Record Length Research Articles

Flood frequency analysis – Part 2: Development of a modified plotting position

The original plotting position concept was suggested more than a century ago. Since then, many alternative plotting position approaches have been developed. Despite a general lack of agreement around which plotting position is theoretically ‘correct’ and the ‘best’ to use, all plotting positions fail to adequately address outliers and data of similar magnitude. Hydrologists generally fail to acknowledge that the plotting position primarily offers an informative display of data, against which distributions can be compared, rather than an absolute measure of probability. This paper does not intend to challenge any of the many lengthy theoretical mathematical arguments, utilised to ‘prove’ why one plotting position is superior to the others. These theoretical arguments may very well be valid for a ‘population’ of flood peaks – the reality, however, is that hydrologists are confronted with the challenge of analysing very limited ‘samples’ of the population. Consequently, the plotting position issue demands a more pragmatic approach, rather than a purely theoretical approach. This paper illustrates various problems with existing plotting position techniques in use and offers an alternative approach and a more sensible plotting position technique, using Z-scores and referred to as the Z-set PP, against which distributions can be checked. The study further illustrates how effectively the Z‑set PP deals with outliers and its robustness with various record lengths. Although derived from a study of flood peak data obtained from South African flow-gauging sites, it is deemed that it will be universally applicable.

Precipitation Scaling With Temperature in the Northeast US: Variations by Weather Regime, Season, and Precipitation Intensity

AbstractWe present a novel hierarchical Bayesian quantile regression model to estimate how precipitation scales with temperature depending on season, weather regime (WR; i.e., large‐scale patterns of atmospheric circulation), and precipitation percentile. The approach develops regional scaling estimates by partially pooling data across sites, accounting for uncertainty stemming from variable record lengths. Results using long‐term records of daily and hourly precipitation and both dry‐bulb and dew point temperature across the Northeast US suggest that regional precipitation scaling rates vary from 0% to 8% per °C. Scaling rate variability is driven most by season and the percentile of precipitation, with only modest variations across WRs. Daily scaling rates are highest in the winter and summer, while hourly rates at the highest percentiles are greatest in summer. Lower scaling rates for more extreme precipitation quantiles are driven by a handful of storms occurring at cooler temperatures but with strong vertical uplift and heavy precipitation.

A Mobile App for the Precise Measurement of Healthcare Provider Activity Times to Support Time-Driven Activity Based Costing Studies.

The duration of activities performed by healthcare providers are pivotal to Time-Driven Activity-Based Costing(TDABC) models. This study examines the use of a smartphone mobile application technology to record activity times. This study validates the accuracy of activity times recorded on a smartphone mobile application, dTool, compared to observed length of time recordings in the operating room. For analysis, we performed two one-sided tests for the measurements "Case Start" and "Case End". Equivalence bounds were specified in terms of raw mean difference of 1min (upper) and -1min (lower). The total number of comparisons in the observer protocol was 72 (32 "case start" patient comparisons and 40 "case end" patient comparisons measured over 45 individual OR cases). Given equivalence bounds of -1.000 and 1.000 (on a raw scale) and an alpha of 0.05, both equivalence tests were significant: provider and third-party observer protocol presented t(40) = 3.228 and p = < 0.001; observer timing protocol presented t(68.68) = 56.762, p = < 0.001. Conclusions: With this novel smartphone technology, a healthcare provider can reliably self-record activity LoT using dTool while providing patient care. Future TDABC studies incorporating this technology will reduce the potential operational barriers to implementation.

Minimum record length for detecting a prospective uniform sea level acceleration at a tide gauge station

Minimum record length for detecting a prospective uniform sea level acceleration at a tide gauge station

Analysis of Rainfall Variability in the Province of Quirino

The temporal variability of rainfall in Quirino Province was analyzed through the use of rainfall data of seven (7) rain gauges within the neighboring provinces like Nueva Vizcaya and Aurora. The length of record analyzed from 1997 to 2016. In this study, rainfall frequency analysis and consistency of rainfall data from the different stations through the use of double mass curve analysis was performed and analyzed. The annual series was used to screen each station’s annual rainfall data while the province’s average Thiessen rainfall was screened using maximum period series process. It was found out that all the data of the seven (7) rainfall stations were consistent. To attain allowable error in estimation of 10%, 5% and 1% for the mean annual rainfall the number of rain gauge station needed in the province should be 18, 72 and 1799, respectively.

