Temporal clustering of floods and impacts of climate indices in the Tarim River basin, China Academic Article uri icon


  • 2016 The occurrence rates of floods in Tarim River basin, the largest arid basin in China, were estimated using the Peak-over-Threshold (POT) technique. The intra-annual, seasonal and inter-annual clustering of floods was then analyzed using the Cox regression model, month frequency method and dispersion index, respectively. Possible impacts of climate indices on the occurrence rates were also investigated. Both NAO and AO are selected as significant covariates to occurrence rates of floods in Tarim River basin by Cox regression model, suggesting occurrence of flood events is not independent, but exhibits temporal clustering in intra-annual scale. On the basis of the results of the station and region-wide modeling by Cox regression model, we suggest using a model in which the rate of occurrence depends on monthly averaged NAO or AO. The Cox regression model not only can be used to assess the time-varying rate of flood occurrence, but also has the capability to forecast the predictors. Flood occurrence time and probability of exceedance are changing with climate index from negative to positive on both station and region scale. In addition, seasonal clustering of station-based floods and regional observed floods are also identified with mainly concentrating from June to August. Meanwhile, dispersion index is used to evaluate the inter-annual clustering of annual number of flood occurrences both on station and region. We found that inter-annual clustering of regional floods is more evident than that of station-based floods, indicating that regional observed flood records are generally over-dispersed with a tendency for flood events to cluster in time.

published proceedings


author list (cited authors)

  • Gu, X., Zhang, Q., Singh, V. P., Chen, Y. D., & Shi, P.

citation count

  • 15

complete list of authors

  • Gu, Xihui||Zhang, Qiang||Singh, Vijay P||Chen, Yongqin David||Shi, Peijun

publication date

  • December 2016