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PEMANFATAAN DATA EQUATORIAL ATMOSPHERE RADAR (EAR) DALAM MENGKAJI TERJADINYA MONSUN DI KAWASAN BARAT INDONESIA(THE VALUABLE OF EQUATORIAL ATMOSPHERE RADAR (EAR) DATA TO STUDY MONSOON IN THE WEST AREA INDONESIA)
Get PDFKototabang, Padang Panjang and Sicincin city are area in the West part of Indonesia and they are relative in the equator line. Otherwise, three of these cities have difference of behaviors of rainfall for Monsoon. In this study, we were used EAR Data, which were including the rainfall Kototabang, Padangpanjang, and Sicincin. Base on this data (i.e EAR data) in Kototabang, there is monsoon in 8-18 km layer and the higher monsoon is in 14 km layer during the April 2002-April 2006 period. Analisis Power Spectral Density (PSD) and Transformasi wavelet were shown that Monsoon oscillation around 12 months. While vertical profile was presented that the stronger monsoon will be in the wet weather on January. The domination of wind in Kototabang city is South Wind, it is because the wind took water vapor mass from South to North. According to analysis of rainfall in Kototabang, Padangpanjang and Sicincin City, meridional wind in the the Sicincin has rainfall pattern the same as with monsoon. Its was indicated that there were local indicator which can cause the monsoon. From the cross correlation between meridionial wind speed with rainfall in Kototabang, Pontianak and Sicincin, they were shown that three of these cities have significant correlation
PENERAPAN IRIGASI MIKRO,TUMPANGSARI DAN MULSA UNTUK MENGANTISIPASI KEHILANGAN HASIL CABAI MERAH PADA PENANAMAN DI MUSIM KEMARAU(APPLICATION OF MICRO IRRIGATION MULTIPLECROPPING AND MULCH FOR PEPPER YIELD LOST ANTICIPATION ON DRY SEASON)
Get PDFPrecipitation indicates system and intensity planting area. For anticipation plant on dry season, farmers from mountain range use water which was saving in plastic tank at teras bangku. This study has done at Canggal, Kledung, Temanggung on dry season;18 September 2006 till 19 December 2006. Climate, agronomy and soil water content were analyzed using dailiy crop water balance (CWB-ETO).The treatment which application were : a)once irrigation every three days, b) irrigation every das, c)not use mulch and d)use mulch. Yield lost on dry season (plant date at March till September) at 100 % and 20 % is plant date at January till Februari. At Sudirjo field, yield lost at 0 % on dry season plant date. Production of pepper at Sudirjo field 0..6 kg/plnt and secundar date of tomat production is 1.6 kg/platn until 1.2 kg/plantt.Difference of yield lost between Sudirjo field and from CWB-ETO simulation needs good analysis which base for CWB-ETO program
PEMODELAN TANAMAN JARAK PAGAR (JATROPHA CURCAS L.) BERBASIS EFISIENSI PENGGUNAAN RADIASI SURYA,KETERSEDIAAN AIR DAN NITROGEN(CROP MODELING OF JARAK PAGAR (JATROPHA CURCAS L.) BASED ON RADIATION USE EFFICIENCY,WATER AND NITROGEN AVAILABLE)
Get PDFA number of crop growth simulation models have been developed using the radiation use efficiency (RUE) concept to predict crop growth and yield in various environments. These models generally calculate daily biomass production as the product of the quantity of radiation intercepted and RUE. Besides that biomass production was deterimined by water and nitrogen available factor. So, this research was carried out to quantify the RUE, biomass and leaf area index on Jatropha under rainfall condition, four levels of nitrogen fertilizer (N) and two and three population densities (P) planted twice. The experiments used a systematic Nelder fan design with 9 spokes and 4 – 5 rings were conducted at SEAMEO-BIOTROP field experiment in 2007. Data from the first experiment were used for parameterization and calibration and the second experiment data for model validation. Based on parameterization, we found that RUE can prediction above ground biomass accumulation of Jatropha were 0.94 (r=0.83) g MJ-1 to 1.3 (r=0.75) g MJ-1. Water availability was between ψ=-30 kpa and ψ=-1.5 MPa for field capacity and wilting point, respectively. Nitrogen demand of root, stem, leaf and grain N were (Ndemr=0.75), (Ndems=0.60), (Ndeml=2.53), and (Ndemg=2.41), respectively. Validation showed that model can simulate crop growth and development of Jatropha
PENGEMBANGAN MODEL PREDIKSI MADDEN JULIAN OSCILLATION (MJO) BERBASIS PADA HASIL ANALISIS DATA REAL TIME MULTIVARIATE MJO (RMM1 DAN RMM2) (PREDICTION MODEL DEVELOPMENT MADDEN JULIAN OSCILLATION (MJO) BASED ON THE RESULTS OF DATA ANALYSIS ...
