Surface Water and Groundwater Interactions in Kosi River Basin
using Surface and Subsurface Hydrological Modelling
Laveti.N.V.Satish
Research Scholar
Water Resources Engineering & Management
Department of Civil Engineering
IIT Guwahati
Under the Guidance of
Prof. Subashisa Dutta
&
Dr. Suresh A. Kartha
Department of Civil Engineering
Indian Institute of Technology Guwahati
OUTLINES
• Introduction
• Objectives
• Surface Water Hydrological Modelling
• Sub-surface hydrological Modelling with LULC changes
• Landuse/Landcover change Analysis using Evapotranspiration Datasets
• Conclusions
• Acknowledgement & References
Introduction
• Surface water and Groundwater interaction is a natural process
and is a complex phenomenon
• It is classified as connected and disconnected systems
• It can take place in three types
A) Water flux entering from aquifer to river (Gaining)
B) Water flux leaving river (Loosing)
C) Combination of both
Surface water-Groundwater Interaction
Figure: General Conditions for Gaining and Losing Streams in an Aquifer, reproduced from
Winter et al. (1998).
Source: http://www.kenai-
watershed.org/spawning/spawning.shtml 5
Objectives
• Quantification of water availability using surface hydrological
Modelling (SWAT)
• To carry out simple water balance study to understand the surface
and groundwater interaction exchange pattern.
• Comparison of water balance study with sub-surface hydrological
model (MODFLOW)
Surface Water Hydrological Modelling
Study Area
Kosi river sub-basin, Ganges system, India.
• Latitude - 29°08'18"N
to 25°18'51"N
• Longitudes - 85°19'50"E
to 88°56'57"E
• Catchment Area - 86,000
Km2
Surface Water Modelling Using SWAT
• ArcSWAT is an ArcGIS –Arc View extension and a graphical user input interface for the SWAT model
.
• SWAT is a river based or watershed, scale model to predict the impact of land management
practices on water, sediment, and agricultural chemical yields in large, complex watersheds with
varying soils, land use , and management conditions over a long period of time.
• SWAT was developed based on SCS Curve number technique
SCS Curve Number Equation is (SCS 1972)
Where, = Runoff depth (mm)
= Rainfall (mm)
= Initial abstraction = 0.2 S
S = Maximum retention after runoff begins = 28.4
2( )
( )
day a
surf
day a
R IQ
R I S
surfQ
dayR
aI1000
( 10)CN
SWAT Model Input
• Digital Elevation Model: USGS, SRTM (shuttle radar topography mission) (90m)
• Landuse/landcover (LULC) data: MODIS Landcover type product (MCD12Q1) at 500_m
resolution for the year 2000 and 2006 have been used
• Soil data: To start with, data at a resolution of 1: 10,000,000 has been obtained from FAO.
However, all efforts are being made to use 1:500,000 digital soil atlas from National Bureau
of Soil survey and Land Use Planning (NBSS&LUP), India.
• Weather data: Daily gridded rainfall data at 0.5°×0.5° resolution has been obtained from
Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation
of Water Resources (Aphrodite), developed by the Climate Research Department,
Meteorological Research Institute, Japan (http://www.chikyu.ac.jp/precip/index.html).
• Daily gridded temperature (max and min) at 1°×1° resolution has been obtained from
Princeton University (http://hydrology.princeton.edu/home.php).
• Stream-flow discharges: Measured stream-flow discharges (available at Baltara gauging
station from 1970-2006) are used for model calibration and validation. This data, in hard
copy form, has been obtained from the Central Water Commission.
