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Prediction of Climate Change Impacts on Groundwater Storage by
Analysis and Modeling of Hydrograph Recession Curves: Application
to the Bar Watershed, Iran
Majid Taie Semiromi1, Manfred Koch1, Siavash Taie Semiromi2
1
Department of Geohydraulics and Engineering Hydrology, University of Kassel, Germany
2
Department of Watershed Management Engineering, Tarbiat Modares University, Iran
Introduction

While 67% of Earth’s surface is covered by
water, only less than 2.7% of global water is
freshwater. Most of the freshwater (2.05%) are
locked in ice caps and glaciers. Only less than
0.7% is available for human use.
2
Volume of water stored in
the water cycle's reservoirs
Reservoir
Ocean
Ice caps & glaciers
Groundwater
Lakes
Soil Moisture
Atmosphere
Streams & rivers
Biosphere
Volume of water
(106 km³)
1370
29
9.5
0.125
0.065
0.013
0.0017
0.0006
Percent
of total
97.25
2.05
0.68
0.01
0.005
0.001
0.0001
0.00004
Climate and climate change
Climate = a region’s long-term pattern of atmospheric conditions
Global climate change = changes in Earth’s climate, including
temperature, precipitation, and other variables
Global warming = an increase in Earth’s average surface
temperature
Climate changes naturally, but the recent rapid warming of the
planet and its change in atmospheric composition are widely
thought to be due to human activities.
Climate Change = Hydrologic Change
http://www.fs.fed.us/emphasis/products/water-climate-brochure.pdf
Groundwater and climate change
Groundwater is the source of 35% of global human water
withdrawals, and even of 42% of global irrigation water withdrawals
Climate change affects on groundwater resources in two ways:
1- Direct impacts: reducing or increasing of recharge
2-indirect impacts: Due to increased temporal variability of surface water
flows, climate change is likely to lead to higher demands for
groundwater
In this regard, using global hydrological models, global groundwater
recharge and thus renewable groundwater resources were estimated to be
13 000–15 000 km3 per year under current climate conditions, and to
account for approximately one third of the total renewable water
resources (Döll and Fiedler 2008, Wada et al 2010).
groundwater resources in Iran
1- Groundwater utilization in 1970: around 20 BCM
2- Groundwater utilization now: more than 74 BCM
3- There is no clear picture of just how much of
groundwater resources is economically and
technically exploitable.
4- Sea water intrusion and groundwater
contamination
5- Drought
6- Climate change
Objectives of the present research
1- Assessing the groundwater storage
during the historical data record (1970 to
2010) by analyzing recession curves of
the annual hydrographs
2- Forecasting the groundwater reserves
under different climate change scenarios
Expected results (hypotheses)
1- The groundwater storage has been
significantly decreasing during reference
period
2- Groundwater storage will be reducing as a
result of climate change under climate
projections
Literature
review
Chen, Z., Grasby, S.E., Osadetz, K.G., 2002
Issar, A. S., 2003.
Brouyère, S., Carabin, G., Dassargues, A., 2004
Holman, I., 2006
Dettinger, M.D., Earman, S., 2007
IPCC, 2007.
Scibek, J., Allen, D.M., Cannon, A.J., Whitfield, P.H., 2007.
Green T.R., Taniguchi, M., Kooi, H., Gurdak, J.J., Allen,
D.M., Hiscock, K.M., Treidel, H., Aureli, A., 2011
Herrera-Pantoja, M.; Hiscock, K.M., Boar, R. R., 2012
Hirata, R.; Conicelli, B.P., 2012
José-Luis Molina, David Pulido-Vel‫ل‬zquez b, José Luis
Garc‫ي‬a-Arَ stegui c, Manuel Pulido-Vel‫ل‬zquez, 2013
IPCC, 2013
Study area: Physiography of Iran
Climatological Condition
Mean Annual Precipitation Iran≈ 250 mm(World: 860 mm)
Mean Annual Evaporation Iran ≈ 2100 mm (World: 700 mm)
Around 85% of the country is covered by arid and
semi- arid areas
Case study
Methodology
1- Selecting rain gauge and hydometery stations
2- Analysis of hydro- climate time series during observed
period (1970- 2010)
3- Simulation of hydro- climate variable by General
Circulation Models (GCMs) and choosing appropriate
GCMs
4- Downscaling with LARS- WG model in the scale of Bar
watershed
5- Using downscaled data including mean, max and min
temperature, precipitation and solar radiation as the inputs
into a rainfall- runoff model
6- Rainfall- runoff simulation by IHACRES model
7- Recession curve modeling (Recession Analysis)
1- The Boussinesq equation is of hyperbolic form:
Qt = Q0/ [1 + α(t − t0)]2
where t = time since the beginning of recession for which the flow
rate is calculated and t0 = time at the beginning of recession usually
(but not necessarily) set equal to zero.
2- The Maillet equation which is more commonly used, is an
exponential function
Qt = Q0 · e−α(t−t0)
The dimensionless parameter α in both equations represents the
coefficient of discharge (or recession coefficient), which depends on
the transmissivity and specific yield of the aquifer. The Maillet
equation, when plotted on a semilog diagram, is a straight line with
the coefficient of discharge (α) being its slope:

