Consistent Dataset Research Articles

Performance Evaluation and Comparison of Satellite-Derived Rainfall Datasets over the Ziway Lake Basin, Ethiopia

Consistent time series rainfall datasets are important in performing climate trend analyses and agro-hydrological modeling. However, temporally consistent ground-based and long-term observed rainfall data are usually lacking for such analyses, especially in mountainous and developing countries. In the absence of such data, satellite-derived rainfall products, such as the Climate Hazard Infrared Precipitations with Stations (CHIRPS) and Global Precipitation Measurement Integrated Multi-SatellitE Retrieval (GPM-IMERG) can be used. However, as their performance varies from region to region, it is of interest to evaluate the accuracy of satellite-derived rainfall products at the basin scale using ground-based observations. In this study, we evaluated and demonstrated the performance of the three-run GPM-IMERG (early, late, and final) and CHIRPS rainfall datasets against the ground-based observations over the Ziway Lake Basin in Ethiopia. We performed the analysis at monthly and seasonal time scales from 2000 to 2014, using multiple statistical evaluation criteria and graphical methods. While both GPM-IMERG and CHIRPS showed good agreement with ground-observed rainfall data at monthly and seasonal time scales, the CHIRPS products slightly outperformed the GPM-IMERG products. The study thus concluded that CHIRPS or GPM-IMERG rainfall data can be used as a surrogate in the absence of ground-based observed rainfall data for monthly or seasonal agro-hydrological studies.

Global land cover trajectories and transitions

Global land cover (LC) changes threaten sustainability and yet we lack a comprehensive understanding of the gains and losses of LC types, including the magnitudes, locations and timings of transitions. We used a novel, fine-resolution and temporally consistent satellite-derived dataset covering the entire Earth annually from 1992 to 2018 to quantify LC changes across a range of scales. At global and continental scales, the observed trajectories of change for most LC types were fairly smooth and consistent in direction through time. We show these observed trajectories in the context of error margins produced by extrapolating previously published accuracy metrics associated with the LC dataset. For many LC classes the observed changes were found to be within the error margins. However, an important exception was the increase in urban land, which was consistently larger than the error margins, and for which the LC transition was unidirectional. An advantage of analysing the global, fine spatial resolution LC time-series dataset is the ability to identify where and when LC changes have taken place on the Earth. We present LC change maps and trajectories that identify locations with high dynamism, and which pose significant sustainability challenges. We focused on forest loss and urban growth at the national scale, identifying the top 10 countries with the largest percentages of forest loss and urban growth globally. Crucially, we found that most of these ‘worst-case’ countries have stabilized their forest losses, although urban expansion was monotonic in all cases. These findings provide crucial information to support progress towards the UN’s SDGs.

Inference and combination of missing data sets for the determination of [formula omitted] thermal decomposition rate uncertainty

The thermal decomposition of H2O2 is an important process in hydrocarbon combustion, playing a particularly crucial role in providing a source of radicals at high pressure where it controls the 3rd explosion limit in the H2-O2 system, and also as a branching reaction in intermediate-temperature hydrocarbon oxidation. As such, estimating the Arrhenius parameters of the rate expression for this reaction and their joint uncertainty is crucial for performing meaningful predictive combustion computations. Raw experimental data is typically unavailable for most reported investigations of elementary reaction rates. Rather, what is typically available are summary statistics on measured quantities. The unavoidable loss of information in the use of summary statistics versus raw data for inference makes the direct computation of the necessary joint uncertainty structure of the parameters in rate expressions difficult. To overcome this situation we perform a statistical inference procedure relying on maximum entropy principles and approximate Bayesian computation methods, and employing a two-level nested Markov Chain Monte Carlo algorithm, to approximate the joint posterior density on the H2O2 decomposition rate parameters for a selected case of laser absorption measurements in a shock tube study, subject to the constraints imposed by the rate constant statistics and available information reported for this experiment. This procedure constructs a set of hypothetical H2O2 concentration decay profiles consistent with the reported statistics in the form of mean values and error bars of rate constants at the given experiment temperatures, and the data fitting model originally employed by the experimentalists - essentially reconstructing the missing experimental data in an approximate form. These consistent hypothetical data sets are then used to determine the joint Arrhenius parameter density through straightforward Bayesian inference within a pooling procedure. We apply the method in the context of combining the information within a single experimental setting (i.e. across a temperature range for fixed pressure) for the purposes of estimating Arrhenius parameters, and then extend the analysis to incorporate information from additional experiments (at a different target pressure) resulting in a decrease in the uncertainty of the parameter estimates as this new information is employed. We also highlight the flexibility of operating in this data-centric framework with a view to generating unbiased consensus rate expressions up to the full mechanism level when using multiple sources of experimental results in different forms.

