Detrending Research Articles

Analysis of spatiotemporal household water consumption patterns and their relationship with meteorological variables

The spatiotemporal patterns of household water consumption can provide valuable information for urban water management. Thus, this paper introduces a novel method based on the Bayesian Estimator of Abrupt Change, Seasonal, and Trend (BEAST) algorithm to extract these patterns. The proposed method divides the study area into different regions using cells of equal sizes. Then the BEAST algorithm estimates the trend and the seasonality of the consumption time series of the buildings within each cell as spatiotemporal patterns. The proposed method is evaluated using 5-year consumption data of 566 residential buildings in Shabestar City, Iran. We also studied how changes in trend and seasonality of average wind speed, precipitation, relative humidity, and temperature affect consumption patterns. The results showed that household water consumption had a linear trend, with only three change points, one of which, because of its synchronicity, can be ascribed to the COVID-19 outbreak. The water consumption data also showed a relatively weak seasonality. The correlation analysis showed no meaningful relationship between the trends of considered meteorological variables and water consumption. However, the seasonality of average wind speed and temperature showed a weak correlation of 0.71 and 0.51 with the seasonality of the water consumption data, respectively.

Challenging the geographic bias in recognising large‐scale patterns of diversity change

AbstractAimGeographic structure is a fundamental organising principle in ecological and Earth sciences, and our planet is conceptually divided into distinct geographic clusters (e.g. ecoregions and biomes) demarcating unique diversity patterns. Given recent advances in technology and data availability, however, we ask whether geographically clustering diversity time‐series should be the default framework to identify meaningful patterns of diversity change.LocationNorth America.TaxonAves.MethodsWe first propose a framework that recognises patterns of diversity change based on similarities in the behaviour of diversity time‐series, independent of their specific or relative spatial locations. Specifically, we applied an artificial neural network approach, the self‐organising map (SOM), to group time‐series of over 0.9 million observations from the North American Breeding Birds Survey (BBS) data from 1973 to 2016. We then test whether time‐series identified as having similar behaviour are geographically structured.ResultsWe find little evidence of strong geographic structure in patterns of diversity change for North American breeding birds. The majority of the recognised diversity time‐series patterns tend to be indistinguishable from being independently distributed in space.Main ConclusionsOur results suggest that geographic proximity may not correspond to shared temporal trends in diversity; assuming that geographic clustering is the basis for analysis may bias diversity trend estimation. We suggest that approaches that consider variability independently of geographic structure can serve as a useful addition to existing organising rules of biodiversity time‐series.

Differing Definitions of First-Ever Stroke Influence Incidence Estimates More than Trends: A Study Using Linked Administrative Data

Introduction: Researchers apply varying definitions when measuring stroke incidence using administrative data. We aimed to investigate the sensitivity of incidence estimates to varying definitions of stroke and lookback periods and to provide updated incidence rates and trends for Western Australia (WA). Methods: We used linked state-wide hospital and death data from 1985 to 2017 to identify incident strokes from 2005 to 2017. A standard definition was applied which included strokes coded as the principal hospital diagnosis or the underlying cause of death, with a 10-year lookback used to clear prevalent cases. Alternative definitions were compared against the standard definition by percentage difference in case numbers. Age-standardised incidence rates were calculated, and age- and sex-adjusted Poisson regression models were used to estimate incidence trends. Results: The standard definition with a 10-year lookback period captured 31,274 incident strokes. Capture increased by 19.3% when including secondary diagnoses, 4.1% when including nontraumatic subdural and extradural haemorrhage, and 8.1% when including associated causes of death. Excluding death records reduced capture by 11.1%. A 20-year lookback reduced over-ascertainment by 2.0%, and a 1-year lookback increased capture by 13.3%. Incidence declined 0.6% annually (95% confidence interval −0.9, −0.3). Annual reductions were similar for most definitions except when death records were excluded (−0.1%, CI: −0.4, 0.2) and with the shortest lookback periods (greatest annual reduction). Conclusion: Stroke incidence has declined in WA. Differing methods of identifying stroke influence estimates of incidence to a greater extent than estimates of trends. Reductions in stroke incidence over time are primarily driven by declines in fatal strokes.

