Threshold Exceedances Research Articles

Past and future coastal flooding in Pacific Small-Island Nations: insights from the Pacific Sea Level and Geodetic Monitoring (PSLGM) Project tide gauges

Sea level rise is increasing the frequency of coastal flooding globally, and low-lying communities are particularly vulnerable. We present an assessment of historical and projected changes in coastal flooding in 11 Pacific small-island nations, using tide gauge data from the Australian Bureau of Meteorology. We derive impact-, event- and percentile-based thresholds to calculate historical exceedance frequencies. Projections of future exceedance frequencies are then made using the recent suite of Shared Socioeconomic Pathways (SSPs) emission scenarios (Sixth Assessment Report). We find that exceedances of the percentile thresholds have increased in the last decade at all locations, with sites seeing exceedances in months where exceedances were previously rare or unseen in the sea level record. In the future, daily threshold exceedances occur after 50–115 cm of sea level rise, depending on location. Such levels are currently projected to be reached between 2080 and 2130 according to high emissions scenario SSP5–8.5. Low emissions scenario, SSP1–1.9, shows sea level rise resulting in 25–75 days of exceedances by 2050 for the 11 locations. This increased frequency of coastal flooding highlights the changing nature of coastal flood risk in the Pacific, with extreme weather and wave events being increasingly unnecessary for inundation to occur. Further, this work highlights how underlying increases in coastal flooding frequency pose a growing risk of exacerbating inundation associated with extreme weather or waves. Better flood monitoring and reporting will improve the accuracy of impact thresholds, strengthening the relevance of the results presented here for coastal emergency and planning managers.

Spatio-temporal analysis of the extent of an extreme heat event

Evidence of global warming induced from the increasing concentration of greenhouse gases in the atmosphere suggests more frequent warm days and heat waves. The concept of an extreme heat event (EHE), defined locally based on exceedance of a suitable local threshold, enables us to capture the notion of a period of persistent extremely high temperatures. Modeling for extreme heat events is customarily implemented using time series of temperatures collected at a set of locations. Since spatial dependence is anticipated in the occurrence of EHE’s, a joint model for the time series, incorporating spatial dependence is needed. Recent work by Schliep et al. (J R Stat Soc Ser A Stat Soc 184(3):1070–1092, 2021) develops a space-time model based on a point-referenced collection of temperature time series that enables the prediction of both the incidence and characteristics of EHE’s occurring at any location in a study region. The contribution here is to introduce a formal definition of the notion of the spatial extent of an extreme heat event and then to employ output from the Schliep et al. (J R Stat Soc Ser A Stat Soc 184(3):1070–1092, 2021) modeling work to illustrate the notion. For a specified region and a given day, the definition takes the form of a block average of indicator functions over the region. Our risk assessment examines extents for the Comunidad Autónoma de Aragón in northeastern Spain. We calculate daily, seasonal and decadal averages of the extents for two subregions in this comunidad. We generalize our definition to capture extents of persistence of extreme heat and make comparisons across decades to reveal evidence of increasing extent over time.

Impact of climate change on landslides in Slovenia in the mid-21st century

Slovenia is affected by extreme and intense rainfall that triggers numerous landslides every year, resulting in significant human impact and damage to infrastructure. Previous studies on landslides have shown how rainfall patterns can influence landslide occurrence, while in this paper, we present one of the first study in Slovenia to examine the impact of climate change on landslides in the mid-21st century. To do this, we used the Representative Concentration Pathway (RCP) 4.5 climate scenario and future climatology simulated by six climate models that differed from each other as much as possible while representing measured values of past climate variables as closely as possible. Based on baseline period (1981-2010) we showed the number of days with exceedance of rainfall thresholds and the area where landslides may occur more frequently in the projection period (2041-2070). We found that extreme rainfall events are likely to occur more frequent in the future, which may lead to a higher frequency of landslides in some areas.

