WATER DEFICIT VS WATER EXCESS
Alejandro Marulanda Aguirre
Civil Engineer, Hydraulic and Environmental Engineering.
Candidate M.Sc Hydraulic Resources.
Coauthors:
Jorge Julián Vélez U. Ph.D Hydrology
Olga Lucia Ocampo L. PhDc Engineering
Universidad Nacional de Colombia - Manizales
▪ Introduction
▪ Objectives
▪ Context
- Climate context
- Climate extremes: Wet and Dry
- Causes, effects and impacts
▪ Methodology
- Use of ArcGis in water supply
▪ Results
▪ Conclusions
AGENDA
OBJECTIVES
General objective
Estimate the water deficit hazard in municipal aqueducts with
water supply problems in the department of Caldas,
Colombia.
Specific objectives
1. Define a methodology to estimate the aqueduct water
supply.
2. Explain climate variability effects.
National climate context:Influence of the Andes Mountains on Colombia, South America
Source: Gifex-HIMC, 2007. IDEAM, 2010.
Caldas
Altitud m.a.s.l.Annual precipitation
Caldas
CONTEXT
National climate context:
Source: IDEAM, 2010.
Annual Precipitation
Legend (mm) (In)
0 - 500 0 - 19,7
500 - 1000 19,7 - 39
1000 - 1500 39 - 59
1500 - 2000 59 - 79
2000 - 2500 79 - 98
2500 - 3000 98 - 118
3000 - 4000 118 - 158
4000 - 5000 158 - 197
5000 - 7000 197 - 276
7000 - 9000 276 - 354
9000 - 11000 354 - 433
> 11000 > 433
Caldas
Annual precipitation
CONTEXT
J F M A M J A S O N D
Source: CORPOCALDAS & Gotta, 2017.
Altitude Annual precipitation
Legend (mm) (In)
660 – 1000 26 – 39
1000 – 1500 39 – 59
1500 – 2500 59 – 98
2500 – 4000 98 – 157
4000 – 6000 157 – 236
> 6000 > 236
Legend m.a.s.l Feet
5289 208
141 5,5
CONTEXT
Local climate context – Caldas, Colombia:
Climate extremes:
Flow changes in the aqueduct water supply.
Fuente: CORPOCALDAS, 2017.
MUNICIPIO DE PENSILVANIA (EXP. 0640 )
MES DE JULIO DE 2014 MES DE ABRIL DE 2015
Quebrada El Dorado
Caudal de la Fuente: 120,5 l/s Caudal de la Fuente: 91 l/s
Caudal Captado: 18 l/s Caudal Captado: 18,84 l/s
REGISTRO FOTOGRÁFICO
Quebrada El Popal
Quebrada El Popal
Quebrada El Dorado
Quebrada El Dorado
MUNICIPIO DE PENSILVANIA (EXP. 0640 )
MES DE JULIO DE 2014 MES DE ABRIL DE 2015
Quebrada El Dorado
Caudal de la Fuente: 120,5 l/s Caudal de la Fuente: 91 l/s
Caudal Captado: 18 l/s Caudal Captado: 18,84 l/s
REGISTRO FOTOGRÁFICO
Quebrada El Popal
Quebrada El Popal
Quebrada El Dorado
Quebrada El Dorado
Manizales Victoria Pensilvania
CONTEXT
Climate extremes:Sea Surface Temperature Anomaly vs Flow regime
Source: CORPOCALDAS, 2017. NOAA, 2018.
MUNICIPIO DE PENSILVANIA (EXP. 0640 )
MES DE JULIO DE 2014 MES DE ABRIL DE 2015
Quebrada El Dorado
Caudal de la Fuente: 120,5 l/s Caudal de la Fuente: 91 l/s
Caudal Captado: 18 l/s Caudal Captado: 18,84 l/s
REGISTRO FOTOGRÁFICO
Quebrada El Popal
Quebrada El Popal
Quebrada El Dorado
Quebrada El Dorado
MUNICIPIO DE PENSILVANIA (EXP. 0640 )
MES DE JULIO DE 2014 MES DE ABRIL DE 2015
Quebrada El Dorado
Caudal de la Fuente: 120,5 l/s Caudal de la Fuente: 91 l/s
Caudal Captado: 18 l/s Caudal Captado: 18,84 l/s
REGISTRO FOTOGRÁFICO
Quebrada El Popal
Quebrada El Popal
Quebrada El Dorado
Quebrada El Dorado
CONTEXT
Climate extremes:
Influence of the ENSO (El Niño South Oscillation)
Source: CORPOCALDAS & Gotta, 2017.
