Original Metrics Research Articles

Readability as a measure of textual complexity: determinants and evidence in Brazilian companies

ABSTRACT The aim of this article was to evaluate the effect of company earnings and of harmonization with IFRS on the readability of Management Reports in the Brazilian stock market. There is a gap to be filled both in the elaboration and adaptation of readability measures to the context studied, as the studies tend to replicate the original formulas, and in identifying the determinants of the readability of Brazilian company reports, as the research in this field remains in its infancy and the results are inconclusive. The results provide indications for investors to identify complex textual information and may help public policymakers to establish a simple writing manual, along the lines of the SEC’s 1998 Plain English Handbook. The modified metrics and the one developed overcome the criticisms regarding the use of readability formulas in accounting research and could be used in substitution of the original metrics in future studies. An econometric model was used that presents the determinants of readability. Readability was calculated for the Results Analysis section of the Management Report. The resulting construct is understood via three attributes: persistence, current performance, and the reference benchmark. Harmonization with IFRS is a dummy variable, which delimits the pre- and post-IFRS periods. The hypotheses were tested in a sample of Brazilian companies made up of 714 company-year observations covering the period from 2006 to 2019. The descriptive results show that there is an apparent improvement in the readability of the reports in the pre- and post-IFRS period comparison. The econometric evidence shows that, in general, companies with persistent and positive earnings present less complex reports and are more likely to have highly readable reports, because managers publish reports with better readability to signal positive results to the market.

Quantifying the cascading effects of passenger delays

Delays in transport networks has adverse implications for infrastructure and service managers as well as travellers. While it is widely acknowledged that delays occurring across the transport networks may be related, there are is lack of knowledge on the underlying properties of these relations and means for quantifying them. To this end, we develop a network-wide data-driven delay analysis method. First, we construct a Bayesian network to represent the relations between delays associated with different transport network elements and assess the reliability of critical infrastructure elements. Second, we propose a series of original metrics denominated informativity indicators for quantifying the spatial extent of the delays observed based on the Bayesian network obtained. The proposed approach is applied to the Washington DC metro network. Time-dependent passenger waiting and transferring delays inferred over more than a year from smartcard data are utilized as input to the Bayesian network. Our findings indicate that passenger delays at few selected stations are directly informative of delays occurring at many other stations. We also examine the relation between the proposed informativity metrics and the topological properties of metro stations, concluding that the latter have a limited value in approximating network-wide delay correlations.

The heat is on: Gulf of Alaska Pacific cod and climate-ready fisheries

Abstract Rapidly changing ocean conditions pose substantial challenges for coastal communities, fishermen, and managers. From 2014 to 2016, the Gulf of Alaska (GOA) experienced a marine heatwave and corollary decline in Pacific cod (Gadus macrocephalus). Here, we explore the 2020 closure of the directed Pacific cod federal fishery in the GOA as a case study of the needs and opportunities for managing fisheries in the context of environmental change. We build on climate-ready fishery research and conversations with experienced commercial fishermen, including Alutiiq fishers, and fishery managers to: (i) discuss ecosystem-based management in Alaska, (ii) explore early warning signs and management challenges preceding the decline of Pacific cod, (iii) recommend tools to enhance adaptive capacity in fishery management. We conclude that a strong foundation of science-based management that incorporates ecosystem information and multiple ways of knowing, increased monitoring and evaluation of indicators, and new tools for managers to respond and adapt will be essential to sustainable fishery management. New mechanisms highlighted in this essay include: a GOA Fishery Ecosystem Plan, climate risk and vulnerability analyses, habitat protections, and the development of original metrics, such as food web production or function targets to inform stock assessments and fishery management.

Quantification of solid waste management system efficiency using input–output indices

The assessment of solid waste management systems is vital for continued improvement in the efficiency of waste management systems (WMSs). Many studies have aimed to develop metrics that examine policy and system effectiveness, but there appears to be a lack of simple and comparable metrics to evaluate the WMS efficiency. This study introduces a set of novel waste diversion indicators, including a jurisdictionally comparable index known as diversion size indicator (DSI), and employs them to analyze WMSs in Canada. DSI increased in only a single province Nova Scotia during the 14-year study period. The DSI variability was largest for all Prairie Provinces, indicating that large efficiency changes in these province’s WMSs over the study period. Combining all proposed metrics into a single rank showed that Saskatchewan outperformed all other Canadian jurisdictions, balancing diversion rate goals well with cost efficiency. Findings indicate that Alberta and Ontario rank very low in diversion efficiency and tonnage hauled efficiency. Also, Nova Scotia, the DSI leader, ranks lowest in terms of costs per tonne handled. Data availability remains a large barrier to a complete evaluation of WMSs. Nonetheless, the proposed original metrics create a framework for creating comparable and easy to use metrics for waste management efficiency.

