A LiDAR-based flood modelling approach for mapping rice cultivationareas in Apalit, Pampanga

Luigi L. Toda a, c, John Colin E. Yokingco a, *, Enrico C. Paringit b, Rodel D. Lasco a, d

a The Oscar M. Lopez Center for Climate Change Adaptation and Disaster Risk Management Foundation, Inc., Pasig City, Philippinesb Phil-LiDAR 1 Hazard Mapping of the Philippines using LiDAR, National Engineering Center, University of the Philippines, Quezon City, Philippinesc Crawford School of Public Policy, The Australian National University, Canberra, ACT, Australiad World Agroforestry Center, International Rice Research Institute, Los Ba~nos, Laguna, Philippines

a r t i c l e i n f o

Article history:Received 23 November 2015Received in revised form22 March 2016Accepted 27 December 2016

Keywords:Flood modellingLiDARGISRice cultivation areas

a b s t r a c t

Majority of rice cultivation areas in the Philippines are susceptible to excessive flooding owing to intenserainfall events. The study introduces the use of fine scale flood inundation modelling to map cultivationareas in Apalit, a rice-producing municipality located in the province of Pampanga in the Philippines. Thestudy used a LiDAR-based digital elevation model (DEM), river discharge and rainfall data to generateflood inundation maps using LISFLOOD-FP. By applying spatial analysis, rice cultivation zone maps werederived and four cultivation zones are proposed. In areas where both depth and duration exceedthreshold values set in this study, varieties tolerant to stagnant flooding and submergence are highlyrecommended in Zone 1, where flood conditions are least favorable for any existing traditional lowlandirrigation varieties. The study emphasizes that a decline in yield is likely as increasing flood extents andlonger submergence periods may cause cultivation areas for traditional irrigated lowland varieties todecrease over time. This decrease in yield may be prevented by using varieties most suitable to theflooding conditions as prescribed in the rice zone classification. The method introduced in this studycould facilitate appropriate rice cultivation in flood-prone areas.© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND

license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

Climate change impacts such as flooding pose threats to foodproduction and agricultural communities in Asia. Located infloodplains, majority of rice cultivation areas in the Philippines aresusceptible to severe flooding due to excessive rainfall. ThePhilippines is one of the countries most exposed to typhoons andfloods due to its location in the western rim of the Pacific Ocean. Anestimated 20 typhoons pass through the Philippine Area of Re-sponsibility (PAR) every year (PAGASA, 2009). These typhoons areusually accompanied by heavy rains which can cause adverse ef-fects particularly flooding.

There are a total of 18 major river basins in the Philippines,covering approximately 36% of the country's landmass (DENR).These river basins provide a natural source of irrigation favorablefor agricultural activities, of which approximately 32% of thecountry’s population is involved in (The World Bank, 2012). Areas

found within these river systems are regularly flooded com-pounded by past flood events (Zoleta-Nantes, 2000). Notwith-standing its susceptibility to flooding, many of the country's highlypopulated areas lie in the banks of river systems, Pampanga riverbasin included. The effect of climate change has increased the fre-quency and intensity of flooding events and is projected to haveadverse effects on crop production particularly in subsistence sec-tors (IPCC, 2007). Flooding is considered a major problem partic-ularly for rice production in the Philippines. From 2007 to 2010alone, an annual average of 23.76 million US dollars of damage torice farming in the Philippines was attributed to flooding (Israel,2012). Consequently, flood damage can lead to long term impov-erishment of affected individuals (Arnall, Thomas, Twyman, &Liverman, 2013). Farmers are particularly affected because in thelong run flood risks affect the viability of crop cultivation as asource of livelihood (Gwimbi, 2009). With 12 million farmersdependent on rice cultivation for livelihood (Altoveros& Borromea,2007), the need to develop strategies to adapt to changing floodconditions now becomes a great priority (Mitin, 2009).

