Climate Change Research Methodology

8/13/2019 Climate Change Research Methodology

1/23

1

1.0INTRODUCTIONResearch Topic:

CLIMATE CHANGE SUSTAINABLE IMPACTS OF HYDROELECTRIC POWERSUPPLY IN MINDANAO, PHILIPPINES

After reviewing the literature based on the research topic, the following research questions

were formed:

Research Questions:

What are the climate parameters that have significant change that were brought aboutin the increase of greenhouse gases?

What are the climate variables that resulted in the change of climate in the Philippines? What is the relationship in rising temperature and precipitation change due the climate

change in the Philippines with Hydroelectric power generation in Mindanao,

Philippines?

What is the effect of El Nino phenomenon with Hydroelectric Power generation inMindanao, Philippines?

What other factors aside from climate change and El Nino that can be considered asinvolved in the Power shortage supply in Mindanao, Philippines?

Hypothesis:

Climate Change in the Philippines affects the hydroelectric power generation in

Mindanao, Philippines.

This type of hypothesis is a relational hypothesis since it will state the relationships between

variables. The variables involved in climate change are the temperature and precipitation.

Other variables are the temperature and precipitation change brought about by the El Nino

phenomenon. An increase in temperature will increase evaporation; this will affect the water

resources required in hydroelectric generation. Decrease in precipitation will also affect

water slow therefore will affect also power supply in hydroelectric facilities. Hypotheses can

be tested qualitatively because variables such as temperature and precipitation can be

measured. Hydroelectric power output can also be quantified.


2/23

2

Research Methodology and Research Method:

Secondary data source can be used to allow the translation of climatic variables into estimates

of river flow. This will enable to show the relationship between climate variables and

hydropower generation performance. A model can be developed in order to quantify the

relationship between changing climate and hydroelectric power generation viability. All

variables can be quantified; therefore, quantitative methodology will be used. Based on the

quantitative methodology, this research will be appropriately used the Correlational Research

Method. Since, this will examine the co-variation of two or more variables such as

temperature and precipitation change in relation with hydropower generation performance.

After a second review of literatures a revised hypothesis was drawn:

Increase in temperature, decrease in precipitation and decrease river runoff due to

climate change in the Philippines has a negative impact of the hydroelectric power

generation in Mindanao, Philippines.

The potential impact of climate change on water resources can be shown in terms of

variations in temperature and precipitation. There is a relationship between increased

temperatures with variations in river runoff due to changes in precipitation. Studies show not

only the effect on the river flows but also the impact on generation from hydroelectric

stations.

2.0RESEARCH METHODOLOGYIn order to systematically find a way to solve the research problem an appropriate research

methodology should be carried out. Research methodology is essentially the procedures that

will be used to describe, explain and predict this phenomenon and also gaining more

knowledge (Rajasekar , Philominathan & Chinnathambi 2006) .

In this research the procedure that will be used in describing or explaining climate change in

the Philippines is by using change of temperature and precipitation. The capacity of the

natural water resources used in Mindanao, Philippines in hydroelectric power generation can

be assessed against projected climate change and variability. This required the definition of

the water source and the hydroelectric facilities. After the definition, water resource model

can be used to simulate hydrological systems (Jose & Cruz 1999).


3/23

3

Based on the above assessment, Quantitative research method will be applied that will

primarily base on the measurement of change of temperature and precipitation consequently

the estimates of river flow or river runoff. This research method will be based on simulation

case studies; therefore, it will generally classify as quantitative research (Melville & Goddard

1996).

Secondary data source can be used to allow the translation of climatic variables into estimates

of river flow. This will enable to show the relationship between climate variables and

hydropower generation performance. A model can be developed in order to quantify the

relationship between changing climate and hydroelectric power generation viability (Jose &

Cruz 1999).

This will examine the co-variation of two variables such as temperature and precipitation

change in relation with hydropower generation performance. Collections of empirical data

can be analysed and simulated to develop a model. A model can described and will enable to

evaluate the relationship between the changes in climate and the hydroelectric power

performance (Harisson & Whittington 2002).

Enhanced levels of greenhouse gas concentrations are predicted to cause a significant rise in

temperature over the next century. Predictions of future climate are based on the output ofcomplex numerical Global Circulation Models (GCMs) which simulate physical processes in

the atmosphere and oceans (Harisson, Whittington & Gundry n.d.).

