Agromet is published twice a year by the Indonesian Association of Agricultural Meteorology (PERHIMPI) in collaboration with Department of Geophysics and Meteorology, Faculty of Mathematics and Natural Sciences, IPB University. Agromet publishes original research articles or reviews that have not been published elsewhere. The scope of publication related to weather and climate issues (agriculture, forestry, water resources, environment, ecology, and health as affected by weather and climate variability and change).
PERHIMPI (Indonesian Association of Agricultural Meteorology)
Water Use Efficiency and Adaptive Responses of Oil Palm under El Niño-Induced Drought and Haze
Oil palm plays an important role in the tropical carbon cycle but is highly sensitive to climatic variability. Understanding the coupled dynamics of carbon and water fluxes in such ecosystems essential for sustainable management under variable climatic conditions. This study analyses the Water Use Efficiency (WUE) and coupled carbon–water fluxes of an 18-year-old oil palm plantation in Jambi, Indonesia, during the 2015 El Niño event using data from an eddy covariance flux tower. The analysis focused on the diurnal variations of Net Ecosystem Production (NEP), evapotranspiration (ET), and water use efficiency (WUE) during wet, dry, and dry with haze periods, which were determined based on rainfall data. Our results show that WUE reached its highest value during the dry-with-haze period (7,484 g CO₂ kg⁻¹ H₂O), more than double that of the wet (3,440) and dry (3,347) periods. This increase resulted from reduced evapotranspiration (ET) due to stomatal regulation, despite lower Net Ecosystem Production (NEP) caused by light limitation from haze. Diurnal analyses showed WUE peaking in the morning and declining at midday as the Vapor Pressure Deficit (VPD) increased (up to 0.88 kPa under haze). These findings highlight oil palm’s adaptive strategy to conserve water under stress while maintaining productivity. However, severe haze markedly weakens carbon sequestration. The results provide critical insights for optimizing irrigation and water management in the face of increasing climate variability.
Copyright (c) 2025 Kharisma Rinandyta, Prof. Dr. Ir. Tania June, MSc https://creativecommons.org/licenses/by-nc/4.0
Bias Correction of CMIP6 Models for Assessment of Wet and Dry Conditions Over Sumatra
The performance of CMIP6 models in capturing local and regional precipitation patterns often requires refinement due to inherent biases. This study evaluates eleven CMIP6 models for their applicability over Sumatra Island and applies two bias correction methods namely Linear Scaling (LS) and Quantile Delta Mapping (QDM). We used ERA5 precipitation datasets as a reference bias correction during 1981-2014. The performance was assessed using MAE, correlation, and PBIAS. Results reveals that raw model of CMIP6 generally underestimate precipitation, particularly during the DJF and SON seasons, with the largest errors over the mountainous western Sumatra. LS tends to overcorrect and shift precipitation estimates toward a wetter bias, while QDM significantly improves the accuracy and seasonal consistency of the simulations. The multi-model ensemble mean (CMIP6-avg) outperforms individual models, and its performance is further enhanced with QDM, yielding higher correlation and lower error metrics. Spatial and seasonal analyses demonstrate that QDM more effectively reduces both dry and wet biases, especially during peak rainfall seasons. These findings underscore the importance of robust bias correction techniques to improve climate projections for hydrological and climate impact studies in Sumatra and other tropical regions with complex terrain.
Copyright (c) 2025 Rahmat Hidayat, Muh Taufik
Assessing the Influence of Climate Services and Climate Change Adaptation Strategies on Smallholder Agriculture: A Systematic Literature Review
Climate services and climate change adaptation practices are increasingly recognized as essential for supporting smallholder farmers. Despite numerous studies on climate impacts and adaptation strategies, limited systematic evidence exists on how climate services and adaptation interventions influence farming practices across regions. This study addresses the gap through a systematic literature review of Scopus-indexed publications over the past decade. Using the PRISMA approach, 1981 articles were screened, with 31 meeting the eligibility criteria. Of these, 23 focused on adaptation interventions and 8 on climate services. Geographically, 30 studies were concentrated in tropical regions Africa (n =16) and in Asia (n=14), while one study was outside the tropics. Findings show that climate information strongly supports the adoption of adaptation strategies (>60%), especially in technological interventions such as Climate-Smart Agriculture, ecosystem management, irrigation, and climate risk reduction. In terms of service delivery, basic climate service provision demonstrated greater effectiveness (80%) compared to advisory-based agricultural services (40%). Socio-demographic factors, particularly education and age, consistently influenced farmers’ decision-making in adopting both climate services and adaptation practices. Overall, this review highlights the need for more integrated approaches that explicitly connect climate services with adaptation interventions. Strengthening these linkages is especially critical in tropical regions, where smallholder farmers remain highly vulnerable to climate variability and long-term climate change risks.
