Water Resources Research

Modeling the hydrologic responses of the Pampanga River basin, Philippines: A quantitative approach for identifying droughts



[1] Drought in the Philippines has been monitored for agricultural and economic losses, but spatial and temporal characterization at the basin scale has not been quantified. The relationship between different drought types, and how these can be integrated into timely water-resource-management planning in the agriculture and water sector of the Pampanga River basin, were considered. Specifically, the objectives of this study are as follows: (1) to propose a standardized anomaly (SA) index for assessing different types of drought impacts at the basin scale; (2) to quantify vulnerability of the agriculture and water sectors using physically consistent hydrological parameters with temporal variation and spatial heterogeneity; (3) to develop a method for combining the drought index calculated from the inputs and outputs of WEB-DHM on the basis of sturdy algorithms for the physics of water and energy movement in the basin using monthly and seasonal differences of various drought types; and (4) to combine hydrological parameters with crop production to determine its effects on rice. The SA was calculated for the variables related to each drought type: rainfall (meteorological), streamflow and groundwater (hydrological), and soil moisture (agricultural) during 1983, 1987, 1990–1992, and 1998 droughts. El Niño is one of the major driving forces leading to drought in the country. The drought intensified on the second year of the average two-year El Niño Southern-Oscillation (ENSO) composites with a 1 to 7 month time lag between parameters and hot spots in the upland and central plains of the basin. Recommended adaptation strategies include crop scheduling, crop/livelihood substitutes, and alternative water sources.

1. Introduction

[2] By the time drought is recognized by local farming communities in Philippine agriculture, crop stress or crop losses have already occurred. Drought is a natural hazard causing severe damage in many regions globally [Dracup et al., 1980; Fleig et al., 2006]. This has even worsened with the changing climate, as reported by the IPCC Working Group I, which stated that it is likely (66%) that droughts will increase as a result of climate change [Solomon et al., 2007]. Furthermore, the IPCC Working Group II also projected with high confidence that changes in the frequency and severity of extreme climate events have significant consequences for food, forestry production, and food security, which will severely affect smallholder and subsistence farmers, pastoralists, and artisanal fisher folk [Easterling et al., 2007]. In the Philippines, droughts affect the most number of people compared to other natural disasters. From 1978 to 2007, the emergency events database [Centre for Research on the Epidemiology of Disasters (CRED), 2010] of the World Health Organization Collaborating Centre for Research on the Epidemiology of Disasters records eight droughts occurring in the country affecting 6,553,207 people (on average, 819,151 people per drought event) and causing $64,453,000 (on average, $8,056,000 per drought event) damage to livelihoods and properties. Although the death toll caused by drought is low as compared to other natural disasters, it occurs on large regional scales and over longer time scales, and remains unchecked until economic losses are felt. Drought records in the Philippines are usually related to agriculture because of their effects on crop yield. The following years of agricultural drought occurred in the country for the study period 1982 to 2000: 1982, 1983, 1987, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1997, and 1998 [Steyaert et al., 1981; Department of Environment and Natural Resources, 1999; U.S. Department of Agriculture, 2005; Pandey et al., 2007; CRED, 2010]. A study by Lasco et al. [2010] on the Pantabangan-Carranglan watershed reported four major drought episodes that occurred in the upper portion of the Pampanga River basin. These episodes occurred in 1983, 1987, 1990–1992 (represented by the year 1991, since drought occurred December 1990 to July 1992 [CRED, 2010]), and 1998.

[3] There are several definitions of drought in literature. For uniformity, drought is defined here as a prolonged and abnormal moisture deficiency [Huschke, 1959; Palmer, 1965]. There are several kinds of droughts: atmospheric, meteorological, hydrological, agricultural, physiological, and socioeconomic [Wilhite and Glantz, 1985; Keyantash and Dracup, 2002]. For this study, three types of droughts are considered: (1) meteorological droughts characterized by precipitation deficit causing significant economic losses [Smakhtin and Hughes, 2006]; (2) hydrological drought characterized by inadequate streamflows (as determined by discharge and groundwater level deficit) supplying established uses under a given water-management system [Linsley et al., 1988]; and (3) agricultural drought characterized by soil-moisture deficit insufficient to meet crop requirements leading to increased plant stress and reduced crop yield [Pandey et al., 2007]. Droughts caused by different deficits tend to be positively correlated and are likely to respond to the same triggers, but they exhibit diverse temporal and spatial scales. A study by Mishra and Cherkauer [2010] using the variable infiltration capacity (VIC) model on the three drought types in the midwestern United States showed that crop yield is strongly correlated with meteorological drought and maximum daily temperature by using three indices (SPI, SRI, and SMP). In the case of other regions, such as this basin in the Philippines where environmental conditions are quite different, these indices have to be validated for usability. In addition, it is difficult to produce an overall drought indicator because of the complicated physical connections between variables used to characterize droughts [Kao and Govindaraju, 2010]. Quantifying drought intensity in various categories is also difficult because of the economic and social differences that contribute to the subjectivity in drought characterization. Limitations in available data also aggravate the difficulty in quantifying drought temporally and spatially in remote areas. To date, there is still no single standard method to completely and accurately quantify drought occurrences from these different indicators. This study addresses these limitations, and determines how different parameters are affected individually, and in combination, to identify spatially drought-prone areas in the Pampanga River basin using a standard drought index. The relationship between the different spatial and temporal effects of drought on the hydrological parameters leading to the evolution of different drought types, as well as the localized effects of drought in this basin, may be useful for the planning of the agriculture (especially rice farming) and water sectors.

[4] There are several commonly used drought indices, but they vary depending on the drought parameters considered. Recent studies formulated new indices using statistical methods such as copulas on joint effects of precipitation and streamflow for the joint drought index (JDI) [Kao and Govindaraju, 2010], the standardized precipitation evapotranspiration index (SPEI) [Vicente-Serrano et al., 2010], the modified effective drought index (EDI) for long-term drought analysis, which enhanced the index's accuracy in predicting drought [Byun and Wilhite, 1999; Kim et al., 2009], and several other methods that quantify drought at different time scales and for different purposes. Most of these indices have their own statistical or physical limitations, and there is no consensus on which method is optimal. The more common indices for measuring soil-moisture deficits are the crop-specific drought index (CSDI) of Meyer et al. [1993], the soil-moisture drought index (SMDI) of Hollinger et al. [1993], and the commonly implemented Palmer drought severity index (PDSI) of Palmer [1965]. There are also other simple and effective methods for quantifying drought. The popular ones are the percent Normal; the Bhalme and Mooley drought index (BMDI) of Bhalmey and Mooley [1980]; the Deciles index (DI) of Gibbs and Maher [1967]; the rainfall anomaly index (RAI) of van Rooy [1965]; and the standardized precipitation index (SPI) of Mckee et al. [1993]. The SPI is the simplest index to estimate and is commonly used for quantifying drought using precipitation that has also been used for runoff and soil moisture [Mo et al., 2009]. This index is based on the cumulative probability of a given rainfall event occurring at a meteorological station by fitting rainfall data to a gamma distribution and transforming it into a standard normal distribution. The categories of SPI outputs are shown in Table 1 (available at http://www.drought.unl.edu/whatis/indices.htm#spi) [McKee et al., 1993]. For this study, SPI was also considered for other hydrologic parameters and renamed as the standardized index (SI). To increase SI versatility, we employed a modified version of SI using the SA index for different parameters with varying distribution patterns. This is done by (1) normalizing the varying distribution patterns of the hydrological parameters and (2) standardizing the normalized distribution by taking the anomalies of the transformed distribution from the transformed climatic means (long-term monthly means) and dividing them with the transformed standard deviation. The SA was used to identify significant differences during two-year composites of El Niño and La Niña years, and to determine the behavior of the different drought types within the four years when drought occurred.

