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Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests., Global Change Biology, 28 (23), ISSN:1354-1013, ISSN:1365-2486

Mitigation of adverse effects of global warming relies on accurate flow projections under climate change. These projections usually focus on changes in hydrological signatures, such as 100-year floods, which are estimated through statistical analyses of simulated flows under baseline and future conditions. However, models used for these simulations are traditionally calibrated to reproduce entire flow series, rather than statistics of hydrological signatures. Here, we consider this dichotomy by testing whether performance indicators (e.g. Nash-Sutcliffe coefficient) are informative about model ability to reproduce distributions and trends in the signatures. Results of streamflow simulations in 50 high-latitude catchments with the 3DNet-Catch model show that high model performances according to traditional indicators do not provide assurance that distributions or trends in hydrological signatures are well reproduced. We therefore suggest that performance in reproducing distributions and trends in hydrological signatures should be included in the process of model selection for climate change impact studies.

In this paper, a set of best practice data sharing guidelines for wind turbine fault detection model evaluation is developed, which can help practitioners overcome the main challenges of digitalisation. Digitalisation is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle. One of the largest challenges in successfully implementing digitalisation is the lack of data sharing and collaboration between organisations in the sector. In order to overcome this challenge, a new collaboration framework called WeDoWind was developed in recent work. The main innovation of this framework is the way it creates tangible incentives to motivate and empower different types of people from all over the world to share data and knowledge in practice. In this present paper, the challenges related to comparing and evaluating different SCADA-data-based wind turbine fault detection models are investigated by carrying out a new case study, the “WinJi Gearbox Fault Detection Challenge”, based on the WeDoWind framework. A total of six new solutions were submitted to the challenge, and a comparison and evaluation of the results show that, in general, some of the approaches (Particle Swarm Optimisation algorithm for constructing health indicators, performance monitoring using Deep Neural Networks, Combined Ward Hierarchical Clustering and Novelty Detection with Local Outlier Factor and Time-to-failure prediction using Random Forest Regression) appear to exhibit high potential to reach the goals of the Challenge. However, there are a number of concrete things that would have to have been done by the Challenge providers and the Challenge moderators in order to ensure success. This includes enabling access to more details of the different failure types, access to multiple data sets from more wind turbines experiencing gearbox failure, provision of a model or rule relating fault detection times or a remaining useful lifetime to the estimated costs for repairs, replacements and inspections, provision of a clear strategy for training and test periods in advance, as well as provision of a pre-defined template or requirements for the results. These learning outcomes are used directly to define a set of best practice data sharing guidelines for wind turbine fault detection model evaluation. The guidelines can be used by researchers in the sector in order to improve model evaluation and data sharing in the future.

Air pollution continues to be of concern for Bulgarian cities, mainly due to particulate matter of aerodynamic diameter smaller than 10 μm (PM10). There is public and expert interest in the improvement of two operational air quality modeling systems: the Bulgarian Chemical Weather Forecast System (BgCWFS) and the Local Air Quality Management System (LAQMS) for the city of Plovdiv. The aim of the study is to investigate the effects of satellite data assimilation in BgCWFS on surface concentrations over Bulgaria (resolution 9 km), to downscale BgCWFS output to LAQMS (resolution 250 m), and to examine effects on PM10 in Plovdiv. Data from the Global Ozone Monitoring Experiment-2 (GOME-2) (MetOP satellites) for aerosols, nitrogen dioxide (NO2), and sulfur dioxide (SO2) were assimilated in BgCWFS using objective analysis. Simulation experiments with and without satellite data were conducted for a summer and a winter month. The comparison to surface observations in the country showed improvement of results when using satellite data, especially in the summer due to mineral dust events captured by satellites. The decrease in the normalized mean bias (NMB) over the two months was 43% (PM10) and 73% (SO2). The LAQMS estimated background contributions to PM10 in the city as 32%. The absolute NMB by LAQMS decreased by 38%.

This study presents a comparison of the usual statistical methods used for crop model assessment. A case study was conducted using a data set from observations of the total dry weight in diploid potato crop, and six simulated data sets derived from the observations aimed to predict the measured data. Statistical indices such as the coefficient of determination, the root mean squared error, the relative root mean squared error, mean error, index of agreement, modified index of agreement, revised index of agreement, modeling efficiency, and revised modeling efficiency were compared. The results showed that the coefficient of determination is not a useful statistical index for model evaluation. The root mean squared error together with the relative root mean squared error offer an excellent notion of how deviated the simulations are in the same unit of the variable and percentage terms, and they leave no doubt when evaluating the quality of the simulations of a model. RESUMEN Este artículo presenta una comparación de los métodos estadísticos habituales que se utilizan para la evaluación de modelos de cultivos. Se realizó un estudio de caso utilizando un conjunto de datos observados del peso seco total en un cultivo de papa diploide y seis conjuntos de datos simulados destinados a predecir las observaciones. Los parámetros estadísticos evaluados fueron el coeficiente de determinación, la raíz cuadrada del cuadrado medio del error, la raíz cuadrada del cuadrado medio del error relativo, el error medio, el índice de concordancia, el índice de concordancia modificado, el índice de concordancia revisado, el índice de eficiencia y el índice de eficiencia revisado. Los resultados mostraron que el coeficiente de determinación no es un índice estadístico útil para la evaluación de modelos de cultivo. La raíz cuadrada del cuadrado medio del error junto a la raíz cuadrada del cuadrado medio del error relativo, ofrecen una excelente idea de cuánto están desviadas las simulaciones en la misma unidad de medida de la variable y en términos porcentuales. La raíz cuadrada del cuadrado medio del error y la raíz cuadrada del cuadrado medio del error relativo no dejan dudas al evaluar la calidad de las simulaciones de un modelo respecto a las observaciones

Evaluating whether hydrological models are right for the right reasons demands reproducible model benchmarking and diagnostics that evaluate not just statistical predictive model performance but also internal processes. Such model benchmarking and diagnostic efforts will benefit from standardized methods and ready-to-use toolkits. Using the Jupyter platform, this work presents HydroBench, a model-agnostic benchmarking tool consisting of three sets of metrics: 1) common statistical predictive measures, 2) hydrological signature-based process metrics, including a new time-linked flow duration curve and 3) information-theoretic diagnostics that measure the flow of information among model variables. As a test case, HydroBench was applied to compare two model products (calibrated and uncalibrated) of the National Hydrologic Model - Precipitation Runoff Modeling System (NHM-PRMS) at the Cedar River watershed, WA, United States. Although the uncalibrated model has the highest predictive performance, particularly for high flows, the signature-based diagnostics showed that the model overestimates low flows and poorly represents the recession processes. Elucidating why low flows may have been overestimated, the information-theoretic diagnostics indicated a higher flow of information from precipitation to snowmelt to streamflow in the uncalibrated model compared to the calibrated model, where information flowed more directly from precipitation to streamflow. This test case demonstrated the capability of HydroBench in process diagnostics and model predictive and functional performance evaluations, along with their tradeoffs. Having such a model benchmarking tool not only provides modelers with a comprehensive model evaluation system but also provides an open-source tool that can further be developed by the hydrological community.

