A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Calibration of ECMWF SEAS5 based streamflow forecast in Seasonal hydrological forecasting for Citarum river basin, West Java, Indonesia
Study region:: Citarum river basin, West Java — Indonesia. Study focus:: 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. New hydrological insights for the region:: 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.
Calibration of ECMWF SEAS5 based streamflow forecast in Seasonal hydrological forecasting for Citarum river basin, West Java, Indonesia
Study region:: Citarum river basin, West Java — Indonesia. Study focus:: 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. New hydrological insights for the region:: 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.
Calibration of ECMWF SEAS5 based streamflow forecast in Seasonal hydrological forecasting for Citarum river basin, West Java, Indonesia
Dian Nur Ratri (author) / Albrecht Weerts (author) / Robi Muharsyah (author) / Kirien Whan (author) / Albert Klein Tank (author) / Edvin Aldrian (author) / Mugni Hadi Hariadi (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Assessment of hydrological characteristics in the upper Citarum catchment, West Java
| British Library Conference Proceedings | 2003
Rainfall Trend Analysis in the Upper Citarum River Basin, Indonesia
| Springer Verlag | 2019