Philippine Journal of DevelopmentNumber 66, First Semester 2009

Volume XXXVI, No. 1

El Niño Southern Oscillationin the Philippines: Impacts, Forecasts, and Risk Management

fLaviana HiLario, rosaLina de guzman, daisy ortega, Peter Hayman, and Bronya aLexander1

ABSTRACTThe climate of the Philippines is highly influenced by the El Niño Southern Oscillation (ENSO). El Niño is associated with an increased chance of drier conditions and La Niña is associated with an increased chance of wetter conditions. Changes in rainfall are associated with changes to tropical cyclone activity in the western equatorial Pacific, the strength of the monsoon, and changes in the onset and/or termination of monsoon rains.

ENSO is a naturally occurring phenomenon and has both negative and positive impacts on the various sectors of the society and environment, but experience would show that there are more adverse impacts than beneficial ones. These adverse impacts may be mitigated through using seasonal climate forecasts.

This paper looks at the effects of ENSO on droughts, flood, and tropical cyclones in the Philippines before discussing the challenge of using knowledge about the effects of ENSO for decisionmaking and risk management.

1 Flaviana Hilario, Rosalina de Guzman, and Daisy Ortega are from the Philippine Atmospheric, Geophysical, and Astronomical Services Administration of the Department of Science and Technology, Philippines. Email for correspondence: fhilarioph@yahoo.com. Peter Hayman and Bronya Alexander are from South Australian Research and Development Institute, Climate Applications Unit, Adelaide, South Australia.

10 PhiliPPine Journal of DeveloPment 2009

INTRODUCTIONAs recently as the early 1970’s, the notion of forecasting the climate was seen as farfetched, even by many atmospheric scientists. But the last three decades have seen a revolution in the understanding and beliefs about the predictability of the climate (Nicholls 2005). This has come about largely through increased understanding of the El Niño Southern Oscillation (ENSO) (Allen et al. 1996; Stern and Easterling 1999; Hansen 2002).

The acronym ENSO refers to the ocean component (El Niño) and the atmospheric component (southern oscillation) of a naturally occurring phenomenon that originates in the Pacific Ocean. El Niño and La Niña refer to the pattern of above or below average sea surface temperatures in the central and eastern Pacific that leads to a major shift in weather patterns across the Pacific. ENSO is the most important source of interannual variability of rainfall in the Philippines along with many other areas of the world (Ropelewski and Halpert 1987; Allen et al. 1996). The role of ENSO has influenced world history (Davis 2001) and the association of warm episodes in the Pacific Ocean and drought in the Philippines has been documented since the mid-1980s (Jose 1989, 1990).

The southern oscillation index (SOI) is calculated from the monthly or seasonal fluctuations in the air pressure difference between Tahiti and Darwin, and has been linked to rainfall variability and used in seasonal forecasting (Stone and Auliciems 1992). Regions influenced by ENSO have higher variability than otherwise expected for their latitude (Nicholls 1988). While ENSO amplifies the variability, it also gives some means of predictability for this variability which has the potential to be used in risk management. ENSO-based forecasts can help people in regions affected by ENSO to be more prepared and cope better with ENSO-related climate variability (Hansen 2002; Jose 2002; Meinke et al. 2003).

Climate variability in the PhilippinesIn the Philippines, a rain-year is defined as the 12-month rainfall from April through the end of March the following year. This is divided into first semester (Figure 1a), from April to September synchronous with the southwest monsoon (northern hemisphere summer monsoon), and the second semester, October to March (Figure 1b) synchronous with the northeast monsoon (northern hemisphere winter monsoon). Rainfall distribution is influenced by the complex interactions of the various factors such as geography and topography, principal airstreams, and weather systems that affect the country at different times. During the first semester, rainfall less than 1000mm is found in the Cagayan Valley, the interior portions of Visayas, and southern part of Mindanao (Figure 1a), and rainfall greater than 1800mm is found in the western sections of Luzon and Visayas. During the second semester, the eastern section of the country is more

11hilario, De guzman, ortega, hayman anD alexanDer

exposed to the prevailing northeasterlies while rainfall in the western region is less than 500mm.

Figure 1a. Average rainfall for the first semester (April to September), 1951–2000

NORMAL RAINFALLAPRIL - SEPTEMBER

LEGEND :≤ 450 (MM)451 - 900

901 - 1800> 1800

Figure 1b. Average rainfall for the second semester (October to March), 1951–2000

NORMAL RAINFALLOCTOBER - MARCH

LEGEND :≤ 450 (MM)451 - 900

901 - 1800> 1800

12 PhiliPPine Journal of DeveloPment 2009

Impacts of ENSO in the PhilippinesThe extreme phases of the ENSO phenomenon have a strong modulating effect on seasonal rainfall in the Philippines, with mature ENSO warm events (El Niño) often associated with drought and stresses on water resources and agriculture, while cold events (La Niña) often result in excessive rainfall (Jose 2002; Lyon et al. 2006). There are also impacts on the onset and length of the rainy season and importantly the number of tropical cyclones.

Since 1949 there have been 17 El Niño events based on the National Oceanic and Atmospheric Administration’s classification (www.cpc.noaa.gov), many of which have brought adverse socioeconomic impacts in the Philippines. The 1982–1983 El Niño event caused an estimated 13 billion dollars in global damages, with the Philippines suffering an estimated 450 million dollars. However, the 1997–1998 El Niño was the strongest El Niño of the century, surpassing the intensity of the 1982–1983 event (WMO 1998; Changnon and Bell 2000). In the same period, there have been 12 La Niña events, aggravating further the increasing socioeconomic problems of the country. Nine of these events are listed in Table 1 along with their intensities. The intensities are based on the average sea surface temperature (SST) anomaly for the Niño 3.4 region. Excessive rains brought about by strong monsoon activity during these ENSO events have created massive floods and landslides in the urban and rural areas. Notable increases in strong tropical cyclones surrounding the Philippines have generated devastating winds and increased storm surges over coastal areas. Thus, millions of dollars in infrastructure have been damaged with major economic losses and millions of families affected. The following sections will discuss the three main impacts of ENSO under the headings of droughts, floods, and tropical cyclones.