The fungus Sclerotinia sclerotiorum causes serious losses to several agricultural crops worldwide. By using systems analysis, we retrieved the available knowledge concerning S. sclerotiorum from the literature and then analyzed and synthesized the data to develop a mechanistic, dynamic, weather-driven model for the prediction of epidemics on different crops. The model accounts for i) the production and survival of apothecia; ii) the production, dispersal, and survival of ascospores; iii) infection by ascospores; and iv) lesion onset. The ability of the model to predict the occurrence of apothecia was evaluated for epidemics observed with different climates, soil types, and host crops (soybean, white bean, and carrot) using independent data obtained from trials conducted in Ontario (Canada) in 1981, 1982, and from 1999 to 2002; in Michigan (U.S.A.) in 2015 and 2016; and in Wisconsin (U.S.A.) in 2016. The model showed 0.82 accuracy and 0.73 specificity in predicting the presence of apothecia, with a posterior probability of correctly predicting apothecia to be present or absent of 0.804 and 0.876, respectively. The model was also validated for its ability to predict disease progress on soybean and sunflower in Ontario in 1981 and 1982, in Manitoba (Canada) in 2001 and 2002, and in Michigan in 2015 and 2016. Comparison of model output with observations showed a concordance correlation coefficient of 0.948, and a root mean square error of 0.122. The model represents an improvement of previous S. sclerotiorum models and could be useful for making decisions on disease control.

Q9 is widely used in industries handling flammable fluids and is central to explosion risk assessment (ERA). Q9 transforms complex flammable clouds from pressurised releases to simple cuboids with uniform stoichiometric concentration, drastically reducing the time and resources needed by ERAs. Q9 is commonly believed in the industry to be conservative but two studies on Q9 gave conflicting conclusions. This efficacy issue is important as impacts of Q9 have real life consequences, such as inadequate engineering design and risk management, risk underestimation, etc. This paper reviews published data and described additional assessment on Q9 using the large-scale experimental dataset from Blast and Fire for Topside Structure joint industry (BFTSS) Phase 3B project which was designed to address this type of scenario. The results in this paper showed that Q9 systematically underpredicts this dataset. Following recognised model evaluation protocol would have avoided confusion and misinterpretation in previous studies. It is recommended that the modelling concept of Equivalent Stoichiometric Cloud behind Q9 should be put on a sound scientific footing. Meanwhile, Q9 should be used with caution, users should take full account of its bias and variance.

In this paper we propose the Single-equation Penalized Error Correction Selector (SPECS) as an automated estimation procedure for dynamic single-equation models with a large number of potentially (co)integrated variables. By extending the classical single-equation error correction model, SPECS enables the researcher to model large cointegrated datasets without necessitating any form of pre-testing for the order of integration or cointegrating rank. Under an asymptotic regime in which both the number of parameters and time series observations jointly diverge to infinity, we show that SPECS is able to consistently estimate an appropriate linear combination of the cointegrating vectors that may occur in the underlying DGP. In addition, SPECS is shown to enable the correct recovery of sparsity patterns in the parameter space and to possess the same limiting distribution as the OLS oracle procedure. A simulation study shows strong selective capabilities, as well as superior predictive performance in the context of nowcasting compared to high-dimensional models that ignore cointegration. An empirical application to nowcasting Dutch unemployment rates using Google Trends confirms the strong practical performance of our procedure. (c) 2020 Elsevier B.V. All rights reserved.

Recent upgrades to China’s radar network now allow for polarimetric measurements of convective systems in central China, providing an effective data set with which to evaluate the microphysics schemes employed in local squall line simulations. We compared polarimetric radar variables derived by Weather Research and Forecasting (WRF) and radar forward models and the corresponding hydrometeor species with radar observations and retrievals for a severe squall line observed over central China on 16 March 2022. Two microphysics schemes were tested and were able to accurately depict the contrast between convective and stratiform regions in terms of the drop size distribution (DSD) and reproduce the classical polarimetric signatures of the observed differential reflectivity (ZDR) and specific differential phase (KDP) columns. However, for the convective region, the simulated DSDs in both schemes exhibited lower proportions of large drops and lower liquid water content; by contrast, for the stratiform region, the proportion of large drops was found to be too high in the Morrison (MORR) scheme. The underprediction of ice-phase processes in the convective region, particularly the riming processes associated with graupel and hail, was likely responsible for the bias toward large raindrops at low levels. In the stratiform region, raindrop evaporation in the WRF Double-Moment 6-Class (WDM6) scheme, which partially offsets the overestimation of ice-phase processes, produced ground DSDs that more closely matched the observational data, and did not exhibit the overly strong warm-rain collisional growth processes of MORR.

The satellite signal deteriorates as it propagates from the source to the ground antenna on the surface of the earth. While some losses involving the amplitude and energy of the signal are unavoidably recovered due to the long slant path through the atmosphere, the impairment due to precipitation, especially rain, can be mitigated. Site diversity is one of the ways to obtain a less rain-attenuated signal, simply by preparing the other site to receive a link similar to that of the main site. Dual site diversity is common in practice with a site separation distance of at least a rain cell extent. This article presents an overview of the site diversity concept and a description of all parameters involved regarding the factor that contributed to the gain, which has been used as a performance metric to measure the effectiveness of a site diversity scheme. A detailed assessment of the capability of the gain prediction model is presented using site diversity experimental data conducted in two different climatic groups, namely, Greece in a temperate region and India in a tropical climatic region. The models involved in the evaluation were the ITU-R, Hodge, and Panagopoulos models, which were validated using temperate regional data, and the Semire and Yeo models, which were validated using tropical regional data. The observations of model behavior revealed that the Semire and Yeo models of the tropics were consistent with the measured gain obtained from India and Greece, respectively; thus, this demonstrated that the direction of future prediction models should consider both climates in the validation process.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

OHEJP Project: DiSCoVeR

Importance of evaluation of global hydrological models (gHMs) before doing climate impact assessment was underlined in several studies. The main objective of this study is to evaluate the performance of six gHMs in simulating observed discharge for a set of 57 large catchments applying common metrics with thresholds for the monthly and seasonal dynamics and summarize them estimating an aggregated index of model performance for each model in each basin. One model showed a good performance, and other five showed a weak or poor performance in most of the basins. In 15 catchments, evaluation results of all models were poor. The model evaluation was supplemented by climate impact assessment for a subset of 12 representative catchments using (1) usual ensemble mean approach and (2) weighted mean approach based on model performance, and the outcomes were compared. The comparison of impacts in terms of mean monthly and mean annual discharges using two approaches has shown that in four basins, differences were negligible or small, and in eight catchments, differences in mean monthly, mean annual discharge or both were moderate to large. The spreads were notably decreased in most cases when the second method was applied. It can be concluded that for improving credibility of projections, the model evaluation and application of the weighted mean approach could be recommended, especially if the mean monthly (seasonal) impacts are of interest, whereas the ensemble mean approach could be applied for projecting the mean annual changes. The calibration of gHMs could improve their performance and, consequently, the credibility of projections., BMBF, JSPS KAKENHI, NSFC

The research focuses on finding a superior forecasting technique to predict stock movement and behavior in the Shanghai Stock Exchange. The author’s interest is in stock market activities during high volatility, specifically 13 years from 2002 to 2015. This volatile period, fueled by events such as the dot-com bubble, SARS outbreak, political leadership transitions, and the global financial crisis, is of interest. The study aims to analyze changes in stock prices during an unstable period. The author used advanced computer sciences, Machine Learning through information processing and training, and the traditional statistical approach, the Multiple Linear Regression Model, with the least square method. Both techniques are accurate predictors measured by Absolute Percent Error with a range of 1.50% to 1.65%, using a data file containing 3,283 observations generated to record the daily close prices of individual Chinese companies. The t-test paired difference experiment shows the superiority of Neural Network in the finance sector and potentially not in other sectors. The Multiple Linear Regression Model performs equivalent to the Neural Network in other sectors.

