Reliable Projections Research Articles

Determination of long-term deformation behaviours with InSAR data at a dump site of an open-pit coal mine in Turkey

Dump sites with broken overburden layers in surface mines have the risk of instability due to their loose bulk structures. In this study, deformations at a dump site of the Kangal/Kalburçayırı open-pit coal mine and their long-term behaviours were revealed with InSAR data for a total period of 4.5 years. Firstly, 9-periods involving different time ranges were created considering the presence of LiCSAR products. Then, the deformation velocities of each period were calculated by LiCSBAS software using LiCSAR products. The area where high deformations were concentrated from the LiCSBAS result products was determined and divided into 4 zones for more detailed evaluations. At the final stage, the period-deformation velocity graphs were drawn separately for each zone with the data obtained using two different approaches from the deformation velocities of all periods, and long-term deformation behaviours were determined. More reliable future behaviour projections could be made considering the current increasing or decreasing trend of deformation behaviours. It was revealed that vertical deformations at the dump site had an increasing trend in the first periods and a decreasing trend in the later periods. Maximum vertical deformation velocities ranging between −111.4 mm/year and −178.0 mm/year in the first 4 months reached the highest values (ranging from −141.9 mm/year to −261.8 mm/year) at the end of two years and decreased to values ranging from −33.0 mm/year to −44.3 mm/year in the later periods. It was found that horizontal deformation velocities ranged between +0.5 mm/year and +55.4 mm/year in the east–west direction and between −7.0 mm/year and −56.8 mm/year in the west-east direction, were approximately 5 times lower than vertical velocities and did not have a constantly increasing or decreasing trend in any direction. It was observed that deformations exhibited a behaviour resulting from the structural property of the dump bulk material. The results of the evaluations showed that the risk of instability gradually decreased at the dump site. The methodology of this study can enable the determination of similar areas that may pose a threat in a short time, the prioritization of risky areas, the planning of more detailed studies, and taking the necessary measures in a timely manner.

Automated system for high-accuracy quantity takeoff using BIM

Reliable construction project cost estimation relies on accurate quantity takeoffs (QTOs), traditionally done with error-prone 2-D drawings, but the emergence of Building Information Modeling (BIM) has improved QTO speed, accuracy, and reliability. However, the quality of BIM models can influence quantity accuracy, an aspect often overlooked in research on automating QTO with BIM. This paper develops an integrated framework to automatically extract and visualize construction quantities from BIM models. The system includes a Quantity Precision Check (QPC) module for automating more accurate QTO outputs. These outputs are then automatically transferred to an external database and visualized through a user-friendly dashboard on the Microsoft Power BI platform. This dashboard allows for comparisons between 2-D and BIM quantities and helps track changes in quantities over time due to design changes. The system was successfully validated in an infrastructure construction project in Canada, where it identified a 39% inconsistency in wall elements' material quantities and reduced steel quantity by 10% due to design changes. Overall, the system offers a speedy, reliable, and efficient tool for construction estimators, site managers, and cost control teams to provide accurate quantities to the procurement team and make swift decisions in response to design change impacts on construction costs.

Bringing an EU Joint Action to TikTok: the case of eCAN

Abstract Issue The Joint Action (JA) on strengthening eHealth including telemedicine and remote monitoring for health care systems for cancer prevention and care (eCAN) is a project co-funded by the European Union from 2022 to 2024. The Work Package on Communication (WP2) disseminates the JA's activities and outputs. Social media (SM) is one of the pillars of the communication strategy to promote eCAN and engage target audiences. Although the health sector is often present on LinkedIn, Twitter and Facebook, there are few examples of TikTok use in this area, making it difficult to identify role models. Description of the problem After reviewing other JA's communication channels, eCAN opens six SM accounts and divides them into two groups: written-content focused (Facebook, LinkedIn & Twitter) and audiovisual-content focused (Instagram, YouTube & TikTok). The TikTok account aims at innovating the way EU initiatives communicate and disseminate results. At the same time, it is meant to try to achieve a broader scope of audiences by exploring original means to reach them. Results Main results of eCAN's TikTok account for the period mid-January to mid-April 2023*: Followers: 18; Likes: 181; Comments: 1; Nº of posts: 10; Views: 3.355 * Data to be updated with results up to mid-October at the conference. Lessons WP2 faces the barrier of reducing the gap existing in bringing healthcare literacy and scientific accuracy into the much less rigid format TikTok is, particularly regarding cancer and telemedicine outreach. Besides, this social network's rules and algorithms might be challenging in terms of achieving visibility. The success of dissemination strategy depends on an adequate planification of what contents need to be communicated altogether with what the best formats should be used to widen the audiences while maintaining the project's reliability. Key messages • The eCAN JA is the first initiative to incorporate TikTok as a platform for disseminating the projects updates, outcomes and issues related to telemedicine, cancer and digital transformation. • This project explores new tools to be used in the health sector and within EU initiatives for reducing the gap in bringing health literacy and scientific accuracy to broader audiences.