Although rice are cultivated in flooded fields, prolonged sub-mergence can cause serious damage (Ram et al., 2002).

* Corresponding author.E-mail address: yokingco.jce@omlopezcenter.org (J.C.E. Yokingco).

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Applied Geography

journal homepage: www.elsevier .com/locate/apgeog

http://dx.doi.org/10.1016/j.apgeog.2016.12.0200143-6228/© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Applied Geography 80 (2017) 34e47

Submergence can cause mechanical damage to the plants, but moreimportantly it inhibits desirable bio-chemical processes of theplant-like photosynthesis (Jackson & Ram, 2003; Ram et al., 2002;Setter et al., 1997). Multiple approaches to address these impacts offlooding to rice have been put in place. One of these is the devel-opment of flood-tolerant varieties (Septiningsih et al., 2009) thatexhibit desirable physiological traits helpful for the plant’s survival(Setter et al., 1997) and are embodied in the sub1-a gene (Bailey-Serres et al., 2010). The sub1 gene has been isolated and bred intorice varieties (Khanh, Linh, Linnh, Ham, & Xuan, 2013) includingthose available in the Philippines such as the IR64 sub1(International Rice Research Institute (IRRI) 2009) and NSIC Rc 194(PhilRice, 2010).

Submergence-tolerant rice varieties can survive up to twoweeks of complete submergence without any adverse effects onproduction (IRRI, 2009). However, while tolerance to submergenceis a desirable quality, other traits should be considered for adaptiveagricultural strategies. For instance, farmers have other trait pref-erences such as yield and panicle quality (Manzanilla et al., 2010).Also, depending on the flooding conditions, for instance those thatlast for more than one week and with depth of one meter or more,would require varieties that are tall or have stems that rapidlyelongate (Mackill et al., 2010).

In order to optimize the use of submergence tolerant varieties,the flooding conditions should be identified and mapped. Byidentifying the flood extent and duration in rice cultivation areas,potential damage to property and/or crops can be estimated andother countermeasures can be developed (Chau, Holland, Cassels,&Tuohy, 2013; Wang, Colby, & Mulcahy, 2002; Thieken, Merz,Kreibach, & Apel, 2006). Flood conditions are usually character-ized through flood inundation modelling and are then displayedthrough flood hazard maps. The hazard categories used in thesemaps are a function of the product of depth and extent (Baky,Zaman, & Khan, 2012) and/or velocity (Daffi, Otun, & Ismail,2014; Kourgialas & Karatzas, 2014; Purwandari, Hadi, & Kingma,2011; Toriman et al., 2009). These hazard maps are then used formitigation and preparedness purposes such as hazard assessments(Daffi et al., 2014; Purwandari et al., 2011), flood propagation andhazard estimation (Kourgialas & Karatzas, 2014), risk management(Baky et al., 2012), and flood management (Toriman et al., 2009).These hazard maps can take into consideration possible damageand potential risks to agriculture (Daffi et al., 2014; Kourgialas &Karatzas, 2014; Baky et al., 2012) but may not necessarily suggestadaptation options. Flood modelling has been used to estimatepotential impact on agricultural land through either mapping floodextents in different rain return scenarios (Chau et al., 2013) orcombining extent with flood duration (Hamdani & Kartiwa, 2014).Flood models have also been used in combination with risk man-agement to specifically optimize rice planning (Samantaray,Chatterjee, Singh, Gupta, & Panighry, 2015) using flood extent,duration, and depth to map suitable environments for specific ricevarieties.

