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At least 19 recordsLinked to original sources

The application of prototype point processes for the summary and description of California wildfires

A method for summarizing repeated realizations of a space‐time marked point process, known as prototyping, is discussed and applied to catalogues of wildfires in California. Prototype summaries are constructed for varying time intervals using California wildfire data from 1990 to 2006. Previous work on prototypes for temporal and space‐time point processes is extended here to include methods for computing prototypes with marks and the incorporation of prototype summaries into hierarchical clustering algorithms, the latter of which is used to delineate fire seasons in California. Other results include summaries of patterns in the spatial‐temporal distribution of wildfires within each wildfire season.

California

Periodic autoregressive-moving average (PARMA) modeling with applications to water resources

Results involving correlation properties and parameter estimation for autoregressive-moving average models with periodic parameters are presented. A multivariate representation of the PARMA model is used to derive parameter space restrictions and difference equations for the periodic autocorrelations. Close approximation to the likelihood function for Gaussian PARMA processes results in efficient maximum-likelihood estimation procedures. Terms in the Fourier expansion of the parameters are sequentially included, and a selection criterion is given for determining the optimal number of harmonics to be included. Application of the techniques is demonstrated through analysis of a monthly streamflow time series.

Journal of the American Water Resources Associatio

A novel regression method for harmonic analysis of time series

Harmonic analysis of time series is an important technique in remote sensing to reveal seasonal land surface dynamics. However, frequency selection in the harmonic analysis is often difficult because high-frequency components are useful for delineating seasonal dynamics but sensitive to noise and gaps in time series. On the other hand, it is challenging to obtain temporally continuous satellite data with high quality because of atmospheric contamination. We developed a novel regression method named Harmonic Adaptive Penalty Operator (HAPO) for harmonic analysis of unevenly distributed time series. We introduced a new penalty function to minimize unexpected fluctuations in the model, which can substantially reduce the overfitting issue of regression in time series with temporal gaps. Specifically, the new penalty function minimizes the length of the model curve and the value range difference between the model and the time series observations. We compared HAPO with three widely used regression methods (OLS: Ordinary Least Squares; LASSO: Least Absolute Shrinkage and Selection Operator; and Ridge) in different scenarios using Landsat time series data across the United States. First, we evaluated methods using the Landsat surface reflectance time series within a single year. HAPO showed low and consistent monthly Root Mean Square Deviation (RMSD) values, in which most of the time RMSD of predicted reflectance were less than 0.04. More importantly, HAPO showed consistent and less bias given varying density and irregularity of time series. Second, we evaluated methods using multi-year time series. HAPO was a better predictor of relatively short time series (< 4 years) with steady low RMSD values. When a longer time series ( 4 years) was used, all four methods showed similar RMSD values, but HAPO outperformed the other methods if there were temporal gaps. Therefore, for places with large seasonal observation gaps or for time series that are relatively short (less than 4 years), HAPO can provide more consistent and accurate results in harmonic analysis of time series.

ISPRS Journal of Photogrammetry and Remote Sensing

Assessing gap-filled Landsat land surface temperature time-series data using different observational datasets

Landsat Analysis Ready Data (ARD)-based time-series present challenges in monitoring surface urban heat islands (SUHI) due to rapid changes in land surface temperature (LST) compared to cloud-free satellite observations. This research investigates the use of a spatiotemporal gap-filling model as a feasible and cost-effective solution to produce Landsat time-series LST products with both high spatial resolution and temporal frequency. The study identified and filled Landsat ARD thermal times-series data gaps due to missing data, cloud and shadow effects, and data quality. The accuracy of Landsat gap-filled products was assessed using randomly selected clear observations of Landsat and uncertainty products from the gap-filling model and was evaluated using various existing temperature datasets, including climate data from NOAA Global Historical Climate Network station observations, Daily Surface Weather and Climatological Summaries (DAYMET), and LST including MODIS, VIIRS and ECOSTRESS. The result suggests that the gap-filled Landsat LST has significant correlations with existing datasets including field observation and remote sensing data derived from other sensors that have similar monthly and seasonal variation patterns. The uncertainty maps show spatial distributions of uncertainty for gap-filled pixels that have high or low uncertainties. The Landsat gap-filled time-series datasets can be used to measure annual, seasonal, or even monthly landscape thermal conditions, which are useful for SUHI and relevant research, and to perform multi-decade time-series LST change analysis under climate change conditions.

