Search

Much of the Southeastern United States (SeUS) has experienced an increasing number of extreme precipitation events in recent decades. Characterizing these extreme precipitation events is critical for assessing risk from future hydroclimatic extremes and potential flash flooding. A threshold of one inch per hour (1IPH) was used to indicate an extreme precipitation event. Non-parametric tests were run to identify trends in 1IPH event frequency and locate time series change points. In the last 20 years, 1IPH events increased by 53 percent in the SeUS, and 21/61 stations recorded significant increasing trends. A change point is identified in 15/61 stations. June, July, and August are generally the peak time for 1IPH events, but Florida, Louisiana, and Mississippi recorded longer peak seasons. For the time between events, 17/61 stations recorded significant decreasing trends, implying that 1IPH events are increasing in frequency. Four teleconnection indices were positively correlated with 1IPH events. The SeUS experiences considerable tropical cyclone-induced extreme precipitation, yet only seven percent of 1IPH events overlapped with tropical cyclones. Therefore, the increasing frequency of 1IPH events is likely the result of a combination of baroclinic frontal zones or regional and mesoscale convective features. Causes for the increasing frequency of 1IPH events require further research. [ABSTRACT FROM AUTHOR]

In this study, we aimed to evaluate the performance of various machine learning (ML) classifiers to predict mode choice of movement-challenged persons (MCPs) based on data collected through a questionnaire survey of 384 respondents in Dhaka, Bangladesh. The mode choice set consisted of CNG-driven auto-rickshaw, bus, walking, motorized rickshaw, and non-motorized rickshaw, which was found as the most prominent mode used by MCPs. Age, sex, income, travel time, and supporting instrument (as an indicator of the level of disability) utilized by MCPs were explored as predictive variables. Results from the different split ratios with 10-fold cross-validation were compared to evaluate model outcomes. A split ratio of 60% demonstrates the optimum accuracy. It was found that Multi-nominal Logistic Regression (MNL), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) show higher accuracy for the split ratio of 60%. Overfitting of bus and walking as a travel mode was found as a source of classification error. Travel time was identified as the most important factor influencing the selection of walking, CNG, and rickshaw for MNL, KNN, and LDA. LDA and KNN depict the supporting instrument as a more important factor in mode choice than MNL. The selection of rickshaw as a mode follows a relatively normal probability distribution, while probability distribution is negatively skewed for the other three modes. [ABSTRACT FROM AUTHOR]

Information and material biological legacies that persist after catastrophic forest disturbance collectively constitute the ecological memory of the system and may strongly influence future stand development. Catastrophic disturbances often result in an influx of coarse woody debris (CWD), and this material legacy may provide beneficial microsites that affect successional and structural developmental pathways. We examined how microenvironmental characteristics influence the regeneration of woody plants in a subtropical woodland that experienced a large influx of CWD from a catastrophic wind disturbance. Specifically, we asked (1) what microenvironmental factors best explain woody plant density, richness, and height in the regeneration layer and (2) does woody plant density, richness, and height benefit from the large influx of CWD to a degree that competition dynamics and succession may be modified? Data were collected in a Pinus palustris woodland that had experienced an EF3 tornado and was subjected to a four-year prescribed fire rotation. We documented live woody plants <5 cm diameter at breast height, soil, and site characteristics and tested for differences in seedling and sapling density, species richness, and height in relation to CWD proximity. We used a random forest machine learning algorithm to examine the influence of microenvironmental conditions on the characteristics of woody plants in the regeneration layer. Woody plant density and species richness were not significantly different by proximity to CWD, but plants near CWD were slightly taller than plants away from CWD. The best predictors of woody plant density, richness, and height were abiotic site characteristics including slope gradient and azimuth, organic matter depth and weight, and soil water content. Results indicated that the regeneration of woody plants in this P. palustris woodland was not strongly influenced by the influx of CWD, but by other biological legacies such as existing root networks and soil characteristics. Our study highlights the need to consider ecological memory in forest management decision-making after catastrophic disturbance. Information and material legacies shape recovery patterns, but, depending on the system, some legacies will be more influential on successional and developmental pathways than others. [ABSTRACT FROM AUTHOR]

After being repeatedly struck by droughts in the last few decades, water managers and stakeholders in the Southeast U.S. dread the future extremes that climate change might cause. In this study, the length of future dry periods is assessed using a sub-ensemble of downscaled CMIP5 climate models, which are proven to perform well in precipitation estimations. The length of a dry spell with a twenty-year return period is estimated for the cold and warm seasons for two time periods; 2020–2059 and 2060–2099, and considering two emission scenarios: RCP 4.5 and 8.5. The estimates are then compared with historical dry spells and differences in length and geospatial distribution analyzed. Based on the findings of this paper, little change can be expected in dry spell length during the warm season. Greater changes are to be expected in the cold season in the southern half of Florida, where dry spells are expected to be up to twenty days shorter, while dry spells in Alabama, Mississippi and Tennessee are predicted to be up to twenty days longer. The changes predicted by the models are positively associated with emission trajectory and future time period. [ABSTRACT FROM AUTHOR]