Moving Earth (not heaven): A novel approach to tropical cyclone impact modelling, demonstrated for New Zealand

Seagrass has been acknowledged as a productive blue carbon ecosystem that is in significant decline across much of the world. A first step toward conservation is the mapping and monitoring of extant seagrass meadows. Several methods are currently in use, but mapping the resource from satellite images using machine learning is not widely applied, despite its successful use in various comparable applications. This research aimed to develop a novel approach for seagrass monitoring using state-of-the-art machine learning with data from Sentinel–2 imagery. We used Tauranga Harbor, New Zealand as a validation site for which extensive ground truth data are available to compare ensemble machine learning methods involving random forests (RF), rotation forests (RoF), and canonical correlation forests (CCF) with the more traditional maximum likelihood classifier (MLC) technique. Using a group of validation metrics including F1, precision, recall, accuracy, and the McNemar test, our results indicated that machine learning techniques outperformed the MLC with RoF as the best performer (F1 scores ranging from 0.75–0.91 for sparse and dense seagrass meadows, respectively). Our study is the first comparison of various ensemble-based methods for seagrass mapping of which we are aware, and promises to be an effective approach to enhance the accuracy of seagrass monitoring.

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Track-Pattern-Based Model for Seasonal Prediction of Tropical Cyclone Activity in the Western North Pacific

Journal of Climate ◽

10.1175/jcli-d-11-00236.1 ◽

2012 ◽

Vol 25 (13) ◽

pp. 4660-4678 ◽

Cited By ~ 30

Author(s):

Hyeong-Seog Kim ◽

Chang-Hoi Ho ◽

Joo-Hong Kim ◽

Pao-Shin Chu

Keyword(s):

Tropical Cyclone ◽

North Pacific ◽

Western North Pacific ◽

Correlation Coefficients ◽

Seasonal Prediction ◽

Track Density ◽

Good Prediction ◽

Seasonal Characteristics ◽

Novel Approach ◽

Track Pattern

Abstract Skillful predictions of the seasonal tropical cyclone (TC) activity are important in mitigating the potential destruction from the TC approach/landfall in many coastal regions. In this study, a novel approach for the prediction of the seasonal TC activity over the western North Pacific is developed to provide useful probabilistic information on the seasonal characteristics of the TC tracks and vulnerable areas. The developed model, which is termed the “track-pattern-based model,” is characterized by two features: 1) a hybrid statistical–dynamical prediction of the seasonal activity of seven track patterns obtained by fuzzy c-means clustering of historical TC tracks and 2) a technique that enables researchers to construct a forecasting map of the spatial probability of the seasonal TC track density over the entire basin. The hybrid statistical–dynamical prediction for each pattern is based on the statistical relationship between the seasonal TC frequency of the pattern and the seasonal mean key predictors dynamically forecast by the National Centers for Environmental Prediction Climate Forecast System in May. The leave-one-out cross validation shows good prediction skill, with the correlation coefficients between the hindcasts and the observations ranging from 0.71 to 0.81. Using the predicted frequency and the climatological probability for each pattern, the authors obtain the forecasting map of the seasonal TC track density by combining the TC track densities of the seven patterns. The hindcasts of the basinwide seasonal TC track density exhibit good skill in reproducing the observed pattern. The El Niño–/La Niña–related years, in particular, tend to show a better skill than the neutral years.

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Social Vulnerability Indicators for Flooding in Aotearoa New Zealand

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18083952 ◽

2021 ◽

Vol 18 (8) ◽

pp. 3952

Author(s):

Kylie Mason ◽

Kirstin Lindberg ◽

Carolin Haenfling ◽

Allan Schori ◽

Helene Marsters ◽

...

Keyword(s):

Climate Change ◽

New Zealand ◽

Natural Hazards ◽

Social Vulnerability ◽

Social Connectedness ◽

Population Data ◽

Holistic View ◽

Aotearoa New Zealand ◽

Novel Approach ◽

Vulnerability Indicators

Social vulnerability indicators are a valuable tool for understanding which population groups are more vulnerable to experiencing negative impacts from disasters, and where these groups live, to inform disaster risk management activities. While many approaches have been used to measure social vulnerability to natural hazards, there is no single method or universally agreed approach. This paper proposes a novel approach to developing social vulnerability indicators, using the example of flooding in Aotearoa New Zealand. A conceptual framework was developed to guide selection of the social vulnerability indicators, based on previous frameworks (including the MOVE framework), consideration of climate change, and a holistic view of health and wellbeing. Using this framework, ten dimensions relating to social vulnerability were identified: exposure; children; older adults; health and disability status; money to cope with crises/losses; social connectedness; knowledge, skills and awareness of natural hazards; safe, secure and healthy housing; food and water to cope with shortage; and decision making and participation. For each dimension, key indicators were identified and implemented, mostly using national Census population data. After development, the indicators were assessed by end users using a case study of Porirua City, New Zealand, then implemented for the whole of New Zealand. These indicators will provide useful data about social vulnerability to floods in New Zealand, and these methods could potentially be adapted for other jurisdictions and other natural hazards, including those relating to climate change.

