This is the just preliminary result of groundwater flow modeling in Guimaras Island. Further study is in process.

1. 7th ASEAN Environmental Engineering Conference
GROUNDWATER FLOW SIMULATION IN
GUIMARAS ISLAND, PHILIPPINE
By: Ratha DOUNG1; Ariel BLANCO2 and Jiro TAKEMURA3
1 PhD Student, Environmental Engineering Department, University of the Philippines – Diliman
2 Chairman of Department of Geodetic Engineering, University of the Philippines – Diliman, Philippines
3 Prof. in Department of Civil Engineering, Tokyo Institute of Technology, Tokyo, Japan
21-22 November 2014
Puerto Princesa, Palawan, Philippines 1

2. Content
1. Introduction
1.1. Background
1.2. Problem statement
1.3. Objective
2. Methodology
2.1. Rainfall
2.2. Temperature
2.3. Groundwater recharge
2.4. Hydraulic conductivity
2.5. Assign grid & orientation
2.6. Aquifer geometry & assign boundary
2.7. Assign hydraulic conductivity
2.8. Assign starting head
2
3. Results and discussions
3.1. Steady state simulation
- Observation well
- Steady state calibration
3.2. Transient simulation
- Transient data
- Transient head
- Transient calibration
4. Conclusion

3. 1. Introduction
1.1. Background
• Well-known for its mangoes, Guimaras is an island province
located Southeast of Panay and Northwest of Negros Island
in Western Visayas, Philippines.
• The Province of Guimaras is composed of five (5)
municipalities and ninety eight (98) barangay (villages)
Luzon
Visayas
Mindanao
3

4. 1.1. Background
• Guimaras Province has a total
coastline length of 470.29km
and covers a land area of
60,457 ha. It consists of four
major rivers, namely
– Igang River,
– Sibunag River,
– Cabano River and
– Mantangingi River
Length of coastline of the five municipalities in Guimaras
4

5. 1.1. Background
Population growth
Tourism growth
57, 600 (1960)
73, 000 (1970)
92,4000 (1980)
118, 000 (1990)
141, 500 (2000)
151, 238 (2007)
162, 943 (2010)
- Regarding to the national census, the
population in Guimaras increased from
57, 600 in 1960 to 162, 943 in 2010.
- At the national level Guimaras shared
0.18% to the total Philippine population
of 92.34 million as recorded in the
Census 2010.
- Tourism is a growing industry in the
province.
- The visitor arrivals showed an increasing
trend from 2000 until 2005 with average
annual growth rate of 2.5% and decreased
by 7.3% in the succeeding two year due to
the oil spill incident. 5

6. 1.2. Problem statement
• Due to dramatically increasing of population, tourist, settlement and
agricultural field, groundwater needs and extraction rate also increase year
by year.
• Excessive groundwater extraction may lead to freshwater scarcity, saltwater
intrusion into coastal aquifers, and hence excessive salinity. Therefore, it is
important that groundwater resources in the study site have to manage have
their own management strategies (different from surface water).
• Groundwater simulation models are useful method of the management tool
in order to understand the behavior of the aquifer system in this particular
study area.
6

7. 1.3. Objectives
- The research tends to use Groundwater Modeling System (GMS
10.0.3) to assess ground water head in study site in 2 cases
(steady-state & Transient)
To visualize and determine general ground water head for
entire of Guimaras Island in the current condition based on
average provided and observed data (Steady-state Simulation)
To figure out the effect of the rainfall variation densities on
groundwater water head based on time series monitoring
data set (Transient Simulation)
1
2
7

8. - Rainfall data is provided by MNRDC (National
Mango Research and Development Center)
department of agriculture, republic of the
Philippine, during 32 years from 1980 to 2011.
- Min=1856 (1992), Max= 3167 (1984)
2. Methodology
2.1. Rainfall
3500
3000
2500
2000
1500
1000
500
0
Annual Rainfall 32 years (mm)
500
400
300
200
100
0
Rainfall average
Dry season Dry Season Average
1975 1980 1985 1990 1995 2000 2005 2010 2015
Precipitation (mm)
Year
Different density of rainfall  recharge  effect groundwater table 8

9. 2.2. Temperature
15 years of temperature recorded (1987-2011 MNRDC)
- Hottest in May
- Coolest in December
30
29
28
27
26
25
24
Average Monthly Temperature 15 years
(mm)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
9