Table 1. La Niña events and the Oceanic Niño Index (ONI) where lower values indicate a stronger La Niña

La Niña event ONI Value

JJA 1970 – DJF 1971/72 –1.4

AMJ 1973 – JJA 1974 –2.0

SON 1984 – ASO 1985 –1.1

AMJ 1988 – AMJ 1989 –2.0

ASO 1995 – FMA 1996 –0.8

JJA 1998 – MJJ 2000 –1.7

SON 2000 – JFM 2001 –0.7

Early 2006 –0.8

JAS 2007 – AMJ 2008 –1.5

Source: Climate Prediction Center: http://www.cpc.noaa.gov/.

13hilario, De guzman, ortega, hayman anD alexanDer

Figure 2 reveals the seasonal pattern of rainfall in a number of locations in the Philippines (see Appendix Table 3 for the meteorological station numbers and length of data used) and how this pattern is modified in El Niño and La Niña years. Figure 2 uses the El Niño and La Niña years defined by the Australian Bureau of Meteorology (Wright 2001) covering the years 1900 to 2007 in the following categories: El Niño (1902, 1905, 1911, 1913, 1914, 1919, 1925, 1940, 1941, 1946, 1952, 1953, 1959, 1965, 1969, 1972, 1977, 1982, 1987, 1991, 1993, 1994, 1997, 2002, and 2006); La Niña (1903, 1906, 1909, 1910, 1916, 1917, 1924, 1928, 1938, 1950, 1955, 1958, 1964, 1970, 1971, 1973, 1974, 1975, 1988, 1996, and 1998); and the remainder being neutral years.

Figure 2 shows the general impact of drier conditions during warm events and wetter conditions during cool events and that the main impact of ENSO is in the fourth quarter of the year and the first quarter of the following year for the Philippines. A number of sites in north-central Philippines show a summer reversal in the ENSO rainfall signal (Lyon et al. 2006), whereby it has often been wetter than average during July to September before the drier than normal conditions in the later months of the year and the following year associated with El Niño. The reverse is true of La Niña. Sites where this is apparent in Figure 2 are in northern Luzon (Laoag, Tuguegarao, Vigan, and Baguio), Central Luzon (Dagupan, Iba, and Cabanatuan), and the Visayas (Catbalogan and Iloilo).

Drought and drought eventsDrought is lower than expected precipitation that, when extended over a season or longer period of time, is insufficient to meet the demands of human activities and the environment (WMO 2006). It is important to recognize that drought is a relative term (what constitutes a drought in a wet region is different from a drought in an arid region) and drought relates to human expectations of the climate. In the Philippines, ENSO-related droughts are due to fewer tropical cyclones, delayed onset of the rains, and weak monsoon activity.

Table 2 (Jose 2002) shows drought events and areas affected in the Philippines during the last four decades. The four most significant drought events in the past two decades were 1982–1983, 1986–1987, 1989–1993, and 1997–1998. The droughts were associated with El Niño events except the 1989–1990 drought event which occurred during a neutral condition. The 1986–1987 drought started with unusually dry conditions during the peak month of the southwest monsoon rain (August) throughout Negros province located in the western Philippines. The dry conditions became more pronounced during the second semester rain period (October 1986–March 1987) across the Bicol region, Samar province, Western and Southern Mindanao (Figure 3a). The first drought advisory from the Philippine Atmospheric, Geophysical, and Astronomical Services Administration

14 PhiliPPine Journal of DeveloPment 2009

Figure 2. Average monthly rainfall (shaded) in selected locations for available data between 1902 and 2007

Northern Luzon

Central Luzon

15hilario, De guzman, ortega, hayman anD alexanDer

Visayas

Mindanao

Note: Also shown is the monthly rainfall averaged over El Niño years starting January of the first year defined by El Niño through July of the following year. Average rainfall is shown similarly for La Niña years.

16P

hiliPPine Journal of DeveloPment 2009

Table 2. Drought events, 1968–1998Drought event Areas affected Damages1968–1969 • moderate to severe drought over most of the Philippines with Bicol region as most

severely affected• total of 5x105mt of rice and corn production

1972–1973 • Central Luzon, Palawan, Visayas, and Mindanao • total loss of 6.3x105mt of rice and corn production

1977–1978 • all of Mindanao except Davao • total loss of 7.5x105mt of rice and corn production1982–1983October 1982–March 1983April 1983–September 1983

• Western and Central Luzon, Southern Tagalog provinces, Northern Visayas, Bohol, and Western Mindanao

• moderate to severe drought affected most of Luzon, Negros Occidental, and Iloilo

• loss of 6.4x105mt of rice and corn • insurance claims mounted to P38m • hydropower generation loss was P316m

1986–1987October 1986–March 1987April 1987–September 1987

• severe drought affected Bicol region, Southern Negros, Cebu, and Western Mindanao• severe drought affected mainland Luzon, Central Visayas, and Western Mindanao

• estimated agricultural damages of P47m • estimated hydroenergy generation loss was P671m

1989–1990October 1989–March1990

• drought affected Cagayan Valley, Panay Island, Guimaras, Palawan, and Southern Mindanao

• affected rice and corn area totalled 283,562 hectares • major multipurpose water reservoirs reduced inflow

• estimated 5x105mt of rice and corn production losses • hydropower generation loss of P348m • 10% cutback in water production in Metro Manila

1991–1992 • severe drought affected Manila, Central and Western Visayas, and Cagayan Valley• Affected agricultural area- 461,800 hectares• 20% shortfall in Metro Manila’s water supply

• P4.09 billion in agricultural losses

1997–1998 • about 70% of the Philippines experienced severe drought • about 292,000 hectares of rice and corn area completely damaged

• 622,106mt of rice production loss and 565,240mt of corn amounting to P3b

• water shortages• forest fires and human health impacts

Source: Jose 2002.