Copyright (c) 2025 Marjuki Marjuki, Yonny Koesmaryono, I Putu Santikayasa, Ardhasena Sopaheluwakan https://creativecommons.org/licenses/by-nc/4.0
Water Use Efficiency in Pineapple Plants Fertilization Based on Water Balance Analysis
Most fertilization in pineapple cultivation occurs through foliar application, which involves dissolving fertilizers in water and spraying them on the pineapple plants. The provision of irrigated water to plants refers to water balance analysis, which corresponds to the available water supply. The research aims to determine the effect of various foliar volumes on the growth and production of pineapple plants. The research was carried out in April 2022 - July 2023 at PT. Great Giant Pineapple Lampung using a Completely Randomized Block Design (CRBD). We applied four treatments of foliar water, which comprised of 1500 l/ha, 2000 l/ha, 3000 l/ha (as control), and based on water balance analysis. The treatment has three replication each. The results showed there was not significant different of growth and yield between treatments of water balance approach and the 3000 l/ha foliar water volume (control), in which both have reduced water usage by 22% for one cycle of pineapple cultivation. The findings provide a more efficient water manage-ment strategy for foliar fertilization, reducing water usage without affecting plant performance, and supporting sustainable agricultural practices in pineapple cultivation. Further, the findings can serve as a reference for optimizing irrigation scheduling and input costs in large-scale plantations.
Copyright (c) 2025 Nungki Kusuma Astuti, Rinaldi Nur Rahman Putra, Yuni Arda Br Saragih https://creativecommons.org/licenses/by-nc/4.0
ECMWF SEAS5 Seasonal Rainfall Assessment: A Study Case in Papua
Papua, Indonesia’s easternmost island, is prone to seasonal hydrometeorological disasters, necessitating high-quality climate forecasts for effective risk management. This study evaluates the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) Seasonal Forecast System 5 (SEAS5) in predicting seasonal (3-monthly) rainfall across Papua from 1982 to 2016, using the blended Climate Hazards group InfraRed Precipitation with in-situ rainfall data (CHIRP+Pos) as the observational reference. SEAS5 forecasts at 1 to 3 month lead times were assessed across seasons which defined as July-August-September (JAS), August-September-October (ASO), September-October-November (SON), and December-January-February (DJF), and El Niño-Southern Oscillation (ENSO) phases (El Niño, La Niña, Neutral), using Pearson correlation coefficient (Corr), root mean square error (RMSE), and Kling-Gupta Efficiency (KGE) metrics. Results show stronger SEAS5 skill in JAS–SON (Corr up to 0.939) compared to DJF-JFM (Corr as low as -0.208), with a robust ENSO-rainfall relationship in JJA-SON. SEAS5 performed best during El Niño, particularly in lowlands and exhibited greater variability skill during La Niña and Neutral phases. Benchmarking against a linear regression baseline showed SEAS5’s superior Corr in 76.2% of grids but higher RMSE in 60.6%. Despite limitations in mountainous regions and at longer lead times, SEAS5 offers reliable forecasts for lowland areas during JAS-SON under El Niño, supporting operational applications like drought preparedness and agricultural planning in the regions.
