Articles | Volume 29, issue 21
https://doi.org/10.5194/hess-29-6181-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-6181-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Nov 2025
Research article |
| 11 Nov 2025
| 11 Nov 2025
Integrating historical archives and geospatial data to revise flood estimation equations for Philippine rivers
Trevor B. Hoey
Department of Civil and Environmental Engineering, Brunel University London, London, UB8 3PH, United Kingdom
Pamela Louise M. Tolentino
CORRESPONDING AUTHOR
School of Geographical and Earth Sciences, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
National Institute of Geological Sciences, University of the Philippines, Diliman, the Philippines
Esmael Guardian
National Institute of Geological Sciences, University of the Philippines, Diliman, the Philippines
John Edward G. Perez
National Institute of Geological Sciences, University of the Philippines, Diliman, the Philippines
University of Vienna, Vienna, Austria
Richard D. Williams
School of Geographical and Earth Sciences, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
Earth Sciences New Zealand, Kirikiriroa / Hamilton, 3216, Aotearoa / New Zealand
Richard Boothroyd
Department of Geography and Planning, University of Liverpool, Liverpool, L69 7ZT, United Kingdom
Carlos Primo C. David
National Institute of Geological Sciences, University of the Philippines, Diliman, the Philippines
Enrico C. Paringit
Department of Geodetic Engineering, University of the Philippines, Diliman, the Philippines
Department of Science and Technology – Philippine Council for Industry, Energy and Emerging Technology Research and Development, Manila, the Philippines
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Cited articles
Asquith, W.: Package `lmomco' [code], https://cran.r-project.org/web/packages/lmomco/ (last access: 19 October 2025), https://doi.org/10.32614/CRAN.package.lmomco, 2020.
Asquith, W. H., Kiang, J. E., and Cohn, T. A.: Application of at-site peak-streamflow frequency analyses for very low annual exceedance probabilities, US Geological Survey Scientific Investigation Report 2017-5038, US Geological Survey, https://doi.org/10.3133/sir20175038, 2017.
Bagtasa, G.: Contribution of tropical cyclones to rainfall in the Philippines, J. Climate, 30, 3621–3633, https://doi.org/10.1175/JCLI-D-16-0150.1, 2017.
Bocchiola, D., De Michele, C., and Rosso, R.: Review of recent advances in index flood estimation, Hydrol. Earth Syst. Sci., 7, 283–296, https://doi.org/10.5194/hess-7-283-2003, 2003.
Boothroyd, R. J., Williams, R. D., Hoey, T. B., MacDonell, C., Tolentino, P. M. L., Quick, L., Guardian, E. L., Reyes, J. C. M. O., Sabillo, C. J., Perez, J. E. G., and David, C. P. C.: National-scale geodatabase of catchment characteristics in the Philippines for river management applications, PLoS ONE, 18, e0281933, https://doi.org/10.1371/journal.pone.0281933, 2023.
Cabrera, J. S. and Lee, H. S.: Flood risk assessment for Davao Oriental in the Philippines using geographic information system-based multi-criteria analysis and the maximum entropy model, J. Flood Risk Manage., e12607, https://doi.org/10.1111/jfr3.12607, 2020.
Coronas, J.: The climate and weather of the Philippines, 1903–1918, in: vol. 25, Bureau of Printing, https://name.umdl.umich.edu/AGH9000.0001.001 (last access: 19 October 2025), 1920.
Cunnane, C.: Unbiased plotting positions – a review, J. Hydrol., 37, 205–222, https://doi.org/10.1016/0022-1694(78)90017-3, 1978.
Dalrymple, T.: Flood frequency analyses, United States Geological Survey Water Supply Paper 1543A, United States Geological Survey, 11–51, 1960.
Davison, A. C. and Gholamrezaee, M. M.: Geostatistics of extremes, P. Roy. Soc. A, 468, 581–608, https://doi.org/10.1098/rspa.2011.0412, 2012.
Fischer, S. and Schumann, A. H.: Handling the stochastic uncertainty of flood statistics in regionalization approaches, Hydrolog. Sci. J., https://doi.org/10.1080/02626667.2022.2091410, 2022.
François, B., Schlef, K. E., Wi, S. and Brown, C. M.: Design considerations for riverine floods in a changing climate – A review, J. Hydrol.., 574, 557–573, https://doi.org/10.1016/j.jhydrol.2019.04.068, 2019.
Franco-Villoria, M., Scott, E. M., and Hoey, T. B.: Spatiotemporal modeling of hydrological return levels: a quantile regression approach, Envirometrics, 30, e2522, https://doi.org/10.1002/env.2522, 2019.
Fuentes, M., Henry, J., and Reich, B.: Nonparametric spatial models for extremes: Application to extreme temperature data, Extremes, 1–27, https://doi.org/10.1007/s10687-012-0154-1, 2012.
Grafil, L. and Castro, O.: Acquisition of IfSAR for the production of nationwide DEM and ORI for the Philippines under the unified mapping project, Infomapper, 21, 12–13 and 40–43, ISSN 0117-1674, https://www.namria.gov.ph/jdownloads/Info_Mapper/21_im_2014.pdf, 2014.