17hilario, De guzman, ortega, hayman anD alexanDer

(PAGASA) was issued in early February 1987 covering the Bicol region, with a countrywide advisory soon to follow due to deteriorating rainfall conditions. The drought situation persisted for the following season (April–September 1987) shown in Figure 3b with about half of the country having experienced less than 80 percent of the normal rainfall. Areas with less than 60 percent of the normal values included the mainland of Luzon, Central Visayas, and most areas of Western and Northern Mindanao.

The second semester rain period of 1989 (October 1989–March 1990) was not an El Niño episode but a neutral phase right after the cold event (La Niña) in 1988–1989. However, drought conditions were experienced during this semester where below normal rainfall conditions were noted over the most of the country for five consecutive months (Figure 2c).

Figure 3. Percent of normal rainfall using 1950 to 1990 as the base period

(a) Oct–Mar 1986–1987

(d) Oct–Mar 1991–1992 (e) Oct–Mar 1997–1998

(b) Apr–Sep 1987 (c) Oct–Mar 1989–1990

18 PhiliPPine Journal of DeveloPment 2009

Drought conditions recurred during the 1991–1992 El Niño event affecting most areas of Central Luzon, Southern Luzon, Visayas, and Mindanao (Figure 3d). The agricultural production damages caused by this drought amounted to more than 4 billion pesos as shown in Appendix Table 1. Water inflows and runoff into reservoirs were significantly reduced, particularly for the multipurpose Angat water reservoir which is the main source of potable water in Metro Manila. Table 3 shows the effect on water levels in the three major reservoirs in Luzon during the 1991–1992 El Niño event. During this event, Angat reservoir recorded its lowest water level of 150 meters (the average is 180 meters). As well as providing water for Metro Manila, Angat reservoir also supplies irrigation water to thousands of farms planted to rice and vegetables and serves as flood control for areas downstream. As one of several sources of hydropower in the island of Luzon, it contributes to Luzon’s power requirement with an annual average of 500GWh generating capacity.

Reductions in inflow were also recorded for the other water reservoirs of Magat and Pantabangan during the 1991–1992 drought (Table 3). The National Power Corporation, a state-owned company that serves as the largest provider and generator of electricity in the country, reported that reductions in reservoir levels had drastically limited the generating capacity of the various hydropower plants, particularly in Luzon and Mindanao. In Luzon alone, the three major multipurpose dams of Angat, Magat, and Pantabangan experienced power generation losses of about 31 percent of the expected power generation for October 1991 to March 1992. With the decrease of hydroenergy due to low

Table 3. Actual monthly inflows compared with normal values for three major reservoirs in Luzon (million cubic meters)

ANGAT MAGAT PANTABANGANMonth Actual Normal % of

NormalActual Normal % of

NormalActual Normal % of

Normal1991 Oct 68 318 21 362 1046 35 98 193 51Nov 101 308 59 294 641 46 50 149 34Dec 113 204 55 226 313 72 14 30 471992 Jan 62 102 61 132 215 61 9 13 68Feb 29 59 49 38 149 26 5 8 63Mar 15 51 29 38 134 28 4 7 57

Total 468 1042 45 1090 2498 44 180 400 45

Source: Jose et al. 1999.

19hilario, De guzman, ortega, hayman anD alexanDer

water elevations, there was an increase in the load of thermal plants to cover the deficiencies which proved costly.

The severe 1997–1998 El Niño event began in early 1997 when below normal rainfall was observed in most areas of Northern and Southern Luzon, Panay Island, and Western Mindanao. This condition became more pronounced and extended over most of the country during the second semester rain period. Figure 3e shows the six-month total rainfall as percent of the normal for October 1997 to March 1998. The areas affected are indicated with observed values less than 80 percent of the normal values. The most severely affected areas with less than 40 percent of the normal rainfall were in Western Luzon, Western Visayas, Northern Samar, and the southern part of Western Mindanao. This was based on 50 years of rainfall (1950–1999).

FloodsMonsoon surges and slow moving tropical cyclones in the Philippines can cause prolonged flooding inflicting damage to agricultural crops, public and private property, disruption of commercial activities, and loss of lives. Intensity of the monsoon is linked to La Niña events. Studies on the seasonal rainfall abnormalities (Asuncion and Jose 1981) have showed that for the period 1900–1939 and 1947–1976, the worst flood years for the southwest monsoon were 1962 and 1972, and for the northeast monsoon were 1924, 1964, 1970–1971, 1973–1975. Likewise, 2000–2001 and 2006 are the updated worst flood years for the northeast monsoon period. The prolonged flood conditions are due to the occurrences of tropical cyclones and the intensification of the southwest and northeast monsoons, both of which are related to La Niña events.

The floodplains of several major river basins in the country are highly vulnerable to floods. Intense rainfall in the floodplains leads to accelerated soil erosion on highly farmed areas and deforested mountain slopes. The flood that devastated Ormoc, Leyte in 1991 was induced by excessive rainfall associated with tropical storm Uring. The flooding in Camarines Sur which occurred at the height of typhoon Loleng in 1998 was also exacerbated by the effects of the La Niña phenomenon which existed during that period. Another La Niña impact was the 2006 Guinsaugon, Southern Leyte landslide which occurred after strong rains (200cm in 10 days) and killed over 1000 residents. The rain was enhanced by the northeast monsoon, affected by the 2006 La Niña episode.

In July 1972, one of the most disastrous floods in the country took place due to intense monsoon flow causing prolonged rains. Low-lying areas in Pampanga and Bulacan were submerged when floodwater rose up to rooftop level of houses. Most streets in Manila were also submerged. Laguna de Bay overflowed and inundated the surrounding towns. It is interesting to note that the 1972 great flood

20 PhiliPPine Journal of DeveloPment 2009

over Luzon occurred during an El Niño episode. This is consistent with the seasonal reversal of the ENSO rainfall signal (Lyon et al. 2006). Most parts of western and central Luzon were flooded during July, when two intense tropical cyclones formed over northern Philippines. This was further aggravated by intensification of the southwest monsoon over the western Philippines. Meanwhile, a large portion of Mindanao was experiencing droughts brought about by the warm episode over the central and eastern equatorial Pacific.

