IUKWC Workshop November 2016: Developing Hydro-climatic Services for Water Security Session 4.3 Sekhar Muddu

1. Modeling hydrological changes using
multiscale remote sensing in India
Indo-UK Workshop on Developing Hydro-Climatic Services for Water Security
29th
– 30th
November 2016, IITM Pune
http://civil.iisc.ernet.in/~mudduhttp://civil.iisc.ernet.in/~muddu
Sekhar Muddu
Department of Civil Engineering &
Interdisciplinary Centre for Water Resources
Indian Institute of Science, Bangalore
Email: sekhar.muddu@gmail.com

2.  Water Budget components from RS.
 ET estimation from Energy Balance
method & India product at 5 km grid resolution.
Soil moisture estimation from remote sensing &
mapping crop-water stress regions.
 Groundwater budget & Baseflow modeling:
Hydrology outlook.
 Framework for addressing climate change impacts
on groundwater.
Outline of the presentation
2

3. θ∆−∆−−=+ GWEPQQ GWs
Surface & Groundwater Water Budget
• Qs = Surface water runoff
• QGW = Base flow
• P = Precipitation
• E = Evapotranspiration
• ∆GW = Groundwater storage change
• ∆θ= Change in soil moisture
3

4. Hydrological Modeling (conventional)
Optical RS
(e.g. IRS-LISS)
Optical RS
(e.g. IRS-LISS)
Soil/crop
parameters
Weather
variables
DHMDHM
Runoff, Recharge,
Root zone soil moisture
AET
SSM
Root zone soil moisture,
AET & recharge at plot scale
for entire watershed
Root zone soil moisture,
AET & recharge at plot scale
for entire watershed
Model
4
A
C
Q
Rf ET
Ro
OVF2
TF1
TF2
TFn
OVF1
OVFn
S1
S2
SnP1
P2
Pn
Rf
Int
Ovf
TF
Q
Hydrological
model
BF
GWD

5. Modeling approach with Remote Sensing
MODISMODIS
Active
microwave
Active
microwave
Passive
microwave
Passive
microwave
AET
SSM
SSM
Soil/crop
parameters
Weather
variables
DHMDHM
Runoff, Recharge,
Root zone soil moisture
AET
SSM
Data assimilation/parameter estimationData assimilation/parameter estimation
Improved root zone soil moisture,
AET & recharge at plot size
for entire watershed
Improved root zone soil moisture,
AET & recharge at plot size
for entire watershed
Model
Satellite
5
Optical RS
(e.g. IRS-LISS)
Optical RS
(e.g. IRS-LISS)
TRMM/
GPM
TRMM/
GPM SARSAR

6. θ∆−∆−−=+ GWEPQQ GWs
Surface & Groundwater Water Budget
• Qs = Surface water runoff
• QGW = Base flow
• P = Precipitation
• E = Evapotranspiration
• ∆GW = Groundwater storage change
• ∆θ= Change in soil moisture
6

7. Evapotranspiration Modeling – Energy Balance Method
7
 The ET was estimated using S-SEBI and Triangle
methods. Five sites with about 200 plus clear
sky images from Terra and Aqua were used during
2009-12. It was observed that the Triangle method
performed well with ground observations from the
BREB towers of Energy, Mass Exchange project of SAC,
ISRO and further extended through INCOMPASS
(NERC-MoES) project
Eswar, Sekhar, Bhattacharya (2013) Journal of Geophysical Res- Atmosphere
0
10
20
30
40
50
01-Jan 26-Feb 22-Apr 17-Jun 12-Aug 07-Oct 02-Dec
PET
AET
Evapotranspiration(mm)
Berambadi, Karnataka
0
50
100
150
200
250
300
350
400
0 50 100 150 200 250 300 350 400
LatentHeatfromSatelite(Wm-2)
LatentHeat from AMS towers (Wm-2)
Madhya Pradesh
Dehradun
Karnataka
Rajasthan
WestBengal
R2 = 0.57
RMSE = 46 Wm-2
Bias = 24 Wm-2
MAE = 38 Wm-2
BREB Tower @
Beechanahalli
Eddy flux Tower @
Beechanahalli

8. Modeling Evapotranspiration from Remote Sensing
8
 The EF was estimated using S-SEBI and Triangle methods. Five sites and 150 plus clear
sky images from Terra and Aqua were used during 2009-12. It was observed that the
Triangle method compared well with the EF obtained from the BREB observations. EF
at 250m was disaggregated using DEFrac model.
0
10
20
30
40
50
01-Jan 26-Feb 22-Apr 17-Jun 12-Aug 07-Oct 02-Dec
PET
AET
Evapotranspiration(mm)
Eswar, Sekhar, Bhattacharya (2013) Journal of Geophysical Res- Atmosphere