Flood inundation models entail the use of flood depth, extent,duration and flow velocity based on advanced algorithm and high-quality data (Samantaray et al., 2015). Flood modelling softwaresuch as Lisflood, HEC-RAS, and MIKE typically require rainfall,discharge, topography, and Digital Elevation Model (DEM) data togenerate flood models. The accuracy of the models depends on thequality of input data and the quality of DEMs are considereddirectly proportional to the reliability of the models (Brandt & Lim,2012). To increase the accuracy of flood models, high resolutionDEMs from sources such as LiDAR are highly favored (Sole, Giosa,Nol�e, Medina, & Bateman, 2008). LiDAR is a remote sensing tech-nology that uses rapid laser pulses to map out the surface of theearth and can be used to create high resolution terrain and surface

DEMs. LiDAR has been widely used because of its vertical accuracyof ± 15 centimeter and spatial resolution of up to 1 meter. ThePhilippines has recently obtained airborne LiDAR technology(Paringit, Fabila,& Santillan, 2012) tomapmajor river basins, with amuch higher degree of accuracy than what is currently available.

Apalit is one of the rice producing municipalities of Pampangawhich suffer from prolonged inundation and has a relatively flatterrain due to its location within a flood plain. For the purpose ofthis study, LiDAR surface models were used in the flood simulationin the municipality of Apalit. Other topographic mapping technol-ogies have difficulty in capturing flat terrains (Sole et al., 2008), butLiDAR is able to produce highly accurate flood models due to itscapability to capture gentle terrain changes. The aim of this study isto develop LiDAR-derived flood models to map rice cultivationareas based on depth and duration. The maps allow the identifi-cation of suitable rice varieties for each rice cultivation map. Thestudy therefore provides a tool in determining appropriate areas forrice cultivation and in selecting and developing suitable rice vari-eties based on different flooding condition sets.

2. Study area

The study was conducted in Apalit, one of the 20 municipalitiesin the Province of Pampanga (Fig. 1). Apalit is located in the southeastern portion of the province and has a total area of 6147 ha withan estimated population of 101,537 (NSCB, 2010). The municipalityhas an estimated 3799.95 has of agricultural land, as derived fromdigitized orthophotos. The rice-producing barangays (villages) areBalucuc, Calantipe, Cansinala, Capalangan, Paligui, Sampaloc, SanJuan, San Vicente, Sucad, Sulipan, and Tabuyuc. Barangay Colganteand portions of barangay Paligui practice aquaculture throughfishponds.

The topography of the municipality is characterized by largelyflat and low with slopes ranging from 0 to 3% and an elevation notexceeding 20 m (Municipal Government of Apalit, 2000). It is alsoalluvial, dominated by streams and rivers attributed to its locationwithin the Pampanga floodplain. The Pampanga River runs throughthe mid-eastern portion of the municipality and is a source ofirrigation in the municipality (Fig. 1). Its cropping cycle runs be-tween the dry season from November to December and the wetseason from June to August. An average of 644 mm of rain isexperienced during the wet season. The riverine morphology andflat terrain makes the municipality favorable for rice cultivationwhile rendering it prone to flooding during the wet season. ThePinatubo eruption have also significantly altered flooding condi-tions (Siringan & Rodolfo, 2003). Flooding is largely characterizedby surface water inundation worsened by heavy rainfall during thewet season. This is as verified from the focus group discussion(FGD) and reports from Pampanga River Basin Flood Forecastingand Warning Center (2012).

3. Data used

3.1. LiDAR DEM

The airborne LiDAR data used for the study areawas acquired bythe University of the Philippines Disaster Risk and ExposureAssessment for Mitigation (UP-DREAM) Program from October toDecember 2012. The data was processed to produce a 1-meterspatial resolution for the Digital Surface Model (DSM) and theDigital Terrain Model (DTM) (Fig. 2) with a point density of two tothree points per square meter. The LiDAR point cloud data wasprocessed and refined using Terra Scan (Terrasolid, Inc.) to elimi-nate stray points and normalizing elevations throughout the flightpaths.

L.L. Toda et al. / Applied Geography 80 (2017) 34e47 35

The final DEM used for the model was resampled from the 1-meter resolution DSM into a 5-meter resolution DEM to fit the

flood model resolution limit of 12 million cells. The resampledmodel does not affect the resolution accuracy since the land use of

Fig. 1. Location map of Pampanga.

