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The recent development of FYRE (French hYdroclimate REanalysis) Climate, a high-resolution ensemble daily reanalysis of precipitation and temperature covering the 1871–2012 period and the whole of France, offers the opportunity to derive streamflow series over the country from 1871 onwards. The FYRE Climate dataset has been used as input for hydrological modelling over a large sample of 661 near-natural French catchments using the GR6J (Génie Rural à 6 Paramètres Journaliers) lumped conceptual model. This approach led to the creation of the 25-member hydrological reconstructions, HydRE (Hydrological REconstruction), spanning the 1871–2012 period. Two sources of uncertainties have been taken into account: (1) the climate uncertainty using forcings from all 25 ensemble members provided by FYRE Climate and (2) the streamflow measurement error by perturbing observations used during the calibration. Further, the hydrological model error based on the relative discrepancies between observed and simulated streamflow has been added to derive the HydREM (Hydrological REconstruction with Model error) streamflow reconstructions. These two reconstructions are compared to other hydrological reconstructions with different meteorological inputs, hydrological reconstructions from a machine learning algorithm, and independent and dependent observations. Overall, the results show the added value of the HydRE and HydREM reconstructions in terms of quality, uncertainty estimation, and representation of extremes, therefore allowing us to better understand the variability in past hydrology over France.

Rivers are rich in biodiversity and act as ecological corridors for plant and animal species. With climate change and increasing anthropogenic water demand, more frequent and prolonged periods of drying in river systems are expected, endangering biodiversity and river ecosystems. However, understanding and predicting the hydrological mechanisms that control periodic drying and rewetting in rivers is challenging due to a lack of studies and hydrological observations, particularly in non-perennial rivers. Within the framework of the Horizon 2020 DRYvER (Drying River Networks and Climate Change) project, a hydrological modelling study of flow intermittence in rivers is being carried out in three European catchments (Spain, Finland, France) characterised by different climate, geology, and anthropogenic use. The objective of this study is to represent the spatio-temporal dynamics of flow intermittence at the reach level in mesoscale river networks (between 120 and 350 km2). The daily and spatially distributed flow condition (flowing or dry) is predicted using the J2000 distributed hydrological model coupled with a random forest classification model. Observed flow condition data from different sources (water level measurements, photo traps, citizen science applications) are used to build the predictive model. This study aims to evaluate the impact of the observed flow condition dataset (sample size, spatial and temporal representativity) on the performance of the predictive model. Results show that the hybrid modelling approach developed in this study allows the spatio-temporal patterns of drying to be accurately predicted in the three catchments, with a sensitivity criterion above 0.9 for the prediction of dry events in the Finnish and French case studies and 0.65 in the Spanish case study. This study shows the value of combining different data sources of observed flow condition to reduce the uncertainty in predicting flow intermittence.

Large datasets of long-term streamflow measurements are widely used to infer and model hydrological processes. However, streamflow measurements may suffer from what users can consider anomalies, i.e. non-natural records that may be erroneous streamflow values or anthropogenic influences that can lead to misinterpretation of actual hydrological processes. Since identifying anomalies is time consuming for humans, no study has investigated their proportion, temporal distribution, and influence on hydrological indicators over large datasets. This study summarizes the results of a large visual inspection campaign of 674 streamflow time series in France made by 43 evaluators, who were asked to identify anomalies falling under five categories, namely, linear interpolation, drops, noise, point anomalies, and other. We examined the evaluators' individual behaviour in terms of severity and agreement with other evaluators, as well as the temporal distributions of the anomalies and their influence on commonly used hydrological indicators. We found that inter-evaluator agreement was surprisingly low, with an average of 12 % of overlapping periods reported as anomalies. These anomalies were mostly identified as linear interpolation and noise, and they were more frequently reported during the low-flow periods in summer. The impact of cleaning data from the identified anomaly values was higher on low-flow indicators than on high-flow indicators, with change rates lower than 5 % most of the time. We conclude that the identification of anomalies in streamflow time series is highly dependent on the aims and skills of each evaluator, which raises questions about the best practices to adopt for data cleaning.

Surface observations are usually too few and far between to properly assess multidecadal variations at the local scale and characterize historical local extreme events at the same time. A data assimilation scheme has been recently presented to assimilate daily observations of temperature and precipitation into downscaled reconstructions from a global extended reanalysis through an Ensemble Kalman fitting approach and to derive high-resolution fields. Recent studies also showed that assimilating observations at high temporal resolution does not guarantee correct multidecadal variations. The current paper thus proposes (1) to apply the data assimilation scheme over France and over the 1871–2012 period based on the SCOPE Climate reconstructions background dataset and all available daily historical surface observations of temperature and precipitation, (2) to develop an assimilation scheme at the yearly timescale and to apply it over the same period and lastly, (3) to derive the FYRE Climate reanalysis, a 25-member ensemble hybrid dataset resulting from the daily and yearly assimilation schemes, spanning the whole 1871–2012 period at a daily and 8 km resolution over France. Assimilating daily observations only allows reconstructing accurately daily characteristics, but fails in reproducing robust multidecadal variations when compared to independent datasets. Combining the daily and yearly assimilation schemes, FYRE Climate clearly performs better than the SCOPE Climate background in terms of bias, error, and correlation, but also better than the Safran reference surface reanalysis over France available from 1958 onward only. FYRE Climate also succeeds in reconstructing both local extreme events and multidecadal variability. It is freely available at https://doi.org/10.5281/zenodo.4005573 (precipitation, Devers et al., 2020b) and https://doi.org/10.5281/zenodo.4006472 (temperature, Devers et al., 2020c).