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Estimation of Global Synthetic Tropical Cyclone Hazard Probabilities using the STORM dataset

10.5194/egusphere-egu2020-2568 ◽

2020 ◽

Author(s):

Nadia Bloemendaal ◽

Ivan Haigh ◽

Hans de Moel ◽

Sanne Muis ◽

Jeroen Aerts

Keyword(s):

Tropical Cyclone ◽

Spatial Information ◽

Risk Mitigation ◽

Global Scale ◽

Generation Model ◽

Risk Modeling ◽

Climate Conditions ◽

Wind Speeds ◽

South Pacific Ocean ◽

Novel Approach

<p>Tropical cyclones (TCs), also referred to as hurricanes or typhoons, are amongst the deadliest and costliest natural disasters, affecting people, economies and the environment in coastal areas around the globe when they make landfall. In 2017, Hurricanes Harvey, Irma and Maria entered the top-5 costliest Atlantic hurricanes ever recorded, with combined losses estimated at $220 billion. Therefore, to minimize future loss of life and property and to aid risk mitigation efforts, it is crucial to perform accurate TC risk assessments in low-lying coastal regions. Calculating TC risk at a global scale, however, has proven to be difficult, given the limited temporal and spatial information on landfalling TCs around much of the global coastline.</p><p>In this research, we present a novel approach to calculate TC risk under present and future climate conditions on a global scale, using the newly developed Synthetic Tropical cyclOne geneRation Model (STORM). For this, we extract 38 years of historical data from the International Best-Track Archive for Climate Stewardship (IBTrACS). This dataset is used as input for the STORM algorithm to statistically extend this dataset from 38 years to 10,000 years of TC activity. Validation shows that the STORM dataset preserves the TC statistics as found on the original IBTrACS dataset. The STORM dataset is then used to calculate global-scale return periods of TC-induced wind speeds at 0.1&#176;resolution. This return period dataset can then be used to assess the low probabilities of extreme events all around the globe. Moreover, we demonstrate the application of this dataset for TC risk modeling on small islands in e.g. the Caribbean or in the South Pacific Ocean.</p>

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Statutory Sentencing Reform in Israel: Exploring the Sentencing Law of 2012

Israel Law Review ◽

10.1017/s0021223713000162 ◽

2013 ◽

Vol 46 (3) ◽

pp. 455-479 ◽

Cited By ~ 8

Author(s):

Julian V Roberts ◽

Oren Gazal-Ayal

Keyword(s):

United States ◽

New Zealand ◽

The United States ◽

Judicial Discretion ◽

England And Wales ◽

Sentencing Reform ◽

Novel Approach ◽

Sentencing Reforms ◽

The Law

In 2012 the Knesset approved a new sentencing law. Israel thus became the latest jurisdiction to introduce statutory directions for courts to follow in sentencing. The approach of the United States to structuring judicial discretion often entails the use of a sentencing grid with presumptive sentencing ranges. In contrast, the Sentencing Act of Israel reflects a less prescriptive method: it provides guidance by words rather than numbers. Retributivism is clearly identified as the penal philosophy underpinning the new law, which takes a novel approach to promoting more proportionate sentencing. Courts are directed to construct an individualised proportionate sentencing range appropriate to the case in hand. Once this is established, the court then follows additional directions regarding factors and principles related to sentencing. Although other jurisdictions have placed the purposes and principles of sentencing on a statutory footing, this is the first such legislative declaration in Israel. The statute also contains a methodology to implement a proportional approach to sentencing as well as detailed guidance on sentencing factors. This article describes and explores the new Sentencing Act, making limited comparisons to sentencing reforms in other jurisdictions – principally England and Wales, New Zealand and the United States. In concluding, we speculate on the likely consequences of the law: will it achieve the goals of promoting more consistent and principled sentencing?