Copula density-driven metrics for sensitivity analysis: Theory and application to flow and transport in porous media

We introduce original sensitivity analysis metrics with the aim of assistingdiagnosis of the functioning of a given model. We do so by characterizing model-induced dependencies between a target model output and selected model input(s) through the associated bivariate copuladensity. The latter fully characterizes the dependencies between two random variables at any order (i.e., without being limited to linear dependence), independent of the marginal behavior of the two variables. As a metric to assess sensitivity, we rely on the absolute distance betweenthe copuladensity associated with the target model output and a model input and its counterpart associated with two independent variables. We then provide two sensitivity indices which allow characterizing (i) the global (with respect to the input) value of the sensitivity and (ii) the degree of variability (across the range of the input values) of the sensitivity for each value that the prescribed model output can possibly undertake, as driven by the governing model. In this sense, our approach to sensitivity is global with respect to model input(s) and local with respect to model output, thus enabling one to discriminate the relevance of an input across the entire range of values of the modeling goal of interest. We exemplify the use of our approach and illustrate the type of information it can provide by focusing on an analytical test function and on two scenarios related to flow and transport in porous media.

3D Magnetic Resonance Spirometry

Spirometry is today the gold standard technique for assessing pulmonary ventilatory function in humans. From the shape of a flow-volume loop measured while the patient is performing forced respiratory cycles, the Forced Vital Capacity (FVC) and the Forced Expiratory Volume in one second (FEV1) can be inferred, and the pulmonologist is able to detect and characterize common respiratory afflictions. This technique is non-invasive, simple, widely available, robust, repeatable and reproducible. Yet, its outcomes rely on the patient’s cooperation and provide only global information over the lung. With 3D Magnetic Resonance (MR) Spirometry, local ventilation can be assessed by MRI anywhere in the lung while the patient is freely breathing. The larger dimensionality of 3D MR Spirometry advantageously allows the extraction of original metrics that characterize the anisotropic and hysteretic regional mechanical behavior of the lung. Here, we demonstrated the potential of this technique on a healthy human volunteer breathing along different respiratory patterns during the MR acquisition. These new results are discussed with lung physiology and recent pulmonary CT data. As respiratory mechanics inherently support lung ventilation, 3D MR Spirometry may open a new way to non-invasively explore lung function while providing improved diagnosis of localized pulmonary diseases.

Positive Energy Functional for Massless Scalars in Rotating Black Hole Backgrounds of Maximal Ungauged Supergravity.

We outline a proof of the stability of a massless neutral scalar field ψ in the background of a wide class of four dimensional asymptotically flat rotating and "electrically charged" solutions of supergravity, and the low energy limit of string theory, known as STU metrics. Despite their complexity, we find it possible to circumvent the difficulties presented by the existence of ergo regions and the related phenomenon of superradiance in the original metrics by following a strategy due to Whiting, and passing to an auxiliary metric admitting an everywhere lightlike Killing field and constructing a scalar field ψ (related to a possible unstable mode ψ by a nonlocal transformation) which satisfies the massless wave equation with respect to the auxiliary metric. By contrast with the case for ψ, the associated energy density of ψ is not only conserved but is also non-negative.

Detecting Lung Cancer Trends by Leveraging Real-World and Internet-Based Data: Infodemiology Study.

BackgroundInternet search data on health-related terms can reflect people’s concerns about their health status in near real time, and hence serve as a supplementary metric of disease characteristics. However, studies using internet search data to monitor and predict chronic diseases at a geographically finer state-level scale are sparse.ObjectiveThe aim of this study was to explore the associations of internet search volumes for lung cancer with published cancer incidence and mortality data in the United States.MethodsWe used Google relative search volumes, which represent the search frequency of specific search terms in Google. We performed cross-sectional analyses of the original and disease metrics at both national and state levels. A smoothed time series of relative search volumes was created to eliminate the effects of irregular changes on the search frequencies and obtain the long-term trends of search volumes for lung cancer at both the national and state levels. We also performed analyses of decomposed Google relative search volume data and disease metrics at the national and state levels.ResultsThe monthly trends of lung cancer-related internet hits were consistent with the trends of reported lung cancer rates at the national level. Ohio had the highest frequency for lung cancer-related search terms. At the state level, the relative search volume was significantly correlated with lung cancer incidence rates in 42 states, with correlation coefficients ranging from 0.58 in Virginia to 0.94 in Oregon. Relative search volume was also significantly correlated with mortality in 47 states, with correlation coefficients ranging from 0.58 in Oklahoma to 0.94 in North Carolina. Both the incidence and mortality rates of lung cancer were correlated with decomposed relative search volumes in all states excluding Vermont.ConclusionsInternet search behaviors could reflect public awareness of lung cancer. Research on internet search behaviors could be a novel and timely approach to monitor and estimate the prevalence, incidence, and mortality rates of a broader range of cancers and even more health issues.

