•Suitability of probability distributions to model wind speed in Quebec (Canada).•Two-component mixture distributions were fitted to the data.•The L-moment ratio diagram is used for assessment of ...one-component distributions.•Mixture distributions provide better fits than one-component distributions.
The assessment of wind energy potential at sites of interest requires reliable estimates of the statistical characteristics of wind speed. A probability density function (pdf) is usually fitted to short-term observed local wind speed data. It is common for wind speed data to present bimodal distributions for which conventional one-component pdfs are not appropriate. Mixture distributions represent an appropriate alternative to model such wind speed data. Homogeneous mixture distributions remain rarely used in the field of wind energy assessment while heterogeneous mixture models have only been developed recently. The present work aims to investigate the potential of homogeneous and heterogeneous mixture distributions to model wind speed data in a northern environment. A total of ten two-component mixture models including mixtures of gamma, Weibull, Gumbel and truncated normal are evaluated in the present study. The estimation of the parameter of the mixture models are obtained with the least-squares (LS) and the maximum likelihood (ML) methods. The optimization of the objective functions related to these estimation methods is carried out with a genetic algorithm that is more adapted to mixture distributions. The case study of the province of Québec (Canada), a Northern region with an enormous potential for wind energy production, is investigated in the present work. A total of 83 stations with long data records and providing a good coverage of the territory of the province are selected. To identify the most appropriate one-component distribution for the selected stations, the newly proposed method of L-moment ratio diagram (MRD) is used. The advantages of this approach are that it is simple to apply and it allows an easy comparison of the fit of several pdfs for several stations on a single diagram. One-component distributions are compared with the selected mixture distributions based on model selection criteria. Results show that mixture distributions often provide better fit than conventional one-component distributions for the study area. It was also observed that the ML method outperforms the LS method and that the mixture model combining two Gumbel distributions using ML is the overall best model.
•Random Forest Regression (RFR) is used for regional flood frequency analysis (RFA).•RFR is also combined with Canonical Correlation Analysis (CCA): CCA-RFR.•The two techniques are compared to other ...linear and non-linear RFA models.•CCA-RFR leads to the best performance in terms of root mean squared error.•RFR is simple to apply and more efficient than more complex models.
Flood quantile estimation at sites with little or no data is important for the adequate planning and management of water resources. Regional Hydrological Frequency Analysis (RFA) deals with the estimation of hydrological variables at ungauged sites. Random Forest (RF) is an ensemble learning technique which uses multiple Classification and Regression Trees (CART) for classification, regression, and other tasks. The RF technique is gaining popularity in a number of fields because of its powerful non-linear and non-parametric nature. In the present study, we investigate the use of Random Forest Regression (RFR) in the estimation step of RFA based on a case study represented by data collected from 151 hydrometric stations from the province of Quebec, Canada. RFR is applied to the whole data set and to homogeneous regions of stations delineated by canonical correlation analysis (CCA). Using the Out-of-bag error rate feature of RF, the optimal number of trees for the dataset is calculated. The results of the application of the CCA based RFR model (CCA-RFR) are compared to results obtained with a number of other linear and non-linear RFA models. CCA-RFR leads to the best performance in terms of root mean squared error. The use of CCA to delineate neighborhoods improves considerably the performance of RFR. RFR is found to be simple to apply and more efficient than more complex models such as Artificial Neural Network-based models.
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•Variables used as sensitivity analysis models are identified.•Evidence of pitfalls in such practices is provided.•Nonlinear sensitivity analysis models prevail in wind resource ...assessment.•One-at-a-time sensitivity analysis prevails in wind resource assessment.•One-at-a-time sensitivity analysis does not apply to nonlinear models.
A review of sensitivity analysis in wind resource assessment is presented, offering classifications by sensitivity analysis output variable (or model), method, application, country, and software. No review of sensitivity analysis in wind resource assessment is currently available in the literature. The review pool consists of 102 articles with models dealing with statistical and economic aspects of wind resource assessment (goodness-of-fit metrics, wind power, wind energy, the net present value, the payback period, the internal rate of return, the payback period, the levelized cost of energy, capital and operational expenses). Sensitivity analysis studies, where the wind is predicted with weather research and forecasting models, sensitivity analysis of hybrid energy systems with a wind component, and sensitivity analysis of wind turbine fatigue loads, are beyond the scope of this review. This review reveals the lack of collective agreement on the definition of sensitivity analysis in the literature, the dominance of nonlinear models (100%), and the prevalence of one-at-a-time sensitivity analysis method (82%). The review highlights the existing gaps in the field, provides evidence of the common pitfalls, possibly leading to costly misinterpretations of the data at the site and hence to erroneous feasibility assessments. The review urges to rethink how to conduct sensitivity analysis in wind resource assessment. It also includes comparison of one-at-a-time sensitivity analysis and global sensitivity analysis for a linear and nonlinear models.
