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Ecosystem state transitions can be ecologically devastating or be a restoration success. State transitions are common within aquatic systems worldwide, especially considering human‐mediated changes to land use and water use. We created a transferable conceptual framework to enable multiscale assessments of state resilience and early warnings of state transitions that can inform strategic restorations and avoid ecosystem collapse. The conceptual framework integrated machine learning predictions with ecosystem state concepts (e.g., state classification, gradients of vulnerability, and recovery potential leading to state transitions) and was devised to investigate possible environmental drivers. As an application of the framework, we generated prediction probabilities of submersed aquatic vegetation (SAV) presence at nearly 10,000 sites in the Upper Mississippi River (United States). Then, we used an interpretability method to explain model predictions to gain insights into possible environmental drivers and thresholds or linear responses of SAV presence and absence. Model accuracy was 89% without spatial bias. Average water depth, suspended solids, substrate, and distance to nearest SAV were the best predictors and likely environmental drivers of SAV habitat suitability. These environmental drivers exhibited nonlinear, threshold‐type responses for SAV. All the results are also presented in an online dashboard to explore results at many spatial scales. The habitat suitability model outputs and prediction explanations from many spatial scales (4 m to 400 km of river reach) can inform research and restoration planning. [ABSTRACT FROM AUTHOR]

Rainbands are essential to tropical cyclones (TCs), significantly affecting TC structure and intensity change. High‐resolution synthetic aperture radar (SAR) imagery can capture the footprints of rainbands caused by rain‐induced sea surface roughness modification. Using 464 SAR TC images, we investigated the rainband‐occurrence probability of TCs under different hemispheres, local times (LTs), intensities, and ocean basins. Results show that the rainband‐occurrence probability is highest in the downshear‐left quadrant for Northern Hemisphere TCs (downshear‐right quadrant for Southern Hemisphere TCs). For Northern Hemisphere TCs, the rainband‐occurrence probability is overall higher in the early morning (LT), and the peak region of rainband‐occurrence probability appears farther from the TC center in the evening (LT). Compared with weak TCs, the rainband‐occurrence probability becomes higher for strong TCs in the Northern Hemisphere. Furthermore, TCs have a higher rainband‐occurrence probability in the Northwest Pacific than in the North Atlantic and Northeast Pacific. Plain Language Summary: Rainbands are a salient feature of tropical cyclones (TCs) and are closely related to TC structure and intensity change. Synthetic aperture radar (SAR) can capture the sea surface imprint of rainbands beneath clouds caused by rain‐induced sea surface roughness modification. Using 464 SAR TC images, we made 464 rainband‐annotated data. The data were mapped to grid nodes spaced at 0.027 times the radius of max winds in a coordinate system with the origin at the TC center and the y‐axis in the vertical wind shear direction. Then, the data were composited to estimate and further investigate the rainband‐occurrence probability of TCs under different hemispheres, local times (LTs), intensities, and ocean basins. Results show that the rainband‐occurrence probability is highest in the downshear‐left quadrant for Northern Hemisphere TCs (downshear‐right quadrant for Southern Hemisphere TCs). For Northern Hemisphere TCs, the rainband‐occurrence probability is overall higher in the early morning (LT), and the peak region of rainband‐occurrence probability appears farther from the TC center in the evening (LT). Compared with weak TCs, the rainband‐occurrence probability becomes higher for strong TCs in the Northern Hemisphere. Furthermore, TCs have a higher rainband‐occurrence probability in the Northwestern Pacific than in the North Atlantic and Northeast Pacific. Key Points: The sea surface imprint of tropical cyclone (TC) rainbands in many synthetic aperture radar images reveals their occurrence probabilityThe rainband‐occurrence probability is overall higher in the early morning than in the evening. The feature is more obvious in strong TCsThe peak region of the probability appears farther from the TC center in the evening than in the early morning [ABSTRACT FROM AUTHOR]

A recent study by Liu et al. (2020) suggested that due to the saturation of equatorially trapped planetary waves with different dynamical types, temporal periods, meridional and baroclinic modes, complex layer structures of vertical velocity shear and hence turbulent mixing could frequently occur in the thermocline of the eastern equatorial Pacific. We investigated the occurrence of the interior turbulent mixing as indicated by shear instabilities, above the Equatorial Undercurrent (EUC) core at three equatorial sites along 140°W, 170°W, and 165°E, respectively, based mainly on data from the Tropical Atmosphere and Ocean (TAO) mooring array. We found that turbulent mixing bursts persisted in the thermocline of all three sites. Specifically, the interior turbulent mixing layers (ITMLs) could occur in probability of approximately 68%, 53%, and 48% at the three sites, respectively. The overall occurrence probability shows obvious and similar biannual variations at 140°W and 170°W, which is higher in boreal from late summer to winter and lower in spring. Vertically, the ITMLs are primarily located above the EUC core and prevail in deeper (shallower) layers from late summer to winter (spring). Most ITMLs (70%) lasted for hours to 3 days, and a few of them (15%) for more than 7 days. The thicknesses of ITMLs were concentrated between 15 and 55 m. At 165°E, the vertical distribution of ITML occurrence probability was different from that at 140°W and 170°W, as it did not show a preference for depths; the durations of ITMLs are short (also from hours to several days) and their thicknesses were between 5 and 25 m. These properties, particularly the high occurrence probability, and short durations demonstrated the persistence of thermocline mixing in the western to eastern equatorial Pacific thermocline and confirmed the generation mechanism by persistent equatorial waves as well. [ABSTRACT FROM AUTHOR]

Abstract Rainbands are essential to tropical cyclones (TCs), significantly affecting TC structure and intensity change. High‐resolution synthetic aperture radar (SAR) imagery can capture the footprints of rainbands caused by rain‐induced sea surface roughness modification. Using 464 SAR TC images, we investigated the rainband‐occurrence probability of TCs under different hemispheres, local times (LTs), intensities, and ocean basins. Results show that the rainband‐occurrence probability is highest in the downshear‐left quadrant for Northern Hemisphere TCs (downshear‐right quadrant for Southern Hemisphere TCs). For Northern Hemisphere TCs, the rainband‐occurrence probability is overall higher in the early morning (LT), and the peak region of rainband‐occurrence probability appears farther from the TC center in the evening (LT). Compared with weak TCs, the rainband‐occurrence probability becomes higher for strong TCs in the Northern Hemisphere. Furthermore, TCs have a higher rainband‐occurrence probability in the Northwest Pacific than in the North Atlantic and Northeast Pacific.