Table 1. Meteorological Conditions Considered for the Range of SA Valuesa
SA ValuesMeteorological Condition
2.0+Extremely wet
1.5 to 1.99Near normal
1.0 to 1.49Moderately wet
−0.99 to 0.99Near normal
−1.49 to −1.0Moderately dry
−1.5 to −1.99Severely dry
−2 and lessExtremely dry

[5] A study by Kumar et al. [2006] for droughts in India shows that El Niño (warm ENSO) does not always lead to drought, but the enhanced convection associated with the ENSO has the downward branch of the Walker circulation located over the Philippines; hence, drought has been likely to occur during the El Niño years as evidenced by studies of decadal trends of sea surface temperature (SST) anomalies in the north Pacific and the tropical Pacific influencing drought occurrence [Dai et al., 2004]. This is also further verified in previous studies by Jose et al., [1996], who observed a decrease in streamflow during El Niño events in several Philippine watersheds, relating it to droughts. The Pampanga River basin is one of the major rice-producing areas in the country (it is commonly called the Philippine “rice bowl”), so this study focused specifically on agricultural drought and its effects on rice production.

[6] Drought impacts in Philippine agriculture have caused severe problems. To address this, some water-resources-management strategies have been proposed by previous studies: water reuse by recycling of drainage water and groundwater [Maraseni et al., 2010]; optimizing irrigation practices (alternate soil wetting and drying); supplemental or microirrigation [Rockstrom et al., 2002]; crop substitution during dry years (e.g., maize or other more drought-tolerant high-yielding crops, as opposed to rice); or abandoning second and third cropping altogether [Roberts et al., 2009]. Land-use change because of clearance may also cause droughts, so this also needs consideration. At the local level, adaptation strategies suggested by local communities in the upper Pampanga River integrated irrigation system (UPRIIS) area include the following [Lasco et al., 2010]: reforestation/agroforestry; soil and water conservation measures; water impoundment (to store rainfall or streamflow during wet periods); well construction (to access groundwater); cloud seeding; use of appropriate crops/varieties (e.g., more drought-resistant crops); irrigation management; using other water sources (e.g., rivers, groundwater); repairing damaged infrastructure; changes in livelihood; strict implementation of forest laws; and capacity-building activities. However, these recommendations from previous studies need to be reviewed further prior to implementation depending on the spatial and temporal characteristics of the drought as well as on the existing environmental conditions of the basin.

[7] This paper aims to look at the behavior of the different drought types in the Pampanga River basin to identify basin-specific water-resources management that can be adapted by the local communities in the area. Specifically, this study will (1) propose a standardized anomaly index for assessing different types of drought impacts at the basin scale; (2) determine quantitatively the vulnerability of the agriculture and water sectors using physically consistent hydrological parameters with temporal variation and spatial heterogeneity; (3) develop a method for combining the drought index calculated from the inputs and outputs of water and energy budget-based distributed hydrological model (WEB-DHM) on the basis of sturdy algorithms for the physics of water and energy movement in the river basin using monthly and seasonal differences of various drought types (e.g., meteorological, hydrological, and agricultural); and (4) combine hydrological parameters with crop production using the SA to determine drought effects on crop production.

2. Methods

[8] The methods used in the study were as follows (Figure 1): Input data from various sources were collected and processed at the watershed scale using Arc Info, FORTRAN, GrADS, and C++ scripts and integrated into the WEB-DHM [Wang et al. 2009a, 2009b]. The WEB-DHM scripts are currently available in FORTRAN for ease in adjusting different input data types. The Pampanga River basin had some localized input data limitations, which were addressed using high-resolution global data sets (section 3). Model outputs were spatial coverages and hourly basin averages (total discharge was collected at the outlet flowing to Manila Bay). Simulated discharges from the upper (Pantabangan Dam) and middle portion (San Agustin, Arayat) of the watershed were calibrated with observation data. After calibration, temporal and spatial analyses of selected parameters (precipitation, discharge, soil moisture (surface and root zone), and groundwater level) were conducted using SA, and the combined effects of different drought types were used to identify hot spots prone to drought during the dry months of the reported severe drought years. We identified suitable watershed management practices to mitigate drought effects on the agriculture and water sectors.

Figure 1.

Diagram of the methods for quantifying drought.

2.1. Web-DHM

[9] The water and energy budget-based distributed hydrological model (or WEB-DHM) [Wang et al., 2009a, 2009b, 2009c] was developed by fully coupling a simple biosphere scheme SiB2 [Sellers et al., 1996] with a hillslope hydrological model GBHM [Yang et al., 2004]. This is a distributed biosphere hydrological model that allows consistent descriptions of water, energy, and CO2 fluxes at the basin scale [Wang et al., 2009a, 2009b, 2009c]. The model has shown reliable accuracies in simulations of fluxes, discharge, and surface soil moisture in river basins. For this study, inputs (spatial and temporal distribution of rainfall) and outputs (spatial and temporal distribution of soil moisture, runoff, and groundwater level) of WEB-DHM were utilized.

[10] A digital elevation model (DEM) was used to define the basin and subbasins using the Pfafstetter system (Figure 2a). For each subbasin, flow intervals were calculated to represent time lags and accumulation in the river network (Figure 2b). Each flow interval comprises several model grids. Each cell was ascribed one land-use type and one soil type, and the land-surface submodel was used to independently calculate turbulent fluxes between atmosphere and land surface (Figures 2b and 2d). The vertical water distributions for all grid cells, such as ground interception storage and soil-moisture profile, were obtained through this biophysical process. Each model grid was subdivided into a number of geometrically symmetrical hillslopes (Figure 2c). Hillslopes (angle and length) were calculated from a finer-resolution DEM. The subgrid scheme (grid-hillslope discretization) was used to simulate lateral water redistributions and to calculate runoff composed of overland, lateral subsurface, and groundwater flows (Figures 2c and 2d). Simplifications of streams located in one flow interval were done by combining into a single virtual channel. All flow intervals were linked by the river network generated from the DEM. All runoff from the grid cells in a given flow interval was accumulated into a virtual channel leading to the outlet of the river basin. The flow routing for the river network in the basin was modeled using the kinematic wave approach.

