Abstract
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.
Highlights
As floods become increasingly more frequent, intense and devastating due to changing climatic conditions and anthropogenic factors [1], reliable hydrological information is required by flood risk managers and stakeholders alike to inform the deployment of interventions to mitigate flood impact [2].Typically, networks of river gauging stations are established across several locations of interest to collect the necessary data over a given period [3]
The coefficients of determination (R2 ) for the relationship between radar altimetry (RA) and in situ water level data points presented in Table 3 were higher at gauging stations where the distances between virtual and in situ gauge stations was minimal, as well as where the influence of tributaries discharging into the main rivers is reduced and river width is considerable
Missing data is a recurring challenge for flood management in many developing regions, where hydrological data is often manually collected and where peak flood events result in restricted access for data collection and damage to measuring equipment
Summary
As floods become increasingly more frequent, intense and devastating due to changing climatic conditions and anthropogenic factors [1], reliable hydrological information is required by flood risk managers and stakeholders alike to inform the deployment of interventions to mitigate flood impact [2].Typically, networks of river gauging stations are established across several locations of interest to collect the necessary data over a given period [3]. Water 2018, 10, 1483 institutional (lack of technical capacity and commitment), operational (inaccessibility to remote gauge stations due to logistical and security challenges), and technical (equipment malfunction, replacement, damage, modification, discontinuity and manual data entry procedures prone to errors) factors [4,5,6]. These factors contribute to hydrological network inadequacy, the decline in functional stations, and gaps in available historical records, that impact on the outcome of flood modelling processes required to inform decision making. Several approaches have been explored to compensate for data deficiencies to estimate flows for ungauged or sparsely gauged river basins, including remote sensing [8,9], hydrodynamic modelling [10], combined remote sensing and hydrodynamic models [11,12], catchment geomorphological and meteorological data integration [13], and hydrological regionalization [14], resulting in the estimation of river water levels and discharge with reduced levels of uncertainty
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