Secondary historical climate data of change in temperature and precipitation from the

Philippines government statistics website can be investigated to convert into primary date

using regression analysis. To assess the effect of climate change, a model can be develop

using this method to provide estimates of potential and actual evapotranspiration in terms of

river runoff based on temperature and precipitation change. The effect of climate change on

hydrogenation can then be determined by using the expected values for river runoff and the

regression analysis Harisson, Whittington & Gundry n.d.).

3.0RESEARCH METHODSResearch methods are the various procedures, schemes and algorithms used in research.

These include theoretical procedures, experimental studies, numerical schemes and statistical


4/23

4

approaches that are essentially planned, scientific and value-neutral. Research methods will

assist the researcher to collect samples, data in order to find solution to the research problem.

Research method also will explain scientifically phenomena based on collected facts,

measurements and observations in which these explanations can be verified by experiments

(Graciano & Raulin 2004).

3.1SELECTION OF RESEARCH METHODSBased on the variables identified in the research hypothesis which are temperature and

precipitation, Quantitative research method should be applied in this research. Since

temperature and precipitation data can be objectively and reliably drawn from the

Philippines Statistics website, quantitative research method can be used by basing on

this assumption. Furthermore, this collection of data can be manipulated

mathematically to objectively quantify differences and relationships. On the other

hand if Qualitative research method if considered, the phenomena which is climate

change in the Philippines will be assumed as complex for this matter (Borrego,

Douglas & Amelink 2009).

Quantitative research method in the case of climate data analysis can make

predictions, produce causal statements, establish relationships and generalise findings.

Qualitative research also is use in conducting research to gain insight into complex

phenomena such as climate change. Thus, this will describe the said event or patterns

of changes in the Philippines climate (Schloss & Smith 1999).

In this method, there will be a process that will involve in demonstrating control of

dependent variables from the research hypothesis through statistical techniques that

will then support the generalisation of findings. In this research, historical studies are

involved such as Philippines historical climate data and related case studies. The

variables in the research hypothesis such as temperature and precipitation will be

drawn from historical data website and a case study will be used to correlate these

variables (Schloss & Smith 1999).


5/23

5

3.2DETAIL LITERATURE STUDYFurther data should be sought by using case studies to correlate hydroelectric power

potential with climate change variables that can be found which are temperature,

precipitation and river runoff in the research hypothesis. In order to assess the climate

impact on hydroelectric power generation, simulation and model will be used from

case studies. The following literatures for this matter were reviewed for this research.

Climate change impacts on hydroelectric power

G.P. Harrison, H.W. Whittington and S.W. Gundry

Anthropogenic emissions of greenhouse gases are expected to lead to significant

changes in climate over the next century. One of the many potential effects is that

river catchment runoff may be altered. This could have implications for the design,

operation and viability of hydroelectric power stations. This describes attempts to

predict and quantify these impacts. It details a methodology for computer based

modelling of hydroelectric resources and proposes analysis of the impacts on the

electrical system and on the performance of hydroelectric power generation.

Predictions of future climate are based on the output of complex numerical Global

Circulation Models (GCMs) which simulate physical processes in the atmosphereand oceans (Harisson, Whittington & Gundry n.d.).

Climate change impacts and responses in the Philippines: water resources

Aida M. Jose, Nathaniel A. Cruz

The Philippines, like many of the worlds poor countries, will be among the most

vulnerable to the impacts of climate change because of its limited resources. As

shown by previous studies, occurrences of extreme climatic events like droughts and

floods have serious negative implications for major water reservoirs in the country. A

preliminary and limited assessment of the countrys water resources was undertaken

through the application of general circulation model (GCM) results and climate

change scenarios that incorporate incremental changes in temperature and rainfall and

the use of a hydrological model to simulate the future runoff-rainfall relationship.

Results showed that changes in rainfall and temperature in the future will be critical to

future inflow in the Angat reservoir and Lake Lanao, with rainfall variability having agreater impact than temperature variability. In the Angat reservoir, runoff is likely to


6/23

6

decrease in the future and be insufficient to meet future demands for water. Lake

Lanao is also expected to have a decrease in runoff in the future. With the expected

vulnerability of the countrys water resources to global warming, possible measures to

cope with future problems facing the countrys water resources are identified (Jose &

Cruz 1999).

