MSc Defence ppt_presnetation at ITC

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a case study of a case study of Nakhon Ratchasima, Thailand

Spatial Modeling and Timely Prediction of Salinization Spatial Modeling and Timely Prediction of Salinization using using SAHYSMODSAHYSMOD in GIS environment in GIS environment

Thesis Assessment Board

Prof. Dr.V. G.Jetten ChairmanDr.T.W.J. Van Asch External Examiner Dr.A. (Abbas) Farshad First Supervisor Dr.D. B. (Dhruba) Pikha Shrestha Second Supervisor

Tsegay Fithanegest Desta

Applied Earth Sciences: GEOHAZARDS

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Presentation outlinePresentation outline

1.1. IntroductionIntroductionResearch Objective Research Objective Research questionsResearch questions

2.2. The Study AreaThe Study AreaSalient features Salient features

3.3. Materials and MethodsMaterials and MethodsMaterials Materials MethodsMethods

4.4. Result discussionResult discussionModel calibrationModel calibrationModel validationModel validationSalinity predictionSalinity prediction

5.5. Conclusion and Recommendation Conclusion and Recommendation Applied Earth Sciences: GEOHAZARDS

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1.0 IntroductionIntroduction

1. Salt and Salinization def. 1.salt is presences of excessive soluble salts in the root zone, while 2. salinization is process of its accumulation in root zone area, Together threaten

Biophysical ecology Crop physiological processes Biogeochemical processes of agricultural soils National socio-economy

2. General objective To detect soil salinity change both in time and spaces dimension and to model salinization as a process.

3. General research questions Could soil salinity change be detected in spaces & time dimension? and

salinization be modelled as a process? Applied Earth Sciences: GEOHAZARDS

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2.0 The Study AreaStudy area salient featuresName: Nong sung

1. Geographical• 795858 – 821786 E• 1659635 –1688797 N• Elevation: 114 – 209 Meter amsl.

2. Administrative• Province: Nakhon Ratchasima• Region: Northeast• Country: Thailand

3. Area size:816 km2

4. Climatic features• Aver. Rainfall=1030mm/yr• Aver. Temperature = 27oc

5. Dominant soil and Rocks• Sandy loam • Mahasarakham formation


2.0 Layout of the study Area

5Applied Earth Sciences: GEOHAZARDS

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3.0 Literature review The millstone literature reviews of the study:- 1. Greiner (1997) Saline soils are identified by their:-

• EC, SAR, ESP, and degree of acidity (pH) soil extract @ 250C• ECe > 4 mS/m, threshold for deleterious effects to occur

2. The findings of Slavich and Petterson (1993), • EC1:X soil-water solutions, is > 2 to 3*higher than the FC H

2O

content3. Oosterbaan (2005) SAHYSMOD model works based on electrical

conductive at filed capacity (ECfc) so needs conversions.

f = texture dependant conversion factor ECe = standardized electrical conductivity(EC of saturation extract) EC1:5 = Electrical conductivity of 1gm soil to 5 ml of distilled water.


ECe = f * EC1:5 ECFc = ECe*2

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4.0 Materials and Methods1. Materials

Hardware Gramin GPS 12X, pH meter, EC meter, Topographic map,

Geopedological map, DEM map and Aerial photo, lab chemicals and buffer solutions and user manuals. Software

ArcInfo Map, Erdas, ILWIS, ENVI, SPSS, MS-Excel, MS-Word, and

SAHYSMOD2. Breakdown of methods followed

Pre field workAt field workPost field work


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4.1 Methods followed Pre

field w

ork

activit

ies

Literature reviewEstablished historical database

Preparation for field work

Field w

ork ac

tivities

Data collection

Post fi

eld wo

rk act

ivities

Data processing and analysis

Error analysis

Proposal writing

· Soil-water· EC 1:5 · pH

Data on · Meteorology· Beneficiaries

Data on· Soil· Geology

Process

Decision

Data

Legend

Problem identification

Documents

steady-state model calibration

Revise model

Problem with model

Unacceptable error

Acceptable error

Nodal net work establishment

Sensitivity aanalysis

Libratory analysis

Data & map· Salinity · Borehole · Crop· irrigation

evaluation and validation

20 year Prediction

DEM Class

GIS

Salinity map

SlopeClass

LanduseClass

Salinity Attributes

of 8polygons

DSS

Predefined Decision

Report writing

Accuracy problem


Extrapolated

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020406080

100120140160180200

0.908 1.816 7.264 175.75 0.454 2.27 11.21 3.99

Observed_ECFc[dS/m]

Sim

ulat

ed_E

CFc[

dS/m

]

observed_ECFc[dS/m] Simulated_ECFc[dS/m]

y = 0.833x - 0.3642R2 = 0.9999

0

20

40

60

80

100

120

140

160

0 50 100 150 200

Series1

Linear (Series1)

5.0 Model calibration and evaluation


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Determining of leaching efficiency

0

20

40

60

80

100

120

0.01 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

percentage of leaching efficency

ECFC

(dS/

m)