Fig. 2. LiDAR digital terrain model (DTM) with elevation values and outline of Pampanga River.

L.L. Toda et al. / Applied Geography 80 (2017) 34e4736

the municipality is largely agricultural and does not greatly affectflood behavior as opposed to urban areas (Tamiru& Rientjes, 2005).

3.2. Rainfall and river discharge data

The Rainfall Intensity-Duration-Frequency (RIDF) analysis datafor the flood models and the hypothetical, extreme validationmodel were obtained from the Philippine Atmospheric, Geophys-ical and Astronomical Administration (PAGASA). The RIDF datawere spatially interpolated across the region through the theissenpoligon technique in ArcGIS 10.2. Port Area rain gauge station wasselected.

To produce the discharge for the flood scenarios, RIDF analysisdata gathered by PAGASA and water-level readings from ASTI wereused as inputs in the 2D HEC-HMS flood models of Pampanga forthe different rainfall-runoff scenarios. Table 1 shows peak values ofoutflow and rainfall at the Sto. Ni~no station for the 5, 25 and 100year rain return periods.

3.3. Land cover and roughness coefficient

Land cover was manually classified and digitized at a scale of

1:1250 from a combination of orthophotos obtained by UP-DREAMProgram from October to December 2012 and stitch maps takenfrom Google Earth™ (Fig. 5). Land cover classes were assigned withappropriate roughness coefficients based on Chow (1959) andstandardized by Brunner (2010).

4. Methodology

4.1. Framework for rice cultivation area mapping (Fig. 3)

4.1.1. Flood model developmentThe development of the flood models for the Pampanga River

Basinwas done using LISFLOOD-FPmodel (version 5.9.6) developedby the School of Geographic Sciences of the University of Bristol.LISFLOOD-FP is capable of integrating 1D and 2D parameters into asingle model and can depict not only riverine flooding, but alsosurface flooding. For 1D parameters, the study used LISFLOOD-FP'sdiffusive solver. The diffusive solvermodels the behavior of the flowof water within a river or stream over time by running thedischarge values through the channel and then computing the ef-fects to water depth within the channel in relation to the channel'scross-section, friction values, and gravity. When the river depth

Table 1Peak values of the Sto. Ni~no outflow.

RIDF Period Total Precipitation (mm) Peak Rainfall (mm. in 10 min) Sto. Ni~no Peak Outflow (m3/s) Time to Peak

5yr 223.5 29.7 4591.2 2 days, 1 h, 12 min25yr 328.8 41.4 7646.8 2 days, 2 h, 50 min100yr 415.6 51.0 10,210.4 2 days, 6 h, 10 min

Source 1 Philippine Astronomical, Geophysical and Atmospheric Administration (PAGASA) and Disaster Risk and Exposure for Assessment of Mitigation (DREAM) Program.

Run LISFLOOD using inputs to produce time series of

water depths and total inundation time outputs

Stack time series images and convert to .img file

Rice cultivation zone map

Validate model results using historical flood maps and focus group discussion

Obtain flood modelling inputdata: rainfall-discharge, LiDAR DEM,

roughness coefficients, river cross-section

Calculate mean depth by using stacked time series .img file as input in compute mean

statistics tool in ENVI

Input mean depth raster and total inundation time output in

Python program to produce zone map

Clip zone map extent to rice cultivation extent

t

d

Fig. 3. Framework for rice cultivation area mapping.