Susceptibility of the Batoka Gorge hydroelectric scheme to climate change

Gareth P. Harrison, H. (Bert) W. Whittington

The continuing and increased use of renewable energy sources, including hydropower,

is a key strategy to limit the extent of future climate change. Paradoxically, climate

change itself may alter the availability of this natural resource, adversely affecting the

financial viability of both existing and potential schemes. Here, a model is described

that enables the assessment of the relationship between changes in climate and the

viability, technical and financial, of hydro development. The planned Batoka Gorge

scheme on the Zambezi River is used as a case study to validate the model and to

predict the impact of climate change on river flows, electricity production and scheme

financial performance. The model was found to perform well, given the inherentdifficulties in the task, although there is concern regarding the ability of the

hydrological model to reproduce the historic flow conditions of the upper Zambezi

Basin. Simulations with climate change scenarios illustrate the sensitivity of the

Batoka Gorge scheme to changes in climate. They suggest significant reductions in

river flows, declining power production, reductions in electricity sales revenue and

consequently an adverse impact on a range of investment measures (Harisson &

Whittington 2002).

A modelling methodology for assessing the Impact of climate variability and

climatic Change on hydroelectric generation

J. Ricardo Munoz and David J. Sailor

A new methodology relating basic climatic variables to hydroelectric generation was

developed. The methodology can be implemented in large or small basins with any

number of hydro plants. The method was applied to the Sacramento, Eel and Russian

river basins in northern California where more than 100 hydroelectric plants are


7/23

7

located. The final model predicts the availability of hydroelectric generation for the

entire basin provided present and near past climate conditions, with about 90%

accuracy. The results can be used for water management purposes or for analysing the

effect of climate variability on hydropower generation. Climate change scenarios

were defined to investigate the impact of global warming on the hydropower

generation availability in the basin. A wide range of results can be obtained depending

on the climate change scenario used (Munoz & Sailor 1998).

Climate change/variability implications on hydroelectricity generation in the

Zambezi River Basin

Francis Davison Yamba & Hartley Walimwipi & Suman Jain & Peter Zhou &

Boaventura Cuamba & Cornelius Mzezewa

The study has analysed the effects of various factors on hydroelectric power

generation potential to include climate change/variability, water demand, and

installation of proposed hydroelectric power schemes in the Zambezi River Basin. An

assessment of historical (19702000) power potential in relation to climate

change/variability at existing hydroelectric power schemes (Cahora Bassa, Kariba,

Kafue Gorge and Itezhi-Tezhi) in the Zambezi River Basin was conducted. The

correlation of hydroelectric power potential with climate change/variability aimed at

observing the link and extent of influence of the latter on the former was investigated.

In order to predict the future outlook of hydroelectric power potential, General

Circulation Models (GCM) were used to generate projected precipitation. The

monthly simulated precipitation was extracted from the GCM for every sub basin and

used to compute future precipitation. Further, future water demand in the sub basins

of the Zambezi River Basin were estimated based on the respective population growth

rate in each sub basin. Subsequently, water balance model, with projected

precipitation and water demand input was used to determine projected run-offs of sub

basins of the Zambezi River Basin. .Based on the projected run-offs of sub basins,

reservoir storage capacities at existing hydroelectric power schemes was estimated.

The baseline assessment revealed a strong relationship between hydroelectric power

potential and climate change/variability. The study also revealed that the main climate

and other risks associated with current and future hydroelectric power generation

include projected dry years, floods and increasing water demand. The results indicate

that the hydroelectric power potential has a tendency towards gradual reduction in its


8/23

8

potential in all existing and proposed hydroelectric power schemes owing to climate

change and increasing water demand (Yamba et al. 2011).

3.3TESTING HYPOTHESIS

A fundamental principle in research is the formulation of hypothesis. After the

formulation of this hypothesis and the accumulation of data, analysis of the collected

data will be done. This analysis will accepts or rejects the hypothesis. The purpose of

this hypothesis is to predict a relationship between variables in this case temperature,

precipitation and river runoff which can be tested (Melville & Goddard 1996).

The first step of hypothesis testing is to convert the research question into null and

alterative hypotheses. We start with the null hypothesis (H0). The null hypothesis is a

claim of no difference. The opposing hypothesis is the alternative hypothesis (H1).

The alternative hypothesis is a claim of a difference in the population, and is the

hypothesis in which this research often hopes to bolster. It is important to keep in

mind that the null and alternative hypothesis reference population values, and not

observed statistics (Melville & Goddard 1996).

