Isnaeni Murdi Hartanto was born in Kendal, Indonesia 1978. He has great interest in science, physics, computing technology, and modelling. He had completed his undergraduate degree in civil engineering from Diponegoro University (Undip) in Indonesia at year 2001. From 2006 until now, he joins Ministry of Public Works of Indonesia, in Directorate General of Water Resources. In 2008, he started his MSc in Water Science and Engineering (specialization Hydraulic Engineering and River Basin Development) at IHE Delft Institute for Water Education, Delft, The Netherlands. Two years upon completion of his Master study, he undertakes his PhD study in IHE Delft, which is funded by MyWater Projects under European Community's Seventh Framework Programme focusing in integrating various data into water management forecasting to reduce uncertainty.
TopicIntegrating multiple sources of information and hydrological modelling to reduce uncertainty in operational water management
- Hartanto, I. M. (2010). The application of coastal modelling software in fluvial environments ; XBEACH. WSE-HERBD. Delft, IHE Delft. MSc Thesis.
- Hartanto IM, Beevers L, Popescu I, Wright NG (2011) Application of a coastal modelling code in fluvial environments. Environmental Modelling & Software 26: 1685-1695
- Hartanto, I.M., Van Andel, S.J., Solomatine, D.P. (2012) Integrating earth observation data into hydrological modeling and water management, European Geoscience Union (EGU) General Assembly 2012, 22-27 April 2012 Vienna Austria, Poster presentation
- Hartanto, I.M., Van Andel, S.J., Solomatine, D.P. (2013) Hydrological modelling of low-lying catchments in deltas using multiple data sources and SIMGRO modelling system, European Geoscience Union (EGU) General Assembly 2013, 7-12 April 2013 Vienna Austria, Accepted for poster presentation.
The availability of Earth Observation (EO) and Numerical Weather Prediction (NWP) data for hydrology has increased significantly during the past years. With advantages in wide spatial coverage and cost effectiveness, integration into water managements improves accuracy of a forecast and further reduces uncertainties. The research is aiming in developing effective methodology to integrate EO data, NWP predictions, hydrological model and in-situ measurement to improve forecasting capabilities used in operational management and reduce the uncertainty. Multiple data sources are also integrated, such as EO data from different satellites and NWP from several providers. The research is revolving around the utilization of feedback loops and ensemble modelling.
Funding: MyWater Project, EU FP7
MyWater Web Site: http://www.mywater-fp7.eu/