Blood pressure monitoring with piezoelectric bed sensor systems

ObjectiveBallistocardiography (BCG) measures vital signals without direct contact, which shows great potential for continuous sleep monitoring. This study proposed a BCG-based blood pressure (BP) estimation algorithm framework using piezoelectric bed sensor systems. Methods: To derive BP, a combination of morphological features, spectral features, and fractal dimensions of the BCG signal were utilized. Bayesian neural networks were employed to weigh the contribution of each feature and generate input-dependent coefficients. Two data balancing procedures were tested, and the proposed system's effectiveness was evaluated, including in-hospital patients and healthy subjects. Transfer learning techniques were employed to further improve the system's performance and showcase the similarity between bed systems with different piezoelectric sensors. Results: The combination of morphological and spectral features significantly improves BP estimation accuracy. The fractal dimension captures short-term BP fluctuations, improving intra-subject BP trend estimation. For young and healthy subjects, calibration-free mean absolute error (MAE) for systolic BP (SBP) and diastolic BP (DBP) is 4.20/4.25 mmHg at a 5-second time resolution. In the case of in-hospital patients, the best MAE for overnight SBP/DBP is 9.96/7.59 mmHg. Transfer learning, combined with data balancing techniques, substantially enhances BP estimation accuracy for in-hospital patients, providing insights for future algorithm designs. Conclusion: The study concludes that bed-sensor systems can track BP changes in relatively healthy subjects. However, BCG alone may not be sufficient for subjects with severe cardiovascular dysfunctions to obtain reliable BP readings. Transfer learning and proper data balancing may facilitate the fast development of new bed sensor systems with similar technologies.

Rigorous hybrid machine learning approaches for interfacial tension modeling in brine-hydrogen/cushion gas systems: Implication for hydrogen geo-storage in the presence of cushion gas

In light of the environmental consequences linked to the burning of fossil fuels, there is a mounting push to devise cleaner means of energy generation. The production of hydrogen is gaining momentum as a favored avenue for enabling the shift toward sustainable energy. Effective hydrogen storage is crucial for sustainable energy solutions, and the inclusion of cushion gas in geological storage could aid in maintaining formation pressure during hydrogen reproduction while expanding pore volumes for gas by preventing water presence in pores. Accurately controlling hydrogen behavior and interactions in porous media is crucial for successful storage, with interfacial tension between gas and water playing a significant role in the trapping and mobilizing the phases. Thus, developing a predictive tool to simulate interfacial tension measurements is essential. This study employs various intelligent modeling techniques, including Adaptive Neuro-Fuzzy Inference System, Multilayer Perceptron optimized with Bayesian Regularization, Levenberg-Marquardt, and Scaled Conjugate Gradient algorithm, Grey Wolf Optimizer-based Least Squares Boosting (GWO-LSBOOST), GWO-based Radial Basis Function, GWO-based Least Squares Support Vector Machine, and Extreme Learning Machine (ELM), to develop interfacial tension models for brine-hydrogen/cushion gas systems. Applying these models developed based on diverse theories, structures, and performance characteristics helps identify the optimal predictor for the target parameter. The evaluation of the models was carried out using 2868 experimental data points. The achieved results demonstrated the satisfactory performance of different modeling techniques (with R2 falling within the range of 0.8979 to 0.9960), however, GWO-LSBOOST outperformed others (improving R2 from 0.8979 to 0.9960 and AARE% from 5.5714 % to 0.8060 %, compared to ELM). Leverage outlier identification and trend estimation analysis confirmed the statistical reliability of the dataset and the GWO-LSBOOST model. The formulated strategies offer a promising framework applicable for incorporating the gas-water interfacial tension in simulating various stages of gas injection/ reproduction in underground hydrogen storage.

