Urban heat mitigation by green and blue infrastructure: Drivers, effectiveness, and future needs.

The combination of urbanization and global warming leads to urban overheating and compounds the frequency and intensity of extreme heat events due to climate change. Yet, the risk of urban overheating can be mitigated by urban green-blue-grey infrastructure (GBGI), such as parks, wetlands, and engineered greening, which have the potential to effectively reduce summer air temperatures. Despite many reviews, the evidence bases on quantified GBGI cooling benefits remains partial and the practical recommendations for implementation are unclear. This systematic literature review synthesizes the evidence base for heat mitigation and related co-benefits, identifies knowledge gaps, and proposes recommendations for their implementation to maximize their benefits. After screening 27,486 papers, 202 were reviewed, based on 51 GBGI types categorized under 10 main divisions. Certain GBGI (green walls, parks, street trees) have been well researched for their urban cooling capabilities. However, several other GBGI have received negligible (zoological garden, golf course, estuary) or minimal (private garden, allotment) attention. The most efficient air cooling was observed in botanical gardens (5.0 ± 3.5°C), wetlands (4.9 ± 3.2°C), green walls (4.1 ± 4.2°C), street trees (3.8 ± 3.1°C), and vegetated balconies (3.8 ± 2.7°C). Under changing climate conditions (2070-2100) with consideration of RCP8.5, there is a shift in climate subtypes, either within the same climate zone (e.g., Dfa to Dfb and Cfb to Cfa) or across other climate zones (e.g., Dfb [continental warm-summer humid] to BSk [dry, cold semi-arid] and Cwa [temperate] to Am [tropical]). These shifts may result in lower efficiency for the current GBGI in the future. Given the importance of multiple services, it is crucial to balance their functionality, cooling performance, and other related co-benefits when planning for the future GBGI. This global GBGI heat mitigation inventory can assist policymakers and urban planners in prioritizing effective interventions to reduce the risk of urban overheating, filling research gaps, and promoting community resilience.

Microsatellite Analysis and Urediniospore Dispersal Simulations Support the Movement of Puccinia graminis f. sp. tritici from Southern Africa to Australia.

The Australian wheat stem rust (Puccinia graminis f. sp. tritici) population was shaped by the introduction of four exotic incursions into the country. It was previously hypothesized that at least two of these (races 326-1,2,3,5,6 and 194-1,2,3,5,6 first detected in 1969) had an African origin and moved across the Indian Ocean to Australia on high-altitude winds. We provide strong supportive evidence for this hypothesis by combining genetic analyses and complex atmospheric dispersion modeling. Genetic analysis of 29 Australian and South African P. graminis f. sp. tritici races using microsatellite markers confirmed the close genetic relationship between the South African and Australian populations, thereby confirming previously described phenotypic similarities. Lagrangian particle dispersion model simulations using finely resolved meteorological data showed that long distance dispersal events between southern Africa and Australia are indeed possible, albeit rare. Simulated urediniospore transmission events were most frequent from central South Africa (viable spore transmission on approximately 7% of all simulated release days) compared with other potential source regions in southern Africa. The study acts as a warning of possible future P. graminis f. sp. tritici dispersal events from southern Africa to Australia, which could include members of the Ug99 race group, emphasizing the need for continued surveillance on both continents.

Communicating Geographical Risks in Crisis Management: The Need for Research.

In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatiotemporal uncertainty. Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane. Estimating spatiotemporal probabilities is usually a difficult task, but that is not our primary concern. Rather, we ask how analysts can communicate spatiotemporal uncertainty to those handling the crisis. We comment on the somewhat limited literature on the representation of spatial uncertainty on maps. We note that many cognitive issues arise and that the potential for confusion is high. We note that in the early stages of handling a crisis, the uncertainties involved may be deep, i.e., difficult or impossible to quantify in the time available. In such circumstance, we suggest the idea of presenting multiple scenarios.

An early warning system to predict and mitigate wheat rust diseases in Ethiopia.

