An Alternative Method for Estimating the Peak Flow for a Regional Catchment Considering the Uncertainty via Continuous Simulation

Abstract

Estimating peak flow for a catchment is commonly undertaken using the design event method; however, this method does not allow for the understanding of uncertainty in the result. This research first presents a simplified method of fragments approach to rainfall disaggregation that ignores the need to consider seasonality, offering a greater diversity in storm patterns within the resulting sub-daily rainfall. By simulating 20 iterations of the disaggregated sub-daily rainfall within a calibrated continuous simulation hydrologic model, we were able to produce multiple long series of streamflow at the outlet of the catchment. With these data, we investigated the use of both the annual maximum and peaks over threshold approaches to flood frequency analysis and found that for a 1-in-100-year annual exceedance probability peak flow, the peaks over threshold method (333 m3/s ± 50 m3/s) was significantly less uncertain than the annual maximum method (427 m3/s ± 100 m3/s). For the 1-in-100-year annual exceedance probability, the median peak flow from the peaks over threshold method (333 m3/s) produced an outcome comparable to the design event method peak flow (328 m3/s), indicating that this research offers an alternative approach to estimating peak flow, with the additional benefit of understanding the uncertainty in the estimation. Finally, this paper highlighted the impact that length and period of streamflow has on peak flow estimation and noted that previous assumptions around the minimum length of gauged streamflow required for flood frequency analysis may not be appropriate in particular catchments.

Exploring Climate Sensitivity in Hydrological Model Calibration

“…Here, Y i s , Y i o , and Y o represent the simulated time series, observed time series, and mean of the observed time series, respectively. KGE (Kling-Gupta Efficiency) is a coefficient proposed by Gupta et al [54] and is calculated through Equation (11).…”

“…Hydrologists have conducted extensive research on the selection [1], calibration [2][3][4][5], and uncertainty assessment [6][7][8][9][10][11] of hydrological models to obtain optimal information from these models. In the calibration or uncertainty assessment of hydrological models, observational data play a pivotal role, with the critical elements being the length of the data and the climatic conditions (e.g., dry years, normal years, and wet years) to which the data belong [12].…”