9. Mean diurnal cycles: Energy fluxes

10. Disaggregation of LST : Comparative analysis of different
vegetation indices
Temperature
vegetation
dryness index
(Downscaled
Soil Moisture?)
Eswar, R., Sekhar, M., Bhattacharya, B. (2015) Disaggregation of LST over India: Comparative analysis of different vegetation
indices, International Journal of Remote Sensing

11. 2010
ET Product
ET product is from 2001-
2014 at 0.05 degree
spatial resolution with 8-
day frequency.
The approach used for ET
estimate is based on the
Triangle method using
MODIS (Eswar et al.,
2013 & 2016). The
method was validated
using data from BREB
tower sites of Energy,
Mass Exchange project of
SAC, ISRO.
Eswar, Sekhar, Bhattacharya (2013) Journal of
Geophysical Res- Atmosphere
Eswar, Sekhar, Bhattacharya (2016) International
Journal of Remote Sensing
11

12. Validation Sites
0
1
2
3
4
5
0 1 2 3 4 5
ET (mm/day)
RS Model
FluxTower
RMSE = 0.7 mm
0
10
20
30
40
50
60
70
80
90
100
0
1
2
3
4
5
21-03-2012
14-04-2012
08-05-2012
01-06-2012
25-06-2012
19-07-2012
12-08-2012
05-09-2012
29-09-2012
23-10-2012
16-11-2012
10-12-2012
01-01-2013
25-01-2013
18-02-2013
14-03-2013
07-04-2013
01-05-2013
25-05-2013
18-06-2013
12-07-2013
05-08-2013
29-08-2013
Rainfall
RS Model
Flux Tower
Evapotranspiration(mm/day)
Rainfall(mm/week)
Kabini CZO
12

14. Landuse&LandCover
Sub-basins&Grids
750
800
850
900
950
2003 2005 2007 2009 2011 2013 2015
Kabini
Lakshmanthirtha
Hemavathy
Shimsha
Suvarnavathy
Arakavathy
AnnualET(mm)
2005 2007 2009 2011 2013 2015
Kabini Lakshmanthirtha Hemavathy
Shimsha Suvarnavathy Arakavathy
700
750
800
850
900
950
2003 2005 2007 2009 2011 2013 2015
AnnualET(mm)
700
750
800
850
900
950
2003 2005 2007 2009 2011 20
Bhavani Chinar2 Chinar1
Noyal Amaravati Delta
AnnualET(mm)
14

15. Cauvery:Districts
0.2
0.4
0.6
0.8
1.0
1.2
Kodagu
Chikmagalur
Hassan
Mandya
BangaloreUrban
Tumkur
BangaloreRural
Mysore
Chamarajanagar
Annual(ET/P)
2014-2013
0.4
0.6
0.8
1.0
1.2
2002 2004 2006 2008 2010 2012 2014
Karnataka
Tamilnadu
Annual(ET/P)
Karnataka
15

16. • Groundwater storage changes can be estimated at a good
spatial resolution if the RHS is known - depletion is strongly
linked to (P-AET) where ET/P =1 or >1
• Crop/ AET is a good sensor of excessive groundwater draft
and so these hot spots can be delineated and modeled in
priority.
• In regional modeling, AET can be proxy for spatial variations
of draft.
GWEP ∆∝−
( )GWs QQEPGW +−−=∆
Modeling Groundwater storage changes at finer resolution
16

17. Mean (P-ET)
= 200 mm
For 10 years
(2003-2013)
(P-AET)
= 2000mm
= 2m
GWL change =
2m / Sy ≈
= 20 m or
= 40 m
Precipitation – ET Trends
450 484
961
1071
681
997
927
848 867 841
539
652
806
-517 -473
24 25
-310
43
-136 -179 -206 -164
-444
-317
-194
-800
-300
200
700
1200
2002 2004 2006 2008 2010 2012 Mean
Rainfall (mm/y)
ET in mm/yearRainfall,(P-ET) P-ET mm/y
17
Sy = 0.1 or 0.05

18. Major crops – Sunflower, Marigold,
Sorghum, Maize, Turmeric = 64%
18
October 2013
Major crops – Sunflower, Marigold,
Sorghum, Maize, Turmeric = 66%
September 2014
Comparison of ET from Energy & Water balance
0
5
10
15
20
25
May-12 Jun-12 Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13
Water balance
Energy balance
Evapotranspiration(mm)
P-ET mm/y
0
2
4
6
8
25-11-200102-02-200215-04-200226-06-200206-09-200217-11-200225-01-200307-04-200318-06-200329-08-200309-11-200317-01-200429-03-200409-06-200420-08-200431-10-200409-01-200522-03-200502-06-200513-08-200524-10-200501-01-200614-03-200625-05-200605-08-200616-10-200627-12-200606-03-200717-05-200728-07-200708-10-200719-12-200726-02-200808-05-200819-07-200829-09-200810-12-200818-02-200901-05-200912-07-200922-09-200903-12-200910-02-201023-04-201004-07-201014-09-201025-11-201002-02-201115-04-201126-06-201106-09-201117-11-201125-01-201206-04-201217-06-201228-08-201208-11-201217-01-201330-03-201310-06-201321-08-201301-11-2013
Mean daily Evapotranspiration (mm)
Evapotranspiration(mm)