Environmental assessment based surface water quality prediction using hyper-parameter optimized machine learning models based on consistent big data

Prediction of dissolved oxygen (DO) and total dissolved solids (TDS) are of paramount importance for water environmental protection and analysis of the ecosystem. The traditional methods for water quality prediction are suffering from unadjusted hyper-parameters. To effectively solve the hyper-parameter setting problem, the present study proposes a framework for tuning the hyper-parameters of feed forward neural network (FFNN) and gene expression programming (GEP) with particle swarm optimization (PSO). Thereafter, the PSO coupled hybrid feed forward neural network (PSO-FFNN) and hybrid gene expression programming (PSO-GEP) were used to predict DO and TDS levels in the upper Indus River. Based on thirty years consistent dataset, the most influential input parameters for DO and TDS prediction were determined using principal component analysis (PCA). The impact on the model performance was evaluated employing five statistical evaluation techniques. Modeling results indicated excellent searching efficiency of the PSO algorithm in optimizing the structure and hyper-parameters of the FFNN and GEP. Results of PCA revealed that magnesium, chloride, sulphate, bicarbonates, specific conductivity, and water temperature are appropriate inputs for DO modeling, whereas; calcium, magnesium, sodium, chloride, bicarbonates and specific conductivity remained the influential parameters for TDS. Both the proposed hybrid models showed better accuracy in predicting DO and TDS, however, the hybrid PSO-GEP model achieves better accuracy than the PSO-FFNN with R value above 0.85, the root mean squared error (RMSE) below 3 mg/l and performance index value close to 1. The external validation criteria confirmed the resolved overfitting issue and generalized results of the models. Cross-validation of the model output attained the best statistical metrics i.e. (R = 0.87, RMSE = 2.67) and (R = 0.895, RMSE = 2.21) for PSO-FFNN and PSO-GEP model, respectively. The research findings demonstrated that the implementation of artificial intelligence models with optimization routine can lead to optimized models for accurate prediction of water quality.

Consistency of total column ozone measurements between the Brewer and Dobson spectroradiometers of the LKO Arosa and PMOD/WRC Davos

Abstract. Total column ozone measured by Brewer and Dobson spectroradiometers at Arosa and Davos, Switzerland, have systematic seasonal variations of around 1.5 % using the standard operational data processing. Most of this variability can be attributed to the temperature sensitivity of approx. +0.1 %/K of the ozone absorption coefficient of the Dobson spectroradiometer (in this study D101). While the currently used Bass and Paur ozone absorption cross-sections produce inconsistent results for Dobson and Brewer, the use of the ozone absorption cross-sections from Serdyuchenko et al. (2014) in conjunction with an effective ozone temperature dataset produces excellent agreement between the four Brewers investigated (of which two are double Brewers) and Dobson D101. Even though other ozone absorption cross-sections available in the literature are able to reduce the seasonal variability as well, all of those investigated produce systematic biases in total column ozone between Brewer and Dobson of +2.1 % to −3.2 %. The highest consistency in total column ozone from Brewers and Dobson D101 at Arosa and Davos is obtained by applying the Rayleigh scattering cross-sections from Bodhaine et al. (1999), the ozone absorption cross-sections from Serdyuchenko et al. (2014), the effective ozone temperature from either ozone-sondes or the European Centre for Medium-Range Weather Forecasts (ECMWF), and the measured line spread functions of Brewer and Dobson. The variability of 0.9 % between Brewer and Dobson for single measurements can be reduced to less than 0.1 % for monthly means. As shown here, the applied methodology produces consistent total column ozone datasets between Brewer and Dobson spectroradiometers, with average differences of 0.0 % and a remaining seasonal variability of 0.11 %. For collocated Brewer and Dobson spectroradiometers, as is the case for the Arosa and Davos total column ozone times series, this allows for the merging of these two distinct datasets to produce a homogeneous time series of total column ozone measurements. Furthermore, it guarantees the long-term future of this longest total column ozone time series, by proposing a methodology for how to eventually replace the ageing Dobson spectroradiometer with the state-of-the art Brewer spectroradiometer.

