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At least 865 records · Page 48Linked to original sources
USGS2003

Post-seismic relaxation theory on laterally heterogeneous viscoelastic model

Investigation was carried out into the problem of relaxation of a laterally heterogeneous viscoelastic Earth following an impulsive moment release event. The formal solution utilizes a semi-analytic solution for post-seismic deformation on a laterally homogeneous Earth constructed from viscoelastic normal modes, followed by application of mode coupling theory to derive the response on the aspherical Earth. The solution is constructed in the Laplace transform domain using the correspondence principle and is valid for any linear constitutive relationship between stress and strain. The specific implementation described in this paper is a semi-analytic discretization method which assumes isotropic elastic structure and a Maxwell constitutive relation. It accounts for viscoelastic-gravitational coupling under lateral variations in elastic parameters and viscosity. For a given viscoelastic structure and minimum wavelength scale, the computational effort involved with the numerical algorithm is proportional to the volume of the laterally heterogeneous region. Examples are presented of the calculation of post-seismic relaxation with a shallow, laterally heterogeneous volume following synthetic impulsive seismic events, and they illustrate the potentially large effect of regional 3-D heterogeneities on regional deformation patterns.

Geophysical Journal International↗
USGS2002

Using simple environmental variables to estimate below-ground productivity in grasslands

In many temperate and annual grasslands, above-ground net primary productivity (NPP) can be estimated by measuring peak above-ground biomass. Estimates of below-ground net primary productivity and, consequently, total net primary productivity, are more difficult. We addressed one of the three main objectives of the Global Primary Productivity Data Initiative for grassland systems to develop simple models or algorithms to estimate missing components of total system NPP. Any estimate of below-ground NPP (BNPP) requires an accounting of total root biomass, the percentage of living biomass and annual turnover of live roots. We derived a relationship using above-ground peak biomass and mean annual temperature as predictors of below-ground biomass (r2 = 0.54; P = 0.01). The percentage of live material was 0.6, based on published values. We used three different functions to describe root turnover: constant, a direct function of above-ground biomass, or as a positive exponential relationship with mean annual temperature. We tested the various models against a large database of global grassland NPP and the constant turnover and direct function models were approximately equally descriptive (r2 = 0.31 and 0.37), while the exponential function had a stronger correlation with the measured values (r2 = 0.40) and had a better fit than the other two models at the productive end of the BNPP gradient. When applied to extensive data we assembled from two grassland sites with reliable estimates of total NPP, the direct function was most effective, especially at lower productivity sites. We provide some caveats for its use in systems that lie at the extremes of the grassland gradient and stress that there are large uncertainties associated with measured and modelled estimates of BNPP.

Global Ecology and Biogeography↗
USGS1994

Multicomponent-flow analyses by multimode method of characteristics

For unsteady open-channel flows having N interacting unknown variables, a system of N mutually independent, partial differential equations can be used to describe the flow-field. The system generally belongs to marching-type problems and permits transformation into characteristic equations that are associated with N distinct characteristics directions. Because characteristics can be considered 'wave' or 'disturbance' propagation, a fluvial system so described can be viewed as adequately definable using these N component waves. A numerical algorithm to solve the N families of characteristics can then be introduced for formulation of an N-component flow-simulation model. The multimode method of characteristics (MMOC), a new numerical scheme that has a combined capacity of several specified-time-interval (STI) schemes of the method of characteristics, makes numerical modeling of such N-component riverine flows feasible and attainable. Merging different STI schemes yields different kinds of MMOC schemes, for which two kinds are displayed herein. With the MMOC, each characteristics is dynamically treated by an appropriate numerical mode, which should lead to an effective and suitable global simulation, covering various types of unsteady flow. The scheme is always linearly stable and its numerical accuracy can be systematically analyzed. By increasing the N value, one can develop a progressively sophisticated model that addresses increasingly complex river-mechanics problems.

