Journal metrics

Journal metrics

  • IF value: 4.256 IF 4.256
  • IF 5-year value: 4.819 IF 5-year 4.819
  • CiteScore value: 4.10 CiteScore 4.10
  • SNIP value: 1.412 SNIP 1.412
  • SJR value: 2.023 SJR 2.023
  • IPP value: 3.97 IPP 3.97
  • h5-index value: 58 h5-index 58
  • Scimago H index value: 99 Scimago H index 99
Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-379
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 23 Jul 2018

Research article | 23 Jul 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Attributing the 2017 Bangladesh floods from meteorological and hydrological perspectives

Sjoukje Philip1, Sarah Sparrow2, Sarah F. Kew1, Karin van der Wiel1, Niko Wanders3,4, Roop Singh5, Ahmadul Hassan5, Khaled Mohammed2, Hammad Javid2,6, Karsten Haustein6, Friederike E.L. Otto6, Feyera Hirpa7, Ruksana H. Rimi6, AKM Saiful Islam8, David C.H. Wallom2, and Geert Jan van Oldenborgh1 Sjoukje Philip et al.
  • 1Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands
  • 2Oxford e-Research Centre, Department of Engineering Science, University of Oxford, United Kingdom
  • 3Department of Physical Geography, Utrecht University, Utrecht, The Netherlands
  • 4Department of Civil and Environmental engineering, Princeton University, Princeton, NJ, U.S.A.
  • 5Red Cross Red Crescent Climate Centre, The Hague, the Netherlands
  • 6Environmental Change Institute, Oxford University Centre for the Environment, Oxford, United Kingdom
  • 7School of Geography and the Environment, University of Oxford, United Kingdom
  • 8Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, India

Abstract. In August 2017 Bangladesh faced one of its worst river flooding events in recent history. This paper presents for the first time an attribution of this precipitation-induced flooding from a combined meteorological and hydrological perspective. Experiments were conducted with three observational data sets and two climate models to estimate changes in extreme 10-day precipitation event frequency over the Brahmaputra basin. The precipitation fields were then used as meteorological input for four different hydrological models to estimate the corresponding changes in river discharge, allowing for comparison between approaches and for the robustness of the attribution results to be assessed.

In all three observational precipitation data sets the climate change trends for extreme precipitation similar to observed in August 2017 are not significant, however in two out of three series, the sign of this insignificant trend is positive. One climate model shows a significant positive influence of anthropogenic climate change, whereas the other simulates a cancellation between the increase due to greenhouse gases and a decrease due to sulphate aerosols. Considering discharge rather than precipitation, the hydrological models show that attribution of the change in discharge towards higher values is somewhat less uncertain than for precipitation, but the 95% confidence interval still encompasses no change in risk. For the future, all models project an increase in probability of extreme events at 2°C global heating since pre-industrial times, becoming more than 1.7 times more likely for high 10-day precipitation, and about a factor 1.5 more likely for discharge. Our best estimate on the trend in flooding events similar to the Brahmaputra event of August 2017 is derived by synthesizing the observational and model results: We find the change in risk to be greater than one and of similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach. This study shows that, for precipitation-induced flooding events, investigating changes in precipitation is useful, either as an alternative when hydrological models are not available, or as an additional measure to confirm qualitative conclusions. Besides, it highlights the importance of using multiple models in attribution studies, particularly where the climate change signal is not strong relative to natural variability or is confounded by other factors such as aerosols.

Download & links
Sjoukje Philip et al.
Interactive discussion
Status: open (until 18 Sep 2018)
Status: open (until 18 Sep 2018)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Sjoukje Philip et al.
Sjoukje Philip et al.
Viewed
Total article views: 288 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
229 58 1 288 8 2 2
  • HTML: 229
  • PDF: 58
  • XML: 1
  • Total: 288
  • Supplement: 8
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 23 Jul 2018)
Cumulative views and downloads (calculated since 23 Jul 2018)
Viewed (geographical distribution)
Total article views: 288 (including HTML, PDF, and XML) Thereof 288 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited
Saved
No saved metrics found.
Discussed
No discussed metrics found.
Latest update: 15 Aug 2018
Publications Copernicus
Download
Short summary
In August 2017 Bangladesh faced one of its worst river flooding events in recent history. For the large Brahmaputra basin using precipitation alone as a proxy for flooding might not be appropriate. In this paper we explicitly test this assumption by performing an attribution of both precipitation and discharge as a flooding-related measure to climate change. We find the change in risk to be of similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach.
In August 2017 Bangladesh faced one of its worst river flooding events in recent history. For...
Citation
Share