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.
翻译:潮流观测数据对洪水监测、农业和住区规划至关重要,然而,由于环境条件恶劣、业务资源有限等各种原因,这种流流数据通常会受到缺漏的观察,这些问题往往在撒哈拉以南非洲等资源不足的地区更为普遍;在这项工作中,我们通过对贝宁共和国十个河流测量站GEGOGloWS ECMWF流流流服务(GESS)预测的偏差校正来重建流流时间序列数据;我们在有限的培训期间,通过配对量子绘图、高山进程和埃利斯蒂斯特网回归来纠正偏差。我们通过模拟缺漏情况,显示GESS预报在诸如撒哈拉以南非洲等资源不足的地区具有显著的偏差性,导致对十个贝宁站的预测技能较低。我们的研究结果表明,通过埃利斯特网和戈西亚进程回归,总体偏差纠正在随机森林、K-Near Rous和GESS外望程中取得了优超能力。我们通过在有限的培训阶段进行纠正偏差现象。我们通过模拟测算工作的结果为全球GESS流数据纳入运行中的预警决策系统提供了基础。我们发现,因为这些国家面临极端的干旱的气候。