Agromet

Propagation Characteristics of Madden Julian Oscillation in the Indonesian Maritime Continent: Case Studies for 2020-2022

2024-02-02T14:59:08+07:00

Madden-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.

Climate Influences on Latex Yield in South Sumatra, Indonesia

2024-02-05T09:41:37+07:00

This study addresses the impact of climate variability on latex yield. Field research was carried out in the Indonesian Rubber Research Institute Experimental Field, located in South Sumatra, Indonesia for 2020 to 2022. The study used mature IRR 118 clones of rubber (Hevea brasiliensis) planted in clay loam soil. Latex yields for dry and rainy seasons were compared to obtain the effects of climatic factors. A purposive sampling of latex clone IRR 118 was applied in the field. The results showed that declined rainfall and soil moisture content contributed to the low latex yield during dry season. A declined water availability acts as a limiting factor resulting in decreased latex yield. Latex yield consistently decreased when soil moisture content fell below 21.5%. Based on statistical analysis, the correlation between latex yield and climate factor was 0.36, 0.42, and 0.52 for rainfall, soil moisture content, and evapotranspiration, respectively. Our findings highlight the crucial influence of climatic factors, emphasizing the significance of optimal water availability for latex production.

A Comparison of the Performance of the Weighted Ensembles Means in CORDEX-SEA Precipitation Simulations

2024-03-19T10:31:52+07:00

Numerous studies stated that the performance of ensemble mean derived from multiple climate models generally surpassed the individual member model, and applying weighting factors potentially increase the ensemble mean of performance. This study aims to assess the performance of unweighted and weighted ensemble means of 9-modelled precipitation datasets in the CORDEX-SEA multi-model simulations for 1981-2005. The 9 datasets included: CNRM_a, ECE_b, GFDL_b, IPSL_b, HadGEM2_a, HadGEM2_c, HadGEM2_d, MPI_c, and NorESM1_d. The weighting factors were derived from the models' skill scores measured using five statistical-based metrics, namely Taylor, Pierce (SS), Tian skill score (Tian), Climate prediction index (CPI), and Performance and Independence (PI). The ERA5 and GPCP precipitation datasets were used as the references for comparison. Then, reliable metrics will be used to determine the weighting factor. The results found that three metrics namely Taylor, SS, and Tian were more reliable than the other two metrics (CPI and PI). Spatially, the weighted ensemble mean based on a random method was superior to other ensemble mean methods and individual models. We found that the CNRM_a and GFDL_b models were spatially performed best. In contrast, most the ensemble means was temporally less performed compared to the individual model. Our findings suggested that by removal of low performance models will significantly influence on the overall ensemble model performance. Further, the research may provide valuable considerations of climate models selection for climate projection assessments, especially in the Southeast Asia region.

Extreme Rainfall Analysis in the Bengawan Solo Watershed, Java

2024-06-20T18:03:29+07:00

As the largest watershed in Java Island, the Bengawan Solo watershed has experienced recurrent hydrometeorological hazards, leading to infra-structure damage, casualties, and environmental degradation. Research on extreme rainfall causing the hazards in the Bengawan Solo watershed is still limited. This study examines extreme rainfall events by analyzing daily rainfall data (1991-2020) at three observation stations namely Musuk, Tinap, and Lowayu, which represent the upstream, middle, and downstream of the Bengawan Solo watershed. The Extreme Value Theory (EVT) using the Block Maxima approach with a Generalized Extreme Value (GEV) method was used to determine the rainfall return period of 5, 10, 20, 30, and 50-year. We applied the Mann-Kendall test to assess the annual trends of extreme rainfall indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The results found that the highest estimated annual maximum of daily rainfall was in Musuk station (226.7 mm), followed by Tinap station (159.3 mm) and Lowayu station (149.4 mm). While no significant trend was observed for Musuk, other stations showed a significant trend for the decrease of the daily rainfall intensity, the increase of the number of annual rainy days, the decrease of the annually maximum amount of five consecutive precipitation days, and the increase of the annually number of consecutive wet days. There is also an increase in the maximum amount of annual rainfall for one day (Rx1day) at Lowayu station, which indicates a higher risk of disaster due to high rainfall. Additionally, an increasing trend in the total annual rainfall (PRCPTOT) at Musuk, Tinap, and Lowayu stations suggests a greater potential for water storage to meet water needs in these areas.

A Preliminary Study on the Parameter Configuration of Weather Research Forecasting in Tropical Peatland, Central Kalimantan

2024-06-24T17:13:30+07:00

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.

Correlation between Climate Data and Yields of Some Prominent Food Crops in Manokwari, West Papua, Indonesia

2024-06-28T20:26:57+07:00

Environmental 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.