Remote sensing and GIS application in Water Resources Management- By S.P. Aggarval spa@iirs.gov.in Indian Institute of Remote sensing ISRO, Department of space, Dehradun
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Iirs overview -Remote sensing and GIS application in Water Resources Management
1. Remote Sensing and GIS Application in
Water Resources Management
By
Dr. S.P. Aggarwal
spa@iirs.gov.in
Indian Institute of Remote Sensing
(National Remote Sensing Centre)
ISRO, Dept. of Space, Govt. of India
Dehradun
2. Indian scenario
Water Resources of India – A Glance
Average Annual Rainfall - 4000 billion cu.m.
Natural Runoff - 1953 billion cu.m.
( Surface & Ground Water)
Estimated Utilisable Surface Potential - 690 billion cu. m.
Ground Water Resource - 432 billion cu. m.
Available Groundwater Resource for Irrigation - 361 billion cu. m.
Net Utilisable Ground Water Resources for Irrigation - 325 billion cu. m.
Groundwater provision for Domestic, Industrial - 71 billion cu. m.
and Other
About 1700 liter/person/day
Source : ICID NEWS, March, 2000
3. Problem!
Spatial variation
120 mm to 12000 mm
Seasonal Variation
Rainy season: 70%-90%
Annual variation
Some times drought year
some times wet year
5. For effective Water Management we need
Near real time hydrological information
Temporal information : Seasonal variation
Spatial information: Point v/s Spatial
Synopticity : think globally and act locally
Remote Sensing provides
Near real time hydrologic information : within few
hours to few days
Temporal : 30 minutes to few days
Spatial information :1 Meter to few km. spatial
resolution
Synoptic coverage: 25 km to 800 km. or more
Geographic Information System is used …..
Develop spatial database
Integrate databases ( remote sensing , topographic,
Socioeconomic etc . ) and develop water resources management
strategies through SDSS
6. Imaging capability of India
Imaging
‘Hot spot’ Met/Ocean
Observation
communication
All weather
High mapping
resolution
imaging
Laser
Terrain Mapper Real Time
Mapping
7. 1999
1995/1997
2003
IRS-1C/1D LISS-3 (23/70M, RESOURCESAT-1
STEERABLE PAN (5.8 M); INSAT-2E CCD LISS3 - 23 M; 4 XS
WiFS (188M) (1KM RESOLUTION; LISS4 - 5.8 M; 3-
1996 EVERY 30 MNUTESS) XS 2005
1994 AWIFS - 70 M; 4-
IRS-P3
XS
WiFS MOS 1999 CARTOSAT - 1
IRS-P2 X-Ray PAN - 2.5M, 30 KM,
IRS-P4 F/A
LISS-2
OCEANSAT OCM, MSMR 2007
1988/91
IRS-1A/1B LISS-1&2 (72/36M,
INDIAN
CARTOSAT-2
4 BANDS; VIS & NIR) IMAGING PAN – 0.8M
1982 SYSTEMS 2009
RS-D1 IMAGING IMPROVEMENTS MEGHA-
1979 1KM TO 0.8 M RESOLUTION TROPIQUES
GLOBAL COVERAGE
APPLICATION-SPECIFIC
BHASKARA
8. OBSERVATION CAPABILITY
INSAT – IRS LISS-3
VHRR/CCD
EVERY 30 MIN. IMAGING EACH
EVERY 22 DAYS IMAGING
IRS – OCM IRS – PAN
towards….