Figure 2.

(a) The WEB-DHM: division from basin to subbasin; (b) subdivision from subbasin to flow intervals comprising several model grids; (c) discretization from a model grid to a number of geometrically symmetrical hillslopes; (d) and description of the water moisture transfer from atmosphere to river. Rsw and Rlw are downward solar radiation and long wave radiation, respectively. H is the sensible heat flux and equation image is the latent heat of vaporization. Here, the land-surface submodel is used to describe the transfer of the turbulent fluxes (energy, water, and CO2) between the atmosphere and the ground surface for each model grid. The hydrological submodel simulates both surface and subsurface runoff using grid-hillslope discretization simulating flow routing in the river network.

[11] Two different soil subdivision schemes were used to describe land-surface and hydrological processes [Wang et al., 2009a]. For the land-surface processes, the three-layer soil structure for the unsaturated zone is the same as in SiB2 (Figure 3). Thickness of the soil surface (D1) is set at 5 cm; the root-zone depth (D1 + D2) depends on the vegetation type considered by default in SiB2. The thickness of the deep soil zone (D3) changes with water table fluctuation and is equal to the depth of the groundwater level minus the thickness of the upper two layers. The root zone and deep soil zone were subdivided into several sublayers to calculate vertical interlayer flows and lateral runoff. Details of the equations used are by Wang et al. [2009a].

Figure 3.

Soil model of the WEB-DHM: Two different soil subdivision schemes are used for describing land-surface and hydrological processes, respectively. The three-layer soil structure used in SiB2 is retained to represent the unsaturated zone in the calculations of land-surface processes. The unsaturated zone is divided into multiple sublayers when simulating water flows within it and water exchanges with the groundwater aquifer.

2.2. Drought Indices

[12] We used a standardized anomaly index, a variation of the standardized precipitation index [Mckee, 1993], to quantify droughts. The SI utilized an equation fitting a gamma distribution to the data set, and then converted it to a normal distribution. The method is limited to using only a single gamma distribution for the entire data set. Hence, variations in the mean monthly values resulting from the differences in distribution pattern of the data set are neglected. This assumption may be true for hydrological parameters such as rainfall, if it follows a gamma distribution. From previous studies on spatial patterns of precipitation, for different time scales, distribution patterns vary significantly [Vicente-Serrano, 2006]. For this study, several hydrological parameters with different land and atmospheric conditions (tropical conditions) that result in varying monthly distribution patterns were considered. Another limitation pointed out by Kim et al. [2009] is that it utilizes a simple average of precipitation for each period. This means that the SI cannot accurately account for the effects of monthly differences or seasonality, nor for the fact that substantial water resources generated by rainfall that occurred many months ago have already been lost because of outflow and evaporation [Kim et al. 2009].

[13] To differentiate the modifications made in the original method of calculating for SI, we used a SA for temporal and spatial drought classifications. This index fits a distribution pattern (Table 2) to the monthly hydrological parameter values from the inputs and outputs of the WEB-DHM simulations. This is transformed to the normal distribution (equation (1)) [Walpole, 2000] and then standardized by taking the anomaly (calculated as the difference of the parameter value from its climatic mean (long-term monthly mean)), divided by the standard deviation of the transformed parameter (equation (2)). The equations used are

equation image

where equation image and equation image and f(x) is the distribution function of any hydrological parameter x. Then, we use these equations to transform to normal distribution, and calculate the SA using the equation

equation image
Table 2. Monthly Best-Fit Distribution Patterns for the Different Hydrological Parameters
MonthRainfallDischargeSoil Moisture: SurfaceSoil Moisture: Root ZoneGroundwater Level
JanThreshold WeibullFrechetLEVThreshold WeibullLogistic
MarThreshold WeibullThreshold WeibullLog logisticLEVLognormal
AprWeibullThreshold WeibullWeibullLEVNormal
MayWeibullWeibullGeneralized GammaThreshold WeibullLognormal
JunFrechetLog NormalWeibullSEVLognormal
JulWeibullWeibullSEVLog Generalized GammaLEV
AugLognormalLognormalGeneralized GammaLog Generalized GammaLEV
SeptWeibullWeibullGeneralized GammaSEVLognormal
NovLognormalWeibullLEVNormalThreshold Weibull
DecThreshold WeibullFrechetLognormalLognormalLogistic

[14] The SA values were compared with the SI to determine if similar results were obtained. Similar trends were observed for both SI and SA for precipitation with r2 = 0.94 for monthly, 0.95 for 3 month, and 0.96 for 6 to 12 month running averages. However, the Q (discharge), SM (soil moisture), and GW (groundwater) level showed lower correlations (r2 < 0.5) between SA and SI indicating that they have significantly different monthly distribution patterns. The SI categories [McKee et al., 1993] were used to classify droughts from SA outputs. For analysis of parameters using SA, monthly (for within-year droughts) and 3 month time scales (because of available crop volume data limitations) were used in comparison with ENSO and crop production. Other time scales can also be used for the SA equation and from WEB-DHM outputs, although studies on the time scales for SI indicate that shorter time scales are more sensitive to identifying droughts, and spatial complexity and uncertainty occurred in longer time scales [Vicente-Serrano and Lopez-Moreno, 2005]. The effects of monthly and seasonal differences can be identified by SA, and the quantitative effects of evapotranspiration are integrated into calculations of other parameters using the physically consistent model WEB-DHM. Another advantage of the SA is the ease with which it can be combined with different parameters in spatially identifying the average effects contributing to drought at the basin scale.

2.3. ENSO Years

[15] The ENSO affecting the Philippines was determined from the Niño 3.4 index (Figure 4), categorized as either weak, moderate, or strong El Niño/La Niña using the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) method of analyzing 3 month running means of SSTAs exceeding 0.4° for 6 months or more. These were then categorized as +0.5° to +1.0°C (or −0.5° to −1.0°C) for weak El Niño/La Niña; +1.0° to +1.5°C (or −1.0° to −1.5°C) for moderate El Niño/La Niña, and more than +1.5°C (or less than −1.5°C) for strong El Niño or La Niña [Philippine Institute for Developmental Studies, 2007; Lyon et al., 2006]. A comparison of El Niño/La Niña and drought severity was conducted to determine their correlation at the basin scale. The Niño 3.4 is a SST index in the region located 120°W to 170°W (190°E to 240°E), 5°N to 5°S, and was used for analysis of the Pampanga River basin because the location where this index was derived is close to the Pacific warm pool and main centers of convection [Trenberth, 1997]. Data from the Hadley Center sea surface temperature (HadSST) with base period 1870 to 2009 were used to identify the Niño 3.4 index for the period 1982 to 2000. To further analyze the impacts of El Niño, 2 year composites were considered using the method of Berri [2002], which was based on the incidence of five or more consecutive months of warm or cold ENSO events per year occurring for two consecutive years. The study period is limited (data availability), so six cases of El Niño and four cases of La Niña are averaged for the 2 year composites. There are also limited non-ENSO years because usually the tail end or beginning of ENSO partly affected most of the cases; so, for this study, non-ENSO years anomalies are set to 0.

