REPUBLIC OF THE PHILIPPINES THE NATIONAL IRRIGATION ADMINISTRATION Bago River Irrigation System Rehabilitation and Improvement Project (BRISRIP) Assignment Report Climate & Hydrological Forecasting By Jose Edgardo L. ABAN, Ph.D. February 2007 SPACEVISION MAPPING SERVICES
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REPUBLIC OF THE PHILIPPINES
THE NATIONAL IRRIGATION ADMINISTRATION
Bago River Irrigation System
Rehabilitation and Improvement Project
(BRISRIP)
Assignment Report
Climate & Hydrological Forecasting
By
Jose Edgardo L. ABAN, Ph.D.
February 2007
SPACEVISION MAPPING SERVICES
Bago River Irrigation System Rehabilitation and Improvement Project
(BRISRIP)
Table of Contents
Pages
1 Summary Assignment…………………………………………………. 1
1.1 Name and Assignment …………………………………………… 1
1.2 Assignment Period………………………………………………... 1
2 Basic Approach to Study……………………………………………… 1
2.1 Collection of Bago River Watershed Meteorological and
Hydrological Parameters………………………………………………
1
2.2 Analysis of Bago River Watershed Hydrological and
Meteorological Parameters…………………………………………….
2
3 Forecasted Data and Analyses………………………………………… 5
3.1 Forecasted Mean Monthly Discharge (Years 2003-2016)………. 5
3.2 Forecasted Mean Monthly Intake Discharge Data
(Years 2003-2016)……………………………………………………
5
3.3 Forecasted Mean Monthly Rainfall Data (Years 2003-2016)…. 6
3.4 Forecasted Mean Monthly Evaporation (2003-2016)…………... 7
3.5 Forecasted Annual Mean Dry Season Rainfall…………………. 9
3.6 Forecasted Annual Mean Wet Season Rainfall………………... 10
List of Tables
Table 1.1 Forecasted Mean Monthly Discharge for the years 2003-
2016 based on the Estimated Bago River Discharge Data
(1994-2002) …………………………………………………
T- 1
Table 1.2 Forecasted Mean Monthly Intake Discharge for the years
2003-2016 based on the Estimated Bago River Discharge
Data (1994-2002) …………………………………………...
T- 12
Table 1.3 Forecasted Mean Monthly Rainfall for the years 2003-2016
based on the La Granja Agromet Research Data Center, La
Carlota City PAGASA Weather Station (1994-2002) ……..
T- 25
Table 1.4 Forecasted Mean Monthly Evaporation for the years 2003-
2016 based on the La Granja Agromet Research Data
Center, La Carlota City PAGASA Weather Station
(1994-2002) ………………………………………………...
T- 36
Table 1.5 Forecasted Annual Mean Dry Season Rainfall, for the Years
2003-2016 ………………………………………………….
T- 47
Table 1.6 Forecasted Annual Mean Cumulative Wet and Dry Seasons
Rainfall, for the Years 2003-2016 ………………………….
T- 48
Table 1.7 Forecasted Annual Mean Wet Season Rainfall, for the
Years 2003-2016 …………………………………………….
T- 49
List of Figures
Figure 1.1 Estimated Mean Monthly Discharge for the years 1994-2002
based on the Estimated Bago River Discharge …………………
F- 1
Figure 1.2 Forecasted Mean Monthly Discharge for the years 2003-2016
based on the Estimated Bago River Discharge …………………
F- 2
Figure 1.3 Estimated Mean Monthly Intake Discharge for the years 1990-
2002 based on the Estimated Bago River Discharge …………...
F- 3
Figure 1.4 Forecasted Mean Monthly Intake Discharge for the years 2003-
2016 based on the Estimated Bago River Discharge …………...
F- 4
Figure 1.5 Estimated Mean Monthly Rainfall for the years 1994-2002 based
on the La Granja Agromet Research Center Data ………………
F- 5
Figure 1.6 Forecasted Mean Monthly Rainfall for the years 2003-2016
based on the La Granja Agromet Research Center Data ……….
F- 6
Figure 1.7 Mean Monthly Evaporation at La Granja Agromet Research
Center Data (1994-2002) ……………………………………….
