Modification of input datasets for the Ensemble Streamflow Prediction based on large-scale climatic indices and weather generator

Šípek, Václav; Daňhelka, Jan

doi:10.1016/j.jhydrol.2015.07.008

Cited by 13 publications

(7 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…the statistical consistency between observed frequencies and forecast probabilities) and the capacity of ensemble predictions to detect critical events. In the Czech Republic, Šípek et Daňhelka (2015) ran a hydrological model with synthetic series of precipitation and temperature generated from climate forecasts and historical meteorological series. The advantage of this modified ESP approach for forecasting was the gain in sharpness, as well as a better capacity to detect high-and low-flow events.…”

Section: Selecting Ensembles For Long-range 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

Section: Selecting Ensembles For Long-range 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

show abstract

“…In other cases, the value of MI increases with the increasing dependence between variables. The kernel density approach (Silverman 1998) was used to estimate the joint and marginal densities in Eq. (1) according to where x is a d-dimensional point in which the estimate p is calculated, K is the kernel function and h is the bandwidth.…”

Section: Mutual Informationmentioning

confidence: 99%

Catchment Storage and its Influence on Summer Low Flows in Central European Mountainous Catchments

Šípek

Jeníček

Hnilica

et al. 2021

Water Resour Manage

Self Cite

View full text Add to dashboard Cite

The objective of this study was to determine the role of spring catchment water storage on the evolution of low flows in central European mountainous catchments. The study analysed 58 catchments for which catchment storage, represented by snow, soil water and groundwater storages, was determined by the HBV hydrological model over a 35-year period. The spring catchment storage was related to several streamflow indices describing low flow periods using the mutual information criterion. The mean runoff in the summer and autumn periods was mostly related to rainfall sums from the respective season. The median relative contribution of rainfall to the total mutual information value was 48.4% in summer, and 44.2% in autumn period, respectively. The relative contribution of soil water and groundwater storages was approximately 25% for each of the components. In contrast, the minimum runoff, its duration and deficit runoff volume, were equally related to both catchment storage and seasonal rainfall, especially in the autumn period.

show abstract

“…This procedure has been widely used as a post-processing of ensemble meteorological forecast fields for streamflow forecasting (see e.g. Robertson et al, 2013;Verkade et al, 2013;Demargne et al, 2014;Šípek and Daňhelka, 2015). Vrac and Friederichs (2015) also adapted it recently for multivariate bias correction of downscaled climate simulations.…”

Section: Uncertainties In Reconstructed Streamflowmentioning

confidence: 99%

Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871

Caillouet¹,

Vidal²,

Sauquet³

et al. 2017

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatiotemporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989-1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

show abstract

Modification of input datasets for the Ensemble Streamflow Prediction based on large-scale climatic indices and weather generator

Cited by 13 publications

References 60 publications

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Catchment Storage and its Influence on Summer Low Flows in Central European Mountainous Catchments

Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871

Contact Info

Product

Resources

About