Volume XXXIX-B4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B4, 427-430, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B4-427-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B4, 427-430, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B4-427-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  01 Aug 2012

01 Aug 2012

USER FRIENDLY OPEN GIS TOOL FOR LARGE SCALE DATA ASSIMILATION – A CASE STUDY OF HYDROLOGICAL MODELLING

P. K. Gupta P. K. Gupta
  • Indian Institute of Remote Sensing, Indian Space Research Organisation, Dehradun, India

Keywords: 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 easily assimilate large dataset in an organised manner.