In Bayesian inference, predictive distributions are typically in the form of samples generated via Markov chain Monte Carlo or related algorithms. In this paper, we conduct a systematic analysis of how to make and evaluate probabilistic forecasts from such simulation output. Based on proper scoring rules, we develop a notion of consistency that allows to assess the adequacy of methods for estimating the stationary distribution underlying the simulation output. We then provide asymptotic results that account for the salient features of Bayesian posterior simulators and derive conditions under which choices from the literature satisfy our notion of consistency. Importantly, these conditions depend on the scoring rule being used, such that the choices of approximation method and scoring rule are intertwined. While the logarithmic rule requires fairly stringent conditions, the continuous ranked probability score yields consistent approximations under minimal assumptions. These results are illustrated in a simulation study and an economic data example. Overall, mixture-of-parameters approximations that exploit the parametric structure of Bayesian models perform particularly well. Under the continuous ranked probability score, the empirical distribution function is a simple and appealing alternative option.

Using the historical simulations from 27 models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and 27 models in phase 6 (CMIP6), the authors evaluated the differences between CMIP5 and CMIP6 models in simulating the climate mean of extreme temperature over China through comparison with observations during 1979–2005. The CMIP6 models reproduce well the spatial distribution of annual maxima of daily maximum temperature (TXx), annual minima of daily minimum temperature (TNn), and frost days (FD). The model spread in CMIP6 is reduced relative to CMIP5 for some temperature indices, such as TXx, warm spell duration index (WSDI), and warm days (TX90p). The multimodel median ensembles also capture the observed trend of extreme temperature. However, the CMIP6 models still have low skill in capturing TX90p and cold nights (TN10p) and have obvious cold biases or warm biases over the Tibetan Plateau. The ability of individual models varies for different indices, although some models outperform the others in terms of the average of all indices considered for different models. By comparing different version models from the same organization, the updated CMIP6 models show no significant difference from their counterparts from CMIP5 for some models. Compared with individual models, the median ensembles show better agreement with the observations for temperature indices and their means.

The East Asian westerly jet (EAJ) plays a crucial role in affecting the East Asian summer rainfall (EASR). Therefore, evaluations of EAJ simulations are vital for improving the understanding and projections of climate change in East Asia. This study evaluates the simulations of the climatology and interannual variability in the present-day summer EAJ in the CMIP6 models and compares the results with those in the CMIP5 models by analyzing the historical climate simulations of 29 CMIP5 models and 21 CMIP6 models during the period from 1986–2005. In general, the CMIP6 models capture the EAJ more realistically than the CMIP5 models. The results show that the CMIP6 models reasonably capture the spatial features of the climatological zonal wind at 200 hPa and simulate a smaller zonal wind bias along the EAJ. The locations of the EAJ’s core are at the observed location in nearly all CMIP6 models but in only approximately two-thirds of the CMIP5 models. The EAJ’s intensity is closer to the observed value and exhibits a smaller intermodel dispersion in the CMIP6 models. The CMIP6 models also show an improved ability to reproduce the interannual variability in the EAJ’s meridional displacement and have a stronger relationship with the EASR.

Hydrological indicators support analyses about the impact of climate and anthropogenic changes on riverine ecosystems. As these studies often rely on hydrological models for estimating the future value of the indicators, it is important to investigate how well, and under which conditions, we can replicate changes in the indicators. This study looks at these questions by investigating the performance that can be achieved depending on the objective function for calibrating the model, the direction of the change in the indicator, the magnitude of this change and the properties of the catchments. The results indicate that, in general, indicators describing the magnitude of discharge (monthly and annual) can be adequately estimated with hydrological models, but that there are difficulties when estimating the characteristics of flow pulses, flow reversals and timing variables. For some of these indicators, it is not even possible to correctly estimate the direction of large changes. The analysis showed further that these problems cannot be resolved by adjusting the calibrated parameters, but that the model structure is unsuitable for modelling these indicators.

ObjectiveTo construct a simple model containing predictors derived from Chinese Learning Accomplishment Profile (C-LAP) to better the evaluation of the social–emotional development of toddlers aged 24–36 months.MethodThe test results by C-LAP system and demographic information of toddlers aged 24–36 months were collected between 2013 and 2019 in Shanghai, China, whose guardians were voluntary to accept the investigation. We developed a norm with the dataset based on the study population. With the norm, stepwise regression and best subset analysis were applied to select predictors.ResultsRelying on the norm established and stepwise regression and also the best subset analysis, an optimal model containing only 6 indicators was finally determined and the nomogram of the model was constructed. In the training and validation dataset, the AUCs of the optimal model were 0.95 (95% CI: 0.94–0.96) and 0.88 (95% CI: 0.85–0.90), respectively. When the cutoff point of the model was set at 0.04, its sensitivity in training and validation dataset was 0.969 and 0.949, respectively, and the specificity in training and validation dataset is 0.802 and 0.736, respectively.ConclusionA simplified predictive model which includes only 6 items derived from C-LAP is developed to evaluate the probabilities of being at risk of developmental problem in social–emotional development for toddlers aged 24–36 months. Meanwhile, specificity and sensitivity of the model may be high enough for future fast screening.

High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments, including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-m resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for snow water equivalent, recharge, and runoff.

Введение. Оригинальная статья ставит своей целью изучение вопроса о применимости CES-вида производственных функций для описания процесса сельскохозяйственного производства на уровне совокупности сельскохозяйственных организаций на примере данных по Нижегородской области. Материалы и методы. Приводится краткий обзор современных зарубежных исследований в части применения CES-функций, рассматривается вопрос о добавлении неклассических факторов производства в модель, рассматриваются некоторые методологические вопросы, в частности вопросы нормализации данных, математических алгоритмов оценки параметров производственной функции. Описывается план исследования, состав рассматриваемых факторов. Результаты. Построены 35 различных двух-, трехи четырехфакторных CES-моделей отдельно для организаций, занимающихся выпуском только продукции растениеводства, отдельно для занимающихся выпуском только продукции животноводства и отдельно для организаций, занятых производством продукции обеих отраслей. По каждой модели оценена значимость полученных статистических коэффициентов. Для модели, описывающей процесс производства продукции растениеводства, со всеми значимыми параметрами построено соответствующее уравнение и сделаны содержательные выводы. Отмечено, что не удалось подобрать достоверную и адекватную CES-функцию для организаций, занятых производством продукции животноводства и для организаций, занятых производством продукции обеих отраслей. Обсуждение. Подведены итоги исследования. Сделаны выводы о применимости классических CES-функций для описания процесса производства. Выдвинуты предложения о путях изменения уравнения CES-функций для повышения точности аппроксимации, в частности, обоснована необходимость разработки модифицированных форм CES-функции с эластичностью замещения, зависящей от количества и соотношения ресурсов. Заключение. Предложены дальнейшие направления развития исследований., Introduction. The original article aims to study the applicability of the CES-type of production functions to describe the process of agricultural production at the level of the population of agricultural organizations using the data for the Nizhny Novgorod region as an example. Materials and methods. A brief overview of modern foreign research on the use of CES-functions is given, the issue of adding non-classical factors of production to the model is considered, some methodological issues are considered, in particular, the issues of data normalization, mathematical algorithms for estimating the parameters of the production function. The research plan, the composition of the considered factors is described. Results. 35 different two-, three-, and four-factor CES models were built separately for organizations engaged in the production of only crop products, separately for those engaged in the production of only livestock products, and separately for organizations engaged in the production of products from both industries. For each model, the significance of the obtained statistical coefficients was evaluated. For a model that describes the process of crop production, with all significant parameters, an appropriate equation is constructed and meaningful conclusions are made. It is noted that it was not possible to select a reliable and adequate CES function for organizations engaged in the production of livestock products and for organizations engaged in the production of products of both industries. Discussion. The results of the study are summed up. Conclusions are drawn about the applicability of classical CES functions to describe the production process. Suggestions are made on ways to change the equation of CES functions to improve the approximation accuracy, in particular, the need to develop modified forms of the CES function with substitution elasticity depending on the amount and ratio of resources is substantiated. Conclusion. Further directions of research development are proposed.