ECFC_0.1

ECFC_0.2

ECFC_0.3

ECFC_0.4

ECFC_0.5

ECFC_0.6

ECFC_0.7

ECFC_0.8

ECFC_0.9

ECFC_1

6.0 Sensitivity analysis


Model sensitivity analysis for drainage installation

0.001.002.003.004.005.006.00

1 3 5 7 9 11 13 15 17 19 21

Time

ECFC

(dS/

m)

Dd_1m Dd_1.5m Dd_2m calibrated line

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020406080

100120

0.9 1.7 5.9 97.7 0.6 1.7 15.2 4.1

Observ_ECFc(dS/M)Sim

ulat

ed_E

CFc

(dS/

M)

Obse_Validation Simul_Validation

6.0 Validation analysis


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Root Zone Salinity (dS/m)

Poly_ID

Observed Simulated

ME MAE RMS

1 0.91 0.15 0.76 0.76 0.76 2 1.66 1.38 0.28 0.28 0.28 3 5.90 4.90 1.00 1.00 1.00 4 97.70 81.10 16.60 16.60 16.60 5 0.55 0.09 0.46 0.46 0.46 6 1.66 0.27 1.39 1.39 1.39 7 15.18 12.60 2.58 2.58 2.58 8 4.14 3.44 0.70 0.70 0.70 Mean of Errors 2.97 2.97 1.72

Table 4 Mean error of measured Vs simulated root zone salinity of validation

7.1 Validation analysis …Mean of errors


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8.0 Model prediction Groundwater table-Salinity

Correlation between root zone salinity[Cr4] and Excess of groundwater inflow overoutflow[Gaq]

-10.00

-5.00

0.00

5.00

10.00

15.00

20.00

25.00

30.00

0 2 4 6 8

Polygon nr.

Cr4

(dS/

m) a

nd G

aq (c

m/s

easo

n)

Cr4

Gaq


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8.1 Prediction cont…. Numerical Salinity result

Appendix 1 Predicted root zone salinity (dS/m)

Poly_ID Obsereved Year_0 Year_5 Year_10 Year_15 Year_20(dS/m) average average average average average

1 0.91 0.15 1.42 5.47 10.75 16.702 1.82 1.51 3.28 6.58 10.00 13.403 7.26 6.03 1.48 0.42 0.19 0.134 175.75 146.00 40.35 30.05 27.85 26.655 0.45 0.07 4.58 4.55 4.44 4.326 2.27 0.36 0.17 0.13 0.13 0.127 11.21 9.30 20.50 19.70 18.75 17.858 3.99 3.31 10.45 9.82 9.24 8.71

Appendix 1 Predicted transtion zone salinity (dS/m)

Poly_ID Obse Year_0 Year_5 Year_10 Year_15 Year_20(dS/m) average average average average average

1 0.91 0.91 1.34 1.87 2.19 2.412 1.82 1.82 1.70 1.59 1.56 1.573 2.27 2.27 2.53 1.90 1.31 0.894 154.47 154.00 37.30 29.45 27.60 26.455 0.91 0.91 4.60 4.53 4.41 4.296 3.18 3.18 2.13 1.44 1.00 0.727 19.95 20.00 20.40 19.55 18.55 17.658 6.46 6.46 10.35 9.70 9.12 8.61

Appendix 1 Predicted aquifer zone salinity (dS/m)

Polygon Obsereved Year_0 Year_5 Year_10 Year_15 Year_20ID (dS/m) average average average average average

1 1.82 1.82 1.99 2.15 2.30 2.432 1.36 1.36 1.39 1.44 1.49 1.543 6.36 6.36 6.38 6.43 6.50 6.554 31.35 31.40 29.90 28.65 27.65 26.605 4.99 4.99 4.73 4.58 4.44 4.326 2.72 2.72 2.73 2.74 2.75 2.757 21.66 21.70 20.55 19.45 18.55 17.658 11.40 11.40 10.45 9.81 9.23 8.71


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8.3 Prediction spatial salinity distribution based on three aspects


Applied Earth Sciences: GEOHAZARDS16

8.4 DSS for extrapolation model prediction results

Table 6 Designed decision supporting system for extrapolation of model predicted attributes

Applied Earth Sciences: GEOHAZARDS17

8.5 Interim ConclusionWith successful model

• Calibration, sensitivity analysis,• validation and evaluation works

Able to predicate soil salinity perfectly and identify/model 1. Saline geopedologic units2. Salinity rate per geopedologic unit per year. 3. Change of salinization in space and in time.4. Main factor of salinization, Rise of saline GWT.5. Biophysical factors that aggravate salinization :-

I. Hot climate condition, high ETOII. Deforestation.III. Uncontrolled irrigation practiseIV. Traditional salt making remnants

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8.6 Prediction spatial salinity distribution map for the three profiles


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9.0 Recommendation

1. salinity relatedReafforestation indigenous trees.Agronomic packages:

Adding organic matter Using resistance Varity Crop rotation

Installation of drainage network => 4 to 5 times2. Model related

Area selection should be done with care. Grid alignment limitation should be noted before use.Output data reduction should be noted before use. Integrating different factors of salinization is advisable.


I recommend a change detection research to be carried

1. The study area is large enough for bigger grid size creation and alignment.

2. There are enough resources references materials3. Salinization rate of the area is fast & destructive so it needs monitoring

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10.0 Concluding remark