Drought is one of the most destructive and important climate phenomena, whose effect is usually more important on a regional scale. The importance of this phenomenon is more evident in the Karkheh basin due to its size and important role in providing the country's water resources. We aim to monitor hydrologic drought using the accurate calculation of standardizes streamflow index (SSI) in one month time scale based on fitting frequency distribution to monthly data and goodness of fit test for each station in Karkheh basin for 30 years (1986-2016). The findings of this research showed that the generalized Pareto distribution was selected as the most appropriate distribution in most months, unlike the previous research that fitted and used only the Gama distribution on the data. The time series of the standard flow index indicated the occurrence of super-drought in 2008 to 2015 years. Also, the significant impact of the construction of hydraulic structures upstream of the basin on the average flow rate was observed in some stations. The results of direct and annual monitoring of the drought situation showed that the Karkheh basin has experienced hydrological drought in recent years, and the drought trend is increasing.

Drought is one of the most destructive and important climate phenomena, whose effect is usually more important on a regional scale. The importance of this phenomenon is more evident in the Karkheh basin due to its size and important role in providing the country's water resources. We aim to monitor hydrologic drought using the accurate calculation of standardizes streamflow index (SSI) in one month time scale based on fitting frequency distribution to monthly data and goodness of fit test for each station in Karkheh basin for 30 years (1986-2016). The findings of this research showed that the generalized Pareto distribution was selected as the most appropriate distribution in most months, unlike the previous research that fitted and used only the Gama distribution on the data. The time series of the standard flow index indicated the occurrence of super-drought in 2008 to 2015 years. Also, the significant impact of the construction of hydraulic structures upstream of the basin on the average flow rate was observed in some stations. The results of direct and annual monitoring of the drought situation showed that the Karkheh basin has experienced hydrological drought in recent years, and the drought trend is increasing. [ABSTRACT FROM AUTHOR]

In July 2021, Typhoon In‐Fa produced record‐breaking extreme precipitation events (hereafter referred to as the 2021 EPEs) in central and eastern China, and caused serious socioeconomic losses and casualties. However, it is still unknown whether the 2021 EPEs can be attributed to anthropogenic climate change (ACC) and how the occurrence probabilities of precipitation events of a similar magnitude might evolve in the future. The 2021 EPEs in central (eastern) China occurred in the context of no linear trend (a significantly increasing trend at a rate of 4.44%/decade) in the region‐averaged Rx5day (summer maximum 5‐day accumulated precipitation) percentage precipitation anomaly (PPA), indicating that global warming might have no impact on the 2021 EPE in central China but might have impacted the 2021 EPE in eastern China by increasing the long‐term trend of EPEs. Using the scaled generalized extreme value distribution, we detected a slightly negative (significantly positive) association of the Rx5day PPA time series in central (eastern) China with the global mean temperature anomaly, suggesting that global warming might have no (a detectable) contribution to the changes in occurrence probability of precipitation extremes like the 2021 EPEs in central (eastern) China. Historical attributions (1961–2020) showed that the likelihood of the 2021 EPE in central/eastern China decreased/increased by approximately +47% (−23% to +89%)/+55% (−45% to +201%) due to ACC. By the end of the 21st century, the likelihood of precipitation extremes similar to the 2021 EPE in central/eastern China under SSP585 is 14 (9–19)/15 (9–20) times higher than under historical climate conditions. Plain Language Summary: Central and eastern China experienced record‐breaking extreme precipitation events (EPEs) in July 2021. The summer maximum 5‐day accumulated precipitation (Rx5day) of the 2021 EPE in central (eastern) China exceeded the climatology during 1981–2010 by 116.8% (96.1%). Under climate conditions of 1961 and 2021 and based on observations, the 2021 EPE was estimated to be a 1‐in‐76‐year event and 1‐in‐212‐year event in central China, respectively, and a 1‐in‐>10,000‐year and a 1‐in‐256‐year event in eastern China, respectively. Here, we estimate the contribution of anthropogenic climate change (ACC) to precipitation extremes of a similar magnitude to the 2021 EPEs and project how their probability might change in the future, based on climate projections from Coupled Model Intercomparison Project Phase 6. We estimate that ACC is responsible for +47%/+55% of the decrease/increase in the occurrence probability of the 2021 EPE in central/eastern China. By the end of the 21st century under a high emission scenario, the probability of occurrence of precipitation extremes similar to the 2021 EPE in central/eastern China is projected to be 14/15 times larger than under historical climate conditions. Our results highlight that EPEs in central and eastern China are becoming more frequent and more extreme in response to increasing greenhouse gas emissions. Key Points: Global warming might have no (a detectable) contribution to the occurrence probability of a precipitation extreme like the 2021 extreme precipitation event (EPE) in central (eastern) ChinaAnthropogenic climate change contributed to +47%/+55% of the decrease/increase in the occurrence probability of the 2021 EPE in central/eastern ChinaBy the end of the 21st century, the likelihood of such event in central/eastern China would be increased by 14/15 times under SSP585 [ABSTRACT FROM AUTHOR]

Abstract In July 2021, Typhoon In‐Fa produced record‐breaking extreme precipitation events (hereafter referred to as the 2021 EPEs) in central and eastern China, and caused serious socioeconomic losses and casualties. However, it is still unknown whether the 2021 EPEs can be attributed to anthropogenic climate change (ACC) and how the occurrence probabilities of precipitation events of a similar magnitude might evolve in the future. The 2021 EPEs in central (eastern) China occurred in the context of no linear trend (a significantly increasing trend at a rate of 4.44%/decade) in the region‐averaged Rx5day (summer maximum 5‐day accumulated precipitation) percentage precipitation anomaly (PPA), indicating that global warming might have no impact on the 2021 EPE in central China but might have impacted the 2021 EPE in eastern China by increasing the long‐term trend of EPEs. Using the scaled generalized extreme value distribution, we detected a slightly negative (significantly positive) association of the Rx5day PPA time series in central (eastern) China with the global mean temperature anomaly, suggesting that global warming might have no (a detectable) contribution to the changes in occurrence probability of precipitation extremes like the 2021 EPEs in central (eastern) China. Historical attributions (1961–2020) showed that the likelihood of the 2021 EPE in central/eastern China decreased/increased by approximately +47% (−23% to +89%)/+55% (−45% to +201%) due to ACC. By the end of the 21st century, the likelihood of precipitation extremes similar to the 2021 EPE in central/eastern China under SSP585 is 14 (9–19)/15 (9–20) times higher than under historical climate conditions.