Journal of Hydraulic Engineering↗
USGS2007

Application of acoustic doppler velocimeters for streamflow measurements

The U.S. Geological Survey (USGS) principally has used Price AA and Price pygmy mechanical current meters for measurement of discharge. New technologies have resulted in the introduction of alternatives to the Price meters. One alternative, the FlowTracker acoustic Doppler velocimeter, was designed by SonTek/YSI to make streamflow measurements in wadeable conditions. The device measures a point velocity and can be used with standard midsection method algorithms to compute streamflow. The USGS collected 55 quality-assurance measurements with the FlowTracker at 43 different USGS streamflow-gaging stations across the United States, with mean depths from 0.05to0.67m, mean velocities from 13 to 60 cm/s, and discharges from 0.02 to 12.4m3/s. These measurements were compared with Price mechanical current meter measurements. Analysis of the comparisons shows that the FlowTracker discharges were not statistically different from the Price meter discharges at a 95% confidence level. ?? 2007 ASCE.

Journal of Hydraulic Engineering↗
USGS1996

Hydrologic impact of Great Flood of 1993 in south-central Kansas

The writers analyze the hydrologic budget and quantify the ground-water recharge impact of the Great Flood of 1993 on the Great Bend Prairie aquifer of south-central Kansas. During the summer of 1993, rainfall totals exceeded normal levels by 200% in the northern portion of the study area, while air temperature and evapotranspiration were below normal levels. This extreme event provided the opportunity to revisit previously developed recharge-estimation algorithms. Average ground-water recharge for 1993 at four index sites was estimated at 178 mm using the hybrid water-fluctuation method of Sophocleous. Employing the recharge-estimation multiple-regression methodology of Sophocleous for the area, the writers estimated the 1993 recharge to be 145 mm. Both estimates are higher than the maximum annual recharge observed at the index sites during the 1985–1992 period. A January–July 1993 hydrologic balance analysis resulted in 130 mm of recharge. The recharge caused by the flood was three to more than four times the average annual recharge of the previous eight years. The regression-based recharge-estimation methodology proved to be generally reliable, even under extreme conditions.

Journal of Irrigation and Drainage Engineering↗
USGS1991

Seismic response of transamerica building. II. System identification

A detailed analysis of the recorded seismic response of the Transamerica Building during the October 17, 1989 Loma Prieta earthquake is presented. The system identification algorithm used for the analysis is based on the discrete-time linear filtering approach with least-squares approximation, and assumes a multi-input, single-output model for the building. Fifteen modes in the north-south direction, and 18 modes in the east-west direction are identified from the records. The analysis shows that the building's response to the earthquake was dominated by a coupled mode of vibration at 0.28 Hz in the southwest-northeast direction, which is almost parallel to one of the diagonals in the building's square cross section. The reason for this behavior is the symmetry of the building's structural characteristics, as well as the strong polarization of the S-waves of the earthquake. Several higher modes of the building were excited during the strong-motion part of the earthquake. The results also show a significant amount of rocking in the building at a frequency of 2.15 Hz.

Journal of Structural Engineering↗
USGS2011

Finite volume model for two-dimensional shallow environmental flow

This paper presents the development of a two-dimensional, depth integrated, unsteady, free-surface model based on the shallow water equations. The development was motivated by the desire of balancing computational efficiency and accuracy by selective and conjunctive use of different numerical techniques. The base framework of the discrete model uses Godunov methods on unstructured triangular grids, but the solution technique emphasizes the use of a high-resolution Riemann solver where needed, switching to a simpler and computationally more efficient upwind finite volume technique in the smooth regions of the flow. Explicit time marching is accomplished with strong stability preserving Runge-Kutta methods, with additional acceleration techniques for steady-state computations. A simplified mass-preserving algorithm is used to deal with wet/dry fronts. Application of the model is made to several benchmark cases that show the interplay of the diverse solution techniques.

Journal of Hydraulic Engineering↗
USGS2012

Using "big data" to optimally model hydrology and water quality across expansive regions

This paper describes a new divide and conquer approach that leverages big environmental data, utilizing all available categorical and time-series data without subjectivity, to empirically model hydrologic and water-quality behaviors across expansive regions. The approach decomposes large, intractable problems into smaller ones that are optimally solved; decomposes complex signals into behavioral components that are easier to model with "sub- models"; and employs a sequence of numerically optimizing algorithms that include time-series clustering, nonlinear, multivariate sensitivity analysis and predictive modeling using multi-layer perceptron artificial neural networks, and classification for selecting the best sub-models to make predictions at new sites. This approach has many advantages over traditional modeling approaches, including being faster and less expensive, more comprehensive in its use of available data, and more accurate in representing a system's physical processes. This paper describes the application of the approach to model groundwater levels in Florida, stream temperatures across Western Oregon and Wisconsin, and water depths in the Florida Everglades. ?? 2009 ASCE.

