Claire Welty

Bio: Claire Welty is an academic researcher from University of Maryland, Baltimore County. The author has contributed to research in topics: Impervious surface & Surface runoff. The author has an hindex of 24, co-authored 55 publications receiving 3107 citations. Previous affiliations of Claire Welty include University of Maryland, Baltimore & University of Maryland, College Park.

A Critical Review of Data on Field-Scale Dispersion in Aquifers

Abstract: found that field-scale dispersivities are several orders of.magnitude greater than lab-scale values for the same material; it is generally agreed that this difference is a reflection of the influence of natural heterogeneities which produce irregular flow patterns at the field scale. Consequently, laboratory measurements of dispersivity cannot be used to predict field values of dispersivity. Instead field-scale tracer tests are sometimes conducted to estimate dispersivity at a particular site. Early efforts to document the scale dependence of dispersivity [Lallemand-Barres and Peaudecerf, 1978; Anderson, 1979; Pickens and Grisak, 1981; Beims, 1983; Neretnieks, 1985] were based on field values of dispersivity reported in the literature and the test scales associated with those values. These studies were useful in that they indeed documented field evidence of the scale effect, but they were lacking in that they did not assess the reliability of the data presented. Because we felt that the data would be more

Transport and Fate of Microbial Pathogens in Agricultural Settings

Abstract: An understanding of the transport and survival of microbial pathogens (pathogens hereafter) in agricultural settings is needed to assess the risk of pathogen contamination to water and food resources, and to develop control strategies and treatment options. However, many knowledge gaps still remain in predicting the fate and transport of pathogens in runoff water, and then through the shallow vadose zone and groundwater. A number of transport pathways, processes, factors, and mathematical models often are needed to describe pathogen fate in agricultural settings. The level of complexity is dramatically enhanced by soil heterogeneity, as well as by temporal variability in temperature, water inputs, and pathogen sources. There is substantial variability in pathogen migration pathways, leading to changes in the dominant processes that control pathogen transport over different spatial and temporal scales. For example, intense rainfall events can generate runoff and preferential flow that can rapidly transport...

Land Use and Climate Variability Amplify Carbon, Nutrient, and Contaminant Pulses: A Review with Management Implications

Abstract: Nonpoint source pollution from agriculture and urbanization is increasing globally at the same time climate extremes have increased in frequency and intensity. We review >200 studies of hydrologic and gaseous fluxes and show how the interaction between land use and climate variability alters magnitude and frequency of carbon, nutrient, and greenhouse gas pulses in watersheds. Agricultural and urban watersheds respond similarly to climate variability due to headwater alteration and loss of ecosystem services to buffer runoff and temperature changes. Organic carbon concentrations/exports increase and organic carbon quality changes with runoff. Nitrogen and phosphorus exports increase during floods (sometimes by an order of magnitude) and decrease during droughts. Relationships between annual runoff and nitrogen and phosphorus exports differ across land use. CH4 and N2O pulses in riparian zones/floodplains predominantly increase with: flooding, warming, low oxygen, nutrient enrichment, and organic carbon. CH4, N2O, and CO2 pulses in streams/rivers increase due to similar factors but effects of floods are less known compared to base flow/droughts. Emerging questions include: (1) What factors influence lag times of contaminant pulses in response to extreme events? (2) What drives resistance/resilience to hydrologic and gaseous pulses? We conclude with eight recommendations for managing watershed pulses in response to interactive effects of land use and climate change.

Closure of "Watershed-Scale Evaluation of a System of Storm Water Detention Basins"

Abstract: The effectiveness of an existing system of storm water detention basins operating at the watershed scale is evaluated. Data utilized in the study were collected from Valley Creek watershed in Chester County, Pa., which has undergone rapid development from the westward spread of suburban Philadelphia. Since the late 1970s, more than 100 storm water detention basins have been constructed in this 62 km2 (24 mi2 ) watershed, each designed on a site-by-site basis. The design objective of these detention basins is to limit a site’s postconstruction peak flow rate to or below its predevelopment level for 2- through 100-year storms. To evaluate the watershed-wide effectiveness of the network of detention basins, all basins were surveyed and included in a hydrologic model of the watershed. The U.S. Army Corps of Engineers Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model was calibrated by using measured rainfall and observed streamflow from a U.S. Geological Survey (USGS) stream gauge. Res...

Pluvial flood risk and opportunities for resilience

Abstract: Environmental Sciences Initiative, Advanced Science Research Center at the Graduate Center, City University of New York, New York, New York Julie Ann Wrigley Global Institute of Sustainability, Arizona State University, Tempe, Arizona Department of Geography, Portland State University, Portland, Oregon Arizona State University, Tempe, Arizona Department of Chemical, Biochemical, and Environmental Engineering and Center for Urban Environmental Research and Education, University of Maryland, Baltimore County, Baltimore, Maryland Cary Institute of Ecosystem Studies, Millbrook, New York Georgia State University, Atlanta, Georgia Department of Civil and Environmental Engineering, Syracuse Center of Excellence in Environmental and Energy Systems, Syracuse University, Syracuse, New York

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Climate Change 2007: The Physical Science Basis.

Abstract: Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

The restaurant at the end of the random walk: recent developments in the description of anomalous transport by fractional dynamics

Abstract: Fractional dynamics has experienced a firm upswing during the past few years, having been forged into a mature framework in the theory of stochastic processes. A large number of research papers developing fractional dynamics further, or applying it to various systems have appeared since our first review article on the fractional Fokker–Planck equation (Metzler R and Klafter J 2000a, Phys. Rep. 339 1–77). It therefore appears timely to put these new works in a cohesive perspective. In this review we cover both the theoretical modelling of sub- and superdiffusive processes, placing emphasis on superdiffusion, and the discussion of applications such as the correct formulation of boundary value problems to obtain the first passage time density function. We also discuss extensively the occurrence of anomalous dynamics in various fields ranging from nanoscale over biological to geophysical and environmental systems.