1. DEEP BASU
131859
1

2. SMALL HYDRO POWER PROGRAMME (SHP)
•Small Hydro Power (SHP) projects up to 25 MW station
capacities serving a small community or industrial plant.
•Available on small rivers, canal heads and canal drops.
•Potential for power generation in the country from such
plants is over 15,000 MW.
2

3. Fig:1
SHP
3

4. WHY GIS AND RS FOR HYDROPOWER DEVELOPMENT
•Variation in terms of natural and socioeconomic settings.
•Rivers have distinct catchments areas in different physiographic
zones.
•Rugged terrain of Himalayas requires thorough study of geology,
topography, land use patterns, distribution patterns of biodiversity
resources, infrastructure, socio-economic activities etc.
4

5. FIELDS OF APPLICATION
•Digital mapping
•Drainage database system
•Terrain analysis
•Watershed mapping
•Integration of socio-economic variables
5

6. Fig:2
Layer Management in a GIS Database
6

7. Fig. 3. Analytical framework of the SHP
7

8. 8
LITERATURE REVIEW
8
•Todinini CL . (2002) discussed advantages of these tools and their capability to
simulate water content vis-à-vis discharge within miniature spatial extent for three
different flows viz., overland, surface and channel flows.
•Pokharel S. (2000) performed spatial analysis using GIS to develop location
specific energy resource and consumption profile in rural areas of Nepal.
•Mackay DS et al. (2000) showed benefits of GIS integrated process based
hydrological models. It also described that the requirement of large volume of
data concerning land use, soil and climate has been one major limitation of
hydrological modelling.

9. Case study on Assessment of small hydropower
potential using remote sensing data for sustainable
development in India
Authors:
Surekha Dudhania,, A.K. Sinhab, S.S. Inamdara
9
Journal:
Energy Policy (2006)
Area of study:
Tehri, Garwal district

10. Proposed Methodology
•Emphasis on satellite image interpretation and its application for
identification and assessment of water resources and its association for the
development of hydropower in a scientific method.
•Satellite image obtained from IRS-1D LISS III Geocoded False Color
Composite (FCC) data is used for extraction and mapping of water
resources.
•In image processing technique, input and output are still images, but
output is an abstract representation of the input.
•Work is carried out in two steps:
(1) Image clustering and classification (segmentation),
(2) Image interpretation.
10

11. Image clustering and classification
11
•Fuzzy-theoretic technique of pattern clustering and classification used.
•The intensity in terms of distribution of R-G-B values of pixels together with the
knowledge base of the features of the objects is used for image clustering and
classification.
•Fuzzy C-Means (FCM) algorithm is used in the analysis. The number of cluster
and sub
clusters (water, forest, inhabitation and snow) assumed to crisp in nature for
identifying the water resources.
•FCM uses the principle of fuzzy sets to evolve a partition matrix U(X) while
minimizing the measure
(Uik)mD2(Vi,Xk),

C
i 1

C
i 1

C
i 1

C
i 1

12. Image Interpretation
12
•Clear water absorbs relatively little energy having wavelengths less than
about 0.6 mm. High transmittance typifies these wavelengths with a
maximum in the blue-green portion of the spectrum.
•An expert system is developed with computer-based program in Visual
Basic that uses knowledge, facts and different reasoning techniques to
solve visual interpretation of wide variety of remotely sensed data
problems.

13. Fig 4: Domain specific expert system
13
User
User Interface
may employ:
Question and
Answer,
Menu Driven,
Natural
Language,
Graphic
Interface
Styles,
Human Expert
Knowledge
Acquisition
Literature and
others
Knowledge
based editor
Inference engine
Explanation
subsystem
General
knowledge
based
Case specific
data

14. 14
Fig 5: (a) 0-level DFD, (b) 1-Level DFD, (c) 2-level DFD
SATELLIT
E IMAGE
S I A
0
LANDUSE/LAND
COVER/WATER
(a) 0-level DFD
SATELLITE
IMAGE
LOAD
IMAGE
0.1
FCC
FILTER
0.3
PRINT
0.6
SAVE
0.7
ZOOM
0.5
FEATURES
0.4
R G B
0.2
(b) 1-Level DFD

15. 15
LOAD IMAGE
EXTRACTED
IMAGE OF THE
SELECT OBJECT
ADD TO DATA
BASE
WATER
0.4.1
SNOW
0.4.2
INHABITATION
0.4.3
FOREST
0.4.4
(c) 2-level DFD

16. 16
Fig 6 Ganga River
Fig 7 Alaknanda River
.

