Rainfall In Indonesia Research Articles

ANALISIS INTENSITAS CURAH HUJAN DAN KURVA IDF (INTENSITY-DURATION-FREQUENCY) METODE MONONOBE DI KOTA SALATIGA

Extreme rainfall in Indonesia has led to natural disasters in many areas including Salatiga City. Many human activities depend on the amount of rainfall that falls on the earth's surface. In the field of civil engineering, research on rainfall is very common because many activities related to civil engineering use rainfall data such as for water resource management, drainage development, dam construction and other building construction. This study aims to determine the amount of rainfall intensity and IDF curves in Salatiga City for return periods of 2 years, 5 years, 10 years, 25 years, 50 years and 100 years. The data used in this study is the maximum daily rainfall data collected from Salatiga Central Bureau of Statistics (BPS). The type of distribution selected in this study is the Log Pearson Type III probability distribution. The results of the analysis show that the highest rainfall intensity occurs at a short duration (5 minutes) in the 100-year return period of 106.66 mm/hour and the results of the IDF curve show that the shorter the rainfall time, the higher the rainfall intensity while the longer the rainfall time, the smaller the rainfall intensity at each return period T.

Detection of Dominant Rainfall Patterns in Indonesian Regions Using Empirical Orthogonal Function (EOF) and Its Relation with ENSO and IOD Events

Several studies indicate that Indonesia’s position on the equator, where winds from the northern and southern hemispheres converge, leads to year-round rainfall in Indonesia. Previous studies have also shown the presence of monsoon winds over Indonesia, which causes rain in Indonesia to follow the Asian and Australian monsoon wind patterns. Recently, variations in rainfall patterns and intensity in Indonesia have been highly volatile and difficult to predict. This study uses gridded data analysis of ocean-atmospheric rainfall for 69 years (1948–2016) to detect monsoonal, equatorial, and local signals and relate them to ENSO and IOD events. Rainfall analysis reveals that the first mode accounts for 38.57%, the second 13.92%, and the third 8.93% of the total variance. These results show that these variants are very variable, both in space and time. Monsoonal patterns were detected in the southern region of Indonesia, equatorial patterns in northern Sumatra and western Kalimantan, while local patterns were detected in Maluku and the Maluku Islands. Furthermore, the monsoonal and local rainfall patterns are strongly correlated (> 0.85) with local SST, but the equatorial rainfall pattern is weakly correlated (< 0.5) with local SST. Note that the equatorial pattern is associated with the Inter Tropical Convergence Zone (ITCZ) phase. The varied vector correlation map illustrates the asymmetric fluctuations in sea surface temperature and the simultaneous existence of winds coming from the west from the Indian Ocean and winds coming from the east from the Pacific Ocean during the period of heavy precipitation.

MORLET’S WAVELET ANALYSIS ON EL NIÑO SOUTHERN OSCILLATION (ENSO) AND THE INDIAN OCEAN DIPOLE (IOD) FOR 84 YEARS: 1940-2023

As is known, the impact caused by El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) can reach extreme levels, especially rainfall in Indonesia. So, updating information on events and cycles of these phenomena is essential. Using Sea Surface Temperature (SST) data spanning the previous 84 years (1940–2023) from ERA5, we examined Sea Surface Temperature Anomalies (SSTA), which serve as a predictive tool for ENSO and IOD events. Apart from that, in this research, SSTA variance analysis was also carried out using Wavelet. The analysis results show several Positive IOD-Like events (1943, 1944, 1977, 1996) and Negative IOD-Like (1985, 1992, 2016). Apart from that, the results of this research also show that El Niño in 2002/03 coincided with Negative IOD in 2002. The results of Wavelet analysis show that the SSTA DMI variance experienced increased activity in the periods 1940-1968, 1969-1991, and 1992-2023. The Wavelet analysis also shows that ENSO activity increased in 1970-2000 and decreased again in 2000-2023.