Changes of Extreme Agro-Climatic Droughts and Their Impacts on Grain Yields in Rain-Fed Agricultural Regions in China over the Past 50 Years

Climate change and climate extremes and their impacts on agriculture, water resources, and ecosystems have become important issues globally. Agricultural sustainability and food security are facing unprecedented challenges due to the increasing occurrence of extreme climatic events, including, notably, extreme droughts in recent years in China. In this study, a threshold determination model of extreme agro-climatic droughts (EADs) was built based on the cumulative probability distribution functions (CDF) of an agricultural drought index—the consecutive days without available precipitation (CDWAP). The CDWAP was established by combining meteorological data with the characteristics of cropping patterns and the water requirement in different growing periods of crops. The CDF of CDWAP was obtained based on the relationship of CDWAP and its occurrence frequency. Based on the model, the spatial pattern of the thresholds of EADs and the threshold exceedance time series of EADs in 500 meteorological stations were obtained, and then changes in the frequencies and intensities of EADs in China and their impacts on grain yields in rain-fed regions during the past 50 years were analyzed. The results follow: (1) The threshold value of EADs in China gradually increased from southeast to northwest. The stations of the highest value were located in the Northwest China, with the CDWAP more than 60 days, while the lowest value was in the middle reaches of the Yangzi River, with the CDWAP less than 16 days. (2) The frequencies and intensities of the EADs increased mostly in the east areas of the Hu Huanyong line, which was also the main agricultural production region in China. The North China (NC) and Southwest China (SW) regions showed the highest increasing rates of the EADs; their frequencies and intensities were 11.3% and 2.2%, respectively, for the NC region, and 9.3% and 2.7%, respectively, for the SW region. (3) Case studies in the NC, SW, and SE regions indicated that there was a negative correlation between grain yields and EAD frequency and intensity; i.e., the low grain yields often occurred in the year with relatively higher frequency or/and stronger intensity of EADs. The correlation coefficients of grain yield and EAD were generally greater than that of merely extreme climatic droughts; therefore, the study of EAD is necessary when researching the impacts of extreme drought events on grain yield.

Approaches for unsupervised identification of data-driven models for flow forecasting in urban drainage systems

Abstract In this work, an unsupervised model selection procedure for identifying data-driven forecast models for urban drainage systems is proposed and evaluated. Specifically, we consider the case of predicting inflows to wastewater treatment plants for activating wet weather operation (aeration tank settling, ATS) using Box–Jenkins models. The model selection procedure considers different model structures and different objective functions. The hyperparameter search space is constrained based on the time of concentration in the catchment. Objective function criteria that minimize one-step-ahead as well as multi-step prediction errors are considered. Finally, we consider two criteria for unsupervised selection of the best-performing model. These measure the agreement of observed and predicted hydrographs (persistence index), as well as the binary exceedance of critical flow thresholds (critical success index (CSI)). Our work shows that forecast models can be developed in an unsupervised manner, and ATS activation is correctly forecasted in 60–90% of the events. The selected model structures reflect the physical behaviour of the catchment. Models should not be selected on operational criteria like the CSI due to a risk of overfitting. The degree to which rainfall input improves forecasts depends on the specific catchment, and the objective function criterion that should be used for coefficient estimation depends on the application context.

Spatial Warped Gaussian Processes: Estimation and Efficient Field Reconstruction

A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we study both the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators, the multi-point Maximum A-Posteriori (MAP) estimators, an efficient class of multi-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators, and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.

A new seabed mobility index for the Irish Sea: Modelling seabed shear stress and classifying sediment mobilisation to help predict erosion, deposition, and sediment distribution