Weather extremes over Europe under 1.5 and 2.0 °C global warming from HAPPI regional climate ensemble simulations

Abstract. This paper presents a novel dataset of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. This is a unique and physically consistent dataset, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100×10-year simulations and 25×10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. To demonstrate how adaptation-relevant information can be derived from the HAPPI dataset, changes in four climate indices for periods with 1.5 and 2.0 ∘C global warming are quantified. These indices include number of days per year with daily mean near-surface apparent temperature of >28 ∘C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-year return period (RI50yr); and the annual consecutive dry days (CDDs). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature-related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 ∘C.

Long-term NO<sub><i>x</i></sub> measurements in the remote marine tropical troposphere

Abstract. Atmospheric nitrogen oxides (NO + NO2 = NOx) have been measured at the Cape Verde Atmospheric Observatory (CVAO) in the tropical Atlantic (16∘51′ N, 24∘52′ W) since October 2006. These measurements represent a unique time series of NOx in the background remote troposphere. Nitrogen dioxide (NO2) is measured via photolytic conversion to nitric oxide (NO) by ultraviolet light-emitting diode arrays followed by chemiluminescence detection. Since the measurements began, a blue light converter (BLC) has been used for NO2 photolysis, with a maximum spectral output of 395 nm from 2006 to 2015 and of 385 nm from 2015 onwards. The original BLC used was constructed with a Teflon-like material and appeared to cause an overestimation of NO2 when illuminated. To avoid such interferences, a new additional photolytic converter (PLC) with a quartz photolysis cell (maximum spectral output also 385 nm) was implemented in March 2017. Once corrections are made for the NO2 artefact from the original BLC, the two NO2 converters are shown to give comparable NO2 mixing ratios (BLC = 0.99 × PLC + 0.7 ppt, linear least-squares regression), giving confidence in the quantitative measurement of NOx at very low levels. Data analysis methods for the NOx measurements made at CVAO have been developed and applied to the entire time series to produce an internally consistent and high-quality long-term data set. NO has a clear diurnal pattern with a maximum mixing ratio of 2–10 ppt during the day depending on the season and ∼ 0 ppt during the night. NO2 shows a fairly flat diurnal signal, although a small increase in daytime NOx is evident in some months. Monthly average mixing ratios of NO2 vary between 5 and 30 ppt depending on the season. Clear seasonal trends in NO and NO2 levels can be observed with a maximum in autumn and winter and a minimum in spring and summer.

Rate of decline of the production cross section of superheavy nuclei with Z=114–117 at high excitation energies

The production cross sections of superheavy nuclei with charge numbers $114-117$ are predicted in the $(5-9)n$-evaporation channels of the $^{48}$Ca-induced complete fusion reactions for future experiments. The estimates of synthesis capabilities are based on a uniform and consistent set of input nuclear data provided by the multidimensional macroscopic-microscopic approach. The contributions of various factors to the final production cross section are discussed. As shown, the specific interplay between survival and fusion probabilities unexpectedly leads to a relatively slow decline of the total cross-sections with increasing excitation energy. This effect is supported by a favorable arrangement of fission barriers protecting the compound nucleus against splitting concerning energetic thresholds for the emission of successive neutrons. In particular, the probabilities of the formation of superheavy nuclei in the $5n$-, $6n$-, and in some cases even in $7n$-evaporation channels are still promising. This may offer a new opportunity for the future synthesis of unknown neutron-deficient superheavy isotopes.