Understanding uncertainties in projections of western North Pacific tropical cyclogenesis

Reliable projections of tropical cyclone (TC) activities in the western North Pacific (WNP) are crucial for climate policy-making in densely-populated coastal Asia. Existing projections, however, exhibit considerable uncertainties with unclear sources. Here, based on future projections by the latest Coupled Model Intercomparison Project Phase 6 climate models, we identify a new and prevailing source of uncertainty arising from different TC identification schemes. Notable differences in projections of detected TCs and empirical genesis potential indices are found to be caused by inconsistent changes in dynamic and thermodynamic environmental factors affecting TC formations. While model uncertainty holds the secondary importance, we show large potential in reducing it through improved model simulations of present-day TC characteristics. Internal variability noticeably impacts near-term projections of the WNP tropical cyclogenesis, while the relative contribution of scenario uncertainty remains small. Our findings provide valuable insights into model development and TC projections, thereby aiding in adaptation decisions.

Deep Learning for Bias‐Correcting CMIP6‐Class Earth System Models

AbstractThe accurate representation of precipitation in Earth system models (ESMs) is crucial for reliable projections of the ecological and socioeconomic impacts in response to anthropogenic global warming. The complex cross‐scale interactions of processes that produce precipitation are challenging to model, however, inducing potentially strong biases in ESM fields, especially regarding extremes. State‐of‐the‐art bias correction methods only address errors in the simulated frequency distributions locally at every individual grid cell. Improving unrealistic spatial patterns of the ESM output, which would require spatial context, has not been possible so far. Here, we show that a postprocessing method based on physically constrained generative adversarial networks (cGANs) can correct biases of a state‐of‐the‐art, CMIP6‐class ESM both in local frequency distributions and in the spatial patterns at once. While our method improves local frequency distributions equally well as gold‐standard bias‐adjustment frameworks, it strongly outperforms any existing methods in the correction of spatial patterns, especially in terms of the characteristic spatial intermittency of precipitation extremes.

Dementia death rates prediction

BackgroundPrevalence of dementia illness, causing certain morbidity and mortality globally, places burden on global public health. This study primary goal was to assess future risks of dying from severe dementia, given specific return period, within selected group of regions or nations.MethodsTraditional statistical approaches do not have benefits of effectively handling large regional dimensionality, along with nonlinear cross-correlations between various regional observations. In order to produce reliable long-term projections of excessive dementia death rate risks, this study advocates novel bio-system reliability technique, that being particularly suited for multi-regional environmental, biological, and health systems.DataRaw clinical data has been used as an input to the suggested population-based, bio-statistical technique using data from medical surveys and several centers.ResultsNovel spatiotemporal health system reliability methodology has been developed and applied to dementia death rates raw clinical data. Suggested methodology shown to be capable of dealing efficiently with spatiotemporal clinical observations of multi-regional nature. Accurate disease risks multi-regional spatiotemporal prediction being done, relevant confidence intervals have been presented as well.ConclusionsBased on available clinical survey dataset, the proposed approach may be applied in a variety of clinical public health applications. Confidence bands, given for predicted dementia-associated death rate levels with return periods of interest, have been reasonably narrow, indicating practical values of advocated prognostics.

