Background: Thrips (order Thysanoptera) infestations of cotton seedlings result in plant injury, increasing the detrimental consequences of other challenges to production agriculture, such as abiotic stress or infestation by other pests. Using Frankliniella fusca as a thrips species of focus, we empirically developed a composite model of thrips phenology and cotton seedling susceptibility to predict site-specific infestation risk so that monitoring and other resources can be allocated efficiently, to optimize the timing of thrips control measures to maximize effectiveness, and to inform stakeholders about the dynamics of thrips infestation and cotton seedling injury at a time when thrips are evolving resistance to commonly-used pesticides. Results: A mixture distribution model of thrips infestation potential, fit to data describing F. fusca adult dispersal in time, proved best for predicting infestations of F. fusca on cotton seedlings. Thrips generations occurring each year as a function of weather are represented as a probability distribution. A model of cotton seedling growth was also developed to predict susceptibility as a function of weather. Combining these two models resulted in a model of seedling injury, which was validated and developed for implementation as a software tool. Conclusions: Experimental validation of the implemented model demonstrated the utility of its output in predicting infestation risk. Successful implementation and use of the software tool derived from this model was enabled by close cooperation with university extension personnel, agricultural consultants, and growers, underscoring the importance of stakeholder and expert input to the success of applied analytical research.
Abstract. The simultaneous rise of tropical-cyclone-induced flood waters across a large hazard management domain can stretch rescue and recovery efforts. Here we present a means to quantify the connectedness of maximum surge during a storm with geospatial statistics. Tide gauges throughout the extensive estuaries and barrier islands of North Carolina deployed and operating during hurricanes Matthew (n=82) and Florence (n=123) are used to compare the spatial compounding of surge for these two disasters. Moran's I showed the occurrence of maximum storm tide was more clustered for Matthew compared to Florence, and a semivariogram analysis produced a spatial range of similarly timed storm tide that was 4 times as large for Matthew than Florence. A more limited data set of fluvial flooding and precipitation in eastern North Carolina showed a consistent result – multivariate flood sources associated with Matthew were more concentrated in time as compared to Florence. Although Matthew and Florence were equally intense, they had very different tracks and speeds which influenced the timing of surge along the coast.
Abstract. The simultaneous rise of tropical cyclone induced flood waters across a large hazard management domain can stretch rescue and recovery efforts. Here we present a means to quantify the connectedness of maximum surge during a storm with geospatial statistics. Tide gauges throughout the extensive estuaries and barrier islands of North Carolina deployed and operating during Hurricanes Matthew (n = 82) and Florence (n = 123) are used to compare the spatial compounding of surge for these two disasters. Moran's I showed the occurrence of maximum storm tide was more clustered for Matthew compared to Florence, and a semivariogram analysis produced a spatial range of similarly timed storm tide that was four times as large for Matthew than Florence. A more limited data set of fluvial flooding and precipitation in eastern North Carolina showed a consistent result – multivariate flood sources associated with Matthew were more concentrated in time as compared to Florence. Although Matthew and Florence were equally intense, they had very different tracks and speeds, which influenced the timing of surge along the coast. We hope this method could be used for other landfalling tropical cyclones to better understand the drivers that lead to spatially compounded surge events.
Heatwaves are weather hazards that can influence societal and natural systems. Recently, heatwaves have increased in frequency, duration, and intensity, and this trend is projected to continue as a consequence of climate change. The study of heatwaves is hampered by the lack of a common definition, which limits comparability between studies. This applies in particular to the considered time scale. Here, we study which temporal scales of heatwaves are most impact-relevant for various types of impacts. For this purpose, we analyse societal metrics related to health (heat-related hospitalizations, mortality) and public attention (Google trends, news articles) in Germany. Country-averaged temperatures are calculated for the period of 2010-2020 and the warmest periods of all durations between 1 and 90 days are selected. Then, we assess and compare the societal response during those periods to identify the heatwave durations with the most pronounced impacts. The results differ slightly between the considered societal metrics but indicate overall that heatwaves induce the strongest societal response at time scales between 2 weeks and 2 months for Germany. Finally, we show that heatwave duration affects the societal response independent of, and additionally to, heatwave temperatures. This finding highlights the relevance of making informed choices on the considered time scale in heatwave analyses. The approach we introduce here can be extended to other societal indices, countries, and hazard types to reveal more meaningful definitions of climate extremes to guide future research on these events. An improved understanding of weather and climate hazards with their impacts on society, economy and environmental systems will support better preparation, response, and future adaptation.
The frequency, duration, and intensity of heat waves are expected to increase in the coming decades across the globe. In this context it is not clear how the related impacts differ between countries as a result of different vulnerability and exposure characteristics, such as overall climatic conditions and implemented adaptation strategies. Such strategies include the release of official heat warnings. It remains, however, to be tested to which extent they capture days with detectable societal heat responses. Here, we analyze and compare the response of several societal metrics (Google search attention, excess mortality, press attention) to hot temperatures in twelve European countries during 2010-2020. Applying a piecewise regression analysis, we identify countryspecific temperature thresholds above which societal responses start to increase. We find higher thresholds in countries with a warmer climate consistently across societal response variables, indicating overall lower heat vulnerability in southern Europe. At the same time, we find similar numbers of societally relevant hot days across European countries, computed as the sum of days on which more than 50% of the population in a country is experiencing temperatures above the detected thresholds. This indicates that the reduced vulnerability and exposure found for warmer countries are counteracted by hotter heat waves. Finally, the determined number of societally relevant hot days generally exceeds the number of days with heat warnings in five investigated European countries. This suggests that lower temperature thresholds would be better aligned with detectable societal responses and should therefore be considered in the context of (early) warning systems.
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