The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment to the patient. Machine learning applications in the medical niche have increased as they can recognize patterns from data. Using machine learning to classify cardiovascular disease occurrence can help diagnosticians reduce misdiagnosis. This research develops a model that can correctly predict cardiovascular diseases to reduce the fatality caused by cardiovascular diseases. This paper proposes a method of k-modes clustering with Huang starting that can improve classification accuracy. Models such as random forest (RF), decision tree classifier (DT), multilayer perceptron (MP), and XGBoost (XGB) are used. GridSearchCV was used to hypertune the parameters of the applied model to optimize the result. The proposed model is applied to a real-world dataset of 70,000 instances from Kaggle. Models were trained on data that were split in 80:20 and achieved accuracy as follows: decision tree: 86.37% (with cross-validation) and 86.53% (without cross-validation), XGBoost: 86.87% (with cross-validation) and 87.02% (without cross-validation), random forest: 87.05% (with cross-validation) and 86.92% (without cross-validation), multilayer perceptron: 87.28% (with cross-validation) and 86.94% (without cross-validation). The proposed models have AUC (area under the curve) values: decision tree: 0.94, XGBoost: 0.95, random forest: 0.95, multilayer perceptron: 0.95. The conclusion drawn from this underlying research is that multilayer perceptron with cross-validation has outperformed all other algorithms in terms of accuracy. It achieved the highest accuracy of 87.28%.

An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications. In this work, a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean, minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling. These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland. The dynamical downscaling is performed with the Weather Research and Forecasting (WRF) model, and the statistical downscaling method implemented is the Cumulative Distribution Function-transform (CDF-t). The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season. The performance of the two methods is assessed qualitatively, by inspection of quantile-quantile plots, and quantitatively, through the Cramer-von Mises, mean absolute error, and root-mean-square error diagnostics. The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling (for all seasons). The hybrid method proves to be less computationally expensive, and also to give more skillful temperature forecasts (at least for the Finnish near-coastal region)., We acknowledge Botnia-Atlantica, anEU-programme financing cross border cooperation projects inSweden, Finland and Norway, for their support of this workthrough the WindCoE project. We would like to thank the High Per-formance Computing Center North for providing the computer re-sources needed to perform the numerical experiments presented inthis paper. We would like to thank the three anonymous reviewersfor their detailed and insightful comments and suggestions, whichhelped to improve the quality of the paper.

The Lancang–Mekong River basin (LMRB) is under increasing threat from global warming. In this paper, the projection of future climate in the LMRB is explored by focusing on the temperature change and extreme temperature events. First, the authors evaluate the bias of temperature simulated by the Weather Research and Forecasting model. Then, correction is made for the simulation by comparing with observation based on the non-parametric quantile mapping using robust empirical quantiles (RQUANT) method. Furthermore, using the corrected model results, the future climate projections of temperature and extreme temperature events in this basin during 2016–35, 2046–65, and 2080–99 are analyzed. The study shows that RQUANT can effectively reduce the bias of simulation results. After correction, the simulation can capture the spatial features and trends of mean temperature over the LMRB, as well as the extreme temperature events. Besides, it can reproduce the spatial and temporal distributions of the major modes. In the future, the temperature will keep increasing, and the warming in the southern basin will be more intense in the wet season than the dry season. The number of extreme high-temperature days exhibits an increasing trend, while the number of extreme low-temperature days shows a decreasing trend. Based on empirical orthogonal function analysis, the dominant feature of temperature over this basin shows a consistent change. The second mode shows a seesaw pattern.

Chlorophyll-a(chl-a) has been used as an important indicator of water quality. Great efforts have been invested to develop remote-sensing-based chl-a retrieval models. However, due to the spatial difference in chl-a concentration, a single model usually cannot accurately predict the whole range of chl-a concentration. To test the performance of precedent chl-a models, we carried out an experiment along the upper and middle reaches of the Kaidu River and around some small ponds in the Bayanbulak Wetland. We measured water surface reflectance in the field and analyzed the chl-a concentration in the laboratory. Initially, we performed a sensitivity analysis of the spectrum band to chl-a concentration with the aim of identifying the most suitable bands for various chl-a models. We found that the water samples could be divided into two groups with a threshold of 4.50 mg/m3. Then, we tested the performance of 11 precedent chl-a retrieval models and 7 spectral index-based regression models from this study for all the sample datasets and the two separate datasets with relatively high and low chl-a concentrations. Through a complete comparison of the performance of these models, we selected the D3B model for water bodies with high chl-a concentration and OC2 model (ocean color 2) for low chl-a concentration waters, resulting in the hierarchical and piecewise retrieval algorithm OC2-D3B. The chl-a concentration of 4.50 mg/m3 corresponded to the D3B value of −0.051; therefore, we used −0.051 as the threshold value of the OC2-D3B model. The result of the OC2-D3B model showed a better performance than the other algorithms. Finally, we mapped the spatial distribution and seasonal pattern of chl-a concentration in Bayanbulak Wetland using Sentinel-2 images from 2016 to 2019. The results indicated that the chl-a concentration in the riparian ponds was generally in the range of 8–10 mg/m3, which was higher than that in rivers with a range of 2–4 mg/m3. The highest chl-a concentration usually appears in summer, followed by spring and autumn, and the lowest in winter. The correlation between meteorological data and chl-a concentration showed that temperature is the dominant factor for chl-a concentration changes. Our analytical framework could provide a better way to accurately map the spatial distribution of chl-a concentration in complex river systems.

A key challenge in the field of Quantitative Structure Activity Relationships (QSAR) is how to effectively treat experimental error in the training and evaluation of computational models. It is often assumed in the field of QSAR that models cannot produce predictions which are more accurate than their training data. Additionally, it is implicitly assumed, by necessity, that data points in test sets or validation sets do not contain error, and that each data point is a population mean. This work proposes the hypothesis that QSAR models can make predictions which are more accurate than their training data and that the error-free test set assumption leads to a significant misevaluation of model performance. This work used 8 datasets with six different common QSAR endpoints, because different endpoints should have different amounts of experimental error associated with varying complexity of the measurements. Up to 15 levels of simulated Gaussian distributed random error was added to the datasets, and models were built on the error laden datasets using five different algorithms. The models were trained on the error laden data, evaluated on error-laden test sets, and evaluated on error-free test sets. The results show that for each level of added error, the RMSE for evaluation on the error free test sets was always better. The results support the hypothesis that, at least under the conditions of Gaussian distributed random error, QSAR models can make predictions which are more accurate than their training data, and that the evaluation of models on error laden test and validation sets may give a flawed measure of model performance. These results have implications for how QSAR models are evaluated, especially for disciplines where experimental error is very large, such as in computational toxicology. Graphical Abstract