Projected heat wave increasing trends over China based on combined dynamical and multiple statistical downscaling methods

Extensive investigations on the projection of heat waves (HWs) were conducted on the basis of coarse-resolution global climate models (GCMs). However, these investigations still fail to characterise the future changes in HWs regionally over China. PRECIS dynamical downscaling with a horizontal resolution of 25 km × 25 km was employed on the basis of GCM-HadCM3 to provide reliable projections on HWs over the Chinese mainland, and six statistical downscaling methods were used for bias correction under RCP4.5 and RCP8.5 scenarios. The multi-method ensemble (MME) of the top three dynamical downscaling methods with good performance was used to project future changes. Results showed that PRECIS primarily replicated the detailed spatiotemporal pattern of HWs. However, PRECIS overestimated the HWs in the Northwest and Southeast and expanded the areas of HWs in the Northeast and Southwest. Three statistical downscaling methods (quantile mapping, CDF-t and quantile delta mapping) demonstrated good performance in improving PRECIS simulation for reproducing HWs. By contrast, parametric-based trend-preserving approaches such as scaled distribution mapping and ISI-MIP are outperformed by the three aforementioned methods in downscaling HWs, particularly in the high latitudes of China. Based on MME projections, at the end of the 21st century, the national average of the number of HW days each year, the length of the longest HW event in the year and the extreme maximum temperature in HW will increase by 3 times, 1 time and 1.3 °C, respectively, under the RCP4.5 scenario, whilst that under the RCP8.5 scenario will increase by 8 times, 3 times and 3.7 °C, respectively, relative to 1986–2005. The Northwest is regionally projected to suffer long and hot HWs, whilst the South and Southeast will experience frequent consecutive HWs. Thus, HWs projected by the combined dynamical and statistical downscaling method are highly reliable in projecting HWs over China.

Artificial Intelligence's Use in the Diagnosis of Mouth Ulcers: A Systematic Review.

Artificial intelligence (AI) has been cited as being helpful in the diagnosis of diseases, the prediction of prognoses, and the development of patient-specific therapeutic strategies. AI can help dentists, in particular, when they need to make important judgments quickly. It can eliminate human mistakes in making decisions, resulting in superior and consistent medical treatment while lowering the workload on dentists.The existing studies relevant to the study and application of AI in the diagnosis of various forms of mouth ulcers are reviewed in this work. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards were followed in the preparation of the review. There were no rule violations, with the significant exception of the use of a better search method that led to more accurate findings. Using search terms mainly such as AI, oral health, oral ulcers, oral herpes simplex, oral lichen planus, pemphigus vulgaris, recurrent aphthous ulcer (RAU), oral cancer, premalignant and malignant disorders, etc., a comprehensive search was carried out in the reliable sources of literature, namely PubMed, Scopus, Embase, Web of Science, Ovid, Global Health, and PsycINFO. For all papers, exhaustive searches were done using inclusion criteria as well as exclusion criteria between June 28, 2018, and June 28, 2023. An AI framework for the automatic categorization of oral ulcers from oral clinical photographs was developed by the authors, and it performed satisfactorily. The newly designed AI model works better than the current convolutional neural network image categorization techniques and shows a fair level of precision in the classification of oral ulcers. However, despite being useful for identifying oral ulcers, the suggested technique needs a broader set of data for validation and training purposes before being used in clinical settings. Automated OCSCC identification using a deep learning-based technique is a quick, harmless, affordable, and practical approach to evaluating the effectiveness of cancer treatment. The categorization and identification of RAU lesions through the use of non-intrusive oral pictures using the previously developed ResNet50 and YOLOV algorithms demonstrated better accuracy as well as adequate potential for the future, which could be helpful in clinical practice. Moreover, the most reliable projections for the likelihood of the presence or absence of RAU were made by the optimized neural network. The authors also discovered variables associated with RAU that might be used as input information to build artificial neural networks that anticipate RAU.