SWAT
Hydrologic
al
Modelling
Meterological
Data [Rainfall
and
Temperature
etc]
Geospatial Data
[Landuse/Landcover,
soil, Topography]
Calibration of SWAT
Model
Validation of SWAT
Model
Monthly water
availability at reach
scales under baseline
conditions
Daily water balance,
Sediment and pollution load
Methodology
Geospatial Data Sets
Geospatial Data Sets
2000
S.No Class Class Name Area in
Percentage
1 WATR Water 0.44
2 FRSE Evergreen Needleleaf forest 3.12
3 FREB Evergreen Broadleaf forest 2.04
4 FRSD Deciduous Needleleaf forest 3.04
5 FRDB Deciduous Broadleaf forest 4.18
6 FRST Mixed forest 13.36
7 RNGC Closed shrublands 4.13
8 RNGO Open shrublands 10.81
9 RNGW Woody savannas 12.02
10 RNGE Savannas 1.08
11 RNGG Grasslands 15.06
12 WETF Permanent wetlands 0.02
13 AGRR Croplands 16.9
14 URMD Urban and built-up 0.14
15 AGRL Cropland/Natural vegetation
mosaic 2.34
16 SNOW Snow and ice 5.04
17 PAST Barren or sparsely vegetated 6.28
Geospatial Data Sets
2006
S.No Class Class Name Area in
Percentage
1 WATR Water 0.38
2 FRSE Evergreen Needleleaf forest 0.91
3 FREB Evergreen Broadleaf forest 0.60
4 FRSD Deciduous Needleleaf forest 0.02
5 FRDB Deciduous Broadleaf forest 3.12
6 FRST Mixed forest 8.32
7 RNGC Closed shrublands 2.97
8 RNGO Open shrublands 6.85
9 RNGW Woody savannas 8.83
10 RNGE Savannas 0.04
11 RNGG Grasslands 10.52
12 WETF Permanent wetlands 0.10
13 AGRR Croplands 22.47
14 URMD Urban and built-up 0.25
15 AGRL Cropland/Natural vegetation
mosaic 10.42
16 SNOW Snow and ice 4.58
17 PAST Barren or sparsely vegetated 19.61
SWAT Model Calibration & Validation (MODIS 2000)
0
2000
4000
6000
8000
10000
12000
19
91
\1
19
91
\5
19
91
\8
19
91
\11
1
99
2\2
1
99
2\5
1
99
2\8
1
99
2\1
1
19
93
\2
19
93
\5
19
93
\8
19
93
\11
1
99
4\2
1
99
4\5
1
99
4\8
1
99
4\1
1
19
95
\2
19
95
\5
19
95
\8
19
95
\11
1
99
6\2
1
99
6\5
1
99
6\8
1
99
6\1
1
19
97
\2
19
97
\5
19
97
\8
19
97
\11
1
99
8\2
1
99
8\5
1
99
8\8
1
99
8\1
1
19
99
\2
19
99
\5
19
99
\8
19
99
\11
2
00
0\2
2
00
0\5
2
00
0\8
2
00
0\1
1
Dis
ch
arg
e in
cu
me
c
Month
CWC Observed Monthly Dishcarge
Simulated Monthly Observed Dishcharge
0
2000
4000
6000
8000
10000
12000 1
98
0\1
19
80
\5
19
80
\9
19
81
\1
19
81
\5
19
81
\9
19
82
\1
19
82
\5
19
82
\9
19
83
\1
19
83
\5
19
83
\9
19
84
\1
19
84
\5
19
84
\9
19
85
\1
19
85
\5
19
85
\9
19
86
\1
19
86
\5
19
86
\9
19
87
\1
19
87
\5
19
87
\9
19
88
\1
19
88
\5
19
88
\9
19
89
\1
19
89
\5
19
89
\9
19
90
\1
19
90
\5
19
90
\9
Dis
ch
arg
e in
cu
me
c
Month
CWC Observed Monthly Dishcarge
Simulated Monthly Observed Dishcharge
Calibration Period (1980-
1990)
Validation Period (1991-
2000)
R² = 0.8522
0
2000
4000
6000
8000
10000
12000
0 2000 4000 6000 8000 10000 12000
Sim
ula
ted
Dis
ch
arg
e in
cu
me
c
Observed Discharge in cumec
R² = 0.8431
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
0 2000 4000 6000 8000 10000 12000
Sim
ula
ted
Dis
ch
arg
e in
cu
me
c
Observed Discharge in cumec
Calibration Period (1980-1990)
Validation Period (1991-2000)
SWAT Model Calibration & Validation (MODIS 2000)
SWAT Model Evaluation
Scenario Scenario Calibration Validation
1
Nash–Sutcliffe
model efficiency
coefficient
0.82 0.83
1 Correlation
Coefficient 0.84 0.86
2
Nash–Sutcliffe
model efficiency
coefficient
0.82 0.81
2 Correlation
Coefficient 0.84 0.85
Scenario 1: SWAT Model with MODIS Landuse/Landcover Data for the year 2000
Scenario 2: SWAT Model with MODIS Landuse/Landcover Data for the year 2006
Calibration Period
(1991-2000)
Validation Period
(2001-2006)
