1. Faculty of Environmental Sciences, Department of Hydrosciences
INTRODUCTION
Under the current challenges (population growth, climate change, unaccounted groundwater use like in a case study of Punjab region of Pakistan, etc.,) faced to water
resources, there is a need of DSS which can support managers in making decisons and planning for sustainable use of water resources. So that, protection of the water
resources for the future generation can be ensured.
Photo
MASTERTHESIS
A decision support system (DSS) for an integrated irrigation and
groundwater management in the Punjab region of Pakistan
Fahad Ejaz
2013 – 2015 Technische Universität Dresden
2011 – 2013 Junior Engineer, National Engineering Services (PVT) Pakistan
2007 – 2011 University of Engineering and Technology, Lahore - Pakistan
METHODOLOGY
 DSS contains two fundamental components, i.e, database and model processes.
 To calculate actual evapotranspiration (Etc), reference evaporation (Eto) is estimated by using Penman-Montieth approach and crop coefficient curves which are drawn
based on the stage development and growth period of crops. The estimated crop coefficients are adjusted according to local conditions.
 Effective precipitation (Pe) is estimated by using SCS-method, which takes in to account Etc and usable soil water storage depth (D).
 Groundwater abstraction is estimated based on the total water (groundwater and canals supply) required in the fields.
 Groundwater recharge is estimated from canals depending on the water heads as well as from crop fields based on the water application efficiency in the fields.
 Groundwater flow is simulated (12 months) by using (r.gwflow) groundwater flow model which is a module in GIS GRASS software.
Model processes which involves evapotranspiration, effective precipitation groundwater recharge and
abstraction are first calculated on monthly basis and stored in the database. The estimated values along with
other stored spatially distributed parameters in the database as shown in figure 1 are used to estimate the
groundwater levels. For this purpose the database interacts with GIS-GRASS (r.gwflow) groundwater flow
model. The scenarios are developed based on three different copping patterns and three different irrigation
system parameters.
RESULTS
Figure
 HeadingDSS proves its ability to achieve each scenario goal for different crops combination.
 G.W simulations have confirmed the decrease of water level. However, this decrease is spatially varied.
 Maximum groundwater pumping is in the months of May (290.46 MCM) and June (316.04). The maximum groundwater recharges, i.e., 112.26 MCM and 123.27 MCM
are also in these two months.
 Cropping pattern containing crops of cotton, fodder in Kharif season and wheat , fodder in Rabi season is suitable to improve the current situation.
CONCLUSIONS
Responsible professor: Prof. Dr. Axel Fischer
Supervisor(s): Dr. Catalin Stefan
M.Sc. Aybulat Fatkhutdinov
Figure
Figure 3:Trends of total, effective rainfall, and actual evtp. ( ETc ). Figure 4: Monthly averaged simulated water levels of the study
area.
Figure 6: Difference between averaged and referenced water
levels.
Evapotranspiration
(ET)
Effective
Precipitation (Pe)
G.W Recharge
G.W Abstraction
Decision Model
(r.gwflow)
Graphical
Display
(Output)
Format Conversion
GIS GRASS
r.out.gdalr.in.gdal
raster2pgsqlgdal_translate
User Input
Data Base
1
146.15
146.20
146.25
146.30
146.35
146.40
146.45
146.50
146.55
146.60
May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr
G.W.L(m.a.s.l)
0
1
2
3
4
5
6
7
0
50
100
150
200
250
May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr
ETc(mm)
Total,EffectiveRF(mm)
monthly total rainfall monthly effective rainall monthly daily avg. actual evtp.
-0.40
-0.30
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr
G.W.Ldiff(m)
Irrigation System Scenario 3
rice, fodder, wheat cotton, fodder, wheat
sugarcane, fodder
Boundary
conditions
(-)
Recharge (ms-1)
Hydraulic conductivity
Abstraction (m3s-1)
Bottom
(m.a.s.l)
Top
(m.a.s.l)
Initial Heads
(m.a.s.l)
(ms-1)
Groundwater flow
(m.a.s.l)
Bottom Top EffectiveBottom
Porosity
(-)
Figure 1: Workflow of GIS-GRASS (r.gwflow) groundwater model. Figure 2: Structure of a Decision Support System (DSS).
no
Cropping Pattern Scenarios
Irrigation System
Scenarios
Kharif Rabi
Icanal (%
increase)
Ea (%) qf (%)
1
Rice(75%) and
Fodder (21%)
Wheat(79%) and
Fodder(17%)
actual
canal
supplies
75 25
2
Cotton (75%)
and
Fodder(21%)
Wheat(79%) and
Fodder(17%)
30 85 15
3
Sugarcane
(96%)
Sugarcane(79%)
and Fodder(17%)
50 90 10
Figure 5: Scenarios development.
29th September, 2015
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