Conference Paper↗
USGS2012

Bayesian WLS/GLS regression for regional skewness analysis for regions with large crest stage gage networks

This paper summarizes methodological advances in regional log-space skewness analyses that support flood-frequency analysis with the log Pearson Type III (LP3) distribution. A Bayesian Weighted Least Squares/Generalized Least Squares (B-WLS/B-GLS) methodology that relates observed skewness coefficient estimators to basin characteristics in conjunction with diagnostic statistics represents an extension of the previously developed B-GLS methodology. B-WLS/B-GLS has been shown to be effective in two California studies. B-WLS/B-GLS uses B-WLS to generate stable estimators of model parameters and B-GLS to estimate the precision of those B-WLS regression parameters, as well as the precision of the model. The study described here employs this methodology to develop a regional skewness model for the State of Iowa. To provide cost effective peak-flow data for smaller drainage basins in Iowa, the U.S. Geological Survey operates a large network of crest stage gages (CSGs) that only record flow values above an identified recording threshold (thus producing a censored data record). CSGs are different from continuous-record gages, which record almost all flow values and have been used in previous B-GLS and B-WLS/B-GLS regional skewness studies. The complexity of analyzing a large CSG network is addressed by using the B-WLS/B-GLS framework along with the Expected Moments Algorithm (EMA). Because EMA allows for the censoring of low outliers, as well as the use of estimated interval discharges for missing, censored, and historic data, it complicates the calculations of effective record length (and effective concurrent record length) used to describe the precision of sample estimators because the peak discharges are no longer solely represented by single values. Thus new record length calculations were developed. The regional skewness analysis for the State of Iowa illustrates the value of the new B-WLS/BGLS methodology with these new extensions.

Conference Paper↗
USGS2012

Parameter estimation method and updating of regional prediction equations for ungaged sites in the desert region of California

The U.S. Geological Survey (USGS) is currently updating at-site flood frequency estimates for USGS streamflow-gaging stations in the desert region of California. The at-site flood-frequency analysis is complicated by short record lengths (less than 20 years is common) and numerous zero flows/low outliers at many sites. Estimates of the three parameters (mean, standard deviation, and skew) required for fitting the log Pearson Type 3 (LP3) distribution are likely to be highly unreliable based on the limited and heavily censored at-site data. In a generalization of the recommendations in Bulletin 17B, a regional analysis was used to develop regional estimates of all three parameters (mean, standard deviation, and skew) of the LP3 distribution. A regional skew value of zero from a previously published report was used with a new estimated mean squared error (MSE) of 0.20. A weighted least squares (WLS) regression method was used to develop both a regional standard deviation and a mean model based on annual peak-discharge data for 33 USGS stations throughout California’s desert region. At-site standard deviation and mean values were determined by using an expected moments algorithm (EMA) method for fitting the LP3 distribution to the logarithms of annual peak-discharge data. Additionally, a multiple Grubbs-Beck (MGB) test, a generalization of the test recommended in Bulletin 17B, was used for detecting multiple potentially influential low outliers in a flood series. The WLS regression found that no basin characteristics could explain the variability of standard deviation. Consequently, a constant regional standard deviation model was selected, resulting in a log-space value of 0.91 with a MSE of 0.03 log units. Yet drainage area was found to be statistically significant at explaining the site-to-site variability in mean. The linear WLS regional mean model based on drainage area had a Pseudo- 2 R of 51 percent and a MSE of 0.32 log units. The regional parameter estimates were then used to develop a set of equations for estimating flows with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for ungaged basins. The final equations are functions of drainage area.Average standard errors of prediction for these regression equations range from 214.2 to 856.2 percent.