F- 7
Figure 1.8 Forecasted Mean Monthly Evaporation for the years 2003-2016
based on the La Granja Agromet Research Center Data ……….
F- 8
Figure 1.9 Forecasted Annual Mean Dry Season Rainfall for the years
2003-2016 ………………………………………………………
F- 9
Figure 1.10 Forecasted Annual Mean Cumulative Wet and Dry Seasons
Rainfall for the years 2003 to 2016 ……………………………..
F- 10
Figure 1.11 Forecasted Annual Mean Wet Season Rainfall for the years
2003-2016 ………………………………………………………
F- 11
Figure 1.12 Power Spectrum Graph (Normalized) of Annual Mean Dry
Season Rainfall based on annual dry season rainfall
from 1976-2002 …………………………………………………
F- 12
Figure 1.13 Power Spectrum Graph (Normalized) of Annual Mean Wet
Season Rainfall based on annual wet season rainfall
from 1976-2002 …………………………………………………
F- 13
1
1. Summary Assignment
1.1 Name of Assignment: Climate and Hydrological Forecasting
Name : Jose Edgardo L. ABAN, Ph.D.
Assignment Work: Climate Analyst
1.2 Assignment Period: September 15, 2006 to February 15, 2007
Assignment Works:
� Collection, review and evaluation of records, data and information on
meteorology and hydrology for analysis and forecasting of future climatic
and hydrological parameters;
� Identification of important environmental events of significance to the
Bago Watershed and its hydrology;
� Analyses and Forecasting of future climatic/hydrologic scenarios over the
Bago River Watershed.
2. Basic Approach to the Study
2.1. Collection of Bago River Watershed Meteorological and Hydrological Parameters
Retrospective study was conducted on the various parameters and based on the report
made by Sakanashi (2003) of the watershed, particularly on the following:
� Mean Monthly Discharge (1990-20021)
� Mean Monthly Intake Discharge (1990-2002)
� Mean Monthly Rainfall (1994-2002)
� Mean Monthly Evaporation (1994-2002)
� Annual Mean Wet Season rainfall (1976-2002)
� Annual Mean Dry Season Rainfall (1976-2002)
� Annual Mean Cumulative Wet and Dry Season Rainfall (1976-2002)
2
2.2 Analysis of Bago River Watershed Hydrological and Meteorological Parameters
Two non-parametric techniques were employed on the dataset cited above, in order to
extract oscillatory components as well as be able to reconstruct forecasted data from the
year 2003 to about 2016 (or around 13 years of forecasted data. The techniques included
the Blackman-Tukey (BT) Correlogram Analysis or Power Spectrum (PS) Technique
and Singular Spectrum Analysis (SSA).
2.2.1 Blackman-Tukey Correlogram Analysis
The correlogram constructs an estimate of the power spectrum using a windowed fast
Fourier transforms (FFT) of the autocorrelation function of the time series. It was
developed by Blackman and Tukey (1958) and is based on the Wiener-Khinchin theorem,
which states that if the Fourier transform of a series g(t) is G(w), and if the
autocorrelation function of the series is R, then the Fourier transform of R is |G(w)|2 or
the power spectrum of g (e.g., Press et al., 1989). The resulting power-spectrum estimate
is called a correlogram.
The correlogram is usually performed on weighted versions of the time series or
autocorrelation functions in order to reduce power leakage (artificially high power
estimates at frequencies away from the true peak frequencies). Press et al. (1989, pp. 423-
424) note that "when we select a run of N sampled points for periodogram spectral
estimation, we are in effect multiplying an infinite run of ... data ... by a window function
in time, one which is zero except during the total sampling time [NDt], and is unity
during that time." The sharp edges of this window function contain much power at
highest frequencies, which is imparted to the windowed signal and leads to power
leakage. A similar argument can be made for correlograms. Weighting the data or
correlation function by various tapered shapes (high in center and falling off to sides) is
an accepted traditional approach to reducing power leakage. In the Blackman-Tukey
approach, the power spectrum P(w) is estimated by
3
where rj is the autocorrelation function, M is the maximum lag considered and window
length, and wj is the windowing
There are various windows of the same widths give similar results. The more important
choice is how wide the windows should be. The averaging associated with windowing a
series reduces the resolution of the methods, from the frequency intervals of 1/N, to a
windowed frequency intervals of about 1/M (e.g., Kay 1988, p. 81). Thus, wider windows
yield higher spectral resolution, and vice versa.
