Apparent Seasonal Bias in Delta Outflow Estimates as Revealed in the Historical Salinity Record of the San Francisco Estuary: Implications for Delta Net Channel Depletion Estimates

Accurate estimates of freshwater flow to the San Francisco Estuary are important in successfully regulating this water body, in protecting its beneficial uses, and in accurately modeling its hydrodynamic and water-quality transport regime. For regulatory purposes, freshwater flow to the estuary is not directly measured; rather, it is estimated from a daily balance of upstream Delta inflows, exports, and in-Delta water use termed the net Delta outflow index (NDOI). Field research in the 1960s indicated that NDOI estimates are biased low in summer–fall and biased high in winter–spring as a result of conflating Delta island evapotranspiration estimates with the sum of ungauged hydrologic interactions between channels and islands referred to as net channel depletions. In this work, we employed a 50-year observed salinity record along with gauged tidal flows and an ensemble of five empirical flow-salinity (X2) models to test whether a seasonal bias in Delta outflow estimates could be inferred. We accomplished this objective by conducting statistical analyses and evaluating whether model skill could be improved through seasonal NDOI flow adjustments. Assuming that model residuals are associated with channel depletion uncertainty, our findings corroborate the 1960s research and suggest that channel depletions are biased low in winter months (i.e., NDOI is biased high) and biased high in late summer and early fall months (i.e., NDOI is biased low). The magnitude of seasonal bias, which can reach 1,000 cfs, is a small percentage of typical winter outflow but represents a significant percentage of typical summer outflow. Our findings were derived from five independently developed models, and are consistent with the physical understanding of water exchanges on the islands. This work provides motivation for improved characterization of these exchanges to improve Delta outflow estimates, particularly during drought periods when water supplies are scarce and must be carefully managed.

Abiotic Factors That Affect The Distribution of Aquatic Macrophytes In Shallow Lakes Located In Sibley County, Minnesota, USA: A Spatial Modeling Approach

Macrophytes are an integral component of lake communities, therefore understanding the factors that affect macrophyte community structure is important for conservation and management of lakes. In Sibley County, Minnesota, USA, five lakes were surveyed using the point-intercept method. At each point the presence of macrophytes were recorded, water depth was measured, and a sediment sample was collected. Sediment samples were quantified by determining soil particle size and percent organic matter. The richness of macrophytes in all lakes were modeled via generalized linear regression with six explanatory variables: water depth, distance from shore, percent sand, percent silt, percent clay, and percent sediment organic matter. If model residuals were spatially autocorrelated, then a geographically weighted regression was used. Water depth and distance from shore were negatively related to mean species richness, and silt was either negatively or positively related to species richness depending on the lake and macrophytes present. All species richness models had pseudo-R2 values between 0.25 and 0.40. Curlyleaf pondweed (Potamogeton crispus) was related to with water depth, percent silt, and percent sediment organic matter during early season surveys.

Transformation and Preprocessing of Single-Cell RNA-Seq Data

The count table, a numeric matrix of genes × cells, is a basic input data structure in the analysis of single-cell RNA-seq data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so that the variance is similar across the dynamic range. These steps are intended to make subsequent application of generic statistical methods more palatable. Here, we describe three transformations (based on the delta method, model residuals, or inferred latent expression state) and compare their strengths and weaknesses. We conclude with an outlook on future needs for the development of transformations for single-cell count data.

Temporal and geographic analysis of trichinellosis incidence in Chile with risk assessment

Trichinellosis is a foodborne disease caused by several Trichinella species around the world. In Chile, the domestic cycle was fairly well-studied in previous decades, but has been neglected in recent years. The aims of this study were to analyze, geographically, the incidence of trichinellosis in Chile to assess the relative risk and to analyze the incidence rate fluctuation in the last decades. Using temporal data spanning 1964–2019, as well as geographical data from 2010 to 2019, the time series of cases was analyzed with ARIMA models to explore trends and periodicity. The Dickey-Fuller test was used to study trends, and the Portmanteau test was used to study white noise in the model residuals. The Besag-York-Mollie (BYM) model was used to create Bayesian maps of the level of risk relative to that expected by the overall population. The association of the relative risk with the number of farmed swine was assessed with Spearman’s correlation. The number of annual cases varied between 5 and 220 (mean: 65.13); the annual rate of reported cases varied between 0.03 and 1.9 cases per 105 inhabitants (mean: 0.53). The cases of trichinellosis in Chile showed a downward trend that has become more evident since the 1980s. No periodicities were detected via the autocorrelation function. Communes (the smallest geographical administrative subdivision) with high incidence rates and high relative risk were mostly observed in the Araucanía region. The relative risk of the commune was significantly associated with the number of farmed pigs and boar (Sus scrofa Linnaeus, 1758). The results allowed us to state that trichinellosis is not a (re)emerging disease in Chile, but the severe economic poverty rate of the Mapuche Indigenous peoples and the high number of backyard and free-ranging pigs seem to be associated with the high risk of trichinellosis in the Araucanía region.

The Impact of Regulatory Reforms on Demand Weighted Average Prices

Average prices are popularly used in the literature on price modeling. Calculating daily or weekly prices as an average over hourly or half-hourly trading periods assumes the same weight ignoring demand or traded volumes during those periods. Analyzing demand weighted average prices is important if producers may affect prices by decreasing them during low-demand periods and increasing them during high-demand periods within a day. The prediction of this price manipulation might have motivated the regulatory authority to introduce price caps not only on annual average prices but also on annual demand weighted average prices in the England and Wales wholesale electricity market. The dynamics of demand weighted average prices of electricity has been analyzed little in the literature. We show that skew generalized error distribution (SGED) is the appropriate assumption for model residuals. The estimated volatility model is used for evaluating the impact of regulatory reforms on demand weighted average prices during the complete history of the England and Wales wholesale electricity market.

