quaternary

Article Approach for Analysis of Land-Cover Changes and Their Impact on Flooding Regime

Badri Bhakta Shrestha

Department of Civil Engineering, The University of Tokyo, Tokyo 113-8654, Japan; [email protected] or [email protected]



Received: 6 June 2019; Accepted: 26 July 2019; Published: 28 July 2019


Abstract: This study focused on the analysis of land-use/land-cover changes and their impact on flood runoff, flood hazards and inundation, focusing in the Pampanga River basin of the Philippines. The land-cover maps for the years 1996 and 2016 were generated using Landsat images, and the land cover changes were analyzed using TerrSet Geospatial Monitoring and Modeling System (TGMMS). Based on an empirical approach and considering variable factors, the land-cover maps for the future were predicted using Land Change Modeler (LCM). After preparation of land-cover maps for past and future years, flood characteristics were analyzed using a distributed hydrological model named the rainfall runoff inundation (RRI) model with a land-cover map for different years. The impacts of land cover changes on flood runoff, flood volume and flood inundation were analyzed for 50- and 100-year floods. The results show that flood runoff, flood inundation volume and flood extent areas may increase in the future due to land-cover change in the basin.

Keywords: land cover change; machine learning; Landsat images; GIS; flood hazard; rainfall-runoff-inundation

1. Introduction Many places in the world are hotspots of risk from extreme weather-related events; and disaster risk are likely to increase due to a combination of land-use/land-cover changes, population growth, climate change, and development activities [1]. Flood risk, climate change and social change have increasingly become a global concern, and also, vulnerabilities related to land use change and climate change have, potentially, a very strong effect on catchment hydrology, floods and damages. Based on previous literature, Blöschl et al. [2] hypothesized the impact of climate and land-use/land-cover changes on hydrological response as a function of catchment scale as shown in Figure1, which shows that sudden changes in the watershed response can occur due to land use/land cover change [3]. The impact of climate change on magnitude and frequency of floods has been widely investigated [4–6]. Similarly, numerous researches focused on study on the impacts of land cover changes on watershed hydrological response [3,7–9], however, only a few of them focused on analyzing the impact of land-cover change in river floods using distributed hydrological modeling [7]. Since the changes in land use/land cover and their spatial distribution have significant impacts on river flow and flooding [7,9], land-cover change analysis and its impact on hydrological processes become the prominent research topics in recent years [8,10]. To analyze such impacts of land-cover changes, it is essential to analyze dynamics of land cover changes, and it is also necessary to investigate hydrological impacts of land use changes using hydrological modeling [7]. The dynamics of land-cover change can be monitored and observed by using remotely sensed satellite-based data [9], and land-cover maps can also be generated by using satellite data such as Landsat images [3,9,11,12] and Moderate Resolution Imaging Spectroradiometer (MODIS) images [13,14].

Quaternary 2019, 2, 27; doi:10.3390/quat2030027 www.mdpi.com/journal/quaternary Quaternary 2019, 2, 27 2 of 18 Quaternary 2019, 2, x 2 of 18

45 ResearchersResearchers have have used used different different approaches approaches to to analyze analyze impacts impacts of of land-cover land-cover changes changes on on flood flood 46 characteristicscharacteristics or or hydrological hydrological response, response, for for example, example, a a Hydrologic Hydrologic Engineering Engineering Centre–Hydrologic Centre–Hydrologic 47 ModelingModeling System System (HEC–HMS) (HEC–HMS) model model [8,10], [8,10], Curve Curve Number Number based based flood flood runoff runoff estimationestimation [3,13], [3,13], 48 distributeddistributed rainfall rainfall runoff runoff modelmodel [7]. [7]. Most Most of of these these studies studies mainly mainly focused focused on on analysis analysis of of impact impact of of 49 land-coverland-cover change change on on river river runoff. runoff .However, However, invest investigationigation of of impact impact of of land land-cover-cover change change on on flood flood 50 inundationinundation are are limited. limited. In In addition, addition, the the management management and and good good planning planning of of land land cover cover can can provide provide 51 anan important important role role in in the the flood flood management, management, climate climate change change adaptation adaptation and and land land degradation degradation [9]; [9]; 52 therefore,therefore, understandingunderstanding impactsimpacts of of land-use land-use and and land-cover land-cover changes changes on flood on runo floodff and runoff inundation and 53 inundationis necessary is for necessary proper managementfor proper management and planning and of landplanning use as of well land as use to reduceas well the as riskto reduce of flood the in 54 riskthe of future. flood Therefore,in the future. it is Theref very importantore, it is very to analyze important land-cover to analyze changes land-cover and their changes impact and on their flood 55 impactrunoff onand flood flood runoff inundation and flood using inundation the rainfall us runoing theff inundation rainfall runoff (RRI) inundation model. (RRI) model. 56 ThisThis study study focused onon analysis analysis of of land-use land-use/land-cover/land-cover changes changes and and their their impact impact on flood on runofloodff , 57 runoff,flood hazardsflood hazards and inundation and inundation in the Pampanga in the RiverPampanga basin ofRiver the Philippines.basin of the The Philippines. land-cover mapsThe 58 land-coverfor past years maps were for generated past years using were Landsat generated images using and Landsat their changes images wereand their also analyzed.changes were Then, also the 59 analyzed.land-cover Then, maps the for land-cover the future weremaps predicted for the future using Landwere Changepredicted Modeler using (LCM).Land Change After preparation Modeler 60 (LCM).of land-cover After preparation maps for past of andland-cover future years,maps floodfor past characteristics and future wereyears, analyzed flood characteristics using a distributed were 61 analyzedhydrological using model, a distributed namely thehydrological RRI model, model, with anamely land-cover the RRI map model, for diff wierentth a years. land-cover The impacts map for of 62 differentland cover years. changes The onimpacts flood runoof landff, flood cover volume changes and on flood flood inundation runoff, wereflood analyzedvolume and for 50- flood and 63 inundation100-year flood were cases. analyzed for 50- and 100-year flood cases.