The seafloor is increasingly being used for siting renewable energy and telecommunication infrastructure as well as supporting key fisheries and biodiversity. Understanding seabed stability and sediment dynamics is, therefore, a fundamental need for offshore engineering and geoscience and biological studies. In this study we aim to quantify the levels of sediment mobility in the Irish Sea: an area of increasing socio-economic interest and subsequent seabed pressures. The temporal and spatial interaction between bathymetry, hydrodynamics and seabed sediments leads to a complex pattern of erosion, bedload transport and deposition which can affect seabed infrastructure and modify habitats. Information on current and wave conditions were obtained from numerical modelling to assess their role in generating seabed hydrodynamic conditions. These outputs were coupled with observed seabed grain-size data to predict the exceedance of sediment mobility thresholds by bed shear stress values for a period of one year according to empirical formulae. Exceedance frequency values were used to calculate a number of sediment disturbance and mobility indexes to allow for a robust assessment of sediment dynamics. Sediment in the Irish sea, on average, is being mobilised 35% of the time during the year, with 35% of the spatial area studied being mobilised over 50% of the time. Even in areas of low sediment mobilisation frequency (<5%), there are implications for bedform dynamics. The spatial patterns of the calculated sediment mobility are discussed in the context of current seabed geomorphology and the implications for both engineering and environmental considerations.

The ClearSight System for Postoperative Arterial Blood Pressure Monitoring After Carotid Endarterectomy: A Validation Study.

BACKGROUNDThe majority of postoperative events in patients undergoing carotid endarterectomy (CEA) are of hemodynamic origin, requiring preventive strict postoperative arterial blood pressure (BP) control. This study aimed to assess whether BP monitoring with noninvasive beat-to-beat ClearSight finger BP (BPCS) can replace invasive beat-to-beat radial artery BP (BPRAD) in the postoperative phase.METHODSThis study was a single-center clinical validation study using a prespecified study protocol. In 48 patients with symptomatic carotid artery stenosis, BPCS and BPRAD were monitored ipsilateral in a simultaneous manner during a 6-hour period on the recovery unit following CEA. Primary endpoints were accuracy and precision of BP derived by ClearSight (Edward Lifesciences, Irvine, CA) vs. the reference standard (Arbocath 20 G, Hospira, Lake Forest, IL) to investigate if BPCS is a reliable noninvasive alternative for BP monitoring postoperatively in CEA patients. Validation was guided by the standard set by the Association for Advancement of Medical Instrumentation (AAMI), considering a BP-monitor adequate when bias (precision) is <5 (8) mm Hg. Secondary endpoint was percentage under- and overtreatment, defined as exceedance of individual postoperative systolic BP threshold by BPRAD or BPCS in contrast to BPCS or BPRAD, respectively.RESULTSThe bias (precision) of BPCS compared to BPRAD was −10 (13.6), 8 (7.2) and 4 (7.8) mm Hg for systolic, diastolic and mean arterial pressure (MAP), respectively. Based on BPCS, undertreatment was 5.6% and overtreatment was 2.4%; however, percentages of undertreatment quadrupled for lower systolic BP thresholds.CONCLUSIONSNoninvasive MAP, but not systolic and diastolic BP, was similar to invasive BPRAD during postoperative observation following CEA, based on AAMI criteria. However, as systolic BP is currently leading in postoperative monitoring to adjust BP therapy on, BPCS is not a reliable alternative for BPRAD.

Event attribution of Parnaíba River floods in Northeastern Brazil

AbstractThe climate modeling techniques of event attribution enable systematic assessments of the extent that anthropogenic climate change may be altering the probability or magnitude of extreme events. In the consecutive years of 2018, 2019, and 2020, rainfalls caused repeated flooding impacts in the lower Parnaíba River in Northeastern Brazil. We studied the effect that alterations in precipitation resulting from human influences on the climate had on the likelihood of flooding using two ensembles of the HadGEM3‐GA6 atmospheric model: one driven by both natural and anthropogenic forcings; and the other driven only by natural atmospheric forcings, with anthropogenic changes removed from sea surface temperatures and sea ice patterns. We performed hydrological modeling to base our assessments on the peak annual streamflow. The change in the likelihood of flooding was expressed in terms of the ratio between probabilities of threshold exceedance estimated for each model ensemble. With uncertainty estimates at the 90% confidence level, the median (5% 95%) probability ratio at the threshold for flooding impacts in the historical period (1982–2013) was 1.12 (0.97 1.26), pointing to a marginal contribution of anthropogenic emissions by about 12%. For the 2018, 2019, and 2020 events, the median (5% 95%) probability ratios at the threshold for flooding impacts were higher at 1.25 (1.07 1.46), 1.27 (1.12 1.445), and 1.37 (1.19 1.59), respectively; indicating that precipitation change driven by anthropogenic emissions has contributed to the increase of likelihood of these events by about 30%. However, there are other intricate hydrometeorological and anthropogenic processes undergoing long‐term changes that affect the flood hazard in the lower Parnaíba River. Trend and flood frequency analyses performed on observations showed a nonsignificant long‐term reduction of annual peak flow, likely due to decreasing precipitation from natural climate variability and increasing evapotranspiration and flow regulation.

