1. Process Automation for
Hydrological Data Mapping
over GIS Software
By Rohan Jain (08AG1016)

2. Introduction
● Weather data is available from various
organisations like IMD, CWC through their
stations spanning all over the country,
periodically.
● The data available from these places can be
used for further processing.
● Processing is done via various GIS Software
available.
● ArcGIS is one such popular software. It is
used for this project

3. Introduction: Problem
● Data is not available in format ArcGIS support
● So it cannot be directly imported
● Manually importing 10s of thousands of data
is not possible.
● Hence data needs to be automatically
converted into an ArcGIS format.
● But again data from all the sources is not in a
standardised format.
● So each data source needs special attention

4. Objectives
● Automatic conversion of existing
hydrological data of Mahanadi river basin
into a universal time-series format
● Mapping of the data into ArcHydro model of
the ArcGIS software

5. Study Area: Description
● Mahandi river basin, located between
longitudes 800 30' and 870 E, and latitudes
190 21' and 230 35' N
● 4.3% of the total geographical area of India
● Mahanadi was notorious for its devastating
floods.
● Hirakud Dam, one of the longest dams
improved the situation greatly.

6. Mahanadi river
basin

7. Study Area: Data Available
● Data from India Meteorological Department
and Central Water Commission (CWC)
● Rainfall data
● Escape Discharge data
● Water Level Data
● Data from remote sensing

8. Methodology: Requirements
● ArcGIS (Version 9.3)
● ArcHydro tools (Version 1.4) and ArcHydro
data model
● Python Programming Language (Version >
2.6)
● External Python Libraries
○ xlrd (for reading spreadsheets)
○ dbfpy (for writing dBase files)

9. Methodology: Study Material
● Book: ArcHydro - GIS for Water Resources
by David R. Maidment[7]
● Book: Arc Hydro Tools - Tutorials
● GIS Course Content - University of Texas
● Web Resources, Lectures made available by
ESRI[8] (ArcGIS Developer organisation)

10. Methodology
● For interfacing with ArcGIS dBase (*.dbf)
database file format used
● dBase is a popular database and ArcGIS
relies on it itself for storing data, so a good
choice for using it for our task
● Python libraries available (dbfpy)
● For data model to store the time series, used
the TimeSeries model from ArcHydro data
models.

11. Methodology: Data Model
● FeatureID: ID of the feature for which this
time series data exists. IMD Stations, CWC
Gauges etc.
● TSTypeID: ID of the time series type. We
have Precipitaion, Discharge, Water Level
etc defined
● TSDateTime: The date and time of individual
data
● TSValue: Individual data value

12. Methodology: Automation
1. The data obtained from various organisations
is converted into a format which follows
python data structures.
2. Separate (dBase) files contain information
about HydroIDs (which will help find
FeatureID). The information is extracted and
used to find FeatureIDs for station names
3. Time Series is generated and then further
published as dBase files for use with ArcGIS
software.

13. The Data Conversion
Process

14. Methodology: Code Written
● Modules
○ These are for generic tasks which are applicable to
all data sources
○ timeseries.py
■ Takes care of timeseries related internal tasks
■ Also generates the dBase files
○ stations.py:
■ Process the HydroIDs (FeatureIDs in Time
Series database)
■ Fetches ID - Name info about the stations

15. Methodology: Code Written
● Individual Data Source Scripts
○ Since each data source provides information in a
different format, they all need a separate script.
○ These scripts process the raw data to pythonic
format and then generate time series database
● Written in Python Programming Language
● Total roughly 450 lines of python code
● A C/Java equivalent will easily measure 2-3
times

16. Results
● Set up an initial project with correct directory
hierarchy and install python + the required
libraries
● Then, on execution of the scripts the time
series files are generated automatically
● The time series files can then be imported
into ArcGIS table

17. Results: Loading Data
Loading data
into a Time
Series table in
ArcCatalog

18. Result: Loading Data
ArcCatalog data loading dialogs

19. Result: Loading Data
Displaying data
after being
imported.

20. Result: Processing Data
Processing the
data in
ArcMap using
ArcHydro tools

21. Result: Processing Data
ArcMap Processing the Discharge Time Series

22. Future Work
● Rewrite the modules using Object Oriented
Approach to improve the code quality and
future additions of code easier
● Apart from this Rainfall, Discharge, Water
Level series more data can be obtained and
added

23. Thank You