Land use/land cover change

Climate variability Impact

Catchment Scale 64 Figure 1. Land-use and climate change impact on hydrological response [2,3]. 65 Figure 1. Land-use and climate change impact on hydrological response [2,3]. 2. Study Area 66 2. Study Area The Pampanga River basin, which is located in the Region III (Central Luzon) of the Philippines, 67 is theThe fourth Pampanga largest basinRiver in basin, the Philippines. which is Figurelocated2 showsin the theRegion location III map,(Central basin Luzon) boundary of andthe 68 Philippines,digital elevation is the modelfourth (DEM)largest withbasin elevation in the Ph rangesilippines. ofthe Figure study 2 shows site. The the catchment location map, area basin of the 69 boundarystudy site and is approximately digital elevation 10,645 model km2 (DEM)(based with on digital elevation elevation ranges model of the of study study site. area) The and catchment the length 2 70 areaof the of the main study Pampanga site is approximately River is about 10,645 260 km. km About (based 95% on digital basin areaelevation lies in model Nueva of Ecija,study Tarlac,area) 71 andPampanga, the length and of Bulacan the main provinces Pampanga of theRiver Region is about III, while 260 km. the remainingAbout 95% 5% basin area area is part lies of in the Nueva other 72 Ecija,seven Tarlac, provinces Pampanga, of the Region and Bulacan III such provinces as Aurora, of Zambales, the Region Rizal, III, Quezon,while the Pangasinan, remaining Bataan,5% area and is 73 partNueva of Vizcayathe other [15 seven,16]. Theprovinces average of annual the Region precipitation III such in theas basinAurora, is 2155Zambales, mm [17 Rizal,]. There Quezon, are two 74 Pangasinan,multi-purpose Bataan, dams and in the Nueva basin Vizcaya such as the[15,16]. Pantabangan The average dam annual (storage precipitation capacity of 2966in the million basin mis3 ) 75 2155and mm Angat [17]. dam There (storage are two capacity multi- ofpurpose 850 million dams m 3in). the There basin are such also twoas the swamp Pantabangan areas in thedam river (storage basin 3 3 76 capacitysuch as Candabaof 2966 million (250 km m2) and SanAngat Antonio dam (storage (120 km capacity2)[17]. of 850 million m ). There are also two 2 2 77 swampThe areas population in the river density basin in such the studyas Candaba area in (250 2010 km was) and 630 San persons Antonio/km 2(120and km the) annual[17]. average 2 78 populationThe population growth density rate from in 1980the study to 2010 area was in 2010 about was 2.61%, 630 persons/km [17]. The cropland and the areasannual are average widely 79 population growth rate from 1980 to 2010 was about 2.61%, [17]. The cropland areas are widely 80 spread over the central and downstream parts of the Pampanga River basin which are relatively flat Quaternary 2019, 2, 27 3 of 18 Quaternary 2019, 2, x 3 of 18 Quaternary 2019, 2, x 3 of 18 81 spreadand lowland over the areas, central and and built-up downstream area is scattered parts of thein the Pampanga basin [18]. River However, basin the which areas are around relatively three flat 8182 andandmajor lowland lowland cities areas, suchareas, as and and San built-up built-up Fernando, areaarea Angeles is scattered and Cain in banataunthe the basin basin are[18]. [18 continuously]. However, However, the theurbanized. areas areas around around This three river three 8283 majormajorbasin cities citiesis also such such regarded as as San San Fernando,as Fernando, one of the AngelesAngeles most importan and Ca Cabanataunbanataunt river basins are are continuously continuouslyin the Philippines urbanized. urbanized. since Thisthis This riverbasin river 8384 basinbasinmakes is is also an also important regarded regarded contribution as as oneone ofof thethe to mostmostthe country’s importantimportan economyt river river basins basins [19]. Onin in the the the Philippines other Philippines hand, since this since riverthis this basinbasin basin 8485 makesmakesexperiences an an important important at least contribution contributionone flood event toto thethe a country’s year on economyaverage, economy [19].which [19]. On Oncauses the the other othersevere hand, hand, damage this this river to river housebasin basin 8586 experiencesexperiencesbuildings, atinfrastructure, at least least one one flood floodand event agricultureevent a year a year on in average, theon basinaverage, which [16]. which causesFigure causes severe3 shows severe damage the damagenumber to house ofto buildings,houseshouse 8687 infrastructure,buildings,damaged byinfrastructure, andfloods agriculture and theand estimate agriculture in the basind value in [16 theof]. flood Figurebasin damage [16].3 shows Figure to theagriculture 3 numbershows theand of housesnumber infrastructures, damagedof houses in by 8788 floodsdamagedthe Region and theby III estimatedfloods of the and Philippines valuethe estimate of floodwhered damagevalue study of area toflood agriculture is located.damage The andto agriculture figure infrastructures, shows and the infrastructures, inflood the damage Region inIIIto of 89 88 thethehouses, Philippines Region agriculture III whereof the andPhilippines study infrastructure area iswhere located. hasstudy been The area figureincreasing is located. shows in The therecent floodfigure years. damage shows Therefore, the to houses,flood it is damage necessary agriculture to 90 to analyze impact of continuous urbanization and land cover changes in the basin on flood runoff 89 andhouses, infrastructure agriculture has and been infrastructure increasing has in recentbeen increasing years. Therefore, in recent ityears. is necessary Therefore, to it analyze is necessary impact 9091 and inundations for proper adaptation measures and planning of land use to reduce the damage in ofto continuous analyze impact urbanization of continuous and land urbanization cover changes and land in the cover basin changes on flood in the runo basinff and oninundations flood runoff for 9192 andfuture. inundations for proper adaptation measures and planning of land use to reduce the damage in proper adaptation measures and planning of land use to reduce the damage in future. 92 future. Pampanga River basin Major cities Pampanga River basin MajorDams/Reservoirs cities Dams/ReservoirsWater level stations used Pantabangan Waterfor analysis level stations used PantabanganDam forTributary analysis points used for Dam Tributaryanalysis points used for analysisOutlet point Outlet point Mayapap Mayapap Cabanatuan Cabanatuan T2 T2 San Isidro Philippines San Isidro Angeles Arayat Philippines Angeles Arayat San Fernando San Fernando T1 T1

O1 (outlet point) Angat Dam O1 (outlet point) 93 Angat Dam 93 94 Figure 2. Location of the Pampanga River basin of the Philippines and elevation ranges. Figure 2. Location of the Pampanga River basin of the Philippines and elevation ranges. 94 Figure 2. Location of the Pampanga River basin of the Philippines and elevation ranges. (a) (b) 50 14,00014000 (a) Houses Partially Damaged (b) Agricultural Damage 50 Houses Totally Damaged 14,0001400012,00012000 40 Houses Partially Damaged Infrastructure Damage Agricultural Damage Houses Totally Damaged 12,000120001000010,000 40 Infrastructure Damage 30 1000010,0008000 30 20 800060006000

(in MillionPeso) 6000 (in Thousands) (in 20 600040004000 10 (in MillionPeso) Estimated Cost of of EstimatedDamages Cost (in Thousands) (in 4000400020002000

Numebr of Houses Damaged 10

0 of EstimatedDamages Cost 2000200000 Numebr of Houses Damaged 0 00 95 Year Year Year Year 95 Figure 3. Flood damage during past flood events in the Region III of the Philippines (a) number of 96 Figure 3. Flood damage during past flood events in the Region III of the Philippines (a) number of houses damage and (b) estimated cost of agricultural and infrastructure damage [Data source: Office 9697 Figurehouses 3. damage Flood damageand (b) estimatedduring past cost flood of agricultural events in the and Region infrastructure III of the damage Philippines [Data ( asource:) number Office of of Civil Defense Region III, the Philippines]. 9798 housesof Civil damage Defense and Region (b) estimated III, the Philippines]. cost of agricultural and infrastructure damage [Data source: Office 98 of Civil Defense Region III, the Philippines]. Quaternary 2019, 2, x 4 of 18

99 3. Data and Methodology 100 Figure 4 shows the overview of the research method. The research method includes two parts: 101 (1) land-cover change analysis and projection for future, and (2) flood runoff and inundation 102 analysis. 103 The land-cover maps for past years were generated using Landsat images based on 104 supervised-maximum likelihood classification method and land cover changes in the past years 105 were analyzed. The generated land-cover maps were validated by comparing these with Google 106 Earth images. The land cover maps for the future were predicted using LCM based on the analysis of 107 potential transition changes and considering various variables such as elevation, slope factors, 108 aspect factors, distance from the city, distance from the major road, and distance from the river. 109 Then, flood characteristics were analyzed using the RRI model developed by Sayama et al. [20] with 110 landQuaternary cover2019 map, 2, 27 for different years. The RRI model was calibrated and validated by comparing4 of 18 111 calculated flood discharge with observed discharge for past flood events, and calculated flood marks 112 were also compared with recorded flood depth data. The impacts of land-cover changes on flood 3. Data and Methodology 113 runoff, and flood inundation were analyzed for 50- and 100-year floods. The potential affected areas 114 causedFigure by 4flood shows hazards the overview were also of the analyzed research with method. different The research years land method cover includes data. The two details parts: (1)of 115 methodologyland-cover change and data analysis used and are projectiondiscussed forbelow. future, and (2) flood runoff and inundation analysis.

Land Cover Change Analysis Flood Runoff and Inundation Analysis

Preparation of Land Cover Maps Rainfall

Z Change Analysis Subsurface + Surface Vertical Infiltration 1D Diffusion Transition Potential Analysis in River Analysis of Impact of Projection of Land Cover Land Cover Changes Maps for Future on Flood Runoff and Inundation Land cover maps input to the hydrological model (past year, 2D Diffusion projected future land cover map) on Land Rainfall-Runoff-Inundation Model 116 Simulation

117 Figure 4. OverviewOverview of of study study methodology. methodology. The land-cover maps for past years were generated using Landsat images based on 118 3.1. Land-Cover Change Analysis and Projection for Future supervised-maximum likelihood classification method and land cover changes in the past years 119 wereFor analyzed. land-cover The change generated analysis, land-cover Landsat maps images were were validated used because by comparing of their these rich witharchive Google and 120 spectralEarth images. resolution The [21]. land In cover order maps to generate for the futureland-cover were maps predicted for past using years, LCM Landsat based satellite on the analysis images 121 of 30m potential spatial transition resolution changes were acquired and considering from the Ea variousrthExplorer variables database such of as Un elevation,ited States slope Geological factors, 122 Surveyaspect factors,with less distance than 10% from cloud the city,cover distance searching from criteria the major for land road, cloud and distancecover and from scene the cloud river. cover. Then, 123 Basedflood characteristicson visual comparison were analyzed of the images using the for RRI cloud model free developedor less cloud by coverage Sayama et images, al. [20] Landsat with land 8 124 scenescover map acquired for di onfferent 13 February years. The 2016 RRI for model the year was 2016 calibrated and Landsat and validated 5 scenes by acquired comparing on calculated25 March 125 1996flood for discharge the year with 1996 observedwere selected discharge for the for land-cov past flooder classification events, and calculatedin the study flood area. marks Pre-processing were also 126 ofcompared the Landsat with images recorded were flood conducted depth data. using The Terr impactsSet Geospatial of land-cover Monitoring changes and on Modeling flood runo Systemff, and 127 (TGMMS).flood inundation The raw were digital analyzed number for 50-(DN) and values 100-year of each floods. band The of potential the images affected were areas calibrated caused and by 128 convertedflood hazards to reflectance were also analyzed values, and with the diff erentbands years of the land images cover were data. also The detailscorrected of methodology for atmospheric and 129 hazedata usedwith aredark discussed object subtraction below. model in the TGMMS. 130 Figure 5 shows the overall process of generation of land-cover maps for past years. The 131 composite3.1. Land-Cover band Change with combination Analysis and of Projection red, green for and Future blue colors was created using the multiband of 132 LandsatFor image land-cover for better change visualization, analysis, Landsat which is images very useful were usedfor making because training of their samples. rich archive By using and 133 multiband,spectral resolution true color [21 composite]. In order toor generatefalse color land-cover composite maps can be for created past years, to represent Landsat satellitean image. images True 134 colorof 30m composite spatial resolution gives natural were acquiredcolor to image from thewith EarthExplorer combination database of red-green-blue of United (RGB) States Geologicalbands 321 Survey with less than 10% cloud cover searching criteria for land cloud cover and scene cloud cover. Based on visual comparison of the images for cloud free or less cloud coverage images, Landsat 8 scenes acquired on 13 February 2016 for the year 2016 and Landsat 5 scenes acquired on 25 March 1996 for the year 1996 were selected for the land-cover classification in the study area. Pre-processing of the Landsat images were conducted using TerrSet Geospatial Monitoring and Modeling System (TGMMS). The raw digital number (DN) values of each band of the images were calibrated and converted to reflectance values, and the bands of the images were also corrected for atmospheric haze with dark object subtraction model in the TGMMS. Figure5 shows the overall process of generation of land-cover maps for past years. The composite band with combination of red, green and blue colors was created using the multiband of Landsat image for better visualization, which is very useful for making training samples. By using multiband, true color composite or false color composite can be created to represent an image. True color composite Quaternary 2019, 2, 27 5 of 18 Quaternary 2019, 2, x 5 of 18