Spatio-Temporal Variations of Indonesian Rainfall and Their Links to Indo-Pacific Modes

The analysis of rainfall patterns in the Indonesian region utilized the Empirical Orthogonal Function (EOF) method to identify spatial and temporal variations. The study evaluated the dynamic influence of the Tropical Indian Ocean (TIO) and the Tropical Pacific Ocean (TPO) on Indonesian rainfall using monthly data from the Southeast Asian Climate Assessment and Dataset (SACA&D) spanning from January 1981 to December 2016 and encompassing three extreme El Niño events in 1982/1983, 1997/1998 and 2015/2016. Using combined reanalysis and gridded-observation data, this study evaluates the potential impact of the two primary modes in the tropical Indo-Pacific region, namely the Indian Ocean Dipole (IOD) and the El Niño-Southern Oscillation (ENSO) on Indonesian rainfall. The analysis using the EOF method revealed two main modes with variances of 35.23% and 13.07%, respectively. Moreover, the results indicated that rainfall in Indonesia is highly sensitive to sea surface temperatures (SST) in the southeastern tropical Indian Ocean and the central Pacific Ocean (Niño3.4 and Niño3 areas), suggesting that changes in SST could significantly alter rainfall patterns in the region. This research is useful for informing government policies related to anticipating changes in rainfall variability as part of Indonesia’s preparedness for hydrometeorological disasters.

Annual and Interannual Rainfall Variability in Indonesia Using Empirical Orthogonal Function (EOF) Analysis and Its Response to Ocean-Atmosphere Dynamics

We investigate rainfall variability in Indonesia using the Empirical Orthogonal Function (EOF) method. The analysis starts by taking three main modes of EOF results, namely EOF1, EOF2, and EOF3. The EOF1 region is southern Indonesia, from southern Sumatra to Timor Island, parts of Kalimantan, parts of Sulawesi, and parts of Irian Jaya. The EOF2 region is located in northwestern Indonesia and includes the northern part of Sumatra and the northwestern part of Kalimantan. The EOF3 region covers Maluku. This study aims to analyze the annual and inter-annual variability of rainfall in anticipation of the threat of hydrometeorological disasters. Based on the correlation value of the principal component (PC) with the dipole mode index (DMI) and Niño3.4 index, it has a period similar to El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Rainfall in Indonesia is very sensitive to sea surface temperature (SST) in the southeastern Indian Ocean and the central Pacific Ocean, which means that rainfall patterns in Indonesia can change significantly if SST in the region changes.

Future projections of extreme rainfall events in Indonesia

AbstractPrevious evaluations of coupled climate models have indicated that under the influence of global warming precipitation extremes, including those over Indonesia, are expected to intensify. Here, we examine the most recent future projections of extreme rainfall in Indonesia using 24 global climate models from the Coupled Model Intercomparison Project Phase 6, consisting of 11 low resolution (LR) and 13 medium resolution (MR) models. The performance of these models is evaluated against Climate Hazards Group InfraRed Precipitation with Station data observations and realistically reproduce Indonesia's mean climatology over the period assessed (1987–2014). Overall, both LR and MR multi‐model ensemble means (MMEM) outperform individual models. Interestingly, extreme rainfall projections vary across different seasons, time periods, spatial resolutions and climate change scenarios. The LR and MR MMEMs project a continuous increase in wet extremes (R95p and Rx5d) during the wet season across most of Indonesia over the 21st century. Conversely, dry extremes (consecutive dry days [CDD]) are projected to increase (decrease) over the south (north) during the wet season but increase countrywide during the dry season. We show that future extreme wet and dry events are projected to be more frequent and intense, with upper extreme values surpassing historical records. The MR models project smaller changes in extreme wet indices than the LR models but simulate a more prolonged extreme dry index. Key findings here indicate that forthcoming instances of extreme rainfall present substantial hazards, necessitating the implementation of adequate preventative measures by policymakers, particularly within densely inhabited regions, such as Java.