Griffiths, G. A., Singh, S. K., and McKerchar, A. I.: Flood frequency estimation in New Zealand using a region of influence approach and statistical depth functions, J. Hydrol., 589, 125187, https://doi.org/10.1016/j.jhydrol.2020.125187, 2020.
Hoey, T. B., Tolentino, P., Guardian, E., Perez, J. E. G., Williams, R., Boothroyd, R., David, C. P. C., and Paringit, E.: Flood estimation for ungauged catchments in the Philippines: Annual Maximum Flow (AMAX) and catchment properties data [data collection], https://doi.org/10.5525/gla.researchdata.1666, 2024.
Ibarra, D. E., David, C. P. C., and Tolentino, P. L. M.: Technical note: Evaluation and bias correction of an observation-based global runoff dataset using streamflow observations from small tropical catchments in the Philippines, Hydrol. Earth Syst. Sci., 25, 2805–2820, https://doi.org/10.5194/hess-25-2805-2021, 2021.
Irrigation Division: Surface Water Supply of the Philippine Islands 1908–1922, in: Volumes I–V, Bureau of Public Works, Manila, 1923–1924.
Haberlandt, U. and Radtke, I.: Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows, Hydrol. Earth Syst. Sci., 18, 353–365, https://doi.org/10.5194/hess-18-353-2014, 2014.
Hosking, J. R. M.: L-moments: analysis and estimation of distributions using linear combinations of order statistics, J. Roy. Stat. Soc. Ser. B, 52, 105–124, https://doi.org/10.1111/j.2517-6161.1990.tb01775.x, 1990.
Hosking, J. R. M. and Wallis, J. R.: Regional frequency analysis: an approach based on L-moments, Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9780511529443, 1997.
Kalai, C., Mondal, A., Griffin, A., and Stewart, E.: Comparison of Nonstationary Regional Flood Frequency Analysis Techniques Based on the Index-Flood Approach, J. Hydrol. Eng., https://doi.org/10.1061/(ASCE)HE.1943-5584.0001939, 2020.
Kjeldsen, T. R.: How reliable are design flood estimates in the UK?, J. Flood Risk Manage., 8, 237–246, https://doi.org/10.1111/jfr3.12090, 2013.
Kjeldsen, T. R. and Jones, D. A.: Prediction uncertainty in a median-based index flood method using L moments, Water Resour. Res., 42, W07414, https://doi.org/10.1029/2005WR004069, 2006.
Kjeldsen, T. R., and Jones, D. A.: Sampling variance of flood quantiles from the generalised logistic distribution estimated using the method of L-moments, Hydrol. Earth Syst. Sci., 8, 183–190, https://doi.org/10.5194/hess-8-183-2004, 2004.
Kjeldsen, T. R., Jones, D. A., and Bayliss, A. C.: Improving the FEH statistical procedures for flood frequency estimation, Environment Agency Science Report SC050050, Environment Agency, https://www.gov.uk/flood-and-coastal-erosion-risk-management-research-reports/improving-the-flood-estimation-handbook-feh-statistical-index-flood-method-and-software (last access: 19 October 2025), 2008.
Kundzewicz, Z. W., Krysanovab, V., Dankersc, R., Hirabayashid, Y., Kanaee, S., Hattermannb, F. F., Huang, S., Milly, P. C. D., Stoffel, M., Driessenk, P. P. J., Matczaka, P., Quevauvillermand, P., and Schellnhuber, H.-J.: Differences in flood hazard projections in Europe–their causes and consequences for decision making, Hydrolog. Sci. J., 62, 1–14, https://doi.org/10.1080/02626667.2016.1241398, 2017.
Liongson, L. Q.: Regional flood frequency analysis for Philippine rivers, in: 2nd APHW Conference, Asia Pacific Association of Hydrology and Water Resources, Singapore, 2004.
Loebis, J.: Frequency analysis models for long hydrological time series in Southeast Asia and the Pacific region, Proceedings of the Fourth International FRIEND Conference, IAHS Publ., 274, 213–219, 2002.
Lyubchich, V., Newlands, N. K., Ghahari, A., Mahdi, T., and Gel, Y. R.: Insurance risk assessment in the face of climate change: Integrating data science and statistics, WIREs Comput Stat., 11, e1462, https://doi.org/10.1002/wics.1462, 2019.
Macdonald, N., Kjeldsen, T. R., Prosdocimi, I., and Sangster, H.: Reassessing flood frequency for the Sussex Ouse, Lewes: the inclusion of historical flood information since AD 1650, Nat. Hazards Earth Syst. Sci., 14, 2817–2828, https://doi.org/10.5194/nhess-14-2817-2014, 2014.
Mamun, A. A., Hashim, A., and Amir, Z.: Regional statistical models for the estimation of flood peak values at ungauged catchments: Peninsular Malaysia, J. Hydraul. Eng., 17, 547–553, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000464, 2011.
Meigh, J.: Regional flood estimation methods for developing countries, NERC Open Research Archive, https://nora.nerc.ac.uk/id/eprint/8382 (last access: 19 October 2025), 1995.