135 givesin the natural case of color Landsat to image 5 image with combinationand RGB bands of red-green-blue 432 in the case (RGB) of bands Landsat 321 in8 image. the case False of Landsat color 136 5composite image and gives RGB color bands which 432 in theis not case natural of Landsat with 8 image.combination False colorof RGB composite bands gives432 in color the whichcase of is 137 notLandsat natural 5 image with combination and RGB bands of RGB 543 bands in the 432 case in the of case Landsat of Landsat 8 image. 5 image These and combinations RGB bands 543 of inRGB the 138 casebands of in Landsat false color 8 image. composite These combinations make vegetation of RGB and bands water in in false red color and compositeblue shades, make respectively, vegetation 139 andwhich water can inbe reduseful and for blue the shades, better visualization respectively, and which identification can be useful of land for the areas better and visualization water bodies. and In 140 identificationthis study, both of landtrue areascolor andand waterfalse color bodies. comp In thisosite study, images both were true prepared color and and false used color for composite making 141 imagestraining were samples. prepared and used for making training samples.

Landsat Landsat 5 for 1996 Satellite Image Landsat 8 for 2016

Year 1: 1996 DATA Training sample preparation Year 2: 2016 PROCESSING

Composite bands Extraction for study area Image Land Cover Classification Map for 1996 Supervised Classification: Land Cover Maximum likelihood Map for 2016 method

142 Using Terrset Geospatial Monitoring and Modeling System 143 Figure 5. ProcessProcess of land-cover maps preparation using satellite based remote sensing images.

144 There are normally two methodsmethods ofof imageimage classification,classification, i.e.,i.e., supervised and unsupervisedunsupervised 145 classification.classification. SupervisedSupervised classification classification can generatecan generate desired desired classes ofcl land-coverasses of land-cover based on ground-truth based on 146 data,ground-truth while unsupervised data, while unsupervis classificationed attemptsclassification to classify attempts the to pixels classify with the the pixels same with characteristics the same 147 basedcharacteristics on statistical based criteria on statistical [21,22]. Incriteria this study, [21,22]. a supervised In this study, classification a supervised method classification was used, inmethod which 148 trainingwas used, samples in which and training spectral samples signatures and of spectral known categories,signatures suchof known as urban, categories, croplands, such vegetation, as urban, 149 forestcroplands, and others, vegetation, have toforest be developed. and others, Then have image to be classification developed. Then tool in image the TGMMS classification assigns tool each in pixel the 150 inTGMMS the image assigns to the each land-cover pixel in the type image to which to the its land-cover signature type is most to which similar. its Thesignature maximum is most likelihood similar. 151 classificationThe maximum method likelihood was classification used for image method classification, was used which for image is one classification, of the most e whichffective is algorithms one of the 152 formost classification effective algorithms of satellite for images classification [23,24]. The of sate land-coverllite images categories [23,24]. in The the basinland-cover were classifiedcategories into in 153 seventhe basin classes were as classified presented into in Tableseven1 .classes The training as presented samples in forTable each 1. land-coverThe training class samples for 1996 for andeach 154 2016land-cover were selectedclass for as 1996 many and as 2016 possible were throughout selected as themany entire as possible image based throughout on composite the entire images image as 155 wellbased as on google composite earth images.images Aboutas well 672 as traininggoogle earth samples images. (water About bodies: 672 49, training wetlands: samples 70, cropland: (water 156 330,bodies: built-up 49, wetlands: areas: 114, 70, vegetation:cropland: 330, 58, built-up forest: 22, area bares: 114, land: vegetation: 29) were selected58, forest: for 22, land-cover bare land: class 29) 157 forwere 1996, selected while for about land-cover 1128 training class samplesfor 1996, (water while bodies: about 1128 99, wetlands: training 103,samples cropland: (water 552, bodies: built-up 99, 158 areas:wetlands: 245, 103, vegetation: cropland: 80, 552, forest: built-up 30, bare areas: land: 245, 19) werevegetation: selected 80, in forest: the case 30, of bare 2016 (Tableland: 119)). Afterwere 159 makingselected trainingin the case samples of 2016 for (Table 1996 and1). After 2016, making signature training files were samples created for by1996 processing and 2016, Band signature 1 to Band files 160 6were using created the IDRISI by processing image processing Band 1 to toolBand in 6 theusin TGMMS.g the IDRISI Then, image by using processing signature tool files,in the land TGMMS. cover 161 mapsThen, forby theusing years signature 1996 and files, 2016 land were cover generated maps fo basedr the years on maximum 1996 and likelihood 2016 were classification generated based method. on 162 Themaximum generated likelihood land-cover classification maps of 30m method. spatial resolutionThe generated were upscaledland-cover to 450mmaps spatial of 30m resolution, spatial 163 sameresolution as spatial were resolution upscaled ofto DEM 450m used spatial for hydrologicalresolution, same analysis, as spatial using ArcGISresolution 10.3.1 of (Esri,DEM Redlands,used for 164 CA,hydrological USA). The analysis, generated using land-cover ArcGIS maps 10.3.1 were (Esri, compared Redlands, visually CA, USA). with google The generated earth images. land-cover For the 165 fullymaps or were partly compared could coverage visually areas,with google generated earth maps images. were For manually the fully corrected or partly based could on coverage Google areas, Earth 166 imagesgenerated using maps raster were editor manually in ArcGIS corrected 10.3.1 (Redlands, based on California).Google Earth Figure images6 shows using the raster generated editor land in 167 coverArcGIS maps 10.3.1 for (Redlands, the years 1996California). and 2016. Figure The accuracy6 shows assessmentthe generated of generatedland cover land maps cover for mapsthe years was 168 also1996 performedand 2016. byThe checking accuracy random assessment samples of of gene landrated cover land class cover with google maps earthwas images.also performed The overall by 169 accuracychecking andrandom kappa samples value forof land 1996 cover land coverclass with were google about 87.3%earth images. and 0.806, The while overall they accuracy were about and 170 86.5%kappa andvalue 0.801 for 1996 in the land case cover of 2016 were land-cover about 87.3% map. and The 0.806, generated while they land were cover about maps 86.5% for and the years0.801 171 in the case of 2016 land-cover map. The generated land cover maps for the years 1996 and 2016 were 172 in perfect agreement with high value of overall accuracy and kappa value. The land-cover maps Quaternary 2019, 2, x 6 of 18

173 show that the cropland and built-up areas were mostly located in the region with 0–50 m altitude 174 and a slope of 0–5 degrees. The vegetation and forest areas were densely located in the region where 175 the altitude is greater than 200 m. The wetlands areas were mostly located in the coastal areas where 176 water covers the land throughout the year because of the effect of sea/tidal level.