International Journal of Remote Sensing

Detecting sea-level hazards: Simple regression-based methods for calculating the acceleration of sea level

This report documents the development of statistical tools used to quantify the hazard presented by the response of sea-level elevation to natural or anthropogenic changes in climate and ocean circulation. A hazard is a physical process (or processes) that, when combined with vulnerability (or susceptibility to the hazard), results in risk. This study presents the development and comparison of new and existing sea-level analysis methods, exploration of the strengths and weaknesses of the methods using synthetic time series, and when appropriate, synthesis of the application of the method to observed sea-level time series. These reports are intended to enhance material presented in peer-reviewed journal articles where it is not always possible to provide the level of detail that might be necessary to fully support or recreate published results. The purpose of this report is to document and compare three simple methodologies that have previously been used to provide estimates with associated errors of the acceleration of sea-level elevation. These techniques have been used by coastal scientists and planners in assessing coastal risk over a wide range of spatial and temporal scales. Because relative sea-level (SL) elevation time series contain energetic fluctuations at many time scales, extracting what can be relatively small rate and acceleration signals (along with estimates of the error) from much larger &ldquo;noise&rdquo; has proven to be both difficult and controversial. Acceleration is a preferred measure of SL response to recent changes in the Earth&rsquo;s climate because over time scales of 100 years or less slow vertical land motions (such as glacial isostatic adjustment) contribute only to the linear signal and not to acceleration, thus reducing the complexity of the analysis. Hence acceleration is useful if the goal of a study is to characterize and quantify the hazard associated with the changing relative elevation of water with respect to land on decadal time scales. Although in some cases it may be necessary to determine the cause of relative sea level rise, as a first step, it is important to accurately estimate the magnitude of the threat. Most researchers agree that global sea level (GSL) rose persistently through much of the 20th century at about 1.5&ndash;2.0 millimeters per year (mm/yr). There is far less agreement about whether the rate of sea-level rise (SLR) is increasing (that is, an acceleration in SL). Recent studies, and most of their predecessors, use tide gage data to quantify SL acceleration, A SL (t). In the current study, three techniques were used to calculate acceleration from tide gage data, and of those examined, it was determined that the two techniques based on sliding a regression window through the time series are more robust compared to the technique that fits a single quadratic form to the entire time series, particularly if there is temporal variation in the magnitude of the acceleration. The single-fit quadratic regression method has been the most commonly used technique in determining acceleration in tide gage data. The inability of the single-fit method to account for time-varying acceleration may explain some of the inconsistent findings between investigators. Properly quantifying A SL (t) from field measurements is of particular importance in evaluating numerical models of past, present, and future SLR resulting from anticipated climate change.

Open-File Report

Microforms in gravel bed rivers: Formation, disintegration, and effects on bedload transport