Copyright (c) 2025 Steven Andika, I Putu Santikayasa, Permana Donaldi Sukma https://creativecommons.org/licenses/by-nc/4.0
Flood Management Strategy Based on Analysis of Regional Characteristics and Causal Factors in Kendari City
Flooding is a disaster that causes environmental damage, economic losses, and social problem. Kendari City is one of the Indonesian cities that frequently experiences floods due to various factors, including high rainfall, land use changes, poor drainage conditions, and improper spatial management. This study aims to (1) assess the factors contributing to flooding, (2) analyze government governance and community participation in flood management, and (3) formulate an integrated flood management strategy. The methods used were descriptive analysis, spatial approach, SWOT analysis, and Quantitative Strategic Planning Matrix (QSPM). The results showed that there is evidence of land use changes between 2013 – 2023 based on spatial image analysis. We found there were three sub-districts, which is categorized on high flood vulnerability namely Baruga, Kadia, and Kambu sub-districts. Based on the level of community preparedness parameters including knowledge and attitudes, emergency plans, early warning, and resource mobilization, Baruga belongs to the medium category (74.60%), while Kambu (57.42%) and Kadia (59.58%) were in low category. QSPM analysis recommends two priority strategies to reduce flood vulnerability namely accelerating drainage system improvements and replicating the Baruga model in other areas. Future research should focus on climate change-induced flood modeling, gender-sensitive vulnerability assessments, and economic loss estimation to enhance the effectiveness of flood management strategies.
Copyright (c) 2025 wandira agus sakawuna, Budi Indra Setiawan, Perdinan
Climate Change Impact On Rice Productivity Using FAO AquaCrop Model: A Case Study in Lampung
Climate change impacts global food productions, but its local effect on rice production, particularly on monsoon dominated regions remain less understood. Here, we assess climate change impact on rice production situated on Lampung Province, as one of the largest rice productions in Indonesia. The FAO AquaCrop model was used to predict rice productivity, incorporating climate projections from the CMIP5 model under medium (RCP 4.5) and high (RCP 8.5) emission scenarios. We simulated the model for fifteen locations representing districts in Lampung Province. Our results show that by 2050, the decreased rainfall is projected during the dry season and early rainy season, but the average monthly temperatures and evapotranspiration rates are expected to increase across all districts. AquaCrop simulated an increased rice productivity by +0.25 and +0.74 tons/ha for both scenarios in April planting season, but it decreased by -0.41 and -0.75 tons/ha in November planting season due to water stress. This research is important to provide a deeper understanding of the impact of climate change on rice productivity in Lampung Province. These findings highlight the need for adaptive strategies to sustain rice production under future climate conditions.
Copyright (c) 2025 Rizki Priatama, Tumiar Katarina Manik, Paul Benyamin Timotiwu, Eko Pramono, Alimuddin
Assessment of Rice Crop Water Requirements for Planting Season in Moderate Agroclimatic Area of West Sumatra
In changing climate, uncertainty in rice production becomes more frequent leading to threat of food security. However, research on rice cultivation in the rainfed agricultural areas of West Sumatra remains limited. The objectives of the study are to analyze the crop water requirements of rainfed rice and to determine rice planting patterns. The study was conducted in a moderate agro-climate area of West Sumatra based on oldeman agroclimate zone that experienced changes in planting patterns. We used climate data for 1991 – 2020 obtained from TerraClimate, which were utilized for monthly water balance computation based on the Thornwhite and Matter approach. The analysis focused on four major rice production centers, namely: Panti in Pasaman, Lima Kaum in Tanah Datar, Luak in Lima Puluh Kota and Sijunjung. The results showed change in water deficit periods across the study sites have changed planting season. Based on our analysis site in Lima Kaum, Tanah Datar experienced the longest deficit period, which lasted 5 months from May to September. This situation may not suitable to plant rice throughout the year without additional irrigation. Further, adjusting to the secondary crop may be considered to optimize agricultural productivity. These findings can serve as a reference for determining planting seasons and improving water use and distribution strategies in rainfed agricultural systems.