177 Table 1. Land cover classification in the river basin for study and selected training sample sizes. Quaternary 2019, 2, 27 6 of 18 Training Sample Class Land Cover Class Descriptions Sizes (selected) 1996Number and 2016 were in perfect agreement with high value of overall accuracy and kappa value. The 1996 2016 land-cover maps show that the cropland and built-up areas were mostly located in the region with Rivers, lakes, watersheds, streams, 0–50 m1 altitude andWater a slope bodies of 0–5 degrees. The vegetation and forest areas were densely49 located99 in the region where the altitude is greater than 200 m. The wetlandsreservoirs areas were mostly located in the coastal Wetlands 103 areas where2 water covers the land throughoutPermanent the wet year croplands, because of fishponds the effect of sea/tidal 70 level. (permanent) Table 1. Land cover classification inPermanent the river basin croplands, for study paddy and selected field, training sample sizes. 3 Croplands irrigated cropland, rainfed croplands, 330 552 Training Sample Sizes (Selected) Class Number Land Cover Class Descriptionsother crops 1996 2016 1 Water bodies Rivers,Commercial lakes, watersheds, and streams,business reservoirs buildings, 49 99 Wetlands 2 publicPermanent buildings, wet croplands, residential fishponds buildings, 70 103 4 Built(permanent) up 114 245 Permanentinformal croplands, settlements, paddy field, industrial irrigated sites, 3 Croplands 330 552 cropland, rainfedstreets/roads, croplands, otherairports crops Commercial and business buildings, public buildings, residential buildings, informal 4 Built up Naturally occurring multitude of species 114 245 settlements, industrial sites, streets/roads, 5 Vegetation of plants in theairports form of bush, grassland, 58 80 Naturally occurringflora or multitudecollective of speciesplants of 5 Vegetation plants in the form of bush, grassland, flora or 58 80 collective plants 6 6 Forest Forest Open Open forest,forest, dense dense forest, forest, mixed forestmixed forest 22 22 30 30 7 7 Bare Bare areas areas Bare Bare sand sand/soil/soil 29 29 19 19

2016 178 1996

179 Figure 6.6. Generated land-cover maps for the years 1996 andand 2016.2016. Figure7 shows the process of land cover change analysis, transition potential analysis and 180 Figure 7 shows the process of land cover change analysis, transition potential analysis and prediction of land cover maps for the future. To predict land cover maps for future years, land 181 prediction of land cover maps for the future. To predict land cover maps for future years, land cover cover changes between the years 1996 and 2016 were analyzed using the LCM tool of TGMMS. The 182 changes between the years 1996 and 2016 were analyzed using the LCM tool of TGMMS. The potential for land transitions was modeled using transition sub-model of LCM with similarity-weighted 183 potential for land transitions was modeled using transition sub-model of LCM with instance-based machine learning tool (SimWeight) [25]. Six transitions such as cropland to built-up, 184 similarity-weighted instance-based machine learning tool (SimWeight) [25]. Six transitions such as cropland to vegetation, vegetation to cropland, vegetation to built-up, vegetation to forest, and forest to vegetation, were considered for potential transition modeling. The changes in water bodies, wetlands and bare areas were neglected for future projection of land-cover maps. The variables such as elevation, slope, aspect, distance from the road, distance from the city centre, and distance from the river as shown in Figure8 were also considered as static variables in the potential transition modeling. Then, land-cover maps were predicted for years 2018, 2030, 2040 and 2050 based on historical rates of change and the transition potential model. The projected land-cover map for year 2018 was used only for Quaternary 2019, 2, x 7 of 18

185 cropland to built-up, cropland to vegetation, vegetation to cropland, vegetation to built-up, 186 vegetation to forest, and forest to vegetation, were considered for potential transition modeling. The 187 changes in water bodies, wetlands and bare areas were neglected for future projection of land-cover 188 maps. The variables such as elevation, slope, aspect, distance from the road, distance from the city 189 centre,Quaternary and2019 distance, 2, 27 from the river as shown in Figure 8 were also considered as static variables7 of in 18 190 the potential transition modeling. Then, land-cover maps were predicted for years 2018, 2030, 2040 191 and 2050 based on historical rates of change and the transition potential model. The projected 192 land-covervalidationof map the for projected year 2018 map, was and used the only land-cover for validation maps for of years the projected 2030, 2040, map, and and 2050 the were land-cover used for 193 mapsimpact for analysis years 2030, of land 2040, cover and changes 2050 were on flooding used for regime. impact Theanalysis land of use land plan cover of local changes governments on flooding was 194 regime.not considered The land in predictionuse plan of of local land-cover governments maps for was future not dates, considered because in of prediction limited information of land-cover and 195 mapsdata. Thefor LCMfuture tool dates, of TGMMS because can of predictlimited ainformation future scenario and of data. land coverThe LCM for a tool specified of TGMMS future yearcan 196 predictbased on a thefuture historical scenario rates of land of change cover and for thea specified transition future potential year modelingbased on [the26]. historical The LCM rates model of 197 changedetermines and howthe transition the variables potential influence modeling land cover [26]. change The LCM in the model future, determines how much how land-cover the variables change 198 influencetook place land between cover year change 1 and in yearthe future, 2, and thenhow calculatemuch land-cover a relative change amount took of transition place between to the year future 1 199 andyear year [26]. 2, and then calculate a relative amount of transition to the future year [26].

Satellite Images LANDSAT Satellite Images for Year 1 images for Year 2 (2016) (1996) Preparation of land cover map in GIS based on maximum Land cover Land cover likelihood method Change analysis map-2 map-1 using satellite images in LCM (1996) (2016)

• Variables such as DEM, Slope, Aspect, Distance from road, Distance from river, Distance Transition from city were used for potential in LCM transition potential analysis.

Transition potential maps were created.

Projection of Future Land Change Prediction Cover Maps in LCM (2018, 2030, 2040, 2050) 200

201 Figure 7. ProcessProcess of of land-cover land-cover change change analysis and proj projectionection of future land-cover maps. 3.2. Flood Hazard Assessment and Impact Analysis Many rainfall-runoff models, such as HEC-HMS [27], TOPMODEL [28], Tank model [29] and others, which have been applied for runoff prediction, cannot compute flood inundation. Therefore, the RRI model, developed by Sayama et al. [20], was employed to simulate flood characteristics such as flood runoff, inundation depth, flood duration and extent areas. The RRI model is also available for free. The RRI model is a two-dimensional hydrological model, which can simulate rainfall-runoff process, infiltration process and flood inundation simultaneously [20]. The RRI model simulates flow characteristics in the slopes and river channels separately. The flow on the slope grid cells is calculated with a two-dimensional diffusive wave model, while the flow on the river channel is calculated with a one-dimensional diffusive wave model. The details on the governing equations of the RRI model can be found in Sayama et al. [20]. The Interferometric Synthetic Aperture Radar (IfSAR) acquired in 2013 was obtained from the National Mapping and Resource Information Authority (NAMRIA) of the Philippines. A DEM of 15 arc-s (approximately 450 m horizontal resolution), derived from the IfSAR, was used in the RRI model simulation. The river width and depth were calculated at each river grid cell using empirical equations [20]. The constant values of the empirical equations were estimated based on measured cross-sectional information and google earth images at several locations. Based on the past dam release discharge data obtained from the Pantabangan and Angat Dam offices, the Pantabangan dam generally stores all the inflow discharges during the flood event, while the Angat dam mostly releases the discharge during a flood event [30]. Therefore, the flood control function of only Pantabangan dam was considered in the analysis. It was assumed that no outflow from the Quaternary 2019, 2, 27 8 of 18

dam was considered until the flood control storage capacity is filled up (flood storage capacity of the Pantabangan dam is about 1018 million m3), and when the flood storage volume of the dam exceeds the capacity, the outflow discharge from the dam was considered to be the same as the inflow discharge. The ground gauge rainfall data at 17 stations were used and Thiessen polygon method was employed for the spatial distribution of rainfall in the basin. The recent largest flood events i.e. 2011 flood and 2015 flood, were selected for model calibration and validation. The parameters of RRI model were calibrated with September 2011 flood event by comparing calculated discharge with observed data. The results of calculated flood inundation depth were also compared with the recorded flood mark data.Quaternary The calibrated2019, 2, x parameters of the model were validated with October 2015 flood event. 8 of 18

