ClepsHresEns-HbvRhein134-SbkReRhein Medium Range Waterlevel Ensemble Forecasts for Waterway Rhine

The datasets provided here were produced as part of the IMPREX project for work package 9, task 3 “Case studies”. Analysis of the datasets are published in Deliverable 9.2 “Framework for the assessment of forecast quality and value in the navigation sector“(Klein & Meißner 2017) and Deliverable 9.4 “Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning“ (Klein & Meißner 2019). The aim of the dataset was to apply the statistical post-processing method Ensemble Model Output Statistics EMOS (Gneiting et al. 2005) to estimate the predictive uncertainty of the waterlevel ensemble forecasts, in order to provide probabilistic water level forecasts to the end users (Klein & Meißner 2019).

Meteorological forcing data used to calculate water-level forecasts with an extended forecast horizon are based on a 68 member multi-model ensemble: 51 ensemble members from ECMWF ENS (1 control forecast and 50 perturbed members) as well as the control forecast ECMWF HRES with a higher spatial resolution (Leutbecher & Palmer 2008, Owens & Hewson 2018), and the 16 members of the limited-area ensemble prediction system by the consortium for small-scale modelling COSMO LEPS (Montani et al. 2011, Marsigli et al. 2014). Archived meteorological real-time forecasts of the period January 2008 to December 2015 have been used to produce this comprehensive water level re-forecast data set.

The conceptual, semi-distributed rainfall-runoff model HBV-96 (Bergström 1995, Lindstrom et al. 1997) is applied to calculate the flow forecasts used as boundary conditions and lateral inflows of the hydrodynamic model SOBEK (Deltares 2012) used to calculate water level forecasts along the river Rhine. The river Rhine basin is divided into 134 subbasins which are further subdivided into hydrological response units (HRU) according to land use and elevation classes. The flow formation processes are calculated on those HRUs. The model calculates flow with a temporal resolution of 1 h using temperature and precipitation fields that have been interpolated over the subbasins as meteorological input.

The hydrodynamic model suite SOBEK is used as one-dimensional model, which uses cross-section information of the River Rhine as well as its main tributaries. The distance between the cross-sections, which cover the river bathymetry as well as its floodplains, is non-equidistant and ranges between 100 m and 800 m. As the main tributaries of the River Rhine are impounded rivers (e.g. Moselle, Main) the SOBEK-model includes several weirs with their specific control rules in order to simulate the real behaviour of these elements, too.

The flow and water level forecasts were initialized each day at 06:00 UTC, which means that observed real-time meteorological data, interpolated to the subbasins of the hydrological model, up to the forecast date were used as forcings of the hydrological model and observed flow was used as input for the hydrodynamic model to initialize the model states. For the forecast period meteorological ensemble runs from the different Numerical Weather Prediction (NWP) models interpolated to the subbasins were used as forcings of the hydrological model. Flow forecasts of the large tributaries of the river Rhine simulated with HBV were then used as input for the hydrodynamic model. To reduce the error of the input to the hydrodynamic model autoregressive error correction models (Broersen & Weerts 2005) was applied using the differences between the simulation of the model using meteorological observations as forcings and the actually past flow observations as training data. This error correction reduces the error of the hydrological model at the forecast initialization time to zero. To reduce the error of the waterlevel forecasts obtained by running the hydrodynamic model, again autoregressive error correction models were applied using the differences between the water level simulation using observed flow as input and the water-level observations of the past.

Dataset H_OBS_RHINE.nc:

Hourly observed water levels of the gauges Kaub, Koeln, Ruhrort / Rhine for the period 2008–2016 stored as variable h_obs(time=78912, stations=3).

Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG).

float h_obs(time=78912, stations=3);
:units = "cm";
:_FillValue = -9999.0f; // float
:long_name = "observed waterlevel";
:coordinates = "lat lon";

Dataset H_MM_HBV134_SOBEK.nc

Hourly forecasted water level of the hydrodynamic mode SOBEK forced by flow forecasts of the hydrological model HBV134 forced by a multi-model meteorological ensemble. Daily forecasts initialized at 06:00 UTC of the period 2008-01-01 to 2015-12-31 with a lead time of 240 hours. Gauges Kaub, Koeln, Ruhrort / Rhine.

Forecast values are stored in the variable h_fcast_ens(time=2869, lead_time=241, realization=68, stations=3), first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension station. ECMWF-HRES first realization, COSMO-LEPS realization 2 – 17, ECMWF-ENS realization 18- 68.

float h_fcast_ens(time=2869, lead_time=241, realization=68, stations=3);
:_FillValue = -9999.0f; // float
:long_name = "forecast waterlevel ensemble";
:units = "cm";
:coordinates = "lat lon";

Literature

Bergström, S. (1995): The HBV model. In: V. P. Singh (Ed.): Computer models of watershed hydrology. Water Resources Publications, Colorado, USA, 443-476

Broersen, P. & A. Weerts (2005): Automatic Error Correction of Rainfall-Runoff models in Flood Forecasting Systems. Conference Proceedings: IMTC 2005 – Instrumentation and Measurement Technology Conference, Ottawa, Canada, 17-19 May 2005.

Deltares (2012): Technical Reference SOBEK-RE. Deltares, Delft, The Netherlands

Gneiting, T., A. E. Raftery, A. H. Westveld & T. Goldman (2005): Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review 133(5), 1098-1118

Klein, B. & D. Meissner (2017): Framework for the assessment of forecast quality and value in the navigation sector. Deliverable 9.2, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, http://www.imprex.eu/system/files/generated/files/resource/d9-2-imprex-v2-0.pdf

Klein, B. & D. Meissner (2019): Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning. Deliverable 9.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf

Leutbecher, M. & T. N. Palmer (2008): Ensemble forecasting. Journal of Computational Physics 227(7), 3515-3539

Lindstrom, G., B. Johansson, M. Persson, M. Gardelin & S. Bergstrom (1997): Development and test of the distributed HBV-96 hydrological model. Journal of Hydrology 201(1-4), 272-288

Marsigli, C., A. Montani & T. Paccagnella (2014): Perturbation of initial and boundary conditions for a limited-area ensemble: multi-model versus single-model approach. Quarterly Journal of the Royal Meteorological Society 140(678), 197-208

Montani, A., D. Cesari, C. Marsigli & T. Paccagnella (2011): Seven years of activity in the field of mesoscale ensemble forecasting by the COSMO-LEPS system: main achievements and open challenges. Tellus Series a-Dynamic Meteorology and Oceanography 63(3), 605-624

Owens, R. & T. R. E. Hewson (2018): ECMWF Forecast User Guide. ECMWF, Reading, doi: 10.21957/m1cs7h