Spatiotemporal variation in precipitation during rainy season in Beibu Gulf, South China, from 1961 to 2016

The spatiotemporal variation in precipitation is an important part of water cycle change, which is directly associatedwith the atmospheric environment and climate change. The high-resolution spatiotemporal change of precipitation is still unknown inmany areas despite its importance. This study analyzed the spatiotemporal variation in precipitation in Beibu Gulf, South China, during the rainy season (fromApril to September) in the period of 1961–2016. The precipitation datawere collected from 12 national standard rain-gauge observation stations. The spatiotemporal variation in precipitation was evaluated with incidence rate and contribution rate of precipitation. The tendency of variations was analyzed using the Mann–Kendall method. The precipitation in the rainy season contributed 80% to the total annual precipitation. In general, there was an exponential decreasing tendency between the precipitation incidence rate and increased precipitation durations. The corresponding contribution rate showed a downward trend after an initial increase. The precipitation incidence rate decreased with the rising precipitation grades, with a gradual increase in contribution rate. The precipitation incidence rate and contribution rate of 7–9 d durations showed the significant downward trends that passed the 95% level of significance test. The results provide a new understanding of precipitation change in the last five decades, which is valuable for predicting future climate change and extreme weather prevention and mitigation

Analysis of the visual spatiotemporal properties of American Sign Language.

Careful measurements of the temporal dynamics of speech have provided important insights into phonetic properties of spoken languages, which are important for understanding auditory perception. By contrast, analytic quantification of the visual properties of signed languages is still largely uncharted. Exposure to sign language is a unique experience that could shape and modify low-level visual processing for those who use it regularly (i.e., what we refer to as the Enhanced Exposure Hypothesis). The purpose of the current study was to characterize the visual spatiotemporal properties of American Sign Language (ASL) so that future studies can test the enhanced exposure hypothesis in signers, with the prediction that altered vision should be observed within, more so than outside, the range of properties found in ASL. Using an ultrasonic motion tracking system, we recorded the hand position in 3-dimensional space over time during sign language production of signs, sentences, and narratives. From these data, we calculated several metrics: hand position and eccentricity in space and hand motion speed. For individual signs, we also measured total distance travelled by the dominant hand and total duration of each sign. These metrics were found to fall within a selective range, suggesting that exposure to signs is a specific and unique visual experience, which might alter visual perceptual abilities in signers for visual information within the experienced range, even for non-language stimuli

Investigating the Effects of Rainfall on Traffic Operations on Florida Freeways

Rainfall affects the performance of traffic operations and endangers safety. A common and conventional method (rain gauges) for rainfall measurements mostly provide precipitation records in hourly and 15-minute intervals. However, reliability, continuity, and wide area coverage pose challenges with this data collection method. There is also a greater likelihood for data misrepresentation in areas where short duration rainfall is predominant, i.e., reported values may not reflect the actual equivalent rainfall intensity during subintervals over the entire reporting period. With recent weather and climate patterns increasing in severity, there is a need for a more effective and reliable way of measuring rainfall data used for traffic analyses. This study deployed the use of precipitation radar data to investigate the spatiotemporal effect of rainfall on freeways in Jacksonville, Florida. The linear regression analysis suggests a speed reduction of 0.75%, 1.54%, and 2.25% for light, moderate, and heavy rainfall, respectively. Additionally, headways were observed to increase by 0.26%, 0.54%, and 0.79% for light, moderate, and heavy rainfall, respectively. Measuring precipitation from radar data in lieu of using rain gauges has potential for improving the quality of weather data used for transportation engineering purposes. This approach addresses limitations experienced with conventional rain data, especially since conventional collection methods generally do not reflect the spatiotemporal distribution of rainfall