Black Holes and Naked Singularities in an Infinite Momentum Frame

The problem of the construction of shock waves by the boosting method for metrics of black holes and naked singularities is considered. It is noted that singular terms may arise under the boosting, which prevents the generation of shock waves (in particular, in the case of the absence of quasi-Cartesian coordinates). We find that if the Fisher metric of a black hole with a scalar charge and a singular horizon is boosted, then the scalar field disappears (unlike the case of black holes with electric and magnetic charges). It is noted that boosted metrics are solutions of the linearized Einstein equations, while the original metrics are exact nonlinear solutions of these equations. The event horizon disappears under the boosting. Thus, the boosting of naked singularities leads to the same result as the boosting of black holes. This is confirmed by the example of gamma-metrics (Zipoy–Vourhees solution) as well. Finally, it is shown that if a linearized metric is boosted with the Newman–Unti–Tamburino parameter (NUT) along the Misner string, then a shock wave with a nondiagonal structure of the gyraton is obtained.

01 Prediction of Falls in Community-Dwelling Older People using an Accelerometer: The Results of a 1-Year Prospective Study

Abstract Background Fall preventive exercise for community-dwelling older people in Japan has a certain effect. The purpose of this study was to examine the predictors of fall risk in older people engaging in preventive exercise. Methods In this prospective study, we recruited 162 community-dwelling older people. We had them complete a self-report questionnaire containing items on weekly exercise frequency, exercise duration, exercise efficacy, amount of pain, number of diagnosis chronic diseases, health anxiety level, vision impairment, difficulty using the stairs, insomnia, fear of falling, history of falls, and forgetfulness, as well as the Falls Efficacy Scale. Walking speed and trunk acceleration during walking were also measured in all participants. From trunk acceleration, we calculated five gait parameters: fluctuation, weight shift, lateral balance, anteroposterior balance, and rhythm. Participants were classified as fallers or non-fallers according to the incidence of falls over a 1-year period from baseline assessment. Results Of all participants, 19 (11.7%) were classified as fallers and 143 (88.3%) as non-fallers. In comparing the fallers and non-fallers, we found significant differences in their exercise duration, exercise efficacy, amount of pain, history of falls, walking speed, and the gait parameters of weight shift and anteroposterior balance. A logistic regression analysis revealed that walking speed (odds ratio: 0.049, 95% confidence interval [CI]: 0.005-0.265, p=0.008) and weight shift (odds ratio: 0.021, 95%CI: 0.000-0.877, p=0.043) were predictors of falls. Conclusion The gait parameter of weight shift is the amplitude ratio of the auto-correlation function from vertical acceleration. This gait parameter is an original metrics and might show the temporal structure around the terminal stance. Measurement of gait parameters, in addition to physical performance, using an accelerometer seems beneficial for fall risk identification among community-dwelling older people engaged in fall preventive exercise.

National Trends in American Heart Association Revised Life's Simple 7 Metrics Associated With Risk of Mortality Among US Adults