A test statistic from the data can be calculated. There are different types of test

statistics. This research will use in the one-sample z-statistics. The z statistic will

compare the observed sample mean to an expected population mean 0. Large test

statistics indicate data are far from expected, providing evidence against the null

hypothesis and in favour of the alternative hypothesis (Melville & Goddard 1996).

The test statistic is converted to a conditional probability called a P-value. The P-

value answers the question If the null hypothesis were true, what is the probability of

observing the current data or data that is more extreme? (Melville & Goddard 1996).

Small p values provide evidence against the null hypothesis because they say the

observed data are unlikely when the null hypothesis is true. We apply the following

conventions: (Melville & Goddard 1996).

o Whenp value > .10 the observed difference is not significant

o Whenp value .10 the observed difference is marginally significant

o Whenp value .05 the observed difference is significant


9/23

9

o Whenp value .01 the observed difference is highly significant

(Melville & Goddard 1996).

Use of significant in this context will mean that the observed difference is not likely

due to chance. It does not mean of important or meaningful. Alpha () is a

probability threshold for a decision. If P , we will reject the null hypothesis.

Otherwise it will be retained for want of evidence (Melville & Goddard 1996).

This research will use a zstat

to test a sample mean against an expectation. The zstat

needed population standard deviation (without estimating it from the data) to

determine the standard error of the mean. To conduct a one-sample test when the

population standard deviation is not known, we use a variant of thezstat called the tstat.

The advantage of the tstat

is that it can use sample standard deviation s instead of to

formulate the estimated standard error of the mean (Melville & Goddard 1996).

Hypotheses: The null and alternative hypotheses are identical to those used by the z

test. The null hypothesis isH0: =

0. Alternatives are

H1:

0(two-sided)

H1: >

0(one-sided to right)

H1: <

0(one-sided to left)

Test statistic: The one-sample t statistic is:

(Melville & Goddard 1996)

Wherex represents the sample mean, 0

represents the expected value under the null

hypothesis, andsem = s / n. This statistic has n 1 degrees of freedom.

P-value and conclusion: The tstat

is converted to ap value with a computer program

or t table. When using the t table, you will only be able to find boundaries for the p

value. Small values ofPprovide evidence againstH0 (Melville & Goddard 1996).


10/23

10

4.0PRIMARY DATA COLLECTIONThe search for answers to research questions calls of collection of data. Data are facts, figures

and other relevant materials, past and present, serving as bases for study and analysis. The

data serve as the bases or raw materials for analysis. Without an analysis of factual data, no

specific inferences can be drawn on the questions under study. Inferences based on

imagination or guesswork cannot provide correct answers to research questions. The

relevance, adequacy and reliability of data determine the quality of the findings of a study


Data form the basis for testing the hypotheses formulated in this research. Data also provide

the facts and figures required for constructing measurement and tables, which are analysed

with statistical techniques. Inferences on the results of statistical, analysis and tests of

significance provide the answers to research questions. Thus the scientific process of

measurement, analysis, testing and inferences depends on the availability of relevant data and

their accuracy. Hence the importance of data for any research studies(Graciano & Raulin

2004).

Primary sources are original sources from which the researcher directly collects data that

have not been previously collected. Primary data are first-hand information collected through

various methods such as observation, interviewing, mailing etc(Graciano & Raulin 2004).

4.1PRIMARY DATA COLLECTION METHODIn this research the primary data is generated using regression analysis of secondary

data taken from the Philippine government statistic website. Base of the research

hypothesis the variables involve are temperature and precipitation. The following are

historical climate data from previous statistical research (Correlation and Regression

n.d.).


11/23

11

Table 1Historical Climate Data 1966-1982 (Philippine National Statistical

Coordination Board (NSCB) n.d).

Table 2. Historical Climate Data 1983-1996 (Philippine National Statistical

Coordination Board (NSCB) n.d).


12/23

12

Climate Condition

With the increasing emission of greenhouse gases, their concentrations in the

atmosphere also go up which, in turn, cause the temperature of the earth to rise. The

rise in earths temperature, meanwhile, leads to changes in the patterns of

precipitation and the sea level to rise. The changes in climate have adverse effects not

only on our ecological and socioeconomic systems but on human health as well. Thus,

there is a growing concern over various manifestations of climate changes like the

pollution-induced global warming and the El Nio phenomenon (Philippine National

Statistical Coordination Board (NSCB) n.d.).