Preliminary Forecasting of Rainfall-Induced Shallow Landslides in the Wildfire Burned Areas of Western Greece

A new methodology for shallow landslide forecasting in wildfire burned areas is proposed by estimating the annual probability of rainfall threshold exceedance. For this purpose, extensive geological fieldwork was carried out in 122 landslides, which have been periodically activated in Western Greece, after the devastating wildfires that occurred in August 2007 and burned large areas in several parts of Western Greece. In addition, daily rainfall data covering more than 40 years has been collected and statistically processed to estimate the exceedance probability of the rainfall threshold above which these landslides are activated. The objectives of this study are to quantify the magnitude and duration of rainfall above which landslides in burned areas are activated, as well as to introduce a novel methodology on rainfall-induced landslide forecasting. It has been concluded that rainfall-induced landslide annual exceedance probability in the burned areas is higher when cumulative rainfall duration ranges from 6 to 9 days with local differences due to the prevailing geological conditions and landscape characteristics. The proposed methodology can be used as a basis for landslide forecasting in wildfire-affected areas, especially when triggered by rainfall, and can be further developed as a tool for preliminary landslide hazard assessment.

Assessing conservation status with extensive but low-resolution data: Application of frequentist and Bayesian models to endangered Athabasca River rainbow trout.

Use of extensive but low-resolution abundance data is common in the assessment of species at-risk status based on quantitative decline criteria under International Union for Conservation of Nature (IUCN) and national endangered species legislation. Such data can be problematic for 3 reasons. First, statistical power to reject the null hypothesis of no change is often low because of small sample size and high sampling uncertainty leading to a high frequency of type II errors. Second, range-wide assessments composed of multiple site-specific observations do not effectively weight site-specific trends into global trends. Third, uncertainty in site-specific temporal trends and relative abundance are not propagated at the appropriate spatial scale. A common result is the propensity to underestimate the magnitude of declines and therefore fail to identify the appropriate at-risk status for a species. We used 3 statistical approaches, from simple to more complex, to estimate temporal decline rates for a designatable unit (DU) of rainbow trout in the Athabasca River watershed in western Canada. This DU is considered a native species for purposes of listing because of its genetic composition characterized as >0.95 indigenous origin in the face of continuing introgressive hybridization with introduced populations in the watershed. Analysis of abundance trends from 57 time series with a fixed-effects model identified 33 sites with negative trends, but only 2 were statistically significant. By contrast, a hierarchical linear mixed model weighted by site-specific abundance provided a DU-wide decline estimate of 16.4% per year and a 3-generation decline of 93.2%. A hierarchical Bayesian mixed model yielded a similar 3-generation decline trend of 91.3% and the posterior distribution showed that the estimate had a >99% probability of exceeding thresholds for an endangered listing. We conclude that the Bayesian approach was the most useful because it provided a probabilistic statement of threshold exceedance in support of an at-risk status recommendation.

