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All Journal Agromet Indonesian Journal of Geography Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Sumberdaya Lahan Journal of Mathematical and Fundamental Sciences Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) LIMNOTEK - Perairan Darat Tropis di Indonesia International Journal of Remote Sensing and Earth Sciences (IJReSES) JURNAL METEOROLOGI DAN GEOFISIKA Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sumberdaya Lahan Jurnal Tanah dan Iklim Jurnal Meteorologi dan Geofisika The International Journal of Remote Sensing and Earth Sciences (IJReSES)

Akhmad Faqih

Agrometeorology Division, Department Of Geophysics And Meteorology, Faculty Of Mathematics And Natural Sciences, IPB University, Campus IPB Dramaga, Indonesia

Author-ID : 223454

Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Earth & Planetary Sciences Energy Environmental Science Mathematics Physics

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The Impact of the Interaction between Madden-Julian Oscillation and Cold Surge, on Rainfall over Western Indonesia Agita Vivi Wijayanti; Rahmat Hidayat; Akhmad Faqih; Furqon Alfahmi
Indonesian Journal of Geography Vol 53, No 2 (2021): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.64006

The Madden-Julian Oscillation and Cold Surge phenomena have been known to cause increased rainfall, with the capacity to trigger hydrometeorological disasters, in western Indonesia. However, further investigations are required regarding the interaction between these phenomena on rainfall pattern. Therefore, this study aims to analyze the interaction between MJO and CS over western Indonesia, particularly by using land-based rainfall observation data from multiple stations, as previous studies were dominated by the use of gridded data from remote observations. This study utilized in-situ observation data obtained from 4329 weather observations and rain stations between 1989 and 2018. Subsequently, quality control performed based on data availability exceeding 70% over a 30-year period resulted in 303 selected stations to be used for further analysis. Meanwhile, the RMM index, as well as reanalysis data of mean sea level pressure and 925 hPa meridional wind, were also applied for MJO and CS identification. According to the composite analysis, the effect of CS on MJO phases tends to increase precipitation by about 50%, over western Indonesia, with maximum increase ranging from 200 to 400% over the northeastern coast of Sumatra, around Karimata Strait (Riau Islands and West Kalimantan), as well as the northern coast of Java. These areas are exposed to the sea and have direct access to the wind-terrain interaction. In addition, the highest rainfall anomaly due to the MJO-CS interaction occurs around Karimata Strait, followed by northern Sumatra and Java, with spatially averaged rainfall anomaly reaching 5 mm/day over the area.

Vulnerability of Primary Productivity and Its Carbon Use Efficiency to Unfavorable Climatic Conditions in Jambi Province, Indonesia Ummu Ma'rufah; Tania June; Ashehad Ashween Ali; Akhmad Faqih; Yonny Koesmaryono; Christian Stiegler; Alexander Knohl
Journal of Mathematical and Fundamental Sciences Vol. 54 No. 1 (2022)
Publisher : Institute for Research and Community Services (LPPM) ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.math.fund.sci.2022.54.1.4

Climatic conditions and land cover play crucial roles in influencing the process of carbon uptake through vegetation. This study aimed to analyze the effect of climate variability on carbon uptake of four different land covers in Jambi Province, Indonesia. The four land cover types studied were: forest, shrub, grass, and irrigated soybean, based on Community Land Model version 5. Forest was found to have the highest net primary production (NPP) compared to the other land covers. Seasonal climate variability showed no major effect on NPP and gross primary production (GPP). However, GPP and NPP experienced significant declines during El Niño Southern Oscillation (ENSO), particularly in 2015. Carbon use efficiency (CUE = NPP/GPP) was also affected by ENSO, where CUE decreased during El Niño, particularly in October and November with an increased number of days without rainfall. In addition, the difference between latent (LE) and sensible heat (H) flux, denoted as (LE-H), decreased from August to November. This difference was highly correlated with NPP. This result indicates that when water supply is low, stomata will close, thereby reducing photosynthesis and transpiration, and allocating more of the available energy to sensible heat flux rather than latent heat flux.

