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Project 2

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

Source of funding : PEER Science Grant funded by USAID - NAS Cycle 5

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.

Updates on the project can be read at http://sites.nationalacademies.org/PGA/PEER/PEERscience/PGA_174195