Gap-filling of daily streamflow time series using Direct Sampling in various hydroclimatic settings

Dembélé, Moctar; Oriani, Fabio; Tumbulto, Jacob; Mariéthoz, Grégoire; Schaefli, Bettina (2019). Gap-filling of daily streamflow time series using Direct Sampling in various hydroclimatic settings. Journal of hydrology, 569, pp. 573-586. Elsevier 10.1016/j.jhydrol.2018.11.076

[img] Text
Dembele-2019-Gap-filling-of-daily-streamflow-tim.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (6MB) | Request a copy

Complete hydrological time series are necessary for water resources management and modeling. This can be challenging in data scarce environments where data gaps are ubiquitous. In many applications, repetitive gaps can have unfortunate consequences including ineffective model calibration, unreliable timing of peak flows, and biased statistics. Here, Direct Sampling (DS) is used as a non-parametric stochastic method for infilling gaps in daily streamflow records. A thorough gap-filling framework including the selection of predictor stations and the optimization of the DS parameters is developed and applied to data collected in the Volta River basin, West Africa. Various synthetic missing data scenarios are developed to assess the performance of the method, followed by a real-case application to the existing gaps in the flow records. The contribution of this study includes the assessment of the method for different climatic zones and hydrological regimes and for different upstream-downstream relations among the gauging stations used for gap filling. Tested in various missing data conditions, the method allows a precise and reliable simulation of the missing data by using the data patterns available in other stations as predictor variables. The developed gap-filling framework is transferable to other hydrological applications, and it is promising for environmental modeling.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Institute of Geography

UniBE Contributor:

Schaefli, Bettina

Subjects:

900 History > 910 Geography & travel
500 Science > 550 Earth sciences & geology

ISSN:

0022-1694

Publisher:

Elsevier

Language:

English

Submitter:

Bettina Schäfli

Date Deposited:

06 May 2020 12:15

Last Modified:

06 May 2020 12:15

Publisher DOI:

10.1016/j.jhydrol.2018.11.076

Uncontrolled Keywords:

Missing values Discharge Data-driven model Stochastic method Volta River basin West Africa

BORIS DOI:

10.7892/boris.141974

URI:

https://boris.unibe.ch/id/eprint/141974

Actions (login required)

Edit item Edit item
Provide Feedback