This research aims to advance current knowledge on cluster formation and evolution by tackling some of the aspects associated with cluster microtopography and the effects of clusters on bedload transport. The specific objectives of the study are (1) to identify the bed shear stress range in which clusters form and disintegrate, (2) to quantitatively describe the spacing characteristics and orientation of clusters with respect to flow characteristics, (3) to quantify the effects clusters have on the mean bedload rate, and (4) to assess the effects of clusters on the pulsating nature of bedload. In order to meet the objectives of this study, two main experimental scenarios, namely, Test Series A and B (20 experiments overall) are considered in a laboratory flume under well-controlled conditions. Series A tests are performed to address objectives (1) and (2) while Series B is designed to meet objectives (3) and (4). Results show that cluster microforms develop in uniform sediment at 1.25 to 2 times the Shields parameter of an individual particle and start disintegrating at about 2.25 times the Shields parameter. It is found that during an unsteady flow event, effects of clusters on bedload transport rate can be classified in three different phases: a sink phase where clusters absorb incoming sediment, a neutral phase where clusters do not affect bedload, and a source phase where clusters release particles. Clusters also increase the magnitude of the fluctuations in bedload transport rate, showing that clusters amplify the unsteady nature of bedload transport. A fourth-order autoregressive, autoregressive integrated moving average model is employed to describe the time series of bedload and provide a predictive formula for predicting bedload at different periods. Finally, a change-point analysis enhanced with a binary segmentation procedure is performed to identify the abrupt changes in the bedload statistic characteristics due to the effects of clusters and detect the different phases in bedload time series using probability theory. The analysis verifies the experimental findings that three phases are detected in the bedload rate time series structure, namely, sink, neutral, and source. ?? ASCE / JUNE 2004.

Journal of Hydraulic Engineering

Methods of ultimate carbonaceous BOD determination

Studies were conducted to provide an accurate and practical technique for determining the concentration of ultimate carbonaceous biochemical oxygen demand and the rate at which this demand is exerted. The three methods evaluated were carbon derived, nitrification adjusted, and nitrification inhibited. The studies indicate that comparable concentrations and reaction rates can be determined from either non-nitrified samples using no chemical nitrifying inhibitor, or from partially nitrified samples using the chemical inhibitors, 1-allyl-2 thiourea or nitrapyrin, and that the combined use of time-series analysis and Lee's graphical method provide a reliable and accurate technique for determining ultimate biochemical oxygen demand concentration and reaction rate in 5 to 7 days.

Journal of the Water Pollution Control Federation

Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California

Accurate monitoring of surface water location and extent is critical for the management of diverse water resource phenomena. The multi-decadal archive of Landsat satellite imagery is punctuated by missing data due to cloud cover during acquisition times, hindering the assembly of a continuous time series of inundation dynamics. This study investigated whether streamflow volume measurements could be integrated with satellite data to fill gaps in monthly surface water chronologies for the Central Valley region of California, USA, from 1984 to 2015. We aggregated measurements of maximum monthly water extent within each of the study area’s 50 8-digit hydrologic unit code [HUC] watersheds from two Landsat-derived datasets: the European Commission’s Joint Research Centre (JRC) Monthly Water History and the U.S. Geological Survey Dynamic Surface Water Extent (DSWE). We calculated Spearman rank correlation coefficients between water extent values in each HUC and streamflow discharge data. Linear regression fits of the water extent/streamflow data pairs with the highest correlations served as the basis for interpolation of missing imagery surface water values on a HUC-wise basis. Results show strong (ρ > 0.7) maximum correlations in 11 (22.4%) and 25 (51.0%) HUCs for the DSWE and JRC time series, respectively, when comparisons were restricted to imagery and gages co-located in each HUC. Strong maximum correlations occurred in 39 (79.6%; DSWE) and 42 (85.7%; JRC) HUCs when imagery was paired with discharge data from any study area gage, providing a solid basis for reconstruction of water extent values. We generated continuous time series of 30+ years in 35 HUCs, demonstrating that this technique can provide quantitative estimates of historical surface water extents and elucidate flooding or drought events over the period of data collection. Results of a non-parametric trend analysis of the long-term time series on an annual, seasonal, and monthly basis varied among HUCs, though most trends indicate an increase in surface water over the past 30 years.

California

Quantifying and visualizing 32 years of agricultural land use change in Kabul, Afghanistan

Agriculture is a key element of Afghanistan’s economy and plays an essential role supporting the expanding population and urban development of Kabul, the country’s capital. Over the past decades the urban landscape has changed substantially and agricultural land use has shifted in its extent, location, and density. Identifying trends in the amount of agricultural area, as an indication of food production, is important for city planning and humanitarian efforts. While many studies have investigated Afghanistan's agriculture, most are conducted at scales that preclude their use for local-scale decision-making. This study quantifies agricultural extent across 32 years from 1988 to 2020 at local scale using simple and repeatable Landsat multispectral image analysis. The volume of data in time-series analysis complicatesvisualization of key findings and long-term trends. This study also explored visualization methods such as zonal mapping, animations, and the isolation of key themes in a 2D static map.