Vibration Threshold Exceedances in the Groningen Building Vibration Monitoring Network

Vibration assessment criteria for cosmetic building damage are specified in terms of peak velocity thresholds. For masonry buildings, these thresholds are relatively low compared to vibration levels generated by various sources in the built environment. Therefore they will be exceeded numerous times over the lifetime of most buildings. These exceedances are important when one tries to establish or disprove a causal relation between building damage and a vibration source. Despite the relevance of this information, little is known about the number of times velocity thresholds for cosmetic building damage are exceeded in the built environment. The vibration data collected over a period of more than 5 years in 326 buildings, mostly constructed in masonry and randomly distributed over the Province of Groningen, allowed for a unique opportunity to gain insight. First of all, to determine the number of times these thresholds were exceeded, and secondly to make a comparison between shallow earthquakes and other sources. The number of exceedances and the return period were determined for a set of thresholds (i.e. 0.3, 1, 3, 5, 10, and 20 mm/s), which align with peak velocity thresholds typically encountered in vibration assessment guidelines and standards. The results obtained over a 1 year period with very low seismic activity (only earthquakes with M &amp;lt; 2.0) show that the velocity thresholds for cosmetic damage to masonry buildings (i.e. 1, 3, and 5 mm/s) were exceeded on average less than once a month for 80% of the building sensors. For buildings with a large number of exceedances of these thresholds, the most important sources appear to be traffic, construction work, and hits near the sensor. A subset of 59 building sensors was selected, for which the full operational period of about 5 years was analysed to determine the contribution of earthquakes to the threshold exceedances. Less than 1% of the 1 mm/s exceedances measured by the 59 sensors over their full operational period were caused by earthquakes. Only for building with few exceedances (less than 8), the relative influence of induced earthquakes on the number of exceedances is significant (between 30 and 50%).

Leveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness in Peru

Abstract. Disaster planning has historically allocated minimal effort and finances toward advanced preparedness; however, evidence supports reduced vulnerability to flood events, saving lives and money, through appropriate early actions. Among other requirements, effective early action systems necessitate the availability of high-quality forecasts to inform decision making. In this study, we evaluate the ability of statistical and physically based season-ahead prediction models to appropriately trigger flood early preparedness actions based on a 75 % or greater probability of surpassing the 80th percentile of historical seasonal streamflow for the flood-prone Marañón River and Piura River in Peru. The statistical prediction model, developed in this work, leverages the asymmetric relationship between seasonal streamflow and the ENSO phenomenon. Additionally, a multi-model (least-squares combination) is also evaluated against current operational practices. The statistical prediction demonstrates superior performance compared to the physically based model for the Marañón River by correctly triggering preparedness actions in three out of four historical occasions, while both the statistical and multi-model predictions capture all four historical events when the required threshold exceedance probability is reduced to 50 %, with only one false alarm. For the Piura River, the statistical model proves superior to all other approaches, correctly triggering 28 % more often in the hindcast period. Continued efforts should focus on applying this season-ahead prediction framework to additional flood-prone locations where early actions may be warranted and current forecast capacity is limited.

Value of information of structural health monitoring with temporally dependent observations

A Bayesian approach is often applied when updating a deterioration model using observations from inspections, structural health monitoring, or condition monitoring. The observations are stochastic variables with probability distributions that depend on the damage size. Consecutive observations are usually assumed to be independent of each other, but this assumption does not always hold, especially when using online monitoring systems. Frequent updating using dependent measurements can lead to an over-optimistic assessment of the value of information when the measurements are incorrectly modeled as being independent. This article presents a Bayesian network modeling approach for the inclusion of temporal dependency between measurements through a dependency parameter and presents a generic monitoring model based on the exceedance of thresholds for a damage index. Additionally, the model is implemented in a computational framework for risk-based maintenance planning, developed for maintenance planning for wind turbines. The framework is applied for a numerical experiment, where the expected lifetime costs are found for strategies with monitoring with and without dependency between observations, and also for the case where dependency is present but is neglected when making decisions. The numerical experiment and associated parameter study show that neglecting dependency in the decision model when the observations are in fact dependent can lead to much higher costs than expected and to the selection of non-optimal strategies. Much lower costs (down to one quarter) can be obtained when the dependency is properly modeled. In the case of temporally dependent observations, an advanced decision model using a Bayesian network as a simple digital twin is needed to make monitoring feasible compared to only using inspections.