Increased risk of flash droughts with raised concurrent hot and dry extremes under global warming

Flash droughts pose large threats to crop yields and ecosystem services due to their sudden onset and rapid intensification, arousing wide public concern in a warming climate. Their long-term characteristics of change, underlying mechanisms, and especially potential impacts on agriculture, forests, and populations at a global scale, however, remain largely unknown. We used in situ observations, two observation-based global reanalysis data sets, and 22 Earth system models to determine that flash droughts are shifting toward more frequent, accelerated-onset, and longer duration. These changes increased the exposure of agricultural areas, forested areas, and populations to flash droughts by 20.3%, 17.1%, and 30.0%, respectively, during 2001–2020 compared to 1981–2000, with a disproportionate increase in integrated risks across the Amazon Basin and eastern and southern Asia. The increase in concurrent hot and dry climatic conditions driven by warming has been mostly responsible for enabling and intensifying flash droughts over large regions. State-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) models, however, failed to identify the acceleration of the onset time of flash droughts and widely underestimated the occurrence of flash droughts that are driven only by precipitation deficits or by heat waves, probably because they misrepresent the dependence between precipitation and temperature and underestimate the sensitivity of soil moisture to temperature and precipitation at short timescales (e.g., 5 days). These syntheses comprehensively advance our understanding of the characteristics and impacts of flash droughts but also highlight that the CMIP6 models need to be validated to represent the correct covariability between climatic variables at short timescales to provide more reliable projections of flash droughts.

The Impact of Eastern Pacific Warming on Future North Atlantic Tropical Cyclogenesis

AbstractTropical cyclogenesis in the Atlantic is influenced by environmental parameters including vertical wind shear, which is sensitive to forcing from the tropical Pacific. Reliable projections of the response of such parameters to radiative forcing are key to understanding the future of hurricanes and coastal risk. One of the least certain aspects of future climate is the warming of the eastern tropical Pacific Ocean. Using climate model experiments isolating the warming of the eastern Pacific and controlling for other factors including El Niño‐Southern Oscillation (ENSO), changes in Atlantic tropical cyclogenesis potential by the end of this century are ∼20% lower with enhanced eastern Pacific warming. The ENSO signal in Atlantic tropical cyclogenesis potential amplifies with global warming, and that amplification is larger with enhanced eastern Pacific warming. The largest changes and dependencies on eastern Pacific warming are found in the south‐central main development region, attributable to changes in zonal overturning.

Applicability of CMIP5 and CMIP6 Models in China: Reproducibility of Historical Simulation and Uncertainty of Future Projection

Abstract General circulation model (GCM) outputs archived by the Coupled Model Intercomparison Project (CMIP) are widely used for climate projection. A comprehensive evaluation of the applicability of GCMs is crucial for generating reliable projections. However, previous GCM evaluation studies mainly focused on the reproducibility of the historical simulation to the observation data, ignoring the uncertainty involved in the future climate projection. In this study, an innovative and comprehensive index, named the history–future applicability score (HFAS), is proposed for evaluating the applicability of GCMs. Through incorporating the Taylor skill score (S) and the analysis of variance (ANOVA) within a general framework, the HFAS can reflect both the historical reproducibility and future uncertainty of GCMs. The HFAS framework is applied to quantitatively evaluating the skills of 28 GCMs (14 from CMIP5 and 14 from CMIP6) in modeling temperature and precipitation at 631 reference sites across China. Results indicate that (i) both CMIP5 and CMIP6 models underestimate the historical temperature of China and overestimate the historical precipitation; (ii) the total uncertainties in future temperature and precipitation projections increase along with time; compared to CMIP5, temperature uncertainty of CMIP6 is narrowed, while the precipitation uncertainty is increased; (iii) taking into account historical reproducibility and future uncertainty, CMIP6 models generally have increased applicability compared to CMIP5; and (iv) the best-performing GCMs in modeling temperature and precipitation of China are ACCESS-CM2 and MPI-ESM-LR, respectively, with the HFAS being 68.4 and 63.5. The findings are helpful for researchers to select locally appropriate GCMs and generate reliable regional climate projection.