Kabul

A subset of CyberShake ground-motion time series for response-history analysis

This manuscript describes a subset of CyberShake numerically simulated ground motions that were selected and vetted for use in engineering response-history analyses. Ground motions were selected that have seismological properties and response spectra representative of conditions in the Los Angeles area, based on disaggregation of seismic hazard. Ground motions were selected from millions of available time series and were reviewed to confirm their suitability for response-history analysis. The processes used to select the time series, the characteristics of the resulting data, and the provided documentation are described in this article. The resulting data and documentation are available electronically.

California

Long-term monitoring data provide evidence of declining species richness in a river valued for biodiversity conservation

Free-flowing river segments provide refuges for many imperiled aquatic biota that have been extirpated elsewhere in their native ranges. These biodiversity refuges are also foci of conservation concerns because species persisting within isolated habitat fragments may be particularly vulnerable to local environmental change. We have analyzed long-term (14- and 20-y) survey data to assess evidence of fish species declines in two southeastern U.S. rivers where managers and stakeholders have identified potentially detrimental impacts of current and future land uses. The Conasauga River (Georgia and Tennessee) and the Etowah River (Georgia) form free-flowing headwaters of the extensively dammed Coosa River system. These rivers are valued in part because they harbor multiple species of conservation concern, including three federally endangered and two federally threatened fishes. We used data sets comprising annual surveys for fish species at multiple, fixed sites located at river shoals to analyze occupancy dynamics and temporal changes in species richness. Our analyses incorporated repeated site-specific surveys in some years to estimate and account for incomplete species detection, and test for species-specific (rarity, mainstem-restriction) and year-specific (elevated frequencies of low- or high-flow days) covariates on occupancy dynamics. In the Conasauga River, analysis of 26 species at 13 sites showed evidence of temporal declines in colonization rates for nearly all taxa, accompanied by declining species richness. Four taxa (including one federally endangered species) had reduced occupancy across the Conasauga study sites, with three of these taxa apparently absent for at least the last 5 y of the study. In contrast, a similar fauna of 28 taxa at 10 sites in the Etowah River showed no trends in species persistence, colonization, or occupancy. None of the tested covariates showed strong effects on persistence or colonization rates in either river. Previous studies and observations identified contaminants, nutrient loading, or changes in benthic habitat as possible causes for fish species declines in the Conasauga River. Our analysis provides baseline information that could be used to assess effectiveness of future management actions in the Conasauga or Etowah rivers, and illustrates the use of dynamic occupancy models to evaluate evidence of faunal decline from time-series data.

Georgia

The cyclic and fractal seismic series preceding an mb 4.8 earthquake on 1980 February 14 near the Virgin Islands

Seismic activity in the 10 months preceding the 1980 February 14, m b 4.8 earthquake in the Virgin Islands, reported on by Frankel in 1982, consisted of four principal cycles. Each cycle began with a relatively large event or series of closely spaced events, and the duration of the cycles progressively shortened by a factor of about 3/4. Had this regular shortening of the cycles been recognized prior to the earthquake, the time of the next episode of seismicity (the main shock) might have been closely estimated 41 days in advance. That this event could be much larger than the previous events is indicated from time-to-failure analysis of the accelerating rise in released seismic energy, using a non-linear time- and slip-predictable foreshock model. Examination of the timing of all events in the sequence shows an even higher degree of order. Rates of seismicity, measured by consecutive interevent times, when plotted on an iteration diagram of a rate versus the succeeding rate, form a triangular circulating trajectory. The trajectory becomes an ascending helix if extended in a third dimension, time. This construction reveals additional and precise relations among the time intervals between times of relatively high or relatively low rates of seismic activity, including period halving and doubling. The set of 666 time intervals between all possible pairs of the 37 recorded events appears to be a fractal; the set of time points that define the intervals has a finite, non-integer correlation dimension of 0.70. In contrast, the average correlation dimension of 50 random sequences of 37 events is significantly higher, close to 1.0. In a similar analysis, the set of distances between pairs of epicentres has a fractal correlation dimension of 1.52. Well-defined cycles, numerous precise ratios among time intervals, and a non-random temporal fractal dimension suggest that the seismic series is not a random process, but rather the product of a deterministic dynamic system.