L.L. Toda et al. / Applied Geography 80 (2017) 34e47 37

reaches the bank, water flows to the adjacent cells within the DEM.The process is described in the 1D diffusive wave equation (1)(Bates, Trigg, Neal, & Babrowa, 2013) where Qx is volumetric flowrate at a given time t in the x Cartesian direction, A is the crosssectional area of flow, h is the water depth, g is gravity, n is theManning's friction value, R is the hydraulic radius of the river sec-tion, t is time, and x the distance in the x Cartesian direction whichcan also predict backwater effects. The acceleration solver was usedfor the 2D parameters which calculated flows between cells as afunction of friction and water slopes, and local water acceleration.

vQxvt|{z}

local acceleration

þ vvt

Q2xA

!|fflfflfflfflfflffl{zfflfflfflfflfflffl}

convective acceleration

þ gA vðhþ zÞvx|fflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflffl}

water slope

þ gn2Q2x

R4=3A|fflfflffl{zfflfflffl}friction slope

(1)

Input data for the model included the rainfall and dischargedata, raster DEM, river profile, channel and floodplain friction, andmodel time step. Model simulation time was approximated basedon discharge duration. The discharge simulation time was identi-fied at 900,000 s (equivalent to 10.4 days). The input data wereconverted to text formats with specific file extensions that the

model would read based on the parameter file. The data re-quirements and formats are described in Table 2. The model has thecapacity to generate time-series output images of inundationextent and depth, maximum depth, hazard maps, flow velocity, andtotal inundation time. For the purpose of this study, time seriesoutput images were used along with the total inundation time. Atotal of 1500 time series images were produced for each rain returnscenario.

4.2. Model validation

Flood maps showing observed flood events and their extentswere used to subjectively validate the resultant flood models bycomparing actual flood accounts from key informant interviews(KIIs) and focus group discussion (FGD). Observed flood maps wereused for visual comparison with the resultant flood models. Thesemaps were gathered from MODIS satellite images acquired onDecember 4 and 9, 2004 and were digitized using ArcGIS 10.2. Inparticular, model simulated for Habagat 2013 (south-westmonsoon rain) (Fig. 4) event was generated using LISFLOOD-FP forcomparison and validation purposes.

Table 2Model data requirements, format and description.

Data requirement Format Description of format (based on Bates et al., 2013)

Parameter file .par contains the information necessary to run the simulation including file names and locations and the main model and run controlparameters.

Rainfall .rain specifies a time-varying rainfall rate.Discharge .bdy specifies time varying boundary conditions associated with varying discharge values per time step in the river.Raster LiDAR DEM .dem.ascii consists of a 2D raster array of ground elevations in ARC ascii raster formatRiver profile .river river characteristics described in terms of its width, Manning's n friction coefficient and bed elevationRoughness

coefficients.n.ascii channel and floodplain friction - based on Chow (1959) and standardized by Brunner (2010) in a 2D array of friction values in ARC ascii

raster format.Model time step (seconds) user defined time step

Fig. 4. Simulated maximum flood depth of south-west monsoon rain (Habagat) in August 2013.

L.L. Toda et al. / Applied Geography 80 (2017) 34e4738

4.2.1. Focus group discussion and key informant interviewsAn FGD was conducted with farmers not only to validate the

flood maps with their experiences on flooding but also to elicitinformation about local agricultural adaptation practices and vari-etal types used. Twenty-five among the invited thirty six farmersacross all barangays (villages) took part in the FGD. These farmersrepresented nine of the 11 barangays that were mostly agricultural,namely Colgante, Capalangan, Calantipe, San Juan, Cansinala, Tab-uyuc, Sampaloc, Balucuc, and Sucad. Despite lack of representationfrom two barangays, the answers were treated collectively at themunicipal level, not per barangay. Activities included introductionof FGD team and participants, overview of the topic, explanation ofthe FGD process and ground rules, and discussion guided by semi-structured questions. Specific information gathered included (1)historical accounts on flood duration and extent; (2) agriculturaladaptation practices; (3) cropping patterns/calendar; and (4) ricevarieties being used with respect to characteristics, yield, cost andcultivation area coverage.

