1"Assessment the impact of climate change over sediments yields in the

Cesar river basin”

M.Sc. Karen Milena ChAdvisor: Prof. Dr. Rer. nat. Manfred Koch

Department of Geohydraulics and Engineering Hydrology University of Kassel

29.01.13

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Location of study areaArea Cesar District 22.925km2Population: 43.94 Hab/Km2Capital : Valledupar – 365.548 HabTemperature: 28CLocation: 07º41’16’’ 10º52’14’’ N 72º53’27’’ 74º08’28’’ WMain ecosystems:

Sierra Nevada de Santa Marta: (5300 -1230 m.a.s.l)Serranía de Perijá:(750 -1350 m.a.s.l)Valley of River Cesar: (500- 950 m.a.s.l)Cienaga de la zapatosa: :(50 -200 m.a.s.l)

Main characteristics of the Cesar region:

• Important roll for the economic in the region (cotton crop, mining, livestock)

• Intensive agricultural practices• Existence of hydro meteorological and soil use

information for the area.

Table 1:Charactetistic of study area

Serrania de Perija

Sierra nevada de Santa Marta

Valley of River Cesar

Cienaga de la Zapatosa

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Stament of the problem• For the district of Cesar, one of the most important water

resources is the Cesar river.

• One of the major environmental problems in the region has been the massive deforestation of most of the forests to make way for agricultural uses that may lead to ecological imbalances.

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• From the point of view of agriculture, inadequate use of natural resources, such as the use of mechanization without the prior classification of the agro-ecological zone.

• In an area, as the Cesar basin, which is rich in topographical and soil physiological conditions, erosion and sedimentation could occur in several ways. Schumacher T.E, et al. (1999), states that net soil loss from tillage erosion occurs on slope lands as a result of gravity acting on moving soil.

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• According to a study of climate modeling in Colombia conducted by Pabón J.D, (2006), in the Cesar region the trend of air temperature might increase 0.17 °C per decade, and the trend of precipitation 1.46% per decade for that region.

• From the agricultural point of view, the United Nations system in its review of risks and opportunities associated with climate change states that the effects associated with this phenomenon could be the aridity, soil erosion, desertification and changes in the hydrological regime as well as increased risk of flooding in agricultural production affecting crops.

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The main objective• The main objective of this research is to assess the impact of

climatic change over sedimentation yield in the Cesar basin, which is located in the Cesar district in northern of Colombia using SWAT (Soil Water Assessment Tool) model.

• Specific objectives:

Establish the historical behavior of climatic and hydrological variables in the basin of the Cesar River.

Evaluate sedimentation yield in the basin under climate change.

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SWAT Model1 Soil Water Assessment Tool

SWAT is a physically based, watershed-scale, continuous time, distributed-parameter hydrologic model that uses spatially distributed data on topography, land use, soil, and weather for hydrologic modeling and operates on a daily time step (Arnold et al., 1998; Arnold and Fohrer, 2005). Based on topography, this model subdivides a watershed into a number of subbasins for modeling purposes. In latina América SWAT has been used in countries as: México2; Brasil3, Colombia4; República Dominicana5 Venezuela

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1 http://swat.tamu.edu/software/swat-model/2(Torres B. et al., 2005; Johannes,2004), 3(Machado, 2002; Machado et al., 2003; Neves et al., 2006), 4 (Millan and Isaza, 2002); (Oñate and Aguilar, 2003) 5 (Camacho et al.,2003) 6 (Silva, 2004).

The soil–water balance is the primary equation used in the SWAT model, which is represented as:

SW :soil content, R, Q, ET, P and QR are daily precipitation, runoff, evapotranspiration, percolation and return flow, respectly.

Sed :sediment yield on a given day (metric tons)Qsurf : the surface runoff volume (mm/ha)q : the peak runoff rate (m3/s),Khru is the Universal Soil Loss Equation (USLE),CUSLE, PUSLE, LSUSLE CFRG are factors as function of soil.

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The research design and methodology1. Literature review2. Training in SWAT model3. Collection of information

TYPE OF INFORMATION

CHARACTERISTIC OF INFORMATION SOURCE

Digital Elevation Model 90m x 90m ASTER GDEM

Use of soil Scale: 125.000 IGACSoil maps Scale: 125.000 IGACPhysic and chemicalSoil properties

Soil type, Area ratio, Content of sand, Content of clay, Content of fine clay, Content of Organic carbon, Content of total nitrogen, Content of potassium, Content of total phosphorus.

IGAC, ICA

Climatic daily data (30 years) !!!

Daily precipitation, Maximum temperature.Minimum temperature, Solar radiation.Wind speed, Relative humidity , Daily Stream.Sedimentation yield

Hydrology, Meteorology andEnvironmental Studies Institute(IDEAM)

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4. Preparation of background information of entry for the application of the hydrological model.