Geophysical Journal International

Seismological and geodetic constraints on the 2011 M w 5.3 Trinidad, Colorado earthquake and induced deformation in the Raton Basin

The Raton Basin of southern Colorado and northern New Mexico is an actively produced hydrocarbon basin that has experienced increased seismicity since 2001, including the August 2011 M w 5.3 Trinidad normal faulting event. Following the 2011 earthquake, regional seismic observations were used to relocate 21 events, including the 2011 main shock, two foreshocks, and 13 aftershocks. Additionally, interferometric synthetic aperture radar (InSAR) observations of both the 2011 event and preevent basin deformation place constraint on the spatial kinematics of the 2011 event and localized basin subsidence due to ground water or gas withdrawal. We find that the 2011 earthquake ruptured an 8–10 km long segment of a normal fault at depths of 1.5–6.0 km within the crystalline Precambrian basement underlying the Raton Basin sedimentary rocks. The earthquake also nucleated within the crystalline basement in the vicinity of an active wastewater disposal site. The ensuing aftershock sequence demonstrated statistical properties expected for intraplate earthquakes, though the length of the 2011 earthquake is unexpectedly long for an M w 5.3 event, suggesting that wastewater disposal may have triggered a low stress drop, otherwise natural earthquake. Additionally, preevent and postevent seismicity in the Raton Basin spatially correlates to regions of subsidence observed in InSAR time series analysis. While these observations cannot discern a causal link between hydrocarbon production and seismicity, they constrain spatial relationships between active basin deformation and geological and anthropogenic features. Furthermore, the InSAR observations highlight the utility of space-based geodetic observations for monitoring and assessing anthropogenically induced and triggered deformation.

Journal of Geophysical Research B: Solid Earth

The dream and the reality: Meeting decision-making time frames while incorporating ecosystem and economic models into management strategy evaluation

Atlantic herring ( Clupea harengus ) in the Northwest Atlantic have been managed with interim harvest control rules (HCRs). A stakeholder-driven management strategy evaluation (MSE) was conducted that incorporated a broad range of objectives. The MSE process was completed within 1 year. Constant catch, conditional constant catch, and a biomass-based (BB) HCR with a 15% restriction on the interannual change in the quota could achieve more stable yields than BB HCRs without such restrictions, but could not attain as high of yields and resulted in more negative outcomes for terns ( Sterna hirundo ; a predator of herring). A similar range of performance could be achieved by applying a BB HCR annually every 3 years or every 5 years. Predators (i.e., dogfish ( Squalus acanthias ), bluefin tuna ( Thunnus thynnus ), and terns) were generally insensitive to the range of HCRs. While median net revenues were sensitive to some HCRs, time series analysis suggests that most HCRs produced a stable equilibrium of net revenue. To meet management needs, some aspects of the simulations were less than might be considered scientifically ideal, but using “models of intermediate complexity” were informative for managers and formed a foundation for future improvements.

