Multi-variate infilling of missing daily discharge data on the Niger basin

Abstract

The Niger basin has experienced historical drought episodes and floods in recent times. Reliable hydrological modelling has been hampered by missing values in daily river discharge data. We assessed the potential of using the Multivariate Imputation by Chained Equations (MICE) to estimate both continuous and discontinuous daily missing data across different spatial scales in the Niger basin. The study was conducted on 22 discharge stations that have missing data ranging from 2% to 70%. Four efficiency metrics were used to determine the effectiveness of MICE. The flow duration curves (FDC) of observed and filled data were compared to determine how MICE captured the discharge patterns. Mann-Kendall, Modified Mann-Kendall, Pettit and Sen's Slope were used to assess the complete discharge trends using the gap-filled data. Results shows that MICE near perfectly filled the missing discharge data with Nash-Sutcliffe Efficiency (NSE) range of 0.94–0.99 for the calibration (1992–1994) period. Good fits were obtained between FDC of observed and gap-filled data in all considered stations. All the catchments showed significantly increasing discharge trend since 1990s after gap filling. Consequently, the use of MICE in handling missing data challenges across spatial scales in the Niger basin was proposed. Highlights Runoff data of the Niger basin has a large amount of missing data.; The quality of existing runoff data was improved by a multiple imputation technique.; The effectiveness of the multiple imputation algorithm was influenced by the percentage of missing data.; Trend analysis of gap-filled data shows significantly increasing trends from the 1990s.; Modified Mann-Kendall performs better than the original Mann-Kendall test.;

Keywords

• hydrology
• multiple imputation
• river discharge
• spatial scale
• trend analysis
• west africa