3.1. Probabilistic Flood Hazards
For each location along the Shanghai coastline, the return periods of
flood levels (storm tides) under the NCAR/NCEP current climate are
estimated through the flood probability analysis. The spatially
continuous distribution of the estimated flood levels with various
return periods (10-, 100- and 1000-year) along the coast is presented in
Fig. 1. Results indicate that the flood level substantially increases
with the increase of return period: it is between 3.77 and 6.93 m (with
an average value of 4.37 m above Wusong Datum) for the 10-year scenario,
4.57 and 8.48 m (5.19 m) for the 100-year scenario, and 5.88 and 12.10m
(7.03 m) for the 1000-year scenario. The highest flood return levels
(η ) and the largest increases in η over the return periods
can be observed along the northern coast of Chongming Island. This
spatially continuous distribution of simulated flood levels along the
coast provides greater detail and accuracy for flood hazard assessment
compared to a previous study (Yin et al., 2020), in which flood return
levels were interpolated from sparsely distributed tide gauges.
Fig. 2 displays the flood return level curves at selected gauge stations
estimated by our analysis, compared to estimates by SWA using the
officially recommended Pearson-III distribution based on annual maximum
values of recorded historical water levels. Similar to that shown in
Fig. 1, the predicted flood return levels vary greatly from place to
place along the coast. For example, the 1000-year flood height is about
7.76 m at Jinshan Station but only about 6.10 m at Luchaogang Station.
Only very low-probability events (over 3000 ~ 10000
year) will exceed the crest levels of seawall. Results also show a large
uncertainty in flood levels with long return periods (> 100
years). SWA’s analysis predicts significantly higher flood levels,
especially for small return periods (e.g., 5 ~ 6 m for
the 10-year floods). Peak water levels recorded at the gauge stations
during Typhoon Winnie indicate it to be close to a 100-year event by
SWA’s analysis but an 800 ~ 900-year event by our
analysis. This discrepancy can be primarily attributed to the different
distribution models and sample data used in the flood probability
analyses. The occurrence of Typhoon Winnie may have contributed
significantly to lifting the return level curves estimated based on the
limited (less than 60 year) historical data. Otherwise, using
limited-length historical data often underestimates extremes (Lin et
al., 2014; Xu et al., 2020); extrapolating SWA’s estimates for return
periods greater than 1000 years would result in an underestimation of
the extremes compared to our modeling-based estimates.