Figure 4.

El Niño (red) and La Niña (blue) events based on the Niño 3.4 Index (longitude: 190oE to 240oE, latitude: 5oS to 5oN) from the Hadley Center sea surface temperature data set (1870 to 2009) for the period 1982 to 2000.

2.4. The t Test

[16] The Student's t test was used to assess significant differences in the effects of El Niño and La Niña on different hydrological parameters. Significance was set at 0.05 and monthly values from the El Niño and La Niña years' average 2 year composites were compared. The significantly different months were correlated with cropping schedules for rice to determine how drought affects the rice-growing schedules in the river basin.

2.5. Linear Detrending of Agricultural Production

[17] Linear detrending of nationwide data from the Philippine Bureau of Agricultural Statistics (available at www.bas.gov.ph) Gross Value Added of major crops and annual prices were calculated for the period 1967–2009. Linear regression analysis was conducted using the least-squares method to calculate a straight line that best fitted the data (1967–2009). The linear trend was subtracted from annual agricultural prices to detrend the data set, and thus remove the effects of improved technology and consistent misreporting [Simelton et al., 2009], and to allow calculation of expected or normal prices for each crop nationwide.

[18] To further investigate the impacts of ENSO on agricultural production, the standard anomaly for crop volume (similar equation to SA) from 1994 to 2000 was analyzed quarterly to compare with ENSO and drought to determine if crop volume (stock) was affected during ENSO years, and to see if a trend could be identified relating ENSO, soil-moisture drought SA, and crop loss. Limited quarterly agricultural production data for this region from 1994 to 2000 was available, so only preliminary comparisons were done. Further analysis of future crop production and ENSO relationships in this basin will need to be investigated.

3. Data Set

3.1. Study Area

[19] The Pampanga River basin (10,061 km2; see Figure 5a) is located in central Luzon, and lies in the provinces of Pampanga and Bulacan. It is bordered by the provinces of Bataan and Zambales to the west, Tarlac and Nueva Ecija to the north, the rest of Bulacan to the southeast, and drains to Manila Bay. It is a significant water resource for irrigation, hydropower, domestic water use, and industry. Metropolitan Manila obtains ˜97% of its water supply from this basin; therefore, the basin is of economic importance to the country. Major industries within the basin are farming, fishing, and some cottage industries. Major agricultural products include rice, corn, sugarcane, and tilapia.

Figure 5.

The Pampanga River basin: (a) river network and discharge gauges, (b) digital elevation model, (c) land use, and (d) soil map.

3.2. Input Data

[20] Precipitation data were from the Asian precipitation highly resolved observational data integration toward the evaluation of the water-resources-management (APHRODITE) data set. This is a 44 year gridded (0.25° × 0.25°) precipitation data set (1961–2004) for Asia, and utilizes a combination of gauge data and satellite data (TRMM) [Yatagai et al., 2009]. For this study, the years 1982–2000 were utilized for precipitation analysis. Linear interpolation to downscale the data set into 1 km × 1 km grids was conducted for the hydrological simulation.

[21] Discharge data for the period 1980–2009 were obtained from the National Irrigation Authority (NIA) for inflow to the Pantabangan Dam in the upper Pampanga River basin, and discharge data at San Agustin, Arayat, for the period 1982–2000 were obtained from the National Water Resources Board (NWRB). These two stations (Figure 5a) were used in model calibration to achieve an optimal parameter set to obtain discharge at the basin outlet draining to Manila Bay. Meteorological forcing data used in the simulations were from the Japan Meteorological Agency (JMA) Japan Reanalysis (JRA) [Onogi et al., 2007] JRA25 fcst_phy2m data set (air temperature, specific humidity, air pressure, wind speed, downward solar, and long wave radiation).

[22] The leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by the green vegetation canopy (FPAR) were obtained from the NOAA AVHRR PAL 16-km LAI and FPAR satellite data set [Myneni et al., 1997] for the period 1982–2000. The LAI and the FPAR were used to account for dynamic changes in plant photosynthetic activity in the basin.

[23] Digital elevation data used were the 1000-m HYDRO1k, processed using Arc Info (USGS, available at edc.usgs.gov/products/elevation/gtopo30/hydro/index.html, 2009). Subgrid topography used was the NASA SRTM (Shuttle Radar Topographic Mission) using three-arc second digital elevation data (approximately 90 m) resampled to a 100-m DEM. Figure 5b shows the northern and southwestern upland areas (maximum elevation 1835 m) with the central plains at 5 m above sea level.

[24] Land-use type consists mostly of deciduous, broadleaf, and needleleaf evergreen trees (forest areas in the northern and central parts) with short vegetation and grassland areas scattered sparsely, and agricultural areas concentrated in the southwestern part of the watershed (Figure 5c). The land-use data are from the USGS global land cover data set.

[25] Soil in the basin is mostly clay, clay loam, and sandy clay loam (Figure 5d). Soil hydraulic characteristics were obtained from the Food and Agriculture Organization [Food and Agriculture Organization, 2003] global data set (with a spatial resolution of 5 arc minutes). This data included saturated soil-moisture content, residual soil-moisture content, saturated hydrologic conductivity for soil surface, and van Genuchten parameters (equation image and n) [van Genuchten, 1980]. Some soil parameters were optimized using observed discharge during calibration.

4. Results and Discussion

[26] Model calibration against observed discharge was done for the upper Pampanga River basin (Pantabangan Dam) and in San Agustin, Arayat, streamflow to achieve reliable accuracy for simulating the outflow to Manila Bay. This is because the water outflow to Manila Bay is important not only from an agricultural perspective, but for navigation, floods, and drought control in and around metropolitan Manila.

[27] Figures 6a and 6b show the calibrated monthly hydrograph of the Pantabangan Dam and the Arayat gauge, respectively, with the Nash-Sutcliffe (NS) model efficiency coefficient [Nash and Sutcliffe, 1970] and relative error (RE). The NS is defined as

equation image

where Qoi is observed discharge, Qsi is simulated discharge, n is total number of time series for comparison, and equation image is mean discharge value observed over the simulation period. The NS equal to 1 corresponds to perfect matching between the modeled discharge and the observed data. The relative error was used to validate the water budget and is defined as

equation image
Figure 6.

Simulated and observed discharges in (a) Pantabangan dam (inflows) and (b) San Agustin, Arayat (streamflows), by using WEB-DHM from 1982 to 2000 in the Pampanga River basin.

