Search

Satellite remote sensing enhances model predictions by providing insights into terrestrial and hydrological processes. While data assimilation techniques have proven promising, there is a lack of standardized and effective approaches for integrating multiple observations simultaneously. This study presents a novel assimilation framework, the multi‐observation local ensemble‐Kalman‐filter (MoLEnKF), designed to effectively integrate multiple variables, even at scales different than the model. Evaluation of MoLEnKF in the Amazon River basin includes assimilation experiments with remote sensing data only, including water surface elevation (WSE), terrestrial water storage (TWS), flood extent (FE), and soil moisture (SM). MoLEnKF demonstrates improvements in a scenario where regions lack in‐situ hydroclimatic records and when assuming uncertainties of large‐scale hydrologic‐hydrodynamic models. Assimilating WSE outperforms daily discharge and water‐level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. MoLEnKF always remains in second position in a ranking of error and uncertainty reduction, providing an intermediate condition, being able to holistically outperform univariate experiments. MoLEnKF also outperform state‐of‐the‐art models in many cases. This study suggests potential improvements, urging exploration of correlations between assimilated variables and adaptive localization methods based on seasonality. The flexibility and the elegant way of expressing the LEnKF equations by MoLEnKF facilitates their application with different types of variables, compatible with large‐scale hydrologic‐hydrodynamic models and missions such as SWOT. Its robustness ensures easy replicability worldwide, facilitating hydrological reanalysis and improved forecasting, establishing MoLEnKF as a valuable tool for the scientific community in hydrological research. Plain Language Summary: The use of satellites to collect information from far away helps us to understand how water behaves on the continents. But combining all this data with uncertain computer models is complicated. This study introduces a new method called multi‐observation local ensemble‐Kalman‐filter (MoLEnKF) to combine many different kinds of data at once. We tested MoLEnKF in the Amazon River basin, using satellite data on water levels, terrestrial water storage, flood extent and soil moisture. MoLEnKF by using all these observations at the same time obtained better results holistically than the individual experiments, improving our ability to predict aspects such as the amount of discharge, water level and evapotranspiration. This study is a step forward and could be really useful for understanding and predicting water‐related phenomena worldwide, especially in a context of scarce or no availability of in‐situ observations. Key Points: Multi‐observation local ensemble‐Kalman‐filter (MoLEnKF) advances multi‐observation and multi‐scale assimilation, overcoming holistically univariate experimentsMoLEnKF improves the simulation of large‐scale hydrologic‐hydrodynamic uncertain models using only remotely sensed dataMoLEnKF flexibility for global applications: Simplicity and compatibility with various data types make it a robust tool, for example, SWOT mission [ABSTRACT FROM AUTHOR]

The growth of the higher education population and different school paths to access an academic degree has increased the heterogeneity of students inside the classroom. Consequently, the effectiveness of traditional teaching methods has reduced. This paper describes the design, development, implementation and evaluation of a tutoring system (TS) to improve student's engagement in higher mathematics. The TS design was based on the Personalized System of Instruction of the Mastery Learning pedagogical approach and can be implemented in any higher education course with mathematics needs. The TS consists on small self-paced modularized units of educational contents, including tutorial videos, notes and formative e-assessment with personalized feedback. The TS ensures that the student is only allowed to proceed to the next unit after he or she achieves the required mastery criterion of the current unit. The TS was implemented in the Quantitative Methods course of an undergraduate degree and received good acceptance from students. It was also recognized that TS contributed to learning and engagement with the discipline. Through an experimental research experience, it has been shown that the imposition of restrictions on the advance to the next level by a mastery criterion leads to a significant improvement in student's engagement and performance.

