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The quality and completeness of rainfall data is a critical aspect in time series analysis and for the prediction of future water-related disasters. An accurate estimation of missing data is essential for better rainfall prediction results. This study suggests a novel approach for estimating missing rainfall data using Multilayer Perceptron (MLP) neural networks based on three configurations that are represented by the monsoon season (MS), non-monsoon season (NMS), and non-seasonal variation. For this purpose, first, the rainfall dataset was transformed by the wavelet transform method and then, a mathematical model was created to analyze and predict the transformed data in Seoul, South Korea. Missing rainfall data in three time periods from Seoul station were reconstructed using the transformed rainfall data of the other five stations (e.g., Guroguchung, Daegokgyo, Songjeongden, Dongmakgoljuchajang, and Wallgaegyo). The results showed that using the Coiflet wavelet transform with MLP model (named Coi_MLP) estimated missing data more accurately, which is obtained from the results of statistical criteria including root mean square error, mean absolute error, and correlation coefficient of 1.18, 0.49, and 0.99 for transformed MS data and 0.76, 0.18, and 0.99 for transformed NMS data, respectively. The Coi_MLP model can effectively perform rainfall data reconstruction and predict missing rainfall data accurately, especially when the length of the statistical period is limited to the MS and NMS with different volumes of rainfall. [ABSTRACT FROM AUTHOR]

In this study, Vector Autoregressive-Generalized Autoregressive Conditional Heteroskedasticity (VAR-GARCH), copula, and copula-GARCH models were used for joint frequency analysis of storms in the Aras river basin in northwestern Iran in period of 1998–2018. The heteroskedasticity in the series was considered using the vector autoregressive model. Two-dimensional copulas were also used for bivariate analysis. After confirming the correlation between the pair variables of storm with one event lag (S1) and storm with no lag (S0), bivariate frequency analysis was performed. In the simulation step, the residual series of the VAR model was extracted and fitted to the GARCH model. Then, the residual series of the GARCH model was modeled using the copula model. Finally, storm with no lag (S0) affected by storm with one event lag (S1) was simulated by VAR-GARCH, copula, and copula-GARCH models. According to the coefficient of determination (R2) and Nash–Sutcliffe Efficiency coefficient (NSE) and root mean square error (RMSE), the VAR-GARCH model had higher accuracy than copula and copula-GARCH models. The RMSE in the simulation of storm height using the VAR-GARCH model was estimated to be 18% and 11% less than copula and copula-GARCH models, respectively. The VAR-GARCH model provided higher accuracy in the simulations due to the consideration of different lags in the simulations and modeling the variance of the residual series. According to the Taylor diagram, the certainty of all three models in simulating storm height are acceptable. Finally, by two-dimensional analysis of pair variables of storm height and storm duration, typical curve was produced that can estimate the storm duration with different probabilities. In fact, having the storm information that has happened in the present can accurately predict the next storm information. It can be very useful in flood management and the generated curves can be used as a flood warning system in the basin. [ABSTRACT FROM AUTHOR]