Seasonal Variation of Rainfall in Indonesia under Normal Conditions without ENSO and IOD Events from 1981-2021

This study explores the seasonal rainfall variation in Indonesia under normal conditions, excluding the influence of El-Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events. Monthly rainfall data is sourced from the CHIRPS dataset, and wind and atmospheric movement data are obtained from the ECMWF ERA-5 dataset. Identification of normal conditions relies on the ONI and DMI indices. Results reveal that the seasonal rainfall patterns during these normal conditions align with previous studies encompassing all atmospheric conditions. Indonesia typically witnesses one or two peaks in rainfall annually, notably in December-January-February (DJF), May-June (MJ), and March-April-May (MAM), consistent with established rainfall zone classifications in earlier research. A nuanced discrepancy emerges in the timing of the May-June peak, previously noted in June-July. The congruence in peak rainfall patterns during normal conditions, as observed in prior comprehensive research, implies a negligible influence of ENSO and IOD on the timing of seasonal rainfall peaks. Although ENSO and IOD may impact the amplitude of seasonal variation, particularly in Indonesia's eastern and western regions, they do not alter the temporal occurrence of rainfall peaks. Consequently, the timing of rainfall peaks in Indonesia predominantly reflects the combined influence of the Asian and Australian monsoons.

Extreme Rainfall Analysis Of The West Papua Province Using Schlather’s Model Of Max Stable Process

Data from Badan Pusat Statistik (BPS) in 2021 notes the province of West Papua as the province with the 5th highest rainfall in Indonesia with a rainfall of 3,811 mm. The province also recorded 268 rainy days, the most amongst all provinces in the country. The excess amount of rain is one of the causes of disasters such as floods. This research uses rainfall data from the Regencies of Manokwari, Fakfak, and Kaimana. The method used is Spatial Extreme Value particularly Schlather's Model of the Max Stable Process. The data used is hourly rainfall for the period of 13 March 2022 to 17 October 2022 with the proportion of training and testing data respectively 85.84% and 14.16%. Extreme data collection was carried out using the Block Maxima method with a fitting to the Generalized Extreme Value (GEV) distribution before being transformed into the Frechet Z margin units. The calculation of the extreme coefficient resulted in a value between 1.4 to 1.85, indicating a relationship between the locations. Next, the best trend surface model was determined, which involves latitude coordinates for the calculation of the location parameter and both longitude and latitude coordinates for the calculation of the scale parameter. The spatial parameter estimation is carried using the powered exponential correlation function. Then, model validation was carried out using MAPE based on a comparison of return levels and testing data. The MAPE values obtained was 22.61% for the BFGS iteration method. The final step is to calculate return levels for periods of 2, 4, 6, 8, and 10 years ahead. All the results were categorized under very heavy rain. These results can be used by related parties to carry out disaster mitigation efforts.

Correlation Analysis of GNSS-Derived Precipitable Water Vapor (PWV) with Rainfall Data in Indonesia

GNSS (Global Navigation Satellite Systems) have become an important tool for various activities related to positioning, such as navigation, construction projects, and deformation measurement. Additionally, GNSS can also estimate Precipitable Vapor Vapor (PWV) for meteorological purposes. PWV is a measure of atmospheric water vapor, which can eventually precipitate as rain. Since Indonesia’s climate is mainly characterized by the change in rainfall, monitoring precipitation is a crucial step in understanding its pattern. In this study, we aim to analyze the correlation between PWV and rainfall in Indonesia. We used GNSS observations from the InaCORS network across Indonesia, as well as rainfall data from GSMaP at the InaCORS stations in 2019 from 1st January to 31st December. Both of these data are normalized every five days (pentad days) and compared to each other to obtain the Pearson’s correlation coefficients. From these results, we generated a heatmap of the Pearson’s correlation coefficients between pentad PWV and rainfall at InaCORS locations in Indonesia, with the highest correlation of 0.753819 in Garut Regency and the lowest correlation of 0.090146 in Fakfak Regency. Moreover, we also analyzed the timeseries of PWV and rainfall comparison at several sampling stations. From the results we found, PWV and rainfall correlate each other in positive way. Southern regions of Indonesia have higher correlation compared to northern regions of Indonesia.

