remote sensing and gis applications for hilly watersheds · remote sensing and gis applications for...
TRANSCRIPT
Remote Sensing and GISApplications for Hilly Watersheds
SUBASHISA DUTTA DEPARTMENT OF CIVIL ENGINEERING
IIT GUWAHATI
Topics to be coveredTopics to be covered
• Hydrological response of a hillslope under• Hydrological response of a hillslope under
extreme rainfall events
• GIS as a tool for watershed management
• Analysis of digital elevation model ( terrain
data))
• Case studies and discussion
5
Hillslope HydrologyHillslope HydrologyCritical Hydrologic Processes
InfiltrationOverland FlowSubsurface StormflowSubsurface StormflowSoil Macropores and Water Flow
Consequences :Flash FloodinggSlope Failures and SlidesSoil Erosion
7
Debris FlowGroundwater Pollution
Hillslope Experimental Plot
Geographic Location - 26°12′ N latitude and 91°42′ EGeographic Location 26 12 N latitude and 91 42 E longitude
T h8
Topography – Average slope 20%, COV 10.49% Microtopographic variation not significant
Hillslope Experimental Setup
UPSTREAM CHANNELPROFILE PROBE LOCATION
Upper Channel
SIDE PLATES
OVERLAND
VENTURIMETER
VALVE
LOCATION
18 mPiezometer
Side Plate
DOWNSTREAM CHANNEL
OVERLANDFLOW
PUMPPUMP
Piezometer
PONDMEASURING
TANKOUTLET
Lower Channel
6 m
Extreme Storm Intensity – about 50 - 400 mm/hr
9
mm/hrStorm Durations – 15 -120 minutes
Overland Flow - Results6
3
4
5
entra
tion,
t c(m
in) Sparse
Moderate
Dense
0
1
2
Tim
e of
Con
ce
Outflow hydrographFig. 5
00 100 200 300 400 500
Equivalent Rainfall Intensity, i (mm/hr)Fig. 6Relationship between tc and i
300Sparse
Similar response in sparse and moderrate vegetationSimilar macropore network150
200
250
n R
ate,
f b (m
m/h
r)
SparseModerateDense
Distinct changes in behavior in dense vegetationSignificant change in macropore 0
50
100
0 100 200 300 400 500
Infil
tratio
n
11
connectivity and network0 100 200 300 400 500
Equivalent Rainfall Intensity, i (mm/hr)Fig. 7Relationship between fb and i
At t 22 iInitial state
Temporal dynamics of subsurface storm flow
At t=22minAt t=18min.Initial state
At t=42 rainfall ceased At t=51min.At t=29 min.
At t=42 rainfall ceased At t 51min.
12
Temporal dynamics of subsurface storm flow ( Continued)
At t=66min. At t=85 min. At t=99 min.
C i i l Ob iCritical Observations :
1. Fast subsurface stormflow ( within 1-2 hours of the storm event)
2. Initiation of subsurface storm flow occurs for even a storm event of 50 mm/hr
3 T ll h d t t bl f ti th b d k
13
3. Temporally perched water-table formation on the bed-rock
Hydrological effect of land use/land cover changey g g
• Change in top soil macroporosity, more likely to have overland flowgeneration
• Blocking of subsurface stormflow path, more concentrated flowgeneration, leading high sheet erosionB i th h ld fl h i it i t ll d b i f ll i t it• Being a threshold flow mechanism, it is controlled by rainfall intensity,vegetation condition, soil layers
• Wetness index, based on DEM, predicts the subsurface storm flowpathspaths
• Identification of “Hotspots” in a hilly watershed, related to flash floodsand soil erosion, sediment transport capacity and natural sedimenttrapper
• Land use/land cover planning to be carried out by integrating“hydrological knowledge” on geospatial database
17
Why GIS?
C h dl hi ll f d d tCan handle geographically referenced data or spatial data as well as non-spatial data
Can handle relational numerical expressions between these data sets
Ideal for natural resource management
19
B i F ti f GISBasic Functions of GIS
Capturing dataStoring dataManipulating dataRetrieving and Querying dataRetrieving and Querying dataAnalyzing dataDi l i d tDisplaying data
20
Data TypesSpatial Data Non-spatial Data
TopographyLand Use Land Cover S il
Descriptive Attributes
Soil TypeSoilWater bodiesState District Blocks
Soil TypeLand Use TypeVillage Name
State, District, BlocksVillagesForests
Street Name
ForestsGeologyRoad Network
21
Spatial Data Models
Vector Data Model Raster Data Model
Based on geometry of Digital RepresentationG id C ll
Pointas Grid Cells
Satellite Images
Line
P l
Aerial Photographs
Polygon Digital Elevation Models (DEM)
24
Arc-Node Data Structure
Polygon Arc ListPolygon Arc ListPolygonPolygon Arc ListArc ListA rc N u m b e r
S ta r t n o d e
V ertices E n d n o d e
A rc N u m b e r
S ta r t n o d e
V ertices E n d n o d e
A rc N u m b e rA rc N u m b e r
S ta r t n o d eS ta r t n o d e
V erticesV ertices E n d n o d eE n d n o d e
Nodes & VerticesArc-node structure Polygon structure
A 1,2
B 2 3
A 1,2
B 2 3
AA 1,21,2
BB 2 32 3
1 2 0 d ,c ,b .a 10
2 1 0 e 20
1 2 0 d ,c ,b .a 10
2 1 0 e 20
11 2 02 0 d ,c ,b .ad ,c ,b .a 1010
22 1 01 0 ee 2020
27
B 2,3B 2,3BB 2,32,33 1 0 f,g ,h ,i,j 203 1 0 f,g ,h ,i,j 2033 1 01 0 f,g ,h ,i,jf,g ,h ,i,j 2020
Topology : Defining Spatial Relationships
Three major topological concepts:
Connectivity: Arcs connect to each other at nodes.