The American Heart Association (AHA) introduced the Life's Simple 7 (LS7) metrics to assess and promote cardiovascular health. However, several shortcomings of these metrics have been identified. Therefore, a revised set of LS7 metrics was developed. To evaluate national trends in the metrics addressed by the revised LS7 and the individual and combined associations of the revised LS7 metrics with all-cause and cause-specific mortality and to compare these measures with the AHA recommended LS7 metrics. This national cross-sectional study used data from the National Health and Nutrition Examination Survey from 1988 to 2016. The revised LS7 metrics included a combination of the body mass index and waist to hip ratio, Healthy Eating Index-2010, and a lower blood pressure threshold of greater than or equal to 130/80 mm Hg in addition to physical activity, smoking, total cholesterol, and fasting blood glucose. Data for this study were analyzed from June 1, 2017, to December 31, 2017. The primary outcome was all-cause mortality. The secondary outcome was cancer and cardiovascular disease (CVD) mortality. Data were available for 13 606 adults in 1988 to 1994 (7329 [53%] female; mean [SD] age, 47 [17.7] years), 6360 in 1999 to 2004 (3442 [54%] female; mean [SD] age, 47 [18.6] years), 10 618 in 2005 to 2010 (5428 [51%] female; mean [SD] age, 47 [17.5] years), and 10 773 in 2011 to 2016 (5474 [50%] female; mean [SD] age, 48 [17.4] years). Compared with a revised LS7 score of 0 to 1, the adjusted hazard ratios for a revised LS7 score of 5 to 7 were 0.46 (95% CI, 0.35-0.61) for all-cause mortality, 0.42 (95% CI, 0.25-0.68) for cancer mortality, and 0.37 (95% CI, 0.24-0.55) for CVD mortality, respectively. The adjusted hazard ratios for participants who met 6 or more AHA recommended ideal LS7 metrics were 0.49 (95% CI, 0.33-0.74) for all-cause mortality, 0.60 (95% CI, 0.29-1.25) for cancer mortality, and 0.24 (95% CI, 0.13-0.47) for CVD mortality. Participants with a body mass index of 29.9 or less but without central obesity were independently associated with lower risk of all-cause and CVD mortality. Blood pressure was associated with 36.7% or more of the observed population-attributable fraction of mortality. The individual revised LS7 metrics with modified criteria regarding weight, blood pressure, and diet provide more information about factors associated with cancer mortality than the original AHA LS7 metrics.

Multi-document extractive text summarization: A comparative assessment on features

Text summarization is the process of generating a brief version of a text that preserves the salient information of the text. For information retrieval, it is a good dimension reduction solution. In addition, it reduces the required reading time. This study focused on extracting informative summaries from multiple documents using commonly used hand-crafted features from the literature. The first investigation focused on the generation of a feature vector. The features were the number of sentences, term frequency, similarity with the title, term frequency-inverse sentence frequency, sentence position, sentence length, sentence–sentence similarity, bushy-path results, phrases of the sentence, proper nouns, n-gram co-occurrence, and length of the document. Secondly, several combinations of these features were examined and a shallow multi-layer perceptron and two differently modeled fuzzy inference systems were used to extract salient sentences from texts in the Document Understanding Conference (DUC) dataset. The summarization performances of these models were evaluated using original classification performance metrics, and recall-oriented understudy for gisting evaluation (ROUGE)-n. This study recommended the use of fuzzy systems based on a feature vector and a fuzzy rule set for extractive text summarization. The extraction methods were evaluated against a changing compression ratio. Results of experiments showed that the implemented neural model tended to incorrectly infer sentences that were not considered salient by human annotators. However, for distinguishing between summary-worthy and summary-unworthy sentences, the fuzzy inference systems performed better than the utilized neural network, as well as better than the existing fuzzy inference-based text summarization approaches in the literature.

Correlation of Lung Cancer Incidence and Mortality with Internet Search Data in US States: 2004 to 2018

Background: Internet search data reflect population's concern on health topics in near-real time and can serve as an supplementary metric of disease characteristics. We present a detailed analysis of the association among Google relative search volumes (RSV), lung cancer incidence and mortality rates in the United State. We forecast the trends of lung cancer on this basis. Methods: We obtained RSV of lung cancer from Google Trends. We collected incidence and mortality rates from the US Centers for Disease Control and Prevention. We further created a smoothed time series of RSV so as to eliminate the effects of irregular changes and obtain the long-term trend. We performed a cross-sectional analysis of original Google RSV data and disease metrics at the national and state level. We also performed a analysis of the decomposed RSV and disease metrics. Findings: Trend of lung cancer related internet searches is consistent with the trend of lung cancer incidence nationally. Ohio had the highest RSV. At the state level, the RSV is statistically significantly correlated with lung cancer incidence in 42 regions, with correlation ranging from 0.578 to 0.943. RSV is also statistically significantly correlated with mortality in 47 regions. Both the lung cancer incidence and mortality were correlated with decomposed RSV in 50 regions, except Vermont. Interpretation: Internet searches indeed reflect public awareness of cancer. Research on internet search behaviors could offer an innovative and timely approach to monitoring and estimating lung cancer incidence and mortality. Funding Statement: This research was funded by National Natural Science Foundation of China (#91746205). Declaration of Interests: The authors state: None declared. Ethics Approval Statement: Not required.