The occurrence probability of equatorial plasma bubbles and the associated spread F (ESF) irregularities have been derived from ground-based and space-borne measurements. In general, ESF occurrence depends on season and longitude and is high in equinoctial months and low around June solstice. In the West Pacific sector, previous statistical results show that the ESF occurrence probability increases gradually and continuously from March to August. In this study, we use trans-ionospheric VHF data received at Kwajalein Atoll in 2012 to derive the occurrence characteristics of scintillation. It is found that the occurrence probability of strong scintillation had two maxima in June and September and a minimum in July in the evening and midnight sector but only one maximum in June in the post-midnight sector. The monthly variations of scintillation occurrence at Kwajalein are different from almost all previous studies on ESF and scintillation at or near this longitude. To identify the cause for the June peak and the July minimum of scintillation, the ion density and velocity data measured by the Communication/Navigation Outage Forecasting System (C/NOFS) satellite in 2011–2012 are used to derive the ESF occurrence and the post-sunset vertical ion drift near Kwajalein. The ESF occurrence probability and the ion drift measured by the C/NOFS satellite showed two maxima in May/June and August/September and a minimum in July, verifying that the June peak and the July minimum of the VHF scintillation are realistic and caused by the similar variations in the ionospheric ion drift and density. [ABSTRACT FROM AUTHOR]

While detecting targets on the sea by radar that looks downward, sea spikes similar to the target echoes cause false alarms, especially at low grazing angles. To suppress the interference of sea spikes on target detection, it is important to study the factors influencing the occurrence probability of sea spikes during radar inspections. Based on measured sea clutter data, this study uses the amplitude threshold, duration time, and interval time to identify sea spikes from sea clutter and uses the pulse number per unit time to characterize their occurrence probability. The influences of the grazing angle, wave height, wave direction, and wind speed on the occurrence probability of sea spikes were analyzed. The results indicate that the occurrence probability of sea spikes increases exponentially with decreasing grazing angle in the range from 0.7° to 7.1°, and linearly with increasing wind speed. Wave direction has little or no influence on the probability of sea spikes. At the smaller grazing angles, from 0.7° to 1.7°, the influence of wave height on the probability of sea spikes' occurrence is obvious, showing a linear trend, but in the range of 2.6° to 7.1°, the influence is not obvious. In addition, the occurrence probability of sea spikes is found greater at wave heights from 0.9 m to 1.1 m relative to other wave heights, which is worthy of further study. [ABSTRACT FROM AUTHOR]

Based on the Logistic regression model, this study analyzed the relationship between the occurrence of forest fires and meteorological factors, determined the main meteorological factors, and established the prediction model for Heihe region in Heilongjiang Province. Historical forest fire and meteorological data from 1981 to 2015 in Heihe region were randomly divided into 80% building data and 20% validation data. R software was used to establish five sub-models and screen out significant meteorological factors by Logistic stepwise regression (P＜0.05) . Moreover, a forest fire prediction model was established using the Logistic regression model to analyze the main meteorological factors. The results showed that daily maximum wind speed, daily maximum temperature, Daily average water vapor pressure, daily average relative humidity, and daily precipitation are the significant meteorological factors affecting the occurrence of forest fires (P＜0.05) . The former two meteorological factors are positively correlated with forest fire occurrence, whereas the latter three factors are negatively correlated with such occurrence. The prediction accuracy of the Logistic regression model is 82.83%～86.12%. Area under the curve ranges from 0.884 to 0.920. The Logistic regression model has good prediction accuracy and high goodness of fit, which is suitable for forest fire occurrence probability prediction research based on meteorological factors in Heihe region. [ABSTRACT FROM AUTHOR]

The occurrence probability of plasma irregularities has long been used to quantify that of ionospheric scintillations. We use the Hilbert-Huang transform to examine the occurrence probability and the instantaneous total amplitude of plasma irregularities in the low-latitude nighttime ionosphere observed by the low-inclination satellites, ROCSAT-1 during 1999–2004 and FORMOSAT-7/COSMIC-2 (F7C2) in 2020, as well as a high-inclination satellite, DEMETER, during 2006–2010. Generally, the longitudinal distribution of the occurrence probability is similar to that of the total amplitude, except that the probability of ion density irregularity observed by F7C2 and DEMETER yields nonphysical anomalous enhancements over the South American sector during May–August of 2007–2010 and 2020, respectively. S4 scintillations observed by F7C2 show that the anomalous enhancement results from the low ambient density in calculating the occurrence probability during the low solar activity year of 2020. We suggest the instantaneous total amplitude as a better indicator for describing ionospheric plasma irregularities. [ABSTRACT FROM AUTHOR]

The objective of this study is to estimate the occurrence probabilities of severe accident phenomena based on a fuzzy elicitation technique. Normally, it is difficult to determine these probabilities due to the lack of information on severe accident progression and the highly uncertain values currently in use. In this case, fuzzy set theory (FST) can be best exploited. First, questions were devised for expert elicitation on technical issues of severe accident phenomena. To deal with ambiguities and the imprecision of previously developed (reference) probabilities, fuzzy aggregation methods based on FST were employed to derive the occurrence probabilities of severe accidents via four phases: 1) choosing experts, 2) quantifying weighting factors for the experts, 3) aggregating the experts’ opinions, and 4) defuzzifying the fuzzy numbers. In this way, this study obtained expert elicitation results in the form of updated occurrence probabilities of severe accident phenomena in the OPR-1000 plant, after which the differences between the reference probabilities and the newly acquired probabilities using fuzzy aggregation were compared, with the advantages of the fuzzy technique over other approaches explained. Lastly, the impact of applying the updated severe accident probabilities on containment integrity was quantitatively investigated in a Level 2 PSA model.