Temporal and geographic analysis of human trichinellosis incidence in Chile with risk assessment

Trichinellosis is a foodborne disease caused by several Trichinella species around the world. In Chile, the domestic cycle was fairly well-studied in previous decades, but has been neglected in recent years. The aims of this study were to analyze, geographically, the incidence of trichinellosis in Chile to assess the relative risk, as well as to analyze the temporal fluctuation in the incidence rates in the last decades. Using temporal data spanning 1964–2019, as well as geographical data from 2010–2019, the time series of cases was analyzed with ARIMA models to explore trends and periodicity. The Dickey–Fuller test was used to study trends, and the Portmanteau test was used to study white noise in the model residuals. The Besag–York–Mollie (BYM) model was used to create Bayesian maps of the level of risk relative to that expected by the overall population. The association of the relative risk with the number of farmed swine was assessed with Spearman’s correlation. The number of annual cases varied between 5 and 220 (mean: 65.13); the annual rate of reported cases varied between 0.03 and 1.9 cases per 105 inhabitants (mean: 0.53). The cases of trichinellosis in Chile showed a downward trend that has become more evident since the 1980s. No periodicities were detected via the autocorrelation function. Communes (the smallest geographical administrative subdivision) with high incidence rates and high relative risk were mostly observed in the Araucanía region. The relative risk of the commune was significantly associated with the number of farmed pigs and boar (Sus scrofa Linnaeus, 1758). The results allowed us to state that trichinellosis is not an (re)emerging disease in Chile, but local conditions must be further studied to identify the factors favoring the presence of outbreaks in some communes, particularly in Araucanía.

A Novel Method for Regional Short-Term Forecasting of Water Level

The water level forecasting system represented by the hydrodynamic model relies too much on the input data and the forecast value of the boundary, therefore introducing uncertainty in the prediction results. Tide tables ignore the effect of the residual water level, which is usually significant. Therefore, to solve this problem, a water level forecasting method for the regional short-term (3 h) is proposed in this study. First, a simplified MIKE21 flow model (FM) was established to construct the regional major astronomical tides after subdividing the model residuals into stationary constituents (surplus astronomical tides, simulation deviation) and nonstationary constituents (residual water level). Harmonic analysis (HA) and long short-term memory (LSTM) were adopted to forecast these model residuals, respectively. Finally, according to different spatial background information, the prediction for each composition was corrected by the inverse distance weighting (IDW) algorithm and its improved IDW interpolation algorithm based on signal energy and the spatial distance (IDWSE) from adjacent observation stations to nonmeasured locations. The developed method was applied to Narragansett Bay in Rhode Island. Compared with the assimilation model, the root-mean-square error (RMSE) of the proposed method decreased from 12.3 to 5.0 cm, and R2 increased from 0.932 to 0.988. The possibility of adding meteorological features into the LSTM network was further explored as an extension of the prediction of the residual water level. The results show that the accuracy was limited to a moderate level, which is related to the difficulty presented by using only wind features to completely characterize the regional dynamic energy equilibrium process.

Extending vegetation site data and ensemble models to predict patterns of foliage cover and species richness for plant functional groups

Context Ensembles of artificial neural network models can be trained to predict the continuous characteristics of vegetation such as the foliage cover and species richness of different plant functional groups. Objectives Our first objective was to synthesise existing site-based observations of native plant species to quantify summed percentage foliage cover and species richness within four functional groups and in totality. Secondly, we generated spatially-explicit, continuous, landscape-scale models of these functional groups, accompanied by maps of the model residuals to show uncertainty. Methods Using a case study from New South Wales, Australia, we aggregated floristic observations from 6806 sites into four common plant growth forms (trees, shrubs, grasses and forbs) representing four different functional groups. We coupled these response data with spatially-complete surfaces describing environmental predictors and predictors that reflect landscape-scale disturbance. We predicted the distribution of foliage cover and species richness of these four plant functional groups over 1.5 million hectares. Importantly, we display spatially explicit model residuals so that end-users have a tangible and transparent means of assessing model uncertainty. Results Models of richness generally performed well (R2 0.43–0.63), whereas models of cover were more variable (R2 0.12–0.69). RMSD ranged from 1.42 (tree richness) to 29.86 (total native cover). MAE ranged from 1.0 (tree richness) to 20.73 (total native foliage cover). Conclusions Continuous maps of vegetation attributes can add considerable value to existing maps and models of discrete vegetation classes and provide ecologically informative data to support better decisions across multiple spatial scales.

VirES for Swarm & Virtual Research Environment: Software, guides & infrastructure to boost accessibility of Swarm

<p>ESA's Swarm mission continues to deliver excellent data providing insight into a wide range of geophysical phenomena. The mission is an important asset whose data are used within a number of critical resources, from geomagnetic field models to space weather services. As the product portfolio grows to better deliver on the mission's scientific goals, we face increasing complexity in accessing, processing, and visualising the data and models. ESA provides “VirES for Swarm” [1] (developed by EOX IT Services) to help solve this problem. VirES is a web-based data retrieval and visualisation tool where the majority of Swarm products are available. VirES has a graphical interface but also a machine-to-machine interface (API) for programmable use (a Python client is provided). The VirES API also provides access to geomagnetic ground observatory data, as well as forwards evaluation of geomagnetic field models to give data-model residuals. The "Virtual Research Environment" (VRE) adds utility to VirES with a free cloud-based JupyterLab interface allowing scientists to immediately program their own analysis of Swarm products using the Python ecosystem. We are augmenting this with a suite of Jupyter notebooks and dashboards, each targeting a specific use case, and seek community involvement to grow this resource.</p><p>[1] https://vires.services</p>