A study made by the NSCB in 1998 on the various climate data generated by

PAGASA from 1966 to 1996 indicated a shift to a warmer climate. A close

examination of the temperature in the Philippines from the period 1966 to 1996

revealed that from 1987 onwards, the average minimum temperatures recorded were

higher than the normal minimum temperature of 22.95 degrees C, suggesting that the

climate in the country is getting warmer. Similarly, the average mean temperature

observed in the same period has not fallen below the normal mean temperature of

27.03 degrees C(Philippine National Statistical Coordination Board (NSCB) n.d).

Figure 1. Average Minimum Temperature 1966-1996 (Philippine National

Statistical Coordination Board (NSCB) n.d).


13/23

13

Figure 2. Average Mean Temperature 1966-1996 (Philippine National Statistical

Coordination Board (NSCB) n.d.

Other climate variables were tested in terms of the effect of El Nino covering the

same period (1966-1996). El Nino is a phenomenon characterised by the relaxing of

trade winds, which normally blow from the east to west, causing westerly winds to

intensify. This drives the warm waters to the east causing intensified cloud formation

and resulting to heavy rainfall which leads to floods, hurricanes, etc. On the other

hand, formation of clouds in the west is weakened, thereby, reducing rainfall and

causing dry spell 3. Simultaneous occurrences of above normal Pacific sea surface

temperature (SST) and below normal Southern Oscillation Index (SOI) indicate a

global-scale climate variation defined as the El Nio Southern Oscillation (ENSO)

phenomenon. The SOI is the difference in standardized atmospheric pressures over

the south eastern Pacific and the Indian Ocean and Australia. A total of eight El Nio

or ENSOrelated drought episodes/periods have been listed by PAGASA as occurringin the country from 1966 to 1996. These were in 1968-69, 1972-73, 1976-77, 1982-83,

1986-87, 1989-90, 1991-92 and 1994-95. Data on the various climate variables for

the past three decades are given in Table 1 and Table 2. Annual data presented were

obtained by averaging the data recorded by the different PAGASA stations located

nationwide (Philippine National Statistical Coordination Board (NSCB) n.d.

.


14/23

14

Regression analysis includes many techniques for modelling and analysing several

variables, when the focus is on the relationship between a dependent variable and one

or more independent variables. By using this method the secondary data from the

mathematically describes the dependence of the Y variable on the X variable and

constructs an equation which can be used to predict any value of Y for any value of X.

It is more specific and provides more information. Regression analysis assumes that

each of the variables is normally distributed with equal variance. In addition to

deriving the regression equation, regression analysis also draws a line of best fit

through the data points of the scattergram. These regression lines may be linear, in

which case the relationship between the variables fits a straight line, or nonlinear, in

which case a polynomial equation is used to describe the relationship (Correlation and

Regression n.d.).

Regression (also known as simple regression, linear regression, or least squares

regression) fits a straight line equation of the following form to the data: (Correlation

and Regression n.d.)

Y = a + bX

Where Y is the dependent variable, X is the single independent variable, a is the

Y-intercept of the regression line and b is the slope of the line (also known as the

regression coefficient)(Correlation and Regression n.d.).

Once the equation has been derived, it can be used to predict the change in Y for any

change in X. It can therefore be used to extrapolate between the existing data points as

well as predict results which have not been previously observed or tested (Correlation

and Regression n.d.).

A t test is utilized to ascertain whether there is a significant relationship between X

and Y, as in correlation, by testing whether the regression coefficient, b, is different

from the null hypothesis of zero (no relationship). If the correlation coefficient, r, is

known therefore the regression coefficient can be derived (Correlation and Regression

n.d.) .


15/23

15

The regression line is fitted using a method known as least squares which

minimizes the sum of the squared vertical distances between the actual and predicted

values of Y. Along with the regression equation, slope, and intercept; regression

analysis provides another useful statistic: the standard error of the slope (Correlation

and Regression n.d.).

Just as the standard error of the mean is an estimate of how closely the sample mean

approximates the population mean, the standard error of the slope is an estimate of

how closely the measured slope approximates the true slope. It is a measure of the

goodness of fit of the regression line to the data and is calculated using the standard

deviation of the residuals. A residual is the vertical distance of each data point from

the least squares fitted line (Correlation and Regression n.d.).

Figure 3. Regression line (Correlation and Regression n.d.)