M. Bermudez acknowledges funding from EU's Horizon 2020 Programme under Marie Skodowska-Curie Grant Agreement 754446 and UGR Research and Knowledge Transfer Fund-Athenea3i. Funding for open access charge: Universidad de Granada/CBUA., Flood assessment in coastal river areas is subject to complex dependencies and interactions between flood drivers. In addition, coastal areas are especially vulnerable to climate change, and thus its effects should be considered in the evaluation of future flood hazard. In the present study, we propose a methodology for a robust evaluation of historical and future flooding in coastal river areas. It follows a continuous simulation approach in which a hydrologic-hydraulic modeling cascade is run for several years, driven by a simultaneous series of flood drivers. The method differs from other existing methodologies in: (a) jointly considering the relevant flood drivers, both coastal and inland, together with their dependencies and interactions, to represent the simultaneous occurrence of extreme water levels correctly, and (b) obtaining local-scale physically consistent projections of flood drivers to account for future climate change effects on compound flood hazard. The methodology is tested for a coastal river reach in NW Spain. The results show that, although mean sea level rise is usually expected to be the main driver of increased flood hazard, climate change effects on river discharge can play a very significant role in regions with compound flooding potential., European Commission 754446, UGR Research and Knowledge Transfer Fund-Athenea3i, Universidad de Granada/CBUA

In this report, we have evaluated the performance of nearly 40 global climate models (GCMs) participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). The focus is on the northern European area, but the ability to simulate southern European and global climate is discussed as well. Model evaluation was started with a technical control; completely unrealistic values in the GCM output files were identified by seeking the absolute minimum and maximum values. In this stage, one GCM was rejected totally, and furthermore individual output files from two other GCMs. In evaluating the remaining GCMs, the primary tool was the Model Climate Performance Index (MCPI) that combines RMS errors calculated for the different climate variables into one index. The index takes into account both the seasonal and spatial variations in climatological means. Here, MCPI was calculated for the period 1981—2010 by comparing GCM output with the ERA-Interim reanalyses. Climate variables explored in the evaluation were the surface air temperature, precipitation, sea level air pressure and incoming solar radiation at the surface. Besides MCPI, we studied RMS errors in the seasonal course of the spatial means by examining each climate variable separately. Furthermore, the evaluation procedure considered model performance in simulating past trends in the global-mean temperature, the compatibility of future responses to different greenhouse-gas scenarios and the number of available scenario runs. Daily minimum and maximum temperatures were likewise explored in a qualitative sense, but owing to the non-existence of data from multiple GCMs, these variables were not incorporated in the quantitative validation. Four of the 37 GCMs that had passed the initial technical check were regarded as wholly unusable for scenario calculations: in two GCMs the responses to the different greenhouse gas scenarios were contradictory and in two other GCMs data were missing from one of the four key climate variables. Moreover, to reduce inter-GCM dependencies, no more than two variants of any individual GCM were included; this led to an abandonment of one GCM. The remaining 32 GCMs were divided into three quality classes according to the assessed performance. The users of model data can utilize this grading to select a subset of GCMs to be used in elaborating climate projections for Finland or adjacent areas. Annual-mean temperature and precipitation projections for Finland proved to be nearly identical regardless of whether they were derived from the entire ensemble or by ignoring models that had obtained the lowest scores. Solar radiation projections were somewhat more sensitive. Raportissa on arvioitu lähes 40 maailmanlaajuisen CMIP6-ilmastomallin (CMIP6 = Coupled Model Intercomparison Project, 6. vaihe) toimivuutta. Arviointi painottuu Pohjois-Euroopan alueelle, mutta lisäksi on tarkasteltu myös mallien kykyä simuloida Etelä-Euroopan ja koko maapallonkin ilmastoa. Mallien arvioiminen aloitettiin teknillisellä ennakkotarkastuksella; mallien tulostiedostoissa esiintyvät täysin epärealistiset arvot saatiin selville etsimällä kustakin tiedostosta absoluuttisesti pienimmät ja suurimmat arvot. Tässä vaiheessa yksi malli jouduttiin hylkäämään kokonaan, ja sen lisäksi kahden muunkin mallin yksittäiset tulostiedostot osoittautuivat kelvottomiksi. Jäljelle jääneitä malleja arvioitaessa ensisijainen työkalu oli MCPI-indeksi (MCPI = Model Climate Performance Index), joka kokoaa eri ilmastomuuttujille lasketut normitetut RMS-virheet yhdeksi indeksiksi. Indeksi ottaa huomioon sekä ilmastosuureitten vuodenaikaiset että alueelliset vaihtelut. MCPI-indeksi laskettiin jaksolle 1981—2010 vertaamalla mallien tuloksia ERA-Interim-uusanalyyseihin. Arvioinnissa käytetyt ilmastomuuttujat olivat pintalämpötila, sademäärä, merenpinnan tasolle redukoitu ilmanpaine ja pinnalle tuleva auringonsäteily. MCPI-indeksin ohella tutkittiin erikseen jokaisen ilmastomuuttujan aluekeskiarvojen RMS-virheitä. Lisäksi arvioinnissa otettiin huomioon mallien kyky simuloida maapallon keskilämpötilan tähänastisia muutoksia, eri kasvihuonekaasuskenaarioita vastaavien tulevien muutosten keskinäinen yhteensopivuus sekä käytettävissä olevien skenaarioajojen lukumäärä. Vuorokauden alimpien ja ylimpien lämpötilojen hyvyyttä tutkittiin ainoastaan kvalitatiivisesti, sillä näistä suureista tiedot puuttuivat varsin monesta mallista; kyseisiä muuttujia ei siis otettu mukaan hyvyysindeksejä laskettaessa. Neljä mallia niistä 37:stä, jotka olivat läpäisseet alustavan teknillisen tarkastuksen, osoittautui eri syistä täysin sopimattomiksi skenaariolaskelmiin. Kahdessa mallissa eri kasvihuonekaasuskenaarioitten tuottamat vasteet olivat ristiriidassa keskenään, ja kahdessa muussa mallissa tiedot jostakin neljästä keskeisestä ilmastomuuttujasta puuttuivat. Lisäksi mallien välisten riippuvuuksien vähentämiseksi mukaan kelpuutettiin kustakin mallista enintään kaksi versiota; tämä johti yhden mallin hylkäämiseen. Loput 32 mallia jaettiin arvioinnin tuloksen perusteella kolmeen laatuluokkaan. Laadittaessa Suomen tai sen lähialueiden ilmastoennusteita mallitietojen käyttäjät voivat hyödyntää tätä luokitusta valitessaan itselleen malleista sopivan osajoukon. Suomen vuotuiset keskilämpötilan ja sademäärän muutosarviot kylläkin osoittautuivat lähes samoiksi riippumatta siitä, käytetäänkö ennustusta laadittaessa koko mallijoukkoa vai jätetäänkö alhaisimmat pisteet saaneet mallit pois. Auringon säteilyn ennusteet olivat hieman herkempiä.