Tropical cyclone variability Tropical cyclones can be disastrous and threatening to the country’s economy if they track in the Philippine Area of Responsibility (PAR) and worse if they reach land. The PAR encompasses the Philippines and immediate surrounding oceans, particularly the northwestern Pacific, where PAGASA takes responsibility in monitoring and classifying tropical cyclones. It is bounded by the coordinates 25ºN–120ºE, 25ºN–135ºE, 5ºN–135ºE, 5ºN–115ºE, 15ºN–115ºE, and 21ºN–120ºE and is shown in Figure 4f. Strong winds inherent in a cyclone generate storm surges and can enhance the monsoon activity which may bring moderate to heavy rains in any of the affected areas in the country. Historical records reveal many incidences of loss of lives and damages to property from storm surges that struck many sections of the Philippine coastline. The storm surges accompanying a typhoon in October 1997 destroyed towns and killed more than 1,500 people of southern Samar and northern Leyte islands. In November 1987, 200 of the 882 victims of typhoon Sisang drowned in a storm surge in the Bicol region. Based on these experiences and inherent physical configurations of the coastal approaches, storm surge prone areas have been identified (Jose et al. 1999).

Appendix Table 2 shows some statistics of disastrous tropical cyclones which occurred in the Philippine area of responsibility during the last 60 years (1970–2006). While there has been no trend in the mortality rate from cyclones since 1970, there has been a slight increase in monetary damages from cyclones, but a decrease in the maximum recorded wind speeds. The maximum 24-hour rainfall for each cyclone event shows a range from 100mm up to 1000mm.

Figure 4a shows the annual number of tropical cyclones that have entered the PAR for the period 1948–2005. The figures show that there is no trend in the number of tropical cyclones but there is a year-to-year variability. Of these cyclones, about 50 percent have been typhoons (winds >117kph), 28 percent tropical storms (winds 64–117kph), and 22 percent tropical depressions (winds <64kph). The average annual number of cyclones entering the PAR is about 20, however, only an average of 8–9 of these have made landfall. Figure 4b shows that Northern Luzon is the most frequently hit by tropical cyclones, followed by Catanduanes, and northern Samar, while most of Mindanao is rarely hit. Tropical

21hilario, De guzman, ortega, hayman anD alexanDer

Freq

uenc

y of

Tro

pica

l Cyc

lone 31

26

21

16

11

6

1948 1953 1958 1963 1968 1973

Year

Annual Number Tropical Cyclones and five-year running mean

Number of Tropical Cyclones 5 per. Mov. Aug. (Number of Tropic Cyclones)------ Linear (Number of Tropical Cyclones)

1978 1983 1988 1993 1998 2003

1

Figure 4. Characteristics and effects of tropical cyclones in the Philippines

(a) Trend in the number of tropical cyclones entering the PAR between 1948–2005

Note: Grid boxes indicate the frequency of cyclones passing through each 10x10 lat/long grid

Philippine Area of Resposibility

(b) Frequency of tropical cyclones over the Philippines during 1948–2005

Legend:2 in 1 yr

N

4 in 3 yrs1 in 11 in 2 yrs1 in 3 yrs1 in 10 yrs1 in 30 yrs1 in 50 yrs

22 PhiliPPine Journal of DeveloPment 2010

(c) Percent of normal rainfall for July–September 1997

(d) Percent of normal rainfall for October–December 1997

Percent of NormalJuly-September

1997Percent of NormalOctober-September

1997

≤ 40 % ≤ 40 % 41 - 80 41 - 80 81 - 120 81 - 120> 120 > 120

Legend: Legend:

cyclone statistics during the 1997–1998 El Niño showed that almost 60 percent of the cyclones that entered the PAR during 1997 had cyclone intensities (>117kph), but most of their tracks carried them to the north, sparing the country from their effects. No tropical cyclone entered or developed in the PAR during the month of September 1997 compared to the expected number of four. Below normal numbers of tropical cyclones were recorded during the year, with only 14 compared to the average of 20 cyclones entering the PAR. This affected normal rainfall patterns in the country and major drought events occurred in many areas. Figures 4c and 4d are the quarterly rainfall distribution as a percent of normal rainfall for 1997 showing large rainfall deficits in most parts of the country for more than three consecutive months.

The tropical cyclone season coincides with monsoon activity in the Philippines. The season starts during the first semester of the rain-year, covering the months from April to September. This is during the southwest monsoon season where the peak months of rainfall (July–September) also coincides with the highest number of tropical cyclones. This is followed by the second semester rain-period from October to March where tropical cyclone activity occurs during the months of October to December. Since ENSO dynamics affects large-scale atmospheric circulation and causes seasonal changes in the global climate system, it is not surprising that tropical cyclone characteristics are influenced by ENSO in the PAR and globally (Landsea 2000). Figure 4e shows the mean number of tropical cyclones that entered/developed in the PAR during normal, El Niño (warm), and La Niña (cool) conditions. During the warm episode, less

tropical cyclones occur during the last quarter (October–December) of the year. This period is the first half of the northeast monsoon season in the country and because the warmth in the ocean surface is directed toward the central and eastern equatorial Pacific during an El Niño, tropical cyclone formation is favorable in these areas which explains the lower frequency of cyclones that enter the PAR. However, higher number of tropical cyclones of greater intensities (>118 kph) enter the PAR during the warm episode. But since the formation is farther to the east of the western equatorial Pacific, tracks of the cyclones with higher intensities tend to move farther to the north or northeast of the Philippines during the warm event, sparing the country. Figure 4f shows the actual tracks of tropical cyclones during strong El Niños (1948–2005). Based on the National Oceanic and Atmospheric Administration (NOAA) standard that uses Oceanic Niño Index (ONI) for identifying intensities of El Niño and La Niña events, a strong El Niño is defined as having an ONI of equal or greater than 1.5. ONI is the running 3-month mean SST anomaly for the Niño 3.4 region (i.e., 50N–50S, 120–1700W). Events are defined as five consecutive months at or above the +0.50 anomaly for warm (El Niño) events and at or below the –0.5 anomaly for cold (La Niña) events. These are further broken down into weak (with a 0.5 to 0.9 SST anomaly),

23hilario, De guzman, ortega, hayman anD alexanDer

(e) Mean number of tropical cyclones during normal, El Niño (warm) and La Niña (cold) conditions

Source: Cinco et al. 2006.

