P23

A precipitation event extraction method for processing 5-km d4PDF and developing high-resolution
national discharge quantile maps
〇Jiachao CHEN, Takahiro SAYAMA

Introduction

precipitation data. As illustrated in Figure 1, Step 1

Climate change has become an acknowledged reality,

entails extracting precipitation data for singular grid.

ushering in shifts in flood risk. High-resolution national

Potential extreme precipitation is detected using aflood quantile maps, which enhance public awarenessthreshold of 10 mm/h. An interval with physicalof changes in flood frequency, are deemed essential

significance of approximated basin concentration timefrom the perspectives of decision-making, transparent(T) was used to ensure the independence of events. Stepdissemination

2of disaster risk information, andcollaborative scientific research.
Downscaled

calculating

theT-hour

Maximum

Precipitation (TMP) for each event. According to thedetailed

concept of unit hydrograph, precipitation within T

precipitation data for understanding future floods.

hours contributes entirely to the peak discharge. Events

However, dealing with hourly data spanning centuries

are then arranged in descending order of TMP, andand large-scale, high-resolution simulations challenges

events with annual exceedance probability less than 0.1

themodels.

are extracted. Steps 1 and 2 are applied to all grids at

Continuous simulations are no longer a feasible

all resolutions to obtain a preliminary event set. Thesolution.

preliminary event set is arranged according to their

Given that flood studies primarily focus on extreme

starting time in Step 3, where overlapping events are

scenarios below a certain frequency, theoretically, it isaggregated, independent events are retained. As Step 1

possible to simulate only the corresponding events.

sets a threshold, aggregated events need to undergo

However, due to the spatiotemporal variability of

Step 4 to search for the actual start and end times ofprecipitation and the complexity of interactions withevents. The corresponding precipitation data can thenbasins, there is currently no method to extract extreme

be extracted based on the full event period.

limitsclimateinvolves

ofensembles

existing

offerhydrodynamic

precipitation events corresponding to all river sections
comprehensively. To address this, this study proposes amethod to identify and extract extreme dischargeassociated precipitation events for all river sections.
This facilitates the direct simulation of the events ofinterestusinghydrodynamicmodels

andtheestablishment of frequency curves.
MethodFigure 1 Aggregating Grid Events (AGE) method

The Aggregating Grid Events (AGE) method primarily

To assess whether the events extracted by the AGE

involves the following four steps in processing

method have the capability to provide extreme

discharge data for constructing flood frequency curves

the AGE method and all valid events. These peaks arefor all river sections, this study conducted detailed

derived for all river sections, with the largest 72 values

simulations in the Shikoku Island using the 150m

extracted for each section. The results indicate that theRainfall-Runoff-Inundation (RRI) model. Precipitation

discharges based on the AGE method exhibit highdata were derived from the historical simulation results

accuracy, with BIAS ranging from -1% to -2% acrossof the 5 km climate ensemble d4PDF, encompassing

various basins. Using these peaks, we estimated12×60 years of hourly data. Any precipitation event

quantiles for each river section using the Peaks Overwith intensity exceeding 10 mm/h at any grid at anyThreshold method, as the evaluation results shown intimestep is considered a valid event. The discharge

Figure 2. The BIAS is primarily distributed between 0%

simulation results of all valid events serve as theand -10%. The results in Figure 3 indicate that larger

evaluation benchmark. Assessment is conducted forBIAS values predominantly occur in river sections withboth discharge peaks and quantiles.

basin areas less than 25 km², while BIAS in the middle

The results were assessed by correlation coefficientsand lower reaches narrows to the range of 0% to -5%.

(R), relative bias (BIAS), and root mean square errors(RMSE).
𝑅=∑(𝑦𝑒 −𝑦̅̅̅)(𝑦̅̅̅)𝑒𝑟 −𝑦𝑟22√∑(𝑦𝑒 −𝑦̅̅̅)̅̅̅)𝑒 ∑(𝑦𝑟 −𝑦𝑟∑ 𝑦𝑒 −∑ 𝑦𝑟

Eq. 1

× 100%Eq. 2

𝑅𝑀𝑆𝐸 = √ 𝑛 ∑(𝑦𝑒 − 𝑦𝑟 )2

Eq. 3

𝐵𝐼𝐴𝑆 =∑ 𝑦𝑟
1

𝑦𝑒 is the predictions from the model to be evaluated.
𝑦𝑟 is the predictions from the reference model.

ResultFigure 3 Spatial distribution of discharge quantile BIAS
at different return periods

Figure 4 Changes of BIAS of discharge quantile withbasin area

Conclusion
TheAGE

computational
Figure 2 Comparison of discharge peaks from all river
sections

Figure 1 shows the comparison of peak discharges frommethodsignificantly

burdenrequired

reduces

thefor constructing

discharge quantile maps, enablinghydrodynamic

simulations over large areas, at high resolutions, andutilizing multiple climate ensembles.