Residuals represent the difference between the observed value of Y and that which is

predicted by X using the regression equation. If the regression line fits the data well,

the residuals will be small. Large residuals may point to the presence of outlying data

which, as in correlation, can significantly affect the validity of the regression equation

(Correlation and Regression n.d.).

The steps in calculating a regression equation are similar to those for calculating a

correlation coefficient. First, a scattergram is plotted to determine whether the data

assumes a linear or nonlinear pattern. If outliers are present, the need for nonlinear

regression, transformation of the data, or non-parametric methods should be


16/23

16

considered. Assuming the data are normally distributed, the regression equation is

calculated. The residuals are then checked to confirm that the regression line fits the

data well. If the residuals are high, the possibility of non-normally distributed data

should be reconsidered (Correlation and Regression n.d.).

4.2ADVANTAGES AND DISADVANTAGES OF PRIMARY DATACOLLECTION METHOD

Primary data collection method in this case should be the actual measurement of

temperature and precipitation in the Philippines for a certain number of years to get

the pattern of climate change (Melville & Goddard 1996).

One of the main advantages of using this method of collecting data is the amount of

control the researcher will have in terms of the quality of data acquired. Aside from

the quality of date, this method will allow the researcher to choose the type of

method or process in measuring temperature and precipitation and the amount of time

use in data collection. Therefore, this method will enable the researcher to focus on

specific aspects of the research (Melville & Goddard 1996).

The other advantage is that primary data collection focuses on the specific variablesneeded, unlike secondary data used in this research which contain data that are not

needed by the researcher. Aside from this, the other thing about using primary

collection method is that it is presented with original and unbiased data.

The measurement of temperature and precipitation of long span of years will consume

a significant amount of time and can be considered as one of its disadvantage. It will

also demand huge resources in terms of man power and facilities (Melville &

Goddard 1996).

5.0 SECONDARY DATA COLLECTION

For some research questions such as in this case, it is practical to use data collected earlier by

other credible researchers or for other purposes than research which is an official statistics

records kept routinely by a government organisation. By principle of being archived and

made available, this type of primary data will serve as secondary data. In this research, the

main sources of information are the official data archive which is the Philippine National


17/23

17

Statistical Coordination Board (NSCB). This is the gateway to official Philippine social and

economic statistics. Although the actual measurement of the temperature, precipitation and

other climate variables are done by the Philippine Atmospheric, Geophysical and

Astronomical Services Administration (PAGASA) (Melville & Goddard 1996).

5.1SECONDARY DATA COLLECTION METHODThe potential impact of climate change on water resources has been suggested since

the 1980s, as work progressed on predicting climate change. Although Global

Circulation Model (GCM) can be used to predict runoff directly, the coarse scale used

means that this information is only useful for the most general studies. As a result,

many studies have been carried out on individual basins, showing that river basins

display a range of sensitivities to climate change. Figure 1 shows the response of a

typical river basin to variations in precipitation and temperature. It can be seen that

increased temperature results in non-linear variations in runoff due to changes in

precipitation (Harisson, Whittington & Gundry n.d.) .

Figure 4 River Basin Response to Climate Change (Harisson, Whittington &

Gundry n.d.)

Later studies have considered not only the effect on river flows but also the impact on

generation from hydroelectric stations. In particular, one study examined a number of

international river basins. The study drew upon existing hydrological and dedicated

basin models and the experience of international experts. For example, for one GCMscenario (GFDL), hydroelectric production on the Indus River would fall by 22%.


18/23

18

Another study qualitatively examined the effects of reduced hydroelectric output on

sub-Saharan Africa and central Europe (Harisson, Whittington & Gundry n.d.).

Climate impact assessment requires scenarios of future climate to be translated into

potential changes on natural and human systems. To assess climate impacts on

hydropower production a number of key steps must be taken : (Harisson, Whittington

& Gundry n.d.)

1. A river basin is selected and its rainfall-runoff processes are modelled and

calibrated;

2. Climate data emanating from different GCM or arbitrary climate scenarios

is applied to the model and the runoff computed;

3. River runoff values are converted into estimates of hydroelectric power

production.

The first step involves the accurate modelling of the hydrology of the chosen river

basin. A wide variety of modelling techniques have been applied to simulating runoff

processes. Three basic approaches exist: (Harisson, Whittington & Gundry n.d.)

