RCP8.5-ECEARTH-RACMO-LARSIM_ME Climate Flow Projection Data for German Waterways

The datasets provided here were produced as part of the IMPREX project for work package 4, task 4 „Improving prediction on the climate scale“ and work package 9, task 3 “Case studies”. Analysis of the datasets are published in Deliverable 4.4 „Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale“ (Falloon et al 2019). The aim was to study the impact of internal climate model variability and bias correction method on the climate change signal of relevant flow indicators for the German waterways Rhine, Elbe and Danube.

To assess the impact of internal variability of the global climate model on future changes of flow, precipitation, temperature and global radiation of the 16-member ensemble generated with the RCM KNMI-RACMO2 driven by the GCM EC-EARTH 2.3 provided by WP3 of IMPREX were used. EC-EARTH was run 16 times from 1850 to 2100, each member starting from a slightly different initial state, under forcing of historical emissions until 2005 and the RCP8.5 greenhouse gas concentration pathway from 2006 onwards. Each of the EC-EARTH members was subsequently dynamically downscaled using KNMI-RACMO2 on a 0.11° (~12 km) resolved domain (Aalbers et al. 2018).

To correct the systematic model biases of climate models different bias correction methods were applied: (1) no bias correction, (2) linear scaling (Lenderink et al. 2007) and (3) quantile-quantile mapping (Piani et al. 2010). Bias correction relationships were derived for five-day periods (for each variable and location, in total 73 bias correction relationships were derived) including 13 days before and after the considered five-day period (total window size was 31 days) from the observations and values of the regional climate simulations. The period used to estimate the bias correction relationships was 1971-2000.

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. As observed meteorological forcings, precipitation, air temperature and global radiation from the HYRAS data set (Rauthe et al. 2013) available for the 5 km x 5 km model grid and the period 1951-2015 were used. The hydrological model was calibrated using the automatic calibration scheme Shuffled Complex Evolution SCE-UA algorithm (Duan et al. 1994). For more details about the model see Meißner et al. (2017).

The meteorological variables air temperature, precipitation and global radiation produced by the KNMI RACMO-EC-EARTH 16 member ensemble (period 1951-2100) were interpolated to a 25 km x 25 km grid and afterwards bias corrected with respect to the observation data (HYRAS) used for calibration of the hydrological model LARSIM. From this 25 km x 25 km grid the bias corrected variables were downscaled to the 5 km x 5 km model grid of LARSIM using monthly background climatology fields on the 5 km x 5 km target grid of the HYRAS dataset. The bias-corrected and downscaled data was then used as meteorological forcing of LARSIM to calculate flow projections for the rivers Rhine, Elbe and Upper Danube (up to the German/Austrian border).

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)

float q_obs(time=24472, stations=8);
:units = "m3/s";
:_FillValue = -9999.0f; // float
:long_name = "observed streamflow";
:coordinates = "lat lon";

Dataset Q_HYRAS_LME.nc:

Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from the HYRAS dataset stored as variable q_sim (time=23741, stations=8). Period 1951-2015, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.

float q_sim(time=23741, stations=8);
:units = "m3/s";
:_FillValue = -9999.0f; // float
:long_name = "simulated streamflow";
:coordinates = "lat lon";

Q_RCP85_ECEARTH_RACMO_[bc]_LME.nc:

Mean daily projected flow of the hydrological model LARSIM-ME forced by 16 realizations of RCP8.5-ECEARTH-RACMO stored as variable q_sim(time=54787, realization=16, stations=8), first dimension time, second dimension realization and third dimension stations. Bias correction of meteorological forcings [bc]: NOBC: no bias correction, LS: linear scaling, QQMAP Quantile-Quantile Mapping. Period 1951-2100, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.

float q_sim(time=54787, realization=16, stations=8);
:units = "m3/s";
:_FillValue = -9999.0f; // float
:long_name = "projected streamflow";
:coordinates = "lat lon";

Literature

Aalbers, E. E., G. Lenderink, E. van Meijgaard & B. J. J. M. van den Hurk (2018): Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? Climate Dynamics 50(11), 4745-4766

Duan, Q., S. Sorooshian & V. K. Gupta (1994): Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of Hydrology 158(3–4), 265-284

Falloon, P., K. Williams, J. Andreu, A. Solera, S. Suárez-Almiñana, B. Klein, D. Meissner, J. Hunink, J. Eekhout & J. de Vente (2019): Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale. Deliverable 4.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, https://imprex.eu/system/files/generated/files/resource/imprex-deliverablereport-d4-4-final-1.pdf

Lenderink, G., A. Buishand & W. van Deursen (2007): Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrology and Earth System Sciences 11(3), 1143-1159

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

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

Rauthe, M., H. Steiner, U. Riediger, A. Mazurkiewicz & A. Gratzki (2013): A Central European precipitation climatology - Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorologische Zeitschrift 22(3), 235-256