Hydro-climatic extremes are influenced by climate change and climate variability associated to large-scale oscillations. Non-stationary frequency models integrate trends and climate variability by ...introducing covariates in the distribution parameters. These models often assume that the distribution function and shape of the distribution do not change. However, these assumptions are rarely verified in practice. We propose here an approach based on L-moment ratio diagrams to analyze changes in the distribution function and shape parameter of hydro-climate extremes. We found that important changes occur in the distribution of annual maximum streamflow and extreme temperatures. Eventual relations between the shapes of the distributions of extremes and climate indices are also identified. We provide an example of a non-stationary frequency model applied to flood flows. Results show that a model with a shape parameter dependent on climate indices in combination with a scale parameter dependent on time improves significantly the goodness-of-fit.
Persistent extreme heat events are of growing concern in a climate change context. An increase in the intensity, frequency and duration of heat waves is observed in several regions. Temperature ...extremes are also influenced by global-scale modes of climate variability. Temperature-Duration-Frequency (TDF) curves, which relate the intensity of heat events of different durations to their frequencies, can be useful tools for the analysis of heat extremes. To account for climate external forcings, we develop a nonstationary approach to the TDF curves by introducing indices that account for the temporal trend and teleconnections. Nonstationary TDF modeling can find applications in adaptive management in the fields of health care, public safety and energy production. We present a one-step method, based on the maximization of the composite likelihood of observed heat extremes, to build the nonstationary TDF curves. We show the importance of integrating the information concerning climate change and climate oscillations. In an application to the province of Quebec, Canada, the influence of Atlantic Multidecadal Oscillations (AMO) on heat events is shown to be more important than the temporal trend.
ABSTRACT
Wind energy accounts for a small share of the global energy consumption in spite of its widespread availability. One of the obstacles hindering exploitation of wind energy is the lack of ...proper wind speed assessment models. The wind energy field credibility has occasionally suffered from wind power potential estimation studies that were conducted based on very short wind speed records and which did not give consideration to inter‐annual wind variability. The objective of this paper is to examine the long‐term variability of wind speed in the United Arab Emirates (UAE) and its teleconnections with various global climate indices by using wind speed collected from six ground stations and a reanalysis dataset. Linear correlation analysis and wavelet analysis were used to characterize the interaction. The modified Mann–Kendall test and linear regression indicated that half of the stations show a significant wind speed trend at the 5% level. The cumulative sum and Bayesian change detection methods indicated that five of the stations present change points. Continuous wavelet transform of wind speed showed biannual periodicity in some stations, in addition to the annual one. Wavelet coherence analysis demonstrated that wind speed in the UAE is mainly associated with the North Atlantic Oscillation, East Atlantic Oscillation, El Niño Southern Oscillation and the Indian Ocean Dipole indices. The first two indices simultaneously modulate wind speed in the summer while the last two influence winter and autumn wind speeds. Step‐wise multiple linear regression models were developed to select appropriate predictors among the various climate indices.
Abstract
This study assesses the deterministic and probabilistic forecasting skill of a 1-month-lead ensemble of Artificial Neural Networks (EANN) based on low-frequency climate oscillation indices. ...The predictand is the February-April (FMA) rainfall in the Brazilian state of Ceará, which is a prominent subject in climate forecasting studies due to its high seasonal predictability. Additionally, the study proposes combining the EANN with dynamical models into a hybrid multi-model ensemble (MME). The forecast verification is carried out through a leave-one-out cross-validation based on 40 years of data. The EANN forecasting skill is compared with traditional statistical models and the dynamical models that compose Ceará’s operational seasonal forecasting system. A spatial comparison showed that the EANN was among the models with the smallest Root Mean Squared Error (RMSE) and Ranked Probability Score (RPS) in most regions. Moreover, the analysis of the area-aggregated reliability showed that the EANN is better calibrated than the individual dynamical models and has better resolution than Multinomial Logistic Regression for above-normal (AN) and below-normal (BN) categories. It is also shown that combining the EANN and dynamical models into a hybrid MME reduces the overconfidence of the extreme categories observed in a dynamically-based MME, improving the reliability of the forecasting system.
