College of Forestry and Environmental Science, Central Mindanao University, Musuan, Maramag, Philippines


BACKGROUND AND OBJECTIVES: The study involved developing a two-dimensional flood model to analyze the risk exposure of land use/land cover based on the generated flood hazard maps for the six return period scenarios in the Solana watershed.
METHODS: The approach consisted of applying hydrologic and hydraulic numerical flood models and the suite of advanced geographic information systems and remote sensing technologies. The process involved utilizing a high-resolution digital elevation model and a set of high-precision instruments such as the real-time kinematic-global position system receiver, digital flow meter, deep gauge, and automatic weather station in collecting the respective data on bathymetry, river discharge, river depth, and rainfall intensity during a particular climatic event, needed for the model development, calibration and validation.
FINDINGS: The developed two-dimensional flood model could simulate flood hazard with an 86% accuracy level based on the coefficient of determination statistics. The flood risk exposure analysis revealed that coconut is the most affected, with 31.3% and 37.1% being at risk across the 2-year and 100-year return period scenarios, respectively. Results also showed that rice and pineapple are at risk of flooding damage with the increasing rate of exposure by a magnitude of 42.9 and 9.3 across the 2-year and 100-year flood scenarios, respectively.
CONCLUSION: The study highlighted the integration of the findings and recommendations in the localized comprehensive land use plan and implementation to realize the challenge of building a climate change proof and a flood-resilient human settlement in the urbanizing watershed of Solana.

©2021 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

Graphical Abstract

Two-dimensional flood model for risk exposure analysis of land use/land cover in a watershed


  • The manuscript considers the importance of high precision instruments like the real-time kinematic-global positioning system receiver for accurate collection of geospatial points needed in the whole process of flood modeling;
  • The study has calibrated and validated the 2D flood model and generated flood hazard maps for the six return period scenarios with an 89% level of accuracy;
  • The study generated information on the flood risk exposure of land use/land cover potential in making a comprehensive land-use plan relative to building a flood-resilient community.


Main Subjects

Addabbo, P.; Focareta, M.; Marcuccio, S.; Votto, C.; Ullo, S. L., (2016). Contribution of sentinel‐2 data for applications in vegetation monitoring. Acta Imeko, 5(2): 44–54 (11 pages).

Apollonio, C.; Bruno, F.M.; Iemmolo, G.; Molfetta, M.G.; Pellicani, R., (2020). Flood risk evaluation in ungauged coastal areas: The case study of Ippocampo (Southern Italy). Water, 2020(12): (25 pages).

Bahari, N.I.S; Ahmad, A.; Aboobaider, B.M., (2014). Application of support vector machine for classification of multispectral data. IOP Conf. Ser.: Earth Environ. Sci., 20(2014): 1-8 (8 pages).

Bhattacharjee, K.; Behera, B., (2018). Does forest cover help prevent flood damage? Empirical evidence from India. Global Environ. Change, 53: 78-89 (12 pages).

Bogoliubova, A.; Tymkow, P., (2014). Accuracy assessment of automatic image processing for land cover classification of St. Petersburg protected area. Acta Sci. Pol. Geod. Descr. Terr., 13 (1-2): 5-22 (18 pages).

Castro, C.; Maidment, C.V., (2020). GIS preprocessing for rapid initialization of HEC-HMS hydrological basin models using web-based data services. Environ. Modell. Software, 130: 104732 (12 pages).

Delgado, M.E.M.; Canters, F., (2012). Modeling the impacts of agroforestry systems on the spatial patterns of soil erosion risk in three catchments of Claveria, the Philippines. Agrofor. Syst., 85: 411-423 (13 pages).

De Vera-Ruiz, E., (2020). Typhoon ‘Rolly’ may be as strong as 185 kph; may trigger signal No. 3 or 4 – PAGASA. Manila Bulletin.

Diez-Herrero, A.; Garrote, J., (2020). Flood risk analysis and assessment, applications and uncertainties: a bibliometric review. Water, 12(2050): 1-24 (24 pages).

Garcia-Alvarez, D.; Olmedo, M.T.C.; Paegelow, M., (2019). Sensitivity of a common Land Use Cover Change (LUCC) model to the Minimum Mapping Unit (MMU) and Minimum Mapping Width (MMW) of input maps. Compt. Environ. Urban Syst., 78: 101389 (14 pages).

