eprintid: 13898 rev_number: 11 eprint_status: archive userid: 353 dir: disk0/00/01/38/98 datestamp: 2016-10-28 06:51:26 lastmod: 2021-08-27 17:27:55 status_changed: 2016-10-28 06:51:26 type: article metadata_visibility: show item_issues_count: 1 creators_name: Revilla-Romero, B. creators_name: Wanders, N. creators_name: Burek, P. creators_name: Salamon, P. creators_name: de Roo, A. creators_id: 2091 creators_orcid: 0000-0001-6390-8487 title: Integrating remotely sensed surface water extent into continental scale hydrology ispublished: pub divisions: prog_wat keywords: Data Assimilation; Ensemble Kalman filter (EnKF); Global Flood Detection System (GFDS); LISFLOOD model; Continental hydrology; Surface Water abstract: In hydrological forecasting, data assimilation techniques are employed to improve estimates of initial conditions to update incorrect model states with observational data. However, the limited availability of continuous and up-to-date ground streamflow data is one of the main constraints for large-scale flood forecasting models. This is the first study that assess the impact of assimilating daily remotely sensed surface water extent at a 0.1° x 0.1° spatial resolution derived from the Global Flood Detection System (GFDS) into a global rainfall-runoff including large ungauged areas at the continental spatial scale in Africa and South America. Surface water extent is observed using a range of passive microwave remote sensors. The methodology uses the brightness temperature as water bodies have a lower emissivity. In a time series, the satellite signal is expected to vary with changes in water surface, and anomalies can be correlated with flood events. The Ensemble Kalman Filter (EnKF) is a Monte-Carlo implementation of data assimilation and used here by applying random sampling perturbations to the precipitation inputs to account for uncertainty obtaining ensemble streamflow simulations from the LISFLOOD model. Results of the updated streamflow simulation are compared to baseline simulations, without assimilation of the satellite-derived surface water extent. Validation is done in over 100 in situ river gauges using daily streamflow observations in the African and South American continent over a one year period. Some of the more commonly used metrics in hydrology were calculated: KGE’, NSE, PBIAS%, R2, RMSE, and VE. Results show that, for example, NSE score improved on 61 out of 101 stations obtaining significant improvements in both the timing and volume of the flow peaks. Whereas the validation at gauges located in lowland jungle obtained poorest performance mainly due to the closed forest influence on the satellite signal retrieval. The conclusion is that remotely sensed surface water extent holds potential for improving rainfall-runoff streamflow simulations, potentially leading to a better forecast of the peak flow. date: 2016-10-27 date_type: published publisher: Elsevier id_number: doi:10.1016/j.jhydrol.2016.10.041 creators_browse_id: 46 full_text_status: none publication: Journal of Hydrology volume: 543 number: Part B pagerange: 659-670 refereed: TRUE issn: 1879-2707 coversheets_dirty: FALSE fp7_project: no fp7_type: info:eu-repo/semantics/article citation: Revilla-Romero, B., Wanders, N., Burek, P. ORCID: https://orcid.org/0000-0001-6390-8487 , Salamon, P., & de Roo, A. (2016). Integrating remotely sensed surface water extent into continental scale hydrology. Journal of Hydrology 543 (Part B) 659-670. 10.1016/j.jhydrol.2016.10.041 .