Operational aspects of asynchronous filtering for flood forecasting

Rakovec, Oldřich; Weerts, Albrecht; Sumihar, Julius; Uijlenhoet, R.

doi:10.5194/hess-19-2911-2015

Cited by 42 publications

(29 citation statements)

References 50 publications

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“…For operational hydrological forecasting, high‐resolution forcing data are needed to derive the best estimates of the initial conditions at the start of the forecast. Subsequently, data assimilation techniques (Liu et al, ; Rakovec et al, ) may be used to further improve these initial state estimates. High‐resolution historical precipitation data are also needed to resolve the model tendencies of the weather forecasts with equally high accuracy.…”

Section: Introductionmentioning

confidence: 99%

genRE: A Method to Extend Gridded Precipitation Climatology Data Sets in Near Real‐Time for Hydrological Forecasting Purposes

Osnabrugge

Weerts

Uijlenhoet

2017

Water Resources Research

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To enable operational flood forecasting and drought monitoring, reliable and consistent methods for precipitation interpolation are needed. Such methods need to deal with the deficiencies of sparse operational real‐time data compared to quality‐controlled offline data sources used in historical analyses. In particular, often only a fraction of the measurement network reports in near real‐time. For this purpose, we present an interpolation method, generalized REGNIE (genRE), which makes use of climatological monthly background grids derived from existing gridded precipitation climatology data sets. We show how genRE can be used to mimic and extend climatological precipitation data sets in near real‐time using (sparse) real‐time measurement networks in the Rhine basin upstream of the Netherlands (approximately 160,000 km2). In the process, we create a 1.2 × 1.2 km transnational gridded hourly precipitation data set for the Rhine basin. Precipitation gauge data are collected, spatially interpolated for the period 1996–2015 with genRE and inverse‐distance squared weighting (IDW), and then evaluated on the yearly and daily time scale against the HYRAS and EOBS climatological data sets. Hourly fields are compared qualitatively with RADOLAN radar‐based precipitation estimates. Two sources of uncertainty are evaluated: station density and the impact of different background grids (HYRAS versus EOBS). The results show that the genRE method successfully mimics climatological precipitation data sets (HYRAS/EOBS) over daily, monthly, and yearly time frames. We conclude that genRE is a good interpolation method of choice for real‐time operational use. genRE has the largest added value over IDW for cases with a low real‐time station density and a high‐resolution background grid.

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Section: Introductionmentioning

confidence: 99%

genRE: A Method to Extend Gridded Precipitation Climatology Data Sets in Near Real‐Time for Hydrological Forecasting Purposes

Osnabrugge

Weerts

Uijlenhoet

2017

Water Resources Research

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“…Furthermore, for linear dynamics, 4DEnKF is equivalent to the instantaneous assimilation of the measured data (Hunt et al, 2004). Similarly to 4DEnKF, Sakov et al (2010) proposed a modification of the EnKF, the asynchronous ensemble Kalman filter (AEnKF), to assimilate asynchronous observations (Rakovec et al, 2015). Contrary to the EnKF, in the AEnKF, current and past observations are simultaneously assimilated at a single analysis step without the use of an adjoint model.…”

Section: Introductionmentioning

confidence: 99%

Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?

Mazzoleni

Verlaan

Alfonso

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate these observations into mathematical water models have also been developed. Besides, in recent years, the continued technological advances, in combination with the growing inclusion of citizens in participatory processes related to water resources management, have encouraged the increase of citizen science projects around the globe. In turn, this has stimulated the spread of low-cost sensors to allow citizens to participate in the collection of hydrological data in a more distributed way than the classic static physical sensors do. However, two main disadvantages of such crowdsourced data are the irregular availability and variable accuracy from sensor to sensor, which makes them challenging to use in hydrological modelling. This study aims to demonstrate that streamflow data, derived from crowdsourced water level observations, can improve flood prediction if integrated in hydrological models. Two different hydrological models, applied to four case studies, are considered. Realistic (albeit synthetic) time series are used to represent crowdsourced data in all case studies. In this study, it is found that the data accuracies have much more influence on the model results than the irregular frequencies of data availability at which the streamflow data are assimilated. This study demonstrates that data collected by citizens, characterized by being asynchronous and inaccurate, can still complement traditional networks formed by few accurate, static sensors and improve the accuracy of flood forecasts.

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“…The question of the number of reservoir to update is more complex as few global patterns emerge from the results. It is common practice to update all model states variable but this does not systematically lead to the best results (see also McMillan et al, 2013;Rakovec et al, 2015). In Fig 8, model 13 illustrates this since the updating of some state variable sub-ensembles shows improved performance for first, second and third quartiles.…”

Section: Influence Of the Choice Of States Variablesmentioning

confidence: 91%

On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments

Thiboult

Anctil

2015

Journal of Hydrology

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Forecast reliability and accuracy is a prerequisite for successful hydrological applications. This aim may be attained by using data assimilation techniques such as the popular Ensemble Kalman filter (EnKF). Despite its recognized capacity to enhance forecasting by creating a new set of initial conditions, implementation tests have been mostly carried out with a single model and few catchments leading to case specific conclusions. This paper performs an extensive testing to assess ensemble bias and reliability on 20 conceptual lumped models and 38 catchments in the Province of Québec with perfect meteorological forecast forcing. The study confirms that EnKF is a powerful tool for short range forecasting but also that it requires a more subtle setting than it is frequently recommended. The success of the updating procedure depends to a great extent on the specification of the hyper-parameters. In the implementation of the EnKF, the identification of the hyper-parameters is very unintuitive if the model error is not explicitly accounted for and best estimates of forcing and observation error lead to overconfident forecasts. It is shown that performance are also related to the choice of updated state variables and that all states variables should not systematically be updated. Additionally, the improvement over the open loop scheme depends on the watershed and hydrological model structure, as some models exhibit a poor compatibility with EnKF updating. Thus, it is not possible to conclude in detail on a single ideal manner to identify an optimal implementation; conclusions drawn from a unique event, catchment, or model are likely to be misleading since transferring hyper-parameters from a case to another may be hazardous. Finally, achieving reliability and bias jointly is a daunting challenge as the optimization of one score is done at the cost of the other.

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Operational aspects of asynchronous filtering for flood forecasting

Cited by 42 publications

References 50 publications

genRE: A Method to Extend Gridded Precipitation Climatology Data Sets in Near Real‐Time for Hydrological Forecasting Purposes

genRE: A Method to Extend Gridded Precipitation Climatology Data Sets in Near Real‐Time for Hydrological Forecasting Purposes

Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?

On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments

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