California↗
USGS1991

Fractal analysis of multiscale spatial autocorrelation among point data

The analysis of spatial autocorrelation among point-data quadrats is a well-developed technique that has made limited but intriguing use of the multiscale aspects of pattern. In this paper are presented theoretical and algorithmic approaches to the analysis of aggregations of quadrats at or above a given density, in which these sets are treated as multifractal regions whose fractal dimension, D, may vary with phenomenon intensity, scale, and location. The technique is illustrated with Matui's quadrat house-count data, which yield measurements consistent with a nonautocorrelated simulated Poisson process but not with an orthogonal unit-step random walk. The paper concludes with a discussion of the implications of such analysis for multiscale geographic analysis systems. -Author

Environment and Planning A↗
USGS2021

Linking decomposition rates of soil organic amendments to their chemical composition

The stock of organic carbon contained within a soil represents the balance between inputs and losses. Inputs are defined by the ability of vegetation to capture and retain carbon dioxide, effects that management practices have on the proportion of captured carbon that is added to soil and the application organic amendments. The proportion of organic amendment carbon retained is defined by its rate of mineralisation. In this study, the rate of carbon mineralisation from 85 different potential soil organic amendments (composts, manures, plant residues and biosolids) was quantified under controlled environmental conditions over a 547 day incubation period. The composition of each organic amendment was quantified using nuclear magnetic resonance and mid- and near-infrared spectroscopies. Cumulative mineralisation of organic carbon from the amendments was fitted to a two-pool exponential model. Multivariate chemometric algorithms were derived to allow the size of the fast and slow cycling pools of carbon to be predicted from the acquired spectroscopic data. However, the fast and slow decomposition rate constants could not be predicted suggesting that prediction of the residence time of organic amendment carbon in soil would likely require additional information related to soil type, environmental conditions, and management practices in use at the site of application.

Soil Research↗
USGS2020

Cost-effective fuel treatment planning: A theoretical justification and case-study

Modelling the spatial prioritisation of fuel treatments and their net effect on values at risk is an important area for applied work as economic damages from wildfire continue to grow. We model and demonstrate a cost-effective fuel treatment planning algorithm using two ecosystem services as benefits for which fuel treatments are prioritised. We create a surface of expected fuel treatment costs to incorporate the heterogeneity in factors affecting the revenue and costs of fuel treatments, and then prioritise treatments based on a cost-effectiveness ratio to maximise the averted loss of ecosystem services from fire. We compare treatment scenarios that employ cost-effectiveness with those that do not, and use common tools and models in a case study of the Sisters Ranger District on the Deschutes National Forest in central Oregon, USA. Using cost-effectiveness not only increases the expected averted losses from fuel treatments, but it also allows a larger area to be treated for the same cost, simply by incorporating costs and cost-effectiveness into the prioritisation routine. These results have considerable implications for policymakers and land managers trying to minimise risk. Incorporating costs into the spatial planning of treatments could allow more effective outcomes without increasing fuel treatment budgets.

Oregon↗
USGS2020

Mapping fire regime ecoregions in California

The fire regime is a central framing concept in wildfire science and ecology and describes how a range of wildfire characteristics vary geographically over time. Understanding and mapping fire regimes is important for guiding appropriate management and risk reduction strategies and for informing research on drivers of global change and altered fire patterns. Most efforts to spatially delineate fire regimes have been conducted by identifying natural groupings of fire parameters based on available historical fire data. This can result in classes with similar fire characteristics but wide differences in ecosystem types. We took a different approach and defined fire regime ecoregions for California to better align with ecosystem types, without using fire as part of the definition. We used an unsupervised classification algorithm to segregate the state into spatial clusters based on distinctive biophysical and anthropogenic attributes that drive fire regimes – and then used historical fire data to evaluate the ecoregions. The fire regime ecoregion map corresponded well with the major land cover types of the state and provided clear separation of historical patterns in fire frequency and size, with lower variability in fire severity. This methodology could be used for mapping fire regimes in other regions with limited historical fire data or forecasting future fire regimes based on expected changes in biophysical characteristics.