IMPACT OF CHANGES IN CLIMATE AND LAND USE ON THE FUTURE STREAMFLOW FLUCTUATION Suria Tarigan; Akhmad Faqih
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol. 9 No. 1 (2019): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Graduate School Bogor Agricultural University (SPs IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.9.1.181-189

Beside land use change, future climate change potentially alters streamflow fluctuation of a river basin in Indonesia. We investigated relative impact of changes in climate and land use on the streamflow fluctuation of a watershed for future condition (2025). To account for the climate change, we simulated future rainfall and temperature scenarios using the downscaled rainfall and mean surface temperature of 24 CMIP5 GCM outputs with moderate scenario of RCP4.5. We used distributed hydrologic model (SWAT) to simulate relative impact of changes in climate and plantation expansion on the future streamflow fluctuation. The SWAT model performed well with the Nash-Sutcliff efficiency values of 0.80-0.85 (calibration) and 0.84-0.86 (validation). The results indicated that the climate change caused 32% decrease of the low flows during dry season and 96% increase of the flooding peak discharge during rainy season. Meanwhile, the plantation expansion led to 40% decrease of the low flow in the dry season and 65% increase of the flooding peak discharge in wet season. Both changes indicated strong impact on the extreme events such as flooding peak discharge and low flows. The impact of the climate change on the increased peak discharge was stronger compared to that of land use change. Meanwhile, the impact of the land use change on the low flow was stronger compared to that of the climate change. The results of this study pointed out that both climate change and the plantation expansion potentially become crucial factors for the future water security in Indonesia.

UTILIZATION OF NEAR REAL-TIME NOAA-AVHRR SATELLITE OUTPUT FOR EL NIÑO INDUCED DROUGHT ANALYSIS IN INDONESIA (CASE STUDY: EL NIÑO 2015 INDUCED DROUGHT IN SOUTH SULAWESI) Amsari Mudzakir Setiawan; Yonny Koesmaryono; Akhmad Faqih; Dodo Gunawan
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 13, No 2 (2016)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Drought is becoming one of the most important issues for government and policy makers. National food security highly concerned, especially when drought occurred in food production center areas. Climate variability, especially in South Sulawesi as one of the primary national rice production centers is influenced by global climate phenomena such as El Niño Southern Oscillation or ENSO. This phenomenon can lead to drought occurrences. Monitoring of drought potential occurrences in near real-time manner becomes a primary key element to anticipate the drought impact. This study was conducted to determine potential occurrences and the evolution of drought that occurred as a result of the 2015 El Niño event using the Vegetation Health Index (VHI) from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellite products. Composites analysis was performed using weekly Smoothed and Normalized Difference Vegetation Index (or smoothed NDVI) (SMN), Smoothed Brightness Temperature Index (SMT), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). This data were obtained from The Center for Satellite Applications and Research (STAR) - Global Vegetation Health Products (NOAA) website during 35-year period (1981-2015). Lowest potential drought occurrences (highest VHI and VCI value) caused by 2015 El Niño is showed by composite analysis result. Strong El Niño induced drought over the study area indicated by decreasing VHI value started at week 21st. Spatial characteristic differences in drought occurrences observed, especially on the west coast and east coast of South Sulawesi during strong El Niño. Weekly evolution of potential drought due to the El Niño impact in 2015 indicated by lower VHI values (VHI < 40) concentrated on the east coast of South Sulawesi, and then spread to another region along with the El Nino stage.

Kejadian Iklim Ekstrem dan Dampaknya Terhadap Pertanian Tanaman Pangan di Indonesia Elza Surmaini; Akhmad Faqih
Jurnal Sumberdaya Lahan Vol 10, No 2 (2016)
Publisher : Indonesian Center for Agriculture Land Resource Development

