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Abstract. Estimation of peak flow quantiles in ungauged catchments is a challenge often faced by water professionals in many parts of the world. Approaches to address such problem exist, but widely used techniques such as flood frequency regionalisation is often not subjected to performance evaluation. In this study, the jack-knifing principle is used to assess the performance of the flood frequency regionalisation in the complex and data-scarce River Nile basin by examining the error (regionalisation error) between locally and regionally estimated peak flow quantiles for different return periods (Q T ). Agglomerative hierarchical clustering based algorithms were used to search for regions with similar hydrological characteristics. Hydrological data employed were from 180 gauged catchments and several physical characteristics in order to regionalise 365 identified catchments. The Generalised Extreme Value (GEV) distribution, selected using L-moment based approach, was used to construct regional growth curves from which peak flow growth factors could be derived and mapped through interpolation. Inside each region, variations in at-site flood frequency distribution were modelled by regression of the mean annual maximum peak flow (MAF) versus catchment area. The results showed that the performance of the regionalisation is heavily dependent on the historical flow record length and the similarity of the hydrological characteristics inside the regions. The flood frequency regionalisation of the River Nile basin can be improved if sufficient flow data of longer record length of at least 40 yr become available.

How well do climate models reproduce variability in observed rainfall? A case study of the Lake Victoria basin considering CMIP3, CMIP5 and CORDEX simulations STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 33(3), 687-707.

Estimation of peak flow quantiles in ungauged catchments is a challenge often faced by water professionals in many parts of the world. Approaches to address such problem exist but widely used technique such as flood frequency regionalization is often not subjected to performance evaluation. In this study we used the jack-knifing principle to assess the performance of the flood frequency regionalization in the complex and data scarce River Nile basin by examining the error (regionalization error) between locally and regionally estimated peak flow quantiles for different return periods (<i>Q</i>_<i>T</i>). Agglomerative hierarchical clustering based algorithms were used to search for regions with similar hydrological characteristics taking into account the huge catchment area and strong climatic differences across the area. Hydrological data sets employed were from 180 gauged catchments and several physical characteristics in order to regionalize 365 identified catchments. The GEV distribution, selected using <i>L</i>-moment based approach, was used to construct regional growth curves from which peak flow growth factors (<i>Q</i>_<i>T</i>/MAF) could be derived and mapped through interpolation. Inside each region, variations in at-site flood frequency distribution were modeled by regression of the mean annual maximum peak flow (MAF) versus catchment area. The results show that the performance of the regionalization is heavily dependent on the historical flow record length and the similarity of the hydrological characteristics inside the regions. The flood frequency regionalization of the River Nile basin can be improved if sufficient flow data of longer record length × 40 become available

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