Area definition: Arcs that connect to surround d fi lan area define a polygon
Contiguity: Arcs have direction and left and i ht idright sides
28
Vector Data Model
Points: represent discrete point features
each point locationhas a record in thetable
airports are point featuresh i t i t d
32
each point is stored as a coordinate pair
Vector Data Model
Lines: represent linear features
each road segmenthas a record in thetable
33roads are linear features
Vector Data Model
Polygons: represent bounded areas
each bounded polygonhas a record in thetable
polygonal features
34
polygonal features
Data Structures
“where” of GIS is determined by coordinatewhere of GIS is determined by coordinate (map) data structures, but …
“what” of GIS is determined by tabular (relational database) data structures( )
GIS Database = Coordinate data + Attribute Data
36
Attribute Data Structures
Attribute data are stored in database tables.
Tables are composed of:
Fi ldFields
andand
Records
37
Use of Tabular Data
Making queries
Promoting and sorting recordsPromoting and sorting records
Displaying selected sets
Modifying selected sets
Basic descriptive statisticsBasic descriptive statistics
38
Displaying Selected Sets
Selecting records from tables also select features from themesfrom themes
40
Buffering
Quantifying a spatial entity to influence its neighbours or the neighbours to influence the character of aor the neighbours to influence the character of aSpatial entity
Point
Line
Polygon42
Polygon
Digital Elevation ModelsRemotely Sensed Satellite ImagesDigital Elevation Models (DEM)
Raster Data Structure
48
A Simple Digital Elevation Model
67 56 49 46 50
cell size
53 44 37 38 4850
(cell value)58 55 22 31 24
61 47 21 16 19
(cell value)
12 11 123453 cell
51
DEM Data Sources
1 km DEM of the earth (GTOPO)
100 m DEM from 1:250,000 scale maps
30 m DEM from 1:24,000 scale map, p
90 m Shuttle Radar Topography Mission (SRTM)p g p y ( )
52
Using DEM Data
1 1Direction of Steepest Descent
67 56 49 67 56 49
53 44 37 53 44 37
58 55 22 58 55 22
26.1624467
=−
141
5367=
−Slope:
53
2 1
Flow Accumulation Grid
0 0 000 0 0 00 0
0
0 0
03 2 2
1
0
0 0
0
0
3 2 2
11 1
00
0 0
0
011 1
1 15
0
0
0
0 0
011 1
1 15
0 2 524 1 0 12 245
59
Fl A l i 5 C ll Th h ldFlow Accumulation > 5 Cell Threshold
St Li
0 0 00 0
Stream Lines
0
0 03 2 2
0 0 011 1
0
0
0 01
12
15
245
60
0 2 245
St N t k f 5 ll Th h ldStream Network for 5 cell Threshold Drainage Area
0 0 000
0
0
03 2 2
00
0 00
111
1 0
0
0 0 115
2 5 161
024
1
DEM Data Pre-processing
Raw DEM Data
InteractiveInteractive Sink Filling
NO
ContinuousFlow LinesYES
Generate Select O tletGenerate
Watershed70
Stream Lines Select Outlet Watershed Boundary
Case study : Shiwalik hill in DehradunDEM t CARTOSAT iDEM : stereo CARTOSAT imagery
Wetness index image Ln(As/S) Stream power index (A *S) Sediment transport index
75Source: suresh kumar et al, 2008, ISRS-36, 159-165
Case study-2: Spatial distribution of annual sediment yield estimation
Source: Manish and Suresh
76Study area: Jhikhu Khola watershed in NEPAL
Conclusion and DiscussionsExtreme hydrological response of a hillslope :
discussed, their prediction based on wetness index,, p ,
their knowledge for land use/land cover planning
GIS: introduced its use in watershed managementGIS: introduced, its use in watershed management
Digital elevation model: extraction of watershed
parameters, wetness index, stream power index
Recent case studies using high-resolution DEM andg g
satellite remote sensing
77