Spatiotemporal patterns of extreme sea levels along the western North-Atlantic coasts

Gauging mixed climate extreme value distributions in tropical cyclone regions

In tropical cyclone (TC) regions, tide gauge or numerical hindcast records are usually of insufficient length to have sampled sufficient cyclones to enable robust estimates of the climate of TC-induced extreme water level events. Synthetically-generated TC populations provide a means to define a broader set of plausible TC events to better define the probabilities associated with extreme water level events. The challenge is to unify the estimates of extremes from synthetically-generated TC populations with the observed records, which include mainly non-TC extremes resulting from tides and more frequently occurring atmospheric-depression weather and climate events. We find that extreme water level measurements in multiple tide gauge records in TC regions, some which span more than 100 years, exhibit a behaviour consistent with the combining of two populations, TC and non-TC. We develop an equation to model the combination of two populations of extremes in a single continuous mixed climate (MC) extreme value distribution (EVD). We then run statistical simulations to show that long term records including both historical and synthetic events can be better explained using MC than heavy-tailed generalised EVDs. This has implications for estimating extreme water levels when combining synthetic cyclone extreme sea levels with hindcast water levels to provide actionable information for coastal protection.

Exploring deep learning capabilities for surge predictions in coastal areas

To improve coastal adaptation and management, it is critical to better understand and predict the characteristics of sea levels. Here, we explore the capabilities of artificial intelligence, from four deep learning methods to predict the surge component of sea-level variability based on local atmospheric conditions. We use an Artificial Neural Networks, Convolutional Neural Network, Long Short-Term Memory layer (LSTM) and a combination of the latter two (ConvLSTM), to construct ensembles of Neural Network (NN) models at 736 tide stations globally. The NN models show similar patterns of performance, with much higher skill in the mid-latitudes. Using our global model settings, the LSTM generally outperforms the other NN models. Furthermore, for 15 stations we assess the influence of adding complexity more predictor variables. This generally improves model performance but leads to substantial increases in computation time. The improvement in performance remains insufficient to fully capture observed dynamics in some regions. For example, in the tropics only modelling surges is insufficient to capture intra-annual sea level variability. While we focus on minimising mean absolute error for the full time series, the NN models presented here could be adapted for use in forecasting extreme sea levels or emergency response.

Estimation of global tropical cyclone wind speed probabilities using the STORM dataset

Tropical cyclones (TC) are one of the deadliest and costliest natural disasters. To mitigate the impact of such disasters, it is essential to know extreme exceedance probabilities, also known as return periods, of TC hazards. In this paper, we demonstrate the use of the STORM dataset, containing synthetic TCs equivalent of 10,000 years under present-day climate conditions, for the calculation of TC wind speed return periods. The temporal length of the STORM dataset allows us to empirically calculate return periods up to 10,000 years without fitting an extreme value distribution. We show that fitting a distribution typically results in higher wind speeds compared to their empirically derived counterparts, especially for return periods exceeding 100-yr. By applying a parametric wind model to the TC tracks, we derive return periods at 10 km resolution in TC-prone regions. The return periods are validated against observations and previous studies, and show a good agreement. The accompanying global-scale wind speed return period dataset is publicly available and can be used for high-resolution TC risk assessments.

Complex Extreme Sea Levels Prediction Analysis: Karachi Coast Case Study

In this study, the analysis of the extreme sea level was carried out by using 10 years (2007-2016) of hourly tide gauge data of Karachi port station along the Pakistan coast. Observations revealed that the magnitudes of the tides usually exceeded the storm surges at this station. The main observation for this duration and the subsequent analysis showed that in June 2007 a tropical Cyclone "Yemyin" hit the Pakistan coast. The joint probability method (JPM) and the annual maximum method (AMM) were used for statistical analysis to find out the return periods of different extreme sea levels. According to the achieved results, the AMM and JPM methods erre compatible with each other for the Karachi coast and remained well within the range of 95% confidence. For the JPM method, the highest astronomical tide (HAT) of the Karachi coast was considered as the threshold and the sea levels above it were considered extreme sea levels. The 10 annual observed sea level maxima, in the recent past, showed an increasing trend for extreme sea levels. In the study period, the increment rates of 3.6 mm/year and 2.1 mm/year were observed for mean sea level and extreme sea level, respectively, along the Karachi coast. Tidal analysis, for the Karachi tide gauge data, showed less dependency of the extreme sea levels on the non-tidal residuals. By applying the Merrifield criteria of mean annual maximum water level ratio, it was found that the Karachi coast was tidally dominated and the non-tidal residual contribution was just 10%. The examination of the highest water level event (13 June 2014) during the study period, further favored the tidal dominance as compared to the non-tidal component along the Karachi coast.

Land Use Change in Coastal Cities during the Rapid Urbanization Period from 1990 to 2016: A Case Study in Ningbo City, China

Coastal cities have been experiencing tremendous land use changes worldwide. Studies on the consequences of land use change in coastal cities have provided helpful information for spatial regulations and have attracted increased attention. Changes in forests and water bodies, however, have rarely been investigated, challenging the formation of a holistic pattern of land use change. In this study, we selected Ningbo, China, as a case study area and analyzed its land use change from 1990 to 2016. Random forest (RF) classification was employed to derive land use information from Landsat images. Transition matrices and a distribution index (DI) were applied to identify the major types of land use transitions and their spatial variations by site-specific attributes. The results showed that the entire time period could be divided into two stages, based on the manifestations of land use change in Ningbo: 1990–2005 and 2005–2016. During 1990–2005, construction land expanded rapidly, mainly through the occupation of agricultural land and forest, while during 2005–2016, the main change trajectory turned out to be a small net change in construction land and a net increase in agricultural land sourced from construction land, forests, and water bodies. In terms of land use change by site-specific attributes, the rapid expansion of construction land around the municipal city center during 1990–2005 was restrained, and similar amounts of land conversion between construction and agricultural use occurred during 2005–2016. During the study period, areas undergoing land use change also showed trends of moving outward from the municipal city center and the county centers located adjacent to roads and the coastline and of moving up to hilly areas with steeper slopes and higher elevations. Protecting reclaimed agricultural land, improving the efficiency of construction land, and controlling forest conversion in hilly areas are suggested as spatial regulations in Ningbo city.