Empirical Conceptual Deterministic

The first type will be used in this research which will require in establishing a

relationship between climate inputs, the temperature and precipitation and

hydrological outputs such as river runoff. Given that climate data from the GCMs

can be converted into a form suitable for use, the output from an appropriate

calibrated hydrological model that will look like the figure below. In this figure, it

can be seen that climate change affects the magnitude and timing of river runoff. The

potential for hydroelectric generation approximately follows runoff, so here it can be

seen that hydroelectric potential would also be affected. To quantify the relationship

between changing climate and hydroelectric power generation, a model can be

developed (Harisson, Whittington & Gundry n.d.).


19/23

19

Figure 5 Effect of Climate Change on River Runoff (Harisson, Whittington &

Gundry n.d.)

The GCM will indicate annual electricity production. This will show variations of

hydro energy production in relationship with variations of river flow or river runoff.

This can be seen in Figure which shows the percentage of maximum energy

production achieved on average each month (Harisson & Whittington 2002)..

Figure 6 Energy production using GCM (Harisson & Whittington 2002).


20/23

20

5.2ADVANTAGES AND DISADVANTANGES OF SECONDARYDATA COLLECTION METHOD

There are many advantages in using secondary data collection method. This includes

the relative ease of access to many sources of secondary data. In this case, the source

is the Philippines National Statistical Coordination Board (NSCB) website. This

means that in todays technology of online availability access, secondary data is more

openly accessed. Furthermore, the use of secondary data has allowed researchers

access to valuable information for little or no cost to acquire. Therefore, this

collection of data is much less expensive than the primary data. Secondary data

collection is often used to clarify the research question. It is usually used prior to

primary research to help clarify the research focus. The use of secondary data

collection is often used to help align the focus of large scale primary research. When

focusing on secondary research, the researcher may realize that the exact information

they were looking to uncover is already available through secondary sources. This

would effectively eliminate the need and expense to carry out primary research


There are some disadvantages to using secondary research. The originators of theprimary research are largely self-governed and controlled by the organisation that

measured and keep the data. Like in this case, PAGASA for the measurement and

NSCB for archiving and analysing primary data from PAGASA. Therefore, the

secondary data used must be scrutinised closely since the origins of the information

may be questionable. Moreover, the researcher needs to take sufficient steps to

critically evaluate the validity and reliability of the information provided (Graciano &

Raulin 2004).

In many cases, secondary data is not presented in a form that exactly what the

researchers needs. Therefore, the researcher needs to rely on secondary data that is

presented and classified in a way that is similar to the research requirements.

In many cases, researchers find information that appears valuable and promising. The

researcher may not get the full version of the research to gain the full value of the

study. When using secondary research, one must exercise caution when using dated

information from the past. In this case, the location and date of where the data was

measured will be a factor that should be considered (Graciano & Raulin 2004).


21/23

21

6.0VALIDITY OF DATA COLLECTION AND ANALYSISThe use of reliability and validity are common in quantitative research. For quantitative

research, reliability seeks to determine the extent to which the data or measurement is

consistent. In this case, the measurement of the temperature and precipitation as the climate

variables should be consistent. This means that consistency refers to what degree an

instrument measures the same way each time is used under similar conditions with the same

location. Since the data collections was conducted by Philippine Atmospheric, Geophysical

and Astronomical Services Administration (PAGASA), which the Philippine national

institution dedicated to provide flood and typhoon warnings, public weather forecasts and

advisories, meteorological, astronomical, climatological, and other specialized information

and services primarily for the protection of life and property and in support of economic,

productivity and sustainable development, it can be assume that data collection was done

consistently and scientifically (Library & Information Science Research 2009).

By the same basis, the issues of credibility, transferability, dependability and conformability

in this qualitative research method can be positively addressed. On the other hand, in the

analysis of this secondary data, by conducting literature reviews, it is important to examine

the research design and methodology and how the case studies asserted some degree of

reliability and validity. Validity can be determined by the findings that the research has the

correct or best interpretation of the findings from case studies and other factors or variables

have been acknowledged (Library & Information Science Research 2009).

7.0CONCLUSION OF RESEARCH METHODOLOGYIn order to systematically find a way to solve the research problem an appropriate research

methodology should be carried out. In this research the procedure that will be used in

describing or explaining climate change in the Philippines is by using change of temperature

and precipitation. The capacity of the natural water resources used in Mindanao, Philippines

in hydroelectric power generation can be assessed against projected climate change and

variability.