Copyright (c) 2025 Via Yulianti, Rizky Armei Saputra, Hermansah
Land Management of Tidal Swamp Type B with Surjan System as Climate Change Anticipation
Agriculture is one of the most vulnerable sectors to climate change, which can significantly impact national food security. In addition to climate change, agricultural development faces challenges, including the conversion of agricultural land for non-agricultural purposes. As a result, agricultural extensification has expanded into marginal lands, such as tidal swamplands. This paper presents a literature review on the characteristics of tidal swamplands, the principles of the surjan system, and its relevance in addressing climate change, particularly in the context of food security and ecosystem sustainability. Various literature sources were analyzed to assess the advantages, challenges, and sustainable management strategies of tidal swamplands. The review highlights the importance of effective land management to create suitable soil conditions for optimal plant growth and increased productivity. The surjan system, a land management approach practiced by tidal swampland farmers, demonstrates high adaptability in mitigating the impacts of climate change. This system integrates cultural, ecological, and economic perspectives by combining local knowledge with technological advancements. Key components of the surjan system include a one-way water management system with flap-gates and stoplogs, as well as the use of climate-adaptive crop varieties on tidal swamplands.
Copyright (c) 2025 Ani Susilawati, Lutfi Izhar, Achmad Adi Surya Sustama
Contribution of Small Rainwater Reservoirs to Performance Off-season Vegetable Farming
Climate Smart Agriculture (CSA) has become essential in ensuring sustainable agricultural production amidst ongoing climate change. This study aims to analyze the impact of small rainwater reservoirs (SRRs) on the performance of off-season vegetable farming in Netpala Village, East Nusa Tenggara, Indonesia. The SRRs, constructed using plastic tarpaulin with storage capacities of 3.6 m³ and 4.3 m³, were applied to support the cultivation of mustard greens, Chinese cabbage, cabbage, carrots, and eggplant during the December 2017 to April 2018 growing season. An on-farm research (OFR) approach was used to assess the effects of SRR implementation compared to traditional water management practices. Key performance indicators include cultivated land area, planting index, crop diversification, and farmer income. Results revealed that SRRs expanded the cultivated area by 21.07%, increased the planting index by 0.52 for mustard greens and 0.64 for cabbage, and boosted farmer income by 29.38%. Income levels were also influenced by factors such as market absorption, commodity prices, and land availability. These findings demonstrate that SRRs can enhance the resilience and productivity of smallholder vegetable farming systems by improving water availability during the rainy or off-season. SRRs offer a practical and scalable solution to address water scarcity and promote sustainable intensification in vulnerable agricultural regions.
Copyright (c) 2025 Tony Basuki, Bernard deRosari, Debora Kana Hau, Jacob Nulik, Yiyi Sulaeman, Apolinaris S Geru
The Use of Artificial Neural Networks to Estimate Reference Evapotranspiration
Evapotranspiration is defined as the loss of water from soil and vegetation to the atmosphere, driven by weather conditions. It reduces the availability of water for agricultural purposes, which affects the amount of irrigation water, particularly during the dry season. The objective of this paper is to present a comparative analysis of the estimated reference evapotranspiration value based on artificial neural networks (ANN) with backpropagation bias 1 (BP-1) and backpropagation bias 0 (BP-0) architectures. The model was fed with data of air temperature, relative humidity, and solar radiation. The model is utilized to calculate the evapotranspiration using the Hargreaves method as the training data. The performance of ANN model was evaluated using the mean square error (MSE), root mean square error (RMSE), and coefficient determination (R2). Our results showed that both ANN models performed well as indicated by low error (MSE < 0.01) and high R2 (>0.99). Also, we found that air temperature and relative humidity determine the optimal prediction. Further, this proposed model can serve as a reference for other models seeking to determine the most appropriate computational model for evapotranspiration value estimation.
Copyright (c) 2025 Abdul Haris, Marimin, Sri Wahjuni, Budi Indra Setiawan
Canopy Microclimate Modification with Reflective Mulches Under Oil Palm and Its Role to Soybean Growth
Land utilization under oil palm plantation is constrained by the condition of low light intensities. Modification of the microclimate through the use of reflective mulch, as a reflector, will increase its ability to reflect the land surface radiation under the tree stand. This modification may suitable for intercropping system between soybean and oil palm. The study aimed to determine the effect of microclimate modification, using reflective mulch, under the stand of oil palm, and to evaluate its effect on soybean productivity. The research was conducted at PTPN-VIII Cimarga Banten using a nested random design with two factors and three replications each. The first factor is the oil palm age, which consists of: (i) control (open land), (ii) 4 years, (iii) 5 years, and (iv) 8 years age of oil palm. The second factor is the reflective mulch, as a solar radiation reflector, which consists of three levels: (i) without mulch (control), (ii) inorganic reflective mulch/silver black plastic mulch, and (iii) organic reflective mulch/dried oil palm leaves. The application of inorganic and organic reflective mulch increased the distribution of reflected land surface radiation (59%-157%), reduced the soil temperature fluctuation (0.30C-1.20C), and maintained soil water content (45.2%-45.8%). An increased of plant growth rates (56%-86%), relative growth rates (16%-21%), and seed weight production per plant (74.8%-86.2%) also reported, as well as the reduction of the etiolation ratio (9.6%-12.5%). The use of organic and inorganic reflective mulches can improve the microclimate and increase the production of soybean under intercopping system with oil palm.