However, there is a trade-off between higher resolution and increasing variance of the
spectral estimate. At the extreme, a single (M=N) direct application of FFT to an
unwindowed time series results in a periodogram with a theoretical standard deviation of
the estimates equal to the estimates at each frequency, regardless of the number of
observations in the time series (Press et al. 1989, p. 423). Averaging the results from
many short data windows throughout the series (or autocorrelation) effectively increases
the number of independent samples used in estimation and thereby reduces the estimation
variance. Kay (1988, section 4.5) shows that the variance of a power spectrum obtained
by a windowed correlogram is 2M/3N of the estimated power at each frequency. Thus a
narrower window should be used to smooth the spectrum and reduce the sampling errors
on the estimate. In practice, Kay (1988) recommends that windows should be no more
than one-fifth to one-tenth the total number of data points (to obtain desired estimate-
variance reductions) and not too much smaller (in order to retain the ability to distinguish
between powers at neighboring frequencies and to obtain the desired leakage reductions).
References:
Press, W.H., Flannery, B.P., Teukolsky, S.A., and Vetterling, W.T., 1989:
Numerical recipes--The art of scientific computing (FORTRAN version).
Cambridge University Press, 702 p.
Kay, S.M., 1988: Modern spectral estimation--Theory and application. Prentice-
Hall, 543 p.
4
2.2.2. The Singular Spectrum Analysis (SSA) of a Time Series
In recent years a powerful technique of time series analysis has been developed and
applied to many practical problems. This technique is based on the use of the Singular-
value decomposition of the so-called trajectory matrix obtained from the initial time
series by the method of delays. It is aimed at an expansion of the original time series f(t)
into a sum of a small number of 'independent' and 'interpretable' components:
f(t) = f'(t) + f''(t)+... + u(t) (*)
where the time series f'(t),f''(t)... are 'independent' and 'interpretable' and u(t) stands for a
random noise. The expansion (*) can be used for different purposes, for instance, for
extracting trend, seasonalities and other harmonic components, for separating
deterministic and random components, for interpolating and forecasting.
References:
Goljandina N.E., Nekrutkin V.V., Zhigljavsky A.A. (2001) Analysis of Time
Series Structure: SSA and related technique, Chapman & Hall / CRS, Boca Raton,
xii+306pp
Danilov D., Zhigljavsky A.A., ed. (1997) Principal Components of Time Series:
The Caterpillar Method. University of St. Petersburg, 308 pp.
5
3.0. Forecasted Data and Analyses
3.1 Forecasted Mean Monthly Discharge (Years 2003-2016)
The Forecasted Mean Monthly Discharge for the years 2003-2016 based on the Estimated
Bago River Discharge indicate an increasing trend for the thirteen-year forecasted data
(see Figure 1.2).
This increase in the River Discharge may be attributed to the decreasing forest cover
in the watershed leading to a lessened water holding capacity by the watershed itself.
This finding corroborates earlier assumptions made by Sakanashi ( 2003) whereby it had
been cited that according to the Negros Forests and Ecological Foundation Inc.
(NFEFI), the forest area of the watershed has been estimated at 6% in 1984 and 4% in
1992.
The increasing discharge of the Bago River is also corroborated by the interviews made
with Mr. Wilson Ciocon, the flood gatekeeper of NIA. Mr. Ciocon observed that the
flash floods that occurred most especially during rainy seasons have increasingly
subsided faster comparing these with subsidence rates twenty years ago. It had been
observed that twenty years ago, flash floods have longer retention times as long as 5
to 6 hours. Recent flash flood events have been observed to have retention times of only
1 to 2 hours at the most, according to Mr. Ciocon. By 2016, the forecasted mean monthly
discharge is estimated at 175,115 liters per second during the June-July 2016 rainy season
episode.