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/jsdl.v10n2.2016.%p

Abstrak. Perubahan iklim telah menganggu sistem iklim global dan menyebabkan meningkatnya frekuensi dan intensitas kejadian iklim ekstrem. Tulisan ini merupakan tinjauan mengenai proyeksi skenario iklim, faktor pengendali kejadian iklim ekstrem, serta dampaknya terhadap sektor pertanian di Indonesia. Dampak kejadian iklim ekstrim yang dominan pada sektor pertanian adalah kerusakan tanaman akibat kekeringan dan banjir. Akibat perubahan iklim, kekeringan dan banjir diproyeksikan akan meningkat frekuensi dan intensitasnya di masa akan datang. Informasi prediksi musim dapat digunakan untuk mengetahui intensitas dan wilayah yang terdampak dalam 1-2 musim ke depan. Sedangkan dampak jangka panjang 2-3 dekade ke depan dapat diketahui berdasarkan skenario proyeksi iklim. Prediksi musim telah banyak di manfaatkan untuk menyusun strategi dan kebijakan operasional seperti menyesuaikan waktu tanam, pemilihan komoditas, dan distribusi peralatan pertanian. Namun, kajian proyeksi iklim dan dampaknya terhadap produksi pangan masih sangat terbatas. Informasi tersebut diperlukan dalam perencanaan arah dan pembangunan pertanian ke depan. Oleh karena itu, kajian proyeksi iklim dan dampaknya terhadap produksi pangan perlu menjadi prioritas penelitian pertanian di Indonesia.Abstract. Climate change has disrupted the global climate system and lead to increase frequency and intensity of extreme climate events. This paper is an overview of future climate scenarios, driving force of extreme climate events, and its impacts on the agricultural sector in Indonesia. The common impacts of extreme climate events in Indonesia’s agriculture are crop damaged due to drought and flood. Due to climate change, drought and flood events is projected to intensify in the future. Seasonal prediction have been widely used to formulate operational strategies and policies such as planting time, commodity choice, and distribution of agricultural equipment. While, the climate projections are required for the forthcoming decades. However, the study of climate projections and their impact on food production for the next decades is still very limited. The information are required for planning and direction of future agricultural development. Therefore, the study of climate projections and their impact on food crop should be a priority of agricultural research in Indonesia.

Optimasi CNN dengan GA Pada Prediksi Awal Musim Hujan Berdasarkan Data GCM: Kabupaten Pacitan1) (CNN Optimization Using GA for Rainy Season Onset Prediction Based on GCM Output:Pacitan District) Fildza Novadiwanti; Agus Buono; Akhmad Faqih
Jurnal Tanah dan Iklim (Indonesian Soil and Climate Journal) Vol 41, No 1 (2017)
Publisher : Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/jti.v41n1.2017.69-77

Abstrak: Di Indonesia, pertanian menjadi sektor penting dalam pembangunan nasional dan pembangunan ekonomi. Awal musim hujan merupakan salah satu variabel iklim yang dapat memengaruhi produksi pertanian. Perubahan awal musim hujan dapat berdampak pada terjadinya gagal panen. Penelitian ini mengembangkan model untuk memprediksi awal musim hujan menggunakan cascade neural network yang dioptimasi menggunakan genetic algorithm berdasarkan data global circulation model pada Kabupaten Pacitan. Data observasi menggunakan data awal musim hujan dari 3 stasiun cuaca, yaitu Arjosari, Kebon Agung, dan Pringkuku. Data prediktor menggunakan data global circulation model antara tahun 1983 – 2011 dari 3 model, yaitu CMC1-CanCM3, CMC1-CanCM4, dan NCEP-CSFv2. Optimasi cascade neural network dengan genetic algorithm dilakukan dengan mengoptimasi jumlah hidden neuron dan menghasilkan peningkatan nilai koefisien korelasi (r). Penelitian ini menghasilkan model terbaik dari setiap stasiun cuaca dengan parameter yang berbeda. Nilai r stasiun Arjosari adalah 0.89. Nilai r stasiun Kebon Agung adalah 0.86. Nilai r stasiun Pringkuku adalah 0.87. Abstract. In Indonesia, agriculture becomes an important sector for national development and national economy. The onset of the rainy season is one of the rainfall variables that affect agricultural production. The changing of the onset of rainy season can impact on crop failure. This research aims to develop a model for predicting the onset of rainy season using optimized cascade neural network with genetic algorithm based on global circulation model in Pacitan district. Observational data used is the onset of rainy season of 3 weather stations in Pacitan: Arjosari, Kebon Agung, and Pringkuku. Predictor data used is global circulation model output data between 1983 – 2011 from 3 models: CMC1-CanCM3, CMC1-CanCM4, and NCEP-CSFv2. Optimization of cascade neural network with genetic algorithm has been done by optimizing the amount of hidden neuron and obtained an increase value of correlation coefficient (r). This research obtained the best model from each weather stations with different parameters. R value of Arjosari weather station is 0.89. R value of Kebon Agung weather station is 0.86. R value of Pringkuku weather station is 0.87.