Elevation Slope Aspect

Cabanataun

Angeles

San Fernando

Distance from road Distance from city Distance from river 202 Figure 8. 203 Figure 8.Variables Variables used used for for potential potential transition transition analysis analysis (Data (Data source source for for road, road, city city and and river: river: NWRB NWRB and JICA [18]). 204 and JICA [18]). In general, flood risk assessment is conducted for a specific return period flood event or for the 205 3.2. Flood Hazard Assessment and Impact Analysis recorded past largest flood event [31]. The target scale of flood risk assessment varies by river according 206 to socio-economicMany rainfall-runoff activities, models, past disasters, such as and HEC-HMS expected damage[27], TOPMODEL in the basin [28], [31 ].Tank In this model study, [29] flood and 207 hazardothers, assessment which have was been conducted applied for by runoff using predic RRI modeltion, cannot for 50- compute and 100-year flood flood inundation. cases using Therefore, past 208 yearsthe RRI land model, cover datadeveloped (1996 andby Sayama 2016) and et al. also [20], using was predicted employed future to simulate land-cover flood maps characteristics for 2030, 2040 such 209 andas flood 2050. Statisticalrunoff, inundation analysis was depth, conducted flood duration using 48-hours and extent maximum areas. annualThe RRI precipitation model is also to estimateavailable 210 rainfallfor free. intensity The RRI for model different is a two-dimensional return periods. Spatial hydrological and temporal model, rainfallwhich can distribution simulate ofrainfall-runoff September 211 2011process, flood infiltration were selected process to design and rainfall flood hyetographinundation forsimultaneously different return [20]. periods, The RRI and itmodel was estimated simulates 212 byflow multiplying characteristics the rainfall in the of slopes September and river 2011 channels flood event separately. by a rainfall The conversion flow on the factor. slope The grid rainfall cells is 213 conversioncalculated factorwith a for two-dimensional each return period diffusive can be wave estimated model, as thewhile ratio the of flow the correspondingon the river channel rainfall is 214 volumecalculated of the with return a one-dimensional period and the diffusive rainfall volumewave model. of September The details 2011 on [ 30the,31 governing]. Flood hazard equations was of 215 simulatedthe RRI model using RRIcan modelbe found with in rainfall Sayama hyetograph et al. [20]. of The diff erentInterferometric return periods Synthetic considering Aperture diff erentRadar 216 years(IfSAR) land acquired cover map. in 2013 was obtained from the National Mapping and Resource Information 217 Authority (NAMRIA) of the Philippines. A DEM of 15 arc-s (approximately 450 m horizontal 218 resolution), derived from the IfSAR, was used in the RRI model simulation. The river width and 219 depth were calculated at each river grid cell using empirical equations [20]. The constant values of 220 the empirical equations were estimated based on measured cross-sectional information and google 221 earth images at several locations. Based on the past dam release discharge data obtained from the 222 Pantabangan and Angat Dam offices, the Pantabangan dam generally stores all the inflow 223 discharges during the flood event, while the Angat dam mostly releases the discharge during a flood 224 event [30]. Therefore, the flood control function of only Pantabangan dam was considered in the 225 analysis. It was assumed that no outflow from the dam was considered until the flood control 226 storage capacity is filled up (flood storage capacity of the Pantabangan dam is about 1018 million 227 m3), and when the flood storage volume of the dam exceeds the capacity, the outflow discharge from 228 the dam was considered to be the same as the inflow discharge. The ground gauge rainfall data at 17 229 stations were used and Thiessen polygon method was employed for the spatial distribution of 230 rainfall in the basin. The recent largest flood events i.e. 2011 flood and 2015 flood, were selected for 231 model calibration and validation. The parameters of RRI model were calibrated with September 2011 232 flood event by comparing calculated discharge with observed data. The results of calculated flood Quaternary 2019, 2, 27 9 of 18

4. Results and Discussions

4.1. Results of Land-Cover Change Analysis and Projection for Future Table2 shows the area of each land cover class in 1996, 2016, 2030, 2040, and 2050. In 1996, the study area was largely dominated by croplands (5488.67 km2), about 51.5% of the total area. The built-up areas were about 227.34 km2 (2.14% of the study area). In 2016, the cropland areas decreased to 5125.07 km2 (48.14% of the study area) and built-up areas increased to 600.4 km2 (5.64% of study area). Figure9 shows the gain /loss area and net change area of land cover between 1996 and 2016. The figure shows that cropland, vegetation and bare areas decreased from 1996 to 2016, while other areas increased. Figure 10 shows the contribution to net change from 1996 to 2016 for each land-cover class and Figure 11 shows the distribution of loss, no change and gain areas in the case of built-up, cropland and vegetation land-cover classes. The results show that areas gain in water bodies were from the forest, vegetation and cropland, particularly in the upstream of dams. The loss areas of water bodies were mainly converted to wetland, particularly in coastal areas. The gain of permanent wetland areas was mainly from the cropland. Some cropland areas were used for fishponds in recent years and also some areas located in coastal areas become wet throughout the year. The contribution to net change in cropland shows that cropland areas were mainly converted to built-up area. The land-cover maps of 1996 and 2016 shows that the built-up areas were scattered in the large areas. However, the areas around San Fernando, Angeles and Cabanatuan were largely urbanized in 2016 compared to the year 1996. The gain of built-up area was mainly from cropland and some from bare areas and vegetation. The croplands areas are mostly located in the low-lying areas where built-up areas can be easily expanded. Vegetation areas were mainly converted to forest areas. The bare areas, particularly which was created by Mt. Pinatubo eruption in 1991, were converted mainly to cropland, built-up and vegetation.

Table 2. Area of each land cover class in 1996, 2016, 2030, 2040, and 2050.

Area (km2) Class Number Land Cover Class 1996 2016 2030 2040 2050 1 Water bodies 80.9 100.44 100.44 100.44 100.44 2 Wetlands (permanent) 260.84 351.54 351.54 351.54 351.54 3 Croplands 5488.67 5125.07 4824.76 4651.63 4548.15 4 Built up 227.34 600.4 882.1 1049.76 1160.33 5 Vegetation 2458.64 2289.8 2190.04 2127.87 2075.83 6 Forest 1951.73 2158 2276.1 2343.74 2388.69 Quaternary 20197, 2, x Bare areas 177.16 20.04 20.04 20.04 20.0410 of 18

(a) (b) Loss Gain Loss Gain Bare Areas Bare Areas Forest Forest Vegetation Vegetation Built Up Built Up Cropland Cropland Permanent Wetland Permanent Wetland Water Bodies Water Bodies

-1000 -500 0 500 1000 -1000 -500 0 500 1000 2 272 Gain / Loss Area (km2) Net Change Area (km ) 273 FigureFigure 9.9. GainGain and loss area of each land cover class class from from 1996 1996 to to 2016 2016 ( (aa)) gain/loss gain/loss area area and and (b (b) )net net 274 change area. change area.

(a) (b)

Bare Areas Bare Areas Forest Forest Vegetation Vegetation Built Up Built Up Cropland Cropland Per. Wetland Per. Wetland Water Bodies Water Bodies

-10 0 10 20 30 0 20406080100 Gain / Loss Area (km2) 2 (c) (d) Gain / Loss Area (km )

Bare Areas Bare Areas Forest Forest Vegetation Vegetation Built Up Built Up Cropland Cropland Per. Wetland Per. Wetland Water Bodies Water Bodies

-400 -300 -200 -100 0 100 200 -100 0 100 200 300 400 2 2 Gain / Loss Area (km ) (e)Gain / Loss Area (km ) (f)

Bare Areas Bare Areas Forest Forest Vegetation Vegetation Built Up Built Up Cropland Cropland Per. Wetland Per. Wetland Water Bodies Water Bodies

-300 -200 -100 0 100 -50 0 50 100 150 200 250 2 Gain / Loss Area (km2) (g) Gain / Loss Area (km )

Bare Areas Forest Vegetation Gain Built Up Loss Cropland Per. Wetland Water Bodies

-150 -100 -50 0 275 Gain / Loss Area (km2)

276 Figure 10. Contribution to net change from 1996 to 2016 in (a) water bodies, (b) permanent wetland, 277 (c) cropland, (d) built-up areas, (e) vegetation, (f) forest, and (g) bare areas. Quaternary 2019, 2, x 10 of 18

(a) (b) Loss Gain Loss Gain Bare Areas Bare Areas Forest Forest Vegetation Vegetation Built Up Built Up Cropland Cropland Permanent Wetland Permanent Wetland Water Bodies Water Bodies

-1000 -500 0 500 1000 -1000 -500 0 500 1000 2 272 Gain / Loss Area (km2) Net Change Area (km ) 273 Figure 9. Gain and loss area of each land cover class from 1996 to 2016 (a) gain/loss area and (b) net Quaternary 2019, 2, 27 10 of 18 274 change area.

