Precipitation and Land Cover Change in Komodo National Park During El Nino and La Nina

Komodo National Park is located in East Nusa Tenggara province which has a dry tropical climate. Air temperature in this area is relatively high with a lower rainfall compared to most of other Indonesian regions. This condition causes ecosystem in Komodo National Park to be unique with a wide area of savannah and dryland forest. This study aims to identify the change of rainfall and land cover in Komodo National Park in 2018-2020. The analysis was conducted using secondary data from observations and satellite products. The result shows that West Manggarai is classified in Aw climate type. The value of rainfall follows the pattern of ENSO events with a correlation between tree-month data of rainfall and the Ocean Nino Index (ONI) is 42% in average of June-December. The land cover of vegetation in March/April has decreased by 2,240 Ha (2018-2019) and 2,517 Ha (2019-2020) or around 4% and 5%​​ of total area. La Nina has occurred during wet season 2017/2018 followed by El Nino in the coming years. There was decreasing of rainfall during November-February period in 2019 and 2020, which was 17% and 37% lower compared to 2018

Rainfall Prediction Using Artificial Neural Network

Artificial neural network (ANN) is widely used for modelling in environmental science including climate, especially in rainfall prediction. Current knowledge has used several predictors consisting of historical rainfall data and El Niño Southern Oscillation (ENSO). However, rainfall variability of Indonesian is not only driven by ENSO, but Indian Ocean Dipole (IOD) could also influence variability of rainfall. Here, we proposed to use Dipole Mode Index (DMI) as index of IOD as complementary for ENSO. We found that rainfall variability in region with a monsoonal pattern has a strong correlation with ENSO and DMI. This strong correlation occurred during June-November, but a weak correlation was found for region with rainfall’s equatorial pattern. Based on statistical criteria, our model has R2 0.59 to 0.82, and RMSE 0.04-0.09 for monsoonal region. This finding revealed that our model is suitable to be applied in monsoonal region. In addition, ANN based model likely shows a low accuracy when it uses for long period prediction

Land Use Change Impact on Normalized Difference Vegetation Index, Surface Albedo, and Heat Fluxes in Jambi Province: Implications to Rainfall

Jambi covers various land uses with different characteristics related to biogeophysical cycle. Land use plays an important role in the atmosphere-surface interaction and energy balance partition, which influenced rainfall pattern. Two proxies widely used to differentiate various land uses are albedo and normalized difference vegetation index (NDVI). However, study on albedo and NDVI relationship with rainfall in Jambi is still limited. This study aims to analyze the correlation of NDVI and albedo with rainfall and their distribution in Jambi and Muaro Jambi in 2013 and 2017. The research used Landsat 8 OLI TIRS satellite image data to derived NDVI and albedo, and CHIRPS data for rainfall. A simple linear regression was used to calculate the correlation of NDVI and albedo with rainfall. The results showed that the distribution of albedo for each land use class from the lowest to the highest was forest, plantation, cropland, shrubs, and settlements, respectively. On the contrary, the distribution of NDVI and rainfall is the inverse to albedo. Albedo and NDVI had a strong influence on rainfall through surface energy balance partition. This was indicated by the high R-square between albedo and rainfall (0.99) and between NDVI and rainfall (0.97). Increasing upward latent heat flux from the land surface to atmosphere leads to a rainfall increase. In other words, rainfall may also increase with the decrease in albedo, increase in NDVI, or land use change

Estimation of Oil Palm Total Carbon Fluxes Using Remote Sensing

Net primary production (NPP) is one of the approaches used to estimate the amount of carbon sequestration by plants. This research aims to estimate the total carbon flux exchanged from different ages of oil palm using remote sensing.  The study site was at the PTPN VI Batang Hari, Jambi, Sumatra, Indonesia. The amount of carbon sequestration by oil palm plantations at PTPN VI Batang Hari, Jambi can be estimated using remote sensing based on the light use efficiency (LUE) model.  The results showed that the oil palm age affects the amount of carbon sequestrated.  The lowest Net primary production value was found at one year of planting 4.28 gCm-2day-1, and the highest was 9.38 gCm-2day-1 at 20 years of planting. The model LUE output was validated using Eddy covariance data and the results showed a low error and a high accuracy rate with RMSE = 0.05 gCMJ-1, R2 = 92%, and p-value = 0.04. We concluded that the LUE model can be used with high accuracy to estimate the amount of carbon absorption of oil palm when direct measurement is unavailable

Micrometeorological Method in Determining Plant Capacity to Absorb Pollutant: Oil Palm Case Study

