Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts

As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human–water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of sociohydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023, https://doi.org/10.5880/GFZ.4.4.2023.001)

The challenge of unprecedented floods and droughts in risk management

Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3

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Made available in DSpace on 2015-06-08T14:06:19Z (GMT). No. of bitstreams: 2 Mucopolysaccharidosis type II Identification of 30 novel mutations among Latin American patients.pdf: 188876 bytes, checksum: ae8b735010afc03b6cf726def963e227 (MD5) license.txt: 1914 bytes, checksum: 7d48279ffeed55da8dfe2f8e81f3b81f (MD5) Previous issue date: 2014Hospital de Clínicas de Porto Alegre. Serviço de Genética Médica. Porto Alegre, RS, Brasil.Universidade Federal do Rio Grande do Sul. Programa de Pós-Graduação em Ciências Médicas. Porto Alegre, RS, Brasil / Universidade Federal do Rio Grande do Sul. Departamento de Genética. Porto Alegre, RS, Brasil / Hospital de Clínicas de Porto Alegre. Serviço de Genética Médica. Porto Alegre, RS, Brasil.Hospital de Clínicas de Porto Alegre. Serviço de Genética Médica. Porto Alegre, RS, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Puericultura e Pediatria Martagão Gesteira. Serviço de Genética. Rio de Janeiro, RJ, Brasil.Universidade Federal da Bahia. Departamento de Pediatria. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Fernandes Figueira. Rio de Janeiro, RJ, Brasil.Universidade Estadual de Ciências da Saúde de Alagoas. Departamento de Pediatria. Maceió, AL, Brasil.Universidade Estadual de Ciências da Saúde de Alagoas. Departamento de Pediatria. Maceió, AL, Brasil.Faculdade de Medicina de São José do Rio Preto. Departamento de Biologia Molecular. São José do Rio Preto, SP, Brasil.Universidade Estadual de Campinas. Departamento de Genética Médica. Campinas, SP, Brasil.Instituto de Medicina Integral Professor Fernando Figueira. Serviço de Genética Médica. Recife, PE, Brasil.Universidade do Estado do Rio de Janeiro. Departamento Mãe e Criança. Rio de Janeiro, RJ, Brasil.University of Chile. International Trademark Association. Genetics and Metabolic Diseases Unit. Chile.ILCS-UNA. Department of Genetics. Asunción, Paraguay.Universidad Peruana Cayetano Heredia. Lima, Peru.Royal Manchester Children's Hospital. Willink Biochemical Genetics Unit. Manchester, UK.Hospital Roberto del Río. Unidad de Genética Clínica. Santiago, Chile.Universidade Federal do Rio Grande do Sul. Programa de Pós-Graduação em Ciências Médicas. Porto Alegre, RS, Brasil. / Universidade Federal do Rio Grande do Sul. Departamento de Genética. Porto Alegre, RS, Brasil. / Hospital de Clínicas de Porto Alegre. Serviço de Genética Médica. Porto Alegre, RS, Brasil.Universidade Federal do Rio Grande do Sul. Programa de Pós-Graduação em Ciências Médicas. Porto Alegre, RS, Brasil. / Universidade Federal do Rio Grande do Sul. Departamento de Genética. Porto Alegre, RS, Brasil. / Hospital de Clínicas de Porto Alegre. Serviço de Genética Médica. Porto Alegre, RS, Brasil.In this study, 103 unrelated South-American patients with mucopolysaccharidosis type II (MPS II) were investi-gated aiming at the identification of iduronate-2-sulfatase (IDS) disease causing mutations and the possibility of some insights on the genotype–phenotype correlation The strategy used for genotyping involved the identifica-tion of the previously reported inversion/disruption of theIDSgene by PCR and screening for othermutations by PCR/SSCP. The exons with altered mobility on SSCP were sequenced, as well as all the exons of patients with no SSCP alteration. By using this strategy, we were able tofind the pathogenic mutation in all patients. Alterations such as inversion/disruption and partial/total deletions of theIDS gene were found in 20/103 (19%) patients. Small insertions/deletions/indels (b22 bp) andpointmutationswere identified in83/103 (88%) patients, includ-ing 30 novel mutations; except for a higher frequency of small duplications in relation to small deletions, the fre-quencies of major and minor alterations found in our sample are in accordance with those described in the literature

Mucopolysaccharidosis type II: identification of 30 novel mutations among Latin American patients

In this study, 103 unrelated South-American patients with mucopolysaccharidosis type II (MPS II) were investigated aiming at the identification of iduronate-2-sulfatase (IDS) disease causing mutations and the possibility of some insights on the genotype-phenotype correlation The strategy used for genotyping involved the identification of the previously reported inversion/disruption of the IDS gene by PCR and screening for other mutations by PCR/SSCP. The exons with altered mobility on SSCP were sequenced, as well as all the exons of patients with no SSCP alteration. By using this strategy, we were able to find the pathogenic mutation in all patients. Alterations such as inversion/disruption and partial/total deletions of the IDS gene were found in 20/103 (19%) patients. Small insertions/deletions/indels (<22 bp) and point mutations were identified in 83/103 (88%) patients, including 30 novel mutations; except for a higher frequency of small duplications in relation to small deletions, the frequencies of major and minor alterations found in our sample are in accordance with those described in the literature1112133138CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESRoscoe Brady program of NORD (National Organization of Rare Disorders); Institutional FIPE-HCPA fund

A Parsimonious Rainfall-Runoff Model for Flood Forecasting: Incorporating Spatially Varied Rainfall and Soil Moisture

Compared to distributed models, lumped hydrological models for converting rainfall into runoff - such as units hydrographs - require less information, have a relatively simple structure, and do not suffer from over-parameterization. As an input to such models, though, we need to identify a single value of precipitation for the whole watershed, at each time step. Point depth measurements from rain gages are scarce, giving only very limited information regarding the areal coverage of rainfall. Furthermore, the response of a watershed to a rainfall event varies according to antecedent conditions, which can be indexed by considering either its initial soil moisture or else the magnitude of base flow at the beginning of the event. We explore solutions to such issues with a study on an 1,860 km2 sub-basin of the Obion-Forked Deer River system in Western Tennessee, which has a USGS stream-gaging station at Owl City. We use National Center for Environmental Prediction (NCEP) stage IV quantitative precipitation estimates (QPE) radar hourly precipitation data to analyze numerous events spanning 2010-2014. These data help us understand the spatial distribution of every rainfall event and identify its effective coverage and duration. To derive direct runoff for the basin, we utilize gaged streamflow data in conjunction with average radar precipitation, but only for those events with a spatial coverage in excess of 80% of the watershed\u27s surface area. We use the European Space Agency (ESA) climate change initiative (CCI) soil moisture (SM) combined active-passive dataset V4.4 to find daily average soil moisture over our basin; these data give us the pre-existing watershed conditions. Finally, across all of the analyzed events, we check the correlations between the runoff coefficient, rainfall abstraction (-index), average precipitation depth, base flow at the beginning of the event, and soil moisture, and specify equations for calculating some of these variables, based on linear regression analyses. The validation of these equations with 11 storms of varying magnitude shows satisfactory results. Instead of representing the complex physical processes of runoff generation, we focus on the variability introduced in the hydrological response by the input rainfall and antecedent conditions. The unit hydrograph, derived from concurrent rainfall and streamflow records, is presented as a parsimonious rainfall-runoff model with very few parameters, that can use spatially varied rainfall data, and can be improved by using either soil moisture or streamflow data, as a measure of initial watershed conditions