SWAT Model Calibration & Validation (MODIS 2006)
0
2000
4000
6000
8000
10000
12000 1
/1/1
99
1
5/1
/19
91
9/1
/19
91
1/1
/19
92
5/1
/19
92
9/1
/19
92
1/1
/19
93
5/1
/19
93
9/1
/19
93
1/1
/19
94
5/1
/19
94
9/1
/19
94
1/1
/19
95
5/1
/19
95
9/1
/19
95
1/1
/19
96
5/1
/19
96
9/1
/19
96
1/1
/19
97
5/1
/19
97
9/1
/19
97
1/1
/19
98
5/1
/19
98
9/1
/19
98
1/1
/19
99
5/1
/19
99
9/1
/19
99
1/1
/20
00
5/1
/20
00
9/1
/20
00
Dis
ch
arg
e in
cu
me
c
Time (Month)
Observed Discharge Series Simulated Discharge Series
0.00
1000.00
2000.00
3000.00
4000.00
5000.00
6000.00
7000.00
8000.00
9000.00
1/1
/20
01
4/1
/20
01
7/1
/20
01
10
/1/2
00
1
1/1
/20
02
4/1
/20
02
7/1
/20
02
10
/1/2
00
2
1/1
/20
03
4/1
/20
03
7/1
/20
03
10
/1/2
00
3
1/1
/20
04
4/1
/20
04
7/1
/20
04
10
/1/2
00
4
1/1
/20
05
4/1
/20
05
7/1
/20
05
10
/1/2
00
5
1/1
/20
06
4/1
/20
06
7/1
/20
06
10
/1/2
00
6
Dis
ch
arg
e in
cu
me
c
Time (Month)
Observed Discharge Series Simulated Discharge Series
Calibration Period (1991-2000)
Validation Period (2001-2006)
R² = 0.8183
0
2000
4000
6000
8000
10000
12000
0 2000 4000 6000 8000 10000 12000
Sim
ula
ted
Dis
ch
arg
e in
cu
me
c
Observed Discharge Series in cumec)
SWAT Model Calibration & Validation (MODIS 2006)
R² = 0.8513
0.00
1000.00
2000.00
3000.00
4000.00
5000.00
6000.00
7000.00
0 2000 4000 6000 8000 10000
Sim
ula
ted
Dis
ch
arg
e in
cu
me
c
Observed Discharge Series in cumec)
Groundwater Hydrological Modelling
Study Area
Kosi river sub-basin, Ganges system, India.
Latitude - 25°15'41"N to 26°53'45"N Longitudes - 85°15'3"E to 87°20'4"E Catchment Area – 19,129 Km2
Drainage Map of Koi River Basin
• Digital Elevation Model: USGS, SRTM (shuttle radar topography mission) (90m)
• Aquifer Characteristics : Aquifer characteristics such as hydraulic conductivity, specific storage, specific yield and porosity have been obtained from Central Groundwater Board (CGWB), Patna and also from literature (Heath, 1983 and Ferris et al. 1962)
• Soil Characteristics: Soil types those are existing in the study area have been obtained from fence diagram map prepared by CGWB, Patna.
• Groundwater Draft and Recharge: External stresses such as pumping (groundwater draft) and recharge values for the time periods 2000-06 have been obtained from CGWB,Patna
• Evapotranspiration Information: Evapotranspiration gridded data obtained from MODIS Satellite data (1k ×1km) was used as external stress to construct the model (Bhattacharya et al. 2010; Mallick et al. 2007)
• Historical Groundwater Level Information: Measured groundwater levels for the periods of 2000-2006 have been obtained from India-WRIS website (http://www.india-wris.nrsc.gov.in/GWLevelApp.html?UType=R2VuZXJhbA==?UName=).
• Riverbed Conductance: To calculate river bed conductance, river bed soil hydraulic conductivity and river bed thickness are taken from literature (Domenico and Schawartz, 1990). River width is taken from google earth.
Data Used
MODFLOW Aquifer
Characteristics
Initial, Boundary
Conditions and
External Stresses
Calibration
Steady State Simulation
Groundwater head
variations/Initial head for
transient modelling
Transient Groundwater
Flow Simulation Boundary Conditions and
External Stresses
Calibration and
Validation
Groundwater head
variations River-aquifer Exchange Flow
Methodology
Where
h = hydraulic head (L)
SS = specific storage (L-1)
K = hydraulic conductivity (LT-1)
t = time (T)
W (x,y,z,t) = a volumetric flux per unit volume (T-1)
Kxx , Kyy and Kzz are the principal components of hydraulic conductivity tensor (LT-1).