3.2. Forecasted Mean Monthly Intake Discharge Data (Years 2003-2016)
Based on the simulations made from the years 1990 to 2002, there will be constant
volume rate (liters per second) of intake water at the Head Gate station at the diversion
dam. The forecasted volume rate for the rainy seasons for the years 2003 to 2016 will
6
remain relatively constant rates based on the simulated/forecasted data in the range and
on a monthly average of 18,300 liters per second.
The simulated dry season intake discharge data, on the other hand, indicates that there is
a tapering/narrowing of values (please see Figure 1.4), increasing in rates from 2003 to
2016. As such, it expected that there will be more and more intake discharge at the head
gate station diversion dam during dry months in the coming years. This behavior of the
intake discharge should be taken in to consideration since, there is expectedly, more and
more water that will be available for irrigation purposes during dry months if this trend
will continue. This simulated behavior of intake discharge during dry months may indeed
be consistent with the previous finding of an increasing trend in the overall water
discharge rates, as well as forecasted increasing rainfall over the area (based on Table
1.3 and Figure 1.6) out of the Bago River and complicated by that the watershed’s water
holding capacity may be diminishing through time.
Summary Table of Forecasted Mean Monthly Discharge
PARAMETER FORECASTED
OBSERVED TREND
FROM 2003 TO 2016
Simulated volume rate for the rainy seasons in
succeeding years
Constant
Simulated volume rate for the dry seasons in
succeeding years
Increasing
3.3. Forecasted Mean Monthly Rainfall Data (Years 2003-2016)
Data from the La Carlota Agromet Research Center of the Philippine Atmospheric
Geophysical and Astronomical Services Administration (PAGASA) was analyzed using
Singular Spectrum Analysis (SSA) Technique. From the Figure 1.6 and based on
simulated/forecasted data, it can quite easily be interpreted that there are increasing
amounts of rainfall over the area of the Bago Watershed. Hence, expectedly, there will
be increasing amounts of available water for absorption by the Bago watershed and
release through run-off by the Bago River.
7
This increasing trend in the simulated rainfall data is prominent in both rainy and dry
season forecasted data. The average monthly rainfall during the peak of the rainy
season will have increased from approximately 16.7 millimeters in 2006 to 22.3
millimeters by 2016, an increase of around 25% in the amount of rainfall in just ten
years. Likewise, the average monthly rainfall during the peak of the dry season will
have increased from approximately 5.6 millimeters in 2006 to 13.0 millimeters in 2016,
an increase of around 57% in just ten years.
The observed trend of increasing rainfall also corroborates and is consistent with the
earlier two findings of the present study of increasing mean monthly river discharge
(Figure 1.2) and intake discharge (Figure 1.4) of the Bago River.
Summary Table for Forecasted Mean Monthly Rainfall
PARAMETER
FORECASTED
OBSERVED
TREND
PERCENTAGE
CHANGE IN
TEN YEARS
FROM 2006 TO 2016
Average monthly rainfall during
the peak of the rainy season
Increasing 25%
Average monthly rainfall during
the peak of the dry season
Increasing 57%
3.4. Forecasted Mean Monthly Evaporation (2003-2016)
Data from the La Carlota Agromet Research Center of the Philippine Atmospheric
Geophysical and Astronomical Services Administration (PAGASA) was analyzed using
Singular Spectrum Analysis (SSA) Technique.
Based on the simulations made from the years 1994 to 2002, there is a decreasing trend
in the overall evaporation rates over the Bago watershed.
The simulated wet and dry season monthly average evaporation values, indicate that there
is a tapering/narrowing of values (please see Figure 1.8), decreasing in amounts from
8
2003 to 2016. As such, it expected that there will be less and less evaporation rates over
the watershed area during wet and dry seasons in the coming years.
This simulated behavior of decreasing evaporation over the Bago watershed area in both
dry months and wet months may indeed be consistent with the previous finding of the
increasing trend in the overall water discharge rates, providing less water to be
evaporated/ transpirated out from the forested watershed area itself, since less water is
available for sippage /perculation into the forest root system, as well as the diminishing
watershed’s water holding capacity due to the diminishing forest cover (which
transpirates/evaporates water from their canopies) in the watershed.
