https://meetingorganizer.copernicus.org/EGU2010/EGU2010-15247.pdf

Ensemble flood forecasting: a review.

The Prediction in Ungauged Basins (PUB) initiative of the International Association of Hydrological Sciences (IAHS), launched in 2003 and concluded by the PUB Symposium 2012 held in Delft (23–25 October 2012), set out to shift the scientific culture of hydrology towards improved scientific understanding of hydrological processes, as well as associated uncertainties and the development of models with increasing realism and predictive power. This paper reviews the work that has been done under the six science themes of the PUB Decade and outlines the challenges ahead for the hydrological sciences community.Editor D. KoutsoyiannisCitation Hrachowitz, M., Savenije, H.H.G., Bloschl, G., McDonnell, J.J., Sivapalan, M., Pomeroy, J.W., Arheimer, B., Blume, T., Clark, M.P., Ehret, U., Fenicia, F., Freer, J.E., Gelfan, A., Gupta, H.V., Hughes, D.A., Hut, R.W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P.A., Uhlenbrook, S., Wagener, T., Winsemius, H.C., Woods, R.A., Zehe, E., and Cudennec, C., 2013. A d...

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A decade of Predictions in Ungauged Basins (PUB)—a review

[1] The requirements for hydrological models have increased considerably during the previous decades to cope with the resolution of extensive remotely sensed data sets and a number of demanding applications. Existing models exhibit deficiencies such as overparameterization, the lack of an effective technique to integrate the spatial heterogeneity of physiographic characteristics, and the nontransferability of parameters across scales and locations. A multiscale parameter regionalization (MPR) technique is proposed as a way to address these issues simultaneously. Using this technique, parameters at a coarser scale, in which the dominant hydrological processes are represented, are linked with their corresponding ones at a finer resolution in which input data sets are available. The linkage is done with upscaling operators such as the harmonic mean, among others. Parameters at the finer scale are regionalized through nonlinear transfer functions which link basin predictors with global parameters to be determined through calibration. MPR was compared with a standard regionalization (SR) method in which basin predictors instead of model parameters are first aggregated. Both methods were tested in a basin located in Germany using a distributed hydrologic model. Results indicate that MPR is superior to SR in many respects, especially if global parameters are transferred from coarser to finer scales. Furthermore, MPR, as opposed to SR, preserves the spatial variability of state variables and conserves the mass balance with respect to a control scale. Cross-validation tests indicate that the transferability of the global parameters to ungauged locations is possible.

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Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale

https://purehost.bath.ac.uk/ws/files/100824407/ReviewPaper_Accepted.pdf

Review of trend analysis and climate change projections of extreme precipitation and floods in Europe

There is growing concern that flooding is becoming more frequent and severe in Europe. A better understanding of flood regime changes and their drivers is therefore needed. The paper reviews the current knowledge on flood regime changes in European rivers that has traditionally been obtained through two alternative research approaches. The first approach is the data-based detection of changes in observed flood events. Current methods are reviewed together with their challenges and opportunities. For example, observation biases, the merging of different data sources and accounting for nonlinear drivers and responses. The second approach consists of modelled scenarios of future floods. Challenges and opportunities associated with flood change scenarios are discussed such as fully accounting for uncertainties in the modelling cascade and feedbacks. To make progress in flood change research, we suggest that a synthesis of these two approaches is needed. This can be achieved by focusing on long duration records and flood-rich and flood-poor periods rather than on short duration flood trends only, by formally attributing causes of observed flood changes, by validating scenarios against observed flood regime dynamics, and by developing low-dimensional models of flood changes and feedbacks. The paper finishes with a call for a joint European flood change research network.

/pdf/understanding-flood-regime-changes-in-europe-a-state-of-the-2gfidja0l1.pdf

Understanding Flood Regime Changes in Europe: A state of the art assessment

https://www.hydro.tuwien.ac.at/fileadmin/mediapool-hydro/Publikationen/bloeschl/2008_Komma_JH.pdf

Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting

This paper presents a distributed model that is in operational use for forecasting flash floods in northern Austria. The main challenge in developing the model was parameter identification which was addressed by a modelling strategy that involved a model structure defined at the model element scale and multi-source model identification. The model represents runoff generation on a grid basis and lumped routing in the river reaches. Ensemble Kalman Filtering is used to update the model states (grid soil moisture) based on observed runoff. The forecast errors as a function of forecast lead time are evaluated for a number of major events in the 622km^2 Kamp catchment and range from 10% to 30% for 4-24h lead times, respectively.

A spatially distributed flash flood forecasting model

Quantifying the uncertainty of flood forecasts by ensemble methods is becoming increasingly important for operational purposes. The aim of this paper is to ex- amine how the ensemble distribution of precipitation fore- casts propagates in the catchment system, and to interpret the flood forecast probabilities relative to the forecast errors. We use the 622 km 2 Kamp catchment in Austria as an exam- ple where a comprehensive data set, including a 500 yr and a 1000 yr flood, is available. A spatially-distributed contin- uous rainfall-runoff model is used along with ensemble and deterministic precipitation forecasts that combine rain gauge data, radar data and the forecast fields of the ALADIN and ECMWF numerical weather prediction models. The analy- ses indicate that, for long lead times, the variability of the precipitation ensemble is amplified as it propagates through the catchment system as a result of non-linear catchment re- sponse. In contrast, for lead times shorter than the catch- ment lag time (e.g. 12 h and less), the variability of the pre- cipitation ensemble is decreased as the forecasts are mainly controlled by observed upstream runoff and observed pre- cipitation. Assuming that all ensemble members are equally likely, the statistical analyses for five flood events at the Kamp showed that the ensemble spread of the flood forecasts is always narrower than the distribution of the forecast errors. This is because the ensemble forecasts focus on the uncer- tainty in forecast precipitation as the dominant source of un- certainty, and other sources of uncertainty are not accounted for. However, a number of analyses, including Relative Op- erating Characteristic diagrams, indicate that the ensemble spread is a useful indicator to assess potential forecast errors for lead times larger than 12 h.

/pdf/ensemble-prediction-of-floods-catchment-non-linearity-and-2vimlm84c9.pdf

Ensemble prediction of floods – catchment non-linearity and forecast probabilities

Floods in austria

This paper proposes a framework for identifying the parameters of a lumped routing model in small to medium sized catchments where lateral inflows can be large but poorly defined. In a first step, a priori estimates of the parameters are made based on topography, aerial photographs, flood marks and field surveys. In a second step, runoff data are analysed of reservoir release events and convective events where no rainfall in the direct catchments occurred. In a third step the routing model is calibrated to the results of hydrodynamic models for scenarios of different magnitudes. In a fourth step, these pieces of information are combined, allowing for soft expert judgement to be incorporated. In a fifth step, the routing parameters are fine tuned to observed flood events where lateral inflows are estimated by a rainfall—runoff model. The framework is illustrated by the Kamp flood forecasting system in Austria that has been in operational use since 2006.

/pdf/identifying-runoff-routing-parameters-for-operational-flood-ffra2tqdmi.pdf

Christian Reszler

Papers

Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting

A spatially distributed flash flood forecasting model

Ensemble prediction of floods – catchment non-linearity and forecast probabilities

Floods in austria

Identifying runoff routing parameters for operational flood forecasting in small to medium sized catchments