Prioritization of CMIP5 based climate models in simulating precipitation using multicriteria decision making methods

Selecting climate models that are accurate in simulating observed weather is a difficult task for impact modellers. For more reliable future projections, model selection is desirable. The effectiveness of nine GCM-RCM combinations in simulating precipitation over the Meenachil river basin in Kerala is examined in this study using data from the CORDEX-SA datasets. Four performance indicators—correlation coefficient (R), percentage bias (PBIAS), skill score (SS), and root mean square error (RMSE)—were used to evaluate the models’ simulation abilities. The models were ranked based on their ability to simulate observed climate data measured over a baseline period (1980–2005) using the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method and the PROMETHEE-2 (Preference Ranking Organisation Method of Enrichment Evaluation) method of multi-criteria decision making approaches. Rankings of GCM-RCM combinations were carried out in both scenarios of equal and varying weight. The results of the study found that the top three precipitation prediction models for the Meenachil river basin are GFDL-RCA4, GFDL-RegCM4, and CNRM-RCA4.

Multiscale Influences on Rainfall in Northeast Australia

Abstract This study examines the multiscale modulation of mean and extreme rainfall in Northeast (NE) Australia under different background modes of variability, which is a new aspect given the high-resolution and long-term observational datasets. Daily rainfall probability is significantly modified by the Madden–Julian oscillation (MJO), and its influence varies with the seasons and is associated with atmospheric circulation anomalies. Rainfall generally decreases during El Niño and increases during La Niña years; however, there is a notable spatial nuance to El Niño–Southern Oscillation (ENSO)-associated extreme rainfall, with some locations showing the opposite precipitation response to the typical ENSO–rainfall relationship. Despite more precipitation overall in La Niña years, the mean and extreme precipitation responses to the MJO appear to be stronger and more often statistically significant during El Niño compared to La Niña periods. The impact of ENSO on the MJO–rainfall relationship is stronger than the variation of the MJO itself with ENSO, and likely reflects a change in the MJO modulation of rain-bearing atmospheric processes. During El Niño periods, diurnal rainfall amplitude is generally stronger in the central and southern subtropical parts of the study area than during La Niña periods, while the opposite tendency occurs in the northern tropical part. The diurnal cycle of both mean and extreme precipitation is amplified during suppressed convection phases compared to enhanced convection phases of the MJO. In general, the peak time of diurnal cycle does not change with MJO regimes, but there are some notable differences in rainfall propagation between enhanced and suppressed MJO phases. Significance Statement This study presents a new perspective on the relationship between precipitation in Northeast (NE) Australia and two important climate modes, the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO). Rainfall in NE Australia is strongly influenced by the MJO, ENSO, and their interaction, suggesting that climate models need to capture both individual climate modes and their interactions for reliable rainfall projections. These findings have societal climate risk implications, as NE Australia is an area prone to catastrophic flooding due to extreme rainfall.

Hydrological models weighting for hydrological projections: The impacts on future peak flows

Reliable hydrological projections are of great importance for climate change impact studies, especially knowing that these analyses can allow identifying regional adaptation and mitigation strategies for the future. However, the literature has highlighted that hydrological models used under climate conditions that are contrasting to those used during their calibrations can lower their performance and reliability, an issue that lowers the confidence in hydrological projections and adds uncertainty to their analyses. More studies are needed to explore this issue and evaluate potential strategies that might improve hydrological models’ reliability in climate change impact studies. Thus, the present study evaluates the robustness of hydrological models under contrasting climatic conditions and investigates the use of weighting techniques to improve their combined performance and reliability. The robustness of five lumped hydrological models is analysed using a Differential Split-Sample Testing (DSST) that evaluates their performance under cold, warm, humid and dry historical contrasting conditions over 77 basins covering different hydroclimatic conditions (two domains, one located in Quebec, Canada, and one in Mexico). Additionally, four basins were selected from the study area to evaluate the robustness of a more complex semi-distributed and more physically-based hydrological model and compare its simulations against the simpler lumped hydrological models. Based on the resulting performance of each hydrological model, five different weighting methods were applied to evaluate the potential improvements in the multi-model ensemble performance and quantify their effects on hydrological projections, particularly on future peak flows. For each basin, these streamflow projections were produced using two regional climate simulations (one per studied domain) issued from the Canadian Regional Climate Model version 5 (CRCM5) under the Representative Concentration Pathway (RCP) 8.5 for the 1976–2005, 2041–2070 and 2070–2099 periods. The results showed that weighting hydrological models, even with the most simplistic methods, showed better performances over historical contrasting conditions than the best-performing lumped hydrological model. Between the different weighting methods, the Granger-Ramanathan type A showed the overall best performance among the different basins and climate conditions, particularly in peak streamflows. Over the CRCM5-driven peak flow projections, the weighting methods Granger-Ramanathan types A and B produced the largest impacts on the projected floods magnitudes and climate change signals. On the other hand, the additional tests using the semi-distributed and more physically-based hydrological model revealed that this model showed more robust simulations than the weighted lumped hydrological models on low flows over the four selected basins. Additionally, more robust high-flow simulations were observed over a small snow-dominated basin, suggesting a potential added value in adding more complex hydrological models to simulate conditions under a changed climate.

