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Journal : Agromet

Climate influence on Diarrhea Disease in Tropical Regions based on Systematic Literature Review Arinda, Dela; Hidayati, Rini; Taufik, Muh.
Agromet Vol. 37 No. 2 (2023): DECEMBER 2023
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.37.2.99-107

Abstract

Diarrhea 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.
A Preliminary Study on the Parameter Configuration of Weather Research Forecasting in Tropical Peatland, Central Kalimantan Taufik, Muh.; Haikal, Mudrik
Agromet Vol. 38 No. 1 (2024): JUNE 2024
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.38.1.49-57

Abstract

Hydrometeorological variables are sensitively regulated by atmospheric dynamics and variability. Weather research and forecasting (WRF) model is the cutting-edge tool for studying and investigating the dynamics of physical atmospheric conditions, but the configuration scheme of WRF parameters remains a research challenge for topical peatland situated in the maritime continent. Here, we evaluated WRF parametrization based on three kalibration configuration schemes, which influence rainfall, temperature, and soil moisture dynamics. We tested the WRF evaluation for Sebangau-Kahayan peatland for a wet-dry season in August 2020. The best configuration was determined based on three statistical metrics namely mean absolute error, percent bias, and coefficient of correlation. Our results showed that WRF forecasts were greatly depend on a bias correction to improve the model performance, in which it was consistently found in all configurations. Rainfall was barely predicted in station level with a low performance in term of weekly spatial distribution. Other findings revealed that all configurations showed a good performance for temperature and soil moisture forecasts. Further, our findings emphasize the important physical parameter of WRF that control rainfall formation and dynamics. Last, we highlight an urgent need of more ground stations in term of spatial distribution to validate the weather forecast.
Identification of Peatland Burned Area based on Multiple Spectral Indices and Adaptive Thresholding in Central Kalimantan Pratikasiwi, Hilda Ayu; Taufik, Muh.; Santikayasa, I Putu; Domiri, Dede Dirgahayu
Agromet Vol. 38 No. 2 (2024): DECEMBER 2024
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.38.2.68-77

Abstract

Nowadays, spectral index has become popular as a tool to identify fire-burned areas. However, the use of a single index may not be universally applicable to region with diverse landscape and vegetation as peatlands. Here, we propose to develop a procedure that integrates multiple spectral indices with an adaptive thresholding method to enhance the performance of burned area detection. We combined the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) using MODIS imagery from 2002 to 2022 to calculate (Confirmed Burned Pixel) by filtering dNDVI and dNBR. The mean and standard deviation of serve as inputs for image thresholding. We tested our approach in Sebangau peatland, Central Kalimantan, where fires occur annually. The results showed that the model performed well with overall accuracy > of 91%, indicating that the model is effective and reliable for identifying burned areas. The findings also revealed that the frequency of fire is below 2 times/year, with the southeastern is the most fire prone regions. Further, our findings provide an alternative approach for identifying burned areas in locations with diverse vegetation cover and different geographical regions.