Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic

TL;DR

This study improves streamflow data accuracy in Benin by bias-correcting GESS forecasts using advanced statistical methods, enabling better flood and drought management in resource-limited settings.

Contribution

It introduces a bias correction approach using Quantile Mapping, Gaussian Process, and Elastic Net regression for streamflow forecasts in Benin, enhancing predictive skill over traditional methods.

Findings

Elastic Net and Gaussian Process outperform traditional imputation methods

Bias correction significantly improves forecast accuracy

Enhanced data can support early-warning systems for floods and droughts

Abstract

Streamflow observation data is vital for flood monitoring, agricultural, and settlement planning. However, such streamflow data are commonly plagued with missing observations due to various causes such as harsh environmental conditions and constrained operational resources. This problem is often more pervasive in under-resourced areas such as Sub-Saharan Africa. In this work, we reconstruct streamflow time series data through bias correction of the GEOGloWS ECMWF streamflow service (GESS) forecasts at ten river gauging stations in Benin Republic. We perform bias correction by fitting Quantile Mapping, Gaussian Process, and Elastic Net regression in a constrained training period. We show by simulating missingness in a testing period that GESS forecasts have a significant bias that results in low predictive skill over the ten Beninese stations. Our findings suggest that overall bias correction by Elastic Net and Gaussian Process regression achieves superior skill relative to traditional imputation by Random Forest, k-Nearest Neighbour, and GESS lookup. The findings of this work provide a basis for integrating global GESS streamflow data into operational early-warning decision-making systems (e.g., flood alert) in countries vulnerable to drought and flooding due to extreme weather events.