Gumindoga, W.; Rwasoka, D.T.; Nhapi, I.; Dube, T., (2017). Ungauged runoff simulation in Upper Manyame Catchment, Zimbabwe: Application of the HEC-HMS model. Phys. Chem. Earth., 100: 371-382 (12 pages).

Israel, D.C.; Briones, R.M., (2013). Impacts of natural disasters on agriculture, food security, and natural and environment in the Philippines. ERIA Discussion Paper Series, (54 pages).

Janiola, M.D.C.; Puno, G.R., (2018). Land use and land cover (LULC) change detection using multitemporal landsat imagery: A case study in Allah Valley Landscape in Southern, Philippines. J. Biodivers. Environ. Sci., 12(2): 98-108 (11 pages).  

Ji, Y.; Sun, L.; Li, Y.; Li, J.; Liu, S.; Xie, X.; Xu, Y., (2019). Non-destructive classification of defective potatoes based on hyperspectral imaging and support vector machine. Infrared Phys. Technol., 99: 71-79 (9 pages).

Khalfallah, B.C.; Saidi, S., (2018). Spatiotemporal floodplain mapping and prediction using HEC-RAS - GIS tools: Case of the Mejerda river, Tunisia. J. Afr. Earth. Sci., 142: 44-51 (8 pages).

Laks, I.; Sojka, M.; Walczak, Z.; Wrozynski, R., (2017). Possibilities of Using Low Quality Digital Elevation Models of Floodplains in Hydraulic Numerical Models. Water. 9(4): 1-19 (19 pages).

Melaku, N.D.; Wang, J.; Meshesha, T.W., (2020). Improving hydrologic model to predict the effect of snowpack and soil temperature on carbon dioxide emission in the cold region peatlands. J. Hydrol., 587: 124939 (11 pages).

Motlagh, Z.K.; Sayadi, M.K., (2015). Siting MSW landfills using MCE methodology in GIS environment. Case study: Birjand plain, Iran. Waste Manage., 46: 322-337 (16 pages).

Mousavi, S.D.; Roostaei, S.; Rostamzadeh, H., (2019). Estimation of flood land use/land cover mapping by regional modeling of flood hazard at sub-basin level case study: Marand basin. Geomatics, Natural Hazards and Risk. Geomatics Nat. Hazards Risk. 10(1): 1155-1175 (21 pages).

Musa, Z.N.; Popescu, I.; Mynett, A., (2015). A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation. Hydrol. Earth Syst. Sci., (19)9: 3755–3769 (15 pages).

Nguyen, H.T.T.; Doan, T.M.; Tomppo, E.; McRoberts, R., (2020). Land use/land cover mapping using multitemporal Sentinel-2 imagery and four classification methods - A case study from Dak Nong, Vietnam. Remote Sens., 12(9): 1367 (27 pages).

Ogania, J.L.; Puno, G.R.; Alivio, M.B.T.; Taylaran, J.M.G., (2019). Effect of digital elevation model’s resolution in producing flood hazard maps. Global J. Environ. Sci. Manage., 5(1): 95-106 (12 pages).

Pant, R.; Thacker, S.; Hall, J.W.; Alderson, D.; Barr, S., (2016). Critical infrastructure impact assessment due to flood exposure. J. Flood Risk Manage., 11(1) (20 pages).

Phiri, D.; Morgenroth, J., (2017). Developments in Landsat land cover classification methods: A review. Remote Sens. 9(9): 1-25 (25 pages).

Puno, R.C.C.; Puno, G.R.; Talisay, B.A.M., (2019). Hydrologic responses of watershed assessment to land cover and climate change using soil and water assessment tool model. Global J. Environ. Sci. Manage., 5(1): 71-82 (12 pages).

Puno, G.R.; Amper, R.A.L.; Opiso, E.M.; Cipriano, J.A.B., (2019). Mapping and analysis of flood scenarios using numerical models and GIS techniques. Spat. Inf. Res., 28: 215-226 (12 pages).