EVERY 2 DAYS IMAGING EVERY 5 DAYS IMAGING
IRS – WiFS
CARTOSAT
+AIR-
BORNE
EVERY 5 DAYS IMAGING SENSORS
11. Near Real-Time Flood
Inundation Mapping
Flood Damage
Assessment
Flood Risk Zone
Mapping
Flood forecasting and
Spatial Warning
System
12. 1998 Brahmaputra Floods -Basinwise monitoring
Assam
State
Water Resources Group,NRSC
08 Sept, 1998 IRS-1C WiFS
13. 1998 Brahmaputra Floods - Inundation extent
Marigaon District Marooned village
<-- Flood inundation
10 Sept, 1998 IRS-1D WiFS 13 Sept, 1998 IRS-1D WiFS
Inundated area 34,240 ha Villages affected 465
Water Resources Group,NRSC
14. 1998 Brahmaputra Floods - Damage to road network
Part of Marigaon district
Pre-Flood DuringFlood
<--Marooned village
<-- Flood inundation
IRS-1D PAN 03 March, 98 IRS-1C PAN 08 Sept, 98
15. 29 Oct-6gmt
28 Oct-3gmt
30Oct-9gmt
Oct-9gmt
Oct-6gmt
Oct-3gmt
SUPER CYCLONE
OVER ORISSA
COAST
INSAT IMAGES
SHOWING THE
CYCLONE MOVEMENT
DURING 28 OCT TO
30 OCT, 1999
…...AND THE AFTERMATH
• NEARLY 3.75 LAKH Ha. INUNDATED
• ROAD, POWER AND COMMUNICATION
NETWORKS SEVERELY AFFECTED IN 10
COASTAL DISTRICTS
17. 3D VIEW OF FLOOD RISK ZONES OF ALLAHABAD CITY
100Year
79.00mt
50
25 Year
10Year
5
20. September - I
August - II
July - III
July - I
zone 10
June - II
Snow Cover Depletion Curves
May - III
zone 9
May - I
April - II
March - III
zone 8
March - I
February - II
zone 7
January - III
January - I
zone 6
December - II
November - III
November - I
O ctober - II
100
90
80
70
60
50
40
30
20
10
0
Snow cover [% ]
22. SRM calculation
The basic equation of SRM model is
Qn+1 = [CSn an (Tn + ΔTn) Sn+ CRn Pn] A·10000 (1-kn+1)+ Qn kn+1
86400
T, S and P are variables to be measured or determined each day. CR, CS, lapse
rate to determine T, TCRIT, k are parameters which are characteristic for a given
basin.
As an Example for a basin of an elevation range of 1500 m. It is sliced in three
elevation zones A, B and C of 500 m each, the model equation becomes
Qn+1 = {[cSAn · aAn (Tn + ΔTAn) SAn + cRAn · PAn] + AA86400
* 10000
[cSBn · aBn (Tn + ΔTBn ) SBn + cRBn · PBn ] + AB* 10000
86400
[cSCn · aCn (Tn + ΔTCn ) SCn + cRCn · PCn ] +AC* 10000 )} + Qn ·kn+1
(1-kn+1
86400
In this project all the model parameters are derived as 10 daily average values and
used to compute the 10 daily average runoff.
23. Dv(%)=1.8
Dec ember II
estimated
real
Nov ember II
O c tober II
Martinec-Rango SRM (1998-1999)
September II
Augus t II
J uly II
J une II
May II
April II
• Snow Cover Area
Marc h II
• Temperature
• Precipitation
February II
J anuary II
Dec ember II
Nov ember II
O c tober II
September II
Augus t II
J uly II
J une II
240
220
200
180
160
140
120
100
80
60
40
20
0
discharge [m /s]
