MILATION 
lun 
ly fuelled by high level 
GEOS, GeoTools etc.). 
ritten in C++. It allows 
ises on exploiting this 
- Python programming 
1 level gridded rainfall, 
bil data for input into 
0 made to use popular 
out for the same study 
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late large dataset in an 
re importantly handling 
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1999). VIC was run on 
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ace for calibrations. 
GIS 
dels 
(a) 
  
xdels (b) Embedding 
se coupling (d) Tight 
Maggio, 1999) 
2. BACKGROUND OF OPEN SOURCE GIS 
GIS has become an essential tool in hydrology (Goodall and 
Maidment, 2009) and proprietary GIS softwares can fulfil most 
of the requirements of functionality (Steiniger and Hay, 2009). 
Proprietary softwares, however, fall short in terms of 
interoperability, software transparency and data transferability 
(Jolma et al, 2000). The free software foundation (vwww.fsf.org) 
promotes the use of free softwares as they are free from 
restriction; sharing policies and copy; to learn and adapt; to 
work with any system with considerable reliability (Stallman, 
1999). Every aspect of computing is dominated by free and 
open source software (FOSS) as evident from the fact that 
66.17% of all web servers are running Apache, which is an 
open source web server software (Netcraft, 2011). Steiniger and 
Bocher, 2009 have studied the entire gamut of FOSS for Geo 
(FOSS4G) projects and their relationships (Sanz-Salinas and 
Montesinos-Lajara, 2009) as shown in Figure 2. The existence 
of many active and diverse software libraries which act as 
central points for almost all projects has fuelled the current 
study. 
  
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Figure 2 Relationships between FOSS4G Projects (Sanz-Salinas 
and Montesinos-Lajara, 2009) 
3. METHODOLOGY 
The VIC hydrological model requires several RS, GIS inputs, 
soil and vegetation parameters and other calibrating parameters, 
as shown in Table 1. Figure 3 shows few of RS and GIS derived 
inputs (Global Topographic (GTOPO) Digital Elevation Model 
(DEM) and mask file). 
  
Figure 3 DEM and boundary file for VIC input 
Table 1 Various VIC inputs for model run 
  
  
  
  
  
No VIC Input | Derived from Remarks 
1. GRID map No of 
2. Mask map 
file/s: 1 
: 3. DEM s ; 
Soil Dimension 
4. Slope 
1 Parameter of each 
: 5. Aspect 
File ; file: 
6. Soil Type 
: G rows x 
7. . Soil parameters 36:colunins 
8. Calibration parameters 
No of 
l. Fraction of each files: L 
: ; Dimension 
Vegetation vegetation (f) f ach 
2 Parameter 2. Rooting depths of each e à e 
File of the 'v' vegetation le 
G * f rows 
classes 
X 8 
columns 
No of 
l. Vegetation parameters | file/s: 
such as: Dimension 
3 Vegetation i. Albedo of each 
Library File ii. Displacement file: 
iil. Roughness V TOWS X 
iv. LAI 50+ 
columns 
No of 
l. Precipitation file/s: G 
l 2. Maximum temperature Dimension 
Forcing eh 
4 Files 3. Minimum temperature of each 
4. Wind speed (f | file: 
available) N rows x 4 
columns 
  
  
  
  
  
  
Where G is the number of grids in which the study area has 
been divided and N is the number of days the model is run for. 
As seen from Table 1, the large number of input parameters, 
their syntactic and semantic segregation and their scale and 
temporal variation creates a huge challenge along with 
assimilating them to create text/ ASCII input files for VIC 
model run. The study takes advantage of the numerous libraries 
available which can be used to build a GIS on top of it. With 
this approach an application is built which provide a user 
friendly graphical user interface to translate requests to library 
functions (Neumann and Hugentobler, 2008). A prime example 
of such type of software is Quantum GIS or QGIS 
(www.qgis.org). QGIS uses geospatial data abstraction library 
(GDAL/OGR) for reading and writing to data sources, PROJ4 
for reprojecting vector layers on the fly and GEOS for 
intersection tests between geometries and (selection) rectangles. 
QGIS allows users to perform specialised tasks by creating 
plugins in C++ and Python. This research article emphasises on 
exploiting this capability of QGIS to build and implement 
plugins using Python programming language. Using these 
facilities the present study attempts to develop a tool to 
assimilate large spatio-temporal datasets such as national level 
gridded rainfall, temperature, topographic (DEM, slope, aspect), 
landuse/landcover and multi-layer soil data for input into 
hydrological models. In this study, in addition to QGIS 
functionalities, some plugins in Python have been programmed 
for creation of VIC data input files. 
4. RESULTS 
The plugin was developed in Python. The plugin interface was 
developed through Qt4 designer. This application is a tool for 
designing and building graphical user interfaces (GUIs) from