Forest structure and dynamics vary across the Amazon Basin in an east-west gradient coincident with variations in soil fertility and geology. This has resulted in the hypothesis that soil fertility may play an important role in explaining Basin-wide variations in forest biomass, growth and stem turnover rates. Soil samples were collected in a total of 59 different forest plots across the Amazon Basin and analysed for exchangeable cations, carbon, nitrogen and pH, with several phosphorus fractions of likely different plant availability also quantified. Physical properties were additionally examined and an index of soil physical quality developed. Bivariate relationships of soil and climatic properties with above-ground wood productivity, stand-level tree turnover rates, above-ground wood biomass and wood density were first examined with multivariate regression models then applied. Both forms of analysis were undertaken with and without considerations regarding the underlying spatial structure of the dataset. Despite the presence of autocorrelated spatial structures complicating many analyses, forest structure and dynamics were found to be strongly and quantitatively related to edaphic as well as climatic conditions. Basin-wide differences in stand-level turnover rates are mostly influenced by soil physical properties with variations in rates of coarse wood production mostly related to soil phosphorus status. Total soil P was a better predictor of wood production rates than any of the fractionated organic- or inorganic-P pools. This suggests that it is not only the immediately available P forms, but probably the entire soil phosphorus pool that is interacting with forest growth on longer timescales. A role for soil potassium in modulating Amazon forest dynamics through its effects on stand-level wood density was also detected. Taking this into account, otherwise enigmatic variations in stand-level biomass across the Basin were then accounted for through the interacting effects of soil physical and chemical properties with climate. A hypothesis of self-maintaining forest dynamic feedback mechanisms initiated by edaphic conditions is proposed. It is further suggested that this is a major factor determining endogenous disturbance levels, species composition, and forest productivity across the Amazon Basin.

Soil samples were collected in six South American countries in a total of 71 different 1 ha forest plots across the Amazon Basin as part of the RAINFOR project. They were analysed for total and exchangeable cations, C, N, pH with various P fractions also determined. Physical properties were also examined and an index of soil physical quality proposed. A diverse range of soils was found. For the western areas near the Andean cordillera and the southern and northern fringes, soils tend to be distributed among the lower pedogenetic levels, while the central and eastern areas of Amazonia have more intensely weathered soils. This gives rise to a large variation of soil chemical and physical properties across the Basin, with soil properties varying predictably along a gradient of pedogenic development. Nutrient pools generally increased slightly in concentration from the youngest to the intermediate aged soils after which a gradual decline was observed with the lowest values found in the most weathered soils. Soil physical properties were strongly correlated with soil fertility, with favourable physical properties occurring in highly weathered and nutrient depleted soils and with the least weathered, more fertile soils having higher incidence of limiting physical properties. Soil phosphorus concentrations varied markedly in accordance with weathering extent and appear to exert an important influence on the nitrogen cycle of Amazon forest soils.

Xylem density is a physical property of wood that varies between individuals, species and environments. It reflects the physiological strategies of trees that lead to growth, survival and reproduction. Measurements of branch xylem density, ρx, were made for 1653 trees representing 598 species, sampled from 87 sites across the Amazon basin. Measured values ranged from 218 kg m−3for a Cordia sagotii (Boraginaceae) from Mountagne de Tortue, French Guiana to 1130 kg m−3 for an Aiouea sp. (Lauraceae) from Caxiuana, Central Pará, Brazil. Analysis of variance showed significant differences in average ρx across regions and sampled plots as well as significant differences between families, genera and species. A partitioning of the total variance in the dataset showed that species identity (family, genera and species) accounted for 33% with environment (geographic location and plot) accounting for an additional 26%; the remaining "residual" variance accounted for 41% of the total variance. Variations in plot means, were, however, not only accountable by differences in species composition because xylem density of the most widely distributed species in our dataset varied systematically from plot to plot. Thus, as well as having a genetic component, branch xylem density is a plastic trait that, for any given species, varies according to where the tree is growing in a predictable manner. Within the analysed taxa, exceptions to this general rule seem to be pioneer species belonging for example to the Urticaceae whose branch xylem density is more constrained than most species sampled in this study. These patterns of variation of branch xylem density across Amazonia suggest a large functional diversity amongst Amazonian trees which is not well understood.

The article discusses different methods for the early diagnosis of pregnancy and gestational losses in cattle. The difficulties in detecting embryonic mortality and the limitations of available methods are mentioned. Three categories of early pregnancy detection tests are described, including rectal palpation, ultrasonography, and Doppler ultrasonography. It is suggested that determining the concentrations of pregnancy-associated glycoproteins (PAG) in serum samples on day 31 of gestation can predict late embryonic mortality with 95% accuracy. Biomarkers such as progesterone, interferon-tau stimulated genes (ISG), and circulating microRNAs (miRNAs) are also discussed. Additionally, the importance of both maternal and paternal contributions to pregnancy loss is mentioned, and the development of markers to identify bulls with high or low pregnancy loss is suggested. [Extracted from the article]