Puno, G.R.; Amper, R.A.L.; Talisay, B.A.M., (2018). Flood simulation using geospatial and hydrologic models in Manupali Watershed, Bukidnon, Philippines. J. Biodivers. Environ. Sci., 12(3): 294-303 (10 pages). 

Rwanga, S.; Ndambuki, J.M., (2017). Accuracy assessment of land use/land cover classification using remote sensing and GIS. Int. J. Geosci. 8. 611-622 (12 pages).

Santillan, J.R.; Marqueso, J.T.; Makinano-Santillan, M.; Serviano, J.L., (2016). Beyond flood hazard maps: detailed flood characterization with remote sensing, GIS and 2d modeling. In: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W1, 225-235 (11 pages).

Sarchani, S.; Tsanis, I., (2019). Analysis of a flash flood in a small basin in Crete. Water, 11(11): 2253 (22 pages).

Sarmiento, C.J.; Ang, M.R.C.; Paringit E.C., (2015). LiDAR data acquisition and processing for Cagayan de Oro-Iponan river floodplains, disaster risk and exposure for mitigation (DREAM), DOST grants-in-aid program report, (57 pages).

Sharif, H.O.; Al-Juaidi, F.H.; Al-Othman, A.; Al-Dousary, I.; Fadda, E.; Jamla-Uddeen, S.; Elhassan, A., (2016). Flood hazards in an urbanizing watershed in Riyadh, Saudi Arabia. Geomatics Nat. Hazards Risk, 7(2): 702-720 (19 pages).

Shi, D.; Yang, X., (2015). Support Vector Machines for Land Cover Mapping from Remote Sensor Imagery. Monitoring and Modeling of Global Changes: A Geomatics Perspective. Springer Remote Sens./Photogrammetry. 265-279 (15 pages).

Shrestha, S.; Lohpaisankrit, W., (2017). Flood hazard assessment under climate change scenarios in the Yang River Basin, Thailand. Int. J. Sustainable Built Environ., 6(2): 285-298 (14 pages).

Siev, S.; Paringit, E.C.; Yoshimura, C.; Hul, S., (2016). Seasonal changes in the inundation area and water volume of the Tonle Sap River and its floodplain. Hydrol., 3(33): 1-12 (12 pages).

Sothe, C.; de Almeida, C.M.; Liesenberg, V.; Schimalski, M.B., (2017). Evaluating Sentinel-2 and Landsat-8 data to map successional forest stages in a subtropical forest in Southern Brazil. Remote Sens., 9(838): 1-22 (22 pages).

Svetlana, D.; Radovan, D.; Jan, D., (2015). The economic impact of floods and their importance in different regions of the world with emphasis on Europe. Procedia, 34: 649-655 (7 pages).

Teves, C., (2020). 3 Luzon dams brace for ‘Ulysses’. Philippine News Agency.

The World Bank, (2016). Methods in flood hazards and risk assessment. Technical Notes, (28 pages).

Warren, M.A.; Simis, S.G.H.; Martinez-Vincente, V.; Poser, K.; Bresciani, M.; Alikas, K.; Spyrakos, E.; Giardino, C.; Ansper, A., (2019). Assessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland waters. Remote Sens. Environ., 225: 267-289 (23 pages).

Xu, J.; Zhang, Y.; Miao, D., (2020). Three-way confusion matrix for classification: A measure driven view. Inf. Sci., 507: 772-794 (23 pages).

Zang, T.; Su, J.; Liu, C.; Chen, W-H., (2017). Band selection in Sentinel-2 satellite for agriculture applications. Proceedings of the 23rd International Conference on Automation and Computing, University of Huddersfield, Huddersfield, UK.

Zhang, K.; Gann, D.; Ross, M.; Robertson, Q.; Sarmiento, J.; Santana, J.R.; Fritz, C., (2019). Accuracy assessment of ASTER, SRTM, ALOS, and TDX DEMs for Hispaniola and implications for mapping vulnerability to coastal flooding. Remote Sens. Environ., 225: 290-306 (17 pages).

Letters to Editor

GJESM Journal welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in GJESM should be sent to the editorial office of GJESM within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.
[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.
[3] Letters can be no more than 300 words in length.
[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.
[5] Anonymous letters will not be considered.
[6] Letter writers must include their city and state of residence or work.
[7] Letters will be edited for clarity and length.