Year Normal La Niña El NiñoJFM (QTR1) 1.21 1.24 0.38AMJ (QTR2) 3.29 2.87 2.38JAS (QTR3) 9.91 8.86 8.85JFM (QTR4) 6.55 6.79 4.56

TOTAL 20.96 19.76 16.17

24 PhiliPPine Journal of DeveloPment 2009

(f) Actual tropical cyclone tracks reaching the Philippine Area of Responsibility (shown by the boundary surrounding the Philippines) during strong El Niños between 1948–2005

moderate (1.0 to 1.4), and strong (≥ 1.5) events (Null 2009). These were observed during the following El Niño years: 1957–1958; 1965–1966; 1972–1973; 1982–1983; 1991–1992; and 1997–1998.

Climate forecasts and applicationsUnderstanding the influence that ENSO has on rainfall in a region is necessary but not sufficient for this information to be useful.

Hansen (2002) identifies five prerequisites for climate forecasts to be beneficial:

ª forecast information must address a need that is both real and perceived;ª viable decision options that are sensitive to forecast information;ª prediction of the components of climate variability that are relevant to

viable decisions;ª effective communication of relevant information; andª institutional commitment and favorable policies.

To conclude this paper, these five factors shall be discussed as they relate to the Philippines. Forecast information must address a need that is both real and perceived The Philippines is vulnerable to climate variability and some of the variability can be explained by ENSO. At farm, farm adviser, and agricultural policy levels, there has been a shift from a question of “what is an El Niño or La Niña?” to “what will the impact of El Niño or La Niña be on my sector and what can I do about it?”

25hilario, De guzman, ortega, hayman anD alexanDer

The major crops in the Philippines are rice and corn. Figure 5 shows the vulnerability of these crops to El Niño. The concept used in these maps was the assumption that the vulnerability of an area to drought is dependent on the area devoted to rice and corn production per province and the moisture available to crops (Jose et al. 2002). It is comparing the rate of evapotranspiration to the rate of precipitation to assess the degree of dryness (Jose et al. 2002). This utilized the idea of the Moisture Availability Index (MAI) which is equal to the ratio of monthly rainfall to the monthly evapotranspiration (Jose et al. 2002). An average evapotranspiration value of 150mm per month for rice and 140mm for corn was assumed. Months with MAI value of less than 1.0 were considered as dry months. MAI is used to evaluate the water adequacy of the soil during the month to sustain crop growth. The severity of the drought can be deduced from the number of consecutive months with MAI values of less than 1. Assessment of the vulnerability of a province to El Niño is based on the Drought Vulnerability Index (DVI) value. The DVI was computed by multiplying the average number of months for each El Niño year with MAI values less than 1 to the percentage of area devoted to rice and corn production for a particular province. The higher the value, the more vulnerable the area is to drought. Arbitrary assigning of ranges of DVI values to represent the areas least vulnerable (less than or equal to 5), moderately vulnerable (between 5 and 15), and highly vulnerable (more than 15) are done for mapping in Figure 5.

Figure 5. Vulnerable areas for rice and corn production to an El Niño event

(a) Rice (b) Corn

Source: Cinco et al. 2006.

26 PhiliPPine Journal of DeveloPment 2009

Viable decision options that are sensitive to forecast informationBecause agricultural systems are nested hierarchies, climatically sensitive decisions can be usefully identified at the field, farm, region, and national levels (Meinke et al. 2003). Farmers are making many decisions in the face of climate uncertainty; most of these decisions are about allocating resources such as land, labor, and capital. There are decisions about which crop to plant and what area to plant and what levels of inputs to use. All of these decisions are influenced by nonclimatic factors such as costs of inputs and prices of commodities but, in most cases, the optimum decision changes due to rainfall in the coming season.

Prediction of the components of climate variability that are relevant to viable decisionsSeasonal climate forecasting has been shown to have a potential benefit to rainfed rice production (Abedullah and Pandey 1998) and rainfed corn production (Lansigan 2003; Monte et al. 2008; Predo et al. 2008) in the Philippines. Although these studies show a benefit, in many cases the extent of the benefit is fairly modest. In a review of the use of SCFs in Australian context, Hayman et al. (2007) noted that the payoffs for following climate information were low, took some time to accrue, and, while the long-term benefit was positive, farmers could be worse-off following the forecast in any given year.

Effective communication, institutional commitment, and favorable policiesIn the Philippines, SCFs are produced by the Climate Information Monitoring and Prediction Center (CLIMPC), and communicated through its governing body, PAGASA. Inputs used in the generation of CLIMPC products include indicators and indices of rainfall for assessing potential impacts, extracts of outputs from global climate centers, global SST anomalies, and SOI as a basis for SCFs. Forecasts are made monthly and quarterly with one to six months lead time. The CLIMPC conducts research to improve forecast methodologies and models used in effective risk management in various sectors. It also conducts a quarterly climate forum (or monthly if an extreme event arises) to the different government agencies and private entities. Interagency collaboration has been very effective in bringing knowledge of the socioeconomic benefits of climate forecast applications. Feedback from the representatives of the end-users is very important for the validation or confirmation of climate products to determine their accuracy. Accurate seasonal forecasts are vital to formulating short- and long-term plans for disaster preparedness and mitigation strategies. Seasonal climate forecasting by PAGASA is one of the essential inputs in the planning by the National Disaster Coordinating Council, which serves as the adviser of the President of the Philippines on disaster preparedness programs, operations,

27hilario, De guzman, ortega, hayman anD alexanDer

and rehabilitation efforts, of which PAGASA has continuous collaboration. Improvements continue to be made on SCF models by PAGASA to assist with effective risk management strategies.