Distance-based anomaly detectors are among the most efficient unsupervised learning methods due to their non-parametric properties, inexpensive computational requirements, and simplicity. However, some major challenges including severe variability in data, low detectability, and a reliable decision threshold seriously affect overall performances of such techniques for long-term structural health monitoring (SHM). This article proposes a new distance-based anomaly detection method for partially online damage detection using the concepts of unsupervised feature selection and local metric learning. The main objective of unsupervised feature selection is to exploit one-class nearest neighbor search for extracting relevant features and removing irrelevant ones by a local Mahalanobis-squared distance (MSD). Because the choice of adequate nearest neighbors is critical to estimate local covariance matrices needed for distance metric learning, this issue is addressed by developing a hyperparameter optimization algorithm based on a statistical hypothesis test. An enhanced local MSD is also proposed to compute anomaly scores for decision-making. To estimate a reliable decision threshold, this article utilizes the peak-over-threshold technique under extreme value theory and generalized Pareto distribution. Due to the importance of selecting optimal extreme values and a probability rate of false alarm for threshold estimation, two hyperparameter optimization algorithms are designed to choose these unknown parameters. The major contribution of this article is to propose an innovative anomaly detector in conjunction with an enhanced multivariate distance under the concept of distance metric learning. Full-scale concrete and steel bridges under severe environmental variability are considered to validate the proposed method along with several comparisons. Results demonstrate that this method succeeds in long-term SHM under strong environmental variations in modal data and it also outperforms some well-known anomaly detection techniques in terms of effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a detailed review and discussion on the long-range forecast (LRF) of Indian summer monsoon rainfall (ISMR), its present status, problems, and perspectives with an emphasis on the Indian meteorological department (IMD) operational forecasting methods and its forecasts. Verifications of IMD LRF for 1924-1987 revealed a success rate of nearly 64%. IMD operational LRF skills (correlation coefficient) during the period 1988-2020 were found as 0.25 and 0.34 for the 1st -stage (April) and 2nd -stage (June), respectively. From 1988 to 2020 (33-years), 21-years of predictions were predicted out of the confidence band (±4%LRF). Out of these 21-years, 18-years were individual or co-occurring El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) event years. It indicates that IMD operational models have failed to capture ENSO and IOD phenomenon effects in their predictions considerably, despite the developing stage of ENSO and IOD events during the monsoon season. Moderate success and limitations of statistical models of IMD lead to the launching of the Monsoon mission (MM) in 2012 to use the dynamical prediction system by using the climate forecast system. Within a span of 7-years, MM has brought the skill of ISMR prediction to 0.71, which is above the older potentially predictable limit estimate of 0.65. Overall, in the last century, enormous progress has been made on the LRF of the ISMR. The accurate capturing of the ENSO and IOD phenomenon, incorporation of non-stationarity components in the predictors, improvements in the model microphysics and initial conditions could improve prediction skills. [ABSTRACT FROM AUTHOR]

The extrapolation of quantiles beyond or below the largest or smallest observation plays an important role in hydrological practice, design of hydraulic structures, water resources management, or risk assessment. Traditionally, extreme quantiles are obtained using parametric methods that require to make an a priori assumption about the distribution that generated the data. This approach has several limitations mainly when applied to the tails of the distribution. Semiparametric or nonparametric methods, on the other hand, allow more flexibility and they may overcome the problems of the parametric approach. Therefore, we present here a comparison between three selected semi/nonparametric methods, namely the methods of Hutson (Stat and Comput, 12(4):331–338, 2002) and Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) and kernel density estimation. While the first and third methods have already applications in hydrology, Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) is proposed in this context for the first time. After describing the methods and their applications in hydrology, we compare their performance for different sample lengths and return periods. We use synthetic samples extracted from four distributions whose maxima belong to the Gumbel, Weibull, and Fréchet domain of attraction. Then, the same methods are applied to a real precipitation dataset and compared with a parametric approach. Eventually, a detailed discussion of the results is presented to guide researchers in the choice of the most suitable method. None of the three methods, in fact, outperforms the others; performances, instead, vary greatly with distribution type, return period, and sample size. [ABSTRACT FROM AUTHOR]

Investigating the interaction of water resources such as rainfall, river flow and groundwater level can be useful to know the behavior of water balance in a basin. In this study, using the rainfall, river flow and groundwater level deficiency signatures for a 60-day duration, accuracy of vine copulas was investigated by joint frequency analysis. First, while investigating correlation of pair-variables, tree sequences of C-, D- and R-vine copulas were investigated. The results were evaluated using AIC, Log likelihood and BIC statistics. Finally, according to the physics of the problem and evaluation criteria, D-vine copula was selected as the best copula and the relevant tree sequence was introduced. Kendall's tau test was used to evaluate the correlation of pair-signatures. The results of the Kendall's tau test showed that pair-signatures studied have a good correlation. Using D-vine copula and its conditional structure, the joint return period of groundwater deficiency signature affected by rainfall and river flow deficiency signatures was investigated. The results showed that the main changes in the groundwater level deficiency is between 0.3 and 2 m, which due to the rainfall and the corresponding river flow deficiency, return periods will be less than 5 years. Copula-based simulations were used to investigate the best copula accuracy in joint frequency analysis of the studied signatures. Using copula data of the studied signatures, the groundwater deficiency signature was simulated using D-vine copula and a selected tree sequence. The results showed acceptable accuracy of D-vine copula in simulating the copula values of the groundwater deficiency signature. After confirming the accuracy of D-vine copula, the probability of occurrence of groundwater deficiency signature was obtained from the joint probability of occurrence of other signatures. This method can be used as a general drought monitoring system for better water resources management in the basin. [ABSTRACT FROM AUTHOR]

