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

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.

Applying Cascade-Correlation Neural Networks to In-Fill Gaps in Mediterranean Daily Flow Data Series

“…2 A year was considered to be normal/average if its annual volume in natural regime laid between the volume corresponding to 25% and 75% exceedance percentile. 3 A year was considered to be dry if its annual volume in a natural regime was lower than the 196 volume corresponding to the 75% exceedance percentile.…”

“…In order to evaluate resource availability and possible impacts, long records of continuous and reliable data are needed, but they are seldom available. Lack of records (gaps) or discontinuities in data series and quality issues are two of the main problems more often found in databases used for climate studies and water resources management [1][2][3][4][5][6], especially in mountain regions with limited meteorological monitoring and abundant precipitation often associated to extreme events [7]. Flow data series from gauging stations are also affected by these problems.…”

Evaluation of Filling Missing Data Technique in Hydraulic Series

“…While the multiple regression methods is difficult because its need the availability of the data for neighboring stations to the target stations (Sattari et al, 2016). Also some techniques is used an expensive equipment such as Satellite Radar (Ekeu-wei et al, 2018). In this research, the main assumption is that no information about the neighboring stations data is known and the data of target station is available only.…”

Different Approaches to SCADA Data Completion in Water Networks

“…The problem of managing lost data is ubiquitous in many situations and is especially challenging when it manifests itself in long bursts. Dealing with incomplete data is very common in real-life cases, and it is usual to find such cases in water [1] and hydrological data management [2][3][4]. The current work used data from the Drinking Water Treatment Station (DWTS) of Aigües de Vic S.A. For this purpose, data from several DWTS sensors that are stored by the SCADA system in a database were analyzed.…”