Introducing otree_tools: A powerful package to provide process data for attention, multitasking behavior and effort through tracking focus

With the advent of online subject pools, conducting experiments outside the laboratory has become more popular among the scientific community. Unlike the lab, online and field environments tend to be accompanied by a loss of control. In this article we introduce otree_tools, a concise yet powerful add-on for oTree (Chen et al., 2016). otree_tools provides novel ways by which to measure behaviors that are potentially important in the social sciences, such as attention, multitasking and effort. The software also features a novel method of tracking time through the identification of noise. We demonstrate the utility of otree_tools with the help of experimental evidence. The software substantially increases control of the environment. Moreover, the original metrics can be employed for innovative outcome variables, opening the avenue for new research opportunities.

Alignment in regional networks: towards new metrics to assess the effectiveness of technology policy|Alinhamento de redes em uma região em processo de catching-up: em busca de novas métricas para avaliar a efetividade da política tecnológica

This article addresses how regional level network governance and structure influence the effectiveness of technology policy to improve local firms’ innovativeness in a catching-up context. It examines network alignment through an investigation of network governance (i.e., coordination) and structure. Our contribution to the literature relates to the employment of original metrics that allow the understanding of meaning, features and roles of network ties and, therefore, whether network governance and structure have a consistent influence on firms’ engagement in network and their innovative performance. The empirical evidence is based on a Brazilian laggard region that has been the focus of technology policy to promote industrial and regional development, the Recife software network of innovation (Porto Digital). We find that implementation of a technology policy and formation of networks to improve firm-level innovation and regional catch-up should involve careful consideration of the intended effects: membership of a network may not be a sufficient condition for improving innovation at firm level.

Active Fovea-Based Vision Through Computationally-Effective Model-Based Prediction.

What motivates an action in the absence of a definite reward? Taking the case of visuomotor control, we consider a minimal control problem that is how select the next saccade, in a sequence of discrete eye movements, when the final objective is to better interpret the current visual scene. The visual scene is modeled here as a partially-observed environment, with a generative model explaining how the visual data is shaped by action. This allows to interpret different action selection metrics proposed in the literature, including the Salience, the Infomax and the Variational Free Energy, under a single information theoretic construct, namely the view-based Information Gain. Pursuing this analytic track, two original action selection metrics named the Information Gain Lower Bound (IGLB) and the Information Gain Upper Bound (IGUB) are then proposed. Showing either a conservative or an optimistic bias regarding the Information Gain, they strongly simplify its calculation. An original fovea-based visual scene decoding setup is then proposed, with numerical experiments highlighting different facets of artificial fovea-based vision. A first and principal result is that state-of-the-art recognition rates are obtained with fovea-based saccadic exploration, using less than 10% of the original image's data. Those satisfactory results illustrate the advantage of mixing predictive control with accurate state-of-the-art predictors, namely a deep neural network. A second result is the sub-optimality of some classical action-selection metrics widely used in the literature, that is not manifest with finely-tuned inference models, but becomes patent when coarse or faulty models are used. Last, a computationally-effective predictive model is developed using the IGLB objective, with pre-processed visual scan-path read-out from memory, bypassing computationally-demanding predictive calculations. This last simplified setting is shown effective in our case, showing both a competing accuracy and a good robustness to model flaws.

Clinical Evaluation of Shot-Within-Shot Optimization for Gamma Knife Radiosurgery Planning and Delivery

Shot-within-shot (SWS) optimization is a new planning technique that relies on various combinations of shot weighting and prescription isodose line (IDL) to reduce beam-on time. The method differs from other planning techniques that incorporate mixed collimation, multiple stereotactic coordinates, and traditionally low prescription IDLs (<60%). In this work, we evaluate the percentage of brain metastasis for which the method can be applied, the magnitude of the resultant time savings, and the possible tradeoffs in plan quality. A retrospective analysis was performed on 75 patients treated for 241 metastatic lesions in the brain. For each lesion, the original planning metrics related to target coverage, conformity, gradient, and beam-on time were recorded. A subset of lesions were selected for replanning using the SWS technique based on size, shape, and proximity to critical structures. Two replans were done, a reference plan was prescribed at the 50% IDL, and an optimized plan was prescribed at an IDL typically >50%. Planning metrics were then compared among the original plan and the 2 replans. More than a third (39%) of the brain metastases were eligible for the SWS technique. For these lesions, the differences between the original plan and reference SWS plan were as follows: ΔV12Gy < 0.5 cc in 93% of cases, ΔV12Gy < 0.5 cc in 100% of cases, Δselectivity < 0.1 in 79% of cases. Negligible differences were seen between the 2 replans in terms of Δselectivity and ΔV12Gy; ΔGI < 5% in 99% of cases. After optimization, beam-on time was reduced by 25%-30% in approximately 40%-50% of eligible lesions when compared with the reference SWS plan (ΔTmax = 42%). In comparison with the original plan, beam-on time was reduced even further, ΔT > 50% in 20% of cases (ΔTmax = 70%). This work demonstrates clinically that optimization using the shot-within-shot technique can reduce beam-on time without degrading treatment plan quality.