PENERAPAN MODEL REGRESI PANEL KOMPONEN DUA ARAH PADA POLA CURAH HUJAN PROVINSI KALIMANTAN TENGAH

Rainfall is the climate element that is most closely related to supporting the life processes of Indonesian people such as agricultural production, plantations, fisheries, and aviation. In 2014-2016, Indonesia experienced a drought due to a global climate anomaly called the El Nino phenomenon, where annual rainfall at that time tended to decrease from other years. While in 2020-2022, rainfall in Indonesia tends to increase from other years, this event is called the La Nina phenomenon. This study aims to describe the rainfall patterns that occur in each phenomenon and analyze the regression model of rainfall panels in Central Kalimantan province with a two-way component approach. Random Effects Model (REM) is the most appropriate model to be used in the phenomenon of La Nina. Fixed Effect Model (FEM) is the most appropriate model to be used in the El Nino phenomenon. Feasible Generalized Least Square is a parameter estimation method that is focused and used to estimate regression parameters in this study. Based on the results of regression analysis of panel data, for the phenomenon of La Nina obtained R2 value of 51.66% and found that the average air temperature variable tested significant. For the El Nino phenomenon, the value of R2 is 75.35% and it is found that there are no significant independent variables tested. Therefore, it can be expected that the increase in average air temperature can decrease the average rainfall value when the La Nina phenomenon occurs in Central Kalimantan province.

Pemodelan Numerik Hubungan Pola Curah Hujan Wilayah Equatorial di Pulau Sumatera Terhadap Fenomena ENSO dan IOD

A numerical modeling has been built for the relationship between rainfall in the equatorial region of Sumatra to the ENSO and IOD phenomena by taking data from 1991 to 2020 at three stations located in the western region of Sumatra Island, namely SM Japura, SM Minangkabau and SM Hang Nadim. The method used in this study is the method of correlation and linear regression on the Niño3.4 Index variable and the Dipole Mode Index on the amount of rainfall. The data visualization process is carried out with the output in the form of plots and graphs with Goole Colab coding. The resulting graph is a graph of the ENSO and IOD phenomena, as well as a regression graph between The Niño3.4, DMI and the amount of rainfall per month. This study was conducted with the aim of knowing how much influence the El Niño Southern Oscillation (ENSO) variable represented by the Niño3.4 Index and the Indian Ocean Dipole (IOD) represented by DMI had on rainfall in the equatorial region in the 1991 – 2020 period and to see the trend of significance between the two towards the equatorial region on the island of Sumatra. Rainfall in Indonesia's equatorial region tends to be influenced by IOD compared to ENSO. The combination of the two at the same time can cause a significant change in the amount of rainfall. La-Niña and negative IOD occurred in 1992, 1996, 1999, 2000, 2005, 2008, 2011 and 2016. The average rainy day index (SDII) tended to decrease in Japura Island in 1996, 2005, 2011, 2016 , at SM Minangkabau in 1992, 1996, 1999, 2000, 2016 and at SM Hang Nadim in 1996, 1999 and 2008. Meanwhile the longest series of wet days tended to decrease in SM Japura in 1992, 1996, 1999, 2005, in SM Minangkabau in 1992, 1996, 2005, 2008, and in SM Hang Nadim in 1992, 2000, 2005, 2008 and 2011. The longest series of dry days tended to increase in SM Japura in 1996, 2011, in SM Minangkabau in 1996, 2000, 2011, and at SM Hang Nadim in 1996.