A Data Driven Approach to Quantify the Impact of Crashes

The growth of data has begun to transform the transportation research and policy, and open a new window for analyzing the impact of crashes. Currently for the crash impact analysis, researchers tend to rely on reported incident duration, which may not always be accurate. Further, impact of the crashes could linger a much longer time at upstream, even if the records are correct for the crash spot and it is a challenge to quantify the impact of a crash from the complex dynamics of the recurrent and non-recurrent congested condition. Therefore, a difference-in-speed approach is developed in this research to estimate the true crash impact duration using stationary sensor data and incident logs. The proposed method used the Kalman filter algorithm to establish traveler’s anticipated travel speed under incident-free condition and then employ the difference-in-speed approach to quantify the temporal and spatial extent of the crash. Moreover, potential applications such as statistical models for predicting the impact duration and total delay were developed in this research. Later, an analysis on distribution of travel rate was performed to describe and numerically show to what extent crashes influenced travel rates compared with the normal conditions at different periods of the day and by the crash types. This study can help to shape incident management policies for different types of crashes at different periods and illustrates the usages of data to improve the understanding of crashes, their impact, and their distribution in a spatial-temporal domain

An Atmospheric and Spatiotemporal Examination of Lightning-Initiated Terrestrial Gamma-ray Flashes Detected by the Fermi Satellite and TETRA II

Terrestrial Gamma-ray Flashes (TGFs) are sub-millisecond bursts of the highest naturally occurring light-energy found within Earth’s atmosphere. TGFs are associated with the electric fields produced in thunderstorms and are geolocated by coincident sferics from lightning strokes. Though billions of lightning strokes occur globally each year, fewer than 1,000 TGFs are detected via satellite and ground-based sensors and only a small fraction are geolocated via sferics. To date, few studies have focused on individual thunderstorms and climates that produce TGFs. This dissertation examines TGFs from two differing data samples: 1) NASA\u27s Fermi Gamma-Ray Burst Monitor (2013-2018) and 2) The TGF and Energetic Thunderstorm Rooftop Array (TETRA-II) (2016-2019) as a means to identify influences of climate, topography, and electric and atmospheric conditions that produce TGFs. Getis Ord Gi* and Anselin Moran\u27s I spatial cluster analyses reveal several statistically significant cluster patterns of the 1,341 sferic-associated TGFs detected in tropical latitudes by Fermi. Clusters tend to occur in coastal areas heavily influenced by land-sea interaction. A disproportionate number of Fermi TGFs (65\%) occur over ocean, where lightning is infrequent. Additionally, TGFs in this sample do not necessarily coincide with the highest lightning dense regions, suggesting the production of TGFs require a specific atmospheric conditions rather than occurring as a ratio function of lightning activity. TETRA II detected 20 sferic-associated TGF events across three detector arrays in tropical and subtropical climates. An examination of lightning frequency within 10 km of TETRA II indicates that events occur within mature thunderstorm cells exhibiting both high and low frequency lightning flash rates (1-46 flashes/min) within(8km-15.5km). One low-altitude, cold-weather event confirms a probable satellite detection bias as proposed by Chronis et al. 2016. NEXRAD-monitored events occur withinproduction, a relationship to the development of the mixed-phase updraft region is present

Evaluation of Traffic Incident Timeline to Quantify the Performance of Incident Management Strategies