PENGEMBANGAN MODEL PREDIKSI MADDEN-JULIAN OSCILLATION (MJO) BERBASIS HASIL ANALISIS DATA WIND PROFILER RADAR (WPR) Naziah Madani; Eddy Hermawan; Akhmad Faqih
Jurnal Meteorologi dan Geofisika Vol 13, No 1 (2012)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Latar belakang penelitian ini adalah pentingnya kajian mengenai MJO sebagai salah satu osilasi dominan di kawasan ekuator. Penelitian ini bertujuan untuk membuat model prediksi MJO berdasarkan analisis data WPR. Pada penelitian ini kejadian MJO diidentifikasi dari data kecepatan angin zonal pada lapisan 850 mb di kawasan Pontianak, Manado, dan Biak. Sebelum data angin zonal ini dimanfaatkan untuk melihat perilaku MJO, maka data angin tersebut terlebih dahulu dibandingkan dengan data indeks MJO yaitu RMM1 dan RMM2. RMM1 dan RMM2 merupakan sepasang indeks untuk memonitor kejadian MJO secara realtime. Hasil analisis Power Spectral Density (PSD) data kecepatan angin zonal lapisan 850 mb menunjukkan adanya sinyal MJO kuat yang dicirikan dengan adanya osilasi sekitar 45 harian. Hasil korelasi dan regresi juga menunjukkan bahwa terdapat keterkaitan yang signifikan antara kedua data tersebut. Hal tersebut mengindikasikan bahwa data kecepatan angin zonal lapisan 850 mb dapat digunakan untuk analisis MJO. Pada penelitian ini, prediksi MJO didasarkan pada data kecepatan angin zonal menggunakan metode ARIMA Box-Jenkins. Melalui metode ini, model yang mendekati data deret waktu kecepatan angin zonal pada lapisan 850 mb di Pontianak adalah ARIMA(2,0,0), model prediksi untuk Manado adalah ARIMA(2,1,2), sedangkan untuk Biak adalah ARIMA(0,1,3). Model-model tersebut bermanfaat untuk melihat perilaku sinyal MJO pada data angin zonal berkaitan dengan pola curah hujan di wilayah kajian. Background of this research is to study the importance of MJO as one of the predominant peak oscillation in the equator area. This study aims to make prediction models of MJO based on the analysis of zonal wind speed data observed by WPR that compared by the MJO index data, namely RMM1 and RMM2. The results of PSD show strong MJO signal of 45 day periods oscillations. The result of corrrelation and regression analyses also show significant relationship between both data. Therefore, it is suggested that the observed 850 mb zonal wind speed data can be used to analyze the MJO phenomenon. The MJO prediction models were developed by using ARIMA. Then we found the ARIMA model for Pontianak is ARIMA(2,0,0), Manado ARIMA(2,1,2), and Biak ARIMA(0,1,3). Those models used to see the MJO event from zonal wind data that effect to rainfall pattern in study area.