[28] The drought index was used to determine the beginning and end of moisture deficit periods and the degree of deficit severity, and to identify spatially drought-prone areas for the different drought types. The SA was used on all hydrological parameters, using similar categorization to SI, to identify if the droughts of 1983, 1987, 1991, and 1998 were clearly represented, and how drought occurred during these years for the different drought types. Table 2 shows the best-fit distribution pattern per month using the maximum likelihood estimates (MLEs) for each of the hydrological parameters using the corrected Akaike information criterion (AICc) (equation (5)) and the Bayesian information criterion (BIC) (equation (6)) with the best-fit model having the smallest AICc and BIC values [Akaike, 1974]:

equation image

where k is the number of estimated parameters including intercept and error terms in the model and n is the number of observations in the data set.

equation image

where k is the number of estimated parameters and n is the sample size.

[29] It has been found that the Weibull and the threshold Weibull (modified Weibull) distribution functions were the predominant distribution functions for rainfall. For the discharge, Weibull, threshold Weibull, lognormal, and Frechet distributions were identified. Soil moistures had a more variable distribution pattern. For the surface, LEV (largest extreme value), logistic, log logistic, Weibull, generalized gamma, SEV (smallest extreme value), and lognormal were the best-fit distributions, while for root-zone soil moisture, threshold Weibull, LEV, SEV, log generalized gamma, Weibull, normal, and lognormal distribution functions were identified. For the groundwater level, logistic, lognormal, normal, lognormal, LEV, SEV, and threshold Weibull were the best-fit distribution functions. Details of the different types of distribution functions can be found in the work of Meeker et al., [1998]. A similar method for selecting best-fit distribution for flood frequency was done by Hadded and Rahman [2010] for Tasmania in Australia with lognormal distribution to be the best selection.

[30] Monthly rainfall (Figure 7a) fluctuated periodically from 0 to 600 mm per month, while discharge (Figure 7b) ranged from 0 to 500 mm per month. Surface soil moisture (Figure 7c) ranged from 30% to 45%, and root-zone soil moisture (Figure 7d) had a smaller range from 21% to 31%. In both layers, the values ranged just slightly above and below field capacity, but the permanent wilting points were not reached. Depth of groundwater (Figure 7e) measured from the soil surface was also simulated. (For calculating SA of the groundwater level (GW), the negative values (GW) are at first inverted to positive values (−GW), and fitted to different distributions for calculating (−SA). The (−SA) is then inverted again to (SA) for (GW)).

Figure 7.

Basin average for (a) rainfall, (b) discharge, (c) surface soil moisture, and (d) soil moisture at the root zone, as well as (e) groundwater level.

4.1. Temporal Analysis of Drought Parameters

[31] Monthly temporal distribution of SA for meteorological drought (rainfall), hydrological drought (streamflow and groundwater level), and agricultural drought (surface and root-zone soil moisture) are shown in Figure 8. SA values lower than −1.0 were observed during the drought years of 1983, 1987, 1990–1992, and 1998 reported by the local communities (as well as drought years 1993, 1994, and 1995). Three month and yearly running averages were also derived for the different parameters. SA was significantly below −1 during drought years. Longer time scales may not be useful in this basin because the dry period decreased frequency and intensity (e.g., drought for 1990–1992 disappeared for the 12 month running average) similar to the Vicente-Serrano and Lopez-Moreno [2005] study on the Aragon River basin using SI.

Figure 8.

Standardized anomaly index for monthly (a) rainfall, (b) discharge, (c) surface soil moisture, (d) root zone soil moisture, and (e) groundwater level for 1982 to 2000 at the outlet flowing to Manila Bay showing monthly values (blue), 3 month running average (green), and 12 month running average (orange). (Drought occurrence: 1983, 1987, 1990–1992, and 1998).

[32] For within-year droughts (Figure 9) (drought years selected were based on the recorded droughts during the study period), drought did not occur in all months. Individual droughts had different starting and end dates because of the time lag in which precipitation deficit reduced discharge infiltrating to affect soil moisture at the surface, further percolating down to affect soil moisture at the root zone and the groundwater level. For the 1983 drought, rainfall, discharge, and surface soil moisture had severe deficits from May, while root-zone soil moisture and groundwater deficits started in June and July. For 1987, drought conditions occurred in May for rainfall and discharge, while surface and root-zone soil moisture and groundwater deficits were severe in August. For 1991, there were no drought conditions for rainfall and discharge (lowest SA in June), and mild drought for surface soil moisture in July, while no drought conditions were observed for soil moisture and groundwater (lowest SA in July). For 1998, no drought conditions occurred for rainfall (consecutively low from January to August), while very mild drought conditions occurred for discharge (January, July, and August) and surface soil moisture (April and August). For both root-zone soil moisture and groundwater, drought conditions began in January until September. A time lag of 1 to 2 months was observed before drought in the soil surface reached the root zone and the groundwater, delaying agricultural drought by at least 1 month.

Figure 9.

Standardized anomaly index for the basin average (a) rainfall, (b) discharge, (c) surface soil moisture, (d) root zone soil moisture, and (e) groundwater level for drought years 1983, 1987, 1991, and 1998 at the outlet to Manila Bay.

4.2. ENSO and Drought

[33] The general impacts of El Niño on climate in the Philippines by PAGASA included abnormalities such as delayed rainy season onset, early termination of the rainy season, weak monsoon and tropical cyclone activity, below normal rainfall, and above normal temperature.

[34] During the drought years 1983, 1987, 1990–1992, and 1998, Nino 3.4 indices were all greater than 0.5, indicating El Niño during these periods and supporting previous studies relating droughts and warm ENSO events. Table 3 shows the ENSO on the basis of Nino 3.4 SSTs, calculated using PAGASA's method of identifying ENSO events. Although El Niño occurred during drought years, it did not persist for the entire year in all cases. Table 4 shows 2 year composites of warm and cold events considered based on the Niño 3.4 index. These are consistent with the El Niño years (1982/1983, 1986/1987, 1991/1992, 1992/1993, 1994/1995, and 1997/1998) and La Niña years (1984/1985, 1988/1989, and 1995/1996) considered by Berri [2002] and Jaranilla-Sanchez et al. [2009]. It should be noted that all four years of recorded drought events in the basin fell in the second year of the ENSO composites, further verifying that drought effects are significantly higher in the second year of ENSO.