Developing statistical period and simulating the required values in case of data shortage increases certainty and reliability of simulations and statistical analyses, which is very important in studies on hydrology and water resources. Therefore, in this study, for simulating values of potential evapotranspiration at Birjand Station located in eastern Iran, contemporaneous autoregressive moving average (CARMA), CARMA-generalized autoregressive conditional heteroskedasticity (GARCH), and Copula-GARCH models were used in statistical period of 1984–2019. The potential evapotranspiration and relative humidity time series were simulated using these three models. CARMA model has acceptable accuracy for simulating potential evapotranspiration values due to the effect of the second parameter on simulations. Nash–Sutcliffe efficiency (NSE) coefficient of CARMA model for simulating potential evapotranspiration values was estimated as 0.85. NSE coefficient of CARMA-GARCH model was obtained as 0.87 through extracting residuals of CARMA model and simulating variance of data using GARCH model. Comparing the CARMA and CARMA-GARCH models with each other, it was concluded that a combination of two linear and non-linear time series models increases simulation accuracy to some extent. Using Clayton copula (the selected copula from the studied copulas), the mentioned values were simulated by Copula-GARCH model. The results showed that among the three models used, Copula-GARCH model reduced root mean square error of bivariate simulation compared to CARMA and CARMA-GARCH models by 15 and 13%, respectively. The results also showed that the proposed model simulates the average, first, and third quarters and range of changes in the data by 5 and 95% better than the two CARMA and CARMA-GARCH models. [ABSTRACT FROM AUTHOR]

Environmental variability is a major challenging issue in bridge health monitoring because bridges are more prone to such variability than other civil structures. To deal with this challenge, this article proposes a new machine-learning method for early damage detection under environmental variability by means of the k-medoids clustering, a new damage indicator, and an innovative approach for selecting a proper cluster number. Estimation of a reliable alarming threshold is another important challenge for early damage detection via most of the machine-learning methods. On this basis, a novel probabilistic approach using the theory of extreme value and a goodness-of-fit measure is proposed to estimate an alarming threshold. The major contributions of this article include proposing a new damage indicator suitable for decision making by clustering-based algorithms, an innovative cluster selection algorithm for dealing with the problem of environmental variability and increasing damage detectability, and a novel probabilistic method for threshold estimation. Modal-based features of the well-known Z24 Bridge are considered to verify the accuracy and effectiveness of the proposed approaches along with several comparative studies. Results show that the methods presented here are highly able to detect early damage even under strong environmental variations and estimate a reliable threshold. [ABSTRACT FROM AUTHOR]

