Abstract
Less-frequent and inadequate sampling of sediment data has negatively impacted the long and continuous records required for the design and operation of hydraulic facilities. This data-scarcity problem is often found in most river basins of Taiwan. This study aims to propose a parsimonious probabilistic model based on copulas to infill daily suspended sediment loads using streamflow discharge. A copula-based bivariate distribution model of sediment and discharge of the paired recorded data is constructed first. The conditional distribution of sediment load given observed discharge is used to provide probabilistic estimation of sediment loads. In addition, four different methods based on the derived conditional distribution of sediment load are used to give single-value estimations. The obtained outcomes of these methods associated with the results of the traditional sediment rating curve are compared with recorded data and evaluated in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and modified Nash-Sutcliffe efficiency (MNSE). The proposed approach is applied to the Janshou station located in eastern Taiwan with recorded daily data for the period of 1960–2019. The results indicate that the infilled sediments by the sediment rating curve exhibit better performance in RMSE and NSE, while the copula-based methods outperform in MAPE and MNSE. Additionally, the infilled sediments by the copula-based methods preserve scattered characteristics of observed sediment-discharge relationships and exhibit similar frequency distributions to that of recorded sediment data.
Highlights
Hydrologic and climate data play a significant role in water-resources engineering planning, design, and management
The main aim of this study is to infill daily suspended sediment loads based on copulas to provide probabilistic as well as single-value estimations using streamflow discharge
Greater uncertainties exist in an estimation of suspended sediment load for the condition of large discharge
Summary
Hydrologic and climate data play a significant role in water-resources engineering planning, design, and management. Long and complete data are essential for providing accurate statistical analysis in design and establishing efficient operation rules of hydraulic facilities. Hydrologic and climate data are uniquely recorded in time and space. If the data are not recorded at a specific time and location, the lost values can only be estimated [1]. Incomplete and missing data are frequently met in many applications worldwide since a considerable amount factors lead to missing data. These factors include equipment failures, extreme natural disasters (e.g., typhoon, earthquake, and landslide), mishandling of recorded data, malfunction of data storage systems, and others [2,3]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