Drought management plans (DMPs) require an overview of future climate conditions for ensuring long-term relevance of existing decision-making processes. To that end, impact studies are expected to best reproduce decision-making needs linked with catchment intrinsic sensitivity to climate change. The objective of this study is to apply a risk-based approach through sensitivity, exposure and performance assessments to identify where and when, due to climate change, access to surface water constrained by legally binding water restrictions (WRs) may question agricultural activities. After inspection of legally binding WRs from the DMPs in the Rhône–Mediterranean (RM) district, a framework to derive WR durations was developed based on harmonized low-flow indicators. Whilst the framework could not perfectly reproduce all WR ordered by state services, as deviations from sociopolitical factors could not be included, it enabled the identification of most WRs under the current baseline and the quantification of the sensitivity of WR duration to a wide range of perturbed climates for 106 catchments. Four classes of responses were found across the RM district. The information provided by the national system of compensation to farmers during the 2011 drought was used to define a critical threshold of acceptable WR that is related to the current activities over the RM district. The study finally concluded that catchments in mountainous areas, highly sensitive to temperature changes, are also the most predisposed to future restrictions under projected climate changes considering current DMPs, whilst catchments around the Mediterranean Sea were found to be mainly sensitive to precipitation changes and irrigation use was less vulnerable to projected climatic changes. The tools developed enable a rapid assessment of the effectiveness of current DMPs under climate change and can be used to prioritize review of the plans for those most vulnerable basins.

The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatio-temporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989–1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

Instructional leaders require timely and predictive data to guide post-pandemic systemic changes. We investigated the predictability of an observational instrument, the Rigor Appraisal, and its association with achievement and other school effectiveness measures in a sample of 53 schools in Illinois. We found that increases in the Rigor Appraisal had a small to moderate and statistically significant association with achievement, a positive and moderate statistically significant association with attendance, a negative relationship between referrals and suspensions, and a positive and moderate statistically significant association with the 5Essentials. Additionally, the implementation of academic teaming was associated with greater achievement in schools with more low-income students. We also found that leaders who conducted non-evaluative instructional walks experienced a positive and statistically significant impact on achievement. As such, the Rigor Appraisal merits further study in different contexts, as it shows promise as a leading indicator that can be used for the continuous improvement of instructional systems.

The original article [1] contains a small mistake concerning the ARTIC Team members mentioned in the Acknowledgements. The team member, Rocco Salvatore Calabrò had their name presented incorrectly. This has now been corrected in the original article.

As the increasing global consumption of concrete drives notable environmental burdens from its production, particularly greenhouse gas (GHG) emissions, interest in mitigation efforts is increasing. Yet current environmental impact quantification tools rely on user decision-making to select data for each concrete constituent, have inconsistent scopes and system boundaries, and often utilize third-party life cycle inventories. These factors limit customization or tracking of data and hinder the ability to draw robust comparisons among concrete mixtures to mitigate its environmental burdens. To address these issues, we introduce a cohesive, unified dataset of material, energy, and emission inventories to quantify the environmental impacts of concrete. In this work, we detail the synthesis of this open dataset and create an environmental impact assessment tool using this data. Models can be customized to be region specific, expanded to varying concrete mixtures, and support data visualization throughout each production stage. We perform a scenario analysis of impacts to produce a representative concrete mixture across the United States, with results ranging from 189 kg CO2-eq/m3 of concrete (California) to 266 kg CO2-eq/m3 of concrete (West Virginia). The largest driver of GHG, nitrogen oxide, sulfur oxide, and volatile organic compound emissions as well as energy demand is cement production, but aggregate production is the largest driver of water consumption and particulate matter smaller than 2.5 microns (PM2.5) emissions.