The Impact of MJO-C and MJO-B Upon Sea Surface Temperature and Rainfall in Indonesia

Madden Julian Oscillation (MJO) based on its propagation through the maritime continent can be divided into two types, namely MJO-C (Crossing) and MJO-B (Blocked) as evidenced by the spatial diagram of Hovmöller OLR anomaly. This study aimed to determine the effect of MJO variations, namely MJO-C and MJO-B on sea surface temperature and rainfall in the Indonesian region in the period of 1998 to 2015. The data used in this study are reanalysis models including sea surface temperature and rainfall with composite events (one month before propagation, during propagation, and one month after propagation of MJO-C and MJO-B). The results show that when MJO-C and MJO-B are active, sea surface temperature and the amount of rainfall are increased, especially in equatorial Indonesia and the southern hemisphere. When compared, MJO-C has a more significant impact than MJO-B.Keywords: MJO, Sea Surface Temperature, Precipitation

Trend of Rainfall Pattern in Palembang for 20 Years and Link to El-niño Southern Oscillation (ENSO)

One factor that significantly affects rainfall in Indonesia, especially in Palembang City, is the El-Niño Southern Oscillation (ENSO). This study aims to determine how big the correlation is and when the highest correlation occurs between the Niño 3.4 Index and rainfall during the 2001-2020 period. The stages of analysis carried out to carry out this study are as follows: 1) downloading daily rainfall data for 20 years, 2) processing the raw data to make it homogeneous, and 3) correlation analysis to find out when the highest correlation occurs between rainfall and the Niño 3.4 Index. The results show that the highest correlation occurs in September at -0.524, August at -0.481, and October at -0.439. The influence of the Niño 3.4 Index produces a negative relationship. If the Niño 3.4 Index increases, it will affect the maximum temperature at the sea surface so that rainfall will decrease and cause drought. The results obtained can also be used as a prediction or forecast of the inflow of rainfall and the length of the rainy season.

Forecasting rainfall and potential for repeated events to predict flood areas in Banten province, Indonesia

In a period of ten years, from 2011-2020 rainfall in Indonesia is relatively high, with annual rainfall between 460.5-4,627.4 mm. The high rainfall has implications for flooding in several provinces. During this period, almost every year several areas in Banten Province experienced floods. To predict areas of Banten Province that have the potential for flooding, forecasts of rainfall and the potential for repeated occurrences of high rainfall are carried out. In making the forecast, observations were made at the Serang Meteorological Station, the Budiarto Curug Meteorological Station, the South Tangerang Climatology Station, and the Tangerang Geophysics Station. Rainfall data from the four stations were analyzed by Fourier transform, Gumbel method and Mononobe method. Distribution analysis results obtained rainfall in Banten Province between 0.0-607.9 mm with the length of rainy days per month between 0-26 days. Then, the results of the Fourier transform analysis; Banten Province included a monsoon rain pattern with unimodial rainfall. Furthermore, the results of the analysis of the Gumbel method and the Mononobe method, Banten Province included the category of moderate rain and tended to be heavy, even extreme. Based on the results of the analysis using these two methods, in 2025 in Banten Province, it is predicted that 11 % heavy rain, 3 % very heavy rain and 1 % extreme rain are predicted. In that year, it is predicted that there will be 65 sub-districts in Banten Province that have the potential for flooding. The sub-districts that have the potential for flooding are mostly located in Serang Regency, Serang City, Tangerang City, and South Tangerang City. This potential flood is predicted to occur in: January, February, March, April, May, October and November.

Bandung Rainfall Forecast and Its Relationship with Niño 3.4 Using Nonlinear Autoregressive Exogenous Neural Network

The city of Bandung, as the capital city of West Java, is one of several areas in Indonesia with high rainfall. This situation can cause disasters, such as floods and landslides, that can harm many parties. Rainfall in Indonesia, particularly on the island of Java itself, is closely related to the global phenomenon of Niño 3.4. In the period from January 2001–November 2021, the rainfall and Niño 3.4 showed some extreme values. In order to foresee the disasters, an accurate rainfall forecast should be performed. For this reason, we try to construct a model of rainfall forecast and its relation to the global phenomenon of Niño 3.4 using the nonlinear autoregressive exogenous neural network (NARX NN). The result shows that NARX NN (13-7-1) with a Mean Absolute Percentage Error (MAPE) value of 6.26% and R2 of 85.37% is best suited for the prediction of this phenomenon. In addition, this study provides forecast results for the next six periods, which can be used as a reference for the relevant authorities to foresee the possibility of flooding in Bandung city. From the forecast results, it can be concluded that the highest rainfall forecasts in the city of Bandung are in February 2022, and will slowly decrease in March 2022. To prevent hydro-meteorological disasters, such as floods in Bandung city, the community can clear waterways, such as clogged drains, rivers, and dams, as well as prepare tools for evacuation.