Transportation agencies are introducing new strategies and techniques that will improve traffic incident management. Apart from other indicators, agencies measure the performance of the strategies by evaluating the incidents timeline. An effective strategy has to reduce the length of the incident timeline. An incident timeline comprises various stages in the incident management procedure, starting when the incident was detected, and ending when there is the recovery of normal traffic conditions. This thesis addresses three issues that are related to the traffic incident timeline and the incident management strategies. First, co-location of responding agencies has not been investigated as other incident management measures. Co-location of incident responders affects the incident timeline, but there is a scarcity of literature on the magnitude of the effects. Evaluation of the co-location strategy is reflected by the response and verification durations because its effectiveness relies on improving communication between agencies. Investigation of the response and verification duration of incidents, before and after operations of a co-located Traffic Management Center (TMC) is done by using hazard-based models. Results indicate that the incident type, percentage of the lane closure, number of responders, incident severity, detection methods, and day-of-the-week influence the verification duration for both the before- and after- period. Similarly, incident type, lane closure, number of responders, incident severity, time-of-the-day, and detection method influence the response duration for both study periods. The before and after comparison shows significant improvements in the response duration due to co-location of incident response agencies. Second, the incident clearance duration may not necessarily reflect how different types of incidents and various factors affect traffic conditions. The duration at which the incident influences traffic conditions could vary – shorter than the incident duration for some incidents and longer for others. This study introduces a performance measure called incident impact duration and demonstrates a method that was used for estimating it. Also, this study investigated the effects of using incident impact duration compared to the traditionally incident clearance duration in incident modeling. Using hazard-based models, the study analyzed factors that affect the estimated incident impact duration and the incident clearance duration. Results indicate that incident detection methods, the number of responders, Traffic Management Center (TMC) operations, traffic conditions, towing and emergency services influence the duration of an incident. Third, elements of the incident timeline before the clearance duration have been overlooked as factors that influence the clearance duration. Incident elements before the clearance duration include verification time, dispatch duration, and the travel time of responders to the incident scene. This study investigated the influence of incident timeline elements before clearance on the extent of the clearance duration. Also, this study analyzed the impact of other spatial and temporal attributes on the clearance duration. The analysis used a Cox regression model that is estimated using the Least Absolute Shrinkage and Selection Operator (LASSO) penalization method. LASSO enables variable selection from incidents data with a high number of covariates by automatically and simultaneously selecting variables and estimating the coefficients. Results suggest that verification duration, response travel duration, the percentage of lane closure, incident type, the severity of an incident, detection method, and crash location influence the clearance duration

Causative classification of river flood events

A wide variety of processes controls the time of occurrence, duration, extent, and severity of river floods. Classifying flood events by their causative processes may assist in enhancing the accuracy of local and regional flood frequency estimates and support the detection and interpretation of any changes in flood occurrence and magnitudes. This paper provides a critical review of existing causative classifications of instrumental and preinstrumental series of flood events, discusses their validity and applications, and identifies opportunities for moving toward more comprehensive approaches. So far no unified definition of causative mechanisms of flood events exists. Existing frameworks for classification of instrumental and preinstrumental series of flood events adopt different perspectives: hydroclimatic (large‐scale circulation patterns and atmospheric state at the time of the event), hydrological (catchment scale precipitation patterns and antecedent catchment state), and hydrograph‐based (indirectly considering generating mechanisms through their effects on hydrograph characteristics). All of these approaches intend to capture the flood generating mechanisms and are useful for characterizing the flood processes at various spatial and temporal scales. However, uncertainty analyses with respect to indicators, classification methods, and data to assess the robustness of the classification are rarely performed which limits the transferability across different geographic regions. It is argued that more rigorous testing is needed. There are opportunities for extending classification methods to include indicators of space–time dynamics of rainfall, antecedent wetness, and routing effects, which will make the classification schemes even more useful for understanding and estimating floods

Development and Applications of Similarity Measures for Spatial-Temporal Event and Setting Sequences

Similarity or distance measures between data objects are applied frequently in many fields or domains such as geography, environmental science, biology, economics, computer science, linguistics, logic, business analytics, and statistics, among others. One area where similarity measures are particularly important is in the analysis of spatiotemporal event sequences and associated environs or settings. This dissertation focuses on developing a framework of modeling, representation, and new similarity measure construction for sequences of spatiotemporal events and corresponding settings, which can be applied to different event data types and used in different areas of data science. The first core part of this dissertation presents a matrix-based spatiotemporal event sequence representation that unifies punctual and interval-based representation of events. This framework supports different event data types and provides support for data mining and sequence classification and clustering. The similarity measure is based on the modified Jaccard index with temporal order constraints and accommodates different event data types. This approach is demonstrated through simulated data examples and the performance of the similarity measures is evaluated with a k-nearest neighbor algorithm (k-NN) classification test on synthetic datasets. These similarity measures are incorporated into a clustering method and successfully demonstrate the usefulness in a case study analysis of event sequences extracted from space time series of a water quality monitoring system. This dissertation further proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been considered. While modeling event setting sequences, spatial and temporal scales are considered to define the bounds of the setting and incorporate dynamic variables along with static variables. Using a matrix-based representation and an extended Jaccard index, new similarity measures are developed to allow for the use of all variable data types. With these similarity measures coupled with other multivariate statistical analysis approaches, results from a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, support the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. To test the scalability of STES similarity measure in a larger dataset and an extended application in different fields, this dissertation compares and contrasts the prospective space-time scan statistic with the STES similarity approach for identifying COVID-19 hotspots. The COVID-19 pandemic has highlighted the importance of detecting hotspots or clusters of COVID-19 to provide decision makers at various levels with better information for managing distribution of human and technical resources as the outbreak in the USA continues to grow. The prospective space-time scan statistic has been used to help identify emerging disease clusters yet results from this approach can encounter strategic limitations imposed by the spatial constraints of the scanning window. The STES-based approach adapted for this pandemic context computes the similarity of evolving normalized COVID-19 daily cases by county and clusters these to identify counties with similarly evolving COVID-19 case histories. This dissertation analyzes the spread of COVID-19 within the continental US through four periods beginning from late January 2020 using the COVID-19 datasets maintained by John Hopkins University, Center for Systems Science and Engineering (CSSE). Results of the two approaches can complement with each other and taken together can aid in tracking the progression of the pandemic. Overall, the dissertation highlights the importance of developing similarity measures for analyzing spatiotemporal event sequences and associated settings, which can be applied to different event data types and used for data mining, sequence classification, and clustering