3
24. Martinec-Rango SRM (calibration 2000-2001)
September - I
measured
estimated
Dv(%)=15.3
August - II
July - III
July - I
June - II
May - III
May - I
April - II
March - III
March - I
February - II
Year: 1998
January - III
January - I
December - II
November - III
November - I
O ctober - II
180
160
140
120
100
80
60
40
20
0
Discharge [m3 /s]
35. LAND USE /LAND COVER MAP OF KULSI BASIN FOR THE YEAR 2002
36. Land use/ Land cover statistics for the year 1991 & 2002
LULC 1991 2002 Difference
Sq. Km Sq. Km Sq. Km
Dense Forest 849.38 823.17 -26.21 (2621 Ha)
Open Forest 235.09 249.17 14.08
Agriculture 490.44 502.23 11.79
River/Streams 21.32 21.32 -
40. Impact on Runoff
160
140
120
Runoff(mm)
100 Simulated Runoff (mm )
for 1991
80
Simulated runoff (mm ) for
60 2002
40
20
0
v
n
l
p
ar
ay
Ju
No
Ja
Se
M
M
Month
41. Impact on Sediment Yield
1.4
Sediment Yield (T/Ha)
1.2
1 Monthly Sediment
0.8 Yield for 1991
0.6 Monthly Sediment
0.4 Yield for 2002
0.2
0
v
n
l
p
ar
ay
Ju
No
Ja
Se
M
M
Month
42. Comparison
Forest Rain Runoff Sediment
Yield
849.38 sq. km 957mm 75 mm
1991 (53 %) (7.8%)
823.17 sq.km 2915 mm 505 mm
2002 (51.5%) (17.3%)
Model is run for same rainfall
849.38 sq. km 2915 mm 777.5 million M3 2.41 t/ha
1991 (53 %)
Increased Runoff: 27.1 million Cub. M
823.17 sq.km 2915 mm 804.6 million M3 3.88 t/ha
2002 (51.5%)
43. one of the worst natural disasters
causes extensive damage to food
grain production
widespread desert condition in the
long run
affects social and economic life of
millions of people every year
Water Resources Group,NRSC
44. NATIONAL AGRICULTURAL DROUGHT ASSESSMENT AND MONITORING SYSTEM
(NADAMS)
GROUND SYSTEM SATELLITE SYSTEM
CROP VI VI
RAINFALL ARIDITY LANDUSE
CALENDAR STATISTICS MAPS
GEOGRAPHIC
INFORMATION SYSTEM
DROUGHT BULLETIN
AND MAP
45. NATIONAL AGRICULTURAL DROUGHT ASSESSMENT &
MONITORING SYSTEM ( NADAMS )
NOAA BASED VEGETATION INDEX
ANDHRA
PRADESH
ADILABAD
DISTRICT, A.P
DISTRICT & MANDAL-WISE
ASSESSMENT
Water Resources Group,NRSC
46. India
NOAA-AVHRR based NDVI - August 1999
Andhra Pradesh
IRS-WiFS based NDVI - August 1999
Water Resources Group,NRSC
57. Indian Imaging System – Till now
1979/81 1999/2003
BHASKARA –1 /2 Overall INSAT-2E
VIDICON, VHRR, CCD (1 km)
SAMIR Mapping
INSAT-3A
VHRR,CCD
1999
1988/91 IRS-1A & 1B
•Crop Type IRS-P4 (Oceansat– 1)
LISS-1&2 OCM (360m)
(72/36m) •Crop Condition
MSMR
•Crop acreage
2001
1994 Estimation
IRS-P5(Cartosat-1)
TES
IRS-P2 PAN (1m)
LISS-2
1996 Canal2003
IRS-P3 Network IRS-P6 (Resourcesat-1)
WiFS, LISS 3 (23m)
LISS 4 (5.8m)
MOS X-Ray
AWiFS (55m)
2005&7
1995/1997
•Canal
IRS-P5(Cartosat-1)
IRS-1C/1DAlignment
LISS-3 (23/70m) PAN-(2.5 m) F/A
•Structures etc.
