Understanding ‘Problem of Fit’ between Institutions and Environment: Lessons from the Rhine River Basin

Integrated water resources management in the Ruhr River Basin, Germany

Water Science & Technology ◽

10.2166/wst.2003.0674 ◽

2003 ◽

Vol 47 (7-8) ◽

pp. 81-86 ◽

Cited By ~ 5

Author(s):

H. Bode ◽

P. Evers ◽

D.R. Albrecht

Keyword(s):

Water Resources ◽

Water Supply ◽

River Basin ◽

Water Resources Management ◽

Ecological Status ◽

Integrated Water Resources Management ◽

Rhine River ◽

Wastewater Disposal ◽

Resources Management ◽

The People

The Ruhr, with an average flow of 80.5 m3/s at its mouth, is a comparatively small tributary to the Rhine River that has to perform an important task: to secure the water supply of more than 5 million people and of the industry in the densely populated region north of the river. The complex water management system and network applied by the Ruhrverband in the natural Ruhr River Basin has been developed step by step, over decades since 1913. And from the beginning, its major goal has been to achieve optimal conditions for the people living in the region. For this purpose, a functional water supply and wastewater disposal infrastructure has been built up. The development of these structures required and still requires multi-dimensional planning and performance. Since the river serves as receiving water and at the same time as a source of drinking water, the above-standard efforts of Ruhrverband for cleaner water also help to conserve nature and wildlife. Ruhrverband has summed up its environmental awareness in the slogan: “For the people and for the environment”. This basic water philosophy, successfully applied to the Ruhr for more than 80 years, will be continued in accordance with the new European Water Framework Directive, enacted in 2000, which demands integrated water resources management in natural river basins, by including the good ecological status of surface waterbodies as an additional goal.

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Inter-annual variability of runoff and climate within the Upper Rhine River basin, 1808–2007

Hydrological Sciences Journal ◽

10.1080/02626667.2010.536549 ◽

2011 ◽

Vol 56 (1) ◽

pp. 34-50 ◽

Cited By ~ 9

Author(s):

Pascal Hänggi ◽

Rolf Weingartner

Keyword(s):

River Basin ◽

Rhine River ◽

Annual Variability ◽

Upper Rhine

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Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

Hydrology and Earth System Sciences ◽

10.5194/hess-19-2079-2015 ◽

2015 ◽

Vol 19 (4) ◽

pp. 2079-2100 ◽

Cited By ~ 52

Author(s):

N. Tangdamrongsub ◽

S. C. Steele-Dunne ◽

B. C. Gunter ◽

P. G. Ditmar ◽

A. H. Weerts

Keyword(s):

Correlation Coefficient ◽

River Basin ◽

Hydrological Cycle ◽

Water Storage ◽

Model Parameters ◽

Hydrological Models ◽

Rhine River ◽

Terrestrial Water Storage ◽

Grace Data ◽

Terrestrial Water

Abstract. The ability to estimate terrestrial water storage (TWS) realistically is essential for understanding past hydrological events and predicting future changes in the hydrological cycle. Inadequacies in model physics, uncertainty in model land parameters, and uncertainties in meteorological data commonly limit the accuracy of hydrological models in simulating TWS. In an effort to improve model performance, this study investigated the benefits of assimilating TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data into the OpenStreams wflow_hbv model using an ensemble Kalman filter (EnKF) approach. The study area chosen was the Rhine River basin, which has both well-calibrated model parameters and high-quality forcing data that were used for experimentation and comparison. Four different case studies were examined which were designed to evaluate different levels of forcing data quality and resolution including those typical of other less well-monitored river basins. The results were validated using in situ groundwater (GW) and stream gauge data. The analysis showed a noticeable improvement in GW estimates when GRACE data were assimilated, with a best-case improvement of correlation coefficient from 0.31 to 0.53 and root mean square error (RMSE) from 8.4 to 5.4 cm compared to the reference (ensemble open-loop) case. For the data-sparse case, the best-case GW estimates increased the correlation coefficient from 0.46 to 0.61 and decreased the RMSE by 35%. For the average improvement of GW estimates (for all four cases), the correlation coefficient increases from 0.6 to 0.7 and the RMSE was reduced by 15%. Only a slight overall improvement was observed in streamflow estimates when GRACE data were assimilated. Further analysis suggested that this is likely due to sporadic short-term, but sizeable, errors in the forcing data and the lack of sufficient constraints on the soil moisture component. Overall, the results highlight the benefit of assimilating GRACE data into hydrological models, particularly in data-sparse regions, while also providing insight on future refinements of the methodology.

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