This paper describes a new empirical watershed model, the prime feature of which is its parsimony. It involves only three free parameters, a characteristic unparalleled by continuous process models able to work on a wide array of catchments. In spite of its crude simplicity, it achieved, on average, worthwhile results on a set of 140 French catchments and overwhelmingly outperformed a linear model involving 16 parameters. It performed roughly as well as a conceptual model with five free parameters, derived from the well-known TOPMODEL. GR3J: un modèle pluie-débit journalier à trois paramètres testé en FranceRésumé Cet article décrit un nouveau modèle dont la principale caractéristique est le faible nombre de paramètres, trois seulement, ce qui n'a jamais été réalisé dans le domaine des modèles capables de travailler en continu sur un large éventail de bassins. En dépit d'une très grande simplicité, ce modèle a donné en moyenne de bons résultats sur 140 bassins versants situés en France et s'est révélé beaucoup plus efficace qu'un modèle pseudo-linéaire comportant 16 paramètres. Il a donné des résultats peu différents de ceux obtenus avec un modèle conceptuel à cinq paramètres, version simplifiée de TOPMODEL.

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Climate change has significant impacts on changing precipitation patterns causing the variation of the reservoir inflow. Nowadays, Indonesian hydrologist performs reservoir inflow prediction according to the technical guideline of Pd-T-25-2004-A. This technical guideline does not consider the climate variables directly, resulting in significant deviation to the observation results. This research intends to predict the reservoir inflow using the statistical downscaling (SD) of General Circulation Model (GCM) outputs. The GCM outputs are obtained from the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP/NCAR Reanalysis). A new proposed hybrid SD model named Wavelet Support Vector Machine (WSVM) was utilized. It is a combination of the Multiscale Principal Components Analysis (MSPCA) and nonlinear Support Vector Machine regression. The model was validated at Sutami Reservoir, Indonesia. Training and testing were carried out using data of 1991–2008 and 2008–2012, respectively. The results showed that MSPCA produced better extracting data than PCA. The WSVM generated better reservoir inflow prediction than the one of technical guideline. Moreover, this research also applied WSVM for future reservoir inflow prediction based on GCM ECHAM5 and scenario SRES A1B.

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Abstract--It is certainly necessary for a practical, applicative and accurate method to predict the missing data. IDW (Inverse Distance wighting) method is one of the methods and it is quite well known among practitioners. This method is used to predict the missing data from several measured data (multipoint interpolation). In predicting the missing data, it will be better to use the closest data, than using the far data. Therefore, it is necessary for accuracy improvement of the IDW method by bringing data into closer points to the concerned data points, namely the points located between two data and having perpendicular position to the addressed data or called as Perpendicular Line.To know the reliability of IDW Perpendicular method, so it is necessary to test its ability to result the value in the addressed point rather than by the measured rain data as the exact function. The exact function is a surface value (z) as the function from x and y coordinates. The testing has done by using an exact surface value using 3,4, and 5 data. The calculation results of MAD, MSE, MPE show that IDW-P method (1,43%) has much smaller error than the original IDW (10,18 %) while the determination coefficient for IDW-P method is 0.997 which is greater than the original IDW method where is 0.901. In the application process in curved area, IDW-P method has average error percentage of 1,35 %, while the original IDW method has 6,02 %. In the application process using surface data, the IDW-P method has average error percentage of 6,11 %, while the original IDW method has 7,44 %, so that have improvement 1,33%. Generally, the multipoint interpolation of IDW-P method is better than the original IDW method.