KARAKTERISTIK SPASIAL DAN TEMPORAL HOTSPOT DI PULAU SUMATERA Mulyono R. Prabowo; Yonny Koesmaryono; Akhmad Faqih; Ardhasena Sopaheluwakan
Jurnal Meteorologi dan Geofisika Vol 21, No 1 (2020)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Kebakaran hutan di Indonesia telah menjadi masalah global yang terjadi setiap tahun, terutama di Pulau Sumatra. Identifikasi kebakaran hutan dan lahan dalam penelitian ini didasarkan pada jumlah dan distribusi hotspot, berdasarkan data citra satelit dari Moderate Resolution Imaging Spectroradiometer (MODIS) pada 2009-2018. Investigasi pada kondisi meteorologi juga didasarkan pada faktor-faktor global dari data Oceanic Nino Index (ONI), Dipole Mode Index (DMI) dan berdasarkan pada indeks kekeringan dari data Standardized Precipitation Index (SPI). Metode yang digunakan adalah metode analisis spasial dan temporal. Tujuan dari penelitian ini adalah untuk mengetahui karakteristik pola distribusi hotspot di Pulau Sumatra, baik secara spasial dan temporal. Ada perbedaan karakteristik spasial dan temporal dari distribusi hotspot di pulau Sumatra, yang didasarkan pada karakteristik topografi, fase ENSO, serta periode musim hujan dan kemarau. Hujan orografis yang terjadi akibat topografi gunung di Aceh dan pantai barat Sumatra mengakibatkan berkurangnya titik api di daerah tersebut. Sementara itu, El Nino meningkatkan jumlah hotspot, sedangkan La Nina mengurangi jumlah hotspot. Dibandingkan dengan IOD, ENSO lebih berpengaruh pada terjadinya peristiwa hotspot di pulau Sumatra. Perbedaan periode musim kemarau di Sumatera utara, tengah, dan selatan juga memberikan perbedaan waktu terjadinya hotspot maksimum di Sumatera. Pola distribusi hotspot di Sumatera utara dan tengah memuncak pada bulan Februari dan Juni, sedangkan di selatan pada bulan September. Konsentrasi titik api yang tinggi (> 50 kejadian perbulan) pada umumnya terjadi di lahan gambut, yang umumnya ditemukan di Sumatra timur (Sumatera Utara, Riau, dan provinsi Sumatra Selatan). Forest fires in Indonesia have become a global problem that occurs every year, especially on the island of Sumatra. The identification of forest and land fires in this study is based on the number and distribution of hotspots, based on satellite image data from the Moderate Resolution Imaging Spectroradiometer (MODIS) in 2009-2018. Investigations on meteorological conditions are also based on global factors from Oceanic Nino Index (ONI) data, Dipole Mode Index (DMI) and based on the drought index from the Standardized Precipitation Index (SPI) data. The method used is a spatial and temporal analysis method. The purpose of this study was to determine the characteristics of hotspot distribution patterns on the island of Sumatra, both spatially and temporally. There are differences in the spatial and temporal characteristics of the hotspot distribution on the island of Sumatra, which is based on the characteristics of the topography, ENSO phase, as well as the wet and dry season periods. Orographic rain that occurred due to mountain topography in Aceh and the west coast of Sumatra resulted in reduced hotspots in the area. Meanwhile, El Nino increased the number of hotspots, while La Nina reduced the number of hotspots. Compared to IOD, ENSO is more influential on the occurrence of hotspot events on the island of Sumatra. The difference in the dry season period in northern, central and southern Sumatra also gives a difference in the time of the occurrence of maximum hotspots in Sumatra. The pattern of hotspot distribution in northern and central Sumatra peaked in February and June, while in the south in September. High hotspots (> 50 monthly events) with high concentrations occur on peatlands, which are commonly found in eastern Sumatra (province of North Sumatra, Riau, and South Sumatra).

