Full text: Technical Commission IV (B4)

USER FRIENDLY OPEN GIS TOOL FOR LARGE SCALE DATA ASSIMILATION 
— A CASE STUDY OF HYDROLOGICAL MODELLING 
Prasun Kumar Gupta * *, 
? Indian Institute of Remote Sensing, Indian Space Research Organisation, Dehradun 
prasun@iirs.gov.in 
XXII ISPRS Congress 
KEY WORDS: Open systems, GIS, Software 
ABSTRACT: 
Open source software (OSS) coding has tremendous advantages over proprietary software. These are primarily fuelled by high level 
programming languages (JAVA, C++, Python etc.) and open source geospatial libraries (GDAL/OGR, GEOS, GeoTools etc.). 
Quantum GIS (QGIS) is a popular open source GIS package, which is licensed under GNU GPL and is written in C++. It 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 across multiple platforms using the easy to learn — Python programming 
language. 
In the present study, a tool has been developed to assimilate large spatio-temporal datasets such as national level gridded rainfall, 
temperature, topographic (digital elevation model, slope, aspect), landuse/landcover and multi-layer soil data for input into 
hydrological models. At present this tool has been developed for Indian sub-continent. An attempt is also made to use popular 
scientific and numerical libraries to create custom applications for digital inclusion. 
In the hydrological modelling calibration and validation are important steps which are repetitively carried out for the same study 
region. As such the developed tool will be user friendly and used efficiently for these repetitive processes by reducing the time 
required for data management and handling. Moreover, it was found that the developed tool can casily assimilate large dataset in an 
organised manner. 
1. INTRODUCTION 
Water is essential for all life forms on Earth and various models 
have been successfully used to study this movement of water in 
its successive stages of the hydrologic cycle. Data intensive 
simulations are being carried out since 1960’s to model the 
spatio-temporal dynamics of important hydrological processes. 
The use of Geographic Information System (GIS) has enabled 
scientists to use well established practices and standards to 
replicate hydrologic systems. The GIS data sets used are 
primarily derived from remote sensing (RS) and field derived 
data-sets. Sui and Maggio, 1999 have detailed, different widely 
used approaches to integrate GIS with hydrological modelling 
(Figure 1). As evident the four coupling (or integration of GIS 
with hydrological modelling) approaches discussed, are 
constrained by technology available at the time. Today 
Geographic Information (GI) systems provide customization 
facilities allowing users to step beyond standard softwares 
owing to difficulties in system integration of modelling, 
statistical data analysis and databases (Bivand and Lucas, 
2000). 
The current study challenges the use of readymade off-the-shelf 
software capabilities in making a semi-distributed macro-scale 
hydrological model called Variable Infiltration Capacity (VIC) 
model (Gao et al, 2010), efficient in terms of usage, calibration 
and validation. The scientific challenge in addressing all 
environmental problems in not only the development of remote 
  
* Corresponding author. 
sensing sensors and algorithms but more importantly handling 
large data sets, processing them efficiently and generating 
usable products off the same (Barnsley, 1999). VIC was run on 
Indian sub-continent and required pre-post processing of 
geospatial data and a user friendly interface for calibrations. 
  
Hydralogical Models Gls 
Hydrological 
sis Models | 
(a) (b) 
GIs 
Gis 
Hydrological Statistical 
Statistical Models Tools 
Models Tools 
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Figure 1 Integrating GIS with hydrological modeling (a) 
Embedding GIS in Hydrological models (b) Embedding 
hydrological models in GIS (c) Loose coupling (d) Tight 
coupling (Adapted from Sui & Maggio, 1999) 
  
  
  
  
  
   
  
  
   
  
   
  
  
  
  
  
  
   
  
  
  
  
  
  
  
    
  
  
  
  
  
  
  
   
    
  
   
  
   
   
   
  
   
  
  
  
  
  
  
  
  
  
  
  
   
  
   
  
  
  
  
  
     
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Figure 3 |
	        
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