Development of global monthly dataset of CMIP6 climate variables for estimating evapotranspiration

Reliable projection of evapotranspiration (ET) is important for planning sustainable water management for the agriculture field in the context of climate change. A global dataset of monthly climate variables was generated to estimate potential ET (PET) using 14 General Circulation Models (GCMs) for four main shared socioeconomic pathways (SSPs). The generated dataset has a spatial resolution of 0.5° × 0.5° and a period ranging from 1950 to 2100 and can estimate historical and future PET using the Penman-Monteith method. Furthermore, this dataset can be applied to various PET estimation methods based on climate variables. This paper presents that the dataset generated to estimate future PET could reflect the greenhouse gas concentration level of the SSP scenarios in latitude bands. Therefore, this dataset can provide vital information for users to select appropriate GCMs for estimating reasonable PETs and help determine bias correction methods to reduce between observation and model based on the scale of climate variables in each GCM.

Projection of Sea Level Change in the South China Sea Based on Dynamical Downscaling

The projection of future sea level change is usually based on the global climate models (GCMs); however, due to the low spatial resolution of the GCMs, the ability to reproduce the spatial heterogeneity of sea level is limited. In order to improve the sea level simulation capability in the South China Sea (SCS), a high-resolution ocean model has been established by using the dynamic downscaling technology. By evaluating and testing 20 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), average results of seven models were selected as the forcing condition of the high-resolution ocean model. The ocean model conducted the historical (1980~2014) and future (2015~2100) simulation under three scenarios of Shared Socio-economic Pathways (SSP1–2.6, SSP2–4.5 and SSP5–8.5). The selected average results of seven models in CMIP6 are better than any of them individually. The downscaled dynamic ocean model provides fruitful spatial characteristics of the sea level change, with a decrease in the dynamic sea level (DSL) in the central and southeastern parts of the SCS, and with a significant increase in the coastal DSL. The local steric sea level (SSL) is dominated by the local thermosteric sea level (TSSL), and the changes of local TSSL more than half of the sea level rise in SCS, indicate the magnitude of total sea level rise is dominated by local TSSL. But the spatial variation in total sea level is dominated by the spatial variation in DSL. Compared with CMIP5, the rise magnitude of the DSL and the local TSSL have been increased under the CMIP6 scenarios. The dynamic downscaling of sea level reveals more spatial details, provides more reliable projection of future sea level under the background of global warming, and can provide a new reference for coastal areas in the SCS to cope with the increasing risk of extreme water level disasters in the future.

The application of Neural Prophet Time Series in predicting rice stock at Rice Stores

Efficient inventory management and consistent rice supply are pivotal for the sustainability of small-scale food stalls. This research introduces an innovative approach to address this challenge through the Neural Prophet algorithm. By synergizing neural networks with additive regression models, the Neural Prophet captures intricate temporal patterns and trends within rice sales data. Our study evaluates the Neural Prophet's effectiveness in predicting rice sales, specifically for essential food vendors. Leveraging historical sales data from June 2022 to April 2023, the algorithm incorporates seasonality and trends and integrates external events, such as holidays, to heighten prediction precision. Our findings underscore the Neural Prophet's remarkable prowess in forecasting rice sales at primary food kiosks, adeptly discerning data trends and fluctuations, culminating in reliable future sales projections. The model boasts compelling performance metrics: MAE = 12.90, RMSE = 15.80, and Loss = 0.0313. Beyond its technical merits, this research carries significant practical implications, empowering proprietors and suppliers of basic food stalls to streamline inventory management, avert stockouts, and curtail overstocking by harnessing the precision of rice demand forecasting facilitated by the Neural Prophet algorithm.

