The mitigating effect of street trees, urban flora, and the suburban environment on seasonal peak UV indices: A case study from Brisbane, Australia.

Tree shade, particularly shade that obscures direct sunlight near peak periods of midday solar exposure can have a pronounced effect on potentially harmful ultraviolet radiation, and in turn, strongly influence the maximum daily UV index (UVI). In this study, the seasonal influence of tree shade on the UVI is evaluated from 210 hemispherical sky view images collected alongside public walkways and footpaths from 10 residential Brisbane suburbs. The effective sidewalk UV index is calculated underneath planted tree canopies, adjacent residential gardens, buildings and background tree species. Results are presented with respect to seasonal variations in the diurnal solar elevation for each month of the year at Brisbane's latitude. The research also examines the total reduction in UVI due to the presence of individual tree species, showing reductions in the midday UVI of up to 91% of an equivalent unimpeded sky hemisphere when overhead tree canopies are present. Important footpath tree species for peak midday UVI mitigation include Pongamia pinnata, Xanthostemon chrysanthus, Senna siamea, and Libidibia ferrea. The planting and maintenance of existing tree species already growing alongside residential Brisbane streets will improve the shade characteristics of suburbs and enhance UV protection for local residents.

The Playground Shade Index: A New Design Metric for Measuring Shade and Seasonal Ultraviolet Protection Characteristics of Parks and Playgrounds.

Current shading strategies used to protect outdoor playgrounds from harmful solar radiation include the placement of artificial cloth weaves or permanent roofing over a playground site, planting trees in proximity to playground equipment, and using vegetation or surface texture variations to cool playground surfaces. How and where an artificial shade structure is placed or a tree is planted to maximize the shade protection over specific playground areas, requires careful assessment of local seasonal sun exposure patterns. The Playground Shade Index (PSI) is introduced here as a design metric to enable shade and solar ultraviolet exposure patterns to be derived in an outdoor space using conventional aerial views of suburban park maps. The implementation of the PSI is demonstrated by incorporating a machine learning design tool to classify the position of trees from an aerial image, thus enabling the mapping of seasonal shade and ultraviolet exposure patterns within an existing 7180 m2 parkland. This is achieved by modeling the relative position of the sun with respect to nearby buildings, shade structures, and the identified evergreen and deciduous tree species surrounding an outdoor playground.

New double decomposition deep learning methods for river water level forecasting.

Forecasting river water levels or streamflow water levels (SWL) is vital to optimising the practical and sustainable use of available water resources. We propose a new deep learning hybrid model for SWL forecasting using convolutional neural networks (CNN), bi-directional long-short term memory (BiLSTM), and ant colony optimisation (ACO) with a two-phase decomposition approach at the 7-day, 14-day, and 28-day forecast horizons. The newly developed CBILSTM method is coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods to extract the most significant features within predictor variables to build a hybrid CVMD-CBiLSTM model. We integrate three distinct datasets (satellite-derived, climate mode indices, and ground-based meteorological observations) to improve the forecasting capability of the CVMD-CBiLSTM model, applied at nineteen different gauging stations in the Australian Murray River system. This proposed model returns a significantly accurate performance with ~98% of all prediction errors within less than ±0.020 m and a low relative root mean square of ~0.08%, demonstrating its superiority over several benchmark models. The results show that using the new hybrid deep learning algorithm with ACO feature selection can significantly improve the accuracy of forecasted river water levels, and therefore, the method is attractive for adopting remote sensing data to the model ground-based river flow for strategic water savings planning initiatives and dealing with climate change-induced extreme events such as drought events.

Adiabatic decay of internal solitons due to Earth's rotation within the framework of the Gardner-Ostrovsky equation.

The adiabatic decay of different types of internal wave solitons caused by the Earth's rotation is studied within the framework of the Gardner-Ostrovsky equation. The governing equation describing such processes includes quadratic and cubic nonlinear terms, as well as the Boussinesq and Coriolis dispersions: (ut + c ux + α u ux + α1 u2 ux + ß uxxx)x = γ u. It is shown that at the early stage of evolution solitons gradually decay under the influence of weak Earth's rotation described by the parameter γ. The characteristic decay time is derived for different types of solitons for positive and negative coefficients of cubic nonlinearity α1 (both signs of that parameter may occur in the oceans). The coefficient of quadratic nonlinearity α determines only a polarity of solitary wave when α1 < 0 or the asymmetry of solitary waves of opposite polarity when α1 > 0. It is found that the adiabatic theory describes well the decay of solitons having bell-shaped profiles. In contrast to that, large amplitude table-top solitons, which can exist when α1 is negative, are structurally unstable. Under the influence of Earth's rotation, they transfer first to the bell-shaped solitons, which decay then adiabatically. Estimates of the characteristic decay time of internal solitons are presented for the real oceanographic conditions.