The occurrence probability of equatorial plasma bubbles and the associated spread F (ESF) irregularities have been derived from ground-based and space-borne measurements. In general, ESF occurrence depends on season and longitude and is high in equinoctial months and low around June solstice. In the West Pacific sector, previous statistical results show that the ESF occurrence probability increases gradually and continuously from March to August. In this study, we use trans-ionospheric VHF data received at Kwajalein Atoll in 2012 to derive the occurrence characteristics of scintillation. It is found that the occurrence probability of strong scintillation had two maxima in June and September and a minimum in July in the evening and midnight sector but only one maximum in June in the post-midnight sector. The monthly variations of scintillation occurrence at Kwajalein are different from almost all previous studies on ESF and scintillation at or near this longitude. To identify the cause for the June peak and the July minimum of scintillation, the ion density and velocity data measured by the Communication/Navigation Outage Forecasting System (C/NOFS) satellite in 2011–2012 are used to derive the ESF occurrence and the post-sunset vertical ion drift near Kwajalein. The ESF occurrence probability and the ion drift measured by the C/NOFS satellite showed two maxima in May/June and August/September and a minimum in July, verifying that the June peak and the July minimum of the VHF scintillation are realistic and caused by the similar variations in the ionospheric ion drift and density.

Freak waves, a typical nonlinear wave disaster, pose a serious threat to human life and property safety. Therefore, the study of Freak waves is crucial for marine disaster prevention. The study employs the High-Order Spectral method to investigate the generation and characteristics of Freak waves and the evolution of wave field parameters in the long time evolution process of modulated wave trains. It reveals that the appearance of Freak waves is accompanied by the aggregation and separation of wave groups. The evolution of freak degree γ , kurtosis μ 4 , skewness μ 3 , wave steepness and spectral bandwidth B all exhibit distinct stage characteristics. In the stage of modulation instability, only one type and a constant number of wave groups exist, with wave field parameters exhibiting periodic recurrence. In contrast, various types and numbers of wave groups emerge in the stage of wave group interaction. And the occurrence probability of Freak waves increases, the fluctuation range of γ also significantly expands, waves become more irregular. μ 4 , μ 3 , local wave steepness ε loc and B all increase significantly. Meanwhile, excessive nonlinearity leads to frequent wave breaking and a decline in mean wave steepness ε m. • Elucidated Freak wave generation under distinct nonlinear mechanisms during long time evolution of modulated wave trains. • Elucidated the variation of wave groups in the generation process of Freak waves under different mechanisms. • Revealed the evolution characteristics and probability distribution of freak degree under different nonlinear mechanisms. • Clarified the occurrence probability and height distribution characteristics of Freak waves. • Explained the evolution of kurtosis, skewness, local and mean wave steepness under different initial conditions. [ABSTRACT FROM AUTHOR]

The rainy and windy weather conditions during the passage of tropical cyclone (TC) usually cause large-amplitude rain-wind induced vibration (RWIV) of stay cables in bridges, threatening the fatigue life of stay cables and even the safety of bridge structures. In this study, the probability analysis of RWIV of stay cables under TC-induced wind and rainfall environment is performed using both wind tunnel test and numerical simulation techniques. RWIV amplitudes associated with various combinations of wind speed and rainfall rate are obtained by wind tunnel test on a prototypic sectional cable model. Surrogate models including polynomial and Kriging methods are introduced to fit the experiment results, allowing for the construction of three-dimensional surface representing RWIV amplitude or the capacity of the stay cable. A case study is performed to the Jintang Cable-stayed Bridge located at the southeast coast of China. Ten-thousand-year synthetic TC tracks around the bridge site are then generated by Monte Carlo technique. Subsequently, wind speeds and rainfall rates at the bridge site for every storm are calculated using the three-dimensional TC wind field model and HuRRDE (Hurricane Rainfall Rate and Distribution Estimator) model, achieving the joint probability distribution of wind speed, wind direction and rainfall rate. The occurrence probabilities of various RWIV amplitudes during TC process are finally calculated to conduct the probability analysis. The framework and results in this study can be utilized as a guideline and reference for the design and early warning of stay cables against RWIV. • Fragility analysis for RWIV of stay cables under TC-induced wind and rainfall environment is performed. • Joint probability distribution of wind speed and rainfall rate is achieved using Monte Carlo technique. • Surrogate models of RWIV amplitudes are developed by fitting experiment results. • A case study is applied to a cable-stayed bridge located at the southeast coast of China. [ABSTRACT FROM AUTHOR]

One of the most pressing questions in ecology and conservation centers on disentangling the relative impacts of concurrent global change drivers, climate and land‐use/land‐cover (LULC), on biodiversity. Yet studies that evaluate the effects of both drivers on species' winter distributions remain scarce, hampering our ability to develop full‐annual‐cycle conservation strategies. Additionally, understanding how groups of species differentially respond to climate versus LULC change is vital for efforts to enhance bird community resilience to future environmental change. We analyzed long‐term changes in winter occurrence of 89 species across nine bird groups over a 90‐year period within the eastern United States using Audubon Christmas Bird Count (CBC) data. We estimated variation in occurrence probability of each group as a function of spatial and temporal variation in winter climate (minimum temperature, cumulative precipitation) and LULC (proportion of group‐specific and anthropogenic habitats within CBC circle). We reveal that spatial variation in bird occurrence probability was consistently explained by climate across all nine species groups. Conversely, LULC change explained more than twice the temporal variation (i.e., decadal changes) in bird occurrence probability than climate change on average across groups. This pattern was largely driven by habitat‐constrained species (e.g., grassland birds, waterbirds), whereas decadal changes in occurrence probabilities of habitat‐unconstrained species (e.g., forest passerines, mixed habitat birds) were equally explained by both climate and LULC changes over the last century. We conclude that climate has generally governed the winter occurrence of avifauna in space and time, while LULC change has played a pivotal role in driving distributional dynamics of species with limited and declining habitat availability. Effective land management will be critical for improving species' resilience to climate change, especially during a season of relative resource scarcity and critical energetic trade‐offs. [ABSTRACT FROM AUTHOR]