Northwest Atlantic

Temporal variations of Escherichia coli concentrations in a large Midwestern river

The Raccoon River used by the Des Moines Water Works to serve more than 400,000 people in central Iowa is threatened by contamination from Escherichia coli bacteria from point and nonpoint sources. The 9389 km2 watershed is highly agricultural, with 73% of the land in row crop production and widespread animal production. Results from 2155 grab samples from 1997 to 2005 for E. coli analysis were examined for temporal variations. E. coli concentrations were found to vary across years, seasons, and flow conditions, with a 9-year mean value of 1156 most probable number (MPN)/100 ml. Monthly concentrations exhibited clear seasonality with highest values in May through July. Although E. coli concentrations were higher during periods of greater discharge, the relation of log E. coli to log discharge was not particularly strong (r2 = 0.35). The variogram of E. coli concentrations showed temporal correlation within a span of 4 days suggesting that concentrations measured on 1 day may be related in time to concentrations measured up to 3 days later and beyond 4 days the concentrations vary randomly. The spectral analysis of the time series of E. coli was also carried out and was fitted well with the spectrum of an exponential covariance function. Deciphering temporal patterns and correlation of E. coli bacteria in streams may be useful for developing future monitoring strategies to track concentration patterns and loads. ?? 2008 Elsevier B.V. All rights reserved.

Journal of Hydrology

Short-term oscillations in avian molt intensity: evidence from the golden eagle Aquila chrysaetos

From a year-long study of molt in the golden eagle Aquila chrysaetos, we recorded 2069 contour feathers replaced in 137 d (6 May-19 September). Very few contour feathers were lost outside this period. From precise daily counts of feathers lost, and using time series analysis, we identified short-term fluctuations (i.e., 19-d subcycles) around a midsummer peak (i.e., a left-skewed normal distribution). Because these subcycles have never before been reported and because the physiological basis for many aspects of avian molt is poorly known, we offer only hypothetical explanations for the controls responsible for the subcycles.

Journal of Avian Biology

A generalized design flood estimation framework under stationary and non-stationary scenarios

With the ever-ongoing debate over the death of stationarity in flood time series, developing methods for flood frequency analysis that can address both stationarity and non-stationarity simultaneously has become increasingly important for design flood estimation. Existing non-stationary flood frequency analysis (NFFQ) methods are either limited to providing time-varying conditional flood quantile estimates or lack closed-form expression to estimate the design flood over the planning period. We propose a novel framework, MM-NFFQ, that introduces marginal moments (MM) estimation techniques to provide a closed-form expression to estimate design flood under non-stationarity. We demonstrate MM-NFFQ using the LP3 distribution for estimating conditional moments, but in principle, it can work for any 3-parameter distribution. We first show the proposed MM-NFFQ collapses to stationary flood frequency analysis analytically using synthetic data and then demonstrate the MM-NFFQ approach for two basins exhibiting non-stationarity in their flood time series. We further extend the analysis to selected 40 basins across CONUS and find that arid basins exhibit higher deviation from stationarity. Thus, the proposed MM-NFFQ framework can estimate traditional flood frequency curves for both, stationary and non-stationary flood processes, and can also be utilized to analyze the changes in conditional moments and marginal moments over different planning horizons.

Journal of Hydrology X

Assessment and significance of the frequency domain for trends in annual peak streamflow

Risk management of nonstationary floods depends on an understanding of trends over a range of flood frequencies representing small (frequent) to large (infrequent) floods. Quantile regression is applied to the annual peak streamflow distributions at 2683 sites in the contiguous United States to test for trends in the 10th quantile (floods with a 0.9 annual exceedance probability), the 50th quantile (median annual flood), and 90th quantile (floods with a 0.1 annual exceedance probability). Trends are most common (36% of sites) for the median annual flood (50th quantile) and often coherent with trends in both frequent small floods (10th quantile) and infrequent large floods (90th quantile). Changes in the at-site variance of annual peak streamflow, indicated by convergence (decreasing variance) or divergence (increasing variance) of the 10th and 90th quantiles over time, are primarily in response to reservoir operation or urban development rather than climate. An analysis of synthetic series generated from nonstationary distributions demonstrates that quantile regression and standard trend tests used in flood frequency analysis have limited power and high rates of false negatives (>70%) when a test has a significance of p = 0.05. Quantile regression and tests with lower significance complement standard trend testing to inform flood risk management.

Journal of Flood Risk Management