Climate-Triggered Drought as Causes for Different Degradation Types of Natural Forests: A Multitemporal Remote Sensing Analysis in NE Iran

Climate-triggered forest disturbances are increasing either by drought or by other climate extremes. Droughts can change the structure and function of forests in long-term or cause large-scale disturbances such as tree mortality, forest fires and insect outbreaks in short-term. Traditional approaches such as dendroclimatological surveys could retrieve the long-term responses of forest trees to drought conditions; however, they are restricted to individual trees or local forest stands. Therefore, multitemporal satellite-based approaches are progressing for holistic assessment of climate-induced forest responses from regional to global scales. However, little information exists on the efficiency of satellite data for analyzing the effects of droughts in different forest biomes and further studies on the analysis of approaches and large-scale disturbances of droughts are required. This research was accomplished for assessing satellite-derived physiological responses of the Caspian Hyrcanian broadleaves forests to climate-triggered droughts from regional to large scales in northeast Iran. The 16-day physiological anomalies of rangelands and forests were analysed using MODIS-derived indices concerning water content deficit and greenness loss, and their variations were spatially assessed with monthly and inter-seasonal precipitation anomalies from 2000 to 2016. Specifically, dimensions of forest droughts were evaluated in relations with the dimensions of meteorological and hydrological droughts. Large-scale effects of droughts were explored in terms of tree mortality, insect outbreaks, and forest fires using field observations, multitemporal Landsat and TerraClimate data. Various approaches were evaluated to explore forest responses to climate hazards such as traditional regression models, spatial autocorrelations, spatial regression models, and panel data models. Key findings revealed that rangelands’ anomalies did show positive responses to monthly and inter-seasonal precipitation anomalies. However, forests’ droughts were highly associated with increases in temperatures and evapotranspiration and were slightly associated with the decreases in precipitation and surface water level. The hazard intensity of droughts has affected the water content of forests higher than their greenness properties. The stages of moderate to extreme dieback of trees were significantly associated with the hazard intensity of the deficit of forests’ water content. However, the stage of severe defoliation was only associated with the hazard intensity of forests’ greenness loss. Climate hazards significantly triggered insect outbreaks and forest fires. Although maximum temperatures, precipitation deficit, availability of soil moisture and forest fires of the previous year could significantly trigger insect outbreaks, the maximum temperatures were the only significant triggers of forest fires from 2010‒2017. In addition to climate factors, environmental and anthropogenic factors could control fire severity during a dry season. The overall evaluation indicated the evidence of spatial associations between satellite-derived forest disturbances and climate hazards. Future studies are required to apply the approaches that could handle big-data, use the satellite data that have finer wavelengths for large-scale mapping of forest disturbances, and discriminate climate-induced forest disturbances from those that induced by other biotic and abiotic agents.Klimagbedingte Waldstörungen nehmen entweder durch Dürre oder durch