Calibration of ECMWF SEAS5 based streamflow forecast in Seasonal hydrological forecasting for Citarum river basin, West Java, Indonesia

• Published in: Journal of hydrology. Regional studies Vol. 45; p. 101305
• Main Authors: Ratri, Dian Nur, Weerts, Albrecht, Muharsyah, Robi, Whan, Kirien, Tank, Albert Klein, Aldrian, Edvin, Hariadi, Mugni Hadi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-02-2023; Elsevier
• Subjects: Bias-corrected; Citarum river basin; ECMWF SEAS5; Hydrological ensemble forecast; Streamflow forecast verification/skill; Wflow_sbm model; Hydrological ensemble forecast; Wflow_sbm model; Bias-corrected; Citarum river basin; Streamflow forecast verification/skill; ECMWF SEAS5
• ISSN: 2214-5818; 2214-5818
• DOI: 10.1016/j.ejrh.2022.101305

Citarum river basin, West Java — Indonesia.
We look at the skill of Empirical Quantile Mapping corrected ECMWF SEAS5 (SEAS5 EQM bias-corrected) based streamflow forecasts in the Citarum river basin. We focus on July to October because these are agriculturally important months in Java. We use a high-resolution hydrologic model (wflow_sbm) with data for the period 1989–2009.
Water users and agricultural practitioners commonly need monthly to seasonal hydrological forecasts. The forecasts should be sufficiently skillful and provide information that is relevant to the decisions makers in order to have practical value to them. We assess if skilful SEAS5 EQM bias-corrected based seasonal forecasts are available with the purpose to support rice production. In this streamflow forecast calibration, we look at different aggregation days and different lead times. For the verification, we use the Continuous Ranked Probability Skill Score (CRPSS), Brier Skill Score (BSS), and Mean Average Error (MAE). We also look at the correlation, the Root Mean Square Error (RMSE), and the Receiver operating characteristic Skill (ROCS). The LT1 and LT2 forecast show higher skills than longer lead times. Meanwhile, streamflow based on the aggregated forecast at 30 to 60 days aggregation days is more skillful than larger aggregations. In Indonesia, this study is a study that initiates using a hydrological model with inputs from a seasonal rainfall forecast.
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•Use wflow_sbm to simulate the hydrological cycle in the Citarum basin.•EQM bias-correction ECMWF SEAS5 generates the streamflow forecasts in Citarum.•The performances of LT1 ad LT2 of streamflow forecast are better than other LTs.•Performance of hydrological forecast helps farmers to adjust agricultural decisions.