Cross-calibration of MODIS and VIIRS long near infrared bands for ocean color science and applications

Generation of consistent multi-sensor datasets is critical to the assessment of long-term global changes using satellite-borne instruments. Recent research suggests, however, that a fundamental assumption in satellite ocean color data processing concerning the calibration of the long near infrared band (i.e., 865 nm for MODIS) may introduce sensor-specific biases in space and/or time, which may also contribute to cross-sensor inconsistency in the derived reflectance data products. As such, it is necessary to assess the calibration of this band across sensors – performed here for MODIS/Aqua and VIIRS/SNPP using ‘simultaneous same view’ matchups (SSV; similar to simultaneous nadir overpass, but allowing for non-nadir measurements). Towards that end, we assess geometric, temporal, and spatial homogeneity metrics to identify SSVs, and develop a band-shifting approach applicable within standard satellite data processing routines to resolve expected spectral differences in the radiometry. We find top-of-atmosphere (TOA) radiance data from VIIRS/SNPP long near infrared band to be approximately 3% higher than the corresponding MODIS/A data. With the expectation that cross-calibrating the NIRL should improve cross-sensor continuity of downstream geophysical products (e.g., chlorophyll-a), we reprocessed VIIRS data using updated calibration coefficients. While we noticed many minor improvements in cross-sensor continuity in such data products, large-scale geographic and temporal biases between these two datasets still remain. These discontinuities may be the result of disparate errors in polarization correction or atmospheric correction, both of which are modulated by radiant path geometry.

Empirical regressions between system parameters and solute descriptors of polyparameter linear free energy relationships (PPLFERs) for predicting solvent-air partitioning

Polyparameter Linear Free Energy Relationships (PPLFERs) are an empirical tool used to predict the equilibrium partitioning of solutes between two phases, referred to as a system. There are experimentally determined solute descriptors for thousands of chemicals, but there are only on the order of 100 systems with calibrated system parameters, the majority of which are solvents and environmental matrixes in equilibrium with air or water. The goal of this work is to create empirical regressions which use the much more numerous solute descriptors to predict the system parameters of systems which have not yet been calibrated due to a lack of partitioning data. The special case of liquid solvents in equilibrium with air is the focus of this work because this is the case in which the relationship between solute and solvent properties is most clear. First a consistent dataset of PPLFER equations was compiled using partition coefficient data from the literature to recalibrate equations for solvent-air partitioning into the Goss form (Goss, K.-U. 2005) for 89 solvents including water. All 89 solvents have also solute descriptors available in a database curated for this work which describe their behaviour as solutes. The pool of descriptors drawn from to create the empirical regressions were the solute descriptors of the solvents normalized to McGowan volume (V), along with interaction parameters between the normalized descriptors. An applicability domain (AD) for the empirical regressions was defined using leverage to measure similarity to the training dataset of solvents, and solvents in the AD typically had lower RMSE for predictions. Some of the empirical regressions for the six system parameters have good predictive power (s, a, b, c) while others are only adequate (v, l). However, when these equations are combined to predict partition coefficients there is a significant cancellation of error and when predicting partition coefficients in an external validation dataset the RMSE is in the range 0.30–0.35. The empirical regressions combined with the PPLFER equations and solvent density can also be used to predict vapor pressures as an additional external validation. Predictions for a dataset of vapor pressures from the literature had an RMSE of 0.26–0.75. Analysing and comparing the results from these two external validation datasets the RMSE for predicting datasets of partition coefficients for arbitrary solutes in arbitrary systems of solvents in equilibrium with air is estimated to be 0.66 on average.

Establishing a global quality of care benchmark report.

