Abstract. Spatially, climate condition is vary within a region and considered as essential information for planning activities such as agro-climate zonation. An approach to understand the spatial climate variability is the utilization of climate regionalization that is applied to rainfall data to distinguish differences in the pattern and magnitude (characteristics) of spatial rainfall variability over a region. Unfortunately, the application of climate regionalization poses a challenging issue in Indonesia, considering the availability of climate data. Recent advances in satellite and reanalysis data measuring climate variability over a large area provided an opportunity for the application of climate regionalization in the country. Using the West Java, one of main crop production regions in Indonesia, climate regionalization techniques were applied to map spatial variability of climate types based on rainfall data recorded by climate stations (point based analysis) and estimated by modeled/reanalysis data and satellite observations (gridded data). The regionalization derived from gridded rainfall data have reasonably better in capturing the zonal pattern of differences in climate types within the study region than the regionalization applied to insufficient numbers of site-based rainfall observation. This indicates that the gridded data offers an alternative for climate regionalization, when site-based observations are unavailable or limited.
The implementation of climate change adaptation for crop production is often ineffective among farmers due to a lack of access to climatic data and inadequate knowledge on how to use the available data. The project introduced the utilization of climate data via the Climate Smart Action "Saung Iklim" application to targeted stakeholders (i.e., agricultural extension workers and farmer representatives) designed to improve farm management. The term “Saung Iklim” originally means a place where people can learn about the use of climate information for farming activities. Therefore, strengthening stakeholders’ capacity is an essential element of "Saung Iklim", which has been conducted via a series of training activities using various modules and focus group discussions. The project selected one of the major production centres of rice in Indonesia, namely the District of Subang. The district government formed a task force named “Tim Iklim”, consisting of the targeted stakeholders, to assist the project team in delivering the targeted project outputs. The project team, in collaboration with Tim Iklim, produced the modules on utilizing simulations of crop models for managing climate risks. The involvement of the stakeholders was to accommodate their input and understanding so that the modules are ready for practical purposes. The project also equipped Tim Iklim with a dedicated website, containing crop simulation model (CSM) outputs, modules, online surveys, and forums, to facilitate information delivery on managing farm risks to climate exposures.
Mangrove plays important role in the coastal ecosystem worldwide, and Indonesia as an archipelagic country occupies about 27% of the global mangrove area. Unfortunately, about 48% of areas of Indonesia’s mangroves have been damaged. Human, biophysics and climate stressors are found to affect the mangrove damages. Using a case study of Pekalongan and Demak, i.e., the prioritized locations for mangrove rehabilitation in Indonesia, this study translated the contributing stressors into quantifiable indicators that can be used to measure the risks. The risk framework on climate change assessment and ecological sensitive evaluation was employed to define the measured indicators and parameters. The indicators were classified into distinctive groups of hazards, exposures, and vulnerability which were composed of sensitivity and capacity. The indicators are then defined with regards to the existence of mangroves as an individual (vegetation), habitat, and ecosystem, and a set of parameters constitute the indicators measured the stressors of socio-economics, biophysics, and climate were determined with regards to the data availability and requirements. The measured indicators can be helpful to identify what strategies or actions should be devised to address the most contributing indicators to the mangrove risks and may be replicated to other coastal areas in the tropical regions.
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