Can we estimate flood frequency with point-process spatial-temporal rainfall models?
Journal
Journal of Hydrology
Journal Volume
600
Date Issued
2021
Author(s)
Abstract
Stochastic rainfall models are commonly used in practice for long-term flood risk management. One of the most widely used model types is based on point processes. Despite the widespread use of such models, whether their known simplifications in describing the space–time structure of rainfall will affect the accuracy of flood estimation has not been quantified. In this study, we quantify the biases introduced by the rainfall model limitations to flood estimates in two medium-sized river catchments (717 km2 and 844 km2) in the South East of the UK. To achieve this, we used nine years of hourly radar rainfall data, a dense network of hourly rain gauges, a spatial–temporal rainfall stochastic model based on point processes, and a fully distributed hydrological model. We modelled the corresponding catchment water dynamics using observed and simulated hourly rainfall and then assessed whether the errors introduced by the stochastic model will propagate in the river flow dynamics. Our results show that the stochastic rainfall model properly captures the point-scale rainfall statistics, including point extremes and the cross-site spatial correlations. However, the model results in a bias on extremes of areal statistics, including an overestimation of the areal reduction factor, extreme areal mean precipitation, and the areal fraction of rain (wet area ratio). Using this as input for continuous hydrological simulations, we find that the flow duration curves are well preserved, particularly in the high flow seasons (relative bias is less than 7%). The model also reproduces well the flood frequency curves at a daily scale with an averaged relative bias of 0.36–16.9% at 10-year return levels, confirming its ability to infer the long-term flood risk for medium-sized catchments. However, the summer-season hourly peak discharge is highly overestimated with a relative bias of over 163.5% at the same return level. The overestimation in summer hourly peak discharge is explained by the dominating convective systems in summer and the misrepresented spatial structure in the model. ? 2021
Subjects
Catchment runoff
Flood frequency analysis
Point process
Space-time variability
Stochastic rainfall
Catchments
Flood control
Floods
Rain
Rain gages
Risk management
Runoff
Stochastic models
Catchment runoffs
Peak discharge
Rainfall models
Return level
Spatial temporals
Stochastic rainfalls
Stochastic-modeling
Stochastic systems
catchment
flood
flood forecasting
flood frequency
peak discharge
rainfall
seasonal variation
SDGs
Type
journal article