10. 2.3. Groundwater recharge
The recharge rate was calculate by using Empirical method
10
where:
Parameter Unit Value
Rain fall P= mm/y 1304.533
Temperature Tm= 0C 27.276
Evapotranspiration Etr= mm/y 1012.550
Study area A= sqkm 605
Run off Ro= mm/y 149.434
Infiltration U= mm/y (mm/d) 142.548 (0.004)
1
2
3

11. 2.4. Hydraulic conductivities
Hydraulic Conductivity
o Hydraulic conductivity
value (K)obtained
from 8 test wells of
the study site.
o K value are calculated
based on SLUG TEST
method.
Location of slug test well
11

12. 12

13. 2.4. Hydraulic conductivities
K value of Hvorslev (1951) is estimated by:
Where:
Slug Test
2
c w r ln L r
( / )
2
0
K
LT

To is basic time lag. A typical result of the test is shown in Fig.6
in a form of semi-logarithmic plot of H-h/H-Ho vs. t, where H-h/
H-Ho= 0.37.
Time (m)
Depth to
water (m)
Change h in water
level (m)
H-h/H-Ho
0 4.15 1.55 1.00
5 4.40 1.30 0.839
10 4.74 0.96 0.619
15 5.00 0.70 0.452
20 5.25 0.45 0.290
25 5.40 0.30 0.194
30 5.50 0.20 0.129
35 5.58 0.12 0.077
40 5.62 0.08 0.052
45 5.69 0.01 0.006
50 5.70 0.00 0.000
13

14. TEST
WELL
Hydraulic Conductivity Value
UTMLOCATION
HORIZONTAL
HYDRAULIC
CONDUCTIVITY KX
(mm/d)
X Y
1 458476.2 1180494 2.637
2 469264.9 1172992 2.531
3 465260.0 1186350 2.145
4 460255.0 1176741 2.146
5 462483.7 1158953 1.665
6 447113.6 1158300 1.134
7 459133.5 1163723 2.326
8 454977.8 1172071 1.882
14

15. 2.5. Assign grid & orientation
• Model grid consist of 60 column and 112 raw.
• Followed the orientation of model boundary,
grid-frame was rotated 340.5 degree
clockwise.
15

16. 2.6. Aquifer geometry & assign boundary
Aquifer geometry & Boundary condition
• Terrain elevation (DEM) is used to interpolate to GMS as
surface elevation.
• Assumed the aquifer basement is flat (horizontally) with
elevation -100 m below mean sea level.
 Aquifer thickness vary from 100 to 365
• Constant-head boundary: constant
head boundary (IBOUND) was
assigned to each gird cells represented
sea located surrounding island.
• Recharge boundary was assigned with
unique value 0.0004m/d
• River stage was assumed equated to -
2.5 m below land elevation. Height of
water in river equaled to 1 m.
16

17. 2.7. Assign hydraulic conductivity
• The properties of aquifer was
assumed to be uniform
laterally (KX=KY). The value of
vertical hydraulic
conductivity was assumed to
be equal to 1/10 of
horizontal hydraulic
conductivity (10 KZ=KX).
• The distribution of horizontal
for each grid-cell is based on
8 value of slug-test wells.
17

18. 2.8. Assign starting head
• The starting-head of the model was assigned based on 2821 groundwater
table measurement conducted by Local Water Utilities Administration
(LWUA) in 1994.
Starting Head
assigned into
MODFLOW
18

19. 3. Results and discussion
3.1. Steady-state simulation
Groundwater Flow Pattern
19
• The maximum head
located in central of island
(hmax= 144m) while the
minimum head (hmin=-1 m)
located at the coastline.
• River  gaining stream.

20. • The simulation of
groundwater head was
calibrated by observed
head measured at the
field from 352 wells.
• The calibration interval
was set with +/- 1.5 m
with confident error=95%.
20
Steady-state calibration
Observation wells

21. Steady-state calibration
• The agreement between computed and observed could be estimated also by using the
specified statistical criteria of Root Mean Square Error (RMSE) and Relative RootMean
Square Error (RRMSE).
1
2

RMSE Y X
 
( )
i i
1
2
1
n
n
100 1

RRMSE Y X
 ( 
)
i i
n
X n
• Where Yi and Xi are the computed and observed value respectively, is the observed
21
X
mean value; the subscript i indicated the index and n is the number of sample.
RMSE 
0.987
and
RRMSE