The Movember funded TrueNTH Global Registry (TNGR) aims to improve care by collecting and analysing a consistent dataset to identify variation in disease management, benchmark care delivery in accordance with best practice guidelines and provide this information to those in a position to enact change. We discuss considerations of designing and implementing a quality of care report for TNGR. Eleven working group sessions were held prior to and as reports were being built with representation from clinicians, data managers and investigators contributing to TNGR. The aim of the meetings was to understand current data display approaches, share literature review findings and ideas for innovative approaches. Preferred displays were evaluated with two surveys (survey 1: 5 clinicians and 5 non-clinicians, 83% response rate; survey 2: 17 clinicians and 18 non-clinicians, 93% response rate). Consensus on dashboard design and three data-display preferences were achieved. The dashboard comprised two performance summary charts; one summarising site's relative quality indicator (QI) performance and another to summarise data quality. Binary outcome QIs were presented as funnel plots. Patient-reported outcome measures of function score and the extent to which men were bothered by their symptoms were presented in bubble plots. Time series graphs were seen as providing important information to supplement funnel and bubble plots. R Markdown was selected as the software program principally because of its excellent analytic and graph display capacity, open source licensing model and the large global community sharing program code enhancements. International collaboration in creating and maintaining clinical quality registries has allowed benchmarking of process and outcome measures on a large scale. A registry report system was developed with stakeholder engagement to produce dynamic reports that provide user-specific feedback to 132 participating sites across 13 countries.

Long-term monitoring for the surveillance of the conservation status of Tursiops truncatus in an EU Natura2000 site in the Mediterranean Sea. A pilot study in the Tuscan Archipelago

A pilot study, using the dataset from the research network ‘FLT Med Net’, which regularly monitors transborder regions in the Mediterranean Sea using ferries as platform for systematic surveys, was undertaken to assess common bottlenose dolphin range and population trends within the Natura 2000 EU marine site “Tutela del Tursiops truncatus”. The site was recently designated by the Tuscany Region (Italy) within the requirement of the EU Habitats Directive. In order to evaluate the conservation status of bottlenose dolphin according to the surveillance scheme of the Directive, two six-year periods (2007-2012; 2013-2018) were compared to assess trends in distribution-occurrence (range); Sightings Per Unit of Effort and Density (population). In total, 18146 NM were surveyed along two fixed transects, recording 90 sightings of Tursiops truncatus and a total of 268 specimens. Between the two periods, slight but not statistically significant differences were assessed, with decreasing trend in range and population of the species; no variation was detected in mean group sizes. Travelling was the most common behaviour, and juveniles were present in 20% of the sightings, concentrated during spring and summer. The consistent FLT Med Net dataset was found to be appropriate to evaluate important parameters for the assessment of trends in the conservation status of Tursiops truncatus at the Natura2000 site scale.

Automated chiral method screening – Evaluation of generated chromatographic data sets to further optimize screening efficiency

We set up an automated screening process to routinely test 10 chiral supercritical fluid chromatography (SFC) methods - five columns combined with two co-solvents - as part of a chiral separation lab workflow. Proprietary software tools enabled automated method screening of racemates, parallel evaluation of the resulting chromatograms for enantiomer separation and report generation. This process is largely automated and resulted in an efficient and reliable lab process with a minimum requirement for human intervention. Screenings were conducted on a test set of 756 racemates that were selected with focus on structural variation and on 2667 proprietary samples from lab routines. Statistical analysis revealed that up to 92% of the tested racemic mixtures could be successfully separated with at least one of the tested conditions of the screening. Process efficiency was further increased by identification of optimal method screening sequence, re-definition of the optimal column set and project-specific adaptations considering reduced structural variation of the analytes. This study illustrates the usefulness of consistent chromatographic data sets to accelerate and facilitate the identification of chiral methods to separate enantiomers by automated processing and statistical analysis.

Quantitative Kinetic Model of CO2 Absorption in Aqueous Tertiary Amine Solvents.