PAN (5.8 m)
WiFS (188m)
Cartosat2 (80 cm)
58. ology COMMAND AREA MAP
d
tho
Me DISTRIBUTORY BOUNDARY MAP
CROP AREA ESTIMATION IN
MET. DATA EACH BOUNDARY
temp.,wind speed,RH,sunshinehr
Kc VALUES CROP WAT MODEL
EFFECIENCES
IWR ETo
Supply Data
Demand-Supply analysis
63. VARIATION OF NDVI WITH PLACE & TIME
0.450
0.41
0.400
0.350
0.300 0.31
NDVI
0.250
0.200 0.21
0.150 0.14
0.12
0.100 0.11
0.050
0.000
PATAN ANANTPURA MAKHEEDA
DISTRIBUTORY DISRIBUTORY DISTRIBUTORY
AVG.OCTOBER NDVI
DISTRIBUTORIES
AVG.JANUARY NDVI
64. KHARIF SEASON
Head Reach Middle Reach
CALCULATED IRRIGATION WATER REQUIREMENT & ACTUAL IRRIGATION WATER
CALCULATED IRRIGATION WATER REQUIREMENT & ACTUAL SUPPLY
IRRIGATION WATER SUPPLY (KHARIF SEASON ,ANANTPURA DISTRIBUTORY)
( KHARIF SEASON, PATAN DISTRIBUTORY)
900
800
500
769.54
425.29
V O L UM E h a-m
400
V O L U M E h a -m
700
600 592.33 337.01
300 287.03
500 235.08
200 182.20
400 401.84
329.07 100 102.67 120.19
300
200 0 11.68
0.00 6.63
0.00 0.00
100 MAY JUNE JULY AUGUST SEPTEMBER OCTOBER
44.80 25.42
0
MAY JUNE JULY AUGUST SEPTEMBER OCTOBER MONTH
CALCULATED IWR ACTUAL IWS
MONTH CALCULATED IWR ACTUAL IWS
Tail End
CALCULATED IRRIGATION WATER REQUIREMENT & ACTUAL
IRRIGATION WATER SUPPLY
(KHARIF SEASON,MAKHEEDA DISTRIBUTORY)
700
664.54
600
500 487.55
VOLUME,ha-m
400
300 314.75
283.35
200
151.16
100 79.35
71.13
40.36
0 0.00 0.00 0.00 0.00
MAY JUNE JULY AUGUST SEPTEMBER OCTOBER
MONTH CALCULATED IWR ACTUAL IWS
68. Estimation of Rain water harvesting potential
in a City using high resolution Data
69. A case Study :
Area: 1115 Sq. m.
Annual Rainfall: 2000 mm
Runoff coefficient : 0.9
Total Runoff to be collected:
1115 x 2000@1000 x 0-9 litre = 20 lakh litre
71. DECISION RULES BY IMSD & INCOH
Farm Ponds: Flat topography and low soil permeability
is required.
Check Dams: Medium slope, low permeability is required.
The available area should be more than 25 hectors, preferably
check dams should be constructed at lower order streams
(upto third order).
Ground Water Recharges: Flat to moderate slope and soil
should be permeable.
Percolation Tanks: Flat topography and pervious strata are
required. The available area should be more than 40 hectares.
Bundhis: Medium permeable soils, adequate area are the
requisites for bundhis and preferably it should be nearer to
cultivated land.
72. FIELD DATA SATELLITE DATA SOI TOPO MAPS
METEOROLIGICAL DATA
DIGITAL IMAGE PROCESSING CONTOUR MAP
GROUND TRUTH
LANDUSE MAP DIGITAL ELEVATION MODEL
RUNOFF POTENTIAL MAP SOIL MAP SLOPE MAP
USING TM MODEL
HIGH RUNOFF
CL.SLOPE MAP
POTENTIAL AREA MAP
BUFFER MAP FOR OVERLAY
VILLAGES &
AGRICULTURE LAND
ANALYSIS
SITE SUITABILITY MAP
75. FINAL CROSS MAP
CROSS MAP OF LULC
SOIL, CL SLOPE AND
RUNOFF POTENTIAL
0 12 km
76. SITE SUITABILITY
MAP FOR
FARMPONDS
Suitable site
(total 85.2 H)
0 12 km
77. SITE SUITABILITY FOR CHECKDAMS
•From these check dams
4.6, 0.4, 5.1 lakh cubic
3 meter water can be
collected
• These can irrigate 8.4 lakh
squire meter land during
2 rabi season
1
LEGEND
checkdam/
Flooded area
0 12 km 1.VOL - 0.46MCUM
2.VOL - .04 MCUM
3.VOL- 0.5 Mcum