Preprints
https://doi.org/10.5194/hess-2016-619
https://doi.org/10.5194/hess-2016-619
09 Dec 2016
 | 09 Dec 2016
Status: this preprint was under review for the journal HESS but the revision was not accepted.

Comparative study of flood projections under the climate scenarios: links with sampling schemes, probability distribution models, and return level concepts

Lingqi Li, Lihua Xiong, Chong-Yu Xu, Shenglian Guo, and Pan Liu

Abstract. Traditional stationarity strategy for extrapolating future design floods requires renovation in response to the possible nonstationarity caused by changing climate. Capable of tackling such problem, the expected-number-of-events (ENE) method is employed with both Annual Maximum (AM) and Peaks over Threshold (POT) sampling schemes expatiated. The existing paradigms of the ENE method are extended focusing on the over-dispersion emerged in POT arrival rate, for which by virtue of the ability to account, the Negative Binomial (NB) distribution is proposed as an alternative since the common assumption of homogeneous Poisson process would likely be invalid under nonstationarity. Flood return levels are estimated and compared under future climate scenarios (embodied by the two covariates of precipitation and air temperature) using the ENE method for both sampling schemes in the Weihe basin, China. To further understand how flood estimation responds to climate change, a global sensitivity analysis is performed. It is found that design floods dependent on nonstationarity are usually but not necessarily more different from those analyzed by stationarity strategy due to the interaction between air temperature and precipitation. In general, a large decrease in flood projection could be induced under nonstationarity if air temperature presents dramatically increasing trend or reduction occurs in precipitation, and vice versa. AM-based flood projections are mostly smaller than POT estimations (unless a low threshold is assumed) and more sensitive to changing climate. The outcome of the biased flood estimates resulting from an unrestricted use of the Poisson assumption suggests a priority to the NB distribution when fitting POT arrival rate with significantly larger variance than the mean. The study supplements the knowledge of future design floods under changing climate and makes an effort to improve guidance of choices in flood inference.

Lingqi Li, Lihua Xiong, Chong-Yu Xu, Shenglian Guo, and Pan Liu
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Lingqi Li, Lihua Xiong, Chong-Yu Xu, Shenglian Guo, and Pan Liu
Lingqi Li, Lihua Xiong, Chong-Yu Xu, Shenglian Guo, and Pan Liu

Viewed

Total article views: 1,307 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
863 379 65 1,307 78 87
  • HTML: 863
  • PDF: 379
  • XML: 65
  • Total: 1,307
  • BibTeX: 78
  • EndNote: 87
Views and downloads (calculated since 09 Dec 2016)
Cumulative views and downloads (calculated since 09 Dec 2016)

Viewed (geographical distribution)

Total article views: 1,260 (including HTML, PDF, and XML) Thereof 1,257 with geography defined and 3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Mar 2024
Download
Short summary
The study offers insights into future design floods that are inferred with both AM and POT samplings under nonstationarity caused by changing climate. Future design floods in nonstationarity context are usually (lower than) but not necessarily more different from stationary estimates. AM-based projection is more sensitive to climate change than POT estimates. The over-dispersion in POT arrival rate leads to the invalidation of Poisson assumption that the misuse may induce overestimated floods.