Table 3. Classification of ENSO Events on the Basis of El Niño 3.4 SSTs From the Hadley Center Sea Surface Temperaturea
  • a

    Base Period: 1870 to 2009. E+ strong El Niño; E moderate El Niño; E- weak El Niño L+ strong La Niña; L moderate La Niña; L- weak La Niña

1982 E-EE+
1983E+E L-
1984L-L- L
1986L-  E
1991 E-E-E
1993 E-  
1994   E
1995E-  L-
1997 E-E+E+
2000LL- L-
Table 4. List of Warm (El Niño) and Cold (La Niña) ENSO Events Considered in the 2 Year Composites for the Years 1982 to 2000a
Warm ENSO Events (El Niño) (Six Cases)1982/1983, 1986/1987, 1991/1992, 1992/1993, 1994/1995, 1997/1998
Cold ENSO Events (La Niña) (Four Cases)1984/1985, 1988/1989, 1995/1996, 1999/2000

[35] The ENSO 2 year composites (Figure 10) of the five parameters showed similar trends during extreme events. The El Niño and La Niña reversals, which were in agreement with the findings of Lyon et al. [2004], were also observed in the behavior of the hydrological parameters in the Pantabangan-Carranglan watershed in the northern portion of the Pampanga River basin [Jaranilla-Sanchez et al., 2009]. Results from the t tests further determined when the average values differed significantly for SA during El Niño and La Niña years (Table 5).

Figure 10.

Standardized anomaly index categorized in the average 2 year composite (six cases of El Niño and four cases of La Niña years) SA for ENSO composites for (a) rainfall, (b) discharge, (c) surface layer soil moisture, (d) root zone soil moisture, and (e) groundwater level.

Figure 11.

Spatial analysis showing mild to severe drought in July 1983, July 1987, June 1991, and August 1998 for rainfall, discharge, surface soil moisture, root-zone soil moisture, and groundwater level.

Figure 12.

Hot spots: Spatial representation of the effects for July 1983, July 1987, June 1991, and August 1998 for meteorological drought (using rainfall parameter), hydrological drought (using the combined effects of discharge and groundwater level), agricultural drought (using soil moisture), and the combined effects of the different drought types.

Table 5. P Values for the Student t Test Comparing El Niño and La Niña 2 Year Composites for Rainfall, Discharge, Soil Moisture at the Surface, Soil Moisture at the Root Zone, and Groundwater Using SA (α = 0.05)
El Niño versus La Niña: Using SA for Year 1
Soil Moisture (Surface)0.300.670.540.860.650.070.990.590.59<
Soil Moisture (Root Zone)0.630.520.540.700.900.400.940.500.360.010.010.13
Groundwater Level0.890.670.640.840.840.610.610.
El Niño versus La Niña: Using SA for Year 2
Soil Moisture (Surface)<0.01<0.01<
Soil Moisture (Root Zone)<0.01<0.01<
Groundwater Level0.400.300.140.07<0.01<0.01<

[36] During the first year, rainfall, discharge, and soil moisture were significantly different in October and November (onset of the dry season), while no significant difference was found for the groundwater level.

[37] During the second year, significant differences for rainfall occurred in March and May, in April and May for discharge, in February to August for surface soil moisture, in March to August for soil moisture at the root zone, and in May to August for the groundwater level. This shows that the more severe effects of El Niño and La Niña appeared in the second year. Similar to the within-year drought analysis described in section 4.1, a time lag of about 1 month was observed in the second year after discharge was affected by rainfall deficit. Surface soil moisture has a slow recovery time from rainfall deficits in year 1 (February is toward the end of dry season), soil moisture at the root zone was delayed for at least 1 month and the groundwater level was further delayed 2 months after. During the early onset of ENSO, it is possible to utilize groundwater as an alternative water source during most of the dry season (November to March) because the effects of ENSO on groundwater start in May. May to August is the beginning of the wet season, and if rainfall is not sufficient to supply water during these months for crops during the planting season (May to June), entire crops will be delayed or abandoned.

[38] Similar studies on soil-moisture simulations and drought analysis [Sheffield et al., 2004; Wang. et al., 2009] in relation to surface warming [Dai et al., 2004] have been done. However, only studies in groundwater verified the time lag during drought events. Anderson and Emanuel [2008] and Frappart et al. [2008] showed that groundwater was significantly affected by ENSO. Precipitation is the mechanism by which the ENSO signal is transmitted to groundwater with a delay of about 1 to 3 months [Anderson and Emanuel, 2008]. In this study, a time delay of approximately 7 months was also found before precipitation deficit affected groundwater. The time lag here was longer because the study area is in the humid tropics, where rainfall is much higher than that in previous studies. It is possible that rainfall and discharge droughts occurred, but soil moisture and groundwater droughts did not. It is important to identify not only temporal variation in drought, but also the combined effects of the different drought types at certain time periods, and the exact location of where these occur in the basin.

4.3. Spatial Analysis of Droughts

[39] In the Philippines, the strongest effects of El Niño on production are during the dry season [Dawe, 2007]. From temporal and statistical analysis of the different hydrological parameters considered, different types of drought do not occur simultaneously and usually have a time lag of around 2–7 months when moisture deficit occurs from rainfall, trickling down to soil moisture and groundwater deficit. Spatial maps (Figures 11 and 12) on the most severe recorded droughts were constructed to show which hydrological parameters are currently affected and/or more drought-prone “hot spots” during selected time periods. The monthly spatial distribution of the different drought types calculated in SA was overlaid to identify hot spots in the basin most susceptible to combined drought effects during the most severe month of the particular drought year. From Figure 9, the months of July 1983, July 1987, June 1991, and August 1998 were selected in the four drought years, since these are the months where the lowest drought indices were observed. Rainfall was used to determine meteorological drought hot spots, discharge and groundwater level were combined to identify hydrological drought hot spots, and surface and root-zone soil moisture were combined to identify agricultural drought hot spots. Here, the coarse-resolution soil-moisture patterns (both at surface and root zone) are because of the use of the 5 min FAO soil properties as the model inputs.

[40] For July 1983, all five parameters (rainfall, discharge, surface soil moisture, root-zone soil moisture, and groundwater level) had mild to severe drought conditions, with severe conditions occurring in soil moisture and groundwater causing severe agricultural and hydrological droughts at the central plains of the basin. During this period, the upland areas in the north was affected mainly by drought conditions at the soil surface.

[41] Similar to the July 1983 results, July 1987 showed mild to severe drought conditions for all five parameters, with agricultural and hydrological drought occurring mainly in the central plains and severe meteorological drought occurring in the northern portion of the basin. During this time, the spatial distribution showed that the central plains of the basin were again affected severely by the combined drought effects.

[42] June 1991 showed that all five parameters were affected mildly by agricultural, hydrological, and meteorological droughts mostly in the central region. This third drought, which began from the end of 1990 up to the beginning of 1992, was the longest drought period recorded (and longest but only weak El Niño), but its overall effects at the basin scale caused only mild drought affecting portions of the central plain but not the northwestern uplands.

[43] The 1998 drought was one of the more severe droughts affecting the basin, and its effects on different drought types had some time lag wherein droughts caused by rainfall and discharge deficits mostly occurred in the central and southern plains of the basin, while droughts from soil moisture are in some portions of the central plains and drought from groundwater deficit was mild to moderate at the uplands and southwestern portion of the basin. The combined effects of the drought in this month were dominated mostly by meteorological and hydrological droughts having the most severe effects southwest of the basin. Soil moisture during this period had already recovered from the severe conditions at the earlier part of the year, resulting from the onset of the rainy season (commencing usually in May or June). Although meteorological and hydrological drought occurred in the area, agricultural drought occurred only in some portions of the basin and was not the major cause of drought.

