Flood estimation for ungauged catchments in the Philippines

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.