Use of MARC21 control field data in University Library OPACs in Karnataka: A study

This study discusses about importance of MARC21 control field and its effective implementation in university libraries OPACs in Karnataka. The catalogue records available through library web OPACs of universities under study formed the data source for this study. The university library web OPACs which we examined in this study have used Koha ILMS. One thousand and eighty-eight (1088) records were randomly selected from the nine university library web OPACs for the study. The study examined the proper implementation of 008 control field of MARC21, which is an important data element in information retrieval. The study found that average length of a cataloguing record was 1572.78 characters. The study as also revealed that libraries web OPACs under study have given scant attention for control fields, 001 and 003 were the two tag numbers that have been used extensively. Overall the university libraries have neglected control fields data element in their OPACs. This adversely affect the data retrieval from the user point of view. The study strongly recommend that libraries should fill up the data required for control fields while cataloguing their records.

Coupling of satellite-derived precipitation products with Bartlett-Lewis model to estimate intensity-frequency-duration curves for remote areas

Intensity-frequency-duration (IFD) curves (also known as intensity-duration-frequency (IDF) curves in some regions) are widely used to take into consideration the probability of occurrence of extreme precipitation for the design of water related infrastructure. Derivation of IFD curves is based on statistical analysis of observed extreme precipitation events and thus the reliability of the curves mostly depends on the record length of observations. At sub-hourly timescales however, ground-based precipitation observations are often absent or very limited in terms of spatial coverage and record length, particularly in remote areas around the world. Given the availability of satellite-derived precipitation products (SPPs) with finer spatiotemporal resolution and near-global coverage, there is great potential to use these products in the planning and design of infrastructure. This paper proposes a novel framework, SIFD (Satellite-derived IFD curves) to combine long-term daily gauge data and sub-hourly SPP to estimate IFD curves for time intervals from 0.5 to 24 h. A recent variant of the Random Parameter Bartlett-Lewis Rectangular Pulse model (RPBLRPX) was used to disaggregate daily gauge data to derive IFD curves. In the absence of sub-daily rain gauge data, the statistics required for the RPBLRPX model were obtained using the Integrated Multi-satellitE Retrievals for GPM (IMERG; 30-min, 0.1° resolution) product derived from the Global Precipitation Measurement (GPM) mission. Performance of the SIFD framework was tested against gauge data from seven Australian sites across a wide range of climatologies. The disaggregation scheme coupled with the RPBLRPX model was capable of reproducing the wet-period mean intensity, wet-period standard deviation, and wet-period fraction with reasonable accuracy across the sub-daily timescales. The coefficient of determination (r2) values were 0.39 (mean intensity), 0.38 (standard deviation), and 0.87 (wet-period fraction) at 0.5 h timescale. Furthermore, despite some differences in methodology and record length, the IFD estimates computed using the SIFD framework were fairly close to those of the Bureau of Meteorology (BoM). The skill metrics for Sydney were the best with a bias ratio (BR) of 1.01, root mean square error (RMSE) of 13% of the mean of the BoM estimates, and a Nash-Sutcliffe coefficient of efficiency (NSE) of 0.97. The worst case was found at Hobart (BR = 1.43, RMSE = 66%, and NSE = 0.26). Overall, the results indicate that the proposed SIFD framework could provide a viable alternative to estimate unbiased IFD curves at the sub-hourly timescale in the absence of adequate sub-hourly ground-based observations.