17. Results
17
•Site location at 1 and 2 on
Alaknanda river shows minimum
coverage of forest/vegetation area
but requires rehabilitation and
resettlement.
•Site location at 3 may not justify
for the site and also being
mountainous region it is difficult
and expensive to lay the
transmission lines to the load
centers.

18. Case study on Tools for Small Hydropower Plant Resource
Planning and Development: A Review of Technology and
Applications
18
Authors: Petras Punys, Antanas Dumbrauskas, Algis Kvaraciejus
and Gitana Vyciene
Journal: Energies (2011)

19. 19
GIS Applications for Evaluating Hydropower Potential
•DEM (Digital Elevation Model) enables the study of land forms and, can access
many hydrological and morphometric characteristics of a river basin.
•Analysing DEM together with other data layers (e.g., soil data, land use, and
protected areas) accurate estimations of hydropower planning are obtained that
take the already existing network of hydropower into consideration.
•By overlaying a segmented river network over the DEM, the elevation drop for
each river reach can be obtained easily. By integrating runoff time series, flow
duration curves, and annual flow data in GIS based models, power and energy
maps can be produced.

20. 20
Figure 8 : GIS hydropower assessment tool. General model process flowchart

21. 21
Detailed scheme of GIS application for hydropower estimation
Input data: Hydrographic network and DEM of
river basin
Input data: Hydrographic network and river
catchment area with DEM

22. 22

23. 23
•Suitable tools are ArcGIS Spatial Analyst and the recently developed ArcGIS
extension, ArcHydro which allows the user to set a number of hydrological
parameters used by hydrologic models as input data.
•Using the DEM of the river basin, soils and land cover thematic layers and the
river network, the number of required input data for the hydrological model, a sub-
basin separation procedure can be quickly and efficiently calculated.
•A separate case of GIS application is the development of the map with a spatial
distribution of FDC (Flow Duration Curves) parameters.

24. Table 1 :GIS-based Small Hydropower Atlases on the Internet
24
Name Developer Applicable
countries
Accessibility
NVE Atlas Norwegian Water
Resources and
Energy
Directorate (NVE)
Norway Open access,
interactive
Web-based maps
Virtual
Hydropower
Prospector (VHP)
Idaho National
Laboratory
US Open access,
interactive
Web-based maps
RHAM Kerr Wood Leidal
Associates Ltd
(KWL)
British
Columbia,
Canada
Open access,
interactive
Web-based maps
Hydrobot Nick Forrest
Associates
Ltd. et al.
Scotland Limited access
VAPIDRO -
ASTE
ERSE SpA, Italy Open access,
interactive
Web-based maps

25. Summary:
25
•Remote Sensing provides a systematic and comprehensive approach to extract
information for identification and assessment of water resources. For
hydropower project studies, remote sensing (e.g., LIDAR), which is becoming
cost effective compared with conventional surveying
•Saving of manpower and time required for surveying and updating the
information of the potential sites will have significant impact on the cost.
•A large step in SHP assessment was the integration of these software tools into
the GIS environment, the penetration of GIS to the Web and the advent of
remote sensing techniques.

26. 26
•GIS is often used for the presentation of results in the form of digital maps.
•Possible to rapidly assess power potential on a widespread basis while
maintaining a relatively high level of detail. In most countries, GIS data are free
of charge.
•Using DEM and regional hydrologic data, these software tools are able to
calculate the amount of hydropower available on all streams in a study area,
screening out sites within environmentally sensitive or excluded areas, and to
estimate project costs.

27. References:
 Klimpt, J.E., Rivero, C., Puranen, H., Koch, F.; Recommendations for
sustainable hydroelectric development. International Journal of Energy
Policy 30 (2002), 1305–1312.
 Petras Punys , Antanas Dumbrauskas, Algis Kvaraciejus and Gitana
Vyciene. Tools for Small Hydropower Plant Resource Planning and
Development: A Review of Technology and Applications. Energies 2011,4;
26 August 2011.
 Surekha Dudhania,, A.K. Sinhab, S.S. Inamdara. Assessment of small
hydropower potential using remote sensing data for sustainable
development in India. Elsevier; Energy Policy 34 (2006) 3195–3205; 28
July 2005.
27