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Propagation Characteristics of Madden Julian Oscillation in the Indonesian Maritime Continent: Case Studies for 2020-2022
Full text linkMadden-Julian Oscillation (MJO) can affect weather and climate variability in the Indonesian Maritime Continent. MJO propagation is not always the same, previous research has classified MJO into 4 categories: slow, fast, stand, and jump. The objective of this study is to investigate the differences in MJO propagation and the factors that impact it. Daily data for variables such as Outgoing Longwave Radiation (OLR), zonal wind, and sea surface temperature are utilized in this research. The collected data is processed using composite methods based on the 8 MJO phases, with a specific focus on the years 2020, 2021, and 2022. The research findings suggest that warm sea surface temperatures in the Pacific Ocean and zonal winds dominated by Kelvin waves are favorable for MJO propagation. Conversely, cooling sea surface temperatures in the Pacific Ocean and zonal winds dominated by equatorial Rossby waves can hinder MJO propagation. Future researchers are expected to examine the impact of MJO propagation during extreme rainfall occurrences in several regions of Indonesia, as well as the application of machine learning and deep learning methods to predict MJO propagation in the future
Correlation between Climate Data and Yields of Some Prominent Food Crops in Manokwari, West Papua, Indonesia
Full text linkEnvironmental factors, particularly climate conditions, play a crucial role in influencing the growth and yield of cultivated crops. Although knowledge on their influence has been revealed in many records, our understanding of their relationship in West Papua is limited due to lack of data. This research leveraged data of monthly climate variables (temperature, rainfall, and radiation intensity) and crop yields from West Papua, Indonesia for period 2011-2020. The Analysis revealed varying trends in the highest average monthly air temperature, humidity, rainfall, and radiation intensity across different months. Despite these fluctuations, there was a general tendency towards increased harvested area and rice, maize, and soybeans production. While the overall impact of climate variables on crop productivity appeared insignificant, certain nuances emerge. Specifically, air humidity demonstrated a notable influence on rice productivity, while air temperature has a stronger effect on maize productivity than other climate variables. However, correlation tests indicated that the relationship between climate variables (air temperature, humidity, rainfall, and radiation intensity) and crop productivity, particularly maize, rice, and soybeans, did not reach statistical significance. This underscores the complexity of the interplay between climate dynamics and agricultural outcomes
Statistical Assessment of High-Resolution Climate Model Rainfall Data in the Ciliwung Watershed, Indonesia
Full text linkThe impact of climate change on hydrometeorological hazards pointed out the necessity for information on rainfall data. Using Climate Hazard Group InfraRed Precipitation with Station (CHIRPS) data could solve the problem of the scarcity of observed rainfall data at a finer spatial resolution. This paper examines the performance of high-resolution rainfall climate model data called CORDEX SEA and NEXGDPP in the Ciliwung watershed, Indonesia. We used CHIRPS data as observed data, which was separately divided for calibration (1981-2005) and validation (2006-2020) of the climate models. Totally 14 climate models were used, comprised of 4 CORDEX and 10 NEXGDPP. The models accuracy was assessed based on three statistical indicators: bias, mean absolute percentage error (MAPE), and mean square error (MSE). We determined the best model based on Taylor Diagram. The results showed that the bias value in the dry season was smaller than in the wet and transitional seasons. All models performed well as shown by the low bias values except for the ACCESS1-0 RCP8.5 model. The findings revealed that MRI-CGCM was the best model for calibration, whereas EC-Earth was the best model in the validation period for both RCP4.5 and RCP8.5 scenarios. Further, the choice of climate model may influence water resource management over watershed scale
Estimation of Oil Palm Total Carbon Fluxes Using Remote Sensing
Full text linkNet 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
Correlation Analysis Between Urban Heat Island Intensity and Temperature Criticality Value in Denpasar City
Full text linkThe compactness of buildings in Denpasar resulted in the formation of urban heat islands (UHI), which can be evaluated through the Urban Thermal Field Variance Index (UTFVI) and Environment Criticality Index (ECI). ECI is the ratio of land surface temperature to the Normalized Difference Vegetation Index (NDVI). It can be transformed into Temperature Criticality Value (TCV) using air temperature and Index-based Built-up Index (IBI). This study aims to identify the UHI intensity, the impact of land cover changes, and its association with the TCV. The study employs Landsat 8 imagery and field measurements data, and the findings demonstrate that the study area was mainly composed of built-up areas that had grown from 2015 to 2021. TFVI indicates the most intense UHI (>0.02) in the built-up areas, whereas the mean value of NDVI suggested a reduction in vegetation density. The density of built-up areas (IBI) had increased, while vegetation had decreased. TCV in 2015 ranged from -11.15°C.IBI to 6.42°C.IBI; 2018 between -9.96°C.IBI to 6.79°C.IBI; and 2021 between -10.84°C.IBI to 6.87°C.IBI showed that the environment had become increasingly critical from 2015 to 2021. A transect analysis revealed that more vigorous UHI intensity, denser buildings, and a more critical environment were present in urban centers compared to the suburbs. The correlation coefficient (r) between TCV and UTFVI was relatively robust (0.75–0.82), indicating that the growth of UHI intensity was associated with a more critical environment. TCV has the strongest (r=0.99) and strong correlation (r>0,80) with Built-up Index but inverse correlation with NDVI. Therefore, limiting the expansion of built-up areas and increasing vegetation could help to control the environment\u27s criticality
