June, Tania, Indonesia

  • Forum Pasca Sarjana Vol. 31 No. 4 (2008): Forum Pascasarjana - Articles

    The field observation of this research was conducted from June 2005 to May 2007 in tropical rainforest at Lore Lindu National Park, Central Sulawesi.  The main objectives of this research were to study (i) the relationships between interception loss and rainfall properties and vegetation characters and to determine the dominant factors affected, (ii) the effect of rainfall interception on energy flux and mass transfer, and (iii) the dynamics of vertical energy flux under the forest and grass land. Rainfall interception was 36.34% of gross rainfall in 200 events, Depth rainfall and LAI factors had strong effects on rainfall interception.  The relationships could be expressed as Ic = 0.980+0.239P+0.035LAI (R2 = 0.78).  The output of this equation was compared with Gash model and the observation value showed that deviation of the equation was 1% and less than Gash model, so that equation gave as good result as Gash model gave.  By considering the data requirements and calculation procedures it was concluded that this equation is applicable for interception prediction.  Land use type had effects on radiative and energy balance.  Net radiation (Rn) in the forest was 10.66 MJ/m2/day and higher than in the grass land where incident global radiation was similar.  Vertical latent heat flux in the forest was 9.53 MJ/m2/day or 89.40% Rn in rainy day and 8.41 MJ/m2/day or 74.56% Rn in dry day, on the other hand, in the grass land, sensible heat flux was higher than latent heat flux of all weather condition.  Direct effect of rainfall interception on energy flux and mass transfer it is needed latent heat about 9.0 MJ/m2/rainy day to evaporate the rain intercepted 3.69 mm/rainy day.  Effect of rainfall interception had positive correlation with  latent heat flux and the negative correlation  with sensible heat flux.


    Key words: interception, energy, mass and tropical rainforest

    Abstract  PDF
  • Forum Pasca Sarjana Vol. 31 No. 2 (2008): Forum Pascasarjana - Articles

    The paper describes about rainfall zoning and rainfall prediction modeling and its use for rice availability and vulnerability analysis.  The study used rainfall data from Station Baros (Banten region), Station Karawang and Station Kasomalang Subang (Northern Coastal of West-Java), and Station Tarogong (Garut).  Fuzzy clustering methods, that was applied for rainfall zoning, used the representative data for El-Nino, La-Nina and normal means condition during 1980-2006 periods.  Neural network analysis technique was applied for rainfall prediction modeling.  Training set of the model based on the rainfall data of 1990-2002 periods, and validation model based on data of 2003-2006 periods.  The model were used to predict the rainfall of 2007-2008 periods.  The distibution of equivalence value between rainfall stations was very variative under El-Nino, La-Nina and Normal condition.  On the certain of equivalence level it could be derivated some different rainfall zone under El-Nino, La-Nina and normal condition.  Model training set could explain 88% of Baros rainfall variability, 89% of Karawang rainfall variability, and 72% of Kasomalang rainfall variability.  At Baros, Karawang and Subang, rainfall was predicted to be increased on November 2007-February 2008 period, and to be decreased on the March-June 2008, and to be increased on July-November 2008.  The rainfall decreasing on the March-June would carry a losses of rice production up to 25%.  But, applying the well irrigation management and suitable growing periods could decrease and mitigate the decreasing of paddy production.


    Key words: rainfall prediction model, fuzzy clustering, neural network analysis, rice vulnerability
    Abstract  PDF