In response to the devastating impact of the past El Niños on the Philippine economy, the Drought Early Warning and Monitoring System (DEWMS) was developed by the PAGASA Weather Bureau to warn of upcoming drought conditions which may affect a particular area in the country. The first drought advisory was issued by PAGASA on February 9, 1987 which covered the drought stricken area of the Bicol region. In March 1987, the Inter Agency Committee on Water Crisis Management (IACWCM) was created, with the objectives of monitoring and coordinating water-related activities of the Philippine national government to mitigate adverse consequences of drought. The IACWCM consists of all water-related agencies in government, specifically those which deal with domestic water consumption in the metropolis, power generation and electrification, irrigation, water rights, watershed management, and the PAGASA which is responsible for the monthly weather outlook in terms of rainfall availability. As part of the continuous effort to support the program, seasonal rainfall research was initiated in response to the information needs of the committee, and is primarily based on ENSO activity. This has been proven to be useful in the early detection of drought and issuance of advisory. Government response during the 1997–1998 El Niño began after the PAGASA initiative of providing a monthly weather outlook to member agencies of the IACWCM.

To enhance PAGASA’s DEWMS, the National ENSO Early Warning and Monitoring System was developed and in 2002, it was expanded into the Climate Information, Monitoring and Prediction Center. The objective is to provide timely assessments of weather conditions and other information needed by various end-users, particularly policy decisionmakers and those concerned with crisis management. The assessment of various weather elements and developments in the global climate system make possible the early detection of an impending extreme climate event, with advisories immediately issued to the public to mitigate adverse impacts. Another government initiative is the creation of the interagency Task Force on the El Niño in September 1997, a new strategy with coordinated and effective course of action and comprehensive plan. The work program of the Task Force focused on interventions in agriculture, domestic water supply, environment, and other sectors (health, energy) and on the information, education, and communication campaign.

Perhaps the greatest challenge in the Philippines is to communicate the idea that El Niño means increased risk of drought, not a guarantee of drought. In the last 100 years, there have been about 24 El Niño events—so each autumn it can be said that the odds for an El Niño are about 1 in 4 (24%). If a bad drought is defined

28 PhiliPPine Journal of DeveloPment 2009

as 1 in 10- or 1 in 20-year event, this is much rarer than an El Niño event. It is important to recognize that, by this definition, there are more El Niño events than bad droughts. While many El Niño years are also drought years, there will be El Niño years that are not associated with bad droughts and bad droughts that are not associated with El Niño years. Therefore, El Niño is best understood as increasing the risk of bad drought and decisionmakers at a policy or farm level need to factor the forecast into their risk management rather than simply planning for drought.

While there is significant institutional support for the application of SCFs, it is important to recognize that there is always tension between policy and politics. An example is the complex interaction between policy and politics that complicates the application of ENSO information in decisions regarding rice importation (Reyes et al. 2008).

CONCLUDING REMARKSIn this paper, it has been shown that there are strong ENSO impacts on the climate in the Philippines. Communicating ENSO information for risk management is an ongoing challenge. In the Philippines, there are many small farm-holders with a complex network of intermediaries. Provincial workshops with local PAGASA and Department of Agriculture staff have been useful forums for two-way communication.

Earlier in the paper, it has been argued that there had been a swing in the question from “what is ENSO?” to “how do we use ENSO information to manage climate risk?” This latter question requires a complex partnership and two-way discussion between climate science and decisionmakers. For institutions, issuing forecasts is relatively easy, listening to and interacting with a range of decisionmakers from policy to farm level requires an additional suite of skills.

29hilario, De guzman, ortega, hayman anD alexanDer

Area affected (hectares)Region Rice Corn Vegetables Fruit

treesOther Crops

Total Estimated Damage (P)

Ilocos and Pangasinan

2,851 679 527 – – 4,057 34,362,920

Cagayan Valley 2,285 6,950 – – – 9,235 92,589,760Cordillera Autonomous Region

429 4,049 262 132 – 4,872 45,059,033

Bicol 4,099 456 43 – – 4,598 30,609,764Western Visayas 70,990 454 368 – 28 71,840 856,743,835Central Visayas 35 187 87 1,767 – 2,076 5,426,660Eastern Visayas 7,591 1,177 1,353 38 28 10,187 86,298,944Western Mindanao

7,974 8,689 2,564 – – 19,227 227,601,981

Northern Mindanao

2,387 28,552 1,381 13,640 130 46,090 232,649,997

Southern Mindanao

21,156 56,589 1,265 90,987 1,684 171,681 1,304,986,686

Central Mindanao

38,263 74,763 2,390 1,320 1,183 117,919 1,178,603,224

Total 158,058 182,543 10,240 107,884 3,053 461,782 4,094,932,804

APPENDIX

Appendix Table 1. Agricultural production damages at various regions in the Philippines caused by the 1991–1992 El Niño-related drought event

Appendix Table 2. Most disastrous tropical cyclones in the Philippines from 1970–2006*Year Name Date Max Wind Max