Wheat rust diseases pose one of the greatest threats to global food security, including subsistence farmers in Ethiopia. The fungal spores transmitting wheat rust are dispersed by wind and can remain infectious after dispersal over long distances. The emergence of new strains of wheat rust has exacerbated the risks of severe crop loss. We describe the construction and deployment of a near realtime early warning system (EWS) for two major wind-dispersed diseases of wheat crops in Ethiopia that combines existing environmental research infrastructures, newly developed tools and scientific expertise across multiple organisations in Ethiopia and the UK. The EWS encompasses a sophisticated framework that integrates field and mobile phone surveillance data, spore dispersal and disease environmental suitability forecasting, as well as communication to policy-makers, advisors and smallholder farmers. The system involves daily automated data flow between two continents during the wheat season in Ethiopia. The framework utilises expertise and environmental research infrastructures from within the cross-disciplinary spectrum of biology, agronomy, meteorology, computer science and telecommunications. The EWS successfully provided timely information to assist policy makers formulate decisions about allocation of limited stock of fungicide during the 2017 and 2018 wheat seasons. Wheat rust alerts and advisories were sent by short message service and reports to 10 000 development agents and approximately 275 000 smallholder farmers in Ethiopia who rely on wheat for subsistence and livelihood security. The framework represents one of the first advanced crop disease EWSs implemented in a developing country. It provides policy-makers, extension agents and farmers with timely, actionable information on priority diseases affecting a staple food crop. The framework together with the underpinning technologies are transferable to forecast wheat rusts in other regions and can be readily adapted for other wind-dispersed pests and disease of major agricultural crops.

Sensitivity of the modelled deposition of Caesium-137 from the Fukushima Dai-ichi nuclear power plant to the wet deposition parameterisation in NAME.

This paper describes an investigation into the impact of different meteorological data sets and different wet scavenging coefficients on the model predictions of radionuclide deposits following the accident at the Fukushima Dai-ichi nuclear power plant in March 2011. Three separate operational meteorological data sets, the UK Met Office global meteorology, the ECMWF global meteorology and the Japan Meteorological Agency (JMA) mesoscale meteorology as well as radar rainfall analyses from JMA were all used as inputs to the UK Met Office's dispersion model NAME (the Numerical Atmospheric-dispersion Modelling Environment). The model predictions of Caesium-137 deposits based on these meteorological models all showed good agreement with observations of deposits made in eastern Japan with correlation coefficients ranging from 0.44 to 0.80. Unexpectedly the NAME run using radar rainfall data had a lower correlation coefficient (R = 0.66), when compared to observations, than the run using the JMA mesoscale model rainfall (R = 0.76) or the run using ECMWF met data (R = 0.80). Additionally the impact of modifying the wet scavenging coefficients used in the parameterisation of wet deposition was investigated. The results showed that modifying the scavenging parameters had a similar impact to modifying the driving meteorology on the rank calculated from comparing the modelled and observed deposition.

World Meteorological Organization's model simulations of the radionuclide dispersion and deposition from the Fukushima Daiichi nuclear power plant accident.

Five different atmospheric transport and dispersion model's (ATDM) deposition and air concentration results for atmospheric releases from the Fukushima Daiichi nuclear power plant accident were evaluated over Japan using regional (137)Cs deposition measurements and (137)Cs and (131)I air concentration time series at one location about 110 km from the plant. Some of the ATDMs used the same and others different meteorological data consistent with their normal operating practices. There were four global meteorological analyses data sets available and two regional high-resolution analyses. Not all of the ATDMs were able to use all of the meteorological data combinations. The ATDMs were configured identically as much as possible with respect to the release duration, release height, concentration grid size, and averaging time. However, each ATDM retained its unique treatment of the vertical velocity field and the wet and dry deposition, one of the largest uncertainties in these calculations. There were 18 ATDM-meteorology combinations available for evaluation. The deposition results showed that even when using the same meteorological analysis, each ATDM can produce quite different deposition patterns. The better calculations in terms of both deposition and air concentration were associated with the smoother ATDM deposition patterns. The best model with respect to the deposition was not always the best model with respect to air concentrations. The use of high-resolution mesoscale analyses improved ATDM performance; however, high-resolution precipitation analyses did not improve ATDM predictions. Although some ATDMs could be identified as better performers for either deposition or air concentration calculations, overall, the ensemble mean of a subset of better performing members provided more consistent results for both types of calculations.