(a) (b)

Bare Areas Bare Areas Forest Forest Vegetation Vegetation Built Up Built Up Cropland Cropland Per. Wetland Per. Wetland Water Bodies Water Bodies

-10 0 10 20 30 0 20406080100 Gain / Loss Area (km2) 2 (c) (d) Gain / Loss Area (km )

Bare Areas Bare Areas Forest Forest Vegetation Vegetation Built Up Built Up Cropland Cropland Per. Wetland Per. Wetland Water Bodies Water Bodies

-400 -300 -200 -100 0 100 200 -100 0 100 200 300 400 2 2 Gain / Loss Area (km ) (e)Gain / Loss Area (km ) (f)

Bare Areas Bare Areas Forest Forest Vegetation Vegetation Built Up Built Up Cropland Cropland Per. Wetland Per. Wetland Water Bodies Water Bodies

-300 -200 -100 0 100 -50 0 50 100 150 200 250 2 Gain / Loss Area (km2) (g) Gain / Loss Area (km )

Bare Areas Forest Vegetation Gain Built Up Loss Cropland Per. Wetland Water Bodies

-150 -100 -50 0 275 Gain / Loss Area (km2)

276 FigureFigure 10. 10. ContributionContribution to to net net change from 19961996 toto 20162016 inin (a(a)) water water bodies, bodies, ( b(b) permanent) permanent wetland, wetland, (c ) 277 Quaternary(ccropland,) cropland, 2019, 2 (,d x )( d built-up) built-up areas, areas, (e) ( vegetation,e) vegetation, (f) ( forest,f) forest, and and (g) ( bareg) bare areas. areas. 11 of 18

(a) (b) (c)

278 279 Figure 11. Distribution of loss, nono changechange andand gain gain areas areas between between 1996 1996 and and 2016 2016 for for land-cover land-cover class class (a) 280 (a)Built-up Built-up areas, areas, (b) ( Croplandb) Cropland areas, areas, and and (c) Vegetation.(c) Vegetation.

281 Figure 12 shows the projected land-cover map for years 2018, 2030, 2040 and 2050. The 282 projected land-cover map for 2018 was compared visually with Google Earth image of February 283 2018. The random samples of land-cover class were also generated in Geographic Information 284 System (GIS), and the Keyhole Markup Language (KML) data of random samples were imported 285 into Google Earth for comparison. The overall accuracy and kappa value in the case of projected 286 land cover map of 2018 were about 90.4% and 0.858, which were in perfect agreement.

(a) (b)

(c) (d)

287

288 Figure 12. Projected land-cover map for (a) 2018, (b) 2030, (c) 2040, and (d) 2050. Quaternary 2019, 2, x 11 of 18

(a) (b) (c)

278 Quaternary 2019, 2, 27 11 of 18 279 Figure 11. Distribution of loss, no change and gain areas between 1996 and 2016 for land-cover class 280 (a) Built-up areas, (b) Cropland areas, and (c) Vegetation. Figure 12 shows the projected land-cover map for years 2018, 2030, 2040 and 2050. The projected 281 land-coverFigure map12 shows for 2018 the was projected compared land-cover visually ma withp Googlefor years Earth 2018, image 2030, of 2040 February and 2018.2050. The 282 projectedrandom samples land-cover of land-cover map for class2018 werewas alsocompared generated visually in Geographic with Google Information Earth image System of (GIS),February and 283 2018.the Keyhole The random Markup samples Language of (KML)land-cover data ofclass random were samplesalso generated were imported in Geographic into Google Information Earth for 284 Systemcomparison. (GIS), The and overall the Keyhole accuracy Markup and kappa Language value (KML) in the casedata ofof projectedrandom samples land cover were map imported of 2018 285 wereinto Google about 90.4% Earth and for 0.858,comparison. which wereThe overall in perfect accuracy agreement. and kappa value in the case of projected 286 land cover map of 2018 were about 90.4% and 0.858, which were in perfect agreement.

(a) (b)

(c) (d)

287 Figure 12. Projected land-cover map for (a) 2018, (b) 2030, (c) 2040, and (d) 2050. 288 Figure 12. Projected land-cover map for (a) 2018, (b) 2030, (c) 2040, and (d) 2050. The comparison of past and projected land-cover maps shows that the croplands and vegetation areas are decreasing in trend, while built-up and forest areas are increasing in trend (Table2, Figure 12). The projected maps also show that the built-up areas are largely increasing around San Fernando, Angeles and Cabanatuan as well as along the roads. The comparison of net change of land cover map between 2016 and 2050 shows that cropland areas and vegetation may decrease in 2050 by 11.25% and 9.3%, respectively. The built-up areas and forest areas may increase in 2050 by 93.2% and 10.6%, respectively.

4.2. Results of Flood Impact Analysis Figure 13 shows the comparison of calculated discharge using RRI model at San Isidro station with observed discharge for 2011 flood and 2015 flood events. The 2011 flood event was used for RRI model calibration and the 2015 flood event was used for validation of the calibrated parameters. The land cover map of 2016 was used for both flood event cases for the simplicity. The model parameters Quaternary 2019, 2, x 12 of 18

289 The comparison of past and projected land-cover maps shows that the croplands and 290 vegetation areas are decreasing in trend, while built-up and forest areas are increasing in trend 291 (Table 2, Figure 12). The projected maps also show that the built-up areas are largely increasing 292 around San Fernando, Angeles and Cabanatuan as well as along the roads. The comparison of net 293 change of land cover map between 2016 and 2050 shows that cropland areas and vegetation may 294 decrease in 2050 by 11.25% and 9.3%, respectively. The built-up areas and forest areas may increase 295 in 2050 by 93.2% and 10.6%, respectively.

296 4.2. Results of Flood Impact Analysis 297 Figure 13 shows the comparison of calculated discharge using RRI model at San Isidro station 298 with observed discharge for 2011 flood and 2015 flood events. The 2011 flood event was used for RRI 299 model calibration and the 2015 flood event was used for validation of the calibrated parameters. The 300 land cover map of 2016 was used for both flood event cases for the simplicity. The model parameters 301 were calibrated with 2011 flood using 2016 land cover map; however, the calibrated parameters of 302 the model were also confirmed/validated with 2015 flood event using the same land cover map of 303 2016. The values of R2 (squared of correlation coefficient) and Nash Sutcliff Coefficient of Efficiency 304 (NSCE), which are commonly used metrics for evaluation of the model performance in hydrology, 305 were 0.91 and 0.82 in the case of discharge at San Isidro for 2011 flood, while 0.82 and 0.8 for 2015 306 flood. The calculated discharge at the San Isidro station matches well with the observations 307 indicating high R2 and NSCE values. The results of calculated flood inundation depth were also 308 compared with recorded flood mark depth at the selected locations as shown in Figure 14. The red 309 circle points in the Figure 14 (b) show the areas of recorded flood depth greater than 1.01 m [32]. The 310 green rectangle and yellow triangle points in the figure show the areas of recorded flood depth 311 ranges from 0.51–1.0 m and less than 0.5, respectively. The calculated flood extent areas and depth Quaternary 2019, 2, 27 12 of 18 312 trends are similar to the recorded flood mark data. 313 The annual maximum 48-hours rainfall of September 2011 flood was about 299.011 mm, and the 314 werevalues calibrated of 48-hours with rainfall 2011 flood for 50-year using 2016flood land and cover100-year map; flood however, based theon calibratedfrequency parametersanalysis, were of 315 theabout model 330 weremm alsoand confirmed369 mm, respectively./validated with The 2015 rain floodfall hyetographs event using thefor same50- and land 100-year cover map return of 316 2016.periods The were values estimated of R2 (squared by multiplying of correlation the rainfa coefficient)ll pattern and Nashof September Sutcliff Coe 2011ffi cientflood of event Efficiency by a 317 (NSCE),rainfall conversion which are commonlyfactor. Figures used 15 metrics and 16 forshow evaluation the calculated of the flood model inundation performance depth in hydrology,and extent 318 wereareas 0.91with and different 0.82 in year the caseland-cover of discharge maps (1996, at San 2016, Isidro 2030, for 20112040, flood, and 2050) while in 0.82 the andcases 0.8 of for 50- 2015 and 319 flood.100-year The floods, calculated respectively. discharge The at the results San Isidro show stationthat flood matches inundation well with depth the observationsand flood extent indicating areas 320 highare increasing R2 and NSCE in trend values. in the The middle results and of calculated lower areas flood of the inundation basin due depth to land-cover were also changes, compared mainly with 321 recordeddue to rapid flood urbanization mark depth of at thethree selected main cities locations such as as shown San Fernando, in Figure Angeles14. The redand circle Cabanataun, points in and the 322 Figurealso expanding 14b show built-up the areas areas of recorded along the flood road depth networ greaterks. The than flood 1.01 inundation m [32]. The depth green and rectangle flood extent and 323 yellowareas may triangle increase points in inthe the future figure due show to theurbanizati areas ofon recorded activities flood and depthconversions ranges of from croplands 0.51–1.0 into m 324 andbuilt-up less thanareas. 0.5, Urbanization respectively. and The growing calculated of floodbuilt-up extent areas areas can and cause depth a large trends amount are similar of pervious to the 325 recordedareas to change flood mark into impervious data. areas by reducing infiltration rates which results increase in runoff.

(a) (b) Rainfall (ground) Calculated discharge Observed discharge Rainfall (ground) Calculated discharge Observed discharge 3000 0.00 3000 0

2500 2500 /s) /s) 3 10.00 3 10 2000 2000

1500 20 1500 20.00 Discharge(m Discharge (m Discharge Rainfall (mm/hour) Rainfall

1000 (mm/hour) Rainfall 1000 30 30.00 500 500

0 40 0 40.00 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 17-Oct 18-Oct 19-Oct 20-Oct 21-Oct 22-Oct 23-Oct 24-Oct 25-Oct 326 Date Date Quaternary 2019, 2, x 13 of 18 327 FigureFigure 13.13. Calculated and observedobserved dischargedischarge atat SanSan IsidroIsidro forfor ((aa)) 20112011 floodflood andand ((bb)) 20152015 flood.flood.

