Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse Regions

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 interve...

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Main Authors: Iguniwari Thomas Ekeu-wei, George Alan Blackburn, Philip Pedruco
Format: Article
Language:English
Published: MDPI AG 2018-10-01
Series:Water
Subjects:
Online Access:http://www.mdpi.com/2073-4441/10/10/1483
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spelling doaj-aed1c8b29ebc411ca8694584ce515fb42020-11-25T02:17:56ZengMDPI AGWater2073-44412018-10-011010148310.3390/w10101483w10101483Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse RegionsIguniwari Thomas Ekeu-wei0George Alan Blackburn1Philip Pedruco2Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UKLancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UKJacobs Engineering Group Inc., Melbourne, VIC 8009, AustraliaIn 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.http://www.mdpi.com/2073-4441/10/10/1483hydrologymissing dataradar altimetrymultiple imputationflood frequency analysisNiger River BasinUngaged River Basin
collection DOAJ
language English
format Article
sources DOAJ
author Iguniwari Thomas Ekeu-wei
George Alan Blackburn
Philip Pedruco
spellingShingle Iguniwari Thomas Ekeu-wei
George Alan Blackburn
Philip Pedruco
Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse Regions
Water
hydrology
missing data
radar altimetry
multiple imputation
flood frequency analysis
Niger River Basin
Ungaged River Basin
author_facet Iguniwari Thomas Ekeu-wei
George Alan Blackburn
Philip Pedruco
author_sort Iguniwari Thomas Ekeu-wei
title Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse Regions
title_short Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse Regions
title_full Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse Regions
title_fullStr Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse Regions
title_full_unstemmed Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse Regions
title_sort infilling missing data in hydrology: solutions using satellite radar altimetry and multiple imputation for data-sparse regions
publisher MDPI AG
series Water
issn 2073-4441
publishDate 2018-10-01
description 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.
topic hydrology
missing data
radar altimetry
multiple imputation
flood frequency analysis
Niger River Basin
Ungaged River Basin
url http://www.mdpi.com/2073-4441/10/10/1483
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