5. Analysis of the series of time.

6. Modeling and calibration of the Cesar River basin hydrological validation.

7. Write final paper

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Partial results3. Collection of information and arrangement of data

Macro

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• Quality of available climatic data

6%2%

13%

6%

6%

3%

6%

5%4%

45%

6%

Max. T

Max. Ws

MF

MHR

MT

MaxWS

mT

TSB

TE

TP

Twt

VariableStations Years

Amount % from to

MaxT 9 6 1980 2010MaxWs 3 2 1980 2009MS 21 13 1970 2009MRH 9 6 1980 2010MT 9 6 1980 2010MWs 5 3 1975 2009mT 9 6 1980 2010TSB 8 5 1980 2009TE 7 4 1980 2009TP 73 45 1980 2009TWt 9 6 1985 2008

MaxT: Maximun temperature, MaxWs: Maximun wind speed, MWs: Main wind speed, MS: Main Stream, MRH: Main relative Humidity, MT: Main temperature, mT: minimum temperature, TSB: Total solar brightness, TE: Total evaporation, TP: Total precipitation, TWt: Total wind track.

Figure 1: Description of available data

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• Delimitation of the basin with SWAT

Pte canoas Station (45

m.a.s.l)

Area Cesar Basin:12.500km2Range of elevation: 40 and 5300 m.a.s.l.

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(Some Problems with soil information)

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FLOW Analysis

1. Annual Analysis2. Monthly Analysis3. Flow duration4. Flood Frecuency

Pte canoas Station (45

m.a.s.l)

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1. Annual Analysis

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

0

20

40

60

80

100

120Mean Annual Flow, Pte canoas Station (45m.a.s.l) 1972-

2009STREAM FLOW

Year

Mea

n An

nual

Flo

w (

m^

3/se

g)

16

0

20

40

60

80

100

120

mean-SD= 31.67

mean= 55.33

mean+SD= 78.99

Ranked Mean Annual Flow, Pta canoas station (45 m.a.l.s) 1972 - 2009

RANKED MEAN ANNUAL F...

Rank of water year

Mean

Ann

ual

flow

(m^3

/seg)

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1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

0

20

40

60

80

100

120

Mean Annual Flow, 5 year Moving mean, 11year moving mean Pte Canoas Station (45 m.a.l.s) 1972 -2009

Mean Annual Flow 5 Year Moving Mean Annual Flow 11 Year Moving Mean Annual Flow

Year

Dis

char

ge (

m°3

/seg

)

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2. Monthly Analysis

JAN FEB MAR APR MAY JUNE JULY AUG SEPT OCT NOV DEC0

20

40

60

80

100

120

140Mean Montly flows(m^3/seg), Pte Canoas (45 m.a.l.s) 1972-2009

Month

Mea

n m

ontl

y fl

ow(m

^3/

seg)

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1973 1978 1983 1988 1993 1998 2003 20080

20

40

60

80

100

120

140

160

11 Year Moving Mean Monthly Flow

JAN JUNE NOV

year

Dis

char

ge m

3/s

20

0 10 20 30 40 50 60 70 800

50

100

150

200

250

300

350

Frecuency Distribution for 20 Equal Classes, Pte Canoas station (45 m.a.l.s) 1972-2009

Percent of time that indicated discharge was equal or exceded (%)

Dis

char

ge (

m^

3/se

g)

3. Flow duration

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4. Flood Frequency Analysis• Log-Pearson Type III Distribution

The Log-Pearson Type III distribution is a statistical technique for fitting frequency distribution data to predict the design flood for a river at some site. Once the statistical information is calculated for the river site, a frequency distribution can be constructed.  The probabilities of floods of various sizes can be extracted from the curve. The advantage of this particular technique is that extrapolation can be made of the values for events with return periods well beyond the observed flood events.

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1 10 100 100010

100

1000

Flood Frequency Analysis for Pte Canoas Station (45 m.a.l.s) Using Log-Pearson Type III analysis Using average Daily Maximum Streamflow values

(1972-2009)

Return Period (years)

Dis

char

ge (

m^

3/se

g)

180

2

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0 10 20 30 40 50 60 70 800

50

100

150

200

250

300

350Frecuency Distribution for 20 Equal Classes, Pte Canoas station (45

m.a.l.s) 1972-2009

Percent of time that indicated discharge was equal or excededd (%)

Dis

char

ge (

m^

3/se

g)

180

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ConclusionsHigher flow rates months are October and November and the lower flow are January and February.

There is evidence that indicates that the discharge in the months of greater and lower discharge has been increasing in recent years.

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Thank you for your attention