This decreasing trend in the simulated monthly evaporation data is prominent in both
rainy and dry season forecasted data. The average monthly evaporation during the peak
of the rainy season will have decreased from 3.6 millimeters in 2006 to 2.5
millimeters by 2016, an decrease of around 31% in the amount of evaporated water in
just ten years. Likewise, the average monthly evaporation during the peak of the dry
season will have decreased from approximately 2.8 millimeters in 2006 to 2.3
millimeters in 2016, a decrease of around 18% in just ten years.
Summary Table for Forecasted Mean Monthly Evaporation
PARAMETER
FORECASTED
OBSERVED
TREND
PERCENTAGE
CHANGE IN
TEN YEARS
FROM 2006 TO 2016
Average monthly evaporation
during the peak of the rainy
season
Decreasing 31%
Average monthly evaporation
during the peak of the dry season
Decreasing 18%
9
3.5. Forecasted Annual Mean Dry Season Rainfall
3.5.1. Forecasted Data
The forecasted annual mean dry season rainfall expectedly conforms with the earlier
forecasted results of the mean monthly rainfall data. Figure 1.9 shows an apparent 5-year
cycle (oscillatory component) in the amount of rainfall. Based on the graph, it can be
assumed that there will be excessive amounts of rainfall that will have occurred in the
years 2005, 2010 and 2015 (above normal dry season rainfall years). Lowest rainfall
events (below normal dry rainfall season years or dry spell/drought years) during
the dry season will have occurred during the years 2002, 2007 (now forecasted and
experienced to be an El Niño Year) and 2012. If this 5-year cycle would ensue further, it
can be assumed that 2017 may possibly experience drought. The rest of the other years
could be surmised as normal dry season rainfall years.
Summary Table of Extreme Dry Season Events
EXTREME EVENTS YEAR(S)
Above Normal Dry Season Rainfall
2005, 2010, 2015
Below Normal Dry Season Rainfall or
“Dry Spell/Drought Years”
2002, 2007, 2012
Normal Dry Season Rainfall Episodes
All other years
between 2003 to 2016
3.5.2. Oscillatory Events
The annual mean dry season rainfall data from 1976-2002 was also analyzed using the
Blackman-Tukey (BT) Correlogram Analyses or Power Spectrum (PS) Technique.
Figure 1.12 is a representation of the same data in the frequency domain. Figure 1.12
corroborates the earlier finding above, about the 5-year oscillatory behavior in the
annual mean dry season rainfall data. Highest data variability occurs at periods of 5-
years, hence the peak power spectra occurring at a 5-year period in the graph. Extreme
10
events such as extreme dry spells and above-normal dry season rainfall occur with
repeat periods of five years, as earlier cited in the text.
3.6. Forecasted Annual Mean Wet Season Rainfall (2003-2016)
3.6.1. Forecasted Data
There is an overall increasing trend over the thirteen year period (2003-2016). However
this increase in the annual mean wet season is seen to be gradual. The increasing annual
mean wet and dry season rainfall are consistent with previous results conveyed in this
study. However, there is a prominent peak in the year 2013 which may indicate
extreme annual mean wet season rainfall for that particular year (Figure 1.10,
approximately 2209 millimeters). Hence, it might be expected that there could be
excessive river runoff during the 2013 wet season, and therefore flooding may be
expected.
Summary Table of Extreme Annual Wet Season Event
EXTREME EVENTS YEAR(S)
Above Normal Wet Season Rainfall,
flooding may be expected
2013
3.6.2. Oscillatory Components
The annual mean wet season rainfall data from 1976-2002 was also analyzed using the
Power Spectrum Technique. Figure 1.13 is a representation of the same data in the
frequency domain. Figure 1.13 indicates two peaks in data variability, one with a
2.75-year cycle and another peak of around 7-year cycle in the annual mean wet season
rainfall data. These behaviors in the annual mean wet season data is consistent with
the El Niño-Southern Oscillation phenomenon (ENSO). Expectedly, extreme rainfall
events such as extreme wet spells and above-normal wet season rainfall should occur
with repeat periods of 2.75 and 7 years.