ANALISIS MODEL PREDIKSI AWAL MUSIM HUJAN DI SULAWESI SELATAN Alimatul Rahim; Rini Hidayati; Akhmad Faqih; Mamenun Mamenun
Jurnal Meteorologi dan Geofisika Vol 16, No 2 (2015)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Model prediksi awal musim hujan merupakan salah satu kunci yang dapat digunakan untuk mengurangi resiko kegagalan panen padi yang disebabkan oleh faktor iklim di provinsi Sulawesi Selatan. Model prediksi awal musim hujan dibangun dengan menggunakan data curah hujan observasi Sulawesi Selatan dan anomali suhu muka laut di kawasan Pasifik dan perairan Sulawesi. Pada studi ini dilakukan analisis pemilihan stasiun hujan observasi, penentuan awal musim hujan, analisis komponen utama dan pengelompokan, analisis korelasi awal musim hujan terhadap anomali suhu muka laut, pembangunan model untuk prediksi awal musimhujan dan verifikasi model.Hasil analisis awal musim hujan menunjukkan setiap stasiun hujan mempunyai perbedaan awal musim hujan dengan rata-rata jatuh pada Julian Date (JD) ke-348 (14 Desember). Berdasarkan hasil analisis PCA dan cluster, diperoleh bahwa di Sulawesi Selatan terbagi menjadi 3 cluster wilayah. Cluster 1 mempunyai pola hujan lokal, sedangkan cluster 2 dan 3 mempunyai pola hujan monsun. Pada peta korelasi antara awal musim hujan di Sulawesi Selatan dan anomali suhu muka laut menunjukkan bahwa terdapat korelasi nyata(r≥0.5) antara kawasan Pasifik dan Laut Sulawesi pada cluster 1 dan 2 pada bulan Juni Juli Agustus September(JJAS). Sedangkan pada cluster 3, korelasi nyata hanya pada bulan Juni di perairan Sulawesi. Model prediksi AMH terbaik, pada cluster 2 terdapat di domain prediktor kawasan pasifik dengan nilai r=0.82, sedangkan pada cluster 1 dan 3, terdapat di domain perairan Sulawesi dengan nilai r=0.78 dan r-0.48. Verifikasi model terpilih pada cluster 3 mempunyai RMSE = 3, sedangkan cluster 1 dan 2, nilai RMSE berturut-turut sebesar 16 dan 29. Model prediction of rainy season onset is one of the keys to reduce the risk of paddy harvest failure because of the climate factor in South Sulawesi province. The model prediction for rainy season onset was build using rainfall data in South Sulawesi and SST anomaly in the Pacific Ocean and Sulawesi Sea. This research is conducted to select the rainfall station, determine onset using rainfall data, analyze PCA and cluster, make a correlation between onset and SST anomaly, develop onset model prediction, and verify the selected model. The onset analysis showed that every rainfall stations have different onset with average is on the 348th of Julian Date (December 14th). Based on the PCA and cluster analysis, there were three clusters of rainfall regions. Cluster 1 has a local pattern, Cluster 2 and 3 have a monsoonal pattern. On the map of correlation between onset in South Sulawesi and SST anomaly showed that there were strong correlations with the Pacific Ocean and Sulawesi Sea in clusters 1 and 2 on JJAS. Moreover, it has a weak correlation in cluster 3 in June in the Sulawesi Sea. The best AMH model prediction for cluster 2 was on the Pacific Ocean domain with r=0.82, on cluster 1 dan 3 was on the Sulawesi sea with r=0.78 and r=0.48. The selected model verification showed that the smallest RMSE (RMSE=3) was on cluster 3, moreover, on clusters 1 and 2, the RMSE model was 16 and 29.

EL NIÑO MODOKI DAN PENGARUHNYA TERHADAP PERILAKU CURAH HUJAN MONSUNAL DI INDONESIA Ela Hasri Windari; Akhmad Faqih; Eddy Hermawan
Jurnal Meteorologi dan Geofisika Vol 13, No 3 (2012)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v13i3.130