4.398%

22. 3.2. Transient simulation
• Groundwater recharge derived from rainfall (U=10.93%P) which was measured
by rain gauges at the site.
• Groundwater water table were monitored from 8 wells at 5 different located
close to the coasts.
• Transient model was running and calibrating for 469 days started from
3/11/2012 to 6/23/2013.
Transient data
0
0.0005
0.001
0.0015
0.002
0.0025
0.003
0.0035
8
6
4
2
0
-2
-4
Tim series of Rainfall and Water table
Recharge SAB01 SUC01 CAB02 TAN01 ZAL01
-6 0.004
0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5
Rainfall (m/month)
Water table (m ASL)
Duration (month)
22

23. • Groundwater head varied based on the recharge densities.
• Variation of transient head was calibrated with observed data monitored from 8
monitoring wells in 5 specific location. Transient head was calibrated from Mar
11, 2012 to Jun 23, 2013.
23
3.2. Transient Head
No Monitoring date Num of Day Stress period
1 Mar 11, 2012 to Mar 31, 2012 21 0-1
2 Apr 01, 2012 to Apr 30, 2012 30 1-2
3 May 01, 2012 to May 31, 2012 31 2-3
4 Jun 01, 2012 to Jun 30, 2012 30 3-4
5 Jul 01, 2012 to Jul 31, 2012 31 4-5
6 Aug 01, 2012 to Aug 31, 2012 31 5-6
7 Sep 01, 2012 to Sep 30, 2012 30 6-7
8 Oct 01, 2012 to Oct 31, 2012 31 7-8
9 Nov 01, 2012 to Nov 30, 2012 30 8-9
10 Dec 01, 2012 to Dec 31, 2012 31 9-10
11 Jan 01, 2013 to Jan 31, 2013 31 10-11
12 Feb 01, 2013 to Feb 28, 2013 28 11-12
13 Mar 11, 2013 to Mar 31, 2013 31 12-13
14 Apr 01, 2013 to Apr 30, 2013 30 13-14
15 May 01, 2013 to May 31, 2013 31 14-15
16 Jun 01, 2013 to Jun 23, 2013 23 15-16

24. 24
3.3. Transient calibration
4
3
2
1
0
0
-1
-2
-3
-4
-5
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Obervation Head (m)
Com vs Obs Head in observation well SAB01
Com Obs
y = 1.2208x - 0.1011
R² = 0.6419
1.8
0.8
-0.2
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
2
1.5
1
0.5
0
2
1.5
1
4
3
2
1
0
-1
-2
-3
-4
-5
-6
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Obervation Head (m)
Com vs Obs Head in observation well CAB02
Com Obs
y = 1.016x + 0.1137
R² = 0.8773
0.5
0
0 0.5 1 1.5
4
3
2
1
0
-1
-2
-3
3
2
1
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Obervation Head (m)
Com vs Obs Head in observation well ZAL01
Com Obs
y = 1.1765x - 0.0175
R² = 0.9238
0
-1
-2
-3
-2 -1 0 1 2
4
3
2
1
0
-1
-2
-3
-4
-5
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Obervation Head (m)
Com vs Obs Head in observation well TAN01
Com Obs
y = 1.7873x + 2.3253
R² = 0.6886
0
-1
-2
-3
-4
-4 -3.5 -3 -2.5 -2 -1.5
-7
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Obervation Head (m)
Com vs Obs Head in observation well SUC01
Com Obs
y = 1.0384x + 0.2279
R² = 0.8738
-6
-6 -4 -2 0

25. 4. Conclusion
• The steady state simulation of groundwater flow shows the reasonable agreement
between field observation and calculation results and it apply to figuring out the
regional groundwater flow in the study site.
• The agreement between computed and observation head represented by RMSE (R2)=
0.987 and RMSE= 4.398%. Groundwater flow pattern directed from inland to the
coastline with maximum and minimum head equaled to 144m and 0 m respectively.
The main rivers are graining stream from groundwater.
• Monitoring data set of rainfall density and groundwater head were use to run
transient model. Out put of transient simulation show the acceptable relationship
between groundwater recharge and hydraulic head within monitoring wells.
However, computing head is slightly sensitive to the variation of groundwater
recharge.
• The error between computed and observed head in transient simulation is occurred
due to ignored of pumping rate variation from the production wells and maybe
because of the assumption of aquifer geometry (layers, basement, etc…)
• This study served as preliminary result and provided basic information for future
25
work (saltwater intrusion).

26. THANK YOU!