Quantifying effects of cold acclimation and delayed springtime photosynthesis resumption in northern ecosystems.

Land carbon dynamics in temperate and boreal ecosystems are sensitive to environmental change. Accurately simulating gross primary productivity (GPP) and its seasonality is key for reliable carbon cycle projections. However, significant biases have been found in early spring GPP simulations of northern forests, where observations often suggest a later resumption of photosynthetic activity than predicted by models. Here, we used eddy covariance-based GPP estimates from 39 forest sites that differ by their climate and dominant plant functional types. We used a mechanistic and an empirical light use efficiency (LUE) model to investigate the magnitude and environmental controls of delayed springtime photosynthesis resumption (DSPR) across sites. We found DSPR reduced ecosystem LUE by 30-70% at many, but not all site-years during spring. A significant depression of LUE was found not only in coniferous but also at deciduous forests and was related to combined high radiation and low minimum temperatures. By embedding cold-acclimation effects on LUE that considers the delayed effects of minimum temperatures, initial model bias in simulated springtime GPP was effectively resolved. This provides an approach to improve GPP estimates by considering physiological acclimation and enables more reliable simulations of photosynthesis in northern forests and projections in a warming climate.

Determining Highway Project Contract Time from Project Duration Estimates

Contract time determination (CTD) by state DOTs is critical to a typical design-bid-build project due to its profound impacts on stakeholders in various aspects, such as the actual amount of time allocated to the contractor, contracting parties’ costs, and motorists’ traveling experiences on roads constructed under traffic. Reviewing DOTs’ CTD manuals and guidance shows that determining contract time is more complex than only estimating a reliable project duration from technical inputs such as work quantities, which has been the focus of previous studies. The type of contract time (e.g., working days or calendar days) affects contractors’ schedule risks, and external factors such as political commitments often affect highway projects significantly. However, those issues were rarely considered in past studies. Furthermore, a systematic procedure for determining contract time from a project duration estimate and effective supporting tools are lacking in DOTs’ current practice. This study proposed such a procedure incorporating typical constraints of highway projects identified by reviewing CTD-related documents and conducting seven face-to-face interviews with CTD experts. Other contributions include designing an accompanied computation algorithm to automate the process and developing a freely available Microsoft Excel tool to help DOT practitioners implement the proposed procedure in their operations.

Regional wave climate projections forced by EURO‐CORDEX winds for the Black Sea and Sea of Azov towards the end of the 21st century

AbstractWave phenomena impact high commercial value coastal and offshore activities, infrastructures and transportation. The knowledge of future wave conditions allows for consistent long‐term planning and decision‐making. The present study aims to provide robust, reliable projections of the potential future wave conditions of the Black Sea under the influence of climate change. For this purpose, an eight‐member dynamic wave climate ensemble that accurately represents the Black Sea's present wave climate has been produced, and future projections are assessed and analysed here. The wave climate ensemble was obtained by forcing the Simulating Waves Nearshore spectral wave model with eight regional wind fields from the EURO‐CORDEX. The future simulations of the regional wind fields are based on the RCP8.5 high‐emission scenario. The historical wave climate of the ensemble was evaluated against ERA5 reanalysis data. Projected changes in the mean wind and wave characteristics are examined by comparing historical and future simulations. In addition, the projected trends in the annual means during the future period of the wave simulations for significant wave height (Hs) and wave energy flux are also analysed. The projections for Hs and wind speeds are pronounced with significant decreases down to −10% across the basin, especially in the eastern region of the Black Sea. Changes in the Hs 99% percentiles of up to 16% are projected to occur. The projected changes in the annual mean of the wave energy flux are close to the projected changes in Hs means, while the seasonal changes (between −15% and 12%) are expected to be higher.