Preprints
https://doi.org/10.5194/hess-2024-188
https://doi.org/10.5194/hess-2024-188
08 Jul 2024
 | 08 Jul 2024
Status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

Flood estimation for ungauged catchments in the Philippines

Trevor B. Hoey, Pamela Louise M. Tolentino, Esmael L. Guardian, John Edward G. Perez, Richard D. Williams, Richard J. Boothroyd, Carlos Primo C. David, and Enrico C. Paringit

Abstract. Flood magnitude and frequency estimation are essential for the design of structural and nature-based flood risk management interventions and water resources planning. However, the global geography of hydrological observations is uneven; in many regions, such as the Philippines, data are spatially and/or temporary sparse, limiting the choice of statistical methods for flood estimation. We evaluate the potential of pooling short historical data series for ungauged catchment flood estimation. Daily mean river discharge data were collected from 842 sites, with data spanning from 1908 to 2018. Of these, 513 candidate sites met criteria to estimate a reliable annual maximum flood. Using the index flood approach, a range of controls were assessed at national and regional scales using land cover and rainfall datasets, and GIS-derived catchment characteristics. Multivariate analysis for predictive equations for 2 to 100 year recurrence interval floods based on catchment area only have R2 ≤ 0.59. Additionally, adding a rainfall variable, the median annual maximum 1-day rainfall, increases R2 to between 0.56 for Q100 and 0.66 for Q2. Very few other variables were significant when added to multiple regression equations. Although the Philippines exhibits regional climate variability, there is limited spatial structure in predictive equation residuals and region-specific predictive equations do not perform significantly better than national equations. Relatively low R2 values are typical of studies from tropical regions. The predictive equations are suitable for use as design equations for the Philippines but uncertainties must be assessed. Our approach demonstrates how combining individually short historical records, after careful screening and exclusion of erroneous data, generates large data sets that can produce consistent results. Extension of continuous flood records is required to reduce uncertainties but national-scale consistency suggests that extrapolation from a small number of carefully selected catchments could provide nationally reliable predictive equations with reduced uncertainties.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Trevor B. Hoey, Pamela Louise M. Tolentino, Esmael L. Guardian, John Edward G. Perez, Richard D. Williams, Richard J. Boothroyd, Carlos Primo C. David, and Enrico C. Paringit

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2024-188', Anonymous Referee #1, 07 Aug 2024
    • AC2: 'Reply on RC1', Pamela Louise M. Tolentino, 01 Oct 2024
  • RC2: 'Comment on hess-2024-188', Anonymous Referee #2, 27 Aug 2024
    • AC1: 'Reply on RC2', Pamela Louise M. Tolentino, 01 Oct 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2024-188', Anonymous Referee #1, 07 Aug 2024
    • AC2: 'Reply on RC1', Pamela Louise M. Tolentino, 01 Oct 2024
  • RC2: 'Comment on hess-2024-188', Anonymous Referee #2, 27 Aug 2024
    • AC1: 'Reply on RC2', Pamela Louise M. Tolentino, 01 Oct 2024
Trevor B. Hoey, Pamela Louise M. Tolentino, Esmael L. Guardian, John Edward G. Perez, Richard D. Williams, Richard J. Boothroyd, Carlos Primo C. David, and Enrico C. Paringit

Data sets

Flood estimation for ungauged catchments in the Philippines: Annual Maximum Flow (AMAX) and catchment properties data T. B. Hoey et al. https://doi.org/10.5525/gla.researchdata.1666

Trevor B. Hoey, Pamela Louise M. Tolentino, Esmael L. Guardian, John Edward G. Perez, Richard D. Williams, Richard J. Boothroyd, Carlos Primo C. David, and Enrico C. Paringit

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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 size 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.
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