The vegetation canopy's height and characteristics directly affect the turbulence that controls the exchange of mass and energy between the vegetation and the surrounding atmosphere. Turbulence also controls the momentum transfer towards the mass-carrying plant canopy and the accompanying atmospheric properties so that vegetation can contribute to pollutant deposition. This study aims to estimate the canopy capacity of oil palms to absorb pollutants based on their momentum transfer, the influence of atmospheric stability dynamics, and rainy and dry periods upon absorbed pollutants from PTPN VI in Jambi province for the period of January to December 2015 used micrometeorological observation data. The results showed that the dry deposition capacity value at the stable, neutral, and unstable atmospheric conditions were 2.06 x 10-3 kg/m2, 3.50 x 10-3 kg/m2, and 4.35 x 10-3 kg/m2, respectively.  The stable or unstable conditions affected the momentum transfer through decreasing or increasing turbulence. In stable conditions, the cooling of the atmosphere impacts the turbulence to be restrained. The result also showed that the dry deposition capacity during the dry and rainy periods were 4.5 x 10-3 kg/m2 and 2.9 x 10-3 kg/m2, respectively. Further, atmospheric conditions tended to be unstable during the dry period, while the rainy period tended to be stable. This research showed that the momentum transfer method can estimate gas type pollutants by vegetation

The health impacts of Indonesian peatland fires

Background Indonesian peatlands have been drained for agricultural development for several decades. This development has made a major contribution to economic development. At the same time, peatland drainage is causing significant air pollution resulting from peatland fires. Peatland fires occur every year, even though their extent is much larger in dry (El Niño) years. We examine the health effects of long-term exposure to fine particles (PM2.5) from all types of peatland fires (including the burning of above and below ground biomass) in Sumatra and Kalimantan, where most peatland fires in Indonesia take place. Methods We derive PM2.5 concentrations from satellite imagery calibrated and validated with Indonesian Government data on air pollution, and link increases in these concentrations to peatland fires, as observed in satellite imagery. Subsequently, we apply available epidemiological studies to relate PM2.5 exposure to a range of health outcomes. The model utilizes the age distribution and disease prevalence of the impacted population. Results We find that PM2.5 air pollution from peatland fires, causes, on average, around 33,100 adults and 2900 infants to die prematurely each year from air pollution. In addition, peatland fires cause on average around 4390 additional hospitalizations related to respiratory diseases, 635,000 severe cases of asthma in children, and 8.9 million lost workdays. The majority of these impacts occur in Sumatra because of its much higher population density compared to Kalimantan. A main source of uncertainty is in the Concentration Response Functions (CRFs) that we use, with different CRFs leading to annual premature adult mortality ranging from 19,900 to 64,800 deaths. Currently, the population of both regions is relatively young. With aging of the population over time, vulnerabilities to air pollution and health effects from peatland fires will increase. Conclusions Peatland fire health impacts provide a further argument to combat fires in peatlands, and gradually transition to peatland management models that do not require drainage and are therefore not prone to fire risks

The health impacts of Indonesian peatland fires

BACKGROUND: Indonesian peatlands have been drained for agricultural development for several decades. This development has made a major contribution to economic development. At the same time, peatland drainage is causing significant air pollution resulting from peatland fires. Peatland fires occur every year, even though their extent is much larger in dry (El Niño) years. We examine the health effects of long-term exposure to fine particles (PM(2.5)) from all types of peatland fires (including the burning of above and below ground biomass) in Sumatra and Kalimantan, where most peatland fires in Indonesia take place. METHODS: We derive PM(2.5) concentrations from satellite imagery calibrated and validated with Indonesian Government data on air pollution, and link increases in these concentrations to peatland fires, as observed in satellite imagery. Subsequently, we apply available epidemiological studies to relate PM(2.5) exposure to a range of health outcomes. The model utilizes the age distribution and disease prevalence of the impacted population. RESULTS: We find that PM(2.5) air pollution from peatland fires, causes, on average, around 33,100 adults and 2900 infants to die prematurely each year from air pollution. In addition, peatland fires cause on average around 4390 additional hospitalizations related to respiratory diseases, 635,000 severe cases of asthma in children, and 8.9 million lost workdays. The majority of these impacts occur in Sumatra because of its much higher population density compared to Kalimantan. A main source of uncertainty is in the Concentration Response Functions (CRFs) that we use, with different CRFs leading to annual premature adult mortality ranging from 19,900 to 64,800 deaths. Currently, the population of both regions is relatively young. With aging of the population over time, vulnerabilities to air pollution and health effects from peatland fires will increase. CONCLUSIONS: Peatland fire health impacts provide a further argument to combat fires in peatlands, and gradually transition to peatland management models that do not require drainage and are therefore not prone to fire risks. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12940-022-00872-w