In addition to the FGD, semi-structured KIIs were also con-ducted on multiple visits to obtain information on actual/observedflood records, flood impacts on agriculture, existing agriculturaladaptation practices and land use changes, among others. To gatherthese information, the Municipal Agricultural Officer (MAO), theMunicipal Planning Officer (MPO) and a fewMunicipal AgriculturalTechnologists (MATs) were chosen for the KIIs because of the depthof their knowledge and experience on the subject on which theywere interviewed.

4.2.2. Estimation of existing rice cultivation extentThe rice cultivation area (Fig. 6) for Apalit was digitized from

interpretation of land use (Fig. 5) from orthophotos that were takenwith the LiDAR data. This was used in conjunctionwith the rice areamaps obtained from IRRI using MODIS images obtained from 2000to 2012.

4.3. Classification of cultivation areas for rice varieties

ArcGIS 10.2 was used to overlay the map layers of rice cultiva-tion area and the extents of inundation time and mean depth togenerate the zone maps. The total area affected by inundation wascalculated by clipping the flood extent given total inundation timeand mean depth. The average depth over the whole inundationtime provides a picture of howdepthwould affect the rice crop overtime. Mean depth was calculated by stacking thewater depth resultproduced from LISFLOOD-FP as raster time-series images and thenthe image processing software ENVI™ version 4.7 computed theaverage of those images. Total inundation time and mean depthwere integrated using a Python script to produce a raster repre-senting the different depth-duration conditions (Table 3) and wasthen converted into polygons and overlain with the rice cultivationarea. The conditions used in the zoning were based on the depththreshold specified for shallow water rice in Mackill et al. (2010)and the duration threshold based on the short duration submer-gence specified in Salam, Biswas, and Rahman (2004).

5. Results and discussion

5.1. Flood inundation models

Fig. 7 shows the west-east elevation (section A-B) profile from

Fig. 5. Land use classification map of Apalit municipality.

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the approximatemid-point of the river. It illustrates howPampangaRiver is planked by alluvial deposits that act as natural levees whichimpered overflow from the Pampanga River. Increased river runoffin the river partly elucidates why no significant overflow wasobserved during rainfall events as confirmed during the FGDs. Theinundationmodels indicate increase in affected areas for both floodinundation time and mean depth with rain-return period (Figs. 8and 9). Of particular interest are the expanded coverage of floo-ded areas located in the southwestern portion with increasing re-turn periods. The apparent absence of overtopping in theinundation models of Pampanga River is consistent with the ac-counts obtained from the FGD.

5.2. Local rice varieties

There are a total of seven rice varieties that farmers usethroughout the year depending on the season, one of which (IR64)is submergence-tolerant. IR64 was introduced and adopted bymajority of the farmers in the 1990s, but is no longer being grown

due to its low pest tolerance. In contrast, NSIC Rc 150 has a highyield and good eating quality apart from its tolerance to submer-gence. However, it fell out of usage due to age and thus was dis-continued and replaced with other varieties by local farmersaccording to our interview (Manzanilla, personal communication).With its unintended floodresistant characteristic, NSIC Rc150 mayprove to be a better substitute to IR 64 due to its lowcost, high yield,and short days to maturity. This enables farmers particularly in thebarangays of Balucuc and Calantipe and some portions of Tabuyucand Cansinala to maximize their resources and have the opportu-nity to harvest at the most three times a year.

5.3. Observed flood conditions

Contrasting flooding conditions were found between theeastern and western portions of the municipality. The easternportion has greater flood depths, but recedes faster. Two of thebarangays, Calantipe and Balucuc, and some portions of Tabuyuc,Capalangan, and Cansinala do not experience detrimental flooding(Fig. 10) attributed to their comparatively higher elevation as dis-cussed in the FGDs. Barangays in the western portion have shal-lower flood depths, but have very long submergence periods. Theworst case is found in the Barangay of Colgante, where observedflooding lasts between 4 and 7 months. The severe flooding con-ditions are also seen as a result of the conversion from agriculturallands to fish ponds of a portion of barangays Paligui and Colgante.According to the FGD participants, the continuous outflow of laharfrom Mt. Pinatubo and the sea-level rise originating from ManilaBay are major contributors to the constricted stream system whichengender the current flooding conditions. It is estimated that

Fig. 6. Extent of areas planted to rice in Apalit, Pampanga.