Leaf chlorophyll content (LCC) is an indicator of leaf photosynthetic capacity. It is crucial for improving the understanding of plant physiological status. SPAD meters are routinely used to provide an instantaneous estimation of in situ LCC. However, the calibration of meter readings into absolute measures of LCC is difficult, and a generic approach for this conversion remains elusive. This study presents an evaluation of the approaches that are commonly used in converting SPAD readings into absolute LCC values. We compared these approaches using three field datasets and one synthetic dataset. The field datasets consist of LCC measured using a destructive method in the laboratory, as well as the SPAD readings measured in the field for various vegetation types. The synthetic dataset was generated with the leaf radiative transfer model PROSPECT-5 across different leaf structures. LCC covers a wide range from 1.40 μg cm−2 to 86.34 μg cm−2 in the field datasets, and it ranges from 5 μg cm−2 to 80 μg cm−2 in the synthetic dataset. The relationships between LCC and SPAD readings were examined using linear, polynomial, exponential, and homographic functions for the field and synthetic datasets. For the field datasets, the assessments of these approaches were conducted for (i) all three datasets together, (ii) individual datasets, and (iii) individual vegetation species. For the synthetic dataset, leaves with different leaf structures (which mimic different vegetation species) were grouped for the evaluation of the approaches. The results demonstrate that the linear function is the most accurate one for the simulated dataset, in which leaf structure is relatively simple due to the turbid medium assumption of the PROSPECT-5 model. The assumption of leaves in the PROSPECT-5 model complies with the assumption made in the designed algorithm of the SPAD meter. As a result, the linear relationship between LCC and SPAD values was found for the modeled dataset in which the leaf structure is simple. For the field dataset, the functions do not perform well for all datasets together, while they improve significantly for individual datasets or species. The overall performance of the linear (LCC=a∗SPAD+b), polynomial (LCC=a∗SPAD2+b∗SPAD+c), and exponential functions (LCC=0.0893∗10SPADα) is promising for various datasets and species with the R2 > 0.8 and RMSE

In this paper, a high-efficiency and stable Cu-Ce@γ-Al2O3 catalyst was prepared by taking the reverse osmosis (RO) concentrated water of a sewage treatment plant as the treatment object and activated alumina as the carrier. The preparation factors that affected the catalytic activity of Cu-Ce@γ-Al2O3 were investigated. SEM, EDS, XRD, BET, XRF, and XPS techniques were applied to characterize the catalyst. Optimal working conditions, and degradation mechanism of RO concentrated water were researched. In comparison with the ozone oxidation alone, the Cu-Ce@γ-Al2O3 catalytic ozonation has more reactive groups, significantly improving the treatment effect. Characterization results show that Cu and Ce are successfully supported on the surface of the activated alumina support and mainly exist in the form of oxides (e.g., CuO and CeO2). The loading of metal led to a larger specific surface area and pore volume. The repeated use had an insignificant effect on the peaks of Cu2p and Ce3d energy spectra and caused a small loss of active components. Under these conditions, the removal rate of COD from RO concentrated water by Cu-Ce@γ-Al2O3 catalyst was 85.2%. The stability and salt tolerance of Cu-Ce@γ-Al2O3 catalysts were investigated by catalyst wear rate and repeated use times, respectively. The degradation of organic matter and residual tryptophan-like organic compounds were observed through UV absorption spectroscopy and 3D-EEM. Hydroxyl radicals participated in organic pollutants degradation. Finally, a multi-level-fuzzy analysis evaluation model was developed to quantitatively assess the catalytic ozone oxidation system of the Cu-Ce @γ-Al2O3 catalyst for the treatment of RO concentrated water.

This study presents a comprehensive evaluation of the mean and extreme surface air temperature and precipitation in the Coupled Model Intercomparison Project Phase 6 (CMIP6) multimodel ensemble simulations over the Tibetan Plateau. Simulations from 29 climate models are compared against gridded observations for the period 1961–2012. Results show that models reasonably reproduce the overall spatial patterns of the 1961–2012 averages of seasonal mean temperature and precipitation. Nevertheless, models tend to underestimate mean temperature and overestimate precipitation accumulations. To be specific, the multimodel mean estimates of seasonal average temperatures over the plateau are 0.5°C–2.6°C colder than observed, while the corresponding estimates of seasonal precipitation accumulations are 218% (spring: March–May), 76% (summer: June–August), 129% (autumn: September–November), and 533% (winter: December–February) of those observed. As in observations, models also well capture increasing trend of mean temperature and precipitation in all seasons but underestimate the rates of increasing trends for both variables in all seasons. Models’ ability to simulate temperature and precipitation extremes is also evaluated in terms of a set of chosen extreme indices defined by the Expert Team on Climate Change Detection and Indices. On average, models tend to underestimate the averages of annual maximum daily maximum temperature, annual minimum daily minimum temperature, frost days, warm nights, and consecutive dry days but overestimate the corresponding averages of annual maximum 5‐day precipitation accumulations and simply daily intensity. Generally, models are able to simulate the signs of the trends in extreme indices but underestimate their magnitudes and misrepresent spatial patterns.

This study evaluates the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the observed climate variability in the historical simulation of the Coupled Model Intercomparison Phase 6 (CMIP6). TaiESM1 is developed on the basis of the Community Earth System Model version 1.2.2, with the inclusion of several new physical schemes and improvements in the atmosphere model. The new additions include an improved triggering function in the cumulus convection scheme, a revised distribution‐based formula in the cloud fraction scheme, a new aerosol scheme, and a unique scheme for three‐dimensional surface absorption of shortwave radiation that accounts for the influence of complex terrains. In contrast to the majority of model evaluation processes, which focus mainly on the climatological mean, this evaluation focuses on climate variability parameters, including the diurnal rainfall cycle, precipitation extremes, synoptic eddy activity, intraseasonal fluctuation, monsoon evolution, and interannual and multidecadal atmospheric and oceanic teleconnection patterns. A series of intercomparisons between the simulations of TaiESM1 and CMIP6 models and observations indicate that TaiESM1, a participating model in CMIP6, can realistically simulate the observed climate variability at various time scales and are among the leading CMIP6 models in terms of many key climate features.

Purpose This paper aims to propose a data mining approach to evaluate a conceptual model in tourism, encompassing a large data set characterized by dimensions grounded on existing literature. Design/methodology/approach The approach is tested using a guest satisfaction model encompassing nine dimensions. A large data set of 84 k online reviews and 31 features was collected from TripAdvisor. The review score granted was considered a proxy of guest satisfaction and was defined as the target feature to model. A sequence of data understanding and preparation tasks led to a tuned set of 60k reviews and 29 input features which were used for training the data mining model. Finally, the data-based sensitivity analysis was adopted to understand which dimensions most influence guest satisfaction. Findings Previous user’s experience with the online platform, individual preferences, and hotel prestige were the most relevant dimensions concerning guests’ satisfaction. On the opposite, homogeneous characteristics among the Las Vegas hotels such as the hotel size were found of little relevance to satisfaction. Originality/value This study intends to set a baseline for an easier adoption of data mining to evaluate conceptual models through a scalable approach, helping to bridge between theory and practice, especially relevant when dealing with Big Data sources such as the social media. Thus, the steps undertaken during the study are detailed to facilitate replication to other models.

We tailored a tropical channel configuration of the Weather Research and Forecasting (WRF) Model to study tropical cyclone (TC) activity and associated climate variabilities. This tropical channel model (TCM) covers from 30°S to 50°N at 27-km horizontal resolution, with physics parameterizations carefully selected to achieve more realistic simulations of TCs and large-scale climate mean states. We performed 15-member ensembles of retrospective simulations from 1982 to 2016 hurricane seasons. A thorough comparison with observations demonstrates that the TCM yields significant skills in simulating TC activity climatology and variabilities in each basin, as well as TC physical structures. The correlation of the ensemble averaged accumulated cyclone energy (ACE) with observations in the western North Pacific (WNP), eastern North Pacific (ENP), and North Atlantic (NAT) is 0.80, 0.64, and 0.61, respectively, but is insignificant in the north Indian Ocean (NIO). Moreover, the TCM-simulated modulations of El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO) on the large-scale environment and TC genesis also agree well with observations. To examine the TCM’s potential for seasonal TC prediction, the model is used to forecast the 2017 and 2018 hurricane seasons, using bias-corrected sea surface temperatures (SSTs) from the CFSv2 seasonal prediction results. The TCM accurately predicts the hyperactive 2017 NAT hurricane season and near-normal WNP and ENP hurricane seasons when initialized in May. In addition, the TCM accurately predicts TC activity in the NAT and WNP during the 2018 season, but underpredicts ENP TC activity, in association with a poor ENSO forecast.