Get PDFBackground of this research is the importance of study on the Madden Julian Oscillation, the dominant oscillation in the equator area. MJO cycle showed by cloud cluster growing in the Indian Ocean then moved to the east and form a cycle with a range of 40-50 days and the coverage area from 10N-10S. Method that used to predict RMM is Box-Jenkins based on ARIMA (Autoregressive Integrated Moving Average) statistical analysis. The data used RMM daily data period 1 Maret 1979–1 Maret 2009 (30 years). RMM1 and RMM2 is an index for monitoring MJO. This is based on two empirical orthogonal functions (EOFs) from the combined average zonal 850hPa wind, 200hPa zonal wind, and satellite-observed Outgoing Longwave Radiation (OLR) data. The results in form of the Power Spectral Density (PSD) graph Real Time Multivariate MJO (RMM) and long wave radiation (OLR = Outgoing Longwave Radiation) at the position 100° BT, 120° BT, and 140°BT that show the wave pattern (spectrum pattern) and clearly shows the oscillation periods. There is a close relation between RMM1 with OLR at the position 100oBT that characterized the PSD value about 45 day. Through Box-Jenkins method, the prediction model that close to time series data of RMM1 and RMM2 is ARIMA (2,1,2), that mean the forecasts of RMM data for the future depending on one time previously and the error one time before. Prediction model for Zt = Zt = 1,681 Zt-1 – 0,722 Zt-2 - 0,02 at-1 - 0,05 at-2.. Prediction model for RMM2 is Zt = 1,714 Zt-1 – 0,764 Zt-2 - 0,109 at-1 - 0,05 at-2.. The flood case in Jakarta January-February 1996 and 2002 are one of real evidence that made the MJO prediction important. MJO with active phase dominant cover almost the entire Indonesia west area at that moment
IDENTIFIKASI KEKUATAN DAN KELEMAHAN KOMPONEN SISTEM INFORMASI IKLIM(STRENGTH AND WEAKNESS IDENTIFICATION OF CLIMATE INFORMATION COMPONENT)
Get PDFBased on the survey of climate information application in many sectors showed that climate informations are inaccurate, lately received, abstrused and not meet to the user activities. There is a big gaps between climate information producer and user, it needs a bridging to handle a problem in interpreting information. These conditions caused to not optimally climate risk anticipation, so that there were still a lot of failures in some sectors, i.e. crops failure, urban floods, food and water shortage, health crisis, forest fire, etc. There are many activities have been done to increase skill to intepret and react to climate information. Providing climate information is one of the methods to minimize the climate risk. By understanding the climate information, climate risk could be managed optimally and it can minimize negative impact of climate extreme and get benefit from good climate conditions. Boer, 2009, said that there are five primary components as a key to climate information application in manage a risk, 1) climate data observation and data analysis, 2) climate forecast/prediction system, 3) climate information production and evaluation system, 4) communication and dissemination system, and 5) climate information system. Valuation of strength and weakness of five components above relatively depends on which angel be used. It needs an objective indicator to evaluate those components. In this paper, strength and weakness of climate information components will be identified. Data was collected from Meteorological, Climatological and Geophysical Agency’s stations and some institutions in Banten Province as climate information users by distributing questionaire. Furthermore, based on the components identification it could be created a strategy how to increase the capacity of climate information applications
WEATHER MONITORING MODEL BASED ON SATELLITE DATA(MODEL PEMANTAUAN CUACA BERDASAR PADA DATA SATELIT)
Get PDFWeather monitoring model is closely related to the problem of objective analysis of the field of meteorology. The amount of meteorological data is quite substantial and hence the processing of these data is one of primary problems is dynamic meteorology. Therefore, a weather system model must consider atmospheric process, which can be built by mechanistic model rather than statistical approach. Integration of numerical model and spatial model will produce spatial weather information. It should be managed in one computerized system called as an information system for weather monitoring. The approach of the research was divided into five tasks. First task was satellite data capturing and extracting, second was development of numerical modeling based on dynamic and thermodynamic of atmospheric process, third was integration of numerical modeling and geographic information system in the spatial model, fourth was to develop graphical user interface and the fifth task was application of system in the real-world. Temporal resolution of this model is one day, however, in reality weather is temporal state of atmosphere condition that change any time. Moreover, this model only describes weather condition when data satellite on the day could be captured. Therefore, to increase the temporal resolution of this model, the input data could be added or integrated with other satellite data such as GMS satellite that has one-hour temporal resolution. Spatial resolution in this model is 50x50 kilometers square for global and 8x8 kilometers for regional area. Actually, for the spatial resolution, this model has been prepared as NOAA’s spatial resolution. This model cannot simulate vertical distribution of atmosphere, so, it does not give information about relative humidity and precipitation. If air movement in vertical area could be simulated, the dew point temperature and lighting condensation level would be known therefore the relative humidity and precipitation could be predicted
PENILAIAN RISIKO IKLIM PADA SISTEM PERTANIAN EKOSISTEM LAHAN RAWA PASANG SURUT (STUDI KASUS DI DELTA TELANG I, DELTA TELANG II DAN DELTA AIR SALEH, BANYUASIN, SUMATERA SELATAN) (CLIMATE RISK ASSESMENT ON AGRICULTURAL SYSTEM IN SWAMP AREAS ...
