Coupling of surface water model and groundwater model at regional scale for a tropical catchment

1. Case study- Netravathi basin
TIPU SULTAN BADAGAN
4NI15CHY17
4th Sem, M.Tech, Hydraulics
NIE, MYSURU
Under the guidance of:
Dr.YUSUF JAVEED
Professor, Dept. of Civil Engineering
NIE, Mysuru

2.  I must offer my profoundest gratitude to my supervisor, Prof. Dr. Yusuf Javeed for his unreserved
help and guidance. It is because of his timely suggestions that lead me to finish my thesis
systematically. His words can always inspire me and promote me to think and explore more in my
research work.
 I wish to express my deepest sense of gratitude to Dr. Richard Winston, Hydrologist at U.S.
Geological Survey (USGS) and Dr. Maheswaran Rathinasamy, IIT Dehli for having guided and
supported me to complete the dissertation work. I am thankful to them for spending their Precious
time to get the work done.
 I also express my sincere gratitude to Dr. R. Yadupathi Putty, PG Co-ordinator for his constant
support and encouragement, and also I express my thanks to all the Professor, lecturers,
classmates, friends, and all those who have directly or indirectly helped me in successful
completion of my dissertation work.
 I express my heartfelt thanks to my parents and family members who are constant source of
inspiration and support in my life. I could not have finished my post graduate study without their
encouragement and support.
2

3.  INTRODUCTION
 OBJECTIVES
 STUDY AREA
 DATA COLLECTED
 METHODOLOGY
 RESULTS & DISCUSSIONS
 CONCLUSIONS
 LIMITATIONS
 FUTURE SCOPE
3

4.  The coupling of surface water and groundwater model allows a more complete
analysis of the land based hydrological cycle.
 Coupling provides the means for evaluating the impacts of land use, irrigation
development and climate changes on both surface and groundwater resources.
 Until now, hydrologic component analysis has concentrated on the management of
the surface water, while problems related to groundwater have not been managed in
rigorous manner. Furthermore, the groundwater model used in previous studies were
not adequately linked to surface water analysis.
 With the advancement in the analysis tools, availability of hydrological data sets and
specialized water management needs, it has become imperative that the surface
water and groundwater system may be looked as a whole at least in studies at
regional scale, and if modelling is attempted, they be modelled as coupled system.
4

5.  In spite of importance of the topic, a very little guidance is available in the scientific
publications about how to approach coupling at regional scale, particularly in Indian
context.
 India has a diversity of climatic, topographical, geological and hydrogeological
conditions.
 Both surface water and groundwater has emerged as the main sources of drinking
water and irrigation through out the country and hence need to be managed equally.
 Recent research efforts on coupling of surface water and groundwater models have
focused the point scale. Much less research has been carried out at local scale. When
looking at larger scales or regional scale, development of coupled models and its
studies are scarce.
5

6. 6
 Coupling of surface water and groundwater model can be done in two ways, as
tight coupling and loose coupling.
 Tight coupling refers to a single software package, where equations governing
surface and subsurface flows are solved simultaneously.
 Loose coupling refers to the coupling, where two or more individual models are
coupled via the exchange of model results, where the output of one model forms
the input of the other.

7. i. Calibration and validation
of surface water model to
predict run-off from the
river basin.
7
ii. Uncertainty and
sensitivity analysis of the
physically dependent
parameters.
iii. Procurement of sub basin
wise groundwater recharge
values from the calibrated
and validated model.
1.Estimation of groundwater recharge for the
netravathi basin from surface water model.