Climate influence on Diarrhea Disease in Tropical Regions based on Systematic Literature Review
Full text linkDiarrhea disease presents a significant public health concern due to its impact on mortality, and research showed that climate plays an important role on diarrhea prevalence. However, effect of climate on diarrhea incidence was inconsistent among climate factors. Here, we investigated this inconsistency thorough systematic literature review. Our review encompassed the formulation of research questions, development of literature search strategies, and the establishment of inclusion/exclusion criteria for systematic data extraction. We carried out an extensive search from peer-review literature databases including Scopus, Pubmed, and Proquest for articles published between January 2000 to March 2023. We found that 74 studies focusing on diarrhea diseases and climate influencing factors met our inclusive criteria. Climate factors that affected diarrhea were rainfall, temperature, humidity, and climate seasonality. Our findings revealed that a positive association between diarrhea and rainfall was consistently observed. Other climate factors (temperature and humidity) indicated a positive correlation as well, although viral diarrhea exhibited a negative correlation with temperature. Further, bacterial and parasitic diarrhea diseases were more prevalent in the rainy season, whereas viral diarrhea occurred more frequently during the dry season with lower temperatures
Sea Surface Temperature Anomaly Characteristics Affecting Rainfall in Western Java, Indonesia
Full text linkWestern Java is densely populated with high socio-economic activity. Climate-related disasters can be mitigated with the support of an understanding of systems that produce reliable climate predictions. One of the climate variables included in hydrometeorological disasters is rainfall. The characteristics of rainfall in Western Java cannot be separated from the sea surface temperature (SST) around the area. This study compares the relationship between SST and rainfall with singular value decomposition (SVD) and compares it with Pearson\u27s correlation. SVD Model performance was evaluated using square covariance fraction (SCF) and Pearson correlation. The results showed that rainfall has a higher correlation with SST Anomaly (SSTA) by using SVD, with a correlation of about 0.63 in 6 to 9 months without lag time. Rainfall in western Java was closely related to the positive SSTA anomaly in southern Indonesia, especially the waters south of Java Island, and negative anomalies in other areas. Furthermore, atmospheric dynamic analysis showed that the positive coefficient expansion is followed by warmer SST, lower surface air pressure, higher water vapor, and higher rainfall, all were respective to their normal conditions around western Java. This study concludes that warmer SSTA around Western Java causes increased rainfall in western Java than normal and potentially impacts the hydrological disaster in West Java
Analysis of Carbon Dioxide Emission from Forest Fires based on Fire Radiative Power in Riau
Full text linkRiau is one of the susceptible regions in Indonesia, which faces frequent land and forest fires. Fires occur in various land covers and soil types, both peat and mineral soils, which emitted huge carbon to the atmosphere. Forest fires emit greenhouse gases, including carbon dioxide (CO2). The objective of the research was to quantify CO2 from land and forest fires. The quantification emission was for 2016 – 2018 based on the fire radiant power (FRP) dataset along with the buffer methodology for assessing fire-affected land extents across different land covers. The FRP dataset we used to be only at a confidence level of 70% or higher, which represents hotspots. The results revealed large numbers of FRP focal points (> 1000) that can be identified as fires for 2016 and 2018, whereas only small numbers (121) were identified for 2017. Then we quantified the area burned of 95,396 Ha in Riau for 2016, which was double to the 2018’s area burned. Further, this burning contributed to CO2 emission equal to 313,456 tCO2 for 2016. Emission in 2017 was a relatively low as not many observed fires detected
The Optimum Planting Time and Cropping Pattern of Potatoes and Other Horticultural Commodities based on Water Balance in Solok, Indonesia
Full text linkMany mountainous regions in Indonesia have been utilized for potato cultivation. But location for the cultivation is mainly a rainfed agriculture, which greatly depend on the weather condition. Lembah Gumanti in Solok, West Sumatra is a rainfed main potato-growing area, which faced a low productivity during dry season. Therefore, efforts to optimize potato production in rainfed area remains research challenge. This study aims toidentify the optimum cropping calendar for potato and other horticultural commodities in Lembah Gumanti for 2018-2021. We used the water balance approach to derive daily water availability at field level. The approach was used to identify the planting time and pattern of potato and other horticultural commodities for 2018-2023 at dekadal (10-day) interval. The results showed that the most suitable planting time and cropping patternvaried annually. In 2018-2019, the cropping calendar was potato (in October 1st 10-day) – shallots (in April 1st 10-day) – chilies (in July 3rd 10-day). For 2020-2021, the best cropping calendar was shallots (in November 3rd 10-day) – potato (in March 3rd 10-day) – shallots (in August 1st 10-day). The findings reveal that water availability determined the cropping calendar of each commodity
Micrometeorological Method in Determining Plant Capacity to Absorb Pollutant: Oil Palm Case Study
Full text linkThe vegetation canopy\u27s 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