The post-fire debris flow (PFDF) is a commonly destructive hazard that may persist for several years following the wildfires. Susceptibility mapping is an effective method for mitigating hazard risk. Yet, the majority of susceptibility prediction models only focus on spatial probability in the specific period while ignoring the change associated with time. This study improves the predictive model by introducing the temporal factor. The area burned by the 30 March 2020 fire in Xichang City, China is selected as an illustrative example, and the susceptibility of the PFDF was predicted for different periods of seven months after the wildfires. 2214 hydrological response events, including 181 debris flow events and 2033 flood events from the 82 watersheds are adopted to construct the sample dataset. Seven conditioning factors consist of temporal factors and spatial factors are extracted by the remote sensing interpretation, field investigations, and in situ tests, after correlation and importance analysis. The logistic regression (LR) is adopted to establish prediction models through 10 cross-validations. The results show that the susceptibility to PFDF has significantly reduced over time. After two months of wildfire, the proportions of very low, low, moderate, high, and very high susceptibility are 1.2%, 3.7%, 24.4%, 23.2%, and 47.6%, respectively. After seven months of wildfire, the proportions of high and very high susceptibility decreased to 0, while the proportions of very low to medium susceptibility increased to 35.4%, 35.6%, and 28.1%, respectively. The reason is that the drone seeding of grass seeds and artificial planting of trees accelerated the natural recovery of vegetation and soil after the fire. This study can give insight into the evolution mechanism of PFDF over time and reflect the important influence of human activity after the wildfire. [ABSTRACT FROM AUTHOR]

Evidential reasoning satisfies a weaker condition than probability theory and can deal with uncertain scenarios. However, there is still no complete and consistent theoretical system in the evidential reasoning now. In this paper, a novel method of evidential network reasoning based on the logical reasoning rules and conflict measure is proposed. The state of nodes is described through several basic probability assignments(BPAs). Two logical reasoning rules are defined to show that the occurrence of some antecedents in the parent nodes will lead to the sub-BPAs of the child node occurrence. Then the occurrence probability of sub-BPA with the child node is computed through antecedent probabilities in the parent nodes. Besides, the support degree of sub-BPAs is computed based on the evidential distance. The final BPA of the child node is obtained by combining weighted sub-BPAs. Two examples about fault diagnosis and threat assessment and prediction are given respectively to illustrate the effectiveness of the proposed method. Different from the previous work, this paper defines a new reasoning rule called OR rule, and the computation based on pure logic makes the reasoning process more intuitive. Besides, the weights of sub-BPAs are computed considering both the occurrence probability and conflict measure.

Construction projects in Iraq face many dangers that cause exceeding the estimated cost of the project and not completing the project on time, and since the risk management process in construction projects is of great importance in controlling and reducing the impact of risks in construction projects, so it is necessary to identify these risks and evaluate them correctly in order to increase accuracy and the health of the subsequent stages of the risk management process in construction projects. This paper aims to identify the most important risks in construction projects in Iraq and to conduct a qualitative assessment of the identified risks and arrange them according to their importance. The researcher adopted the questionnaire method as a tool to determine the risks and used the technique of probability and effect matrix to conduct the qualitative assessment of the identified risks. The study found that there are 48 risk factors that constitute the most dangerous factor in construction projects in Iraq, and 10 of the determining factors were within the high level of risk, and at the forefront of which was the inability of the owner to finance the project. Conclusions Construction projects face many risks that cause exceeding the estimated project cost and not completing the project within the specified time and the identification of these risks is an important phase for managing and controlling them. Therefore, the authors attempt to identify risks that face the construction projects in Diyala governorate which help in construction project management. From the results, the authors concluded the follows: • The study identified 48 risk factors, which represent the most common risks in construction projects in Iraq. • From the result of qualitative risk analyses, 9 risk factors showed in the red zone of probability and impact matrix which reflects the high probability and impact of such factors, and these risks represented the most significant risk factors. • The inability of the owner to finance the project represents the main risk factor that faces the construction projects and has the first rank according to influence level.

Bloom occurrence probability prediction is a critical issue for freshwater resource management and protection. As the mechanism of algal blooms is not understood, the construction of prediction model mainly depends on statistical data. Therefore, knowledge on prior bloom occurrence derived from statistical data plays a significant role in establishing a prediction model. In this study, a naïve Bayesian model incorporating prior information was constructed to predict algal bloom occurrence probabilities 1–7 days in advance under different weather conditions in Dianchi Lake. The proposed model utilised the following data from the MODIS images, the floating algae index (FAI) for the previous 7 days and five meteorological variables (mean wind speed, air pressure, relative humidity on the prediction day, accumulated sunshine hours in the previous 3 days, and accumulated air temperature in the previous 7 days). The prior probabilities of each pixel were calculated on a monthly timescale to highlight the bloom’s temporal-spatial differences, and the 1–7 day posterior probabilities were calculated by combining the prior and conditional probabilities. The predictive effect was tested by the area under the receiver-operating characteristic curve (AUC), and the results showed the number of pixels with estimation predictions classified as ‘Good+’ and ‘Excellent’ were 91.3%, 91.4%, 82.7%, 83.3%, 89.0%, 86.6%, and 90.0% for the predictions on days 1–7, respectively. Additionally, the independent validation datasets showed that the percentage of correctly classified instances (CCI%) of approximately 90% of the pixels were greater than 50%. The proposed algal bloom prediction model based on the naïve Bayesian method has pixel-level prediction abilities, is applicable to other inland water systems, and can provide a reference for water environment risk forecasting and management.