Concrete is a key component of the built environment. However, the manufacture of cement-based materials, such as concrete, produces over 8% of worldwide anthropogenic greenhouse gas (GHG) emissions. While reducing impacts from material production is an important strategy, structural design can also mitigate the environmental impacts of concrete. Designing infrastructure in a manner that uses concrete more efficiently, and thus lowers consumption while meeting the same system demands, holds promise for reducing GHG emissions while avoiding unintended consequences. Researchers at the University of California, Davis developed an initial methodology to evaluate implications of design decisions on the environmental impacts of concrete systems using a multi-criteria selection process to assist decision-makers. They demonstrated the methodology with a case study evaluating a built Caltrans pavement overlay for which comparisons of the GHG emissions and costs of various design alternatives were examined. This policy brief summarizes the findings from that research and provides policy implications.View the NCST Project Webpage

This report demonstrates how considerations across concrete material design and infrastructure design can be used together to change environmental impacts and costs by targeting appropriate constituents, materials, and system longevity. In this early-stage exploration, methods to compare concrete mixtures proportioning as they relate to environmental impacts, comparison indices based on common performance characteristics were used. This work was then built out to explore the role of steel reinforcement on reinforced concrete member environmental impacts to elucidate mechanisms to drive emissions reduction for these multi-material members. Finally, work was extended to understand how the longevity of concrete systems could influence environmental impacts associated with concrete production. Each stage of design considered was shown to have substantial effects on mitigating environmental impacts. In all cases, the primary environmental impact addressed was greenhouse gas emissions; however, this work can be extended to address other environmental impacts in future work. These methods from this work are demonstrated in this report through evaluation of mixtures in literature as well as a case study on an existing pavement overlay and potential alternative designs.View the NCST Project Webpage

The purpose of the study is to close the gap in the literature regarding the Marzano Teacher Evaluation Model (MTEM) that lacks large scale empirical investigations to assess the predictability of the model. The study thoroughly reviews the extant literature from all teacher evaluation frameworks, particularly focusing on the large body of published studies that have investigated the correlations between teacher observation ratings and teacher value-added measures, then outlines the literature specific to the MTEM. The study extracted observation ratings from a collection platform and linked it to the Florida Department of Education teacher value-added measure, which controls for prior testing history and student-level characteristics. The study sample included over 12,000 teachers and validated the use of the MTEM, particularly in Florida, by upholding the magnitude of correlations found in other instructional frameworks and demonstrating that observation ratings were the largest predictor in multilevel models accounting for student, teacher and school level characteristics.

Many factors contribute to the achievement gap in the U.S. As an intervention to help narrow the gap, tutoring presents a potentially powerful solution. Decades of research suggest that tutoring is an effective, evidence-based approach to improve student success, with effect sizes ranging from 0.4. to 2.0. As many school systems in the U.S. move to online and hybrid environments, one-to-one online tutoring could be utilized to support student leaning. The purpose of this project was to explore the impact online tutoring had on student success as measured by Northwest Evaluation Association (NWEA) scores. Overall, online tutoring accounted for between 6% and 18% of the variability in NWEA math growth scores, depending on the number of tutoring hours received and how far behind a student was; the farther behind, the more hours of tutoring needed.

Study techniques that students report using are often ineffective. In this project, text messaging supported students utilizing evidence-based approaches -- low-stakes practice testing, distributed practice, and interleaved practice. Students enrolled in an undergraduate general chemistry course were divided into two groups through matched random assignment. Both participation and performance in the text messaging quizzes correlated to performance in the course. Specifically, performance and participation in the text messaging quizzes, along with class participation, accounted for 67% of the variability in the overall final grades.

Students often report using ineffective study techniques. This project encouraged practice testing, distributed practice, interleaved practice, and metacognitive accuracy to increase learning in an undergraduate general chemistry course. Overall, the purpose of this project was to use text messaging to support evidence-based study practices. Students were provided 20 questions a week via text message and answered four questions per day (Monday through Friday). Both before and after the exam, students rated how well they thought they performed on the exam. Generally, participating in answering practice questions, the accuracy of their quiz question responses, the ability to gauge one's knowledge, and class participation were significant predictors of course scores and subsequent chemistry course scores. Therefore, the use of text messaging to support practice testing, distributed practice, interleaved practice, and metacognition is one way to help students improve learning as measured by final course grades.

The purpose of this project was to explore the impact of receiving nudges (notifications) on enrollment in the On My Way Pre-K program. Past research suggests that nudging is a way to positively impact behavior. Using text messaging, families who demonstrated an interest in On My Way Pre-K (either through completing the initial application or texting to enroll via the webpage) received an enrollment text, a welcome video, and an appointment text. Additionally, after enrolling in the program, families received additional text messages that encouraged attendance, provided additional resources, and gathered data about their experience in the program. Typically, 30% of people who express interest, enroll; however, 55% of those who participated in the texting program enrolled, which suggests there is a positive relationship between texting (nudges) and behavior that impacted enrollment in the program.

Despite the introduction of Common Core State Standards and the National Council of Teachers of Mathematics urging educators to explore fundamental concepts in mathematics at a deeper level, the majority of mathematics education has remained aimed at initial skill acquisition. The result has led to students learning to mimic a standard set of procedures and unable to connect these procedures to new and challenging problems. Further, this surface level understanding leaves learners underprepared for advanced mathematics topics. In our observations, when asked about the mathematical validity of their work, students were largely unable to communicate their ideas or utilized terminology that indicated a significant gap in conceptual understanding. In this article, we identify common conceptual misunderstandings and propose research-based solutions to help students gain a more robust understanding of mathematics in online environments.