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A novel approach to temporary stenting: degradable cardiovascular stents produced from corrodible metal---results 6-18 months after implantation into New Zealand white rabbits

Heart ◽

10.1136/heart.86.5.563 ◽

2001 ◽

Vol 86 (5) ◽

pp. 563-569 ◽

Cited By ~ 330

Author(s):

M Peuster

Keyword(s):

New Zealand ◽

Cardiovascular Stents ◽

Novel Approach ◽

New Zealand White Rabbits

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Recently increased tropical cyclone activity and inferences concerning coastal erosion and inland hydrological regimes in New Zealand and Eastern Australia

Climatic Change ◽

10.1007/bf02423222 ◽

1981 ◽

Vol 3 (3) ◽

pp. 317-332 ◽

Cited By ~ 8

Author(s):

Patrick J. Grant

Keyword(s):

New Zealand ◽

Tropical Cyclone ◽

Coastal Erosion ◽

Tropical Cyclone Activity ◽

Cyclone Activity ◽

Eastern Australia ◽

Hydrological Regimes

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A Novel Approach for Solving CNOP and its Application in Identifying Sensitive Regions of Tropical Cyclone Adaptive Observations

10.5194/npg-2018-17 ◽

2018 ◽

Author(s):

Linlin Zhang ◽

Bin Mu ◽

Shijin Yuan ◽

Feifan Zhou

Keyword(s):

Tropical Cyclone ◽

Mesoscale Model ◽

Search Algorithm ◽

Principal Component ◽

Conditional Nonlinear Optimal Perturbation ◽

Optimal Perturbation ◽

Fifth Generation ◽

Novel Approach ◽

Adaptive Observations ◽

Parallel Particle Swarm Optimization

Abstract. In this paper, a novel approach is proposed for solving conditional nonlinear optimal perturbation (CNOP), named it adaptive cooperation co-evolution of parallel particle swarm optimization and wolf search algorithm (ACPW) based on principal component analysis. Taking Fitow (2013) and Matmo (2014) as two tropical cyclone (TC) cases, CNOP solved by ACPW is used to investigate the sensitive regions identification of TC adaptive observations with the fifth-generation mesoscale model (MM5). Meanwhile, the 60 km and 120 km resolutions are adopted. The adjoint-based method (short for the ADJ-method) is also applied to solve CNOP, and the result is used as a benchmark. To validate the validity of ACPW, the CNOPs obtained from the different methods are compared in terms of the patterns, energies, similarities and simulated TC tracks with perturbations. (1) The ACPW can capture similar CNOP patterns with the ADJ-method, and the patterns of TC Fitow are more similar than TC Matmo. (2) When using the 120 km resolution, similarities between CNOPs of the ADJ-method and ACPW are higher than those using the 60 km. (3) Compared to the ADJ-method, although the CNOPs of ACPW produce lower energies, they can obtain better benefits gained from the reduction of CNOPs, not only in the entire domain but also in the sensitive regions identified. (4) The sensitive regions identified by CNOPs-ACPW has the same influence on the improvements of the TC tracks forecast skills with those identified by CNOPs-ADJ-method. (5) The ACPW has a higher efficiency than the ADJ-method. All conclusions prove that ACPW is a meaningful and effective method for solving CNOP and can be used to identify sensitive regions of TC adaptive observations.

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Integrating the OpenSky Network into GNSS-R Climate Monitoring Research

Proceedings ◽

10.3390/proceedings2020059011 ◽

2020 ◽

Vol 59 (1) ◽

pp. 11

Author(s):

Mike Laverick ◽

Delwyn Moller ◽

Christopher Ruf ◽

Stephen Musko ◽

Andrew O’Brien ◽

...

Keyword(s):

New Zealand ◽

Global Navigation Satellite System ◽

Satellite System ◽

Navigation Satellite ◽

Climate Data ◽

Commercial Aircraft ◽

Climate Monitoring ◽

Novel Approach ◽

Global Navigation ◽

Global Navigation Satellite

Global Navigation Satellite System Reflectometry (GNSS-R) provides a unique means of inferring geophysical conditions of the Earth’s surface without the need for costly, and often infeasible, in-situ climate monitoring systems. As part of NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission, and in conjunction with Air New Zealand, we are taking the novel approach of mounting a GNSS-R receiver on a commercial aircraft, which shall allow for an unprecedented collection of climate data over and around the islands of New Zealand. Such data include inundation and coastal dynamics, and soil moisture content and variability. We report back to the community how the OpenSky Network data support our climate monitoring research. We discuss how we use the historical database state-vectors to simulate and visualise the predicted geographical coverage of the airborne GNSS-R receiver. We also discuss how the live API can help monitor our payload in-flight, our investigations into the OpenSky ADS-B coverage over New Zealand, and our plans to expand the coverage.

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