Figure 1.1 Estimated Mean Monthly Discharge for the years 1994-2002 based on the Estimated Bago River Discharge as presented
by Sakanashi, 2003.
Liters per second
MONTHS
F-1
Figure 1.2 Forecasted Mean Monthly Discharge for the years 2003-2016 based o the Estimated Bago River Discharge as
presented by Sakanashi, 2003. Forecasted data starts from Sample 109 onwards, representing the years 2003 to 2016.
Liters per second
MONTHS
F-2
Figure 1.3 Estimated Mean Monthly Intake Discharge for the years 1990-2002 based on the Estimated Bago River Discharge as
presented by Sakanashi, 2003.
MONTHS
Liters per second
F-3
Figure 1.4 Forecasted Mean Monthly Intake Discharge for the years 2003-2016 based on the Estimated Bago River Discharge
as presented by Sakanashi, 2003. Forecasted data starts from Sample 156 (January 2003) onwards, representing the years
2003 to 2016.
Liters per second
MONTHS
F-4
Figure 1.5. Estimated Mean Monthly Rainfall for the years 1994-2002, based on the La Granja Agromet Research Center Data, La
Carlota City PAGASA Weather Station, (1994-2002), as presented by Sakanashi, 2003.
Millimeters
MONTHS
F-5
Figure 1.6. Forecasted Mean Monthly Rainfall for the years 2003-2016 based on the La Granja Agromet Research Center Data, La
Carlota City PAGASA Weather Station, (1994-2002), as presented by Sakanashi, 2003. Forecasted data starts from Month 109
(January 2003) onwards.
MONTHS
Millimeters
F-6
Figure 1.7. Mean Monthly Evaporation at La Granja Agromet Research Center Data, La Carlota City PAGASA Weather Station,
(1994-2002), as presented by Sakanashi, 2003.
MONTHS
MONTHS
Millimeters
F-7
Figure 1.8. Forecasted Mean Monthly Evaporation for the years 2003-2016 based on the La Granja Agromet Research Center Data,
La Carlota City PAGASA Weather Station, (1994-2002), as presented by Sakanashi, 2003. Forecasted data starts from Month 109
(January 2003) onwards.
MONTHS
F-8
Figure 1.9. Forecasted Annual Mean Dry Season Rainfall, for the Years 2003 to 2016, based on annual dry season rainfall data
from 1976-2002, La Granja Agromet Research Center, La Carlota City (PAGASA Weather Station).
YEARS
Millimeters
F-9
Figure 1.10. Forecasted Annual Mean Cumulative Wet and Dry Seasons Rainfall, for the Years 2003 to 2016, based on cumulative
annual wet and dry seasons rainfall data from 1976-2002, La Granja Agromet Research Center, La Carlota City (PAGASA Weather
Station).
YEARS
Millimeters
F-10
Figure 1.11. Forecasted Annual Mean Wet Season Rainfall, for the Years 2003 to 2016, based on annual wet season rainfall data
from 1976-2002, La Granja Agromet Research Center, La Carlota City (PAGASA Weather Station).
Millimeters
YEARS
F- 11
Figure 1.12. Power Spectrum Graph (Normalized) of Annual Mean Dry Season Rainfall, based on annual dry season rainfall from 1976-2002,
La Granja Agromet Research Center, La Carlota City (PAGASA Weather Station).
VARIANCE
PERIOD ( IN YEARS)
FREQUENCY
5-Year Peak
F-12
Figure 1.13. Power Spectrum Graph (Normalized) of Annual Mean Wet Season Rainfall, based on annual wet season rainfall from 1976-
2002, La Granja Agromet Research Center, La Carlota City (PAGASA Weather Station).
VARIANCE
PERIOD
FREQUENCY
7-Year Peak
2.75 -Year Peak
F-13
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-1
Table 1.1. Forecasted Mean Monthly Discharge for the years 2003-2016 based on the
Estimated Bago River Discharge Data (1994-2002) based on Sakanashi, 2003.