22/23

22

Based on the above assessment, Quantitative research method will be applied that will

primarily base on the measurement of change of temperature and precipitation consequently

the estimates of river flow or river runoff.

Secondary historical climate data of change in temperature and precipitation from the

Philippines government statistics website can be investigated to convert into primary date

using regression analysis. To assess the effect of climate change, a model can be develop

using this method to provide estimates of potential and actual evapotranspiration in terms of

river runoff based on temperature and precipitation change. The effect of climate change on

hydrogenation can then be determined by using the expected values for river runoff and the

regression analysis. The GCM will indicate annual electricity production. This will show

variations of hydro energy production in relationship with variations of river flow or river

runoff.


23/23

23

REFERENCE LIST

Borrego, M, Douglas, E & Amelink C 2009, Quantitative, qualitative, and mixed research

methods in engineering education,Journal of Engineering Education, vol. 98, no. 1, pp. 53-66, (online ProQuest Central).

Correlation and Regression n.d., viewed 28 September 2012,

http://www.surgicalcriticalcare.net/Statistics/correlation.pdf

Graciano, A & Raulin, M 2004, Research Methods A Process of Inquiry, 5th edn., Pearson

Education Group, Inc., Boston.

Harrison, G & Whittington, H 2002, Susceptibility of the Batoka Gorge hydroelectric scheme

to climate change,Journal of Hydrology, vol. 264, pp. 230-241, (online Elsevier).

Harisson, G, Whittington, H & Gundry, S n.d., Climate change impacts on hydroelectric

power, viewed 28 September 2012,www.see.ed.ac.uk/~gph/publications/GPH-Upec98.pdf.

Jose, A & Cruz, N 1999, Climate change impacts and responses in the Philippines: water

resources, Climate Research, vol. 12, pp. 77-84.

Library & Information Science Research 2009, Reliability and validity, vol. 31, pp. 73-74,

(online ScienceDirect).

Melville, S & Goddard, W 1996,Research Methodology, Juta & Co Ltd, Kenwyn.

Munoz J & Sailor, D 1998, A modelling methodology for assessing the impact of climate

variability and climatic change on hydroelectric generation, Energy Convers, Mgmt., vol. 39,

n0. 14, pp. 1459-1469, (online EbsCohost).

Philippine National Statistical Coordination Board (NSCB) n.d., Atmosphere, viewed 28

September 2012,www.nscb.gov.ph/peenra/Publications/Compendium/atmosphere.pdf.

Rajasekar, S, Philominathan, P & Chinnathambi V 2006, Research Methodology, viewed 28

September 2012,arxiv.org/pdf/physics/0601009.pdf.

Schloss, P & Smith M 1999, Conducting Research, Prentice-Hall, Inc., Upper Saddle River.

Yamba, F, Walimwipi H, Jain, S, Zhou, P, Cuamba, B & Mzezewa, C 2011, Climate

change/variability implications on hydroelectricity generation in the Zambezi River Basin,

Mitig Adapt Strateg Glob Change, vol. 16, pp. 617-628, (online Springer).
http://www.surgicalcriticalcare.net/Statistics/correlation.pdfhttp://www.surgicalcriticalcare.net/Statistics/correlation.pdfhttp://www.see.ed.ac.uk/~gph/publications/GPH-Upec98.pdfhttp://www.see.ed.ac.uk/~gph/publications/GPH-Upec98.pdfhttp://www.see.ed.ac.uk/~gph/publications/GPH-Upec98.pdfhttp://www.nscb.gov.ph/peenra/Publications/Compendium/atmosphere.pdfhttp://www.nscb.gov.ph/peenra/Publications/Compendium/atmosphere.pdfhttp://www.nscb.gov.ph/peenra/Publications/Compendium/atmosphere.pdfhttp://arxiv.org/pdf/physics/0601009.pdfhttp://arxiv.org/pdf/physics/0601009.pdfhttp://arxiv.org/pdf/physics/0601009.pdfhttp://arxiv.org/pdf/physics/0601009.pdfhttp://www.nscb.gov.ph/peenra/Publications/Compendium/atmosphere.pdfhttp://www.see.ed.ac.uk/~gph/publications/GPH-Upec98.pdfhttp://www.surgicalcriticalcare.net/Statistics/correlation.pdf

Climate Change Research Methodology

Documents

Transcript of Climate Change Research Methodology