A New and Stable Estimation Method of Country Economic Fitness and Product Complexity.

We present a new metric estimating fitness of countries and complexity of products by exploiting a non-linear non-homogeneous map applied to the publicly available information on the goods exported by a country. The non homogeneous terms guarantee both convergence and stability. After a suitable rescaling of the relevant quantities, the non homogeneous terms are eventually set to zero so that this new metric is parameter free. This new map almost reproduces the results of the original homogeneous metrics already defined in literature and allows for an approximate analytic solution in case of actual binarized matrices based on the Revealed Comparative Advantage (RCA) indicator. This solution is connected with a new quantity describing the neighborhood of nodes in bipartite graphs, representing in this work the relations between countries and exported products. Moreover, we define the new indicator of country net-efficiency quantifying how a country efficiently invests in capabilities able to generate innovative complex high quality products. Eventually, we demonstrate analytically the local convergence of the algorithm involved.

Integrated GIS software for computing landscape visibility metrics

AbstractAs an important component of the quality of the living environment, landscape is increasingly addressed in terms of its visual dimension. In contrast to the point‐like character of in situ observations and photographic analyses, the modeling of landscape visibility from digital data has the advantage of scanning geographical space in a systematic way. However, the tools currently available for visibility modeling are limited to the mapping of viewsheds. They require complementary operations for a complete landscape assessment, including direct and easy computation of landscape metrics. Furthermore, none of those tools integrates recent technical advances to better characterize the visible landscape by tangential vision (i.e., from ground level as opposed to vision by viewshed from above). Starting from this, PixScape software proposes to integrate a large set of functions for modeling landscape visibility while remaining interfaced with GIS software. This software can be used to perform a complete landscape assessment by computing a wide range of original landscape metrics. It performs tangential analysis in addition to viewshed analysis, which can produce more realistic outcomes. Because landscape visibility analysis over large areas implies significant computation time, the software also integrates a multi‐resolution process intended to speed up calculations while also taking into account the cognitive abilities of human vision.

Watershed- to continental-scale influences on winter stormflow in the Southern Blue Ridge Mountains

Spatial and temporal influences on the winter (December-March) stormflow characteristics of fifteen United States Geological Survey (USGS)-gaged watersheds in the Southern Blue Ridge Mountains are identified: (1) watershed-scale differences in geomorphology; (2) continental-scale teleconnections during periods of wetness/dryness (based on the relative amount of winter precipitation over a consistent 20 year dataset); and (3) land cover in the context of soil parent material (e.g., development on alluvium/colluvium). Multiple regression was used to determine how much variance could be explained in five hydrologic variables describing the flashiness of peak flow (three original metrics), total seasonal flashiness (Richards Baker flashiness index), and the ratio of total winter stormflow to total discharge (the stormflow index). Models were constrained to three uncorrelated (|0.65|) variables to prevent overfitting to the dataset. Average-, dry-, and wet-years were subset using the z-scores for winter precipitation derived from the 4 km monthly PRISM (Parameter-elevation Relationships on Independent Slopes Model) dataset, for the period of 1986–2006. Relief, slope, and landscape connectivity explain the majority of explained variance in all five of the hydrologic variables during all time periods. During dry-, average-, and wet-years, atmospheric circulation patterns (i.e., North Atlantic Oscillation and Pacific/North American Pattern) explain more variance than total seasonal precipitation (PRISM), which is not true in the majority of the all-years models. Land cover explains only a small portion of the variance in regional stormflow and only when sub-divided based on soil parent material. Results provide a framework for connecting watershed-scale characteristics to regional- and continental-scale processes.