Metadata

Name

SEAS5/System4-LARSIM_ME Seasonal Streamflow Forecasts for German Waterways

Repository

ZENODO

Identifier

doi:10.5281/zenodo.3696757

Description

The datasets provided were produced as part of the IMPREX project for work package 4, task 1 “Development of the regional and European scale reforecast dataset of hydrological extremes “, work package 4, task 2 “Analysis of the impact of changes in precipitation attributes from short to medium and climatic ranges” and work package 9, task 3 “Case studies”. Analysis of the datasets are published in Meißner et al. 2017, in Deliverable 4.2 „ The sensitivity of sub-seasonal to seasonal streamflow forecasts to meteorological forcing quality, modelled hydrology and the initial hydrological conditions “ (Arnal et al. 2017) and in Deliverable 4.3 “Forecast skill developments” (Weerts et al. 2019). The aim was to evaluate the potential skill of seasonal streamflow forecasting for the German waterways Rhine, Elbe and Danube.

As seasonal meteorological forecast data the reforecast dataset from ECMWF’s Seasonal Forecast System 4 (S4 hereafter) (Molteni et al. 2011) as well as from the fifth generation of ECMWF’s Seasonal forecasting system SEAS5 (ECMWF 2017, Johnson et al. 2018, Owens & Hewson 2018) of the period 1981 – 2016 were used.

The horizontal resolution of System 4 is approximately 80 km. In operational mode the ensemble consists of 51 members generated by using an ensemble of initial conditions and by the use of stochastic physics. Re-forecasts starting on the 1st of each month for the years 1981-2016 are generated with the same model as used for the operational forecast. For the period 1981 – 2011 the ensemble size varies between 15 members (initialization months January, March, April, June, July, September, October, December) and 51 members (for the remaining months). Since 2012, the ensemble size is 51 members all over the year (operational forecasts).

The fifth generation of ECMWF seasonal forecasting system SEAS5 replaced System 4 in November 2017. The horizontal resolution of the model is 0.4°x0.4° (approx. 36 km). The ensemble consists of 51-members created using a combination of Sea Surface Temperature SST and atmospheric initial condition perturbations and the activation of stochastic physics (ECMWF 2017). Re-Forecasts with the ensemble size of 25 members starting on the 1st of each month for the years 1981-2016 are generated with the same model as used for the operational forecast.

The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. For more details about the model see Meißner et al. (2017).

The precipitation and temperature data, used to force the hydrological model in simulation mode up to the initialization of the particular forecast, is taken from the E-OBS dataset, version 18 (Haylock et al. 2008). The downward surface solar radiation is extracted from the ERA-Interim reanalysis (Dee et al. 2011) for the period 1979-2018. For further details on data processing see Meißner et al. (2017).

As meteorological seasonal forecasts tend to drift towards the model climate with increasing lead-time, the outputs daily total precipitation and air temperature from S4, interpolated to a 50 km x 50 km grid (multiple of the 5 km x 5 km model grid) and from SEAS5, interpolated to a 25 km x 25 km grid, respectively, were drift-corrected with the meteorological observation dataset used for the baseline simulation. As drift correction method the quantile-quantile method (Piani et al. 2010) was used. We corrected daily values of the different variables on a monthly basis, which means each daily value of the same month is corrected by the same scaling. Separate drift correction factors were estimated for each forecast initialization date (calendar month) and monthly lead time (month 1 to month 7) based on the reforecast datasets. In the final step the corrected precipitation and temperature were downscaled to the 5 km by 5 km model grid and used as forcing to create the streamflow re-forecast dataset with LARSIM-ME.

Dataset Q_OBS_DE.nc:

Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable q_obs(time=24472, stations=8).