Abstract
Surface Temperature (ST) over India has increased by ~0.055 K/decade during 1860–2005 and follows the global warming trend. Here, the natural and external forcings (e.g., natural and ...anthropogenic) responsible for ST variability are studied from Coupled Model Inter-comparison phase 5 (CMIP5) models during the 20
th
century and projections during the 21
st
century along with seasonal variability. Greenhouse Gases (GHG) and Land Use (LU) are the major factors that gave rise to warming during the 20
th
century. Anthropogenic Aerosols (AA) have slowed down the warming rate. The CMIP5 projection over India shows a sharp increase in ST under Representative Concentration Pathways (RCP) 8.5 where it reaches a maximum of 5 K by the end of the 21
st
century. Under RCP2.6 emission scenarios, ST increases up to the year 2050 and decreases afterwards. The seasonal variability of ST during the 21
st
century shows significant increase during summer. Analysis of rare heat and cold events for 2080–2099 relative to a base period of 1986–2006 under RCP8.5 scenarios reveals that both are likely to increase substantially. However, by controlling the regional AA and LU change in India, a reduction in further warming over India region might be achieved.
•Classical modeling approaches do not take into account interannual variability and trends.•The proposed approach provides wind speed distribution conditionally on a set of predictors.•Annual ...goodness-of-fit at the studied stations improved on average with the non-stationary model.•Influential climatic indices are used as predictors in the non-stationary model.
The assessment of wind energy potential is generally based on the analysis of the statistical distribution of observed wind speed of short time resolution. Record periods of observational data used in practice at sites of interest are often very short, often ranging from a few months to a few years. Predictions based on such small record periods are likely to be biased as it is recognized that wind speed is subject to important interannual variability and long-term trends. Large-scale atmospheric circulation patterns have an important influence on wind speed. Their predictable nature can make them useful for the prediction of wind speed during the lifetime of wind farm projects. This feature is not exploited in practice. It is proposed in this study to introduce predictors of the wind speed in non-stationary statistical models. This approach allows the development of predictions of the wind speed distribution conditionally on the state of the predictors. The predictors used here are indices of atmospheric circulation to account for the interannual variability and a temporal index to account for the long-term temporal trend. The proposed approach was applied to hourly wind speed data at selected meteorological stations in the province of Québec (Canada). 20 stations with long record periods of over 30 years of data were used. The most important circulation indices identified in the study area are the North-Atlantic Oscillation (NAO) during the winter season and the Pacific North American (PNA) during the spring season. Results indicate that the annual goodness-of-fit at the stations of the case study improved on average when the non-stationary model is used compared to the stationary model. The proposed approach can potentially be used to model wind speed during the projected lifetime of wind farms using forecasts of the predictors.
•A nonstationary and multivariate model is proposed.•Parameters of the model are time-varying based on dynamic copula.•The copula parameter is based on moving-average of dependence measures.
To study ...hydrological events, such as floods and droughts, frequency analysis (FA) techniques are commonly employed. FA relies on some assumptions, especially, the stationarity of the data series. However, the stationarity assumption is not always fulfilled for a variety of reasons such as climate change and human activities. Thus, it is essential to check the stationarity or we should develop models that take into account the non-stationarity in a new risk assessment framework. On the other hand, a majority of hydrological phenomena are described by a number of correlated characteristics. To model the dependence structure between these hydrological variables, copulas are the most employed tool. Generally in the literature, the multivariate model is assumed to be the same over time even though multivariate stationarity is required. Considering the non-stationarity in the dependence structure is important because when the copula parameter changes, the multivariate quantile curve changes accordingly. Different scenarios can be considered when choosing a multivariate non-stationary model since several variables and a dependence structure are involved. The objective of the present study is to construct a model that integrates simultaneously multivariate and non-stationarity aspects along with hypothesis testing. For the copula part, we consider versions called Dynamic copulas and series of association measures are obtained through rolling windows of the corresponding series. Adapted versions of the AIC criterion are employed to select the final model (margins and copula). The procedure is applied to a flood volume and peak dataset from Iran. The obtained model constitutes of a lognormal distribution for the margins with linear trend in the peak series, stationary for the volume series and a quadratic trend in the logistic Gumbel copula parameter for the dependence structure.
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