Effect of Rainfall Intensity on Glyphosate Herbicide Effectiveness in Controlling Ageratum conyzoides, Rottboellia exaltata, and Cyperus rotundus Weeds
Glyphosate is one of herbicide active ingredient which is mostly used to control weeds in crops. However, in rain season herbicide effectiveness decreases as it is washed by rain. This research aimed to study effect of rainfall intensity on the effectiveness of herbicide (Round up 486 SL 2.5 l/ha.) with isopropilamina glyphosate as the active ingredient in controlling specific weeds Ageratum conyzoides, Rottboellia exaltata, and Cyperus rotundus. The experiment was consisted of six treatments and arranged in randomized block design with 8 replications. The treatments were level of rain intensity which were 5 mm/hour, 10 mm/hour, 20 mm/hour, 40 mm/hour, no rain and control (no herbicide no rain). Rainfall intensity was determined by conducting simulation trials prior to the treatments and applied 30 minutes after herbicide applications. The results showed that herbicide effectiveness decreased as the rainfall intensity incresed, even though with longer time the herbicide was still able to control the weeds. The effect of rainfall intensity on herbicide effectiveness was different for different weeds. Up to intensity 40 mm/hour herbicide was capable to control weeds but with level of weeds destruction 20-60%.
Implementation of Bayesian Model Averaging Method to Calibrate Monthly Rainfall Ensemble Prediction over Java Island
Bayesian Model Averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from an ensemble prediction in the form of predictive Probability Density Function (PDF). BMA is commonly used to calibrate Ensemble Prediction System (EPS) in a shorter-range forecast. Here, we applied the BMA for a longer forecast at a seasonal interval. This study aimed to develop the implementation of the BMA method to calibrate the seasonal forecast (long range) of monthly rainfall from the RAW output of the EPS European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4). This model was calibrated with observational data from 26 stations over Java Island in 1981-2018. BMA predictive PDF was generated with a gamma distribution, which was obtained based on two training schemes, namely sequential (BMA-JTS) and conditional (BMA-JTC) training windows. Generally, both of BMA-JTS and BMA-JTC were able to produce better distribution characteristics of ensemble prediction than that of RAW model ECS4. Both BMA methods showed a good performance as indicated by a high accuracy, small bias, and small uncertainty to the observed rainfall. Our findings revealed that BMA-JTC was able to improve the quality of probabilistic forecasts of below and above normal events. The improvement was shown in most stations over Java Island, in which the model was a good skill forecast based on Brier Skill Score (BSS).
The Impact of El Niño and La Nina on Fluctuation of Rice Production in Banten Province
Rice production in Indonesia is facing serious problem, in which the production is fluctuated causing the unstability in the food supply. One factor influencing the rice productions is climate extreme. Here, we analysed rice production in Banten Province for 2002-2015. The objective of this reasearh was to analyse the effect of climate variability on the fluctuation of rice production in Banten. We relied on data from BPS Banten, which provided timeseries of rice production for 2002-2015. We used four statistical approaches namely linear, quadratic, exponential, and moving average models to detect trend in rice production. Our results showed that Rice Production fluctuated every year indicating an increased trend for the observartion period. Based on the trend analysis, the growth rate for rice production was 1,66% per year. Climate extreme has affected on rice production, with El Niño resulted in the decreasing on rice production, whereas La Nina caused an increased of rice production. Further, to adapt climate extreme events, the government needs to encourage farmers to join the Rice Farming Insurance (AUTP) program to protect rice farming from economic losses due to the climate extreme impacts.