Get PDFThe characteristics of swampland areas are different from agricultural land of Java, mainly in water availability. In swampland ecosystems there are unique environmental conditions. To assess risks of climate, mainly in climate change, we must assess about capacity and adaptation strategy. From treasure of related institution and interview with farmers,its had been known about climate impact on farming systems application, rainfall pattern and water availability. This paper aims to assess risks of climate on farming systems, application of adaptation strategy to reduce risks of climate and probability to provide of planting pattern alternative in the future in swampland areas (tidal marsh) in Delta Telang I, Delta Telang II and Delta Air Saleh, Banyuasin, South Sumatera
PEMANFAATAN SUMBER AIR PEGUNUNGAN UNTUK MENGANTISIPASI KEKERINGAN PADA MUSIM KEMARAU UNTUK TANAMAN KUBIS(TO EXPLOIT MOUNTAIN WATER RESOURCE FOR ANTICIPATION DROUGHT AT DRY SEASON FOR CABBAGE)
Get PDFOperational step for dry season anticipation among to take schedule in plant pattern at location which often affected El Nino, to evaluate rain characteristic, to evaluaete irrigation availability, to prepare irrigation infrastructure and exploit alternative water resources. Beginning step for to got time and plant pattern with climate data is CWB-Eto program simulation. In this activity, data is taked from climate data at Canggal, Temanggung 2006, cabbage agronomy data and content water data. The result of climate data observation and CWB-Eto program simulation show the rain with 100 mm occurred at January – April and November – December, while the 20 % lost yield occurred.When farmer plants cabbage at January – Mei and September – October. For anticipation drought, micro irrigation and micro climate modification will decrease lost yield. The farmer when he will plant cabbage at dry season must make micro climate among mulc and irrigation with three day one. When we compare between the result research FAO and at Canggal, so cabbage which at Canggal was in good condition. Cabbage production at Canggal was 1,1 kg/plant and cabbage production at the result FAO is between 1 – 1.7 kg / plant
LINKING CLIMATE CHANGE ADAPTATION OPTIONS FOR RICE PRODUCTION AND SUSTAINABLE DEVELOPMENT IN INDONESIA (KETERKAITAN OPSI-OPSI ADAPTASI PERUBAHAN IKLIM UNTUK PRODUKSI BERAS NASIONAL DAN PEMBANGUAN BERKELANJUTAN DI INDONESIA)
Get PDFClimate change is expected to significantly influence Indonesian rice production as this phenomenon will exacerbate extreme climate events such as El Nino and La Nina which have caused serious loss in rice production. This paper is attempted to propose plausible climate change adaptations for rice production by examining the formal documents on climate change studies in Indonesia and rice development strategies and to investigate their linkage with the Sustainable Development in Indonesia. The result shows that climate change adaptations will support Indonesian rice development strategies through options of “change cropping pattern/modified planting season” which has not been addressed by the development strategies. The proposed adaptations which are directed through two major programs for increasing rice production called as Extensification and Intensification, have also already addressed the four pillars of Indonesian sustainable development, namely: pro-job, pro-poor, pro-growth and pro-environment
ESTIMATION OF NET PRIMARY PRODUCTION (NPP) USING REMOTE SENSING APPROACH AND PLANT PHYSIOLOGICAL MODELING(PENDUGAAN NET PRIMARY PRODUCTION (NPP) MENGGUNAKAN PENDEKATAN PENGINDERAAN JAUH DAN MODELING FISIOLOGIS TANAMAN)
Get PDFInformation Net Primary Production (NPP) of tropical forests is important for the development of realistic global carbon budgets and for projecting how these ecosystems will be affected by climate changes. This research utilized remotely sensed data and micrometeorological measurement to provide information on vegetation condition. The objective of this research is to estimate spatial NPP using remote sensing approach and plant physiological/micrometeorological modeling. The estimation of NPP is conducted using modeling approach, which is based on relationship between radiation use efficiency, photosyntetically active radiation and fraction of absorbed photosynthetically active radiation by the plants’s canopy. Trend of NDVI derived using micrometeorological measurement showed an increase from 2001 to 2002, and then decrease from 2002 to 2004. Average different values (delta) between both methods used to derive NDVI is relatively constant around 0.33 with a high correlation of r2 = 0.98. Using remotely sensed data, the highest NPP values estimated is in year 2003 with value range between 2000 – 2500 (gC m-2 yr-1), less than 2% of the whole forest area. In 2003, 75% area has NPP between 1500 – 2000 (gC m-2 yr-1), meanwhile for 2002 and 2004 it is only 21% and 50 %, respectively. NPP values estimated using micrometeorological measurement show the increasing of NPP values from 2002 to 2003, and then decrease from 2003 to 2004. There is strong correlation between NPP values derived from the two methods with r2 = 0.98