al, 2013;Wood et al, 2016). Yossef et al (2013) showed that in western Europe, from July to October, streamflow forecasts are more dependent on meteorolog-ical forcing than they are on initial conditions, even 1 month ahead.…”

Section: Approaches To Seasonal Streamflow Forecastingmentioning

confidence: 99%

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

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.

show abstract

“…We use this term to emphasize the difference of "forecastability" from "predictability", the latter being an objective physical feature of the hydrological system and does not depend on the forecasting methodology (see e.g. Report…, 2002;Shukla et al, 2013). 25 Forecastability maps of four inflow characteristics are shown in Fig.…”

Section: Influence Of the Forecast Issue Date On The Forecast Performmentioning

confidence: 99%

Long-term ensemble forecast of snowmelt inflow into the Cheboksary reservoir under the differently constructed weather scenarios

Gelfan

Moreydo

Motovilov

2017

Preprint

View full text Add to dashboard Cite

Abstract.Development and verification of the long-term ensemble forecast of snowmelt water inflow into the Cheboksary reservoirone of the eleven major river reservoirs of the Volga-Kama Reservoir Cascade, are described. The used forecasting procedure is based on a combination of semi-distributed hydrological model ECOMAG that allows for ensemble calculation 10 of inflow hydrographs with two types of weather ensembles for the lead-time period: (1) observed weather data constructed on the basis of the ESP methodology and (2) synthetic weather data simulated by a weather generator. Ensemble forecasts represented both in deterministic and probability forms were verified by producing hindcasts of water inflow into the Cheboksary reservoir for 35 seasons in 1982 -2016 from the 1st of April for 3 months ahead. Four inflow characteristics were forecasted: volume and maximum discharge of the inflow, as well as amount of days with inflow above two specified 15 thresholds. Results of operational forecast of the recent freshet of 2017 are additionally presented. We found that statistical distributions of the forecasted ensembles calculated under both types of weather scenarios are very close to each other.However, confidence intervals of the forecast statistics is much larger for the ESP-based ensemble. Sensitivity of the forecast performance to changes in the forecast horizon and issue date is analyzed and so-called "forecastability maps" are presented as useful tool for such analysis. 20

show abstract

On the sources of global land surface hydrologic predictability

Cited by 105 publications

References 60 publications

Multimodel seasonal forecasting of global drought onset

Multimodel seasonal forecasting of global drought onset

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Long-term ensemble forecast of snowmelt inflow into the Cheboksary reservoir under the differently constructed weather scenarios

Contact Info

Product

Resources

About