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Ardhasena Sopaheluwakan
Meteorological, Climatological, and Geophysical Agency, Kemayoran, Jakarta Pusat, DKI Jakarta, Indonesia 10610
Agriculture, Biological Sciences & Forestry

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Spatial and Temporal Analysis of El Niño Impact on Land and Forest Fire in Kalimantan and Sumatra Sri Nurdiati; Ardhasena Sopaheluwakan; Pandu Septiawan
Agromet Vol. 35 No. 1 (2021): JUNE 2021
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.35.1.1-10

Abstract

Land and forest fires in Kalimantan and Sumatra, Indonesia occurred annually at different magnitude and duration. Climate and sea interaction, like El Niño, influences the severity of dry seasons preceding the fires. However, research on the influence of El Niño intensity to fire regime in Kalimantan and Sumatra is limited. Therefore, this study aims to analyze the spatial and temporal patterns of the effects of El Niño intensity on land and forest fires in fire-prone provinces in Indonesia. Here, we applied the empirical orthogonal function analysis based on singular value decomposition to determine the dominant patterns of hotspots and rainfall data that evolve spatially and temporally. For analysis, the study required the following data: fire hotspots, dry-spell, and rainfall for period 2001-2019. This study revealed that El Niño intensity had a different impacts for each province. Generally, El Niño will influence the severity of forest fire events in Indonesia. However, we found that the impact of El Niño intensity varied for Kalimantan, South Sumatra, and Riau Province. Kalimantan was the most sensitive province to the El Niño event. The duration and number of hotspots in Kalimantan increased significantly even in moderate El Niño event. This was different for South Sumatra, where the duration and number of hotspots only increased significantly when a strong El Niño event occurred.
Identification of Global Warming Contribution to the El Niño Phenomenon Using Empirical Orthogonal Function Analysis Mochamad Tito Julianto; Septian Dhimas; Ardhasena Sopaheluwakan; Sri Nurdiati; Pandu Septiawan
Agromet Vol. 35 No. 1 (2021): JUNE 2021
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.35.1.11-19

Abstract

Sea surface temperature (SST) is identified as one of the essential climate/ocean variables. The increased SST levels worldwide is associated with global warming which is due to excessive amounts of greenhouse gases being released into the atmosphere causing the multi-decadal tendency to warmer SST. Moreover, global warming has caused more frequent extreme El Niño Southern Oscillation (ENSO) events, which are the most dominant mode in the coupled ocean-atmosphere system on an interannual time scale. The objective of this research is to calculate the contribution of global warming to the ENSO phenomenon. SST anomalies (SSTA) variability rosed from several mechanisms with differing timescales. Therefore, the Empirical Orthogonal Function in this study was used to analyze the data of Pacific Ocean sea surface temperature anomaly. By using EOF analysis, the pattern in data such as precipitation and drought pattern can be obtained. The result of this research showed that the most dominant EOF mode reveals the time series pattern of global warming, while the second most dominant EOF mode reveals the El Niño Southern Oscillation (ENSO). The modes from this EOF method have good performance with 95.8% accuracy rate.
Fire Danger on Jambi Peatland Indonesia based on Weather Research and Forecasting Model Lisnawati; Muh Taufik; Bambang Dwi Dasanto; Ardhasena Sopaheluwakan
Agromet Vol. 36 No. 1 (2022): JUNE 2022
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.36.1.1-10

Abstract

Monitoring drought related to peat fire danger is becoming essentials due to the adverse impacts of peat fires. However, the current monitoring is mostly based on station data and has not yet covered all parts of peatlands. This research was carried out to initiate a spatial monitoring for peat fire, particularly in Jambi province. Our approach was simple by integrating Weather Research Forecasting (WRF) output with a drought-fire model. This research aims to: (i) calibrate rainfall, air temperature and soil moisture data from WRF output; and (ii) analyze temporal drought related to fire danger. A drought-fire model known as Peat Fire Vulnerability Index was applied with daily inputs of WRF output at 5km resolution, which were comprised of rainfall, air temperature, and soil moisture. The results showed that calibration reduced rainfall magnitude, and slightly increased the maximum air temperature and soil moisture. The calibration performance was good as shown by a very low percent bias (less than ±5%), and lower error (RMSE=16.5; MAE=9.5). Our analysis showed that drought triggered by El Niño in 2015 had escalated extreme fire danger class by 38% compared to normal year (2018). This has been confirmed by a low variation of proportion of extreme class during July-August 2015. The results suggested that integrating spatial global climate data will benefit to the improved drought-fire model by providing spatial data. The results are expected to be a reference on drought and peat fires mitigation action.
Co-Authors Dasanto, Bambang Dwi Lisnawati Mochamad Tito Julianto Pandu Septiawan Septian Dhimas Sri Nurdiati Taufik, Muh