Dinâmica dos nutrientes na Ria Formosa: efeitos da interacçäo da laguna com as suas interfaces na reciclagem do azoto, fósforo e sílica

Tese de dout. em Ciências do Mar, Instituto de Investigação das Pescas e do Mar, Univ. do Algarve, 1996

SLOPE STABILITY ANALYSIS OF MOUNT MEAGER, SOUTH-WESTERN BRITISH COLUMBIA, CANADA

The Mount Meager Volcanic Complex (MMVC) in south-western British Columbia is a potentially active, hydrothermally altered massif comprising a series of steep, glaciated peaks. Climatic conditions and glacial retreat has led to the further weathering, exposure and de-buttressing of steep slopes composed of weak, unconsolidated material. This has resulted in an increased frequency of landslide events over the past few decades, many of which have dammed the rivers bordering the Complex. The breach of these debris dams presents a risk of flooding to the downstream communities. Preliminary mapping showed there are numerous sites around the Complex where future failure could occur. Some of these areas are currently undergoing progressive slope movement and display features to support this such as anti-scarps and tension cracks. The effect of water infiltration on stability was modelled using the Rocscience program Slide 6.0. The main site of focus was Mount Meager in the south- east of the Complex where the most recent landslide took place. Two profiles through Mount Meager were analysed along with one other location in the northern section of the MMVC, where instability had been detected. The lowest Factor of Safety (FOS) for each profile was displayed and an estimate of the volume which could be generated was deduced. A hazard map showing the inundation zones for various volumes of debris flows was created from simulations using LAHARZ. Results showed the massif is unstable, even before infiltration. Varying the amount of infiltration appears to have no significant impact on the FOS annually implying that small changes of any kind could also trigger failure. Further modelling could be done to assess the impact of infiltration over shorter time scales. The Slide models show the volume of material that could be delivered to the Lillooet River Valley to be of the order of 109 m3 which, based on the LAHARZ simulations, would completely inundate the valley and communities downstream. A major hazard of this is that the removal of such a large amount of material has the potential to trigger an explosive eruption of the geothermal system and renew volcanic activity. Although events of this size are infrequent, there is a significant risk to the communities downstream of the complex.

Evaluating the Effects of Sea Level Rise on Sea Turtle Nesting Sites: A Case Study of the Archie Carr National Wildlife Refuge

The purpose of this thesis was to determine the extent of sea level rise (SLR) impact on sea turtle nesting beach habitat on Archie Carr National Wildlife Refuge (NWR) as well as impacts on management strategies. The Archie Carr NWR is of exceptional importance due to the high density of Loggerhead, Leatherback, and Green sea turtles that nest there in the summer months. GIS data provided by the Archie Carr NWR and various SLR scenarios, provided by both the Intergovernmental Panel on Climate Change (IPCC) as well as leading scholars, were used to determine inundation area loss across the Refuge as well as nearby parcels targeted for possible acquisition. Inundation losses for the six scenarios were calculated to be in the 20-25% range. Approximately 26% of current lower priority parcels are reclassified as high priority when integrating this information. Therefore, a significant revision to future acquisition strategies is recommended.

How did the AD 1755 tsunami impact on sand barriers across the southern coast of Portugal?

Tsunamis are highly energetic events that may destructively impact the coast. Resolving the degree of coastal resilience to tsunamis is extremely difficult and sometimes impossible. In part, our understanding is constrained by the limited number of contemporaneous examples and by the high dynamism of coastal systems. In fact, longterm changes of coastal systems can mask the evidence of past tsunamis, leaving us a short or incomplete sedimentary archive. Here, we present a multidisciplinary approach involving sedimentological, geomorphological and geophysical analyses and numerical modelling of the AD 1755 tsunami flood on a coastal segment located within the southern coast of Portugal. In particular, the work focuses on deciphering the impact of the tsunami waves over a coastal sand barrier enclosing two lowlands largely inundated by the tsunami flood. Erosional features documented by geophysical data were assigned to the AD 1755 eventwith support of sedimentological and age estimation results. Furthermore, these features allowed the calibration of the simulation settings to reconstruct the local conditions and establish the run-up range of the AD 1755 tsunami when it hit this coast (6– 8 m above mean sea level). Our work highlights the usefulness of erosional imprints preserved in the sediment record to interpret the impact of the extreme events on sand barriers

Data-driven multivariate flood hazard modeling

The present Dissertation shows how recent statistical analysis tools and open datasets can be exploited to improve modelling accuracy in two distinct yet interconnected domains of flood hazard (FH) assessment. In the first Part, unsupervised artificial neural networks are employed as regional models for sub-daily rainfall extremes. The models aim to learn a robust relation to estimate locally the parameters of Gumbel distributions of extreme rainfall depths for any sub-daily duration (1-24h). The predictions depend on twenty morphoclimatic descriptors. A large study area in north-central Italy is adopted, where 2238 annual maximum series are available. Validation is performed over an independent set of 100 gauges. Our results show that multivariate ANNs may remarkably improve the estimation of percentiles relative to the benchmark approach from the literature, where Gumbel parameters depend on mean annual precipitation. Finally, we show that the very nature of the proposed ANN models makes them suitable for interpolating predicted sub-daily rainfall quantiles across space and time-aggregation intervals. In the second Part, decision trees are used to combine a selected blend of input geomorphic descriptors for predicting FH. Relative to existing DEM-based approaches, this method is innovative, as it relies on the combination of three characteristics: (1) simple multivariate models, (2) a set of exclusively DEM-based descriptors as input, and (3) an existing FH map as reference information. First, the methods are applied to northern Italy, represented with the MERIT DEM (∼90m resolution), and second, to the whole of Italy, represented with the EU-DEM (25m resolution). The results show that multivariate approaches may (a) significantly enhance flood-prone areas delineation relative to a selected univariate one, (b) provide accurate predictions of expected inundation depths, (c) produce encouraging results in extrapolation, (d) complete the information of imperfect reference maps, and (e) conveniently convert binary maps into continuous representation of FH.

Private sensors and crowdsourced rainfall data: accuracy and effectiveness for monitoring severe events and mapping pluvial flooding in urban areas

The increasing number of extreme rainfall events, combined with the high population density and the imperviousness of the land surface, makes urban areas particularly vulnerable to pluvial flooding. In order to design and manage cities to be able to deal with this issue, the reconstruction of weather phenomena is essential. Among the most interesting data sources which show great potential are the observational networks of private sensors managed by citizens (crowdsourcing). The number of these personal weather stations is consistently increasing, and the spatial distribution roughly follows population density. Precisely for this reason, they perfectly suit this detailed study on the modelling of pluvial flood in urban environments. The uncertainty associated with these measurements of precipitation is still a matter of research. In order to characterise the accuracy and precision of the crowdsourced data, we carried out exploratory data analyses. A comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national weather services is presented. The crowdsourced stations have very good skills in rain detection but tend to underestimate the reference value. In detail, the accuracy and precision of crowd- sourced data change as precipitation increases, improving the spread going to the extreme values. Then, the ability of this kind of observation to improve the prediction of pluvial flooding is tested. To this aim, the simplified raster-based inundation model incorporated in the Saferplaces web platform is used for simulating pluvial flooding. Different precipitation fields have been produced and tested as input in the model. Two different case studies are analysed over the most densely populated Norwegian city: Oslo. The crowdsourced weather station observations, bias-corrected (i.e. increased by 25%), showed very good skills in detecting flooded areas.