24hr rain

Damages Dead Injured Missing

1970 Sening 10/10/1970 275 235 460 575 1593 193

1970 Yoling 17/11/1970 200 205 116 230 1756 381

1971 Krising 7/10/1971 100 274 13 90 8 80

1972 Asiang 6/01/1972 104 189 145 204 28 5

1972 Konsing 23/06/1972 205 237 100 131 0 0

1973 Narsing 12/10/1973 181 312 39 27 0 30

1974 Susang 9/10/1974 175 781 55 26 0 3

1974 Wening 25/10/1974 269 818 126 23 0 0

1975 Auring 22/01/1975 110 102 17 40 0 8

30 PhiliPPine Journal of DeveloPment 2009

Year Name Date Max Wind Max 24hr rain

Damages Dead Injured Missing

1976 Aring 2/12/1976 95 167 69 100 0 15

1977 Unding 10/11/1977 175 321 477 40 0 0

1978 Atang 18/04/1978 180 222 245 66 47 45

1978 Kading 24/04/1978 125 304 1021 444 749 280

1979 Bebeng 12/04/1979 185 260 267 30 73 63

1980 Aring 1/11/1980 210 699 136 103 0 25

1981 Anding 22/11/1981 260 287 650 280 116 129

1981 Dinang 23/12/1981 165 179 592 18 1838 167

1982 Bising 22/03/1982 185 176 588 112 85 91

1982 Weling 12/10/1982 135 175 627 96 183 30

1983 Bebeng 12/07/1983 165 254 45 18 8 21

1984 Nitang 31/08/1984 220 222 4100 1028 2861 464

1984 Undang 3/11/1984 230 256 1540 895 2526 272

1985 Daling 25/06/1985 165 345 352 55 0 4

1985 Saling 15/10/1985 240 262 2132 88 224 13

1986 Gading 6/07/1986 220 710 621 89 16 20

1986 Miding 17/08/1986 140 313 263 151 17 4

1987 Herming 8/08/1987 185 238 2000 94 468 –

1987 Sisang 23/11/1987 240 236 1119 979 927 –

1988 Unsang 21/10/1988 215 283 5636 157 316 60

1988 Yoning 5/11/1988 175 298 2748 217 149 133

1989 Goring 13/07/1989 155 369 1373 94 382 3

1990 Ilang 28/08/1990 – 224 1502 50 53 0

1990 Ruping 10/11/1990 205 345 10277 508 1274 240

1991 Trining 24/10/1991 150 760 3612 83 58 22

1991 Uring 2/11/1991 95 140 975 5080 292 1264

1992 Maring 18/09/1992 130 370 2155 27 13 18

1993 Goring 23/06/1993 110 534 2774 51 109 5

1993 Kadiang 30/09/1993 110 232 8752 126 37 26

1993 Husing 28/10/1993 165 250 1585 25 7 5

1993 Monang 3/12/1993 185 206 2464 273 607 90

1993 Naning 6/12/1993 160 408 1330 93 579 10

Appendix Table 2 continued

31hilario, De guzman, ortega, hayman anD alexanDer

* The maximum winds are in kilometers per hour, maximum 24-hour rainfall in millimeters, and damages to public and private properties in million pesos.

Year Name Date Max Wind Max 24hr rain

Damages Dead Injured Missing

1994 Katring 18/10/1994 185 188 1433 45 24 1

1995 Mameng 27/09/1995 115 209 4189 133 108 130

1995 Pepang 26/10/1995 100 225 1407 128 165 41

1995 Rosing 30/10/1995 255 334 9330 722 2369 160

1996 Gloring 21/07/1996 130 388 2120 72 50 24

1998 Gading 17/09/1998 110 173 3623 109 22 10

1998 Iliang 11/10/1998 290 220 4476 46 63 29

1998 Loleng 15/10/1998 95 307 5306 303 75 29

1999 Ising 28/07/1999 65 171 1290 – – –

2000 Edeng 3/07/2000 135 322 1102 – – –

2000 Reming 25/10/2000 100 312 3872 6 – 33

2001 Feria 2/07/2001 125 1086 3586 188 241 44

2001 Nanang 6/11/2001 80 214 3246 236 164 98

2003 Chedeng 25/05/2003 105 – 538 44 8 19

2003 Harurot 19/07/2003 190 – 2374 64 154 2

2003 Weng 12/11/2003 100 – 0.045 13 11 5

2004 TY Marce 20/08/2004 120 – 2 64 9 2

2004 TS Unding 14/11/2004 120 – 0.5 71 169 69

2004 TD Violeta 22/11/2004 55 – 0.07 31 187 17

2004 TD Winnie 27/11/2004 55 – 0.7 693 648 443

2004 TY Yoyong 30/11/2004 185 – 0.6 73 168 24

2005 TS Auring 15/03/2005 55 139 21 13 – 63

2005 TS Labuyo 19/08/2005 97 402 496 9 5 5

2006 TY Seniang 7/12/2006 130 200 500 27 42 8

2006 STY Reming 28/11/2006 281 446 5 709 2190 753

2006 TY Paeng 27/10/2006 195 227 1290 32 62 23

2006 TY Milenyo 25/09/2006 110 222 6610 213 660 48

Appendix Table 2 continued

32 PhiliPPine Journal of DeveloPment 2009

Appendix Table 3. Meteorological station identity numbers (ID) and data length for the Philippines

Station ID Station Name Monthly data Years of record222 Vigan, Ilocus Sur 1902–1940, 1951–2007 100232 Aparri, Cagayan 1902–1940, 1951–2007 100223 Laoag City 1908–1940, 1951–2007 94233 Tuguegarao 1903–1940, 1951–2007 99324 Iba, Zambales 1902–1940, 1951–2007 100325 Dagupan City 1902–1940, 1951–2007 100328 Baguio City 1902–1940, 1951–2007 100330 Cabanatuan 1938–19 40,1951–2007 64333 Baler, Quezon 1902–1940, 1951–2007 91538 Roxas City 1902–1940, 1951–2007 98546 Catarman 1951–2007 58548 Catbalogan 1916–1940, 1951–2007 86550 Tacloban City 1903–1940, 1951–2007 99637 Iloilo City 1902–1940, 1951–2007 100642 Dumaguete City 1910–1940, 1951–2007 92741 Dipolog 1907–1940, 1951–2000 95751 Malaybalay 1920–1940, 1951–2007 82753 Davao City 1951–2007 56755 Hinatuan 1951–2007 56

REFERENCESAbedullah and S. Pandey. 1998. Risk and the value of rainfall forecast for rainfed

rice in the Philippines. Philippines Journal of Crop Science 23:159–165.Allen, R., J. Lindesay, and D. Parker. 1996. El Niño Southern Oscillation and

climatic variability. Australia: CSIRO Publishing.Asuncion, J.F. and A.M. Jose. 1981. Onset, peak and recession of the rains during

the northeast and southwest monsoons in the Philippines. Philippine Atmospheric, Geophysical and Astronomical Services Administration.