(a) (b)

328 Figure 14. Comparison of calculated flood inundation with reported data for flood event of September 329 Figure 14. Comparison of calculated flood inundation with reported data for flood event of 2011 (a) calculated flood inundation extent and depth and (b) recorded flood mark depth (Data source 330 September 2011 (a) calculated flood inundation extent and depth and (b) recorded flood mark depth for recorded flood mark: PRFFWC [32]). 331 (Data source for recorded flood mark: PRFFWC [32]). The annual maximum 48-hours rainfall of September 2011 flood was about 299.011 mm, and the values of 48-hours rainfall for 50-year flood and 100-year flood based on frequency analysis, (a) (b) (c) were about 330 mm and 369 mm, respectively. The rainfall hyetographs for 50- and 100-year return periods were estimated by multiplying the rainfall pattern of September 2011 flood event by a rainfall conversion factor. Figures 15 and 16 show the calculated flood inundation depth and extent areas

(d) (e)

> 5.0

332

333 Figure 15. Calculated flood inundation depth and extent areas with different year land-cover maps 334 in the case of 50-year flood (a) 1996, (b) 2016, (c) 2030, (d) 2040, and (e) 2050.

335 Figures 17 and 18 show the change in flow discharge with different years land cover at 336 Mayapap, San Isidro, Arayat, and tributary rivers (T1 and T2), respectively (see Figure 2 for the 337 locations of these points). The results show that the river runoff may increase in the future due to 338 land-cover changes and urbanization activities, particularly in the middle and downstream reach of Quaternary 2019, 2, x 13 of 18

(a) (b)

Quaternary 2019, 2, 27 13 of 18

with different year land-cover maps (1996, 2016, 2030, 2040, and 2050) in the cases of 50- and 100-year floods, respectively. The results show that flood inundation depth and flood extent areas are increasing in trend in the middle and lower areas of the basin due to land-cover changes, mainly due to rapid urbanization of three main cities such as San Fernando, Angeles and Cabanataun, and also expanding 328 built-up areas along the road networks. The flood inundation depth and flood extent areas may 329 increaseFigure in the14. futureComparison due to urbanizationof calculated flood activities inundation and conversions with reported of croplands data for intoflood built-up event of areas. 330 UrbanizationSeptember and 2011 growing (a) calculated of built-up flood areasinundation can cause extent a and large depth amount and ( ofb) perviousrecorded flood areas mark to change depth into 331 impervious(Data source areas for by recorded reducing flood infiltration mark: PRFFWC rates which [32]). results increase in runoff.

(a) (b) (c)

(d) (e)

> 5.0

332

333 FigureFigure 15.15.Calculated Calculated flood flood inundation inundation depth depth and and extent extent areas areas with with di ffdifferenterent year year land-cover land-cover maps maps in 334 thein the case case of 50-yearof 50-year flood flood (a) 1996,(a) 1996, (b) ( 2016,b) 2016, (c) 2030,(c) 2030, (d) ( 2040,d) 2040, and and (e) 2050.(e) 2050.

335 FiguresFigures 17 17 and and 18 18show show the changethe change in flow in dischargeflow discharge with di ffwitherent different years land years cover land at Mayapap, cover at 336 SanMayapap, Isidro, Arayat,San Isidro, and tributaryArayat, and rivers tributary (T1 and rivers T2), respectively (T1 and T2), (see respectively Figure2 for the(see locations Figure 2 of for these the 337 points).locations The of resultsthese points). show that The the results river show runo ffthatmay th increasee river runoff in the may future increase due to land-coverin the future changes due to 338 andland-cover urbanization changes activities, and urbanization particularly activities, in the middle particularly and downstream in the middle reach and of the downstream basin. The changesreach of in the river discharge due to land-cover change are significantly higher at tributaries compared to the main river channel. Quaternary 2019, 2, x 14 of 18 Quaternary 2019, 2, x 14 of 18 the basin. The changes in the river discharge due to land-cover change are significantly higher at 339 tributariesQuaternarythe basin.2019 comparedThe, 2, 27changes to thein the main river river discharge channel. due to land-cover change are significantly higher14 of 18at 340 tributaries compared to the main river channel. (a) (b) (c) (a) (b) (c)

(d) (e) (d) (e)

> 5.0 > 5.0

341 Figure 16. Calculated flood inundation depth and extent areas with different year land-cover maps Figure 16. Calculated flood inundation depth and extent areas with different year land-cover maps in 342 inFigure the case 16. ofCalculated 100-year flood (inundationa) 1996, (b) depth2016, (andc) 2030, extent (d) areas2040, andwith ( edifferent) 2050. year land-cover maps the case of 100-year flood (a) 1996, (b) 2016, (c) 2030, (d) 2040, and (e) 2050. 343 in the case of 100-year flood (a) 1996, (b) 2016, (c) 2030, (d) 2040, and (e) 2050.

(a) (b) (c) 4000 0 2500 0 2000 0 (a) (b) (c) 4000 0 20002500 100 2000 0 /s) /s) /s) 3000 10 3 1500 10 3 3

15002000 2010 /s) /s) /s) 3000 10 3 1500 10 3 2000 20 3 1000 20 10001500 3020 Discharge(m Discharge(m Discharge(m Rainfall (mm/hour) Rainfall Rainfall (mm/hour) Rainfall

2000 20 (mm/hour) Rainfall 1000 20 1000 30 500 30 1000500 4030 Discharge(m Discharge(m Discharge(m Rainfall (mm/hour) Rainfall Rainfall (mm/hour) Rainfall Rainfall (mm/hour) Rainfall 1000 30 500 30 0 40 5000 5040 0 40 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct Date Date Date 0 40 0 50 0 40 (e) 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct (d) 26-Sep 27-SepLU 2030 28-Sep 29-Sep 30-SepLU 2040 1-Oct 2-Oct 3-OctLU 2050 4-Oct 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 500 Date 0 1000 Date 0 Date (e) (d) LU 2030 LU 2040 LU 2050 400500 100 1000800 100 /s) 3 /s) 3 Rainfall (ground) LU-2016

300400 2010 600800 2010 /s) 3 /s) 3 LU-1996Rainfall (ground) LU-2030LU-2016 200 30 Discharge (m Discharge 300 20 400600 3020 Discharge(m Rainfall (mm/hour) Rainfall Rainfall (mm/hour) Rainfall LU-2040LU-1996 LU-2050LU-2030 100200 4030 200 40 Discharge (m Discharge 400 30 Discharge(m Rainfall (mm/hour) Rainfall Rainfall (mm/hour) Rainfall LU-2040 LU-2050 1000 5040 2000 5040 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct Date Date 0 50 0 50 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 344 Date Date Figure 17. Comparison of calculated flow flow discharge with different different year land-cover maps at selected stations and tributary streams in the case of 50-year flood (a) Mayapap station, (b) San Isidro station, (c) 345 stationsFigure 17. and Comparison tributary streams of calculated in the case flow of discharge 50-year flood with (differenta) Mayapap year station, land-cover (b) San maps Isidro at selectedstation, Arayat station, (d) T1 (tributary 1), and (e) T2 (tributary 2). 346 (stationsc) Arayat and station, tributary (d) T1 streams (tributary in the 1), case and of (e 50-year) T2 (tributary flood ( a2).) Mayapap station, (b) San Isidro station, 347 (c) Arayat station, (d) T1 (tributary 1), and (e) T2 (tributary 2). Figure 19a,b show the temporal changes in flood inundation volume and flood extent areas with different year land-cover data in the case of 100-year flood. Figure 19c shows the peak value of flood inundation volume, and the total flood extent areas during the flood event are presented in Figure 19d. The results show that flood inundation volume, peak value of flood volume and flood extent areas Quaternary 2019, 2, 27 15 of 18

may increase in the future due to land-cover changes, particularly due to rapid urbanization of three main cities and expansion of built-up areas along the roads by converting cropland and vegetation areas into built-up areas. The peak value of flood inundation volume and total flood extent areas in the case of using 1996 land-cover map were found to be 1142 million m3 and 1591 km2 respectively, while 1265 million m3 and 1669 km2 in the case of using 2050 land cover map. The flood impact in the basin may increase in the future due to land cover changes and urbanization activities. The cropland areas Quaternary 2019, 2, x 15 of 18 and fishpond areas are mostly located in low land areas of the basin, where flood impact might be Quaternarygreater in 2019 the, 2 future., x 15 of 18 (a) (b) (c) 4000 0 2500 0 2000 0 (a) (b) 2000 10 (c)