Results
Analysis of trend in hydro- climate variables
during observed period (1970- 2010)
Significant test
SD
Average
Max
Min
Variable
0.002*
0.32
0.62
1.7
0.12
River )M3/sec(
discharge
0.051
108
319
505
150
Rainfall (mm)
0.73
1.3
39
42.2
36.7
0.94
3.2
-15.2
-23.6
-10.4
0.005*
226
2850
3196
2130
Maximum temperature
(C)
Minimum temperature
(C)
Hours of sunlight
(hours/yr)
Downscaled GCMs under different scenarios in
comparison with observed data (1970- 2010)
Precipitation
PBIS MAE Nash
R2
Temperature
RMSE
PBIS MAE Nash
R2
RM
SE
GCM
B2
-23
0.23 -0.32 0.01
4.11
0.78
0.1
0.94 0.82 4.20
A2
9.44
0.83
0.01 0.02
3.56
-0.24
0.33
Hadcm
3
0.99 0.84 4.04
A1B
-0.44 0.35 -0.45 0.09
4.13
-0.51
0.06
0.79 0.73 4.52
CGCM

Determination of the model accuracy
 n (Oi  S i ) *100 

PBIAS   i 1 n

i1 (Oi ) 

 O
N
NSE  1 
t 1
i
 Si 
2



O

O

t 1  i

2
N
MAE  1/ ni1 (Si  Oi )
n
1
RMSE  
N

i 1 (Si  Oi ) 
N
1 


S

S
O

O
 n   i  i  


R 2   m1 
 S  O




n

1/ 2
2

2
Simulated monthly rainfall by LARS- WG model
compared to observed rainfall
Month
Simulated maximum monthly temperature by LARSWG model compared to observed data
Simulated minimum monthly temperature by LARSWG model compared to observed data
Relative variations - compared with the 1970-2010 reference
period - of maximum temperature (left) and rainfall
(right) projected by the HadCM3-model under scenario A2 for
the future periods 2010-2039, 2040- 2069 and 2070- 2099.
IHACRES- calibrated (left) and validated (right)
streamflow of the Bar river for the 1970-2010 reference
period
Recession curve analysis for calculating
groundwater reserves
1970
1977
9
30
River discharge (M3/sec)
River discharge (M3/sec)
8
25
20
15
10
5
7
6
5
4
3
2
1
0
0
1
91
181
Time (day)
271
361
1
91
181
271
Time (day)
361
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
14
2089
12
River flow (M3/sec)
River flow (M3/sec)
2091
10
8
6
4
2
1
91
181
271
Time (day)
361
0
1
91
181
Time (day)
271
361
IHACRES-simulated monthly Bar river streamflow (left
panel) and annual recession-computed groundwater storage
(right panel) for the reference- and projected future periods.
Summarized results





1- Predicted mean annual maximum and minimum
temperatures will be increased equally by 1.1, 3.2 and 4.6 ◦C
2-Precipitation will be decreased by 16.4, 17.6 and 31.4 %
during the projected periods 2010- 2039, 2040- 2069 and
2070- 2099, respectively, when compared to the past 19702010 reference period
3- Annual future hydrographs are constructed which
indicate that, compared to the 1970- 2010 reference period,
the Bar river streamflow will be abated by 9, 44 and 66 %,
during the projected periods 2010-2039, 2040- 2069 and
2070- 2099, respectively.
4-Groundwater storage will drop by 36.9, 52 and 61%,
for the three named projected periods, respectively.
Luna Leopold
A river is the report card for its
watershed.
Thank you for your
attention!
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