In developing regions missing data are prevalent in historical hydrological datasets, owing to financial, institutional, operational and technical challenges. If not tackled, these data shortfalls result in uncertainty in flood frequency estimates and consequently flawed catchment management interventions that could exacerbate the impacts of floods. This study presents a comparative analysis of two approaches for infilling missing data in historical annual peak river discharge timeseries required for flood frequency estimation: (i) satellite radar altimetry (RA) and (ii) multiple imputation (MI). These techniques were applied at five gauging stations along the floodprone Niger and Benue rivers within the Niger River Basin. RA and MI enabled the infilling of missing data for conditions where altimetry virtual stations were available and unavailable, respectively. The impact of these approaches on derived flood estimates was assessed, and the return period of a previously unquantified devastating flood event in Nigeria in 2012 was ascertained. This study revealed that the use of RA resulted in reduced uncertainty when compared to MI for data infilling, especially for widely gapped timeseries (>3 years). The two techniques did not differ significantly for data sets with gaps of 1–3 years, hence, both RA and MI can be used interchangeably in such situations. The use of the original in situ data with gaps resulted in higher flood estimates when compared to datasets infilled using RA and MI, and this can be attributed to extrapolation uncertainty. The 2012 flood in Nigeria was quantified as a 1-in-100-year event at the Umaisha gauging station on the Benue River and a 1-in-50-year event at Baro on the Niger River. This suggests that the higher levels of flooding likely emanated from the Kiri and Lagdo dams in Nigeria and Cameroon, respectively, as previously speculated by the media and recent studies. This study demonstrates the potential of RA and MI for providing information to support flood management in developing regions where in situ data is sparse.
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