Date 23/08/02 05/07/92 01/07/87 30/06/87 04/07/92 10/07/97 29/06/87 22/08/02 03/07/92 09/07/97 08/07/97 02/07/92 28/06/87 07/07/97 08/07/91
Mínimum
Flow (L/s)37.3 37.9 38.2 38.3 38.3 38.4 38.7 38.7 38.7 38.8 39.2 39.2 39.3 39.6 39.7
ONI Index 0.9 0.4 1.2 1 0.7 1.6 1.2 0.9 1.6 1.6 1.6 0.4 1.2 1.6 0.7
CONTEXT
J F M A M J A S O N D
Pre
cip
ita
tio
n(m
m/m
es)
Causes, effects and impacts:ENSO: “Refers to the coherent and sometimes very strong year-to-
year variations in sea- surface temperatures, convective rainfall,
surface air pressure, and atmospheric circulation that occur across
the equatorial Pacific Ocean”.
Extreme dry: 1982, 1992, 1997, 2015
Extreme wet: 1988, 2000, 2007, 2010
Source: NOAA, 2018.
CONTEXT
METHODOLOGY
Integrated Water Resources Management (IWRM):
Colombia IWRM:
Source: MINAMBIENTE, 2017.
Supply Demand Quality Risk
Source: ICIWaRM, 2018.
METHODOLOGY
1• Climatic characterization
2• Flow regime
3• Flow recession curve
4• Flow probabilistic estimation
METHODOLOGY
1. Climatic characterization:
Standardised Precipitation Evapotranspiration Index (SPEI).
“The mean for a specified time period divided by the standard deviation
where the mean and standard deviation are determined from past records”.
0
0.2
0.4
0.6
0.8
1
-200 0 200 400 600 800 1000
F(X
)
Di
-200
0
200
400
600
800
1000
Di=
Pi-E
VP
i
-2
-1
0
1
2
3
SP
EI
-2
-1
0
1
2
3S
PE
I 1
MO
NT
H
Source: (McKee et al. 1993)
METHODOLOGY
2. Flow Regime: Measured or simulated flow
TETIS model (Francis et al 2007, Vélez, 2001)
RESULTS
8
13
18
23
280
20
40
60
80
100
01/01/81 21/11/86 10/10/92 30/08/98 19/07/04 08/06/10
T (
ºC)
P (
mm
)
DAILY Pmm Tmed°C
8
10
12
14
160
3000
6000
9000
12000
1 2 3 4 5 6 7 8 9 10 11 12
T (
ºC)
P (
mm
)
MULTI-YEAR MONTHLY Pmm Tmed°C
7
12
17
22
0
1500
3000
4500
1,981 1,984 1,987 1,990 1,993 1,996 1,999 2,002 2,005 2,008
T (
ºC)
P (
mm
)
ANNUAL Pmm Tmed°C
Climatic series:
RESULTS
Climatic characterization: (SPEI).
