Estimation of impact of climate change on the peak discharge probability of the river Rhine View Full Text


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Article Info

DATE

1994-06

AUTHORS

Jaap Kwadijk, Hans Middelkoop

ABSTRACT

RHINEFLOW is a GIS based water balance model that has been developed to study the changes in the water balance compartments of the river Rhine basin on a monthly time basis. The model has been designed to study the sensitivity of the Rhine discharge to a climate change. The calculated discharge has been calibrated and validated on the period 1956 to 1980. For this period the model efficiency of RHINEFLOW is between 0.74 and 0.81 both for the entire Rhine and for its tributaries. Also calculated values for variations in other compartments, e.g. snow storage and actual evapotranspiration, were in good agreement with the measured values. Since a high correlation between monthly discharge and peak discharge was found for the period 1900–1980 The RHINEFLOW model is used to assess the probability of exceedence for discharge peaks under possible future climate conditions. The probabilities of exceedence were calculated from the conditional probabilities of peak discharges for a series of 15 classes of monthly discharges. Comparison of a calculated frequency distribution of high discharge peaks with observed peaks in a test series showed that the method performs well. Scenarios for temperature changes between 0 °C and plus 4 °C and precipitation changes between plus 20% and minus 20% have been applied. Within this range flood frequencies are more sensitive for a precipitation change than for a temperature change. The present two-year return period peak flow (6500–7000 m3/s) decreases by about 6% due to a temperature rise of 4 °C; a precipitation decrease of 20% leads to 30% lower two-year peaks whilst 20% precipitation increase raises them by approximately 30%. Application of a ‘Business As Usual’ (BAU) and an ‘Accelerated Policy’ (AP) climate scenario resulted in a significant increase in probability of peak flows for the BAU scenario, while for the AP scenario no significant change could be found. Due to sampling errors, accurate estimations of recurrence times of discharge peaks⩾7000 m3/s require a longer sampling time series than 90 years. For management purposes the method can be applied to estimate changes of probabilities of events with a relatively long recurrence time. More... »

PAGES

199-224

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf01093591

DOI

http://dx.doi.org/10.1007/bf01093591

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1010978136


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