Table 3Zone value matrix representing the flood depth and duration conditions.

Zone Conditions

Depth (cm) Mean total duration (days)

1 �20 �72 �20 �73 �20

around 1500 has or 25% of Apalit will be affected by a two-meterrise in sea level (PEMSEA, 2012).

5.4. Rice cultivation at risk

Rice cultivation in Apalit is at risk as low-lying barangaysexperience prolonged submergence that extend up to four monthsduring the wet season (June to August). During the FGD, farmersstated that heavy rainfall was the main cause of submergence ofrice fields. In a typical year, floods affect between 50% and 80% ofrice cultivation areas across barangays. Based on the cultivationzone maps (Fig. 11), increasing trend in flood extents of varyingflood depths is observed for 5-year to 100-year rain-return periods.There will also be an increase in submergence period as moreintense rains occur more frequently.

Generally, the varying flooding conditions between the easternand western barangays may indicate the differences in their agri-cultural practices and adaptation strategies. The eastern barangayssuch as Calantipe and Balucuc are able to harvest up to a maximumof three times a year because flooding does not affect them whilebarangays on the western side have reduced their cropping season

to only once each year. Farmers in Colgante and Paligui have evenresorted to converting their lands to fishponds due to recurringfloods.

5.5. Cultivation zone classification

Cultivation areas were classified into four zones (Fig. 12) basedon four flooding conditions. Zone 1 is suitable for flood-tolerantvarieties and those tolerant to stagnant flooding. Depth and dura-tion in this zone exceed the threshold values set in this study andare least favorable for existing traditional lowland irrigation vari-eties. Between 10 and 21% of current cultivated land should growonly varieties that can withstand flooding conditions beyondshallow depth and 7-day flood duration limit (Fig. 13). On the otherhand, there is a decreasing pattern of extent for traditional lowlandirrigation varieties, which means more areas might need to adoptvarieties with known submergence tolerance traits that last longerthan seven days and in depths greater than 20 cm. Areas whichmight need of these varieties are located in the south easternportion mainly in Capalangan and Tabuyuc. Sucad may also adoptthese varieties as the area for Zone 3 decreases in the barangay per

Fig. 7. (a) Elevation map with (b) cross-section of Apalit along the section A-B.

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Fig. 8. Mean depth for (a) 5-, (b) 25- and (c) 100-year rain-return periods.

L.L. Toda et al. / Applied Geography 80 (2017) 34e4742

rainfall scenario.Accounts from the FGD indicate that inundation could extend up

to several months. The need for rice varieties also tolerant tostagnant water for weeks to several months regardless of depth

tolerance is crucial. A decline in yield will be likely as cultivationareas for traditional lowland irrigated varieties may decrease overtime due to expanding flooding and submergence. This decrease inyield may be prevented by planting varieties most suitable to the

Fig. 9. Total inundation time for (a) 5-, (b) 25- and (c) 100-year rain-return periods.

L.L. Toda et al. / Applied Geography 80 (2017) 34e47 43

environment as prescribed in the rice zone classification. However,more than one of the conditions can ensue in any particular flood-prone environment. Therefore, it is desirable to develop varietiesthat have a combination of tolerance traits for flood-prone areas.