Multiple trend tests on air temperature and precipitation anomalies in Vietnam

Abstract An informed decision on building climate resilience and adaptation requires a deep understanding of long-term changes in temperature and precipitation, especially for most vulnerable countries to climate extremes like Vietnam. Hence, this study aimed to comprehend spatiotemporal trend possibilities of temperature and precipitation in Vietnam by employing multiple non-parametric statistical trend tests. This study also suggested a modified procedure, represented as a Hovmöller-like diagram, for enhancing the characterization of long-term spatiotemporal trends by applying multiple monotonic trend tests to all latitude coordinates through all possible 30-year periods. The superiority of multiple trend tests over a single one is that this methodology can assess the sensitivity of trend test results to the beginning years, ending years, and record lengths, thus emphasizing the necessity of performing monotonic trend tests repeatedly. The results show consistent warming trends in all climate sub-regions over the last 4–5 decades, with the estimated trend slopes varying from approximately 0.010–0.042 °C/year. Precipitation anomalies in most climate sub-regions, especially in the southern part, experienced significant increasing trends at the rate of around 0.29–2.76%/year during the last 3–5 decades. These findings are expected to contribute more insights into the spatiotemporal trend patterns of temperature and precipitation in Vietnam.

Recent Changes in Average Recurrence Interval Precipitation Extremes in the Mid-Atlantic United States

AbstractIncreases in the frequency of extreme rainfall occurrence have emerged as one of the more consistent climate trends in recent decades, particularly in the eastern United States. Such changes challenge the veracity of the conventional assumption of stationarity that has been applied in the published extreme rainfall analyses that are the foundation for engineering design assessments and resiliency planning. Using partial-duration series with varying record lengths, temporal changes in daily and hourly rainfall extremes corresponding to average annual recurrence probabilities ranging from 50% (i.e., the 2-yr storm) to 1% (i.e., the 100-yr storm) are evaluated. From 2000 through 2019, extreme rainfall amounts across a range of durations and recurrence probabilities have increased at 75% of the long-term precipitation observation stations in the mid-Atlantic region. At approximately one-quarter of the stations, increases in extreme rainfall have exceeded 5% from 2000 through 2019, with some stations experiencing increases in excess of 10% for both daily and hourly durations. At over 40% of the stations, the rainfall extremes based on the 1950–99 partial-duration series show a significant (p&gt; 0.90) change in the 100-yr ARI relative to the 1950–2019 period. Collectively, the results indicate that, given recent trends in extreme rainfall, routine updates of extreme rainfall analyses are warranted on 20-yr intervals.Significance StatementEngineering design standards for drainage systems, dams, and other infrastructure rely on analyses of precipitation extremes. Often such structures are designed on the basis of the probability of exceeding a specified rainfall rate in a given year. The frequency of extreme rainfall events has increased in the mid-Atlantic region of the United States in recent decades, leading us to evaluate how these changes have affected these exceedance probabilities. From 2000 through 2019, there has been a consistent increase of generally 2.5%–5.0% in design rainfall amounts. The increase is similar across a range of rainfall durations from 1 h to 20 days and also annual exceedance probabilities ranging from 50% to 1% (i.e., from the “2-yr storm” to the “100-yr storm”). The work highlights the need to routinely update the climatological extreme-value analyses used in engineering design, with the results suggesting that a 20-yr cycle might be an appropriate update frequency.

Towards traffic supervision in 6G: a graph neural network-based encrypted malicious traffic detection method

As an important direction for the evolution of next-generation wireless communication technology, 6G will comprehensively promote the wave of economic and social digitization. Services carried by 6G network will rely heavily on the sharing and processing of massive amounts of data between entities, data security is therefore of great importance. Currently, most network applications utilize SSL/TLS protocols to ensure the confidentiality and security of network communications, while encryption mechanism also brings huge challenges to network security supervision. Though encrypted malicious traffic detection in traditional networks has become a research hotspot, existing technologies cannot be directly applied in 6G networks. In a 6G network with massive, instant and unlimited communications between heterogeneous terminals, network communication behavior patterns are much more diversified, which makes the boundary between normal traffic and malicious traffic more blurred in 6G networks than in traditional networks. Existing studies either analyze encrypted traffic in isolation or aggregation, while they all ignore the rich correlations among encrypted traffic. To this end, we propose an encrypted malicious traffic detection framework based on the graph neural network towards the network security problem of future 6G networks, ET-RSGAT. First, considering the characteristics of super high speed and super large connection of 6G network, we design a simple feature extraction method of encrypted traffic: extracting the TLS handshake raw bytes and TLS record length sequence for one single encrypted session. Second, in view of the correlations of large numbers of heterogeneous terminals and the coexistence of multi-source heterogeneous data communication in 6G networks, we analyze the correlations between encrypted sessions from 2 aspects, which are service correlations and communication behavior correlations. Then we construct an encrypted traffic graph, named ETG. On the basis of ETG, we introduce a graph attention network to utilize the correlations between encrypted sessions to enrich the feature representation of nodes. With rich representation, we build the detection model based on a multi-layer perceptron to identify threats. Considering that the simulation environment of 6G networks is immature, we deploy a variety of heterogeneous terminal nodes and run various network services to simulate the 6G communication scenario, and design related experiments for the interconnection of many heterogeneous terminals in 6G networks. The evaluation and experimental results show that our method can obtain satisfactory detection results in both traditional network and simulated environment datasets.