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Vulnerability and hazard are terms that are generally applied to drought risk assessment. Vulnerability can be defined as the capacity of a region to cope with and resist the impacts of natural hazards, while hazard can be defined as the likelihood of a natural or human-induced physical event. In this study, principal component analysis (PCA) was used to generate an aggregate drought vulnerability index (DVI) using multiple socio-economic indicators and copula-based drought frequency analysis was performed to calculate a drought hazard index (DHI) considering meteorological drought occurrence patterns. Finally, regional drought risk was evaluated by combining the DVI and DHI among cities within the Chungcheong province, South Korea. Based on the drought risk index (DRI), Jecheon-si (DRI = 0.50) and Gongju-si (DRI = 0.65) were identified as the most hazardous cities in Chungcheongbuk-do and Chungcheongnam-do, respectively. The overall process of drought risk assessment developed in this study is useful for planning drought management and mitigation at the local level. [ABSTRACT FROM AUTHOR]

Wildfires are responsible for major land use and land cover (LUC) changes. These events are frequent and catastrophic in Portugal and are responsible for great damage and loss of human life. In this study, a map to assess the probability of wildfire occurrence (PWO) in mainland Portugal was created for the period 1975-2017 (first half of 2017). The PWO was obtained by the superimposition of all layers by adding all the burned areas for the total period. It was observed that the occurrences and extent of the burned areas are highly variable from year to year. The PWO map was cross-referenced with LUC geoinformation to evaluate the LUC types that were more affected by the wildfires. The results presented and the PWO maps are important for the management and planning of forest areas and for the creation of guidelines to implement preventive and reactive actions in case of wildfires.

Worldwide there is a huge amount of radioactive waste, including disused sources, decommissioning waste, and naturally occurring radioactive material (NORM) waiting for final disposal, the so-called storage facilities. Results of safety assessment of such facilities are usually required in decision-making during design, modification, safety improvements, periodic safety reviews, and licensing activities. Quantitative safety assessment methodologies used in many areas of nuclear industry involves the evaluation of risk through the definition of scenarios, likelihoods, and consequences, for normal operation and accidents. There are many techniques that can be used in each one of these steps. For screening the accident scenarios, qualitative techniques such as failure modes and effect analysis (FMEA) are available. For a quantitative assessment of occurrence probabilities of undesired events, logical and graphical tools such as fault tree analysis (FTA) are employed. Consequence assessments involve the dose assessment in the workers and the impact of the released materials in the environment and of public exposure to radiation. This work analyzes the application of these traditional safety assessment approaches to storage facilities and how they can be applied to complement specific methodologies used in this area, such as the Safety Assessment Driving Radioactive Waste Management Solutions (SADRWMS), implemented in Safety Assessment Framework (SAFRAN) software tool, made available by International Atomic Energy Agency (IAEA).

The post-fire debris flow (PFDF) is a commonly destructive hazard that may persist for several years following the wildfires. Susceptibility mapping is an effective method for mitigating hazard risk. Yet, the majority of susceptibility prediction models only focus on spatial probability in the specific period while ignoring the change associated with time. This study improves the predictive model by introducing the temporal factor. The area burned by the 30 March 2020 fire in Xichang City, China is selected as an illustrative example, and the susceptibility of the PFDF was predicted for different periods of seven months after the wildfires. 2214 hydrological response events, including 181 debris flow events and 2033 flood events from the 82 watersheds are adopted to construct the sample dataset. Seven conditioning factors consist of temporal factors and spatial factors are extracted by the remote sensing interpretation, field investigations, and in situ tests, after correlation and importance analysis. The logistic regression (LR) is adopted to establish prediction models through 10 cross-validations. The results show that the susceptibility to PFDF has significantly reduced over time. After two months of wildfire, the proportions of very low, low, moderate, high, and very high susceptibility are 1.2%, 3.7%, 24.4%, 23.2%, and 47.6%, respectively. After seven months of wildfire, the proportions of high and very high susceptibility decreased to 0, while the proportions of very low to medium susceptibility increased to 35.4%, 35.6%, and 28.1%, respectively. The reason is that the drone seeding of grass seeds and artificial planting of trees accelerated the natural recovery of vegetation and soil after the fire. This study can give insight into the evolution mechanism of PFDF over time and reflect the important influence of human activity after the wildfire.

Aim: Local suitability estimated with species distribution models (SDMs) could indicate the maximum abundance attainable by species. Often the abundance–suitability relationship is wedge‐shaped because species do not reach their maximum potential in every suitable location. We explore how SDM performance, the amount of information lost when converting continuous abundance into presence–absence data and species prevalence influence the abundance–suitability relationship. Location: Undefined. Time period: Undefined. Major taxa: Virtual species. Methods: Different abundance scenarios were simulated and converted into presence–absence maps. SDMs were parameterized using simulated predictors with different explanatory capacities. Quantile regressions were performed to characterize the abundance–suitability relationship. The strength of the wedge‐shaped pattern was estimated using the difference in slopes between the 90th and 50th quantile lines, the goodness‐of‐fit of the 90th quantile regressions was calculated, and variation of both parameters was analysed as a function of SDM performance, prevalence and maximum abundance. Results: The degree of wedge shape was directly related to maximum abundance. It also depended on SDM quality; the higher the discrimination capacity, the less wedge‐shaped the abundance–suitability relationship and higher the goodness‐of‐fit of the 90th quantile regressions. Prevalence exerted a significant effect on the shape of the relationship by influencing—together with maximum abundance—the extent of information loss. Main conclusions: SDMs cannot predict actual abundance even when the true determinants of abundance are included as predictors. Because presence–absence and abundance data are related but are different variables, wedge‐shaped patterns are unavoidable. Discrimination capacity and prevalence affect the strength of the wedge‐shaped pattern, so understanding their effects is necessary before any biological explanation is provided for the abundance–suitability relationship. Suitability maps derived from SDMs may transmit a false sense of precision at a local scale and should not be used as a perfect surrogate when abundance information is required. [ABSTRACT FROM AUTHOR]

An n-gram in music is defined as an ordered sequence of n notes of a melodic sequence m. Pm(n) is calculated as the average of the occurrence probability without self-matches of all n-grams in m. Then, Pm(n) is compared to the averages Shuffm(n) and Equipm(n), calculated from random sequences constructed with the same length and set of symbols in m either by shuffling a given sequence or by distributing the set of symbols equiprobably. For all n, both Pm(n)−Shuffm(n), Pm(n)−Equipm(n)≥0, and this differences increases with n and the number of notes, which proves that notes in musical melodic sequences are chosen and arranged in very repetitive ways, in contrast to random music. For instance, for n≤5 and for all analyzed genres we found that 1.6<−log(Pm(n))<8.6, while 1.6<−log(Shuffm(n))<14.5 and 1.9<−log(Equipm(n))<18.3. Pm(n) of baroque and classical genres are larger than the romantic genre one. Pm(n) vs n is very well fitted to stretched exponentials for all songs. This simple method can be applied to any musical genre and generalized to polyphonic sequences. [ABSTRACT FROM AUTHOR]