Governing Ground Water Flow Equation in Finite Difference Form
Boundary Conditions: Specified head boundary condition (Dirichlet or first-type boundary conditions) was used along all sides of the boundary Initial Heads or Starting Heads: For constructing the groundwater flow model, initial or starting heads are required. For this purpose, observed averaged groundwater level variations in the study area were used to start the model. Soil Type Information: There are majorly three types of soils exits at different locations at different depths in the study area. They are fine sands, medium sands and clay. Mostly fine sands percentage dominates than other two types.
Initial, Boundary and Aquifer Parameters
Aquifer characteristics: In the study area, aquifer characteristics for fine sands such as hydraulic conductivity, porosity specific storage and specific yield are taken as 0.017 - 43 m/d, 0.3, 0.0015m-1 and 0.33 respectively. River bed conductance: From the literature, in the study area, there exists silty clay soil as river bed material. The hydraulic conductivity of riverbed material, river bed width and river bed thickness values used in the model were 0.8 m/d, 286-771 m and 10 m respectively. External stresses: External stresses such as recharge and pumping and evapotranspiration obtained from above mentioned sources were used to construct the model. Aquifer Top Elevations and Bottom Elevations: SRTM 90m × 90m gridded Digital elevation model was used to obtained top elevations in the study area and bottom elevations were estimated using top elevations and soil strata layer depths.
Initial, Boundary and Aquifer Parameters
Steady state simulation of groundwater flow model of Kosi river basin was constructed by considering the above information
The model was discretized into 100 × 100 grid cells using conceptual approach
The groundwater draft (pumping) was uniformly distributed by considering hypothetical wells (758 numbers) throughout the watershed.
External stress values for recharge, pumping and evapotranspiration were taken as 0.002 m/d, 8640 m3/d per well and 0.0018m/d respectively.
Model was calibrated using calibration parameters (hydraulic conductivity 15m/d and river bed conductance 66 m2/d/m)
Steady State Simulation
Groundwater Flow Contours
R² = 0.9226
22.00
32.00
42.00
52.00
62.00
72.00
82.00
92.00
22.00 32.00 42.00 52.00 62.00 72.00 82.00
Ob
serv
ed H
ead
in m
Simulated Head in m
Simulated head results ranges from 31m to 85m whereas, observed head variation ranges from 30-78 m
Scatter plot between observed head and simulated head for steady state model calibration
for the month of January. 2000 at Darbanga
Calibration of Steady State Simulation
Transient groundwater flow modelling was performed to get seasonal groundwater head variations with above information mentioned using conceptual approach.
External stresses: groundwater draft, recharge and evapotranspiration for the time periods 2000-06 for 4 seasons i.e. January (post monsoon Rabi), May (pre monsoon), August (Monsoon) and November (post monsoon Kharif) were taken to develop the model
The model was calibrated and with observed groundwater level
variations.
The model calibration parameters were hydraulic conductivity ( 10 m/day for Clay, 25 m/day for Fine sand and 120 m/day for medium sand) with and riverbed conductance (24-66 m2/d/m).
Transient Simulation
R² = 0.8599
22
23
24
25
26
27
28
29
22.00 27.00 32.00
Ob
se
rve
d H
ea
d in
m
Simulated Head in m
Piezometric head variations at Kursela station for calibration period 2000-03 Scatter plot between observed and
simulated heads at Kursela for calibration
period 2000-03
Observed head ranges from 24.1- 30.5 m whereas, simulated head ranges from
23.6- 29.4m
18.00
20.00
22.00
24.00
26.00
28.00
30.00
He
ad
in
m
Time
Observed Head in m
Simulated Head in m
Calibration of Transient Simulation
R² = 0.803
47.8 48
48.2 48.4 48.6 48.8
49 49.2 49.4 49.6 49.8
50
46.00 48.00 50.00 52.00
Ob
se
rve
d H
ea
d in
m
Simulated Head in m
Piezometric head variations at Darbanga station for calibration period 2000-03
Scatter plot between observed and
simulated heads at Darbanga for
calibration period 2000-03
Observed head ranges from 47.39- 50.2 m whereas, simulated head ranges
45.00
46.00
47.00
48.00
49.00
50.00
51.00
He
ad
in
m
Time
Observed Head in m
Simulated Head in m
Calibration of Transient Simulation
R² = 0.8332
46
46.5
47
47.5
48
48.5
49
49.5
50
50.5
51
46.00 47.00 48.00 49.00 50.00 51.00
Ob
se
rve
d H
ea
d in
m
Simulated Head in m
Piezometric head variations at Darbanga station for validation time period 2004-06 Scatter plot between observed and
simulated heads at Darbanga for
validation period 2004-06
Observed head ranges from 47- 50.2 m whereas, simulated head ranges from
46.3- 50.1m
45.00
46.00
47.00
48.00
49.00
50.00
51.00
1/1/2004 5/1/2004 9/1/2004 1/1/2005 5/1/2005 9/1/2005 1/1/2006 5/1/2006 9/1/2006
He
ad
in
m
Time
Observed Head in m
Simulated head in m
Validation of transient Simulation
Landuse/Land Cover Change Analysis
using MODIS Satellite Products
Analysis of Evapotranspiration Datasets
• Land Evapotranspiration (ET) is a fundamental
process in the climate system and a terrestrial link
among the water, energy and carbon cycles.