Analysis of the Financial State and Efficiency of Activities of Organic Producers in Russia

Is denoted to the analysis of the financial condition and efficiency of organic producers to assess the potential for the development of organic agriculture in Russia. Methods of comparative and coefficient analysis, estimation of trends in time series, statistical groupings, and studies of the general properties of the sample were used. The source of information was the data of the accounting (financial) statements of organic producers for 2019-2021 from the State Information Resource for Accounting (Financial) Statements of the Russian Federation. Based on the results of the analysis, it was concluded that organic crop production prevails in Russia, although this industry is inferior in terms of financial potential to other areas of agribusiness. The most financially stable and solvent can be called the production of organic livestock products, processing, and canning of organic products. The noted trend is more characteristic of the level of large agricultural enterprises. It should also be taken into account that, in general, only a small part of the enterprises in the analyzed population has reached the standard level of financial stability and performance indicators, which may generally indicate the low financial potential of organic producers in Russia and the need to attract available financial resources from external sources. The novelty of the study lies in the comparative assessment of production efficiency at enterprises of various scales of activity and different specializations in the production of organic products. The practical significance of the study lies in the development of sound proposals of organizational and financial orientation to ensure a realistically achievable increase in the efficiency of organic production in Russia, taking into account the identified resource potential.

Genetic evaluation of growth and reproductive traits for Dawuro sheep in Ethiopia

This study aimed to estimate genetic parameters and genetic trends for growth, reproductive traits, and inbreeding levels in Dawuro sheep. Data on the studied traits were collected between 2017 and 2022.The genetic parameters were estimated by fitting different univariate models, considering/ignoring maternal effects, using WOMBAT software. The estimated direct heritability obtained from the best-fit models were 0.07±0.03, 0.16 ± 0.04, 0.45 ± 0.27, 0.21 ± 0.07, and 0.48 ± 0.04 for birth weight (BWT), weaning weight (3WT), 6-month weight (6WT), average daily gain from birth to weaning (ADG1), and average daily gain from weaning to 6 months of age (ADG2), respectively. The estimated direct heritability for litter size (LS), lambing interval (LI), and annual reproductive rate (ARR) were 0.10 ± 0.10, 0.001 ± 0.10, and 0.09 ± 0.49, respectively. The trait selected for improvement was 6WT, which exhibited a positive genetic trend and moderate heritability estimates. Positive genetic trend suggests the potential for further enhancement of post-weaning lamb growth traits.

Estimation of drought trends and comparison between SPI and SPEI with prediction using machine learning models in Rangpur, Bangladesh

ABSTRACT This study investigates drought trends, SPI-SPEI comparisons, and predictions in Rangpur, Bangladesh, from 1979 to 2020. We employed Modified Mann-Kendall for trend analysis, SPI and SPEI for drought assessment, and Pearson Correlation Coefficient and Simple Linear Regression for evaluating SPI and SPEI relationships. Additionally, we utilized ANN, SVM, and RF for prediction. The study revealed notable negative trends in seasonal and annual drought, with the highest z statistics observed for SPI 06 (-2.75), SPI 09 (-4.50), SPI 12 (5.60), SPI 24 (-8.40), SPEI 06 (-5.13), SPEI 09 (-6.82), SPEI 12 (-8.04), and SPEI 24 (-11.20). Strong correlations were identified across all SPI and SPEI indices, with coefficients peaking at 97%, 98%, 98%, and 97% for 06, 09, 12, and 24-month periods, respectively. The comparative assessment favored SPEI over SPI, highlighting its superiority and accuracy. The ANN prediction model showed significant results for short-term and seasonal drought forecasts, projecting SPEI 03 and SPEI 06 increases of 0.02 and 0.24, respectively. However, long-term drought estimation exhibited insignificant performance across all predictive models. This emphasizes the need for developing essential predictive tools for future drought variability.

Birth spacing and risk of adverse pregnancy and birth outcomes: A systematic review and dose-response meta-analysis.