Extreme streamflow time series analysis: trends, record length, and persistence

Trends can be detected in time series of extreme hydrological events. However, persistence and record length are often ignored in those analyses resulting in contradicting conclusions. The aim of this study is to evaluate their influence on trend detection in extreme streamflow time series. In this study, 108 time series of maximum and minimum streamflow in Brazil were analysed, with a minimum length of 60 years and an average of 76 years. Mann-Kendall (MK), Spearman’s rho, and Pettitt statistical tests were applied to assess trends. Portmanteau and Hurst’s autocorrelation tests were adopted to assess the persistence. Modifications of the MK test were used to remove the persistence effects. We found a strong persistence in the studied time series. Even after removing it, several time series remained non-stationary. Record length significantly affected the results of the analyses, with an increase in the number of trends according to the period analysed.

Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things.

To improve the teaching effect of western music history, the curriculum reform of history education needs to be promoted under the background of the Internet of Things (IoT). At first, a discussion is made on the characteristics of history course, which is combined with the characteristics of teaching data easy to collect under the background of IoT. An analysis is conducted on the related theory of educational data mining. Then, the concept of personalized recommendation is proposed based on deep learning (DL) algorithm. Finally, online and offline experiments are designed to verify the performance of the algorithm from review and investigation, smoothness, and participation of difficulty. The research results show that in terms of offline recommendation accuracy, the average record length in Math data set is 24.5, which is much smaller than that in range data set. The research has obvious innovation significance compared with other studies. In the process of target review and investigation, it is found that the research method here involves a wider range of knowledge and higher reliability. In terms of the difficulty of recommending questions, the Deep Reinforcement Exercise (DRE) recommendation algorithm can adaptively adjust the difficulty of recommending questions. It also allows students to set different learning goals through participation goals. But in the experiments on Math data set, Step 10’s recommendation results are not very good, and the difficulty level varies greatly. If the goal setting is high, the problem recommended to students is too difficult, students may answer these questions wrongly, forcing the algorithm to adjust the difficulty adaptively. According to the above results, DRE recommendation algorithm can adapt to different learning needs and customize the recommendation results, thus opening up a new path for the teaching of western music history. Besides, the combination of DL algorithm and western music history teaching design can recommend learning materials, which is of great significance in the teaching of history courses.

Reconstructing Antarctic winter sea-ice extent during Marine Isotope Stage 5e

Abstract. Environmental conditions during Marine Isotope Stage (MIS) 5e (130–116 ka) represent an important “process analogue” for understanding the climatic responses to present and future anthropogenic warming. The response of Antarctic sea ice to global warming is particularly uncertain due to the short length of the observational record. Reconstructing Antarctic winter sea-ice extent during MIS 5e therefore provides insights into the temporal and spatial patterns of sea-ice change under a warmer-than-present climate. This study presents new MIS 5e records from nine marine sediment cores located south of the Antarctic Polar Front between 55 and 70∘ S. Winter sea-ice extent and sea-surface temperatures are reconstructed using marine diatom assemblages and a modern analogue technique transfer function, and changes in these environmental variables between the three Southern Ocean sectors are investigated. The Atlantic and East Indian sector records show much more variable MIS 5e winter sea-ice extent and sea-surface temperatures than the Pacific sector records. High variability in the Atlantic sector winter sea-ice extent is attributed to high glacial meltwater flux in the Weddell Sea, indicated by increased abundances of the diatom species Eucampia antarctica and Fragilariopsis cylindrus. The high variability in the East Indian sector winter sea-ice extent is conversely believed to result from large latitudinal migrations of the flow bands of the Antarctic Circumpolar Current, inferred from latitudinal shifts in the sea-surface temperature isotherms. Overall, these findings suggest that Pacific sector winter sea ice displays a low sensitivity to warmer climates. The different variability and sensitivity of Antarctic winter sea-ice extent in the three Southern Ocean sectors during MIS 5e may have significant implications for the Southern Hemisphere climatic system under future warming.