19. Salient features
 Works in all weather condition
 Spatial resolution = 500 m
 Temporal resolution = 1 day
SMOS satellite
(CNES)
MAPSM
Algorithm
RISAT-1 satellite
(ISRO)
Soil moisture persistence below a
threshold is important indicator of water
stress to crops
Soil Moisture Modeling from Remote Sensing
θ∆−∆−−=+ GWEPQQ GWs
INNOVATIONS

20. Where,
SM is soil moisture [v/v],
is field capacity [-],
is wilting point [-],
F is the CDF,
BC is the backscatter coefficient [dB],
SM retrieval from active microwave
Existing Enviroscansites
ProposedHydra probe sites
#49

21. Temporal resolution (days)
Spatialresolution
(m)
10 20 30
102
103
104
105
Active
Passive
MAPSM
Passive microwave has good temporal resolution (1-3 days), but poor spatial resolution (~40 km)
Active microwave has good spatial resolution (less than 100 m), but poor temporal resolution (~ 30 days)
MAPSM provides soil moisture at both good temporal (3 days) and spatial resolution (less than 500 m)
Merging Active and Passive Soil Moisture (MAPSM)
Passive microwave:
 SMOS
 SMAP
 AMSR2
Active microwave:
 RISAT
 RADARSAT-2
 PALSAR
MAPSM is applied over Karnataka state using RISAT and SMOS
data. Next slides show the validation results and output for the same. 21

22. RADARSAT-2 retrieved soil moisture
BC= f (Vegetation, Soil roughness,
Soil moisture)
Tomer et al. (2015) Retrieval and multi-scale validation of soil moisture from multi-temporal SAR data in a
tropical region. Remote Sensing, 7(6), 8128-8153 22

23. MAPSM: Merged Active and Passive Soil Moisture
Tomer et al. (2017) MAPSM: A Conceptual Spatio-temporal Algorithm to Merge Active and Passive Soil Moisture.
Remote Sensing.

24. Remote Sensed high resolution relative soil moisture for KarnatakaRemote Sensed high resolution relative soil moisture for Karnataka
Spatial resolution: 500 m; Temporal resolution: 1 day
Relative soil moisture:
0 means driest possible
1 means wettest possible
Latitude
Longitude
24
RMSE 0.057
Correlation
coefficient
0.81
RMSE 0.036
Correlation
coefficient
0.79
Courtesy: Sat Tomer

25. COSMOS at Berambadi
Cosmos, Steven’s Hydra probes & OTT
@ K Madahalli, Chamarajanagar TQ.
Monsoon dynamics and thermodynamics from the land surface,
through convection to the continental scale (INCOMPASS).
Sponsored by MoES and NERC.
Courtesy: Ross Morrison & Jon Evans

26. Cumulative persistence of soil moisture below a threshold
Example: Upper Cauvery
26
INNOVATIONS

27. Relative soil moisture
time series plot
Mandya
Region (red
shaded
region)
Soil moisture time series in location 1 – in
Mandya district (red shaded region) has
indicated relative soil moisture below 0.2 for
nearly three months in Kharif 2016
27
INNOVATIONS

28. θ∆−∆−−=+ GWEPQQ GWs
Surface & Groundwater Water Budget
• Qs = Surface water runoff
• QGW = Base flow
• P = Precipitation
• E = Evapotranspiration
• ∆GW = Groundwater storage change
• ∆θ= Change in soil moisture
28

29. UNESCO (2012)
Irrigated area Map using RS Thenkabail et
al. (2010)
Net Irrigated area = 146 MhaNet Irrigated area = 146 Mha
Major Irrigation = 55 MhaMajor Irrigation = 55 Mha
Minor Irrigation = 91 MhaMinor Irrigation = 91 Mha
Groundwater abstraction trends
29

30. Ground water – Stream flow links
 Modeling the base flow to streams: “Hydrology outlook” for dry
season flow (December-April) mainly depends on groundwater system
30

