38 research outputs found
Characterising droughts in Central America with uncertain hydro-meteorological data
Central America is frequently affected by droughts that cause significant socio-economic and environmental problems. Drought characterisation, monitoring and forecasting are potentially useful to support water resource management. Drought indices are designed for these purposes, but their ability to characterise droughts depends on the characteristics of the regional climate and the quality of the available data. Local comprehensive and high-quality observational networks of meteorological and hydrological data are not available, which limits the choice of drought indices and makes it important to assess available datasets. This study evaluated which combinations of drought index and meteorological dataset were most suitable for characterising droughts in the region. We evaluated the standardised precipitation index (SPI), a modified version of the deciles index (DI), the standardised precipitation evapotranspiration index (SPEI) and the effective drought index (EDI). These were calculated using precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), the CRN073 dataset, the Climate Research Unit (CRU), ECMWF Reanalysis (ERA-Interim) and a regional station dataset, and temperature from the CRU and ERA-Interim datasets. The gridded meteorological precipitation datasets were compared to assess how well they captured key features of the regional climate. The performance of all the drought indices calculated with all the meteorological datasets was then evaluated against a drought index calculated using river discharge data. Results showed that the selection of database was more important than the selection of drought index and that the best combinations were the EDI and DI calculated with CHIRPS and CRN073. Results also highlighted the importance of including indices like SPEI for drought assessment in Central America.Universidad de Costa Rica/[805-B0-810]/UCR/Costa RicaUniversidad de Costa Rica/[805-A9-532]/UCR/Costa RicaUniversidad de Costa Rica/[805-B3-600]/UCR/Costa RicaUniversidad de Costa Rica/[805-B0-065]/UCR/Costa RicaUniversidad de Costa Rica/[805-B3-413]/UCR/Costa RicaUniversidad de Costa Rica/[805-B4-227]/UCR/Costa RicaUniversidad de Costa Rica/[805-B4-228]/UCR/Costa RicaUniversidad de Costa Rica/[805-B5-295]/UCR/Costa RicaUppsala University/[54100006]//SueciaMarie Curie Intra-European Fellowship/[No.329762]//EuropaUCR::VicerrectorÃa de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones GeofÃsicas (CIGEFI)UCR::VicerrectorÃa de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de FÃsic
Impacts of climate change on drought: changes to drier conditions at the beginning of the crop growing season in southern Brazil
Snap-, CLIP- and Halo-tag labelling of budding yeast cells.
Fluorescence microscopy of the localization and the spatial and temporal dynamics of specifically labelled proteins is an indispensable tool in cell biology. Besides fluorescent proteins as tags, tag-mediated labelling utilizing self-labelling proteins as the SNAP-, CLIP-, or the Halo-tag are widely used, flexible labelling systems relying on exogenously supplied fluorophores. Unfortunately, labelling of live budding yeast cells proved to be challenging with these approaches because of the limited accessibility of the cell interior to the dyes. In this study we developed a fast and reliable electroporation-based labelling protocol for living budding yeast cells expressing SNAP-, CLIP-, or Halo-tagged fusion proteins. For the Halo-tag, we demonstrate that it is crucial to use the 6'-carboxy isomers and not the 5'-carboxy isomers of important dyes to ensure cell viability. We report on a simple rule for the analysis of H-1 NMR spectra to discriminate between 6'- and 5'-carboxy isomers of fluorescein and rhodamine derivatives. We demonstrate the usability of the labelling protocol by imaging yeast cells with STED super-resolution microscopy and dual colour live cell microscopy. The large number of available fluorophores for these self-labelling proteins and the simplicity of the protocol described here expands the available toolbox for the model organism Saccharomyces cerevisiae
Estimating drought risk across Europe from reported drought impacts, drought indices, and vulnerability factors
Drought is one of the most costly natural hazards in Europe. Due to its
complexity, drought risk, meant as the combination of the natural hazard and
societal vulnerability, is difficult to define and challenging to detect and
predict, as the impacts of drought are very diverse, covering the breadth of
socioeconomic and environmental systems. Pan-European maps of drought risk
could inform the elaboration of guidelines and policies to address its
documented severity and impact across borders. This work tests the
capability of commonly applied drought indices and vulnerability factors to
predict annual drought impact occurrence for different sectors and macro
regions in Europe and combines information on past drought impacts, drought
indices, and vulnerability factors into estimates of drought risk at the
pan-European scale. This hybrid approach bridges the gap between
traditional vulnerability assessment and probabilistic impact prediction in
a statistical modelling framework. Multivariable logistic regression was
applied to predict the likelihood of impact occurrence on an annual basis
for particular impact categories and European macro regions. The results
indicate sector- and macro-region-specific sensitivities of drought indices,
with the Standardized Precipitation Evapotranspiration Index (SPEI) for a
12-month accumulation period as the overall best hazard predictor.
Vulnerability factors have only limited ability to predict drought impacts
as single predictors, with information about land use and water resources
being the best vulnerability-based predictors. The application of the
hybrid approach revealed strong regional and sector-specific differences
in drought risk across Europe. The majority of the best predictor combinations
rely on a combination of SPEI for shorter and longer accumulation periods,
and a combination of information on land use and water resources. The added
value of integrating regional vulnerability information with drought risk
prediction could be proven. Thus, the study contributes to the overall
understanding of drivers of drought impacts, appropriateness of drought
indices selection for specific applications, and drought risk assessment