How Long Should GPS Recording Lengths Be to Capture the Community Mobility of An Older Clinical Population? A Parkinson's Example.

Wearable global position system (GPS) technology can help those working with older populations and people living with movement disorders monitor and maintain their mobility level. Health research using GPS often employs inconsistent recording lengths due to the lack of a standard minimum GPS recording length for a clinical context. Our work aimed to recommend a GPS recording length for an older clinical population. Over 14 days, 70 older adults with Parkinson’s disease wore the wireless inertial motion unit with GPS (WIMU-GPS) during waking hours to capture daily “time outside”, “trip count”, “hotspots count” and “area size travelled”. The longest recording length accounting for weekend and weekdays was ≥7 days of ≥800 daily minutes of data (14 participants with 156, 483.9 min recorded). We compared the error rate generated when using data based on recording lengths shorter than this sample. The smallest percentage errors were observed across all outcomes, except “hotspots count”, with daily recordings ≥500 min (8.3 h). Eight recording days will capture mobility variability throughout days of the week. This study adds empirical evidence to the sensor literature on the required minimum duration of GPS recording.

A preliminary result of microtremor study to identify the subsurface condition in the Aceh Tenggara region

The Aceh Tenggara region is located in an tectonically active region and it can be categorized as the prone-area. Historically, there were many major earthquakes occured the active fault called the Tripa segment that has generated massive damages and losses. The study of the seismic vulnerability around the highly populated city in the Aceh Tenggara region, Kutacane City, as the center of economy and office activities must be conducted to prepare the suitable earthquake mitigation program. The microtremor have been recorded at 25 sites with one km grid interval between all points. The record lengths have 30 minutes duration at each site, and they were measured with the Taurus Seismometer in miniseed format. The data were analyzed by using the HVSRpy in order to obtain the Horizontal to Vertical Spectral Ratio for each site by means of the geometric-mean and the multiple azimuths methods. Both methods provided a similar result at both focuses with dominant frequency of 0.8 Hz at the AT01 site and 4.8 Hz at the AT14 location. The preliminary result shows that the higher dominant frequency were recorded along the southern part of the Kutacane city which is associated with the Alas formation.

Dual-Frequency Quantum Phase Estimation Mitigates the Spectral Leakage of Quantum Algorithms

Quantum phase estimation is an important component in diverse quantum algorithms. However, it suffers from spectral leakage, when the reciprocal of the record length is not an integer multiple of the unknown phase, which incurs an accuracy degradation. For the existing single-sample estimation scheme, window-based methods have been proposed for spectral leakage mitigation. As a further advance, we propose a dual-frequency estimator, which asymptotically approaches the Cramer-Rao bound, when multiple samples are available. Numerical results show that the proposed estimator outperforms the existing window-based methods, when the number of samples is sufficiently high.