In an era of rapid climate and land transformation, it is increasingly important to understand how future changes impact natural systems. Scenario studies can offer the structure and perspective needed to understand the impacts of change and help inform management and conservation decisions. We implemented a scenario-based approach to assess how two high impact drivers of landscape change influence the distributions of managed wildlife species (n = 10) in the New England region of the northeastern United States. We used expert derived species distribution models (SDMs) and scenarios developed by the New England Landscape Futures Project (NELFP) to estimate how species distributions change under various trajectories (n = 5) of landscape change. The NELFP scenarios were built around two primary drivers – Socio-Economic Connectedness (SEC) and Natural Resource Planning and Innovation (NRPI) – and provide plausible alternatives for how the New England region may change over 50 years (2010–2060). Our models generally resulted in species occurrence and richness declines by 2060. The majority of species (7 of 10) experienced declines in regional occurrence for all NELFP scenarios, and one species experienced a projected increase in mean regional occurrence for all scenarios. Our results indicate that the NRPI and SEC drivers strongly influenced projected distribution changes compared to baseline projections. NRPI had a greater impact on distribution change for five species (coyote, moose, striped skunk, white-tailed deer, and wild turkey), while SEC had a greater impact on four species (American black bear, bobcat, raccoon, and red fox); one species (gray fox) was equally influenced by both NRPI and SEC. These results emphasize the importance of integrating both natural resource planning and socio-economic factors when addressing issues of distribution change and offer insights that can inform proactive management and conservation planning.

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Invasive mosquito species and the pathogens they transmit represent a serious health risk to both humans and animals. Thus, predictions on their potential geographic distribution are urgently needed. In the case of a recently invaded region, only a small number of occurrence data is typically available for analysis, and absence data are not reliable. To overcome this problem, we have tested whether it is possible to determine the climatic ecological niche of an invasive mosquito species by using both the occurrence data of other, native species and machine learning. The approach is based on a support vector machine and in this scenario applied to the Asian bush mosquito (Aedes japonicus japonicus) in Germany. Presence data for this species (recorded in the Germany since 2008) as well as for three native mosquito species were used to model the potential distribution of the invasive species. We trained the model with data collected from 2011 to 2014 and compared our predicted occurrence probabilities for 2015 with observations found in the field throughout 2015 to evaluate our approach. The prediction map showed a high degree of concordance with the field data. We applied the model to medium climate conditions at an early stage of the invasion (2011–2015), and developed an explanation for declining population densities in an area in northern Germany. In addition to the already known distribution areas, our model also indicates a possible spread to Saarland, southwestern Rhineland-Palatinate and in 2015 to southern Bavaria, where the species is now being increasingly detected. However, there is also evidence that the possible distribution area under the mean climate conditions was underestimated. [ABSTRACT FROM AUTHOR]

This study focuses on the longitudinal variations of the ionospheric F1 layer occurrence probabilities from middle to high latitudes (40–70°) in the daytime in summer under low solar activity conditions. The ionospheric electron density profiles retrieved from the Constellation Observing System for Meteorology, Ionosphere, and Climate observations are used. The patterns of the F1 layer occurrence probabilities are constructed with latitude, longitude, and local time in both hemispheres. The results illustrate a new feature that the occurrence probability of the F1 layer changes with longitude in both hemispheres. Specifically, the F1 layer generally occurs more frequently in the longitudes within ~160°E–90°W at 40–70°N and within 0–150°E at 40–70°S. The peak occurrence probabilities occur around 150°W (>0.6) and 60°E (>0.8) in the Northern and Southern Hemispheres, respectively. The valleys with values near 0 are located at about 120°W at 40–70°S. A minor peak (~0.4) also occurs within ~0–90°E at 40–70°N. These locations depend on latitude and local time. A simulation using the Thermosphere Ionosphere Electrodynamics General Circulation Model （(TIEGCM）) reproduces the observed longitudinal pattern well. Further TIEGCM simulations show that the higher F1 layer occurrence probabilities can be largely explained by longitudinal variations of the O/N2 ratios and also that the dynamic processes, such as the neutral winds above the F1 layer, have additional contributions. These results reveal that the low O/N2 ratios deplete the ionospheric electron density above the F1 layer at middle and high latitudes and thus makes the F1 layer stand out. Key Points: Longitudinal variations of the F1 layer occurrence probabilities at middle and high latitudes are foundThe F1 layer occurrence probabilities peak around 150°W at 40–70°N and 60°E at 40–70°S in summer, solar minimum conditionsHigh F1 layer occurrence probabilities are associated with low O/N2 ratios and the consequent electron density depletion above the hmF1 [ABSTRACT FROM AUTHOR]

Increasing water demand due to population growth, economic development, and changes in rainfall patterns due to climate change are likely to alter the duration and magnitude of droughts. Understanding the relationship between low-flow conditions and controlling factors relative to the magnitude of a drought is important for establishing sustainable water resource management based on changes in future drought risk. This study demonstrates the relationship between low-flow and controlling factors under different severities of drought. I calculated the drought runoff coefficient for six types of occurrence probability, using past observation data of annual total discharge and precipitation in the Japanese archipelago, where multiple climate zones exist. Furthermore, I investigated the pattern of change in the drought runoff coefficient in accordance with the probability of occurrence of drought, and relationships among the coefficient and geological, land use, and topographical factors. The drought runoff coefficient for multiple drought magnitudes exhibited three behaviors, corresponding to the pattern of precipitation. Results from a generalized linear model (GLM) revealed that the controlling factors differed depending on the magnitude of the drought. During high-frequency droughts, the drought runoff coefficient was influenced by geological and vegetation factors, whereas land use and topographical factors influenced the drought runoff coefficient during low-frequency droughts. These differences were caused by differences in runoff, which dominated stream discharge, depending on the magnitude of the drought. Therefore, for effective water resource management, estimation of the volume of drought runoff needs to consider the pattern of precipitation, geology, land use, and topography.