Forecasted data starts from Sample 109 onwards, representing the years 2003 to 2016
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-2
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-3
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-4
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-5
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-6
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-7
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-8
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-9
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-10
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-11
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
Table 1.2. Forecasted Mean Monthly Intake Discharge for the years 2003-2016 based
on the Estimated Bago River Discharge Data (1994-2002) based on Sakanashi, 2003.
Forecasted data starts from Sample 109 onwards, representing the years 2003 to 2016.
T-12
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-13
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-14
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-15
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-16
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-17
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-18
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-19
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-20
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-21
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-22
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-23
MONTH-
FORECAST BASE
(LITERS PER
SECOND)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
AVERAGE
(LITERS ER
SECOND)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
T-24
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
AVERAGE
(LITERS ER
SECOND)
MILLIMETERS
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
Table 1.3. Forecasted Mean Monthly Rainfall for the years 2003-2016 based on the La Granja Agromet
Research Data Center, La Carlota City PAGASA Weather Station (1994-2002) based on Sakanashi,
2003. Forecasted data starts from Sample 109 onwards, representing the years 2003 to 2016.
T-25
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
AVERAGE
(LITERS ER
SECOND)
MILLIMETERS
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
T-26
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
AVERAGE
(LITERS ER
SECOND)
MILLIMETERS
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
T-27
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
AVERAGE
(LITERS ER
SECOND)
MILLIMETERS
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
T-28
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
AVERAGE
(LITERS ER
SECOND)
MILLIMETERS
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
T-29
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
AVERAGE
(LITERS ER
SECOND)
MILLIMETERS
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
T-30
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
AVERAGE
(LITERS ER
SECOND)
MILLIMETERS
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
T-31
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
AVERAGE
(LITERS ER
SECOND)
MILLIMETERS
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
T-32
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
AVERAGE
(LITERS ER
SECOND)
MILLIMETERS
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
T-33
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
AVERAGE
(LITERS ER
SECOND)
MILLIMETERS
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
T-34
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
AVERAGE
(LITERS ER
SECOND)
MILLIMETERS
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
MILLIMETERS
T-35
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
AVERAGE
(MILLIMETERS)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
Table 1.4. Forecasted Mean Monthly Evaporation for the years 2003-2016 based on the La
Granja Agromet Research Data Center, La Carlota City PAGASA Weather Station. (1994-2002).
Forecasted data starts from Sample 109 onwards, representing the years 2003 to 2016.
T-36
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
AVERAGE
(MILLIMETERS)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
T-37
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
AVERAGE
(MILLIMETERS)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
T-38
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
AVERAGE
(MILLIMETERS)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
T-39
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
AVERAGE
(MILLIMETERS)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
T-40
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
AVERAGE
(MILLIMETERS)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
T-41
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
AVERAGE
(MILLIMETERS)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
T-42
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
AVERAGE
(MILLIMETERS)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
T-43
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
AVERAGE
(MILLIMETERS)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
T-44
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
AVERAGE
(MILLIMETERS)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
T-45
MONTH-
FORECAST BASE
(MILLIMETERS)
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
AVERAGE
(MILLIMETERS)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETERS)
T-46
Table 1.5. Forecasted Annual Mean Dry Season Rainfall, for the Years 2003-2016
FORECASTED
YEAR-
MILLIMETER
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETER)
AVERAGE BY
BOOTSTRAP
(MILLIMETER)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETER)
T-47
Table 1.6. Forecasted Annual Mean Cumulative Wet and Dry Seasons Rainfall, for the
Years 2003-2016.
FORECASTED
YEAR-
MILLIMETER
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETER)
AVERAGE BY
BOOTSTRAP
(MILLIMETER)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETER)
T-48
Table 1.7. Forecasted Annual Mean Wet Season Rainfall, for the Years 2003-2016.
FORECASTED
YEAR-
MILLIMETER
INITIAL UPPER
CONFIDENCE
BOUND (at 95%)
(MILLIMETER)
AVERAGE BY
BOOTSTRAP
(MILLIMETER)
INITIAL LOWER
CONFIDENCE
BOUND (at 95%)
(MILLIMETER)
T-49
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