In this study, a new method was proposed to model the occurrence of related variables based on the conditional density of copula functions. The proposed method was adopted to investigate the dynamics of meteorological and hydrological droughts in the Zarinehroud basin, southeast of Lake Urmia, during the period 1994–2015. For this purpose, the modified precipitation anomaly percentage and streamflow drought indices were extracted. Finally, the joint frequency analysis of duration-duration and severity-severity characteristics of meteorological and hydrological droughts was analyzed. Analysis of 7 different copulas used to create the joint distribution in the Zarinehroud basin indicated that the Frank copula had the best performance in describing the relationship between the meteorological and hydrological drought severities and durations. By examining the results of the bivariate analysis of duration-duration of meteorological and hydrological droughts at different stations, the expected meteorological and hydrological drought durations were estimated in the years ahead. For example, at Chalkhmaz station, 4- to 7-month duration for the hydrological drought and 9- to 12-month duration for the meteorological drought is expected in the years ahead. The joint frequency analysis of drought characteristics allows to determine the meteorological and hydrological drought characteristics at a single station at the same time using joint probabilities. Also, the results indicated that by knowing the conditional density, the hydrological drought characteristics can be easily estimated for the given meteorological drought characteristics. This could provide users and researchers useful information about the probabilistic behavior of drought characteristics for optimal operation of surface water. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Multilayer perceptron-based predictive model using wavelet transform for the reconstruction of missing rainfall data" published in the journal Stochastic Environmental Research & Risk Assessment. The correction addresses a mistake in the funding section of the original article's online version. The correction has been made, and the original article has been updated. The publisher, Springer Nature, maintains a neutral stance on jurisdictional claims and institutional affiliations. The authors of the article are Roya Narimani, Changhyun Jun, Carlo De Michele, Thian Yew Gan, Somayeh Moghimi Nezhad, and Jongyun Byun. [Extracted from the article]

Timely forecasts of the onset or possible evolution of droughts is an important contribution to mitigate their manifold negative effects; therefore, in this paper, we propose a mathematically-simple drought forecasting framework gaining Mediterranean Sea temperature information (SST-M) to predict droughts. Agro-metrological drought index addressing seasonality and autocorrelation (AMDI-SA) was used in a Markov model in Urmia lake basin, North West of Iran. Markov chain is adopted to model drought for joint occurrence of different classes of drought severity and sea surface temperature of Mediterranean Sea, which is called 2D Markov chain model. The proposed model, which benefits suitability of Markov chain models for modeling droughts, showed improvement results in prediction scores relative to classic Markov chain model not including SST-M information, additionally. [ABSTRACT FROM AUTHOR]

The authors investigate the use of drawable (D-)vine structures to model the dependences existing among the main characteristics of a flood event, i.e., flood volume, flood peak, duration, and peak time. Firstly, different three- and four-dimensional probability distributions were built considering all the permutations of the conditioning variables. The Frank copula was used to model the dependence of each pair of variables. Then, the appropriate D-vine structures were selected using information criteria and a goodness-of-fit test. The influence of varying the data length on the selected D-vine structure was also investigated. Finally, flood event characteristics were simulated using the four-dimensional D-vine structure. [ABSTRACT FROM AUTHOR]

Snow cover maps from Earth Observation (EO) satellites are valuable datasets containing large-scale information on snow cover extent, snow cover distribution and snow cover duration. In evaluating the performances of Regional Climate Models, EO data can be a valid piece of information alternative to in-situ measurements, which require a dense network of stations covering the entire altitudinal range and techniques for interpolating the values. In this context, MODIS snow products play a leading role providing several types of snow cover maps with high spatial and temporal resolutions. Here, we assess snow cover outputs of a high resolution Regional Climate Model (RCM) using MODIS maps of snow covered area over the Po river basin, northern Italy. The dataset consists of 9 years of MODIS data (2003-2011) cleaned from cloud cover by means of a cloud removal procedure. The maps have 500 m spatial resolution and daily temporal resolution. The RCM considered is COSMO-CLM, run at 0.0715° resolution (about 8 km) and coupled with the soil module TERRA_ML. The ERA-Interim reanalyses are used as initial and boundary conditions. The results show a good agreement between observed and simulated snow cover duration and extension. COSMO-CLM is able to reproduce the inter-annual variabilities of snow cover features as well as the seasonal trend of snow cover duration and extension. Limitations emerge when the RCM simulates the progressive depletion of the snow cover in spring. Simulated snowmelt occurs faster than the observed one. Then, we investigate the influence of the spatial resolution of the climate model. The simulation at 0.0715° (about 8 km) is compared to a simulation performed at 0.125° (about 14 km). The comparison highlights the benefits provided by the higher spatial resolution in the accumulation season, reflecting the improvements obtained in temperature and precipitation fields. [ABSTRACT FROM AUTHOR]