The association between extreme birth spacing and adverse outcomes is controversial, and available evidence is fragmented into different classifications of birth spacing. We conducted a systematic review of observational studies to evaluate the association between birth spacing (i.e., interpregnancy interval and interoutcome interval) and adverse outcomes (i.e., pregnancy complications, adverse birth outcomes). Pooled odds ratios (ORs) with 95% confidence intervals (CI) were calculated using a random-effects model, and the dose-response relationships were evaluated using generalized least squares trend estimation. A total of 129 studies involving 46 874 843 pregnancies were included. In the general population, compared with an interpregnancy interval of 18-23 months, extreme intervals (<6 months and ≥ 60 months) were associated with an increased risk of adverse outcomes, including preterm birth, small for gestational age, low birthweight, fetal death, birth defects, early neonatal death, and premature rupture of fetal membranes (pooled OR range: 1.08-1.56; p < 0.05). The dose-response analyses further confirmed these J-shaped relationships (pnon-linear < 0.001-0.009). Long interpregnancy interval was only associated with an increased risk of preeclampsia and gestational diabetes (pnon-linear < 0.005 and pnon-linear < 0.001, respectively). Similar associations were observed between interoutcome interval and risk of low birthweight and preterm birth (pnon-linear < 0.001). Moreover, interoutcome interval of ≥60 months was associated with an increased risk of cesarean delivery (pooled OR 1.72, 95% CI 1.04-2.83). For pregnancies following preterm births, an interpregnancy interval of 9 months was not associated with an increased risk of preterm birth, according to dose-response analyses (pnon-linear = 0.008). Based on limited evidence, we did not observe significant associations between interpregnancy interval or interoutcome interval after pregnancy losses and risk of small for gestational age, fetal death, miscarriage, or preeclampsia (pooled OR range: 0.76-1.21; p > 0.05). Extreme birth spacing has extensive adverse effects on maternal and infant health. In the general population, interpregnancy interval of 18-23 months may be associated with potential benefits for both mothers and infants. For women with previous preterm birth, the optimal birth spacing may be 9 months.

The American Woodcock Singing Ground Survey largely conforms to the phenology of male woodcock migration

AbstractAmerican woodcock (Scolopax minor; woodcock) are monitored, in part, by counts of displaying male woodcock collected via the American Woodcock Singing Ground Survey (SGS), which suggests long‐term, range‐wide declines in woodcock populations. Data from the SGS have been used extensively to develop conservation plans, direct management actions, and understand causes of decline. To avoid bias, the SGS should be timed to avoid spring migration, and the distribution of survey routes should coincide with woodcock breeding distribution. Our objectives for this research were to evaluate SGS timing with the phenology of male woodcock migration, relate the spatial coverage of the SGS to male woodcock breeding distributions, and explore other sources of variation in woodcock migration timing. We marked 133 male woodcock captured throughout eastern North America with global positioning system (GPS) transmitters during 2019–2022, and compared the timing of their spring migration with the spatiotemporal stratification of the SGS. Most woodcock (74%) completed migration prior to the onset of the SGS. In the northernmost SGS zone, a greater percentage of males (34%) continued migration during the survey window; however, the influence of this mismatch is offset because SGS routes were run more frequently during the second half of the window. Young woodcock completing their first spring migration took 8.6 days longer to do so, on average, compared to adults, and so were more likely to migrate during the SGS window. We found little evidence that timing of migration varied among years. Existing SGS routes cover the majority of male woodcock post‐migratory breeding distribution, with 90% of male woodcock establishing final breeding sites within the spatial coverage of the SGS. Our results confirm the SGS includes some migrating males, with the proportion relative to resident breeding males increasing in more northern survey strata. Our data suggests these errors are unlikely to bias trend estimates at large scales (e.g., within woodcock management regions), but there may be potential for bias at more local scales (e.g., state or provincial population indices).

Introducing the Idea of Classifying Sets of Permanent GNSS Stations as Benchmarks for Hydrogeodesy

AbstractWe propose a novel approach to classify sets of Global Navigation Satellite System (GNSS) permanent stations as benchmarks for hydrogeodesy. Benchmarks are trusted sets of GNSS stations whose displacements are classified as significantly and positively correlated with hydrospheric changes and identified in a three temporal‐scales: short‐term, seasonal and long‐term. We use 63 vertical displacement time series processed at the Nevada Geodetic Laboratory for the period 1998–2021 from stations located within Amazon basin and show that estimates of trends and annual signals, including the annual phase maximum, are very coherent with water surface levels provided by altimetry missions. We compute vertical displacements from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On gravity missions and predict those also from Global Land Water Storage (GLWS) v2.0 data set which values are produced by assimilation of GRACE into WaterGAP Global Hydrological Model (WGHM). We divide vertical displacements from the three data sets into the pre‐defined temporal‐scales of short‐term, seasonal and long‐term, using non‐parametric wavelet analysis. For each temporal‐scale, correlation coefficients are computed between GNSS‐measured and GRACE‐derived/GLWS‐predicted displacements. We present the benefits of applying high‐resolution GRACE‐assimilating hydrology model to benchmark GNSS stations, which are particularly evident when using spherical harmonic coefficients higher than 120. Their increase causes the number of stations included in the benchmarks to rise by up to 15% for short‐term. Benchmarking allows hydrogeodesy to take advantage of a broader set of GNSS stations that were previously omitted, such as earthquake‐affected sites and those where a possible poroelastic response is observed.