Penelitian ini bertujuan untuk mempelajari kejadian El Niño Modoki dan pengaruhnya terhadap keragaman curah hujan bertipe monsunal di Indonesia. Selain itu, di dalam penelitian ini juga dikaji perbedaan antara El Niño Modoki dengan El Niño konvensional yang telah dikenal selama ini. Analisis Power Spectral Density terhadap data indeks aSML periode 1979–2010 menghasilkan perbedaan karakteristik temporal antara keduanya. El Niño Modoki memiliki siklus 6–8 tahunan sedangkan El Niño konvensional memiliki siklus 4–5 tahunan. Hasil ini juga didukung oleh hasil analisis Wavelet yang menunjukkan pola osilasi dominan El Niño konvensional ~4 tahunan sedangkan El Niño Modoki hampir mendekati pola dekadal (~10 tahunan). Hasil analisis komposit dari tujuh kejadian El Niño Modoki, yaitu tahun 1986/87, 1990/91, 1992/93, 1994/95, 2002/2003, 2004/2005, dan 2009/2010 menunjukkan bahwa anomali hangat yang berkaitan dengan peristiwa El Niño biasanya konsisten melampaui nilai threshold sekitar periode Juli–Maret. Fase pertumbuhan mulai terlihat sekitar Maret atau April hingga Januari kemudian mulai turun sekitar bulan Februari. Puncak anomalinya terjadi pada bulan Agustus–Januari. Menurut hasil analisis regresi anomali curah hujan terhadap EMI periode 1971-2000, El Niño Modoki memberikan pengaruh yang jelas terhadap penurunan curah hujan di wilayah Sumbawa Besar, Makassar, dan Banjar Baru pada musim JJA dan semakin kuat pengaruhnya pada musim SON. Wilayah Lampung hanya merasakan pengaruh Modoki dengan jelas pada musim JJA saja sedangkan Indramayu pada musim SON. Penggunaan indeks EMI yang memasukkan informasi aSML di sekitar wilayah Indonesia menyebabkan nilai korelasi silang yang signifikan antara anomali curah hujan dengan EMI hanya menghasilkan jeda maksimum satu bulan. This study aims to investigate El Niño Modoki phenomenon and its influence to monsoonal rainfall behavior over Indonesia. The study is also intended to identify the differences between the El Niño Modoki and the well-known El Niño events, referred in this study as Conventional El Niño. Power Spectral Density and Wavelet analysis shows a different strength in the temporal cycle of both events, four years interannual cycles of Nino-4 index and nearly decadal (~10 years) cycles of EMI data. The composite of seven El Niño Modoki events in 1986/87, 1990/91, 1992/93, 1994/95, 2002/03, 2004/05, and 2009/10, shows the El Niño Modoki events indicated by the raise of EMI exceeding its defined threshold occurred from July to March. The growing phase started from March or April until January then continued by decaying phase around February. Regression analysis resulted the El Niño Modoki strongly influence monsoonal rainfall bahavior over Sumbawa Besar, Makassar, and Banjar Baru during both June-July-August (JJA) and September-October-November (SON) periods, over Lampung only during JJA, and Indramayu during SON. The use of EMI which includes SST anomaly around Indonesia led to a significant cross-correlation values between monsoonal rainfall anomaly and EMI with only maximum of one month lag time.

Co-Authors Adyaksa Budi Raharja Agita Vivi Wijayanti Agus Buono Alexander Knohl Alif Akbar Syafrianno Alif Kurniawan Alimatul Rahim Amsari Mudzakir Setiawan Amsari Mudzakir Setiawan Amsari Mudzakir Setiawan Amsari Mudzakir Setiawan Ardhasena Sopaheluwakan Ashehad Ashween Ali Bony Septian Pandjaitan Christian Stiegler Daniel Murdiyarso Dodo Gunawan Dodo Gunawan Eddy Hermawan Ela Hasri Windari Elza Surmaini Erna Nur Aini Fildza Novadiwanti Fithriya Yulisiasih Rohmawati Furqon Alfahmi Furqon Alfahmi Gita Adhani Jadmiko, Syamsu Dwi Kartika, Qurrata A'yun Laode Nurdiansyah M. Mukhlis Mamenun Mamenun Misnawati, Misnawati Mulyono R. Prabowo Mulyono R. Prabowo Naziah Madani P. Perdinan Rahmat Hidayat Rini Hidayati Rizaldi Boer Santikayasa, I Putu Sanusi Sanusi Sitanggang, Imas S. Supari Supari Suria Tarigan Syamsu Dwi Jadmiko Tania June Ummu Ma'rufah Yonny Koesmaryono

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