Mapping data gaps to estimate biomass across Brazilian Amazon forests

Background: Tropical forests play a fundamental role in the provision of diverse ecosystem services, such as biodiversity,climate and air quality regulation, freshwater provision, carbon cycling, agricultural support and culture. To understand the role of forests in the carbon balance, aboveground biomass(AGB) estimates are needed. Given the importance of Brazilian tropical forests, there is an urgent need to improve AGB estimates to support the Brazilian commitments under the United Nations Framework Convention on Climate Change(UNFCCC). Many AGB maps and datasets exist, varying in availability, scale and coverage. Thus, stakeholders, policy makers and scientists must decide which AGB product, dataset or combination of data to use for their particular goals. In this study, we assessed the gaps in the spatial AGB data across the Brazilian Amazon forests not only to orient the decision makers about the data that are currently available but also to provide a guide for future initiatives.Results: We obtained a map of the gaps in the forest AGB spatial data for the Brazilian Amazon using statistics and differences between AGB maps and a spatial multicriteria evaluation that considered the current AGB datasets. The AGB spatial data gap map represents areas with good coverage of AGB data and, consequently, the main gaps or priority areas where further biomass assessments should focus, including the northeast of Amazon State, Amapá and northeast of Pará. Additional y, by quantifying the variability in both the AGB maps and field data on multiple environmental factors,we provide valuable elements for understanding the current AGB data as a function of climate, soil, vegetation and geomorphology.Conclusions: The map of AGB data gaps could become a useful tool for policy makers and different stakeholders working on National Communications, Reducing Emissions from Deforestation and Degradation(REDD+), or carbon emissions modeling to prioritize places to implement further AGB assessments. Only 0.2% of the Amazon biome forest is sampled, and extensive effort is necessary to improve what we know about the tropical forest.

A Data-Adaptive Filter of the Tahiti-Darwin Southern Oscillation Index and the Associate Scheme of Filling Data Gaps

The Tahiti-Darwin Southern Oscillation index provided by Climate Analysis Center of USA has been used in numerous studies. But, it has some deficiency. It contains noise mainly due to high month-to-month variability. In order to reduce the level of noise in the SO index, this paper introduces a fully data-adaptive filter based on singular spectrum analysis. Another interesting aspect of the filter is that it can be used to fill data gaps of the SO index by an iterative process. Eventually, a noiseless long-period data series without any gaps is obtained.

The Additive-multiplicative Hazards Model for Multiple Type of Recurrent Gap Times

Recurrent event gap times data frequently arise in biomedical studies and often more than one type of event is of interest. To evaluate the effects of covariates on the marginal recurrent event hazards functions, there exist two types of hazards models： the multiplicative hazards model and the additive hazards model. In the paper, we propose a more flexible additive-multiplicative hazards model for multiple type of recurrent gap times data, wherein some covariates are assumed to be additive while others are multiplicative. An estimating equation approach is presented to estimate the regression parameters. We establish asymptotic properties of the proposed estimators.

Assessing methods for multiple imputation of systematic missing data in marine fisheries time series with a new validation algorithm

Time series from fisheries often contain multiple missing data.This is a severe limitation that prevents using the data for research on population dynamics,stock assessment,forecasting,and,hence,decision-making around marine resources.Several methods have been proposed to impute missing data in univariate time series.Still,their performances depend not only on the amount of missing data but also on the data structure.This study compares the performance of twelve imputation methods on the time series of marine fishery landings for six species in the Colombian Pacific Ocean.Unlike other studies,we validate the precision of the imputations in the same target time series that include missing data,using the Known Sub-Sequence Algorithm(KSSA),a novelty validation approach that simulates missing data in known sub-sequences of the target time series.The results showed that the best methods for imputation are Seasonal Decomposition with Kalman filters and Structural Models with Kalman filters fitted by maximum likelihood.Results also show that validating the imputation methods with other time series different to the target time series,leads to wrong imputation methods choices.It is noteworthy that these methods and also the validation framework are mainly suited to time series with non-random distribution of missing data,this is,missing data produced systematically in chunks or clusters with predictable frequency,which are common in marine sciences.

Filling the Disaster Data Gap:Lessons from Cataloging Singapore’s Past Disasters

International disaster databases and catalogs provide a baseline for researchers,governments,communities,and organizations to understand the risk of a particular place,analyze broader trends in disaster risk,and justify investments in mitigation.Perhaps because Singapore is routinely identified as one of the safest countries in the world,Singapore’s past disasters have not been studied extensively with few events captured in major global databases such as EM-DAT.In this article,we fill the disaster data gap for postwar Singapore(1950–2020)using specified metrics through an archival search,review of literature,and analysis of secondary sources.We present four key lessons from cataloging these events.First,we expand Singapore’s disaster catalog to 39 events in this time period and quantify the extent of this data gap.Second,we identify the mitigating actions that have followed past events that contribute to Singapore’s present-day safety.Third,we discuss how these past events uncover continuities among vulnerability bearers in Singapore.Last,we identify limitations of a disaster catalog when considering future risks.In expanding the disaster catalog,this case study of Singapore supports the need for comprehensive understanding of past disasters in order to examine current and future disaster resilience.