andere Klimaextreme zu. Dürren können langfristig die Struktur und Funktion der Wälder verändern oder kurzfristig große Störungen wie Baumsterben, Waldbrände und Insektenausbrüche verursachen. Traditionelle Ansätze wie dendroklimatologische Untersuchungen könnten die langfristigen Reaktionen von Waldbäumen auf Dürrebedingungen aufzeigen, sie sind aber auf einzelne Bäume oder lokale Waldbestände beschränkt. Daher werden multitemporale satellitengestützte Ansätze zur ganzheitlichen Bewertung von klimabedingten Waldreaktionen auf regionaler bis globaler Ebene weiterentwickelt. Es gibt jedoch nur wenige Informationen über die Effizienz von Satellitendaten zur Analyse der Auswirkungen von Dürren in verschiedenen Waldbiotopen. Daher sind weitere Studien zur Analyse von Ansätzen und großräumigen Störungen von Dürren erforderlich. Diese Forschung wurde durchgeführt, um die aus Satellitendaten gewonnenen physiologischen Reaktionen der im Nordosten Irans gelegenen kaspischen hyrkanischen Laubwälder auf klimabedingte Dürren auf lokaler und regionaler Ebene zu bewerten. Auf der Grundlage der aus MODIS-Daten abgeleiteten Indizes wurden die 16-tägigen physiologischen Anomalien von Weideland und Wäldern in Bezug auf Wassergehaltsdefizit und Grünverlust analysiert und ihre Variationen räumlich mit monatlichen und intersaisonalen Niederschlagsanomalien von 2000 bis 2016 bewertet. Insbesondere wurden die Dimensionen der Walddürre in Verbindung mit den Dimensionen der meteorologischen und hydrologischen Dürre bewertet. Großräumige Auswirkungen von Dürren wurden in Bezug auf Baumsterblichkeit, Insektenausbrüche und Waldbrände mit Hilfe von Feldbeobachtungen, multitemporalen Landsat- und TerraClimate Daten untersucht. Verschiedene Ansätze wurden ausgewertet, um Waldreaktionen auf Klimagefahren wie traditionelle Regressionsmodelle, räumliche Autokorrelationen, räumliche Regressionsmodelle und Paneldatenmodelle zu untersuchen. Die wichtigsten Ergebnisse zeigten, dass die Anomalien von Weideland positive Reaktionen auf monatliche und intersaisonale Niederschlagsanomalien aufweisen. Die Dürren in den Wäldern waren jedoch in hohem Maße mit Temperaturerhöhungen und Evapotranspiration verbunden und standen in geringem Zusammenhang mit dem Rückgang von Niederschlägen und des Oberflächenwasserspiegels. Die Gefährdungsintensität von Dürren hat den Wassergehalt von Wäldern stärker beeinflusst als die Eigenschaften ihres Blattgrüns. Die Stufen mittlerer bis extremer Baumsterblichkeit waren signifikant mit der Gefährdungsintensität des Defizits des Wassergehalts der Wälder verbunden. Das Ausmaß der starken Entlaubung hing jedoch nur mit der Gefährdungsintensität des Grünverlustes der Wälder zusammen. Die Klimagefahren haben zu deutlichen Insektenausbrüchen und Waldbränden geführt. Obwohl Maximaltemperaturen, Niederschlagsdefizite, fehlende Bodenfeuchte und Waldbrände des Vorjahres deutlich Insektenausbrüche auslösen konnten, waren die Maximaltemperaturen die einzigen signifikanten Auslöser von Waldbränden von 2010 bis 2017. Neben den Klimafaktoren können auch umweltbedingte und anthropogene Faktoren den Schweregrad eines Brandes während einer Trockenzeit beeinflussen. Die Gesamtbewertung zeigt Hinweise auf räumliche Zusammenhänge zwischen aus Satellitendaten abgeleiteten Waldstörungen und Klimagefahren. Weitere Untersuchungen sind erforderlich, um Ansätze anzuwenden, die mit großen Datenmengen umgehen können, die Satellitendaten in einer hohen spektralen Auflösung für die großmaßstäbige Kartierung von Waldstörungen verwenden und die klimabedingte Waldstörungen von denen zu unterscheiden, die durch andere biotische und abiotische Faktoren verursacht werden