Author(s): Steen, VA; Elphick, CS; Tingley, MW | Abstract: Aim: Citizen science data are increasingly used for modelling species distributions because they offer broad spatiotemporal coverage of local observations. However, such data are often collected without experimental design or set survey methods, raising the risk that bias and noise will compromise modelled predictions. We tested the ability of species distribution models (SDMs) built from these low-structure citizen science data to match the quality of SDMs from systematically collected data and tested whether stringent data filtering improved predictions. Location: Northeastern USA. Methods: We evaluated models built from a rapidly growing dataset of avian occurrences reported by birders—eBird—against models built from four independent, systematically collected datasets. We developed SDMs for 96 species using both data sources and compared their predictive abilities. We also tested whether culling eBird data by applying stringent data filters on survey effort or observer expertise improved predictions. Results: We found that SDMs built from low-structure citizen science data matched or exceeded performance of SDMs from systematically collected datasets for 12%–31% of species ((Formula presented.) =n22%), depending on the dataset. At least one culling option produced equivalent or better performance for 40%–70% of species ((Formula presented.) =n49%). Data culling by restricting survey effort improved predictions more than restricting by observer expertise. The optimal effort restriction differed by dataset, and for three of the datasets was further informed by species traits. Main conclusions: Species distribution models developed using low-structure citizen science data sometimes performed as well as those from systematic data. Culling generally improved models, but results were heterogeneous, prohibiting clear recommendations for how to cull. Our results indicate that the growing availability of citizen science data holds potential for creating high-quality spatial predictions, but that time should be invested in determining how best to cull datasets and that one-size-fits-all solutions beyond basic outlier filtering may be hard to find.

The Lake Champlain Basin has socioeconomic and ecological significance for the Northeastern United States and Quebec, Canada. Temperatures and extreme precipitation events have been increasing across this region over the past three decades. Accurate, high-resolution climate simulations are critical to assessing potential climate change risk in the Lake Champlain Basin. We evaluate the performance of a regional climate model, the Weather Research and Forecasting (WRF) model, to downscale ERA-Interim reanalysis data to 4 km for the Lake Champlain Basin. Specifically, we compare an ensemble of five WRF experiments with different physics configurations using a one-way, triple-nested domain (36, 12, and 4 km) over three 5-year periods (1980–1984, 1995–1999, and 2010–2014) to Daymet, a gridded observational dataset. We find that WRF simulations of the Lake Champlain Basin generally reproduce the observed temperature and precipitation seasonal cycles, but have cold and wet biases. The simulation of mean temperature by WRF is most sensitive to the choice of radiation scheme, while the simulation of mean precipitation is most sensitive to the choice of radiation, cumulus, and microphysics scheme. We find that turning the cumulus scheme on improves the simulation of the precipitation seasonal cycle at a 4 km resolution, but also substantially enhances the wet bias. Using a coarser resolution (36 km) produces smaller regionally averaged precipitation biases, but not improved correlations between simulated and observed monthly precipitation. Both spatial resolution and turning the cumulus scheme off have minor effects on simulated temperature.

More than one-third of Earth's landmass is drained by rivers that seasonally freeze over. Ice transforms the hydrologic1,2, ecologic3,4, climatic5 and socio-economic6-8 functions of river corridors. Although river ice extent has been shown to be declining in many regions of the world1, the seasonality, historical change and predicted future changes in river ice extent and duration have not yet been quantified globally. Previous studies of river ice, which suggested that declines in extent and duration could be attributed to warming temperatures9,10, were based on data from sparse locations. Furthermore, existing projections of future ice extent are based solely on the location of the 0-°C isotherm11. Here, using satellite observations, we show that the global extent of river ice is declining, and we project a mean decrease in seasonal ice duration of 6.10 ± 0.08 days per 1-°C increase in global mean surface air temperature. We tracked the extent of river ice using over 400,000 clear-sky Landsat images spanning 1984-2018 and observed a mean decline of 2.5 percentage points globally in the past three decades. To project future changes in river ice extent, we developed an observationally calibrated and validated model, based on temperature and season, which reduced the mean bias by 87 per cent compared with the 0-degree-Celsius isotherm approach. We applied this model to future climate projections for 2080-2100: compared with 2009-2029, the average river ice duration declines by 16.7 days under Representative Concentration Pathway (RCP) 8.5, whereas under RCP 4.5 it declines on average by 7.3 days. Our results show that, globally, river ice is measurably declining and will continue to decline linearly with projected increases in surface air temperature towards the end of this century. ispartof: JOURNAL OF CLIMATE vol:32 issue:20 pages:6899-6915 ispartof: location:England status: Published online

Environmental modelling is transitioning from the traditional paradigm that focuses on the model and its quantitative performance to a more holistic paradigm that recognises successful model-based outcomes are closely tied to undertaking modelling as a social process, not just as a technical procedure. This paper redefines evaluation as a multi-dimensional and multi-perspective concept, and proposes a more complete framework for identifying and measuring the effectiveness of modelling that serves the new paradigm. Under this framework, evaluation considers a broader set of success criteria, and emphasises the importance of contextual factors in determining the relevance and outcome of the criteria. These evaluation criteria are grouped into eight categories: project efficiency, model accessibility, credibility, saliency, legitimacy, satisfaction, application, and impact. Evaluation should be part of an iterative and adaptive process that attempts to improve model-based outcomes and foster pathways to better futures.

International audience; Aims A reliable root architectural model is required to study tree anchorage, including secondary root growth, from seed to the mature stage. Methods We calibrated the generic RootTyp model for the seven root types of Pinus pinaster. We determined most parameter distributions from the means and standard deviations of corresponding features in 92 excavated root systems of various ages. We set the remaining parameters using the literature or an optimisation method. The simulated root systems were compared to 22 additional root systems for validation. Results Branching varies as a function of the distance from the root base, and the growth capacity decreases with branching order. These damping properties had to be implemented in the model to yield realistic outputs beyond the juvenile phase. Diameter growth was satisfactorily modelled from the apex diameter and a tapering-by-branching coefficient. Nevertheless, the diameters of the simulated roots were underestimated near the stump. Growth alterations due to soil constraints were accurately reproduced using the calibrated soil module. Conclusions Root architecture of large-size and long-living plants can be appropriately modelled with the modified RootTyp model. We suppose that diameter underestimation was due to selective acclimation to the prevailing wind, a facet not taken into account at this calibration step.

© 2018 Elsevier BV. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 24 month embargo from date of publication (December 2018) in accordance with the publisher’s archiving policy, Hydrogeological conceptual models are collections of hypotheses describing the understanding of groundwater systems and they are considered one of the major sources of uncertainty in groundwater flow and transport modelling. A common method for characterizing the conceptual uncertainty is the multi-model approach, where alternative plausible conceptual models are developed and evaluated. This review aims to give an overview of how multiple alternative models have been developed, tested and used for predictions in the multi-model approach in international literature and to identify the remaining challenges. The review shows that only a few guidelines for developing the multiple conceptual models exist, and these are rarely followed. The challenge of generating a mutually exclusive and collectively exhaustive range of plausible models is yet to be solved. Regarding conceptual model testing, the reviewed studies show that a challenge remains in finding data that is both suitable to discriminate between conceptual models and relevant to the model objective. We argue that there is a need for a systematic approach to conceptual model building where all aspects of conceptualization relevant to the study objective are covered. For each conceptual issue identified, alternative models representing hypotheses that are mutually exclusive should be defined. Using a systematic, hypothesis based approach increases the transparency in the modelling workflow and therefore the confidence in the final model predictions, while also anticipating conceptual surprises. While the focus of this review is on hydrogeological applications, the concepts and challenges concerning model building and testing are applicable to spatio-temporal dynamical environmental systems models in general.