Meigh, J. R., Farquharson, F. A. K., and Sutcliffe, J. V.: A worldwide comparison of regional flood estimation methods and climate, Hydrolog. Sci. J., 42, 225–244, 1997.
Merz, R. and Blöschl, G.: Flood frequency hydrology: 1. Temporal, spatial, and causal expansion of information, Water Resour. Res., 44, W08432, https://doi.org/10.1029/2007WR006744, 2008.
Muhammad, M. and Lu, A.: Estimating the UK index flood: an improved spatial flooding analysis, Environ. Model. Assess., 25, 731–748, https://doi.org/10.1007/s10666-020-09713-x, 2020.
Nippon Koei: The feasibility study of the flood control project for the Lower Cagayan River in the Republic of the Philippines, 4 Volumes, Japan International Cooperation Agency and Department of Public Works and Highways, the Republic of the Philippines, https://openjicareport.jica.go.jp/pdf/11871175.pdf (last access: 19 October 2025), 2002.
Papalexiou, S. M. and Koutsoyiannis, D.: Battle of extreme value distributions: A global survey on extreme daily rainfall, Water Resour. Res., 49, https://doi.org/10.1029/2012WR012557, 2013.
Parasiewicz, P., King, E. L., Webb, J. A., Piniewski, M., Comoglio, C., Wolter, C., Buijse, A. D., Bjerklie, D., Vezza, P., Melcher, A., and Suska, S.: The role of floods and droughts on riverine ecosystems under a changing climate, Fish Manage. Ecol., 26, 461–473, https://doi.org/10.1111/fme.12388, 2019.
Parkes, B. and Demeritt, D.: Defining the hundred year flood: A Bayesian approach for using historic data to reduce uncertainty in flood frequency estimates, J. Hydrol., 540, 1189–1208, https://doi.org/10.1016/j.jhydrol.2016.07.025, 2016.
Quick, L., Williams, R. D., Boothroyd, R. J., Hoey, T. B., Tolentino, P. L. M., MacDonell, C., Guardian, E., Reyes, J., Sabillo, C., Perez, J., and David, C. P. C.: Confined and mined: anthropogenic river modification as a driver of flood risk change, npj Nat. Hazards, 2, https://doi.org/10.1038/s44304-024-00051-6, 2025.
R Core Team: R: a language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.r-project.org/ (last access: 19 October 2025), 2021.
Reinders, J. B. and Muñoz, S. E.: Improvements to flood frequency analysis on alluvial rivers using paleoflood data, Water Resour. Res., 57, e2020WR028631, https://doi.org/10.1029/2020WR028631, 2021.
Rigon, R., Rodriguez-Iturbe, I., Maritan, A., Giacometti, A., Tarboton, D. G., and Rinaldo, A.: On Hack's Law, Water Resour. Res., 32, 3367–3374, https://doi.org/10.1029/96WR02397, 1996.
Slater, L. J., Singer, M. B., and Kirchner, J. W.: Hydrologic versus geomorphic drivers of trends in flood hazard, Geophys. Res. Lett., 42, 1–7, https://doi.org/10.1002/2014GL062482, 2015.
Stedinger, J. R. and Lu, L.-H.: Appraisal of regional and index flood quantile estimators, Stoch. Hydrol. Hydraul., 9, 49–75, 1995.
Stedinger, J. R., Vogel, R. M., and Foufoula-Georgiou, E.: Frequency analysis of extreme events, in: Chapter 18, Handbook of Hydrology, edited by: Maidment, D., McGraw-Hill, New York, ISBN 0071711775, 1993.
Tolentino, P. L. M., Poortinga, A., Kanamaru, H., Keesstra, S., Maroulis, J., David, C. P. C., and Ritsema, C. J.: Projected impact of climate change on hydrological regimes in the Philippines, PLOS One, 11, e0163941, https://doi.org/10.1371/journal.pone.0163941, 2016.
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and Kitoh, A.: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges, B. Am. Meteorol. Soc., 93, 1401–1415, https://doi.org/10.1175/BAMS-D-11-00122.1, 2012.
Zhao, G., Bates, P., Neal, J., and Pang, B.: Design flood estimation for global river networks based on machine learning models, Hydrol. Earth Syst. Sci., 25, 5981–5999, https://doi.org/10.5194/hess-25-5981-2021, 2021.
Ziegler, A. D., Lim, H. S., Wasson, R. J., and Williamson, F. C.: Flood mortality in SE Asia: Can palaeo-historical information help save lives?, Hydrol. Process., e13989, https://doi.org/10.1002/hyp.13989, 2020.
Short summary
Estimating the sizes of flood events is critical for flood risk management and other activities. We used data from several sources in a statistical analysis of flood sizes for rivers in the Philippines. Flood size is mainly controlled by the size of the river catchment, along with the volume of rainfall. Other factors, such as land use, appear to play only minor roles in flood size. The results can be used to estimate flood size for any river in the country, alongside other local information.
Estimating the sizes of flood events is critical for flood risk management and other activities....