Spatiotemporal Analysis on the Teleconnection of ENSO and IOD to the Stream Flow Regimes in Java, Indonesia

While many studies on the relationship between climate modes and rainfall in Indonesia already exist, studies targeting climate modes’ relationship to streamflow remain rare. This study applied multiple regression (MR) models with polynomial functions to show the teleconnection from the two prominent climate modes—El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)—to streamflow regimes in eight rivers in Java, Indonesia. Our MR models using data from 1970 to 2018 successfully show that the September–November (SON) season provides the best predictability of the streamflow regimes. It is also found that the predictability in 1970–1989 was better than that in 1999–2018. This suggests that the relationships between the climate modes and streamflow in Java were changed over periods, which is suspected due to the river basin development. Hence, we found no clear spatial distribution patterns of the predictability, suggesting that the effect of ENSO and IOD are similar for the eight rivers. Additionally, the predictability of the high flow index has been found higher than the low flow index. Having elucidated the flow regimes’ predictability by spatiotemporal analysis, this study gives new insight into the teleconnection of ENSO and IOD to the Indonesian streamflow.

On the utilization of RDCA method for detecting and predicting the occurrence of heavy rainfall in Indonesia

Accurate prediction of heavy rainfall events is crucial to mitigate hydrometeorological disasters. The real-time cloud observation by weather satellites Himawari-8 has enabled researchers to develop a nowcasting method (short-term prediction) to identify rainfall events in advance, namely the Rapidly Developing Cumulus Area (RDCA) method. The RDCA index, which is the product of the RDCA method, is the probability of occurrence of heavy rainfall. It has been evaluated and successfully utilized in Japan. However, the performance of the RDCA in the tropics has not been assessed yet. Indonesia —as a tropical country consisting of abundant islands— exhibits distinct climate and convective systems due to its geographical characteristics. Different temporal resolution of Himawari-8 data between Japan region (2.5 min) and the tropics (10 min) may also result in different RDCA performance. Therefore, it is important to conduct an evaluation study before operating the RDCA in Indonesia. By performing spatial comparison and using dichotomous and probabilistic verifications with rain gauges at various thresholds, this study finds that the RDCA is able to detect and predict rainfall events in Indonesia. The best lead time is 10 min but the predictive signals extend up to 30 min in advance; it is shorter than that observed in Japan. Lighter rainfall has higher predictability compared with heavier rainfall. The predicted heavy rainfall events show good accuracy, probability of detection, and brier score. The RDCA index and rainfall intensity appear to have a good correlation, where light (heavy) rainfall is better predicted by low (high) RDCA indices. It confirms the usefulness of varying RDCA index in contrast to the classic dichotomous RDCA index. This study reveals the positive prospects of RDCA in Indonesia. We also discuss some future challenges and give possible recommendations regarding an operational RDCA system. This study will benefit the development of early warning systems in Indonesia.

