High-resolution urban change modeling and flood exposure estimation at a national scale using open geospatial data: A case study of the Philippines

Abstract Many developing countries in Asia are experiencing rapid urban expansion in climate hazard prone areas. To support climate resilient urban planning efforts, here we present an approach for simulating future urban land-use changes and evaluating potential flood exposure at a high spatial resolution (30 m) and national scale. As a case study, we applied this model to the Philippines – a country frequently affected by extreme rainfall events. Urban land-use changes were simulated to the year 2050 using a trend-based logistic regression cellular automata model, considering three different scenarios of urban expansion (assuming low/medium/high population growth). Flood exposure assessment was then conducted by overlaying the land-use simulation results onto a global floodplain map. We found that approximately 6040–13,850 ha of urban land conversion is likely to be located in flood prone regions between 2019 and 2050 (depending on the scenario), affecting approximately 2.5–5.8 million additional urban residents. In locations with high rates of future urban development in flood prone areas (Mindanao Island, in particular), climate resilient land-use plans should be developed/enforced, and flood mitigation infrastructure protected (in the case of “nature-based” infrastructure) or constructed. The data selected for our land-use change modeling and flood exposure assessment were all openly and (near-)globally available, with the intention that our methodology can potentially be applied in other countries where rapid urban expansion is occurring. The 2050 urban land-use maps generated in this study are available for download at https://www.iges.or.jp/en/pub/ph-urban2050/en to allow for their use in future works.

[1]  J. Eom,et al.  The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview , 2017 .

[2]  Jakob van Zyl,et al.  The Shuttle Radar Topography Mission (SRTM): a breakthrough in remote sensing of topography , 2001 .

[3]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[4]  Scott Kulp,et al.  CoastalDEM: A global coastal digital elevation model improved from SRTM using a neural network , 2018 .

[5]  G. Di Baldassarre,et al.  GFPLAIN250m, a global high-resolution dataset of Earth’s floodplains , 2019, Scientific Data.

[6]  Paula Beatrice M. Macandog,et al.  Participatory land-use approach for integrating climate change adaptation and mitigation into basin-scale local planning , 2017 .

[7]  Yoshio Yamaguchi,et al.  Land Cover Classification of Palsar Images by Knowledge Based Decision Tree Classifier and Supervised Classifiers Based on SAR Observables , 2011 .

[8]  J. Eom,et al.  Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Shared Socioeconomic Pathways , 2019, Earth's Future.

[9]  Y. Zeng,et al.  Hidden Loss of Wetlands in China , 2019, Current Biology.

[10]  Brian C. O’Neill,et al.  Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways , 2020, Nature Communications.

[11]  Xia Li,et al.  Data mining of cellular automata's transition rules , 2004, Int. J. Geogr. Inf. Sci..

[12]  B. Pijanowski,et al.  Using neural networks and GIS to forecast land use changes: a Land Transformation Model , 2002 .

[13]  C. L. Anderson,et al.  Definitions of “rural” and “urban” and understandings of economic transformation: Evidence from Tanzania , 2020, Journal of rural studies.

[14]  S. Cessie,et al.  Ridge Estimators in Logistic Regression , 1992 .

[15]  Kris A. Johnson,et al.  Estimates of present and future flood risk in the conterminous United States , 2017 .

[16]  Roger White,et al.  Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land-Use Patterns , 1993 .

[17]  Julea Andreea Maria,et al.  Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014 , 2016 .

[18]  B. O’Neill,et al.  Global urbanization projections for the Shared Socioeconomic Pathways , 2017 .

[19]  Andrés Manuel García,et al.  Cellular automata models for the simulation of real-world urban processes: A review and analysis , 2010 .

[20]  Kotaro Iizuka,et al.  Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines , 2017 .

[21]  Iryna Dronova,et al.  Modeling stormwater management at the city district level in response to changes in land use and low impact development , 2017, Environ. Model. Softw..

[22]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[23]  Yatao Zhang,et al.  Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data , 2017, Int. J. Geogr. Inf. Sci..

[24]  Xiaohua Tong,et al.  Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules , 2018 .

[25]  Brian Alan Johnson,et al.  Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment , 2019, Remote. Sens..

[26]  Fulong Wu,et al.  Calibration of stochastic cellular automata: the application to rural-urban land conversions , 2002, Int. J. Geogr. Inf. Sci..

[27]  Xia Li,et al.  Global projections of future urban land expansion under shared socioeconomic pathways , 2020, Nature Communications.

[28]  Chitresh Saraswat,et al.  Assessment of stormwater runoff management practices and governance under climate change and urbanization: An analysis of Bangkok, Hanoi and Tokyo , 2016 .

[29]  E. Luna,et al.  Hidden disasters: Recurrent flooding impacts on educational continuity in the Philippines , 2017 .

[30]  Frieke Van Coillie,et al.  Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX) , 2017 .

[31]  Yuji Murayama,et al.  Examining the potential impact of land use/cover changes on the ecosystem services of Baguio city, the Philippines: A scenario-based analysis , 2012 .

[32]  Makoto Ooba,et al.  Heat health risk assessment in Philippine cities using remotely sensed data and social-ecological indicators , 2020, Nature Communications.

[33]  J. Schilling,et al.  The Nexus of Climate Change, Land Use, and Conflicts , 2019, Current Climate Change Reports.

[34]  Kris A. Johnson,et al.  Validation of a 30 m resolution flood hazard model of the conterminous United States , 2017 .

[35]  M. Huijbregts,et al.  Global patterns of current and future road infrastructure , 2018 .

[36]  Xiaoping Liu,et al.  Integrating ensemble-urban cellular automata model with an uncertainty map to improve the performance of a single model , 2015, Int. J. Geogr. Inf. Sci..

[37]  K. Fukushi,et al.  Assessment of future flood inundations under climate and land use change scenarios in the Ciliwung River Basin, Jakarta , 2018 .

[38]  Man-Hyung Lee,et al.  The Development and Application of the Urban Flood Risk Assessment Model for Reflecting upon Urban Planning Elements , 2019, Water.

[39]  M. Miyamoto,et al.  Flood damage assessment in the Pampanga river basin of the Philippines , 2016 .

[40]  Peng Gong,et al.  Urban growth models: progress and perspective , 2016 .

[41]  Xi-Ying Zhang,et al.  A predator-prey interaction between a marine Pseudoalteromonas sp. and Gram-positive bacteria , 2020, Nature Communications.

[42]  Yuyu Zhou,et al.  Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 , 2020 .

[43]  Le Yu,et al.  A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules , 2014, Int. J. Geogr. Inf. Sci..

[44]  Yuyu Zhou,et al.  An improved urban cellular automata model by using the trend-adjusted neighborhood , 2020, Ecological Processes.

[45]  Achim Roth,et al.  Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data , 2018 .

[46]  Eric Koomen,et al.  Comparing the input, output, and validation maps for several models of land change , 2008 .

[47]  A. Onishi,et al.  A land cover map accuracy metric for hydrological studies , 2017 .