Search for: All records

Flooding during extreme weather events damages critical infrastructure, property, and threatens lives. Hurricane María devastated Puerto Rico (PR) on 20 September 2017. Sixty-four deaths were directly attributable to the flooding. This paper describes the development of a hydrologic model using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), capable of simulating flood depth and extent for the Añasco coastal flood plain in Western PR. The purpose of the study was to develop a numerical model to simulate flooding from extreme weather events and to evaluate the impacts on critical infrastructure and communities; Hurricane María is used as a case study. GSSHA was calibrated for Irma, a Category 3 hurricane, which struck the northeastern corner of the island on 7 September 2017, two weeks before Hurricane María. The upper Añasco watershed was calibrated using United States Geological Survey (USGS) stream discharge data. The model was validated using a storm of similar magnitude on 11–13 December 2007. Owing to the damage sustained by PR’s WSR-88D weather radar during Hurricane María, rainfall was estimated in this study using the Weather Research Forecast (WRF) model. Flooding in the coastal floodplain during Hurricane María was simulated using three methods: (1) Use of observed discharge hydrograph from the upper watershed as an inflow boundary condition for the coastal floodplain area, along with the WRF rainfall in the coastal flood plain; (2) Use of WRF rainfall to simulate runoff in the upper watershed and coastal flood plain; and (3) Similar to approach (2), except the use of bias-corrected WRF rainfall. Flooding results were compared with forty-two values of flood depth obtained during face-to-face interviews with residents of the affected communities. Impacts on critical infrastructure (water, electric, and public schools) were evaluated, assuming any structure exposed to 20 cm or more of flooding would sustain damage. Calibration equations were also used to improve flood depth estimates. Our model included the influence of storm surge, which we found to have a minimal effect on flood depths within the study area. Water infrastructure was more severely impacted by flooding than electrical infrastructure. From these findings, we conclude that the model developed in this study can be used with sufficient accuracy to identify infrastructure affected by future flooding events. 

Abstract Extreme heat events are becoming more frequent and intense. In cities, the urban heat island (UHI) can often intensify extreme heat exposure, presenting a public health challenge across vulnerable populations without access to adaptive measures. Here, we explore the impacts of increasing residential air-conditioning (AC) adoption as one such adaptive measure to extreme heat, with New York City (NYC) as a case study. This study uses AC adoption data from NYC Housing and Vacancy Surveys to study impacts to indoor heat exposure, energy demand, and UHI. The Weather Research and Forecasting (WRF) model, coupled with a multilayer building environment parameterization and building energy model (BEP–BEM), is used to perform this analysis. The BEP–BEM schemes are modified to account for partial AC use and used to analyze current and full AC adoption scenarios. A city-scale case study is performed over the summer months of June–August 2018, which includes three different extreme heat events. Simulation results show good agreement with surface weather stations. We show that increasing AC systems to 100% usage across NYC results in a peak energy demand increase of 20%, while increasing UHI on average by 0.42 °C. Results highlight potential trade-offs in extreme heat adaptation strategies for cities, which may be necessary in the context of increasing extreme heat events. 

Abstract As a consequence of the warm and humid climate of tropical coastal regions, there is high energy demand year-round due to air conditioning to maintain indoor comfort levels. Past and current practices are focused on mitigating peak cooling demands by improving heat balances by using efficient building envelope technologies, passive systems, and demand side management strategies. In this study, we explore city-scale solar photovoltaic (PV) planning integrating information on climate, building parameters and energy models, and electrical system performance, with added benefits for the tropical coastal city of San Juan, Puerto Rico. Energy balance on normal roof, flush-mounted PV roof, and tilted PV roof are used to determine PV power generation, air, and roof surface temperatures. To scale up the application to the whole city, we use the urbanized version of the Weather Research and Forecast (WRF) model with the building effect parameterization (BEP) and the building energy model (BEM). The city topology is represented by the World Urban Database Access Portal Tool (WUDAPT), local climate zones (LCZs) for urban landscapes. The modeled peak roof temperature is maximum for normal roof conditions and minimum when inclined PV is installed on a roof. These trends are followed by the building air conditioning (AC) demand from urbanized WRF, maximum for normal roof and minimum for inclined roof-mounted PV. The net result is a reduced daytime Urban Heat Island (UHI) for horizontal and inclined PV roof and increased nighttime UHI for the horizontal PV roof as compared with the normal roof. The ratio between coincident AC demand and PV production for the entire metropolitan region is further analyzed reaching 20% for compact low rise and open low rise buildings due to adequate roof area but reaches almost 100% for compact high rise and compact midrise buildings class, respectively. 

