scholarly journals Climate Data Interpolation for Shkumbini WEAP Model

Author(s):  
Liljana Lata
1991 ◽  
Vol 11 (3-4) ◽  
pp. 365-366 ◽  
Author(s):  
R.A.I. Norval ◽  
B.D. Perry ◽  
R. Kruska ◽  
K. Kundert

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Zainul Arham ◽  
Ferriyati Masitoh ◽  
Siti Muhajaroh

Nowadays, Technologiy is growing rapidly computer the one of example of technology, helps the human being to fulfill their need of  information. The development of  information technology and computer make the data more regular and can be seen better and faster and also make the updating data easier for example geographical information system. The development of geographical is information system in technology have create wide potention in its application in the agriculture’s sector for example doserve the condition and the growth of plants substances. The write used data interpolation and ModelBuilder method as the design of the study. Data interpolation was used to make a continue grid of theme of the shapefile  point of the data, where as ModelBuilder was a collection of processes which was done on spacial data to procedure certain information in the form of the map. The data which were used in this study were spacial data that Java Province’s map in the form of shapefile and non special data that were climate data including rainfall, temperature and humidity and the appropriateness of mangosteen angoclimate’s data. The data showed that the appropriateness of agroclimate for mangosteen plant in west Java Province’s was dominated by S2 in therm of  climate point of view.   Keywords: geographical information system (SIG), Mangostenn (gatcinia mangostana linn), Agroclimate, West Java Privince’s, ModelBuilder.


Nature ◽  
2019 ◽  
Vol 574 (7780) ◽  
pp. 605-606 ◽  
Author(s):  
Linda Nordling

Agronomie ◽  
2001 ◽  
Vol 21 (1) ◽  
pp. 45-56 ◽  
Author(s):  
Pandi Zdruli ◽  
Robert J.A. Jones ◽  
Luca Montanarella

2019 ◽  
Vol 25 (1) ◽  
Author(s):  
MASROOR ALI KHAN ◽  
KHALID AL GHAMDI ◽  
JAZEM A. MEHYOUB ◽  
RAKHSHAN KHAN

The focus of this study is to find the relationship between El Nino and dengue fever cases in the study area.Mosquito density was recorded with the help of light traps and through aspirators collection. Climate data were obtained from National Meteorology and Environment centre. (Year wise El Nino and La Nina data are according to NOAA & Golden Gate Weather Services). Statistical methods were used to establish the correlation coefficient between different factors. A high significant relationship was observed between Relative Humidity and Dengue fever cases, but Aedes abundance had no significant relationship with either Relative humidity and Temperature. Our conclusion is that the El Nino does not affect the dengue transmission and Aedes mosquito abundance in this region, which is supported by earlier works.


Author(s):  
Mohammad Shohidul Islam ◽  
Sultana Easmin Siddika ◽  
S M Injamamul Haque Masum

Rainfall forecasting is very challenging task for the meteorologists. Over the last few decades, several models have been utilized, attempting the successful analysing and forecasting of rainfall. Recorded climate data can play an important role in this regard. Long-time duration of recorded data can be able to provide better advancement of rainfall forecasting. This paper presents the utilization of statistical techniques, particularly linear regression method for modelling the rainfall prediction over Bangladesh. The rainfall data for a period of 11 years was obtained from Bangladesh Meteorological department (BMD), Dhaka i.e. that was surface-based rain gauge rainfall which was acquired from 08 weather stations over Bangladesh for the years of 2001-2011. The monthly and yearly rainfall was determined. In order to assess the accuracy of it some statistical parameters such as average, meridian, correlation coefficients and standard deviation were determined for all stations. The model prediction of rainfall was compared with true rainfall which was collected from rain gauge of different stations and it was found that the model rainfall prediction has given good results.


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