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.

本研究通过对贝宁共和国的十个河流测量站进行偏差校正，研究发现弹性网络回归和高斯过程回归的整体偏差校正相对于传统的随机森林、k 近邻和 GESS 记录的插补方法具有更强的技能。这一发现为将全球 GESS 流量数据整合到对极端天气事件脆弱的国家的操作性预警决策系统（例如洪水警报）中提供了基础。