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)

https://zenodo.org/record/3696446

Dataset Q_EOBS_LME.nc:

Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from the EOBS dataset and ERA-Interim stored as variable q_sim (time=13880, stations=8). Period 1979-2016, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.

float q_sim(time=13880, stations=8);
:units = "m3/s";
:_FillValue = -9999.0f; // float
:long_name = "simulated streamflow";
:coordinates = "lat lon";

Dataset Q_System4_LME.nc:

Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature and precipitation of ECMWF’s Seasonal Forecast System 4 re-forecasts initialized 1st of each month for the years 1981-2016 with a lead time of 7 months. Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.

Forecast values are stored as variable q_fcast_ens(time=432, lead_time=215, realization=51, stations=8), first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension stations.

loat q_fcast_ens(time=432, lead_time=215, realization=51, stations=8);
:_FillValue = -9999.0f; // float
:long_name = "forecast streamflow ensemble";
:units = "m3/s";
:coordinates = "lat lon";

Dataset Q_SEAS5_LME.nc:

Mean daily forecasted flow of the hydrological model LARSIM-ME forced by air temperature and precipitation of ECMWF’s Seasonal Forecast System SEAS5 re-forecasts initialized 1st of each month for the years 1981-2016 with a lead time of 7 months. Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.

Forecast values are stored in the variable q_fcast_ens(time=432, lead_time=215, realization=25, stations=8), first dimension forecast dates, second dimension lead time, third dimension ensemble member, fourth dimension stations.

float q_fcast_ens(time=432, lead_time=215, realization=25, stations=8);
:_FillValue = -9999.0f; // float
:long_name = "forecast streamflow ensemble";
:units = "m3/s";
:coordinates = "lat lon";

Literature

Arnal, L., H. Cloke, L. Magnusson, B. Klein, D. Meissner, A. de  Tomas, J. Hunink, I. Pechlivanidis, L. Crochemore, S. Suarez, A. Solera, J. Andreu, J. Knight, F. Liggins, A. Weerts, M. H. Ramos & G. Thirel (2017): The sensitivity of sub-seasonal to seasonal streamflow forecasts to meteorological forcing quality, modelled hydrology and the initial hydrological conditions. Deliverable 4.2, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, http://www.imprex.eu/system/files/generated/files/resource/d4-2-imprex-v1-0.pdf

Dee, D. P., S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy, H. Hersbach, E. V. Holm, L. Isaksen, P. Kallberg, M. Kohler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J. J. Morcrette, B. K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J. N. Thepaut & F. Vitart (2011): The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society 137(656), 553-597

ECMWF (2017): SEAS5 user guide - Version 1.1. ECMWF, Reading, UK

Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P. D. Jones & M. New (2008): A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research: Atmospheres 113(D20), D20119

Johnson, S. J., T. N. Stockdale, L. Ferranti, M. A. Balmaseda, F. Molteni, L. Magnusson, S. Tietsche, D. Decremer, A. Weisheimer, G. Balsamo, S. Keeley, K. Mogensen, H. Zuo & B. Monge-Sanz (2018): SEAS5: The new ECMWF seasonal forecast system. Geosci. Model Dev. Discuss. 2018, 1-44

Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.

Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401-6423

Molteni, F., T. Stockdale, M. Balmaseda, G. Balsamo, R. Buizza, L. Ferranti, L. Magnusson, K. Mogensen, T. Palmer & F. Vitart (2011): The new ECMWF seasonal forecast system (System 4). ECMWF Research Department Technical Memorandum n. 656, Shinfield Park, Reading

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

Piani, C., J. O. Haerter & E. Coppola (2010): Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99(1-2), 187-192

Weerts, A., F. Silvestro, L. Magnusson, B. Klein, I. Pechlivanidis, F. Wetterhall, D. Lavers, E. Gascon, J. Day, S. Hagelin, M. Lindskog & B. van Osnabrugge (2019): Forecast skill developments. Deliverable 4.3, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811

Data or Study Types

multiple

Source Organization

Unknown

Access Conditions

available

Year

2020

Access Hyperlink

https://doi.org/10.5281/zenodo.3696757

Distributions

• Encoding Format: HTML ; URL: https://doi.org/10.5281/zenodo.3696757