8. 8
i. Coupling of Groundwater
model with surface water
model, where recharge
values (output) from surface
water model is given as
recharge fluxes (input) to
the groundwater model.
8
ii. Quantification of
discharge from groundwater
system to the stream and
estimation of groundwater
heads.
iii. Estimation of hydraulic
properties of the aquifer
such as specific yield and
hydraulic conductivity.
2. Development of conceptual groundwater model

9.  The present study attempts to develop an approach for surface water and groundwater
coupling with a focus on availability of sparse datasets for the humid tropical basin,
Netravathi river basin, located in southern-west part of India and in the State of
Karnataka forms a typical case.
 The Netravathi river has its origin in the evergreen tropical rainforest of Western Ghats,
at an altitude greater than 1000 m above m.s.l.
 The river basin lies between 12º 29’ 45” N to 13º11’ 00” N and 74º48’ 30” E to 75º45’0” E.
 The river has a length of 103 km, and merges with the Kumaradhara river at
Uppinangadi and joins to the Arabian sea near Mangalore city.
 The Netravathi basin has a drainage area of 3284 km2 up to the gauging station at
Bantwal.
 The basin consists of lateritic mounds underlain by a thin bed of clay, granites, and
gneisses in the interior and coastal alluvium along the coastal belt.
9

10. 10

11. i. Digital Elevation Model (DEM)
ii. Soil Data
iii. Land Use/Land Cover
iv. Rainfall Data
v. Weather Data
vi. Hydrological Data
vii. Groundwater Levels
11

12. 12
 A 30 m by 30m resolution DEM
was downloaded from ASTER
(Advanced Space Borne
Thermal Emission and
Reflection Radiometer) GDEM
(Global Digital Elevation
Model).
 The DEM is used for
delineation of the catchment
and also to analyze the
drainage patterns of the land
surface terrain.
 Topographic parameters such
as slope, gradient, slope length
and stream network
characteristics such as channel
slope, length and width were
derived from DEM.
 The highest point in the study
watershed rises up to 1860 m
above m.s.l and the lowest
point is about 5m above m.s.l.

13. 13
 Soil data was obtained
from Food and Agriculture
Organization (FAO).
 Three types of soils
namely, Coastal Alluvium
(Nd48-2-23b-3817),
Laterite soils (Ap21-2b-
3656) and red loamy soil
(Ne55-2b-3826) are mainly
encountered in the area.

14. 14
 Few important properties of soil
in the Netravathi basin as
provided by FAO are listed.
 The properties of soil
abbreviated as
 Number of layers (NLAYERS),
 Hydraulic Group (HYDGRP),
 Soil Texture (TEXTURE),
 Soil Bulk Density (SOL_BD1,SOL_BD2),
 Soil Actual Water Content (SOL_AWC1,
SOL_AWC2),
 Soil Hydraulic Conductivity (SOL_K1,
SOL_K2),
 Percentage of Clay (CLAY1, CLAY2),
 Percentage of Silt (SILT1, SILT2),
 Percentage of Sand (SAND1, SAND2),
 Percentage of Rock (ROCK1, ROCK2)
and the Suffixes 1 and 2 refers to layer
number.

15. 15
 Land Use/Land Cover (LU/LC) mapping and analysis is crucial
for hydrological modeling.
 It is one of the most important factors that affect runoff, evapo-
transpiration, and soil erosion in a catchment.
 LULC map obtained is developed by NIE and it is reclassified
according to requirements.

16. 16
 The climate of Netravathi river
basin is marked by heavy
rainfall, high humidity and
oppressive weather.
 About 92% of the rainfall occurs
during the south-west and
north-east monsoon seasons
and the remaining 8% of
rainfall occurs in the remaining
period.
 The rainfall data is collected
from WRDO & IMD.
 The maximum rainfall received
in the catchment was 3075 mm
during 1980 and minimum was
1736 mm during 2005. The
catchment receives an average
annual rainfall of 2800 mm
(IMD).
 The months from June to
August receives the highest
rainfall.
 Next slide shows the mean
monthly rainfall and location of
rain gauges in the Nertavathi
basin

17. 17

18.  Weather data such as Solar radiation, Relative humidity and Wind speed are
collected from The National Centers for Environmental Prediction (NCEP)
Climate Forecast System Reanalysis (CFSR).
 The daily CSFR data for given location and time is downloaded from the global
weather website. (www.globalweather.tamu.edu)
 Temperature data is collected from IMD for the study period. The temperature of
the region varies from a minimum of 17°C in December–January to a maximum of
37°C during April–May
18

19. 19
 Observed streamflow data
is required for model
calibration and validation.
 The hydrological data used
for the study are daily
streamflow data of
Netravathi river, gauged
at Bantwal station.
 These data were
downloaded from CWC
portal for the study period
(1997-2000).

