Post on 21-May-2020
Identifying physio-climatic controls onwatershed vulnerability to climate and landuse change
SWAT 2018 IIT Madras, India
Ankit Deshmukh*1,Riddhi Singh2
January 12, 2018
1 Department of Civil Engineering, Indian Institute of Technology Hyderabad, Kandi, Telangana, India 502285 2 Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Maharashtra, India 400076
We need reliable projections of streamflow to manage freshwater
resources in the future
Figure 1: Global extent of water scarce regions in the last decade. (UNEP,
2008)
However, obtaining future projections are challenging! 1
Changing climate will lead to significant and uncertain changes
in future
1960 1980 2000 2020 2040 2060 2080 2100−1
0
1
2
3
4
5
6
7 Historical GCM uncertainty
Model uncertaintyInternal variabilityObservations (GPCP)
Scenario uncertainty
Projected change in global mean precipitation [%]
1960 1980 2000 2020 2040 2060 2080 2100−0.5
0
0.5
1
1.5
2
2.5
3
3.5
4
Projected change in global mean temperature [K]
Adler et al., 2003 Brohan et al., 2006
Decadal mean projections for the 21st century (the total
uncertainty in CMIP-3 global mean)2Hawkins and Sutton [2011]
Uncertainties are present in all the steps of obtaining a hydro-
logic projection
d y
nd
nEmission scenarios
(future CO2 concentration)Determine plausibility o
vulnerabilityGeneral circulation
models
Downscalingal fo
rwar
tion
Hydrologic model
Indicators of hydrologic
Tra
ditio
prop
aga Identify regions in
framework space that leato vulnerability
response
uncertainty about future economy
Initial conditionsParametric uncertainty
Downscaling uncertainty
Model structural uncertaintyParametric uncertainty
Uncertainty about critical thresholds
Climate change
Land use change
Determine if these ranges fall in the vulnerable region
Thus we need a way to provide useful information to stakeholders
in the presence of large uncertainties.
3
We provide an alternative approach to identify vulnerability to
climate change which is independent of future projections of
climate and land use change
y su
su
Determine plausibility ofvulnerability
p an
alys
i
Identify regions inframework space that lead
to vulnerability
Bot
tom
Determine vulnerability ranges forthe hydrologic indicator
Figure 2: Bottom up: A vulnerability based approach
Our vulnerability based approach is useful for decision makers and
water policy makers.4Singh et al. [2014]
We develop a framework which combines strength of bottom-up
approach and comparative hydrology
User should defined vulnerability: One can defined vulnerability as
”streamflow reduced by >50% in the watershed”
ThresholdsBottom-Up Framework Correlation
The framework provides a quantitative measurement (critical
thresholds) of watershed vulnerability to climate and land use
change.
5
We implement the bottom-up approach using exploratory mod-
elling framework
Deep-rooted
Vegetation
Climate and land use combinations
Exploring theprecipitation space
-40% +60%ΔP0oC
12oC
ΔT
0
1
Explo
ring
the
land
use
spa
ce
Exp
lorin
g th
ete
mpe
ratu
re s
pace
Hydrologic model
%Veg
GW store
ET
subsurfaceQ
Qsurface
baseflowQ Str
ea
mflo
w
Random sampling
Parameters θ
Screening (NSE & %Bias)
Mapping of classes to the sample space
-40% +60%ΔP0oC
12oC
ΔT
0
1
Deep-rooted
Vegetation CART
Indicators
Indicator classes
Simulations
C0 C1 C2 C3
6Deshmukh and Singh [2016]
We use a spatial lumped model to simulate runoff which account
land-use as fraction of deep rooted vegetation in the watershed
T
ET
Field Capacity
Shape parameter
LAImin/max
Surface flow
Subsurfaceflow
Baseflow
Max
imum
sto
rage
Ground water store
Recharge coefficient ASS
ABF
Legend
ParameterFluxes
Thresholdtemperature
Degree dayfactor
Precipitation
Rain/Snow
%veg:Fraction of deep rooted vegetation in watershed
Cei
Simulated runoff used to calculate indicator of vulnerability (i.e.
mean annual runoff).7Singh et al. [2014]
We classify an indicators based on class definitions
Indicator mean annual runoff (for water availability in the stream)
classified into 4 classes.