• Several methods are there for estimating
Evapotranspiration.
• In this study Evapotranspiration was estimated
using satellite data (MODIS) of Indian continental
datasets (2000 to 2006)) by energy balance
method.
Analysis of Evapotranspiration Datasets
• Actual evapo-transpiration (AET) (hereafter referred as ET) can be
estimated from latent heat fluxes (λE or LE) and latent heat (L) of
evaporation
• Latent heat flux (λE) is generally computed as a residual of surface
energy balance
• A single (soil-vegetation complex as single unit) source surface energy
balance can be written as,
Where- Rn = net radiation (Wm-2)
H = sensible heat flux (Wm-2)
G = ground heat flux (Wm-2)
M= Energy Component for metabolic activities
S= Canopy Storage component
37
Rn − G = net available energy (Q) in Wm-2
The combination of evaporative fraction and Q results into λE
estimates
Λ - evaporative fraction
38
nE R G H
d
dlET E C
24
E
Q
Evapotranspiration Spatio-Temporal Variations
2000 2006
Water Balance Study
17
Rea
ch 5
3
37
S: Simulated Mean Annual Flow in Cumec
O: Observed(CWC) Mean Annual Flow in Cumec
Barrage
Observed Gauge Point
(2000) Annual
Kosi Barrage
Distance in km
Outlet
NOTE: ALL MEASUREMENTS ARE IN KM & NOT UPTO SCALE
1637(O)
Ground water contribution in cumec
132
27
63
10
5
20
Bal
tara
Rea
ch 5
6
2413(O)
535(s)
57(s)
River Flow Direction
184
Gaining Stream
Kosi river leakage
Effluent or Influent River Reaches
River was gaining in nature during the
time period (2000)
(2000) Annual
Water Balance Study (2006) Annual
17
Rea
ch 5
3
37
Kosi Barrage
1287(O)
132
27
63
10
5
20
Bal
tara
1777(O)
515(s)
23(s)
-136
S: Simulated Mean Annual Flow in Cumec
O: Observed(CWC) Mean Annual Flow in Cumec
Barrage
Observed Gauge Point Distance in km
NOTE: ALL MEASUREMENTS ARE IN KM & NOT UPTO SCALE
Ground water contribution in cumec
River Flow Direction
Kosi river leakage
Loosing Stream
River was partially gaining and loosing in
nature. Overall, river was stream loosing
during this time period (2006) and also
reconfirmed with MODFLOW
Effluent or Influent River Reaches (2006) Annual
Conclusions
Surface water hydrological model was well calibrated and validated. Groundwater delay and soil slope length were found to be most sensitive parameters.
Sub-surface hydrological model was set up with landuse/land cover changes by incorporating evapotranspiration variations and calibrated followed by validation. Hydraulic conductivity and river bed conductance were found to be most sensitive parameters.
As an initial tool, a simple water balance was carried out with SWAT Model to understand the interaction exchange varied annual scale level.
Using MODFLOW, river-aquifer interaction exchange was evaluated and compared the trends for the same time periods with water balance study
Through the water balance study, significant change was observed in the river-aquifer interaction exchange from the year 2000-2006 and it was reflected for the landuse/land cover changes incorporated in the MODFLOW.
The combination of surface and sub-surface hydrological model can be used to understand the effect of LULC on Surface and groundwater interaction exchange process.
ACKNOWLEDGEMENT
And we also thankful to Ganga River Basin
Environmental Plan Management Project Team,
Central Groundwater Board, Patna and Central
Water Commission, Patna for providing valuable
data for hydrological modelling.
We are very thankful to Dr.Bimal Bhattacharya,
Space Application Centre, Ahmedabad for his
support in providing Evapotranspiration data sets
for Bihar State.
45
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