[44] The selected months showed that different drought types occurred at different time scales and locations. However, some common areas usually affected by severe drought conditions for all four drought years include the upland areas (Pantabangan-Carranglan watershed (rain-fed area)) and the central plains of Pampanga. The uplands usually produce rain-fed crops, while the central portion of the watershed is the major rice production area in the basin (both lowland rain fed and some irrigated areas). If these areas are very susceptible to the combined effects of droughts, then adaptation strategies should be implemented to minimize their impacts, especially for rice-growing periods during El Niño years.

4.4. Impacts of Agricultural Drought on Rice Production

[45] Asian agriculture and water-resource sectors will most likely be affected by enhanced climate variability. Droughts associated with the 1997 to 1998 ENSO years in Myanmar, Laos, the Philippines, and Vietnam caused massive crop failures and water shortages, and forest fires occurred in various parts of the Philippines, Laos, and Cambodia [Duong, 2000; Kelly and Adger, 2000; Glantz, 2001; Philippine Atmospheric, Geophysical and Astronomical Services, 2001; Cruz et al., 2007]. Rice will be the agricultural crop most influenced in the short term [Brunner, 2002; Dawe, 2009; Roberts et al., 2009]. In the Philippines, agriculture contributed around 14.9% (CIA, available at https://www.cia.gov/library/publications/the world-factbook/geos/rp.html, 2010) of the country's gross domestic product (GDP) in 2009, with rice the major agricultural product.

[46] The Pampanga River basin is one of the major producers of rice in the country (around 2.09% based on palay (unmilled rice) volume production from 1994 to 2008 for the province of Pampanga, and 16.7% for the entire Region III from the Bureau of Agricultural Statistics [2010]). Rice uses more water [Bouman et al., 2007] than other major crops (e.g., corn and sugarcane) grown in the area, so it is potentially more vulnerable to drought [Roberts et al., 2009]. The total agricultural area in the Pampanga River basin is 355,679 hectares (around 31.2% of basin surface area), and there are two types of rice agro-ecosystems [National Census and Statistics Office, 1971]: irrigated (183,253 hectares, around 51.53%) and rain fed (121,906 hectares, around 34.27%). Previous studies have shown that although rain-fed rice production is lower than irrigated systems because of its high susceptibility to droughts, it remains a vital source of income in the Philippines, especially for many poor farmers [Hossain et al., 2000; Roberts et al., 2009].

[47] El Niño can affect rice yields, especially in areas without irrigation [Dawe et al., 2009]. For this study, rice was the major crop used to identify El Niño effects on agricultural production. Detrending of constant price agricultural production was conducted for palay (unmilled rice), using the following linear equation:

equation image

[48] This was subtracted from the constant annual price. Here y is the annual price in millions of dollars, and x is the year of rice production (Figure 13a). Detrended constant price agricultural production of rice from 1967 to 2009 (Figure 13b) showed that losses (Table 6) occurred during drought years 1983 (−$33.79M ), 1987 (−$28.35M), 1990–1992 (−$32.98M, −$27.19M, and −$78.13M), and 1998 (−$217.11M), totaling to a loss of $417.55M. Since 16.7% comes from the region ($5.64M) in 1983; $4.73M in 1987; $5.51M, $4.54M, and $13.05M from 1990–1992 (similar to regional estimates from Jose et al. [1996]); and $36.26M in 1998, and around 2.09% of this annual average comes from the Pampanga province ($0.71M in 1983; $0.59M in 1987; $0.69M, $0.57M, and $1.63M from 1990–1992; and $5.54M in 1998), this translates to a total of $8.73M in Pampanga, and $69.73M worth of losses for the entire region on rice alone for both rain-fed and irrigated areas.

Figure 13.

Agricultural production of palay (unmilled rice) from 1967 to 2009 detrended using (a) simple linear correlation to calculate (b) annual production gains/losses.

Table 6. Estimated Agricultural Rice Production Losses Occurring During Years With Drought on the Basis of the 1967 to 2009 Production
Drought YearEstimated Nationwide Losses (in million US$)Estimated losses in Region IIIa (in million US$)Estimated Losses in Pampanga Province (in million US$)
  • a

    The Philippines is divided into regions. Central Luzon is categorized as Region III. It consists of the provinces of Bataan, Bulacan, Nueva Ecija, Pampanga, Tarlac, and Zambales. It partly includes the provinces in and around the Pampanga River basin.


[49] To identify the relationship between ENSO, drought, and crop loss, a preliminary analysis on the values for the 3 month drought analysis for surface and root-zone soil moisture was compared with ENSO and loss in agricultural crop volume production. Figure 14 shows that, during El Niño years 1994 to 1995 (mild to moderate) and 1997 to 1998 (moderate to severe), a significant drop in rice volume stock (as shown by the negative crop volume anomaly) occurred. This shows how ENSO affected agricultural drought, resulting in agricultural losses. The crop volume anomalies followed closely the drought indices for both surface and root-zone soil moistures in the two El Niño events. Negative crop volume anomalies were observed during extreme events (moderate to severe El Niño and La Niña). However, because of some data limitations on crop volume, only two El Niño events can be verified in this study.

[50] For 1982 to 2000, the drought years 1983, 1987, 1990–1992, and 1998 showed that rice was very susceptible to drought. However, economic indicators such as annual crop prices and monthly stock volume are insufficient to characterize precisely how much agricultural loss was incurred during a given drought period. This is because of extraneous factors such as market health, increase/decrease in demand, environmental factors such as volcanic eruptions, and the availability of economic data (usually in terms of provincial or countrywide averages). Additional data are necessary for comprehensive quantification of crop yield losses resulting from drought.

[51] The UPRIIS identifies at least two crop-growing seasons for rice in the basin: wet cropping (June 16 to November 16, also called the first cropping, usually begins with the rainy season) and dry cropping (December 16 to May 16, also called the second cropping). On the average, crop volume for the first quarter is highest at 97,853.63 t; the second and third quarter averages follow at 72,388.88 and 71,658.25 t, and the lowest crop volume (the most critical months) is during the third quarter at only 15,596.75 t. In the 2 year ENSO composites, it can be seen that, during El Niño, drought is more significant in the second year (from February to August), indicating that the wet cropping is expected to be severely affected with drought.

