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The effect of climate change on rainfall pattern and deficit of water in tea plantation Dalimoenthe, Salwa Lubnan; Apriana, Y; June, T
Jurnal Penelitian Teh dan Kina Vol 19, No 2 (2016)
Publisher : Research Institute for Tea and Cinchona

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (509.851 KB) | DOI: 10.22302/pptk.jur.jptk.v19i2.104

Abstract

Climate change has been influencing rainfall pattern so that it would be necessary to see the impact of that changed on tea plantation. The experimental area coverage lowland (600 m asl), midland (800-1000m asl) and highland (>1.000 m asl) tea plantation and each altituted represented by three tea estate in West Java. The rainfall data collected since 2005 up to 2014 from each estate and water deficit has been count through the method develop by Wijaya (1996). The results showed that the rainfall pattern has been changed by La-Nina and El-Nino during 2005-2014 in tea estate either in lowland, midland or highland in the last decade. The climate change caused  rainfall decreasing and increasing on dry month (the rainfall < 100 mm). Eventhough on 2009 there is an significantly increasing of the rainfall but after 2009 until 2014, the rainfall tend to decrease. After El-Nino on late 2009 and early 2010, lowland tea estate on Subang Regency facing water deficit until 5 months with R (defisit water index) far below 1 even there is no El Nino. The tea plantation at midland area (Cianjur Regency) facing 5 months water deficit per year, but the R index close to 1. While in highland tea plantation (Bandung Regency), the water deficit only happend on certain month on certain year although there is a month with zero rainfall. Water deficit could be happend because of runoff on soil surface stimulate by low ability of soil to keep the water.
RAINFALL PREDICTION MODELING USING NEURAL NETWORK ANALYSIS TECHNICS AT PADDY PRODUCTION CENTRE AREA IN WEST JAVA AND BANTEN PRAMUDIA, ARIS; KOESMARYONO, Y; LAS, IRSAL; JUNE, T; ASTIKA, I WAYAN; RUNTUNUWU, ELEONORA
Jurnal Tanah dan Iklim (Indonesian Soil and Climate Journal) No 27 (2008): Juli 2008
Publisher : Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/jti.v0n27.2008.%p

Abstract

Rainfall fluctuates with time and changes randomly, which unfavorable for most of the cropping, such as paddy. An early warning system is required to ensure a productive paddy cropping system. This paper describes the rainfall prediction modelling using a neural network analysis at paddy production centre area in the northern coast of Western Java and Banten. Rainfall data from Baros in the northern coast of Banten, Karawang, and Kasomalang Subang in the northern coast of West Java have been used for setting and validating the model. The model provides rainfall prediction for the next three months (Y=CHt+3), using the inputs data of the number of month (X1=t), the rainfall at the current month (X2=CHt), the rainfall atthe following month (X3=CHt+1), the rainfall at the following two months (X4=CHt+2), the southern ossilation index (SOI) at the current month (X5=SOIt) and the NINO-3,4 sea surface temperature anomaly at the current month (X6=AnoSSTt). Rainfall data recorded in the 1990-2002 period have been used for composing the model, and those in the 2003-2006 periods have been used for validating the model. The validated model has been used to predict rainfall in the 2007-2008. The best modelare those that using a combination of those six input variables. These models are able to explain 88-91% of the data variability with 4-8 mm month-1 of the maximum prediction error. At Baros Serang, the predicted rainfall in the 2007-2008 periods will be varied from Normal to Above Normal. At Karawang and Kasomalang Subang, predicted rainfall will be high at the end of 2007 until early 2008, and then will be low in the middle of 2008 and increases at the end of 2008.
Co-Authors Aris Pramudia Eleonora Runtunuwu I Wayan Astika Irsal Las Salwa Lubnan Dalimoenthe, Salwa Lubnan Y Apriana, Y Y KOESMARYONO