Changnon, S.A. and G.D. Bell, Editors. 2000. El Niño 1997–1998: the climate event of the century. United States: Oxford University Press.

Cinco, T.A. F.D. Hilario, L.V. Tibig, V.C. Manalo, C.C. Uson, R.S. Barba, R.D. Guzman, R.F. Frial, and S.R. Espenueva. 2006. Updating tropical cyclone climatology in the PAR. Phil. Met-Hydro Congress.

Davis, M. 2001. Late Victorian holocausts: El Nino famines and the making of the Third World. London: Verso.

33hilario, De guzman, ortega, hayman anD alexanDer

Hansen, J.W. 2002. Realizing the potential benefits of climate prediction to agriculture: issues, approaches, challenges. Agricultural Systems 74:309–330.

Hayman, P.T., J. Crean, J. Mullen, and K. Parton. 2007. How do probabilistic seasonal climate forecasts compare with other innovations that Australian farmers are encouraged to adopt? Australian Journal of Agricultural Research 58:975–984.

Jose, A.M. 1989. Seasonal rainfall prediction in the Philippines. Doctoral thesis. Quezon City: University of the Philippines.

———. 1990. El Niño-related drought events in the Philippines during the past decade (1980–1989). Quezon City: Philippine Atmospheric, Geophysical and Astronomical Services Administration.

———. 2002. ENSO impacts in the Philippines: examples of ENSO-society interactions [online]. http://iri.columbia.edu/climate/ENSO/societal/example/Jose.html.

Jose, A.M. R.V. Francisco, and N.A. Cruz. 1993. A preliminary study on the impacts of climate variability/ change on water resources in the Philippines. Quezon City: PAGASA.

———. 1999. A study on the impact of climate variability/change on water resources in the Philippines. Journal of Philippine Development 26:115–134.

Jose, A.M. F. Hilario, and E. Juanillo. 2002. El Niño vulnerability for rice and corn. Quezon City: PAGASA.

Landsea, C.W. 2000. El Niño/Southern Oscillation and the seasonal predictability of tropical cyclones. In El Niño and the Southern Oscillation: multiscale variability and global and regional impacts, edited by H.F. Diaz, and V. Markgraf. Cambridge University Press.

Lansigan, F.P. 2003. Assessing the impacts of climate variability on crop production and developing coping strategies in rainfed agriculture. In Coping against El Nino for stabilizing rainfed agriculture: lessons from Asia and the Pacific edited by S. Yokoyama and R.N. Concepcion. Proceedings of a joint workshop, Cebu, Philippines, September 17–19, 2002. CGPRT monograph number 43.

Lyon, B., H. Cristi, E.R. Verceles, F.D. Hilario, and R. Abastillas. 2006. Seasonal reversal of the ENSO rainfall signal in the Philippines. Geophysical Research Letters 33:L24710, doi:10.1029/2006GL028182.

Meinke, H., W. Wright, P. Hayman, and D. Stephens. 2003. Managing cropping systems in variable climates. In Principles of field crop production. 4th edition edited by J. Pratley, pp. 26–77. Oxford, UK: Oxford University Press.

34 PhiliPPine Journal of DeveloPment 2009

Monte, E., C. Predo, S. Bulayog, and R. de Guzman. 2008. Using seasonal climate forecast in corn production among smallholder farmers in Cebu, Philippines. 6th Asian Society of Agricultural Economists International Conference.

Nicholls, N. 1988. More on early ENSOs: evidence from Australian documentary sources. Bulletin of the American Meteorological Society 69:4–6.

———. 2005. Climatic outlooks: from revolutionary science to orthodoxy. In A Change in the Weather edited by T. Sherratt, T. Griffiths, and L. Robin, pp. 18–29. Canberra, Australia: National Museum of Australia Press.

Null, J. Revised 2009. El Niño and La Niña years and intensities based on Oceanic Niño Index (ONI). http://ggweather.com/enso/oni.htm.

Predo, C., P. Hayman, J. Crean, J. Mullen, K. Parton, F. Hilario, R. de Guzman, E. Juanillo, C. Reyes, E. Monte, and J. Liguton. 2008. Assessing the economic value of seasonal climate forecasts for corn-based farming systems in Leyte, Philippines. 6th Asian Society of Agricultural Economists International Conference.

Reyes, C. C. Mina, J. Crean, E. Juanillo, R. De Guzman, and K. Parton. 2008. Use of seasonal climate forecasting (SCF) in Philippine Rice Policy. 6th Asian Society of Agricultural Economists International Conference.

Ropelewski, C.F. and M.S. Halpert. 1987. Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Monthly Weather Review 115:1606–1626.

Stern P.C. and W.E. Easterling. Editors. 1999. Making climate forecasts matter, ix–xii. Washington, D.C.: National Academy Press.

Stone, R.C. and A. Auliciems. 1992. SOI phase relationship with rainfall in eastern Australia. International Journal of Climatology 12:625–36.

World Meteorological Organisation (WMO). 1998. WMO statement on the status of the global climate in 1997. WMO No. 877(10). Geneva: World Meteorological Organisation.

———. 2006. Drought monitoring and early warning: concepts, progress and future challenges. Weather and climate information for sustainable agricultural development. WMO No. 1006. Geneva : World Meteorological Organisation.

Wright, W.J. 2001. A review of Australian climate in the 20th century [online]. Preprints, CLI–MANAGE 2000 (Conference on Managing Australian Climate Variability), Albury, NSW, Australia, Bureau of Meteorology, 127–130. http://www.bom.gov.au/climate/enso/australia_detail.shtml.