Questions: Small islands are outstanding model systems to study community assembly. Due to harsher environmental conditions on smaller islands compared to larger ones, environmental filtering may preclude some species, potentially resulting in island size‐dependent species pools. We tested whether the species pool size follows a similar species–area relationship as the observed richness. This can provide new insight into community assembly processes and the elusive small‐island effect (SIE), which states that species richness on smaller islands is less dependent on area than on larger islands. Location: Raja Ampat Archipelago, Indonesia. Methods: We studied the woody vegetation on sixty small islands ranging from 3 m2 to 11,806 m2. For each recorded species, we estimated its area requirements and compared them against random colonization models. We developed a novel method to calculate probabilistic species pools for each island. We compared different species–area models for observed species richness and our index of species pool size to test whether the SIE results from differences in species pool size. Results: We found that most species were restricted to islands significantly larger than expected from random colonization. The occurrence probability of all species increased with island size, indicating a lack of species that are specialized to the conditions on small islands. We found a SIE in observed species richness, but not in species pool size. Conclusion: Woody plants in the studied island system have specific requirements that are linked to island area and determine island‐specific species pools. Lower community completeness on smaller islands compared to larger ones indicated that the SIE is shaped by local limiting processes that have no impact on the species pool, but control how much of it is realized on an island. Together, these results clearly indicate non‐random plant community assembly on small islands. [ABSTRACT FROM AUTHOR]

An increasing demand for assessing the hazard of debris-flow accumulation areas is critical to risk management and policy implication for debris-flow hazard mitigation. In this paper, a framework for hazard assessment in debris flow-prone areas is proposed. Based on the hazard definition, debris-flow hazard is computed by its occurrence probability and disruptive intensity. Debris-flow occurrence probability is defined as a joint probability considering both hydrological and debris supply condition. Debris-flow disruptive intensity is introduced to express the inundated and impact damage of debris flow by the distribution of flow depth and velocity. The distribution is based on an extended geometric model of debris-flow fans, including the maximum length, width, and thickness (or flow depth) of debris-flow fans, and the channel outlet width. A hazard matrix, correlated to debris-flow occurrence probability and disruptive intensity, is used to map the hazard on debris-flow accumulation areas. The proposed procedure is applied to assess the debris-flow hazard on the accumulation area of Shuida gully, located in the Longchi town, Sichuan province of China. Finally, the article considers the effects of hazard maps. [ABSTRACT FROM AUTHOR]

Aim: Data on species occurrences are far more common than data on species abundances. However, a central goal of large‐scale ecology is to understand the spatial distribution of abundance. It has been proposed that species distribution models trained on species occurrence records may capture variation in species abundance. Here, we gauge support for relationships between species abundance and predicted climatic suitability from species distribution models, and relate the slope of this relationship to species traits, evolutionary relationships and sampling completeness. Location: USA. Time period: 1658–2017. Major taxa studied: Mammal and tree species. Methods, Results: To explore the generality of abundance–suitability relationships, we trained species distribution models on species occurrence and species abundance data for 246 mammal species and 158 tree species, and related model‐predicted occurrence probabilities to population abundance predictions. Further, we related the resulting abundance–suitability relationship coefficients to species traits, geographic range sizes, evolutionary relationships and the number of occurrence records to investigate a potential trait or sampling basis for abundance–suitability relationship detectability. We found little evidence for consistent abundance–suitability relationships in mammal (r¯ = .045) or tree (r¯ = −.005) species, finding nearly as many negative and positive relationships. These relationships had little explanatory power, and coefficients were unrelated to species traits, range size or evolutionary relationships. Main conclusions: Our findings suggest that species climatic suitability based on occurrence data may not be reflected in species abundances, suggesting a need to investigate nonclimatic sources of species abundance variation. [ABSTRACT FROM AUTHOR]

Wildfires are responsible for major land use and land cover (LUC) changes. These events are frequent and catastrophic in Portugal and are responsible for great damage and loss of human life. In this study, a map to assess the probability of wildfire occurrence (PWO) in mainland Portugal was created for the period 1975-2017 (first half of 2017). The PWO was obtained by the superimposition of all layers by adding all the burned areas for the total period. It was observed that the occurrences and extent of the burned areas are highly variable from year to year. The PWO map was cross-referenced with LUC geoinformation to evaluate the LUC types that were more affected by the wildfires. The results presented and the PWO maps are important for the management and planning of forest areas and for the creation of guidelines to implement preventive and reactive actions in case of wildfires. [ABSTRACT FROM AUTHOR]

Biological invasions are deemed to be the second most important global driver of biodiversity loss, right behind habitat destruction and fragmentation. In this study, we aimed at testing if community invasibility, defined as the vulnerability to invasion of a community, could be associated with the characteristics of a given habitat, as described by the composition and structure of its native species. Based on a probabilistic sampling of the alien flora occurring in the temperate wetland Lake Doberdò (Friuli Venezia Giulia region, NE Italy) and using a null-model-based approach, the observed occurrence of Invasive Alien Species (IAS) within sampling units was randomized within habitats. While testing the degree of invasibility for each habitat within the wetland, our null hypothesis postulated that habitats are equally invaded by IAS, as IAS can spread homogeneously in the environment thanks to their plasticity in functional traits that makes them able to cope with different ecological conditions. The obtained results comparing observed IAS frequencies, abundance and richness to those obtained by the null model randomizations show that, for all habitats, invasion was selective. Specifically, a marked preference for habitats with an intermediate disturbance level, a high nutrients level and a medium-high light availability was observed, while an avoidance was detected for habitats characterized by lower levels of nutrients and light availability or extreme conditions caused by prolonged submersion. This method allows us to provide useful information using a simple-to-run simulation for the management of the IAS threat within protected areas. Moreover, the method allows us to infer important ecological characteristics leading to habitat invasion without sampling the environmental characteristic of the habitats, which is an expensive operation in terms of time and money.