/s) 3000 10

4000 0 /s)

3 2500 0 /s) 3 20001500 010 3

1500 20 2000 10 2000 20 /s) 3000 10 /s) 3 1000 20 /s) 3 1500 10 3 1000 30 Discharge(m Discharge(m 1500 20 Discharge(m Rainfall (mm/hour) Rainfall Rainfall(mm/hour) 1000 30 (mm/hour) Rainfall 2000 20 500 30 500 40 1000 20 1000 30 Discharge(m Discharge(m Discharge(m Rainfall (mm/hour) Rainfall Rainfall(mm/hour) 0 40 (mm/hour) Rainfall 1000 30 0 50 0 40 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 500 30 50026-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 40 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct Date (d) (e) Date Date 0 40 LU 2030 LU 2040 LU 2050 LU 2030 LU 2040 LU 2050 0 50 0 40 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 1000 0 500 0 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct Date (d) (e) Date Date 400 LU 2030 LU 2040 LU 2050 10 800 LU 2030 LU 2040 LU 2050 10

/s) 1000 0 /s) 500 0 3 3 Rainfall (ground) LU-2016 300 20 600 20 400 10 800 10 /s) /s) 3 3 LU-1996 LU-2030 400 30 Rainfall (ground) LU-2016

200 30 Discharge(m Discharge(m 300 20 600 20 Rainfall (mm/hour) Rainfall Rainfall (mm/hour) Rainfall LU-2040 LU-2050 100 40 200 40 LU-1996 LU-2030 400 30

200 30 Discharge(m Discharge(m Rainfall (mm/hour) Rainfall 0 50 0 50 (mm/hour) Rainfall LU-2040 LU-2050 10026-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 40 20026-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 40 348 Date Date 0 50 0 50 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct 26-Sep 27-Sep 28-Sep 29-Sep 30-Sep 1-Oct 2-Oct 3-Oct 4-Oct Date Date 349 Figure 18. Comparison of calculated flow discharge with different year land cover maps at selected Figure 18. Comparison of calculated flow discharge with different year land cover maps at selected 350 Figurestations 18. and Comparison tributary ofstreams calculated in the flow case discharge of 100-year with flooddifferent (a) yearMayapap land coverstation, maps (b) atSan selected Isidro stations and tributary streams in the case of 100-year flood (a) Mayapap station, (b) San Isidro station, 351 stationsstation, (andc) Arayat tributary station, streams (d) T1 in (tributary the case 1), of and 100-year (e) T2 flood(tributary (a) Mayapap2). station, (b) San Isidro (c) Arayat station, (d) T1 (tributary 1), and (e) T2 (tributary 2). station, (c) Arayat station, (d) T1 (tributary 1), and (e) T2 (tributary 2). (a) LU-1996 LU-2016 LU-2030 (b) LU-1996 LU-2016 LU-2030 LU-2040 LU-2050 LU-2040 LU-2050

) 1400 900 (a)3 LU-1996 LU-2016 LU-2030 (b) LU-1996 LU-2016 LU-2030 800 LU-2040 LU-2050 LU-2040 LU-2050 )

1200 2 ) 1400 900 3 700 800 1000 ) 1200 2 600 700 1000800 500 600 600 400 800 500 300 600400 400 200 Flood inundation area (km area inundation Flood 300 200 400 100 200 Flood inundation area (km area inundation Flood 2000 0 Volume of flood inundation (in million m million (in inundation flood of Volume 100

0 0 Volume of flood inundation (in million m million (in inundation flood of Volume Date Date (c) (d) ) 100 Year Flood 100Date Year Flood

3 Date 1280 1680 ) 2 (c) (d) 1660 )

Millions 100 Year Flood 100 Year Flood

3 1240 1280 1680

) 1640 2 1200 1660 Millions 1240 1620 1640 1160 1600 1200 16201580 1120 1160 16001560 Inundation Area (km Area Inundation 1080 15801540

Peak Inundation Volume (m Volume Inundation Peak 1120 1996 2016 2030 2040 2050 1560 1996 2016 2030 2040 2050 Inundation Area (km Area Inundation 1080 Land Cover Year 1540 Land Cover Year

352 (m Volume Inundation Peak 1996 2016 2030 2040 2050 1996 2016 2030 2040 2050 Land Cover Year Land Cover Year 353 Figure 19.19. ComparisonComparison of of flood flood inundation inundation volume volume and an areasd areas in the in case the of case 100-year of 100-year flood (a) flood temporal (a) flood inundation volume, (b) temporal flood extent areas, (c) peak value of inundation volume, and (d) 354 Figuretemporal 19. flood Comparison inundation of floodvolume, inundation (b) temporal volume flood an extentd areas areas, in the (c )case peak of value 100-year of inundation flood (a) total flood inundation areas during the flood event. 355 temporalvolume, andflood (d )inundation total flood inundationvolume, (b )area temporals during flood the floodextent event. areas, (c) peak value of inundation volume, and (d) total flood inundation areas during the flood event. 356 Figures 19a and 19b show the temporal changes in flood inundation volume and flood extent 357 areasFigures with different 19a and year19b showland-cover the temporal data in changesthe case inof flood100-year inundation flood. Figure volume 19c and shows flood the extent peak 358 areasvalue withof flood different inundation year land-cover volume, dataand inthe the total case flood of 100-year extent areasflood. duringFigure 19cthe showsflood eventthe peak are 359 valuepresented of flood in Figure inundation 19d. The volume results, showand thethat totalflood flood inundation extent volume, areas during peak value the floodof flood event volume are 360 presentedand flood inextent Figure areas 19d. mayThe resultsincrease show in the that future flood inundationdue to land-cover volume, changes, peak value particularly of flood volume due to 361 andrapid flood urbanization extent areas of maythree increase main citiesin the and future expansion due to land-coverof built-up changes, areas along particularly the roads due byto 362 rapidconverting urbanization cropland of and three vegetation main citiesareas andinto built-upexpansion areas. of built-upThe peak areas value along of flood the inundationroads by 363 convertingvolume and cropland total flood and extent vegetation areas inareas the caseinto ofbuilt-up using 1996areas. land-cover The peak mapvalue were of flood found inundation to be 1142 3 2 3 2 364 volumemillion andm and total 1591 flood km extent respectively, areas in thewhile case 1265 of usingmillion 1996 m land-coverand 1669 km map in were the case found of usingto be 11422050 million m3 and 1591 km2 respectively, while 1265 million m3 and 1669 km2 in the case of using 2050 Quaternary 2019, 2, 27 16 of 18

5. Conclusions The land-use/land-cover changes and their impact on flood runoff, flood hazards and inundation were analyzed in the Pampanga River basin of the Philippines. The land-use/land-cover changes were analyzed between 1996 and 2016 using Landsat images and image processing tools in GIS, and the land-cover maps for future years were projected. The main changes observed for the time period of 1996–2016 were cropland and built-up areas. The cropland areas in the basin decreased by approximately 358.03 km2 from 1996 to 2016, and built-up areas increased by approximately 367.3 km2. The vegetation areas decreased by approximately 166.32 km2 and forest areas increased by approximately 203.27 km2. The bare land areas also decreased by approximately 154.76 km2. The cropland areas in the basin were mainly converted to built-up area, and vegetation areas were mainly converted to the forest area. The bare areas were mainly converted to cropland, built-up and vegetation. The result of land-cover changes between 2016-2050 shows that the crop land area and vegetation may decrease in 2050 by 11.25% and 9.3%. The built-up areas and forest areas may increase in 2050 by 93.2% and 10.6%. Flood inundation depth and flood extent areas are increasing in trend in the middle and lower areas of the basin due to land-cover changes, which is mainly due to rapid urbanization of three main cities San Fernando, Angeles and Cabanatuan as well as expansion of built-up areas along the road networks. The flood inundation depth and flood extent areas may increase in the future due to urbanization activities and conversions of cropland into built-up areas. The changes in river discharge due to land-cover change are significantly higher at tributaries compared to the main river channel. The flood inundation volume and flood extent areas may also increase in the future. The results obtained in this study can be useful for proper planning and management of land use in the basin, and also it will be useful to reduce the risk of flood in the future. The flood runoff and inundation analysis considering land-cover change provides useful information for better understanding of land-use/land-cover impacts. In this study, land use plan of local government was not considered for projection of land-cover map for future. If such a plan is available, it is recommended to consider it in further study. The variables such as elevation, slope factors, aspect factors, distance from the city, distance from the major road, and distance from the river, were considered as static variables for future projection of land-cover maps. The dynamic changes of such variables, particularly existing the plan of the future expansion of the road, are also recommended to consider in a further study.

Funding: This research received no external funding. Acknowledgments: The author would like to acknowledge to the all related counterpart organizations for providing data. Conflicts of Interest: The authors declare no conflict of interest.

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