20. 20
 Groundwater levels are used for
validation of groundwater model.
 Groundwater levels data for different
taluks in the basin is being collected
from Mines and Geology Department,
Mangalore for the period of 1996-
2010.
 Figure(a) shows the locations of
different observation wells for which
data is collected.
 Figure (b) show the monthly mean
groundwater levels at the Bantwal
station.
a
b

21.  The main focus of the study is to couple surface water and groundwater model at a
regional scale operating under sparse datasets.
 Loose coupling is carried out using two different models via exchange of results,
where output of one model forms input for other model.
 The first part of methodology deals with surface water modelling and second part
deals with the coupling and groundwater modelling.
 Surface water modelling is carried out using SWAT and the groundwater
modelling is carried out using MODFLOW.
21

22.  The Soil and Water Assessment Tool (SWAT), developed by the United States
Department of Agriculture-Agricultural Research Service (USDA-ARS,)
 Simulates the impact of varying topography, soils, land use, and management
practices on hydrology, water quality, and over long time periods.
 SWAT is a physically based, semi-distributed, hydrological/water quality model that is
capable of simulation on a daily time-step.
 There are multiple components simulated by the SWAT model,
1. Hydrology,
2. Soil erosion and sediment transport,
3. Nutrient cycling and transport,
4. Plant growth, and
5. Land management practices
22

23.  SWAT delineates a watershed/basin
into sub-basin based on topography
characteristics.
 Sub-basins are further discretized into
hydrologic response units (HRUs)
based on homogeneous.
 Land Use,
 Soil Type,
 Slope Characteristics
 Calculations are generally competed at
the HRU level and aggregated to the
sub -basin and Basin scales.
 In this study sub-basin wise
calculations and results are
considered.
23

24. DATA COLLECTION
Rainfall
Temperature
Soil Map
Digital Elevation
Model (DEM)
Preparation of map/data
Input Data
Run Arc SWAT
Sensitivity Analysis and
Calibration
Preparation of
landuse map
Preparation of data
in specified format
of SWAT
24

25.  Surface runoff is calculated using the SCS curve number equation, which is based on
rainfall, surface storage, interception, infiltration prior to runoff, and a retention parameter
based on soils, land use management, slope, and soil water content
 Multiple pathways of water in the soil are simulated, including plant uptake, evaporation,
percolation into shallow and deep aquifers, and lateral flow for streamflow contribution
 Main components of the land phase of the hydrologic cycle are
change in soil water content,
Rainfall volume,
surface runoff,
Percolation or seepage of water from soil to underlying layers,
evapotranspiration,
 groundwater runoff (baseflow)
25

26. 26
 Surface runoff occurs whenever the rate of
water application to the ground surface
exceeds the rate of infiltration.
 Surface runoff is predicted for daily rainfall
by using the SCS curve number equation
 The parameter S is related to curve number
(CN) by the SCS equation
 The SCS curve number (CN) in equation is a
function of the soil’s permeability, land use
and antecedent soil water conditions.
 Typical curve numbers for moisture condition
II are listed by SCS Engineering Division,
1986 for various land covers and soil types.
These values are appropriate for a 5% slope.

27. 27
 SCS defines three antecedent moisture
conditions: I- dry , II – average moisture, and
III – wet
 The slope adjustment is required for curve
number
 The soil retention parameter (S) vary with the
soil profile water content.

28.  The peak runoff rate is estimated using the modified rational formula.
 A stochastic element is included in the rational equation to allow realistic simulation of
peak runoff rates, given only daily rainfall and monthly rainfall intensity.
 Percolation is calculated for each soil layer in the profile.
 Water is allowed to percolate if the water content exceeds the field capacity water content
for that layer and the layer below is not saturated.
 The amount of water that moves from one layer to the underlying layer is calculated
using storage routing methodology.
28

29.  Ground water flow contribution to total streamflow is simulated by creating a
shallow aquifer storage.
 Return flow from the shallow aquifer to the stream is estimated with the equation
 Evapotranspiration is estimated using Penman-Monteith method.
 Water Routing: Manning’s equation is used to define the rate and velocity of flow
in reach segment for a given time step, and water is routed through the channel
network using Muskingum river routing method.
29