Traditional (local) varieties aremostly grown in themunicipalitydespite flood risks. Adoption of submergence-tolerant varietieshowever presents some drawbacks considering their cost and lowtolerance to pest. As some cultivation areas already suffer stagnantinundation of at least a month, medium to deepwater rice varietiesmight thrive for any flood scenario. However, more than one of theconditions can ensue in any particular flood-prone environment.Therefore, it is desirable to develop varieties which possess acombination of tolerance traits for flood-prone areas whenpossible. The potential re-adoption of IR64 and sustainability of useof other submergence-tolerant varieties will be critical in thedevelopment and accessibility of flood-resistant rice cultivars astheir adoption will depend on their productivity, yield, cost andsocio-economic factors. Alongside the development of low-costhigh-yielding flood-tolerant rice varieties, it is crucial that accessto sustainable agricultural services is in place. This, however, willalso depend on the availability of developed flood-tolerantcultivars.

6. Conclusion

The study has demonstrated the use of LiDAR technology forproducing fine scale flood inundation maps to classify cultivationareas for certain rice varieties, the results of which could facilitateagricultural adaptation and help strengthen food security in Apalit,a rice-producing municipality in Pampanga that is generally char-acterized by surface flooding. Using LISFLOOD-FP model, inunda-tion depth and duration conditions were simulated fromintegrating river discharge and rainfall data and LiDAR-based DEM.These conditions were chosen as they are crucial in the survival andproductivity of rice varieties. The inundation models provide an

illustration of the variation in the extent of flood depth and dura-tion across rice cultivation areas given a rainfall scenario. Based onthemodels, there is a potential increase in extent of flood depth andduration as rain-return periods increase. The study found that adecline in yield is likely as cultivation areas for traditional lowlandirrigated varieties may decrease over time due to increasing depthand longer submergence periods. This decrease in yield may beprevented by growing varieties that are more suitable to theenvironment as prescribed in the rice zone classification.

Consequently, four zones are proposed. The flood-tolerant va-rieties and those tolerant to stagnant flooding are recommended inZone 1 where both depth and duration exceed the threshold valuesset in this study, meaning flood conditions are least favorable forany existing traditional lowland irrigation varieties. More than oneof the flooding conditions can ensue in any particular flood-proneenvironment, which makes it is desirable to develop rice varietiesthat possess a combination of tolerance traits for flood-prone areas.Apalit is naturally lowland so medium to deepwater rice varietiesmight thrive for any flood scenario as some parts of the munici-pality already suffer stagnant inundation of at least a month duringextreme rainfall events.

The flood models and the resultant rice zone maps weredesigned to take advantage of LiDAR data sets generated throughthe efforts of Disaster Risk and Exposure Assessment for Mitigation(DREAM) Program of the University of the Philippines and theDepartment of Science and Technology (DOST). While the findingsof the study relies heavily on models, the need for their validationwith observed flood information prompted the conduct of FGD. TheFGD was also necessary to obtain relevant information on impactsof flood on rice cultivation, current rice adaptation strategies, andvarietal types used. Although LiDAR technology offers more accu-rate topographic detail for specific purposes such as flood inun-dation estimation, its accessibility and availability present somedrawbacks for studies that cover larger scale areas where floodingconditions are critical to rice cultivation. Alternatively, free and

Fig. 10. Map of observed flood depths accounted during FGD.

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Fig. 11. Recommended varietal types and characteristics per cultivation zone.

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accessible but scale-appropriate DEMs should be maximized usingthe methodology employed in this study.

Acknowledgments

This study has been done as a part of a project titled ‘‘FloodInundation Mapping using LiDAR and GIS’’ of the Oscar M. LopezCenter for Climate Change Adaptation and Disaster Risk Manage-ment Foundation, Inc., the Philippines (The Oscar M. Lopez Center).The authors would like to acknowledge the Department of Scienceand Technology (DOST) and the University of the Philippines (UP)through the Disaster Risk and Exposure Assessment for Mitigation(DREAM) Program for the LiDAR and discharge data and forallowing us to use their flood modelling software; NAMRIA for theadministrative boundary; and PAGASA for the rainfall information.The authors would also like to acknowledge Mark Joshua Salvacionof DREAM Program who helped in executing the zone maps.

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