Aqueous tertiary amine solutions are increasingly used in industrial CO2 capture operations because they are more energy-efficient than primary or secondary amines and demonstrate higher CO2 absorption capacity. Yet, tertiary amine solutions have a significant drawback in that they tend to have lower CO2 absorption rates. To identify tertiary amines that absorb CO2 faster, it would be efficacious to have a quantitative and predictive model of the rate-controlling processes. Despite numerous attempts to date, this goal has been elusive. The present computational approach achieves this goal by focusing on the reaction of CO2 with OH- forming HCO3-. The performance of the resulting model is demonstrated for a consistent experimental data set of the absorption rates of CO2 for 24 different aqueous tertiary amine solvents. The key to the new model's success is the manner in which the free energy barrier for the reaction of CO2 with OH- is evaluated from the differences among the solvation free energies of CO2, OH-, and HCO3-, while the pKa of the amines controls the concentration of OH-. These solvation energies are obtained from molecular dynamics simulations. The experimental value of the free energy of reaction of CO2 with pure water is combined with information about measured rates of absorption of CO2 in an aqueous amine solvent in order to calibrate the absorption rate model. This model achieves a relative accuracy better than 0.1 kJ mol-1 for the free energies of activation for CO2 absorption in aqueous amine solutions and 0.07 g L-1 min-1 for the absorption rate of CO2. Such high accuracies are necessary to predict the correct experimental ranking of CO2 absorption rates, thus providing a quantitative approach of practical interest.

OceanSODA-ETHZ: a global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification

Abstract. Ocean acidification has profoundly altered the ocean's carbonate chemistry since preindustrial times, with potentially serious consequences for marine life. Yet, no long-term, global observation-based data set exists that allows us to study changes in ocean acidification for all carbonate system parameters over the last few decades. Here, we fill this gap and present a methodologically consistent global data set of all relevant surface ocean parameters, i.e., dissolved inorganic carbon (DIC), total alkalinity (TA), partial pressure of CO2 (pCO2), pH, and the saturation state with respect to mineral CaCO3 (Ω) at a monthly resolution over the period 1985 through 2018 at a spatial resolution of 1∘×1∘. This data set, named OceanSODA-ETHZ, was created by extrapolating in time and space the surface ocean observations of pCO2 (from the Surface Ocean CO2 Atlas, SOCAT) and total alkalinity (TA; from the Global Ocean Data Analysis Project, GLODAP) using the newly developed Geospatial Random Cluster Ensemble Regression (GRaCER) method (code available at https://doi.org/10.5281/zenodo.4455354, Gregor, 2021). This method is based on a two-step (cluster-regression) approach but extends it by considering an ensemble of such cluster regressions, leading to improved robustness. Surface ocean DIC, pH, and Ω were then computed from the globally mapped pCO2 and TA using the thermodynamic equations of the carbonate system. For the open ocean, the cluster-regression method estimates pCO2 and TA with global near-zero biases and root mean squared errors of 12 µatm and 13 µmol kg−1, respectively. Taking into account also the measurement and representation errors, the total uncertainty increases to 14 µatm and 21 µmol kg−1, respectively. We assess the fidelity of the computed parameters by comparing them to direct observations from GLODAP, finding surface ocean pH and DIC global biases of near zero, as well as root mean squared errors of 0.023 and 16 µmol kg−1, respectively. These uncertainties are very comparable to those expected by propagating the total uncertainty from pCO2 and TA through the thermodynamic computations, indicating a robust and conservative assessment of the uncertainties. We illustrate the potential of this new data set by analyzing the climatological mean seasonal cycles of the different parameters of the surface ocean carbonate system, highlighting their commonalities and differences. Further, this data set provides a novel constraint on the global- and basin-scale trends in ocean acidification for all parameters. Concretely, we find for the period 1990 through 2018 global mean trends of 8.6 ± 0.1 µmol kg−1 per decade for DIC, −0.016 ± 0.000 per decade for pH, 16.5 ± 0.1 µatm per decade for pCO2, and −0.07 ± 0.00 per decade for Ω. The OceanSODA-ETHZ data can be downloaded from https://doi.org/10.25921/m5wx-ja34 (Gregor and Gruber, 2020).