AirGIS, a Geographic Information Systems (GIS) based air pollution and human exposure modelling system, is routinely used in conjunction with the Operational Street Pollution Model (OSPM®), across the globe, to assess local- or street-scale air pollution. We developed a substantially revised version of AirGIS (hereafter, new AirGIS) as a new modelling system in open-source GIS i.e. PostgreSQL software with its spatial extension PostGIS to (1) optimize the model performance enabling model calculations for a large number of sites over a large geographical area, with limited computing resources (2) replace the outdated programming language Avenue (3) become independent of commercial GIS software. This paper, therefore, aims to describe the overall structure of new AirGIS modelling system together with its strengths and limitations. Furthermore, the new AirGIS has been evaluated against various measured datasets of ambient air pollution (NOx, NO2, PM10 and PM2.5). In terms of reproducing temporal variation (single location, time series of concentrations e.g. annual, daily etc.) of air pollution, the new model achieved correlations (R) in the range 0.45–0.96. While, in terms of reproducing the spatial variation (several locations, single time interval), the new AirGIS achieved correlations in the range 0.32–0.92. The new model, therefore, can be used for both short- and long-term air pollution exposure assessments to facilitate health related studies. However, the present evaluation of the new modelling system also revealed that the new AirGIS significantly overestimated the observed concentrations for two out of four datasets. The possible reasons for these errors and future directions to reducing the bias in the new model output have been discussed.

It is widely acknowledged that object segmentation is a significant research field for computer vision and a key process for many other visual tasks. In the past unsupervised single-image segmentation, there are often cases where the segmentation result is not good. In the current supervised single-image segmentation, it is necessary to rely on a large number of data annotations and long-term training of the model. Then, people attempted to segment simultaneously the common regions from multiple images. On the one hand, it does not need to use a large amount of labeled data to train in advance. On the other hand, it utilizes the consistency constraint between images to better obtain the object information. This idea can generate better performance than the traditional one did, resulting in many methods related to object co-segmentation. This paper reviews some classic and effective object co-segmentation methods, including saliency-based approaches, joint-processing-based approaches, graph-based approaches, and others. For different methods, we select two or three related models to elaborate, such as a model based on random walks. Moreover, in order to exhibit and evaluate these methods objectively and comprehensively, we not only summarize them in the form of flowcharts and algorithm summaries, but also compare their performance with visualization methods and evaluation metrics, such as intersection-over-union, consistency error, and precision-recall rate. From the experiment, we also attempt to clarify and analyze the existing problems. Finally, we point out the challenges and directions and open new venues for future researchers in the field.

This paper introduces Detection Metrics, an open-source scientific software for the assessment of deep learning neural network models for visual object detection. This software provides objective performance metrics such as mean average precision and mean inference time. The most relevant international object detection datasets are supported along with the most widely used deep learning frameworks. Different network models, even those built from different frameworks, can be fairly compared in this way. This is very useful when developing deep learning applications or research. A set of tools is provided to manage and work with different datasets and models, including visualization and conversion into several common formats. Detection Metrics may also be used in automatic batch processing for large experimental tests, saving researchers time, and new domain-specific datasets can be easily created from videos or webcams. It is open-source, can be audited, extended, and adapted to particular requirements. It has been experimentally validated. The performance of the most relevant state-of-the-art neural models for object detection has been experimentally compared. In addition, it has been used in several research projects, guiding in selecting the most suitable network model architectures and training procedures. The performance of the different models and training alternatives can be easily measured, even on large datasets.

The land surface scheme (LSS) plays a very important role in the forecast of surface meteorological elements in the Central Asia arid region. Therefore, two LSSs viz. Noah and Noah-MP were evaluated over the Central Asia arid region in January and July 2017 using the Weather Research and Forecasting (WRF) model at a 3-km horizontal grid resolution. The objective was to assess the performance of WRF LSSs by calculated the mean error (ME) and the root mean squared error (RMSE) of simulated hourly meteorological elements, such as 2-m air temperature, 10-m wind speed, soil temperature, soil moisture at 5 and 25 cm thickness, and surface soil heat flux at 5 cm thickness. The results showed that, compared to Noah, Noah-MP modeled less surface sensible heat flux in the northern Xinjiang (15~20 W∙m−2) and surface latent heat flux in most areas of Xinjiang (

Climate change will have a considerable impact on forest productivity worldwide. Forecasting the magnitude of such impact, with multiple environmental stressors changing simultaneously, is only possible with the help of process-based models. In order to assess their performance, such models require careful evaluation against measurements. However, direct comparison of model outputs against observational data is often not reliable, as models may provide the right answers due to the wrong reasons. This would severely hinder forecasting abilities under unprecedented climate conditions. Here, we present a methodology for model assessment, which supplements the traditional output-to-observation model validation. It evaluates model performance through its ability to reproduce observed seasonal changes of the most limiting environmental driver (MLED) for a given process, here daily gross primary productivity (GPP). We analyzed seasonal changes of the MLED for GPP in two contrasting pine forests, the Mediterranean Pinus halepensis Mill. Yatir (Israel) and the boreal Pinus sylvestris L. Hyytiälä (Finland) from three years of eddy-covariance flux data. Then, we simulated the same period with a state-of-the-art process-based simulation model (LandscapeDNDC). Finally, we assessed if the model was able to reproduce both GPP observations and MLED seasonality. We found that the model reproduced the seasonality of GPP in both stands, but it was slightly overestimated without site-specific fine-tuning. Interestingly, although LandscapeDNDC properly captured the main MLED in Hyytiälä (temperature) and in Yatir (soil water availability), it failed to reproduce high-temperature and high-vapor pressure limitations of GPP in Yatir during spring and summer. We deduced that the most likely reason for this divergence is an incomplete description of stomatal behavior. In summary, this study validates the MLED approach as a model evaluation tool, and opens up new possibilities for model improvement.

BackgroundAn increasing number of ecological processes have been incorporated into Earth system models. However, model evaluations usually lag behind the fast development of models, leading to a pervasive simulation uncertainty in key ecological processes, especially the terrestrial carbon (C) cycle. Traceability analysis provides a theoretical basis for tracking and quantifying the structural uncertainty of simulated C storage in models. Thus, a new tool of model evaluation based on the traceability analysis is urgently needed to efficiently diagnose the sources of inter-model variations on the terrestrial C cycle in Earth system models.MethodsA new cloud-based model evaluation platform, i.e., the online traceability analysis system for model evaluation (TraceME v1.0), was established. The TraceME was applied to analyze the uncertainties of seven models from the Coupled Model Intercomparison Project (CMIP6).ResultsThe TraceME can effectively diagnose the key sources of different land C dynamics among CMIIP6 models. For example, the analyses based on TraceME showed that the estimation of global land C storage varied about 2.4 folds across the seven CMIP6 models. Among all models, IPSL-CM6A-LR simulated the lowest land C storage, which mainly resulted from its shortest baseline C residence time. Over the historical period of 1850–2014, gross primary productivity and baseline C residence time were the major uncertainty contributors to the inter-model variation in ecosystem C storage in most land grid cells.ConclusionTraceME can facilitate model evaluation by identifying sources of model uncertainty and provides a new tool for the next generation of model evaluation.