C0 C1 C2 C3
Class: Change in streamflowC0: > 0%C1: 0% to -25%C2: -25% to -50C3: < -50%
We use a classification method, CART to identify the combinations
of climate and parameters which leads to a specific class.
8
We select 77 United states watersheds for this study
Removed watersheds
WatershedsNorth0 1000
km
134 km2
8151 km2
We remove 8 watersheds based on model performance criteria and
low runoff ratio.9Deshmukh and Singh [2016]
We use CART to relate climate, land use and parameters sam-
ples into vulnerability classes
Node:1ΔP< 0.85
Samples: 28636
C2
Samples: 2631Samples: 26005
Samples: 2275
Node:2
Node:3
Node:5
C3
C1 C2
C0
C0
Node:4
C3
ΔP ≥ 0.75
M< 0.55
ΔP< 1.05
M ≥ 0.35
ΔP ≥ 0.95
if true if false
C0 C1 C2 C3Class: Change in streamflowC4: > 0%C1: 0% to -25% C2: -25% to -50C3: < -50%
Precipitation threshold = 24.3% (Reduction)
Landuse threshold = 0.550
Critical thresholds are computed by weighted averaging of thresholds for
all watersheds (for precipitation and land use change)10Deshmukh and Singh [2016]
We identity relationship between critical threshold and water-
shed physio-climatic characteristics
TO
PW
ET
1st o
rder
str
eam
4t h or
der
stre
am
Str
eam
den
sity
Ele
vatio
n
Asp
ect E
ast
Are
a
Asp
ect N
orth
Avg
. per
mea
bilit
y
HG
-B
Avg
. silt
Org
anic
mat
ter
Avg
. WT
dep
th
Urb
an
For
est
Agr
icul
ture
Pre
cipi
tatio
n
Eva
pora
tion
Arid
ity In
dex
M.A. runoff
IQdsc
Drought
Flood
-1 1
Correlation
Pre
cipi
tatio
n ch
ange
thre
shol
d La
ndus
e
chan
ge th
resh
old
Significant corrrelation Insignificant correlation (at P-value > 0.05)
Mean annual run
FPIFR
Drought
Flood
2nd o
rder
str
eam
a.
b.
1st o
rder
str
eam
4t h or
der
stre
am
Str
eam
den
sity
Ele
vatio
n
Asp
ect E
ast
Are
a
Asp
ect N
orth
Avg
. per
mea
bilit
y
HG
-B
Avg
. silt
Org
anic
mat
ter
Avg
. WT
dep
th
Urb
an
For
est
Agr
icul
ture
Arid
ity In
dex
Mean annual run
FPIFR
Drought
Flood
Drainage Topography Geology Landuse Climate
Land
use
ch
ange
thre
shol
d
-1 1CorrelationSignificant corrrelation (P-value < 0.05) Insignificant correlation
M.A. runoff
IQdsc
Drought
Flood
2nd o
rder
str
eam
a.
b.
Indi
cato
rsIn
dica
tors
TW
I
M. A
. PE
T
M. A
. pre
cipi
tatio
n
The correlation explain generalized relationship of watershed
physio climatic characteristic with watershed vulnerability.11Deshmukh and Singh [2016]
Thank you
Questions?
References
A. Deshmukh and R. Singh. Physio-climatic controls on vulnerability of watersheds to climate and
land use change across the united states. Water Resources Research, 2016.
E. Hawkins and R. Sutton. The potential to narrow uncertainty in projections of regional
precipitation change. Climate Dynamics, 37(1-2):407–418, 2011.
R. Singh, T. Wagener, R. Crane, M. Mann, and L. Ning. A vulnerability driven approach to
identify adverse climate and land use change combinations for critical hydrologic indicator
thresholds: Application to a watershed in pennsylvania, usa. Water Resources Research, 50(4):
3409–3427, 2014.