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PARTNERSHIPS FOR ENHANCED ENGAGEMENT IN RESEARCH (PEER)
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
U.S. Partner: Justin Sheffield, Princeton University
Project Dates: December 2016 - November 2019

Project Overview

This project focuses on drought as one of the major natural hazards in Indonesia. The primary aim is to improve the predictability of drought events 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. Differences in drought characteristics and community profiles between Indonesia and Africa will introduce interesting challenges for formulating novel strategies towards DCM implementation. One of the challenges will be how to predict future drought events under Indonesia’s unique tropical climate variability. This project will develop a Drought Monitoring and Forecasting System (DMFS) and formulate scenarios to reduce drought risk, based on approaches previously applied by U.S. partner Dr. Sheffield and colleagues. The DMFS will be 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.

Specifically, the goals of the project are (1) to improve the predictability of drought by developing a reliable monitoring and forecasting system; (2) to formulate a best framework for implementing a DCM model in Indonesia that incorporates local drought characteristics and community profiles; and (3) to test the effectiveness of the DCM model to reduce drought risk. To answer these questions, Dr. Kuswanto and his team will collect historical climate and hydrology data to characterize drought and use this to develop a drought prediction model based on climate prediction and statistical models. The two most vulnerable districts have been identified as the site for the pilot study for implementation of DCM: Probolinggo, East Java, and Lombok Utara, Nusa Tenggara Barat. They are listed as top priority districts due to their vulnerability to drought impacts. Based on participatory evaluations conducted on these two districts, statistical evidence will be evaluated to confirm the effectiveness of DCM. The U.S. collaborators will assist with the development of the DMFS for Indonesia, as well as with DCM implementation in the targeted districts. They will also provide remote sensing data required to build the system.

The Government of Indonesia (GoI) has made climate change mitigation and adaptation a national priority. Climate change resilience has been the focus of the GoI as part of the commitment to implementing the Sendai Framework for the Disaster Risk Reduction Framework. Climate change resilience has also become one of the focuses of the USAID mission in Indonesia. This PEER project supports these interests by focusing on a parallel strategy to strengthen extreme weather and climate resilience. 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. Therefore, the DMFS coupled with an effective strategy for easy access to information by communities/smallholders, is urgently required. The DCM will frame how decisions are 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. The project will ensure that communities and smallholders will have access to the drought information generated from the system, which is consistent with the idea of the DCM model.


Summary of Recent Activities


5-125 Andalas Award
Dr. Kuswanto (left) receives an award following his presentation and outreach at Andalas University (photo courtesy of Dr. Kuswanto).
Activities on this project during the first quarter of 2019 focused mainly on analysis of the survey dataset. The household survey collected a lot of information about household characteristics, as well as adaptation and mitigation strategies toward drought events in the two locations studied (East Nusa Tenggara, or NTT, and Probolinggo). Dr. Kuswanto and his team are performing additional statistical analysis to highlight meaningful insights suggested by the data. They have already presented the initial descriptive statistics and performed several inferential analyses, and in the first quarter of 2019 alone they have five scientific papers either under revision or review by various journals. In addition, three of their conference papers presented last year have been published.

On the capacity building and outreach aspects of the project, the PI has organized a paper writing workshop for his team recently, which was helpful in moving forward on various papers being prepared for publication. Dr. Kuswanto and two of his team members also attended a workshop on household survey data analysis organized by Airlangga University February 23-24, 2019. The PI and his group continue to collaborate closely with BMKG in Jakarta to provide regular updates and receive input as they move along on the project. In addition to two meetings with that agency this past quarter, the PI was also invited to a coordination meeting on disaster prevention and preparedness organized by the Disaster Management Office (BPBD) March 12-13, 2019, in Malang, East Java. The PI represented the East Java Chapter of the professional society IABI (Indonesian Experts on Disaster). Other meeting participants included the Vice Governor of East Java, the director of the National Disaster Management Board (BNPB) Jakarta, and the heads of local disaster management offices from all over East Java. This provided the PI with an excellent opportunity to discuss and share project results dealing with maximizing the pentahelix model for development, including five key stakeholder types (administration, society, knowledge, business, and capital).

During the next six months, Dr. Kuswanto and his group will continue their data analysis, focusing on Probolinggo in particular, as the survey data from NTT have already been analyzed. They will also work on refining their analysis for the North American Multi-Model Ensemble (NMME) dataset and test an alternate meteorology dataset that might perform better. Work will also proceed on the drought monitoring system the researchers are preparing, and the team will continue completing and submitting additional research papers, including one written with their U.S. collaborators. Dr. Kuswanto plans to visit his PEER partner in the United States to continue collaboration, likely in September 2019. In addition, he will present his work at two conferences this summer: the International Conference on Disaster Management in conjunction with Annual Disaster Expert Meeting in Jakarta in June 2019 and the Statistical World Congress organized by the International Statistical Institute in August 2019.

Following are some recent papers produced by the team based on their PEER-supported work:
  • Kuswanto, H., Fithriasari, K., Inas, R. (2018) Drought risk mapping in East Nusa Tenggara Indonesia based on return periods. Asian Journal of Scientific Research 11 (4), 489-497. DOI: 10.3923/ajsr.2018.489.497.
  • Kuswanto, H., Rahadiyuza, D. (2018) Multi model calibration of rainfall forecasts in East Nusa Tenggara using ensemble model output statistics. Journal of Physics: Conference Series 1028(1), 012231.
  • Kuswanto, H., Ramadhan, E., Rahadiyusa, D. (2018) Active zones detection of sea surface temperature or drought events in East Nusa Tenggara Indonesia using bootstrap. ARPN Journal of Engineering and Applied Sciences 1310, 1342-1348.

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