Oil palm plantation has a high potency to absorb carbon. Limited observed data and expensive instrumentations to measure the absorbed carbon have caused an inaccurate estimation of carbon storage from oil palm. The objectives of this research were to develop a CO2 absorption model, and to calculate the carbon cycle based on climate factors and plant age. CO2 absorption was derived from gross primary production (GPP) and net primary production (NPP), which were ​​based on solar radiation. From NPP we derived net ecosystem exchange (NEE) by calculating the difference between NPP and soil respiration. Our results showed that age of oil palm has influenced the CO2 absorption from 9.8 (1 year) to 117 tons ha-1 year-1 (19 years), with average of 86.5 tons ha-1 year-1 (over 25-year life cycle). We validated our NPP model with biomass that indicated a very good performance of the model with R2 0.95 and RMSE 1.81. Meanwhile, the performance of NEE model was slightly lower (R2 0.71 and 0.72, for wet and dry conditions), but the model had a similar pattern with the measured NEE. Based on the model performance, the findings imply that the model is useful to estimate CO2 absorption, where there is no eddy covariance measurement. This research suggests that carbon modeling will contribute to global terrestrial carbon modeling.
Modeling of Heavy Rainfall Triggering Landslide Using WRF Model
This study revealed the behavior of heavy rainfall before landslide event based on the Weather Research Forecasting (WRF) model. Simulations were carried out to capture the heavy rainfall patterns on 27 November 2018 in Kulonprogo, Yogyakarta. The modeling was performed with three different planetary boundary layer schemes, namely: Yonsei University (YSU), Sin-Hong (SH) and Bougeault and Lacarrere (BL). Our results indicated that the variation of rainfall distribution were small among schemes. The finding revealed that the model was able to capture the radar’s rainfall pattern. Based on statistical metric, WRF-YSU scheme was the best outperforming to predict a temporal pattern. Further, the study showed a pattern of rainfall development coming from the southern coastal of Java before 13:00 LT (Local Time=WIB=UTC+7) and continued to inland after 13:00 LT. During these periods, the new clouds were developed. Based on our analysis, the cloud formation that generated rainfall started at 10:00 LT, and hit a peak at 13:00 LT. A starting time of cloud generating rainfall may be an early indicator of landslide.
Rainfall and Temperature Change Analysis and their Correlation on Maize Productionin Karawang, West Java
Maize is an important food commodity and its yields can be threatened by changes in climate variables, such as increasing air temperature and decreasing rainfall. The research identifies and detect the change in climate variables and analyze their correlation with maize production. Quantitative and descriptive methods were used namely trend analysis, correlation, and regression. We utilized climate data (temperature and rainfall) and maize production for 1991-2022, with tested study area in Karawang, West Java. We divided the climate data into two periods to analyze any change in climate variables. The results indicated a change in temperature (+0.56 °C) and rainfall (-47.34 mm) per year, but there is no change in the agroclimatic zone. Our findings showed a moderate correlation between rainfall and maize production and productivity, with the mean correlation coefficients of 0.31 and 0.35, respectively. Similarly, air temperature showed a moderate correlation with maize production and productivity, with the mean correlation coefficients of 0.30 and 0.32, respectively. Appropriate anticipatory and adaptation efforts are needed to maintain maize production in rainfed agriculture such as in Karawang Regency.
Copyright (c) 2024 Ruminta Roem, Wicaksono Fiky Yulianto, Napitupulu Grace Ananda
ENSO and IOD Influence on Extreme Rainfall in Indonesia: Historical and Future Analysis
Indonesia, as a maritime continent, is vulnerable to environmental disasters such as floods and landslides due to extreme rainfall. This study aims to identify changes in the influence of ENSO and IOD on extreme rainfall across Indonesia, specifically during the September-October-November period. We used rainfall and sea surface temperature data from the CMIP6 climate model for the historical period (1985-2014), near-future (2031-2060), and far-future (2061-2090) projections under SSP2-4.5 and SSP 5-8.5 climate scenarios. The relation between rainfall dan ENSO/IOD was simply defined by linear regression approach. We analyzed the change of influence by comparing the historical and the future condition. The results indicated that the changes in the teleconnection of ENSO and IOD to extreme rainfall in future is consistently negative, except for Java (near-future) and Kalimantan and southern Sumatra (far-future). Our finding revealed that significant changes in the teleconnection varied throughout maritime continent. The maximum change was found in Northern Kalimantan, which reached values of -80 mm/°C due to ENSO and -180 mm/°C due to IOD for near future. These findings highlight the spatial variability in teleconnection changes across Indonesia, underscoring the need for region-specific climate adaptation measures in response to evolving extreme rainfall patterns.