California↗
USGS1998

Fuel models and fire potential from satellite and surface observations

A national 1-km resolution fire danger fuel model map was derived through use of previously mapped land cover classes and ecoregions, and extensive ground sample data, then refined through review by fire managers familiar with various portions of the U.S. The fuel model map will be used in the next generation fire danger rating system for the U.S., but it also made possible immediate development of a satellite and ground based fire potential index map. The inputs and algorithm of the fire potential index are presented, along with a case study of the correlation between the fire potential index and fire occurrence in California and Nevada. Application of the fire potential index in the Mediterranean ecosystems of Spain, Chile, and Mexico will be tested.

International Journal of Wildland Fire↗
USGS2012

A direct-gradient multivariate index of biotic condition

Multimetric indexes constructed by summing metric scores have been criticized despite many of their merits. A leading criticism is the potential for investigator bias involved in metric selection and scoring. Often there is a large number of competing metrics equally well correlated with environmental stressors, requiring a judgment call by the investigator to select the most suitable metrics to include in the index and how to score them. Data-driven procedures for multimetric index formulation published during the last decade have reduced this limitation, yet apprehension remains. Multivariate approaches that select metrics with statistical algorithms may reduce the level of investigator bias and alleviate a weakness of multimetric indexes. We investigated the suitability of a direct-gradient multivariate procedure to derive an index of biotic condition for fish assemblages in oxbow lakes in the Lower Mississippi Alluvial Valley. Although this multivariate procedure also requires that the investigator identify a set of suitable metrics potentially associated with a set of environmental stressors, it is different from multimetric procedures because it limits investigator judgment in selecting a subset of biotic metrics to include in the index and because it produces metric weights suitable for computation of index scores. The procedure, applied to a sample of 35 competing biotic metrics measured at 50 oxbow lakes distributed over a wide geographical region in the Lower Mississippi Alluvial Valley, selected 11 metrics that adequately indexed the biotic condition of five test lakes. Because the multivariate index includes only metrics that explain the maximum variability in the stressor variables rather than a balanced set of metrics chosen to reflect various fish assemblage attributes, it is fundamentally different from multimetric indexes of biotic integrity with advantages and disadvantages. As such, it provides an alternative to multimetric procedures.

Arkansas, Mississippi↗
USGS2019

Making Recursive Bayesian inference accessible

Bayesian models provide recursive inference naturally because they can formally reconcile new data and existing scientific information. However, popular use of Bayesian methods often avoids priors that are based on exact posterior distributions resulting from former studies. Two existing Recursive Bayesian methods are: Prior- and Proposal-Recursive Bayes. Prior-Recursive Bayes uses Bayesian updating, fitting models to partitions of data sequentially, and provides a way to accommodate new data as they become available using the posterior from the previous stage as the prior in the new stage based on the latest data. ProposalRecursive Bayes is intended for use with hierarchical Bayesian models and uses a set of transient priors in first stage independent analyses of the data partitions. The second stage of Proposal-Recursive Bayes uses the posteriors from the first stage as proposals in an MCMC algorithm to fit the full model. We combine Prior- and Proposal-Recursive concepts to fit any Bayesian model, and often with computational improvements. We demonstrate our method with two case studies. Our approach has implications for big data, streaming data, and optimal adaptive design situations.

American Statistician↗
USGS2009

Development and application of a mark-recapture model incorporating predicted sex and transitory behaviour

We developed an extension of Cormack-Jolly-Seber models to handle a complex mark-recapture problem in which (a) the sex of birds cannot be determined prior to first moult, but can be predicted on the basis of body measurements, and (b) a significant portion of captured birds appear to be transients (i.e. are captured once but leave the area or otherwise become ' untrappable'). We applied this methodology to a data set of 4184 serins (Serinus serinus) trapped in northeastern Spain during 1985-96, in order to investigate age-, sex-, and time-specific variation in survival rates. Using this approach, we were able to successfully incorporate the majority of ringings of serins. Had we eliminated birds not previously captured (as has been advocated to avoid the problem of transience) we would have reduced our sample sizes by >2000 releases. In addition, we were able to include 1610 releases of birds of unknown (but predicted) sex; these data contributed to the precision of our estimates and the power of statistical tests. We discuss problems with data structure, encoding of the algorithms to compute parameter estimates, model selection, identifiability of parameters, and goodness-of-fit, and make recommendations for the design and analysis of future studies facing similar problems.

Bird Study↗