31. Groundwater budget
Runoff
Groundwater
discharge
Groundwater balance
equation
( ) ( ) ( ),y G S net
dh
S R I D B O
dt
= + − − +
Recharge Discharge
,y net
dh
S rR D h
dt
λ= − −
λh corresponds to groundwater
discharge term. In the current
GEC approach this is not used &
hence this information is
missing.
31
Improved & simpler
model

32. Local Scale Modeling for Processes & Parameters
10
15
20
25
30
35
Apr-10 Apr-11 Apr-12 Apr-13 Apr-14 Apr-15
Depthtogroundwater(m)
Observed GWL(m)
Simulated GWL(m)
#103
5.7 5.7 5.3
8.2
9.3
0
5
10
15
2010 2011 2012 2013 2014
Honnegowdanahalli
Rechargefactor(%)
Rainfall (mean) = 900 mm,
Recharge = 65 ± 25 mm
GW Discharge = 40 ± 10 mm,
Draft = 25 ± 5 mm
32UPSCAPE Project (2016-19) Sponsored by MoES and NERC.

33. Lat:11.83 N Long: 76.12
E
Area:1260 km2
Record:1973-2012
Base flow modeling
@ Muthakere
(Kabini Basin)
0
20
40
60
80
15-Nov
30-Nov
15-Dec
30-Dec
14-Jan
29-Jan
13-Feb
28-Feb
Dischargecum/sec
1999-2008
1990-1998
1975-1985
33

34. 0
5
10
15
20
25
Apr-90 Apr-92 Apr-94 Apr-96 Apr-98 Apr-00 Apr-02 Apr-04
SimulatedBaseflow(Cumec)
Modeled
Base flow
 Aquifers have longer memory and
base flows in a particular year would
depend on previous years rainfall & uses.
 Further baseflow modeling is helpful
for closing the groundwater budget in the
basin/sub-basin especially recharge (which
is often uncertain due to specific yield).
34

35. Runoff Modeling and Trend Analysis
over Main Land of India
Source:
ISRO

36. Grid # 6
Prerequisite: Developing gridded groundwater level data set for India to
study impacts of climate change. An Example:
Climate Change - Ground water
36
Error bars
for GCMs

37. Kabini Critical Zone Observatory
• What controls the resilience, response and recovery of a hydrological system to
perturbations of climate and land-use changes?
• How can sensing technology and modeling be integrated for simulation and
forecasting of essential hydrological variables?
• How can theory, data and mathematical models from natural- and social- sciences
be integrated to simulate, manage river basin/catchments goods and services ?
Hydrological observatories in the world
Kabini basin observatory-
Response of tropical
ecosystems to global
changes in Southern India
CEFIRSE LMI
37

38.  Numerical modeling improved through groundwater level data mapped at
higher granularity, AET from RS & simpler groundwater models for
improved total water & groundwater budgets.
 Base flow modeling of river basins/sub-basins and gearing up for
“hydrology outlook”.
 Developing gridded groundwater data, calibrating simpler models for the
grids for modeling the climate change impacts and delineating grids
requiring adaptation measures.
 Soil moisture retrieval at 500 m spatial resolution at 2-day frequency
through active & passive microwave satellites for mapping regions with
persistence of crop-water stress.
SUMMARY
38

40. 40
Source:
CGWB
Ganga basin
(0.65 Million Km2
)
Hydrometeorological feedbacks and changes in water
storage and fluxes in Northern India

41. 41
450 mm
2.5 mm
Pumping (mm)
Fall & Rise
Falling
Falling
2003-2012
Groundwater System behavior
 Specific yield estimated using split-
season approach (GEC, 1997).
Groundwater pumping from Minor
Irrigation Surveys.

42. One degree grids
0
500
1000
1500
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64
(Precipitation - Evapotranspiration) mm
10,
11,
12,
13,
14,
20,
21,
22
27,
28
33,
34
38,
39,
40 43,
44
47,
48
52
(P-AET)
Groundwater Pumping induced
Evapotranspiration
Evapotranspiration simulated is able to capture the trends
of AET induced by groundwater pumping 42
Grid 11 2.5
5.0
7.5
10.0
May-03 May-04 May-05 May-06 May-07 May-08 May-09 May-10 May-11 May-12
Depthtogroundwater(m)
Grid 20
Grid 43
y = 0.5x
R² = 0.803
0
250
500
750
1000
0 500 1000 1500 2000
Hilly areas
Precipitation (mm)
Evapotranspiration(mm)
ET influenced
by groundwater
pumping

43. 43

Gridded GWLs - #87 grids

Spatial resolution : 0.5o

Temporal : Monthly

Period : 1980-2012
Modeling the climate change impact on GW
system

44. 44
Recharge function Pumping function
Subash, Y., et al. In: Sustainable Water Resources Management, ASCE Book Chapter. (In Press).