Climate-Driven Range Shifts Are Rapid Yet Variable Among Recreationally Important Coastal-Pelagic Fishes

Shifts in species distributions are occurring globally in response to climate change, but robust comparisons of redistribution rates among species are often prevented by methodological inconsistencies, challenging the identification of species that are most rapidly undergoing range shifts. In particular, comparable assessments of redistributions among harvested species are essential for identifying climate-driven changes in fishing opportunities and prioritising the development of management strategies. Here we utilise consistent datasets and methodologies to comparably analyse rates of climate-driven range shifts over 21 years for four recreationally important coastal-pelagic fishes (Australian bonito, Australian spotted mackerel, narrow-barred Spanish mackerel, and common dolphinfish) from the eastern Australian ocean warming hotspot. Latitudinal values corresponding to the poleward edge of species’ core oceanographic habitats were extracted from species distribution models (SDMs). Rates of poleward shifts in core oceanographic habitats ranged between 148.7 (i.e., common dolphinfish) and 278.6 (i.e., narrow-barred Spanish mackerel) km per decade over the study period. However, rates of redistribution varied by approximately 130 km per decade among species, demonstrating that subtle differences in species’ environmental responses can manifest in highly variable rates of climate-driven range shifts. These findings highlight the capacity for coastal-pelagic species to undergo rapid, yet variable, poleward range shifts, which have implications for ecosystem structure and the changing availability of key resources to fisheries.

A Quantitative Molecular Orbital Perspective of the Chalcogen Bond.

We have quantum chemically analyzed the structure and stability of archetypal chalcogen‐bonded model complexes D2Ch⋅⋅⋅A− (Ch = O, S, Se, Te; D, A = F, Cl, Br) using relativistic density functional theory at ZORA‐M06/QZ4P. Our purpose is twofold: (i) to compute accurate trends in chalcogen‐bond strength based on a set of consistent data; and (ii) to rationalize these trends in terms of detailed analyses of the bonding mechanism based on quantitative Kohn‐Sham molecular orbital (KS‐MO) theory in combination with a canonical energy decomposition analysis (EDA). At odds with the commonly accepted view of chalcogen bonding as a predominantly electrostatic phenomenon, we find that chalcogen bonds, just as hydrogen and halogen bonds, have a significant covalent character stemming from strong HOMO−LUMO interactions. Besides providing significantly to the bond strength, these orbital interactions are also manifested by the structural distortions they induce as well as the associated charge transfer from A− to D2Ch.

Thickness Characteristics of Pāhoehoe Lavas in the Deccan Province, Western Ghats, India, and in Continental Flood Basalt Provinces Elsewhere

Constraining the eruption rates of flood basalt lava flows remains a significant challenge despite decades of work. One potential observable proxy for eruption rates is flood basalt lava-flow lobe thicknesses, a topic that we tackle here quantitatively. In this study, we provide the first global compilation of pāhoehoe lava-lobe thicknesses from various continental flood basalt provinces (∼ 3,800 measurements) to compare characteristic thicknesses within and between provinces. We refer to thin lobes (∼ ≤5 m), characteristic of “compound” lavas, as hummocky pāhoehoe lava flows or flow-fields. Conversely, we term thicker lobes, characteristic of “simple” flows, as coming from sheet-lobe-dominated flows. Data from the Deccan Traps and Columbia River flood-basalt provinces are archetypal since they have the most consistent datasets as well as established chemo- and litho-stratigraphies. Examining Deccan lobe thicknesses, we find that previously suggested (and disputed) distinct temporal and regional distributions of hummocky pāhoehoe and sheet-lobe-dominated flow fields are not strongly supported by the data and that each geochemically defined formation displays both lobe types in varying amounts. Thin flow-lobes do not appear to indicate proximity to source. The modal lobe thickness of Deccan formations with abundant “thin” lava-lobes is 8 m, while the mode for sheet-lobe-dominated formations is only 17 m. Sheet-lobes up to 75–80 m are rare in the Deccan and Columbia River Provinces, and ones >100 m are exceptional globally. For other flood basalt provinces, modal thickness plots show a prevalence toward similar lobe thicknesses to Deccan, with many provinces having some or most lobes in the 5–8 m modal range. However, median values are generally thicker, in the 8–12 m range, suggesting that sheet-lobes dominate. By contrast, lobes from non-flood basalt flow-fields (e.g., Hawai’i, Snake River Plain) show distinctly thinner modes, sub-5 m. Our results provide a quantitative basis to ascertain variations in gross lava morphology and, perhaps, this will in future be related to emplacement dynamics of different flood basalt provinces, or parts thereof. We can also systematically distinguish outlier lobes (or regions) from typical lobes in a province, e.g., North American Central Atlantic Magmatic Province lava-lobes are anomalously thick and are closely related to feeder-intrusions, thus enabling a better understanding of conditions required to produce large-volume, thick, flood basalt lava-lobes and flows.

