Month: January 2024

Coast and the community: understanding public perceptions towards coastal ecosystems in the Northern Province, Sri Lanka

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Abstract

Coastal ecosystems are diverse and provide essential global functions, supporting biodiversity conservation, economic growth, and human welfare. However, they are under threat from human activities such as overexploitation, coastal degradation, and anthropogenic impacts. The present study aimed to explore the level of public awareness and understanding of coastal ecosystems in four coastal cities in the Northern Province of Sri Lanka namely Jaffna, Kilinochchi, Mullaitivu, and Mannar. A three-part questionnaire survey was administered to respondents (n = 641) chosen using a systematic sampling method across four coastal cities in the North from April to November 2022. A key highlight from the study is that approximately 75% of the respondents demonstrated significant awareness and knowledge regarding the degradation of the coastal ecosystem in their respective local areas and 81% reported observing an increase in the trend. The influence of social media for awareness was found in nearly half of the respondents followed by mass media (21%). Encouragingly, there is a positive trend among the respondents in recognizing the roles and responsibilities of the government and local community (58%) in addressing coastal ecosystem degradation and promoting conservation efforts. Overall, respondents from Jaffna and Mannar demonstrated a comparatively higher awareness of coastal ecosystems and their degradation than those from Kilinochchi and Mullaitivu. Given their role as transitional zones between terrestrial and marine environments, their intricate socio-ecological dynamics, and the requirement for integrated planning and management strategies, it becomes evident that gaining insight into the level of public awareness of coastal ecosystems is of utmost importance.

Evaluation of Cumulus and Microphysical Parameterization Schemes of the WRF Model for Precipitation Prediction in the Paraíba do Sul River Basin, Southeastern Brazil

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Abstract

Three cumulus and five microphysics parameterization schemes of the Weather Research and Forecasting model (WRF) are the basis for simulating ten specific meteorological events of the Paraíba do Sul River Basin (PSRB) in Southeast Brazil. The cases studied are frontal wave systems, thermodynamic instability, and the South Atlantic Convergence Zone (SACZ). Each parameterization combination generated 15 simulations for each event, resulting in 150 tests. The primary domain has a horizontal resolution of 8.0 km and the nested 2.6 km resolution. Three analysis tools underlie the study: (i) punctual verification of the first 24 h of precipitation forecast, using the Taylor diagram; (ii) verification of the prediction of precipitation using categorical binary variable and (iii) the Model´s ability to reproduce patterns of the spatial distribution of precipitation. The Taylor diagram suggests that the combination of the Morrison Double moment and Multiscale Kain–Fritsch schemes produce the best results. The categorical verification indicates that, for dynamic/convective events, the Morrison Double moment and Multiscale Kain–Fritsch and WRF Double Moment 6–class sets showed the best indices. Some configurations presented reliable results for exclusively convective events, and WRF Single–moment 6–class and Grell–Freitas Ensemble is the best combination. The Morrison Double moment and Multiscale Kain–Fritsch parameterizations yielded the best performance for the spatial distribution. Overall, the schemes tested perform better for the upstream region, i.e., the area of greater water uptake for the basin.

Multidimensional well-being of US households at a fine spatial scale using fused household surveys

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Abstract

Social science often relies on surveys of households and individuals. Dozens of such surveys are regularly administered by the U.S. government. However, they field independent, unconnected samples with specialized questions, limiting research questions to those that can be answered by a single survey. The presented data comprise the fusion onto the American Community Survey (ACS) microdata of select donor variables from the Residential Energy Consumption Survey (RECS) of 2015, the National Household Travel Survey (NHTS) of 2017, the American Housing Survey (AHS) of 2019, and the Consumer Expenditure Survey - Interview (CEI) for the years 2015–2019. This results in an integrated microdataset of household attributes and well-being dimensions that can be analyzed to address research questions in ways that are not currently possible. The underlying statistical techniques, designed under the fusionACS project, are included in an open-source R package, fusionModel, that provides generic tools for the creation, analysis, and validation of fused microdata.

Monthly potential evapotranspiration estimated using the Thornthwaite method with gridded climate datasets in Southeastern Brazil

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Abstract

We evaluated the performance of the Thornthwaite (ThW) method using two gridded climate datasets to estimate monthly average daily potential evapotranspiration (PET). The PET estimated from two gridded series were compared to PET and to reference evapotranspiration (ETo) determined, respectively, through the ThW and Penman-Monteith model parameterized on Food and Agriculture Organization–Irrigation and Drainage paper No 56 (PM-FAO56) using data from weather stations. The PET by ThM was based on monthly air temperature series (1961–2010) from two gridded datasets (Global Historical Climatology Network-GHCN and University of Delaware-UDel) and 21 weather stations of the National Institute of Meteorology (INMET) located in Southeastern Brazil. The ETo PM-FAO56 used monthly climate series (1961–2010) on sunshine duration, air temperature, relative humidity, and wind speed from weather stations of the INMET. The PET estimated using UDel gridded series was better overall performance than the GHCN series. Differences in altitude, latitude, and longitude were the main geographic factors determining the performance of the PET estimates using gridded climate series. Depending on the factors, some locations require bias correction, especially locations more than 10 km away from the grid point. The gridded datasets are an alternative for locations without climatic series data or with low-quality non-continuous data series.