Correlation Between Early Trends of a Prognostic Biomarker and Overall Survival in Non-Small-Cell Lung Cancer Clinical Trials.

Overall survival (OS) is the primary end point in phase III oncology trials. Given low success rates, surrogate end points, such as progression-free survival or objective response rate, are used in early go/no-go decision making. Here, we investigate whether early trends of OS prognostic biomarkers, such as the ROPRO and DeepROPRO, can also be used for this purpose. Using real-world data, we emulated a series of 12 advanced non-small-cell lung cancer (aNSCLC) clinical trials, originally conducted by six different sponsors and evaluated four different mechanisms, in a total of 19,920 individuals. We evaluated early trends (until 6 months) of the OS biomarker alongside early OS within the joint model (JM) framework. Study-level estimates of early OS and ROPRO trends were correlated against the actual final OS hazard ratios (HRs). We observed a strong correlation between the JM estimates and final OS HR at 3 months (adjusted = 0.88) and at 6 months (adjusted = 0.85). In the leave-one-out analysis, there was a low overall prediction error of the OS HR at both 3 months (root-mean-square error [RMSE] = 0.11) and 6 months (RMSE = 0.12). In addition, at 3 months, the absolute prediction error of the OS HR was lower than 0.05 for three trials. We describe a pipeline to predict trial OS HRs using emulated aNSCLC studies and their early OS and OS biomarker trends. The method has the potential to accelerate and improve decision making in drug development.

ИЗМЕНЕНИЕ АГРОКЛИМАТИЧЕСКИХ РЕСУРСОВ ЗЕРНОПРОИЗВОДЯЩИХ РЕГИОНОВ РОССИИ И ПРОДУКТИВНОСТИ ЗЕРНОВЫХ КУЛЬТУР В НОВОМ КЛИМАТИЧЕСКОМ ПЕРИОДЕ 1991-2020 ГГ

The transition to a new reference period of 1991-2020 requires updating the estimates of agroclimatic resources in agricultural regions. The paper summarizes the results of the annual monitoring of agroclimatic conditions over 1961-1990 and 1991-2020 obtained by both the statistical analysis and the Climate-Soil-Yield simulation system and provides their comparative evaluation. It has been noted that along with growing aridity of the most fertile areas of the steppe and forest-steppe zones, there is an almost universal growth of spring precipitation, which compensates to some extent the negative impact of high summer temperatures on the yield. A significant decrease in the climate-driven productivity of spring wheat from the period of 1961-1990 to the new reference period of 1991-2020 has been revealed for the main grain-producing regions. Estimates of productivity trends for major grain crops, spring and winter wheat, are given in connection with climate change and the development of agricultural technologies. Estimates of changes in the wintering indices and sown areas for winter wheat are also given.

Hypothesis Scoring for Confidence-Aware Blood Pressure Estimation With Particle Filters.

We propose our Confidence-Aware Particle Filter (CAPF) framework that analyzes a series of estimated changes in blood pressure (BP) to provide several true state hypotheses for a given instance. Particularly, our novel confidence-awareness mechanism assigns likelihood scores to each hypothesis in an effort to discard potentially erroneous measurements - based on the agreement amongst a series of estimated changes and the physiological plausibility when considering DBP/SBP pairs. The particle filter formulation (or sequential Monte Carlo method) can jointly consider the hypotheses and their probabilities over time to provide a stable trend of estimated BP measurements. In this study, we evaluate BP trend estimation from an emerging bio-impedance (Bio-Z) prototype wearable modality although it is applicable to all types of physiological modalities. Each subject in the evaluation cohort underwent a hand-gripper exercise, a cold pressor test, and a recovery state to increase the variation to the captured BP ranges. Experiments show that CAPF yields superior continuous pulse pressure (PP), diastolic blood pressure (DBP), and systolic blood pressure (SBP) estimation performance compared to ten baseline approaches. Furthermore, CAPF performs on track to comply with AAMI and BHS standards for achieving a performance classification of Grade A, with mean error accuracies of -0.16 ± 3.75 mmHg for PP (r = 0.81), 0.42 ± 4.39 mmHg for DBP (r = 0.92), and -0.09 ± 6.51 mmHg for SBP (r = 0.92) from more than test 3500 data points.

