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Cycle 5 (2015 Deadline)
Strengthening resilience to extreme weather related events in Indonesia through improving the predictability of drought risk within the Drought Cycle Management Model
PI: Heri Kuswanto (kuswanto.its@gmail.com), Institut Teknologi Sepuluh Nopember (ITS)
U.S. Partner: Justin Sheffield, Princeton University
Project Dates: December 2016 - September 2020
Project Overview
This PEER project aimed to improve the predictability of drought events in Indonesia as part of disaster risk reduction within the framework of the Drought Cycle Management (DCM) model. The DCM has proven to be a robust and practical approach for drought management in Africa for more than 30 years, but it has never been implemented in Indonesia. The Meteorological Office Indonesia (BMKG) issues drought information from a simple monitoring system but with very low predictive capacity and hence drought forecasts have never been made properly. Moreover, the provided drought information is difficult for smallholders and communities to access directly, which has led to lack of actions to reduce the risk. Dr. Kuswanto and his team therefore aimed to create a DCM model to frame decisions currently made at the smallholder and community levels in response to drought and determine whether decisions can be made (based on forecast information) to reduce drought risk.
This project developed a Drought Monitoring and Forecasting System (DMFS) and formulated scenarios to reduce drought risk. The DMFS was developed by drawing from methods developed by the Terrestrial Hydrology Group of Princeton University, integrated with seasonal drought forecasting derived from downscaled climate forecasts from the North America Multi-Model Ensemble (NMME)-II for predicting drought events in Indonesia. The goals of this project were to improve the predictability of drought by developing a reliable monitoring and forecasting system, to formulate a best framework for implementing a DCM model in Indonesia that incorporates local drought characteristics and community profiles; and to test the effectiveness of the DCM model to reduce drought risk.
Final Summary of Project Activities
 | | Dr. Kuswanto (left) receives an award following his presentation and outreach at Andalas University (photo courtesy of Dr. Kuswanto). |
The PI and his team collected historical climate and hydrology data to characterize drought and develop a drought prediction model. They also identified vulnerable districts for the pilot study for implementation of the DCM: Probolinggo, East Java, and Lombok Utara, Nusa Tenggara Barat. The researchers undertook household surveys in the target districts to gather information on drought cycle management, focusing especially on drought knowledge and perceived impacts among smallholders.
The team compared several models for drought prediction (such as Bayesian Model Averaging (BMA), Ensemble Model Output Statistic (EMOS), etc.) to find the best model representing the Indonesian case. They made the DMFS publicly available, sharing it with government agencies, and aim to develop it into a more easily shareable format for the wider community. A module of the system operation is available in Bahasa Indonesia.
The team members held a variety of workshops and outreach activities, including workshops on climate change for primary school students, teachers, and local officials; a workshop on climate change and extreme events; and a local car-free day, in collaboration with the Directorate of Environment (KLH) of East Java Province. They also communicated findings and recommendations on developing a resilient village with village officials in East Java. A team study on the effectiveness of the car-free day in Surabaya resulted in a policy recommendation to the local government.
Dr. Kuswanto and his group shared their findings and policy recommendations with local government officials and in several published papers. The collaboration with the Indonesian state meteorological agency BMKG was especially intensive. Staff from that agency visited ITS in February 2020 to discuss further cooperation after the PEER project and decided to send staff members to study at ITS. Moreover, two ITS students were selected to conduct internships at BMKG. That same month, Dr. Kuswanto was invited by the PTPN (government company producing sugar) to give a training on data analysis. He shared information about the correlation between sugar cane output and the weather situation and explained the usefulness of applying mid-range forecast information. The PTPN staff expressed interest, stating that the predictions from the system the PI and his group are developing could help support their work, especially for the planting and production planning calendar.