[52] On the basis of the above analyses, other strategies can be devised for water-resources management. However, upland management and rain-fed agriculture practices should be verified to determine their suitability, especially in the identified hot-spot areas. Since water sources at the surface are limited, groundwater utilization, as suggested by local communities, could temporarily (within the 2–7 month time lag) be a viable option, especially since the hot-spot areas for the other parameters did not show synchronous severe water deficits in groundwater level during the dry season in El Niño years. Protection of this alternative water resource from contamination and over extraction is deemed necessary for future use. Another important strategy is rescheduling of agricultural activities and proper crop selection during drought years during the second year of El Niño. In worst cases, when drought is extreme, abandoning of the cropping season during this second El Niño year can be done as a last resort. Planning of alternative livelihoods for the community is necessary. Some time delays of around 1–3 months (during the dry cropping season) have been found before significant moisture deficit occurs in the soil that may lead to agricultural drought, so proper planning is needed to prepare for the inevitable effects to agriculture preceding the effects of meteorological and hydrological droughts.

[53] The Pampanga River basin is primarily agricultural and is one of the major producers of rice in the country. Significant agricultural losses were accrued during drought years, so appropriate adaptation strategies are needed to enable communities to cope with extreme drought conditions, especially in the “hot spots.” Drought is exacerbated by damage caused previously by the 1994 Mt. Pinatubo eruption, which altered the Pampanga River basin network and regional soils through lahars and tephra deposition. This means that some previously irrigated areas are now rain fed, and soil degradation has worsened. Historical data used as model inputs, and for agricultural ecosystems, show best-case scenarios for the outputs of the model on discharge, soil moisture, and groundwater level before and after the eruption of Mt. Pinatubo, as archived in global data sets. Despite the best-case scenarios, moderate to severe drought conditions have been observed, especially in the rain-fed and upland areas of the basin. Therefore, it is important to devise appropriate water-management strategies suited to the current state of the basin.

5. Conclusions

[54] Most drought studies focus on quantifying drought at the regional or global scale, but this generalization can obscure localized effects. We focused on a basin-scale study from 1982–2000, using inputs and outputs of WEB-DHM, to characterize drought type, severity, and duration in the Pampanga River basin to quantify drought and identify specific characteristics that can assist in identifying appropriate adaptation strategies (this can be synchronized with ENSO monitoring currently being done) to minimize its impacts to the agriculture and water sector.

[55] The SA, a variation of the SPI, was used for five hydrological parameters (rainfall, discharge, surface and root-zone soil moisture, as well as groundwater level) to identify different drought types. Results show that the SA was able to identify clearly the beginning and end dates, and the severity, of recorded droughts (1983, 1987, 1990–1992, and 1998) in the basin. This study can also be expanded further to determine the joint behavior of two parameters by utilizing methods used in other SI variations such as the JDI (for the joint behavior of two variables, e.g., streamflow and precipitation for each month). However, for this study, the method focuses on how the spatial and temporal differences for each of the hydrological parameters behave independently by fitting a distribution function to each hydrological parameter at each month and then transforming to normal space, one unified index (SA) can be calculated for any hydrological parameters. This work has a unique contribution for achieving a unified drought index, which can identify different drought types (meteorological, hydrological, and agricultural) by simply overlaying the maps of the index. Furthermore, with this unified form of drought expression (SA and its severity criterion), different drought types can be integrated together to find hot spots in a river basin that is most susceptible to the combined drought effects.

[56] Two to seven months lag time was observed for drought resulting from moisture deficit in rainfall before this deficit occurred in the groundwater level. This is shown in the statistical analyses using average 2 year ENSO composites, where the SA was found to be more significant toward the end of the cropping season of the second year, and the impacts to crops at its reproductive and maturity stage will be reduced crop yield. The significant deficits in the soil moisture and groundwater levels continuing toward the beginning of the wet cropping season will also affect the schedule of the land preparation activities as well as the crop stress that rice, at its initial growing stages, will be exposed to.

[57] Losses in agricultural production occurred during drought years, with the largest losses in 1998 (Figure 13b and Table 6). Temporal and spatial heterogeneity affected significantly the agricultural and water sectors in the Pampanga River basin. Spatial characterization of drought hot spots identified drought-prone areas in the basin with the combined effects of different drought types showing the hot-spot areas in the uplands (mostly rain-fed areas where the marginal communities reside) and lowland central plains (both irrigated and rain fed). Some (especially rain-fed areas) of these areas have very limited access to resources, impeding adaptation of farming communities to extreme weather conditions. Hot-spot identification using this index can be a useful tool in risk management at the basin/watershed scale. A 1994–2000 preliminary analysis of the quantitative relationship between El Niño, agricultural drought, and crop production showed decreasing trend of the negative indices for soil moisture and crop production during El Niño years (Figure 10). The magnitude of El Niño was proportional to the magnitude of agricultural drought and crop production losses. The agriculture sector, specifically rice production, was affected by the onset of droughts brought about by the El Niño phenomenon (Figure 14). Hence, proper crop scheduling and planning is needed to minimize the effects of drought, especially to rice agriculture in this basin.

Figure 14.

El Niño 3.4 index, 3 month soil moisture at the root-zone drought index and crop volume anomaly for 1994 to 2000. ENSO index above 1 (blue dashed line) indicates El Niño while SA index below −1 (red dashed line) indicates drought.

[58] Basin-specific strategies that can be done to minimize the impacts of drought to the agriculture and water sector in the basin include crop scheduling, shift to drought-tolerant crops, and alternative livelihood activities, while implementing appropriate water-resources management, taking advantage of the time lag between the occurrence of the different drought types, and focusing on the hot spots in the central and southwestern regions for irrigated rice farming and the northwestern upland for rain-fed rice farming.

[59] Field verification of actual drought impacts on the different types of communities is further needed to identify and implement appropriate adaptation strategies. Coupled with the sturdy physics in WEB-DHM, the SA can be an effective tool in temporally and spatially identifying drought effects on different parameters. Further studies on groundwater quality and quantity are needed to determine if this can be a suitable alternative for domestic and agricultural water use during the earlier period of drought. Additional investigations on crops suitable to the basin during drought should be carried out for more accurate information, which can be used for crop modeling and identification of appropriate agricultural and water management during drought. Further studies on drought prediction using future climate scenarios (similar to Luo and Wood [2007]) in various regions using methods presented here will be part of the future direction of this study.


[60] This study was supported by grants from the Ministry of Education, Culture, Sports, Science, and Technology of Japan. The authors would like to acknowledge EM-DAT for sharing their disaster data set for the Philippines and other Southeast Asian countries; the National Irrigation Authority for the 1969 to 2009 inflow data for Pantabangan Dam and streamflow data for San Agustin, Arayat, as well as other neighboring areas in the Pampanga River basin; UPRIIS for their generous support in providing meteorological data within and around the Pampanga River basin; the Pampanga River Flood Forecasting and Warning Center Hydromet Division of PAGASA; DOST in San Fernando, Pampanga, for providing historical telemetry station data; and the National Water Resources Board for sharing monthly discharge and precipitation data from several gauges in and around the basin. The authors would also like to thank Rodel D. Lasco of the World Agroforestry Centre for input. Special thanks to Toru Tamura, Muhammed Rasmy, Daniel Edison Husana, and Kentaro Aida for their helpful comments and suggestions.