Copyright (c) 2024 Risyda Hanifa, Joko Wiratmo
Identification of Peatland Burned Area based on Multiple Spectral Indices and Adaptive Thresholding in Central Kalimantan
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.
Copyright (c) 2024 Hilda Ayu Pratikasiwi, Muh. Taufik, I Putu Santikayasa, Dede Dirgahayu Domiri
Extreme Rainfall Analysis in the Bengawan Solo Watershed, Java
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.
Copyright (c) 2024 Indri Hapsari, Perdinan, Amsari Mudzakir Setiawan
Correlation between Climate Data and Yields of Some Prominent Food Crops in Manokwari, West Papua, Indonesia
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.
Copyright (c) 2024 Siska Syaranamual, Yaved Muyan
A Preliminary Study on the Parameter Configuration of Weather Research Forecasting in Tropical Peatland, Central Kalimantan
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.
Copyright (c) 2024 Muh. Taufik, Mudrik Haikal
A Comparison of the Performance of the Weighted Ensembles Means in CORDEX-SEA Precipitation Simulations
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.
Copyright (c) 2024 Tugiyo Aminoto, Akhmad Faqih, Perdinan, Yonny Koesmaryono, Bambang Dwi Dasanto
Climate Influences on Latex Yield in South Sumatra, Indonesia
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.
Propagation Characteristics of Madden Julian Oscillation in the Indonesian Maritime Continent: Case Studies for 2020-2022
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.
Copyright (c) 2024 Fadhilatul Istiqomah, Erma Yulihastin, Joko Wiratmo, Eddy Hermawan, Nurjanna Joko Trilaksono, Dasapta Erwin Irawan, Kristy Natasha Yohanes, Amalia Qurrotu Ayunina
Analysis of Carbon Dioxide Emission from Forest Fires based on Fire Radiative Power in Riau
Riau 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.
Copyright (c) 2023 Mochamad Afif Derma Kusuma, Fithriya Y Rohmawati, Idung Risdiyanto
Climate influence on Diarrhea Disease in Tropical Regions based on Systematic Literature Review
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.
Copyright (c) 2023 Dela Arinda, Rini Hidayati, Muh. Taufik
Thermal Comfort for various Altitudes and Land Covers in North Sumatra
Thermal comfort refers to the suitability of meteorological conditions for humans with the environment. Temperature is the main meteorological variable, which determines the thermal comfort as expressed in various climate indices. This study aims to analysis the distribution of thermal comfort, and to identify environmental factors influencing the comfort situated in North Sumatra, Indonesia. We applied the Universal Thermal Climate Index (UTCI) to determine the heat stress level for 2011 - 2020. The higher UTCI value indicates more uncomfortable conditions related to the higher heat stress. The results showed that the average value of UTCI in North Sumatra was categorised at moderate heat stress. Densely urbanised area in the eastern region contributed to high heat stress, whereas mountainous areas in central to west regions were at low level. Our findings found that climate season affected the distribution of heat stress level. The low heat stress occurred in December-February, while high heat stress happened in June-August. Further, findings revealed that altitude and land cover have contributed to UTCI variation by more than 75% variance.
Copyright (c) 2023 Hanny Nirwani, Rini Hidayati, Perdinan
Sea Surface Temperature Anomaly Characteristics Affecting Rainfall in Western Java, Indonesia
Western 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's 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.
Copyright (c) 2023 Qurrata A'yun Kartika, Akhmad Faqih, I Putu Santikayasa, Amsari Mudzakir Setiawan