Characterizations Of Water Quality In West-Seti and Tamor River Basins, Nepal

This study presents the geochemical composition and water quality of waters in the West-Seti and Tamor River basins in Nepal Himalaya with the aim to reveal their hydrochemical characteristics and to evaluate the water quality. Water samples were collected from 18 sites of the rivers in the pre-monsoon season and analysed the physicochemical parameters to characterize their quality. The parameters- temperature, pH, conductivity, and total dissolved solids were measured in the field, while the major ions (Na+, Ca2+, Mg2+, Si4+, SO42−, NO3−, HCO3−, Cl−, and F−) were analyzed in the laboratory. Overall results of physicochemical parameters revealed that the ionic strength is much distinct; however, the waters are chemically pure in both the river basins with very less electrical conductivity (<250 µS/cm) and total dissolved solids (<120 mg/L). Bicarbonate (HCO3−) has a significant correlation with Ca2+ and Mg2+ suggesting carbonate rock weathering as the dominant geochemical process in both of the basins. The concentrations of Ca2+, Mg2+, and HCO3− in the water of the West-Seti is relatively higher than the waters in the Tamor River basin. Mostly, the geochemical facies of both the rivers are characterized by the Ca-Mg-HCO3- type (88.9%), with dominant carbonate dominated lithology. However, hydrochemical facies clearly suggested spatial discrimination between two basins with dominant geogenic signatures as Ca-SO4-Cl type water facies are also reported from the Tamor River basin. The results exhibited that the concentrations of measured parameters were relatively very low and within the WHO guideline values and currently under a safe level of the water quality for drinking and ecosystem health perspectives; however, further in-depth research is recommended in the periodic basis to assess traces of climate change imprints, and anthropogenic interferences for more consistent and reliable dataset. The findings of this study could be useful for the water quality management in the glacier-fed Himalayan River basins.

Deriving Natural Background Levels of Arsenic at the Meso-Scale Using Site-Specific Datasets: An Unorthodox Method

Arsenic is found in groundwater above regulatory limits in many countries and its origin is often from natural sources, making the definition of Natural Background Levels (NBLs) crucial. NBL is commonly assessed based on either dedicated small-scale monitoring campaigns or large-scale national/regional groundwater monitoring networks that may not grab local-scale heterogeneities. An alternative method is represented by site-specific monitoring networks in contaminated/polluted sites under remediation. As a main drawback, groundwater quality at these sites is affected by human activities. This paper explores the potential for groundwater data from an assemblage of site-specific datasets of contaminated/polluted sites to define NBLs of arsenic (As) at the meso-scale (order of 1000 km2). Common procedures for the assessment of human influence cannot be applied to this type of dataset due to limited data homogeneity. Thus, an “unorthodox” method is applied involving the definition of a consistent working dataset followed by a statistical identification and critical analysis of the outliers. The study was conducted in a highly anthropized area (Ferrara, N Italy), where As concentrations often exceed national threshold limits in a shallow aquifer. The results show that site-specific datasets, if properly pre-treated, are an effective alternative for the derivation of NBLs when regional monitoring networks fail to catch local-scale variability.