The relationship between oceanographic factors and the ENSO period on weather in Maluku

The surrounding waters strongly influence the waters of North Maluku. These waters are the entrance gate for ARLINDO, which is directly related to the Pacific Ocean. To produce scientific knowledge, our study aims to determine the influence of oceanographic factors on the distribution of sea surface temperatures (SST) and wind pattern, which affects the rainfall throughout the west monsoon and transitional seasons in the Indonesian region literacy. We used data from ERDDAP, where SST data was processed using Ocean Data View and wind data using WRPLOT. The SST of North Maluku waters during October 2020-April 2021 ranges from 29.1°C-29.8°C, with the highest sea surface temperature, was seen during November-December 2020 and April 2021. The result demonstrates the highest rainfall data was in February 2021 and the dominant winds come from north and west. These winds bring water vapor particles that become convective clouds that increase rainfall in Indonesia, especially in the eastern region. Meanwhile, SST originating from the Pacific Ocean moved due to the Walker Circulation from the east. This further caused the SST from the Pacific Ocean that brought a warm water move towards Indonesian waters.

Pest and Disease Identification in Parijoto Plant (Medinilla speciosa blume) at Nglurah Tawangmangu

Parijoto (Medinilla speciosa blume) is a tropical plant that has purplish pink fruit often called Asian grape. In Indonesia, parijoto plants are very well known as ornamental plants that have various benefits, from parijoto fruit can be used as anti-inflammatory drugs so that ornamental plants are widely cultivated by the community, especially lovers of ornamental plants and ornamental plant cultivation in Nglurah. Medinilla speciosa blume is very suitable in Indonesia, which has a tropical climate, but the high rainfall in Indonesia in the rainy season should get more attention in caring for it. This makes parijoto plants susceptible to several types of diseases and pests that can damage the beauty of the Medinilla speciosa blume plant. However, the lack of experts in this field makes it very difficult for plant lovers and the cultivation community of Medinilla speciosa blume in Nglurah to solve the problem of diseases and pests that attack the Medinilla speciosa blume plant. The purpose of this study was to determine pests and diseases on Medinilla speciosa blume. This research was conducted in Nglurah Village Rt 01 Rw 02 Tawangmangu for five days on 04 – 08 September 2021. This research uses observation and interview methods. Data were analyzed by qualitative descriptive. This study used 10 samples of the observed Medinilla speciosa blume plant. The tools used are cellphone cameras and stationery. The variables to be observed are pests and diseases. The results of this study found 4 pests and 2 diseases on Medinilla speciosa blume including Fussarium oxysporum, Cercospora sp and Pestalotia sp, mealybugs, caterpillars, snails, grasshoppers. For this reason, the solution for treating parijoto plants to avoid pests and diseases requires extra care such as paying attention to soil moisture, giving appropriate fertilizers, and giving vitamins so that parijoto plants can grow well.

Impacts of the MJO on Rainfall at Different Seasons in Indonesia

The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal variability of rainfall in Indonesia, but its signal is often obscured in individual station data, where effects are most directly felt at the local level. This study aims to investigate the general impacts of MJO on rainfall at different seasons in Indonesia, particularly during boreal summer. Impacts of the MJO on daily rainfall anomaly during the four climatic seasons: DJF, MAM, JJA, and SON in Indonesia have been evaluated using in-situ data from 86 stations during 1981 - 2012 and remote sensing data using GPM IMERGV06 from 2001 - 2019. The greatest impact of the MJO on rainfall over Indonesia occurs during the DJF and MAM seasons (austral summer), with the magnitude varying across regions. Enhanced rainfall generally occurs over the western parts of Indonesia on phases 2 to 4, central parts of Indonesia on phase 4, and eastern parts of Indonesia on phases 4 to 5. Conversely, suppressed rainfall generally occurs over the western parts of Indonesia on phases 5 to 8, central parts of Indonesia on phases 6 to 8, and eastern parts of Indonesia on phases 1 to 2 and 6 to 8. In addition, the MJO influence during the JJA and SON seasons are slightly less, in terms of intensity, than during the DJF and MAM seasons, which is likely due to the northward shift of ITCZ and, hence, the intraseasonal oscillation convective envelope during boreal summer. Generally, enhanced rainfall occurs over the western and northern parts of Indonesia on phases 2 to 3, and suppressed rainfall on phases 6 to 7. The results indicate that convectively active MJO may increase the possibility of daily extreme rainfall in particular regions in Indonesia at different seasons.