Risk from pesticide mixtures – The gap between risk assessment and reality

Pesticide applications in agricultural crops often comprise a mixture of plant protection products (PPP), and single fields face multiple applications per year leading to complex pesticide mixtures in the environment. Restricted to single PPP, the current European Union PPP regulation, however, disregards the ecological risks of pesticide mixtures. To quantify this additional risk, we evaluated the contribution of single pesticide active ingredients to the additive mixture risk for aquatic risk indicators (invertebrates and algae) in 464 different PPP used, 3446 applications sprayed and 830 water samples collected in Central Europe, Germany. We identified an average number of 1.3 different pesticides in a single PPP, 3.1 for complete applications often involving multiple PPP and 30 in stream water samples. Under realistic worst-case conditions, the estimated stream water pesticide risk based on additive effects was 3.2 times higher than predicted from single PPP. We found that in streams, however, the majority of regulatory threshold exceedances was caused by single pesticides alone (69% for algae, 81% for invertebrates). Both in PPP applications and in stream samples, pesticide exposure occurred in repeated pulses each driven by one to few alternating pesticides. The time intervals between pulses were shorter than the 8 weeks considered for ecological recovery in environmental risk assessment in 88% of spray series and 53% of streams. We conclude that pesticide risk assessment should consider an additional assessment factor to account for the additive, but also potential synergistic simultaneous pesticide mixture risk. Additionally, future research and risk assessment need to address the risk from the frequent sequential pesticide exposure observed in this study.

Identifying Chemicals and Mixtures of Potential Biological Concern Detected in Passive Samplers from Great Lakes Tributaries Using High-Throughput Data and Biological Pathways.

Waterborne contaminants were monitored in 69 tributaries of the Laurentian Great Lakes in 2010 and 2014 using semipermeable membrane devices (SPMDs) and polar organic chemical integrative samplers (POCIS). A risk‐based screening approach was used to prioritize chemicals and chemical mixtures, identify sites at greatest risk for biological impacts, and identify potential hazards to monitor at those sites. Analyses included 185 chemicals (143 detected) including polycyclic aromatic hydrocarbons (PAHs), legacy and current‐use pesticides, fire retardants, pharmaceuticals, and fragrances. Hazard quotients were calculated by dividing detected concentrations by biological effect concentrations reported in the ECOTOX Knowledgebase (toxicity quotients) or ToxCast database (exposure–activity ratios [EARs]). Mixture effects were estimated by summation of EAR values for chemicals that influence ToxCast assays with common gene targets. Nineteen chemicals—atrazine, N,N‐diethyltoluamide, di(2‐ethylhexyl)phthalate, dl‐menthol, galaxolide, p‐tert‐octylphenol, 3 organochlorine pesticides, 3 PAHs, 4 pharmaceuticals, and 3 phosphate flame retardants—had toxicity quotients >0.1 or EARs for individual chemicals >10–3 at 10% or more of the sites monitored. An additional 4 chemicals (tributyl phosphate, triethyl citrate, benz[a]anthracene, and benzo[b]fluoranthene) were present in mixtures with EARs >10–3. To evaluate potential apical effects and biological endpoints to monitor in exposed wildlife, in vitro bioactivity data were compared to adverse outcome pathway gene ontology information. Endpoints and effects associated with endocrine disruption, alterations in xenobiotic metabolism, and potentially neuronal development would be relevant to monitor at the priority sites. The EAR threshold exceedance for many chemical classes was correlated with urban land cover and wastewater effluent influence, whereas herbicides and fire retardants were also correlated to agricultural land cover. Environ Toxicol Chem 2021;40:2165–2182. Published 2021. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Impact of COVID climate on global crisis and biodiversity