-3-2-10123
SP
EI
1 M
ON
TH
-2-10123
SP
EI
3 M
ON
TH
-3-2-10123
SP
EI
6 M
ON
TH
-2
-1
0
1
2
SP
EI
12 M
ON
TH
-2
-1
0
1
2
SP
EI
18 M
ON
TH
-2
-1
0
1
2
SP
EI
24 M
ON
TH
RESULTS
0
20
40
60
80
100
120
1400
5
10
15
20
25
Pre
cip
itat
ion
(m
m)
Flo
w r
ate
(m
3/s
) Qsimulado
Precipitación
Less rain, lower flow Higher rain, greater flow
Component Value Component Value
Static maximun storage 350,16 Surface storage 0
Conductivity (mm/day) 5,3 Infiltration exponent 2
Residence time - surface Flow (days) 3,79 Evaporation exponent 0,501
Gravitational storage 257
Hydrological modeling:
RESULTS
1ºC
13ºC
20ºC
0.5 mm
3.6 mm
5.9 mm
Precipitation
Temperature
Land cover
Land use
Source: IGAC, 2010
Inputs for hydrological model:
METHODOLOGY
Tetis Model: Calibration and validation.Puente Juntas flow Station:
Costa Azul Station:
1981 – 1990
1991 – 2000
0
50
100
150
200
250
3000
10
20
30
40
50
60
P (
mm
)
Flo
wra
te(m
3/s
)
Qobservado
Qsimulado
050
100150200
2503000
10
203040
5060
P (
mm
)
Flo
wra
te(m
3/s
)
QobservadoQsimulado
0
50
100
150
200
250
3000
5
10
15
20
25
30
P (
mm
)
Flo
wra
te(m
3/s
)
QobservadoQsimulado
0
50
100
150
200
250
3000
5
10
15
20
25
30
P (
mm
)
Flo
wra
te(m
3/s
)
QobservadoQsimulado
RESULTS
Flow duration curve
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0 10 20 30 40 50 60 70 80 90 100
Flo
wra
tem
3/s
Percentage Exceedance (%)
Q EL Uvito
Flow duration curve
P (%) Q0.01 Q1 Q10 Q25 Q50 Q65 Q75 Q85 Q90 Q95 Q97.5
Q (m³/s) 1.85 1.15 0.66 0.47 0.29 0.21 0.17 0.12 0.10 0.07 0.06
RESULTS
2. Flow Regime:
Supply vs Demand
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Flo
wra
te(L
/s)
Percentage Exceedance (%)
La Merced Aqueduct
Supply
Demand
RESULTS
Flow recession curve:
Theoretical:
Measure:
teQtQ 0
300
350
400
450
500
550
600
1 3 5 7 9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
49
51
Flo
wra
te(L
/s)
Days without rain
Recession Rio Blanco, Manizales.Aguas de Manizales (Aqueduct)
JUN-10-JULIO-2015
RESULTS
Flow recession curve
y = -40.11ln(x) + 415.8R² = 0.9631
0
10
20
30
40
50
600.00
0.00
0.01
0.10
1.00
P (
mm
)
Flo
wra
te(m
3/s
) El Rosario
y = -9.53ln(x) + 99.884R² = 0.9506
0
10
20
30
40
50
600.00
0.01
0.10
1.00P
(m
m)
Flo
wra
te(m
3/s
)
Santana
y = -17.82ln(x) + 186.85R² = 0.9602
0
10
20
30
40
50
600.00
0.01
0.10
1.00
P (
mm
)
Flo
wra
te(m
3/s
)
Santana
y = -2.444ln(x) + 25.328R² = 0.9757
0
10
20
30
40
50
600.00
0.01
0.10
1.00
P (
mm
)
Flo
wra
te(m
3/s
) La Isabela
RESULTS
Flow probabilistic estimation:▪ Deficit: Normal, LogNormal and Gumbel.
▪ Excess: General Extreme Value GEV, Gumbel, Log-Normal, SQRT-
ETmax, Two Components Extreme Value TCEV.
Important situation for the supply: Accumulation of sediments and organic
material in drought season, with drag and swept of these with a medium or
maximum flow.
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0 10 20 30 40 50 60 70 80 90 100
Flo
wra
te(L
/s)
Return period (Year)
Probabilistic estimation of minimum flows
NORMAL
GUMBEL
LOGNORMAL
Rp (Year) Q (L/s)
2.33 0.3
5 0.2
10 0.1
25 0.06
50 0.04
100 0.03
Gumbel General Extreme
Value Rp (Year) Q (L/s)
2.33 24
5 32
10 48
25 80
50 160
100 240
CONCLUSIONS
▪ It’s possible to estimate the hazard of shortage based
on the understanding of the associated natural
processes.
▪ The water regulation and retention not only depends
on the physical conditions (vegetation and soils) but it
is determined by the climate.
▪ The duration without rain is important in a deficit
event, while the magnitude is important in a excess
event due to the lag and event evolution.
CONCLUSIONS
▪ It’s important break a hydro-illogical cycle through risk
management (prevention and adaptation), ending with
crisis management (reactive).
▪ The vulnerability not only depends on the supply, also
on the quality of the water, the water demand and the
infrastructure for the supply.
▪ All flow regime is important to the supply. (adaptation
and mitigation measures).