A semiparametric spatio‐temporal model of crop yield trend and its implication to insurance rating

AbstractWe demonstrate the benefit of spatial smoothing for crop trend estimation with a deterministic spatio‐temporal trend model. The proposed model is semiparametric, where the parametric temporal trend is modeled with a two‐knot spline function for forecasting robustness, and the nonparametric spatially‐varying coefficients are modeled by the radial basis function method for flexibility. To select the smoothing parameter of our trend model, we propose a forward validation criterion tailored to meet the forecasting nature of rating crop insurance. This criterion is based on a rolling regression approach that adds one year of data at a time for validation. We also propose a new criterion for model comparison using relative mean squared error in forecasting insurance payouts. Our empirical results show that the proposed trend model is more efficient and capable of identifying profitable insurance policies than two competing models in most state‐crop combinations.

Smoothing Lexis diagrams using kernel functions: A contemporary approach.

Lexis diagrams are rectangular arrays of event rates indexed by age and period. Analysis of Lexis diagrams is a cornerstone of cancer surveillance research. Typically, population-based descriptive studies analyze multiple Lexis diagrams defined by sex, tumor characteristics, race/ethnicity, geographic region, etc. Inevitably the amount of information per Lexis diminishes with increasing stratification. Several methods have been proposed to smooth observed Lexis diagrams up front to clarify salient patterns and improve summary estimates of averages, gradients, and trends. In this article, we develop a novel bivariate kernel-based smoother that incorporates two key innovations. First, for any given kernel, we calculate its singular values decomposition, and select an optimal truncation point-the number of leading singular vectors to retain-based on the bias-corrected Akaike information criterion. Second, we model-average over a panel of candidate kernels with diverse shapes and bandwidths. The truncated model averaging approach is fast, automatic, has excellent performance, and provides a variance-covariance matrix that takes model selection into account. We present an in-depth case study (invasive estrogen receptor-negative breast cancer incidence among non-Hispanic white women in the United States) and simulate operating characteristics for 20 representative cancers. The truncated model averaging approach consistently outperforms any fixed kernel. Our results support the routine use of the truncated model averaging approach in descriptive studies of cancer.

In-line Raman spectroscopy for characterization of an industrial poultry raw material stream

In this work, we evaluated the feasibility of Raman spectroscopy as an in-line raw material characterization tool for industrial process control of the hydrolysis of poultry rest raw material. We established calibrations (N = 59) for fat, protein, ash (proxy for bone) and hydroxyproline (proxy for collagen) in ground poultry rest raw material. Calibrations were established in the laboratory using poultry samples with high compositional variation. Samples were measured using a wide area illumination Raman probe at varying working distance (6 cm, 9 cm, 12 cm) and probe tilt angle (0°, 30°) to mimic expected in-line variations in the measurement situation. These moderate variations did not significantly affect performance for any analytes. The obtained calibrations were tested in-line with continuous measurements of the ground poultry by-product stream at a commercial hydrolysis facility over the course of two days. Measurements were acquired under demanding conditions, e.g. large variations in working distance. Reasonable estimates of compositional trends were obtained. Validation samples (N = 19) were also reasonably well predicted, with RMSEPcorr = [0.14, 1.37, 2.36, 1.51]% for hydroxyproline, protein, fat and ash, respectively. However, there were indications that further calibration development and robustification of pre-processing would be advantageous, particularly with respect to hydroxyproline and protein models. It is the authors’ impression that with such efforts, potentially in combination with development of practical measurement setup, the use of Raman spectroscopy as a process control tool for the hydrolysis of poultry rest raw materials is within reach.