Climate change has created potential major threats to global biodiversity. The multiple components of climate change are projected to affect all pillars of biodiversity, from genes over species to biome level. Of particular concerns are “tipping points” where the exceedance of ecosystem thresholds will possibly lead to irreversible shifts of ecosystems and their functioning. As biodiversity underlies all goods and services provided by ecosystems that are crucial for human survival and wellbeing, this paper presents potential effects of climate change on biodiversity, its plausible impacts on human society as well as the setting in addressing a global crisis. Species affected by climate change may respond in three ways: change, move or die. Local species extinctions or a rapidly affected ecosystem as a whole respectively might move toward its particular “tipping point”, thereby probably depriving its services to human society and ending up in a global crisis. Urgent and appropriate actions within various scenarios of climate change impacts on biodiversity, especially in tropical regions, are needed to be considered. Foremost a multi sectored approach on biodiversity issues with broader policies, stringent strategies and programs at international, national and local levels is essential to meet the challenges of climate change impacts on biodiversity.

Developments and applications of Shapley effects to reliability-oriented sensitivity analysis with correlated inputs

Reliability-oriented sensitivity analysis methods have been developed for understanding the influence of model inputs relative to events which characterize the failure of a system (e.g., a threshold exceedance of the model output). In this field, the target sensitivity analysis focuses primarily on capturing the influence of the inputs on the occurrence of such a critical event. This paper proposes new target sensitivity indices, based on the Shapley values and called “target Shapley effects”, allowing for interpretable sensitivity measures under dependent inputs. Two algorithms (one based on Monte Carlo sampling, and a given-data algorithm based on a nearest-neighbors procedure) are proposed for the estimation of these target Shapley effects based on the ℓ2 norm. Additionally, the behavior of these target Shapley effects are theoretically and empirically studied through various toy-cases. Finally, the application of these new indices in two real-world use-cases (a river flood model and a COVID-19 epidemiological model) is discussed.

Performance Evaluation of a Nowcasting Modelling Chain Operatively Employed in Very Small Catchments in the Mediterranean Environment for Civil Protection Purposes

The Hydro-Meteorological Centre (CMI) of the Environmental Protection Agency of Liguria Region, Italy, is in charge of the hydrometeorological forecast and the in-event monitoring for the region. This region counts numerous small and very small basins, known for their high sensitivity to intense storm events, characterised by low predictability. Therefore, at the CMI, a radar-based nowcasting modelling chain called the Small Basins Model Chain, tailored to such basins, is employed as a monitoring tool for civil protection purposes. The aim of this study is to evaluate the performance of this model chain, in terms of: (1) correct forecast, false alarm and missed alarm rates, based on both observed and simulated discharge threshold exceedances and observed impacts of rainfall events encountered in the region; (2) warning times respect to discharge threshold exceedances. The Small Basins Model Chain is proven to be an effective tool for flood nowcasting and helpful for civil protection operators during the monitoring phase of hydrometeorological events, detecting with good accuracy the location of intense storms, thanks to the radar technology, and the occurrence of flash floods.

Assessing the Performance of the Discrete Generalised Pareto Distribution in Modelling Non-Life Insurance Claims

The generalised Pareto distribution (GPD) offers a family of probability spaces which support threshold exceedances and is thus suitable for modelling high-end actuarial risks. Nonetheless, its distributional continuity presents a critical limitation in characterising data of discrete forms. Discretising the GPD, therefore, yields a derived distribution which accommodates the count data while maintaining the essential tail modelling properties of the GPD. In this paper, we model non-life insurance claims under the three-parameter discrete generalised Pareto (DGP) distribution. Data for the study on reported and settled claims, spanning the period 2012–2016, were obtained from the National Insurance Commission, Ghana. The maximum likelihood estimation (MLE) principle was adopted in fitting the DGP to yearly and aggregated data. The estimation involved two steps. First, we propose a modification to the μ and μ + 1 frequency method in the literature. The proposal provides an alternative routine for generating initial estimators for MLE, in cases of varied count intervals, as is a characteristic of the claim data under study. Second, a bootstrap algorithm is implemented to obtain standard errors of estimators of the DGP parameters. The performance of the DGP is compared to the negative binomial distribution in modelling the claim data using the Akaike and Bayesian information criteria. The results show that the DGP is appropriate for modelling the count of non-life insurance claims and provides a better fit to the regulatory claim data considered.