Land Subsidence Estimation With Tide Gauge and Satellite Radar Altimetry Measurements Along the Texas Gulf Coast, USA

A double difference (DD) method was employed to estimate vertical land motion (VLM) at 26 tide gauge (TG) sites with record lengths of at least ten years across the Texas Gulf Coast, USA, between 1993 and 2020. In the method, the first difference was conducted by coupling nearby correlated TG stations to remove sea-level variability for both TG and satellite radar altimetry (SRA) data. Upon completion of the first difference, a second difference was performed by subtracting between TG and SRA data. Results obtained from the DD method were compared against that of: 1) a single difference (SD) method through subtraction between measurements from TG and SRA, and 2) a global navigation satellite system (GNSS) precise point positioning (PPP) method. Results showed that the DD method improved the performance of VLM estimation with an uncertainty below 1.0 mm/yr at most TG stations. Meanwhile, the estimated VLM trends acquired from the DD method correlated better to that of the ground-truth GNSS PPP solutions than the SD method. The DD method possesses great potential to discover VLM knowledge, particularly along coastal regions where other techniques such as GNSS and interferometric synthetic aperture radar (InSAR) are of impaired estimation capability.

Long‐term trends in extreme precipitation indices in Ireland

AbstractKnowledge of long‐term changes in climate extremes is vital to better understand climate variability and place present day extreme events in historical context. Analysis of trends in extreme precipitation in Ireland have largely been limited to the second half of the 20th century due to lack of data availability in digital format. Using recently digitized data, this study provides the first assessment of long‐term changes in extreme daily precipitation observed at 30 locations across Ireland. Quality control of rescued data is carried out before selected long‐term stations are tested for homogeneity using the RHtests software. Details of detected breakpoints and the application of adjustments to daily values are discussed. Eleven extreme precipitation indices are calculated on an annual basis and analysed to determine spatial and temporal trends in the frequency, intensity and magnitude of observed precipitation. The persistence of trends for varying record lengths and for two fixed periods (1910–2019 and 1940–2019) of analysis is assessed for all stations and indices. Results show increases in precipitation intensity, especially notable in the east and southeast of the island. Our findings also show that the contribution of heavy and extreme precipitation events to annual totals is increasing, while there was no persistent trends in annual totals or consecutive wet or dry days.

Market screening with limited records

Markets differ in the availability of past trading records of their participants. In a repeated sale model with adverse selection, we study the impact of the availability of such records on trading outcomes. We consider regimes varying with respect to the length of the available records. We characterize a class of equilibria in which the record length has direct welfare implications via the market's need to re-screen the seller, as well as indirect implications via the low quality seller's incentives to mimic the high quality seller. As the record length increases, the market needs to re-screen less frequently, which improves efficiency. In turn, less frequent screening makes mimicking more attractive and limits the market's ability to learn. These considerations lead to a non-monotonic relationship between record length and overall gains from trade.

Unprecedented acceleration of winter discharge of Upper Yenisei River inferred from tree rings

The Yenisei River is the largest contributor of freshwater and energy fluxes among all rivers draining to the Arctic Ocean. Modeling long-term variability of Eurasian runoff to the Arctic Ocean is complicated by the considerable variability of river discharge in time and space, and the monitoring constraints imposed by a sparse gauged-flow network and paucity of satellite data. We quantify tree growth response to river discharge at the upper reaches of the Yenisei River in Tuva, South Siberia. Two regression models built from eight tree-ring width chronologies of Larix sibirica are applied to reconstruct winter (Nov–Apr) discharge for the period 1784–1997 (214 years), and annual (Oct–Sept) discharge for the period 1701–2000 (300 years). The Nov–Apr model explains 52% of the discharge variance whereas Oct–Sept explains 26% for the calibration intervals 1927–1997 and 1927–2000, respectively. This new hydrological archive doubles the length of the instrumental discharge record at the Kyzyl gauge and resets the temporal background of discharge variability back to 1784. The reconstruction finds a remarkable 80% upsurge in winter flow over the last 25 years, which is unprecedented in the last 214 years. In contrast, annual discharge fluctuated normally for this system, with only a 7% increase over the last 25 years. Water balance modeling with CRU data manifests a significant discrepancy between decadal variability of the gauged flow and climate data after 1960. We discuss the impact on the baseflow rate change of both the accelerating permafrost warming in the discontinuous zone of South Siberia and widespread forest fires. The winter discharge accounts for only one third of the annual flow, yet the persistent 25 year upsurge is alarming. This trend is likely caused by Arctic Amplification, which can be further magnified by increased winter flow delivering significantly more fresh water to the Kara Sea during the cold season.