Accounting for seasonality in the metastatistical extreme value distribution

The typical approach to account for non-stationarity in the generalized extreme value distribution (GEV) is to model the temporal behavior of the GEV parameters, e.g.,with linear relationships between the parameters and e.g.,the year. When in addition, seasonality, i.e., sub-yearly patterns are of interest, this relationship needs to be more complex. A natural candidate for nonstationary analysis of precipitation extremes is the metastatistical extreme value distribution (MEVD) assuming the ordinary rainfall events being Weibull distributed. It uses all ordinary events of the underlying rainfall distribution and implicitly considers temporal nonstationary changes in the Weibull parameters by a blockwise estimation. However, the MEVD still does not make use of the sub-yearly evolution of the distribution parameters and thus discards a significant part of information. In this paper a modification of the MEVD is proposed. The temporal MEVD (TMEV) relaxes the assumption of constant coefficients of the underlying distribution. The MEVD in each block estimates the Weibull parameters using the number of wet events in that block, regardless whether an individual wet day contributes much or less to the yearly rainfall sum. The newly proposed TMEV weights the single wet days contributions during parameter estimation and is therefore able to explicitly account for seasonal differences of rainfall events. It is shown that the TMEV provides a very similar error characteristic to the simplified MEVD for the estimation of quantiles with different sample lengths. In addition, we demonstrate the ability of the TMEV to identify longterm trends and seasonal variations of extreme precipitation in Austria and discuss possible implications for general trend estimations. We also present a spatio-temporal TMEV model, which is able to reproduce known patterns of a 50-year return level map of daily rainfall in Austria, thereby lending credence to the TMEV approach.

Estimation of skew surge uncertainties when predicting tides in the past

The study of storm surges and more generally of extreme events is a subject of growing interest in the current context of coastal risk management in a changing climate. Recent studies show that the consideration of past events improves statistical models. In this context, important investigations have been carried out in the last decade to retrieve and quantify water levels and skew surges associated with historical events for the 18th or 19th century. The estimation of the historical surges depends on the calculated tidal prediction, and therefore directly on the tidal components and the mean sea level of the period under consideration.Still, as tidal constituents and mean sea level change over time, it is difficult to estimate these parameters for historical events when sea level data are missing or incomplete. Two cases are considered for calculating tidal predictions. Either short time series are available contemporary to the event of interest, allowing a harmonic analysis and subsequent calculation of tidal constituents and mean sea levels. Or there are no or insufficient contemporary data available for the event of interest and current tidal characteristics are used in combination with a mean sea level adjustment. The present study aims to answer the following questions: depending on the data available for a historical event, which tidal constituents and mean sea level should be used when considering events that occurred several decades or even centuries ago? To what extent does the length of the observation series used in the harmonic analysis influence the tidal predictions? How does a mean sea level correction affect tidal predictions made with current tidal components? Finally, what are the uncertainties associated with the estimation of historical extreme skew surges, when there are not enough contemporary observations to estimate tidal components?Two methods are considered to estimate the uncertainties associated with the calculation of tidal predictions. Either the use of contemporary tidal constituents and mean sea level or the use of current tidal constituents corrected for mean sea level. In this study, 14 sea level records of 100 years or more provided by the Global Extreme Sea Level Analysis (GESLA version 3) were analysed. First, the uncertainties of the astronomical tides are assessed. For this purpose, different lengths of observations are implemented in a harmonic analysis and the resulting tidal constituents and tidal predictions are compared. In a second step, current tidal constituents are used for past tidal predictions and a mean sea level trend is inferred to cope with sea level variations. Different time periods are used to estimate the mean sea level trend and their influence on the tidal predictions is analysed. Furthermore, these two methods are combined to study the effects on the estimated skew surges and to assess the associated uncertainties.This study shows that uncertainties of less than 10 cm can be obtained for skew surges by using contemporary tidal components calculated from at least two years of observations. Larger differences are obtained for microtidal or river-influenced sites. When using current tidal constituents and adjusting a mean sea level trend, linear trend correction gives reliable results when more than 60 years of data are used for the trend estimation. In this case, the extreme skew surges obtained are within a range of more or less 5 cm. Finally, on the basis of the results presented, some suggestions are made on the possible applications of these methods according to the availability of data, stressing the importance of site-specific studies.

Nonparametric estimation of trend for SDEs driven by a Gaussian process

We discuss nonparametric estimation of the trend coefficient in models governed by a stochastic differential equation driven by a Gaussian process with small noise.