On the educational side, two undergraduate students (bachelor’s degree program) and one research assistant (Master’s degree program) working on the project completed and defended their final projects in January 2020. All of them graduated in March 2020, just a day before the university was shut down due to COVID-19. During the project phase, the PI learned a great deal about the U.S. education system from his U.S. partners, which gave rise to his idea of adopting the “Master by Research” and “PhD by Research” program as applied in many U.S. universities. With Dr. Kuswanto now serving as the director of postgraduate programs and academic development at ITS, he was in an appropriate position to propose the idea to be implemented at ITS. After much discussion, the ITS Academic Senate approved the idea, and the programs were launched in late May 2020.
The PI received $26,000 in additional grants during the project period, including one from UNESCO, and was nominated as a member of Indonesian Young Science Academy.
Publications
Heri Kuswanto. 2019. Analysis of Factors Influencing the Usage of Seasonal Forecast in Drought Prone Area. International Journal of Research in Business and Social Science 8(3), 64-71. https://doi.org/10.20525/ijrbs.v8i3.254
H. Kuswanto, I.L. Yuliatin, and H.A. Khoiri. 2019. Statistical downscaling to predict drought events using high resolution satelite based geopotential data. IOP Conference Series: Materials Science and Engineering 546: 052040. https://doi.org/10.1088/1757-899X/546/5/052040
Defi Yusti Faidah, Heri Kuswanto, and Suhartono. 2019. The comparison of Bayesian model averaging with gaussian and gamma components for probabilistic precipitation forecasting. AIP Conference Proceedings 2192, 090003. https://doi.org/10.1063/1.5139173
Heri Kuswanto, Fausania Hibatullah, and Eddy Soedjono. 2019. Perception of weather and seasonal drought forecasts and its impact on livelihood in East Nusa Tenggara, Indonesia. Heliyon 5(8): E02360. https://doi.org/10.1016/j.heliyon.2019.e02360
Heri Kuswanto, Fausania Hibatullah, Eddy Soedjono, and Ferry Efendy. 2019. Survey data of household perceptions of drought, mitigation and adaptation practices in East Nusa Tenggara, Indonesia. Data in Brief 24: 103944. https://doi.org/10.1016/j.dib.2019.103944
Heri Kuswanto, Dedi Setiawan, and Ardhasena Sopheluwakan. 2019. Clustering of precipitation variability in Indonesia using TRMM satellite data. Engineering, Technology & Applied Science Research 9(4): 4484-4489. https://doi.org/10.48084/etasr.2950
Defi Yusti Faidah, Heri Kuswanto, Suhartono, and Kiki Ferawati. 2019. Rainfall Forecast with Best and Full Members of the North American Multimodel Ensembles. Malaysian Journal of Science 38(Sp2): 113-119. https://doi.org/10.22452/mjs.sp2019no2.10
Heri Kuswanto, Dimas Rahadiyuza, and Dodo Gunawan. 2019. Probabilistic Precipitation Forecast in (Indonesia) Using NMME Models: Case Study on Dry Climate Region. In: Chaminé, H., Barbieri, M., Kisi, O., Chen, M., Merkel, B. (eds) Advances in Sustainable and Environmental Hydrology, Hydrogeology, Hydrochemistry and Water Resources. CAJG 2018. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-01572-5_3
Heri Kuswanto and Achmad Naufal. 2019. Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods. MethodsX 6: 1238-1251. https://doi.org/10.1016/j.mex.2019.05.029
Heri Kuswanto, Kartika Fithriasari, and Rosyida Inas. 2018. Drought Risk Mapping in East Nusa Tenggara Indonesia Based on Return Periods. Asian Journal of Scientific Research 11(4): 489-497. https://doi.org/10.3923/ajsr.2018.489.497
Heri Kuswanto, Esis Ramadhan, and Dimas Rahadiyuza. 2018. Active zones detection of sea surface temperature for drought events in East Nusa Tenggara Indonesia using Bootstrap. ARPN Journal of Engineering and Applied Sciences 13(10): 3542-3548.
Heri Kuswanto and Dimas Rahadiyuza. 2018. Multi Model Calibration of Rainfall Forecasts in East Nusa Tenggara Using Ensemble Model Output Statistics. IOP Conference Series: Journal of Physics: Conf. Series 1028: 012231. https://doi.org/10.1088/1742-6596/1028/1/012231
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