30.  Watershed model suffer from large model uncertainties
Conceptual uncertainty
Input uncertainty
Parameter uncertainty
30

31. 31
 Software for the
calibration of SWAT
models.
 Performs sensitivity
analysis, calibration,
validation of a SWAT
model.
 Links
GLUE,ParaSol,SUFI2,MC
MC and PSO procedures
to SWAT.
 SWAT-CUP SUFI2
procedure used.
Relationship between uncertainty and sensitivity analyses in
hydrological modeling.

32. 32

33.  Groundwater model are mathematical and
digital tools for analyzing and predicting
the behavior of aquifer system on local and
regional scale, under varying geological
environments
 MODEL MUSE ----MODFLOW 2005
 The purpose of building a conceptual
model is to simplify the field problem and
organize the associated field data so that
the system can be analyzed more readily
33

34. 34
 Groundwater model is
conceptualized with the
following details.
 Surface water model SWAT
is loosely coupled to
groundwater model
MODFLOW via exchange of
results, where the
percolation values obtained
from SWAT are given as
recharge input to
MODFLOW.
 Next slide shows complete
flow diagram of the
procedure carried out.

35. 35

36.  Total simulation period is 22 years (1979-2000)
 Warm up period -- 6 years (1979-1985)
 Calibration period -- 10 years (1985-1995)
 Validation period -- 5 years (1995-2000)
 Groundwater Modelling – 4 years(1997-2000)
36

37.  No. of sub-basins -09
 No. of HRUs (Hydrological Response Units) -33
 Soil class -3
 Landuse type – 17
 Slope -02
 Timestep – Monthly
 Watershed Area – 3014 sq. km
37

38. 38
 Netravathi basin is delineated into 9 sub
basins.
 Outlet of the basin is considered at Bantwal
gauging station
 HRU’s are formed by overlaying of LULC
map, soil map and Slope map.
 Each HRU represents a unique combination
of these three maps.

39. 39

40. 40
 There are more that 40 parameters
included in the SWAT model of
which 15 parameters were
shortlisted which are found to be
sensitive towards our objective.
 Out of 15 parameters 9 were found
to be more sensitive upon
sensitivity analysis.
 The ranges for minimum and
maximum parameter value is
selected from literature and
professor/expertise suggestions.
 The fitted values of the parameters
are replaced in the model.
 A total 900 of iterations are
performed, with 100 iteration for
each parameter, in order to match
the simulated flows with the
observed stream flow data.

41. 41

42. 42
 Model validation is carried out for a period of 5
years.
 Very good results are achieved.
 There is a mismatch between the peaks but our
objective is concerned with volumes not the peaks.
 Volumes of observed and simulated flow are
matched well.

43. 43
𝑅2 = 0.92
NS = 0.86
P-factor = 0.74
R- factor = 0.31
 The SWAT model shows good
performance in simulating
stream flow in the
Netravathi River with 71%
of the observed data
enveloped by the modelling
results.
 R factor or thickness
coefficient equal to 0.31.

44. 44
Mean annual Hydrology
and Water balance ratios

45. 45
0
200
400
600
800
1000
1200
1400
1600
1800
2000 Jan-96
Mar-96
May-96
Jul-96
Sep-96
Nov-96
Jan-97
Mar-97
May-97
Jul-97
Sep-97
Nov-97
Jan-98
Mar-98
May-98
Jul-98
Sep-98
Nov-98
Jan-99
Mar-99
May-99
Jul-99
Sep-99
Nov-99
Jan-00
Mar-00
May-00
Jul-00
Sep-00
Nov-00
COMPONENTS OF HYDROLOGICAL CYCLE
PRECIPmm
PETmm
PERCmm
GW_Qmm
SURQmm

46. 46
 SWAT generates the percolation
values at HRU level and these
values are aggregated to get
percolation value for each basin.
 Since the results of calibration
and validation of the SWAT
model showed good results, the
SWAT generated percolation
values are assumed to be good
enough to use it for further
process.