Given their high environmental variation over relatively short distances, mountains represent ideal systems for evaluating potential factors shaping diversity gradients. Despite a long‐standing interest in ecological gradients, ant diversity patterns and their related mechanisms occurring on mountains are still not well understood. Here, we (i) describe species diversity patterns (α and β) of leaf‐litter ants along the eastern slope of Cofre de Perote in Veracruz, Mexico; and (ii) evaluate climatic and spatial factors in determining these patterns.

Veracruz, Mexico.

Leaf‐litter ants.

We sampled 320 m²of leaf litter spread across eight equally spaced sites from sea level to 3500 m of elevation. We used regression models to predict α‐diversity patterns with climatic (temperature and precipitation) and spatial (geometric constraints) variables. We also assessed, through multiple regression based on distance matrices (MRM), the relative importance of habitat filtering and dispersal limitations for shaping total dissimilarity (βsor), turnover (βsim) and nestedness (βnes).

A hump‐shaped pattern was observed in the α‐diversity. This pattern is best explained by the temperature gradient. β‐diversity showed a nonlinear pattern along the elevational gradient with total dissimilarity and turnover components better explained by habitat filtering (i.e. temperature distances). Turnover had higher contribution to total dissimilarity rather than the nestedness component.

The significance effect of temperature on both α‐ and β‐diversity patterns reinforces its widespread importance in shaping litter ant diversity patterns across elevational gradients. The hump‐shaped pattern in species richness is probably the result of harsh abiotic conditions at the base and the top of the mountain combined with biotic attrition in lowland sites. The niche specialization of ant species in their optimal thermal zones may explain total dissimilarity and ant species replacement along the studied gradient. Taken all together, these results suggest a high relevance of temperature‐driven mechanisms in the origin and maintenance of the biodiversity of such insects and probably another ectothermic taxa.

 

Heat waves impact a wide array of human activities, including health, cooling energy demand, and infrastructure. Cities amplify many of these impacts by concentrating large populations and critical infrastructure in relatively small areas. In addition, heat waves are expected to become longer, more intense, and more frequent in North America. Here, we evaluate combined climate and urban surface impacts on localized heat wave metrics throughout the 21st century across two emissions scenarios (RCP4.5 and RCP8.5) for New York City (NYC), which houses the largest urban population in the United States. We account for local biases due to urban surfaces via bias correcting with observed records and urbanized 1‐km resolution dynamical downscaling simulations across selected time periods (2045–2049 and 2095–2099). Analysis of statistically downscaled global model output shows underestimation of uncorrected summer daily maximum temperatures, leading to lower heat wave intensity and duration projections. High‐resolution dynamical downscaling simulations reveal strong dependency of changes in event duration and intensity on geographical location and urban density. Event intensity changes are expected to be highest closer to the coast, where afternoon sea‐breezes have traditionally mitigated summer high temperatures. Meanwhile, event duration anomaly is largest over Manhattan, where the urban canopy is denser and taller.

 

Mexico is one of the most biodiverse countries in the world, with an important proportion of endemism mainly because of the convergence of the Nearctic and Neotropical biogeographic regions, which generate great diversity and species turnover at different spatial scales. However, most of our knowledge of the Mexican ant biota is limited to a few well‐studied taxa, and we lack a comprehensive synthesis of ant biodiversity information. For instance, most of the knowledge available in the literature on Mexican ant fauna refers only to species lists by states, or is focused on only a few regions of the country, which prevents the study of several basic and applied aspects of ants, from diversity and distribution to conservation. Our aims in this data paper are therefore (1) to compile all the information available regarding ants across the Mexican territory, and (2) to identify major patterns in the gathered data set and geographic gaps in order to direct future sampling efforts. All records were obtained from raw data, including both unpublished and published information. After exhaustive filtering and updating information and synonyms, we compiled a total of 21,731 records for 887 ant species distributed throughout Mexico from 1894 to 2018. These records were concentrated mainly in the states of Chiapas (n = 6,902, 32.76%) and Veracruz de Ignacio de la Llave (n = 4,329, 19.92%), which together comprise half the records. The subfamily with the highest number of records was Myrmicinae (n = 10,458 records, 48.12%), followed by Formicinae (n = 3,284, 15.11%) and Ponerinae (n = 1,914, 8.8%). Most ant records were collected in the Neotropical region of the country (n = 12,646, 58.19%), followed by the Mexican transition zone (n = 5,237, 24.09%) and the Nearctic region (n = 3,848, 17.72%). Native species comprised 95.46% of the records (n = 20,745). To the best of our knowledge, this is the most complete data set available to date in the literature for the country. We hope that this compilation will encourage researchers to explore different aspects of the population and community research of ants at different spatial scales, and to aid in the establishment of conservation policies and actions. There are no copyright restrictions. Please cite this data paper when using its data for publications or teaching events.