47. 47
 There is a great variation in the aquifer
depths of the Netravathi basin due to its
mountainous topography. Noting that,
the model setup was on a regional scale
and to ease on the computational
processes, a two layered unconfined
aquifer system is considered.
 First layer: 50m thick, high conductivity
 Second layer: 100m thick, low
conductivity
 The range of conductivity values to
search for the optimal parameter value
were from 15-25 m/d and 5-15 m/d for top
and bottom layers respectively and 0.06-
0.2 for specific yield for both the layers
together.

48. 48

49. 49
DRAIN RECHARGE NO FLOW BOUNDARY

50. 50
 To calibrate and validate the model,
SWAT generated base flows and
observed groundwater levels are
used.
 Upon manual calibration the
hydraulic conductivity for top and
bottom layer are finalized to be 22
m/d and 8 m/d respectively.
 And the Specific yield value is
finalized to be 0.16.
 Similarly, river bed conductance is
adopted to be 120m/d.
 These calibrated values yielded
modelled groundwater discharge in
to the drain that are in acceptable
range and follows the trend of base
flows from SWAT

51.  To find out the groundwater level fluctuations
in the system a hypothetical borehole has been
put up which is located on the observation well
near Bantwal.
 Referring to Figure, it is observed that the
total yearly fluctuation is more or less the
same between the modelled and observed.
 This shows that the specific yield adopted in
the model is reasonable.
 However, the observed levels do not have a
smooth graph of fluctuations (where as
modelled fluctuations are smooth) and this can
be attributed to local influences on the
fluctuations.
51

52.  Performance rating of the model calibrated for streamflow can be categorized as very good
(R2 = 0.95, NSE = 0.86 for calibration period and R2 = 0.92, NSE = 0.85 for validation period)
 After uncertainty analysis it was found that the simulated results are very much certain by
bracketing 71% of simulated flows to observed flows with thickness of 0.31 of the 95PPU
band.
 The important water balance ratios streamflow/precipitation, Baseflow/Total flow and Surface
Runoff/Total flow is found to be 0.83, 0.38 and 0.62 respectively.
 The mean annual percentage of percolation to precipitation for the study period is found to be
27%
 The surface water model is successfully coupled to the ground water model by passing the
percolation outputs from SWAT as the recharge inputs of MODFLOW.
52

53.  The MODFLOW model was successfully run and calibrated manually for 4 years
in transient mode. The model results were found to be satisfactory by capturing
the acceptable trend and range of modelled groundwater discharge in to the drain.
 The model results show that the assumptions regarding boundary conditions and
aquifer geometry are reasonable and therefore can be used for future scenario
generation.
 While overall, the results appear to be reasonably satisfactory, with the model
simulated groundwater levels and observed groundwater levels matching quite
well. Further the results (in the form of groundwater levels and water budgets)
fairly substantiate model adopted values of specific yield (0.16) and hydraulic
conductivity (8-22 m/d).
53

54.  The main limitation in surface water modelling is the availability of limited weather
data and physical properties for a large catchment of Netravathi.
 The surface water model is calibrated with streamflow data from only one gauging
station.
 Entire aquifer system is considered to be homogeneous and isotropic and without
discontinuities, which is not true in the real conditions.
 Each layer had a single specific yield and conductivity which are depth and aerially
invariable which is not quite true.
 Incorporation of pumping is neglected by assuming that there is no considerable effect
of pumping on the groundwater system.
 Topography of the catchment surface is considered to be replicating the topography of
the groundwater system.
 Availability of the groundwater data and physical properties of groundwater system is
scarce which influences the modelling protocols.
54

55.  More weather station data could be incorporated in the study area and the
surface water model could be run for analyzing hydrologic variables.
 More data sets and physical parameters for groundwater model can be
collected from the concerned organization to have good control on the model.
 The hydraulic conductivities of the river bed and the aquifer can be more
spatially refined.
 Pumping data can be collected and incorporated in the conceptualization of
groundwater model.
 This study represents a frame work for coupling of surface water model and
groundwater model with sparse datasets, this approach can be used for
coupling at different basins and for future scenarios.
55

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