scholarly journals Reservoir regulation affects droughts and floods at local and regional scales

2021 ◽  
Author(s):  
Manuela Irene Brunner
Keyword(s):  
Flood Hazard ◽  
Regional Scale ◽  
Large Data ◽  
The United States ◽  
Climate Impact ◽  
Data Set ◽  
Drought And Flood ◽  
Hydrological Extremes ◽  
Reservoir Regulation ◽  
Droughts And Floods

Abstract Hydrological extremes can be particularly impactful in catchments with high human presence where they are modulated by human intervention such as reservoir regulation. Still, we know little about how reservoir operation affects droughts and floods, particularly at a regional scale. Here, we present a large data set of natural and regulated catchment pairs in the United States and assess how reservoir regulation affects local and regional drought and flood characteristics. Our results show that (1) reservoir regulation affects drought and flood hazard at a local scale by reducing severity (i.e. intensity/magnitude and deficit/volume) but increasing duration; (2) regulation affects regional hazard by reducing spatial flood connectedness (i.e. number of catchments a catchment co-experiences flood events with) in winter and by increasing spatial drought connectedness in summer; (3) the local alleviation effect is only weakly affected by reservoir purpose for both droughts and floods. We conclude that both local and regional flood and drought characteristics are substantially modulated by reservoir regulation, an aspect that should neither be neglected in hazard nor climate impact assessments.

An Empirical Bargaining Model with Left-Digit Bias: A Study on Auto Loan Monthly Payments

2021 ◽  
Author(s):  
Zhenling Jiang
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Large Data ◽  
The United States ◽  
Nash Bargaining ◽  
Bargaining Model ◽  
Data Set ◽  
Proposed Model ◽  
Lower Interest Rate ◽  
The Empirical Analysis

This paper studies price bargaining when both parties have left-digit bias when processing numbers. The empirical analysis focuses on the auto finance market in the United States, using a large data set of 35 million auto loans. Incorporating left-digit bias in bargaining is motivated by several intriguing observations. The scheduled monthly payments of auto loans bunch at both $9- and $0-ending digits, especially over $100 marks. In addition, $9-ending loans carry a higher interest rate, and $0-ending loans have a lower interest rate. We develop a Nash bargaining model that allows for left-digit bias from both consumers and finance managers of auto dealers. Results suggest that both parties are subject to this basic human bias: the perceived difference between $9- and the next $0-ending payments is larger than $1, especially between $99- and $00-ending payments. The proposed model can explain the phenomena of payments bunching and differential interest rates for loans with different ending digits. We use counterfactuals to show a nuanced impact of left-digit bias, which can both increase and decrease the payments. Overall, bias from both sides leads to a $33 increase in average payment per loan compared with a benchmark case with no bias. This paper was accepted by Matthew Shum, marketing.

scholarly journals List-columns in data.table: Nesting and unnesting data tables and vectors

2019 ◽  
Author(s):  
Tyson S. Barrett
Keyword(s):  
Large Data ◽  
The United States ◽  
Bench Mark ◽  
Data Sets ◽  
Complex Data ◽  
Basketball Players ◽  
Text Data ◽  
Data Set ◽  
Data Tables ◽  
Student Information

The use of list-columns in data frames and tibbles in the R statistical environment is well documented (e.g. Bryan, 2018), providing a cognitively efficient way to organize results of complex data (e.g. several statistical models, groupings of text, data summaries, or even graphics) with corresponding data. For example, one can store student information within classrooms, player information within teams, or even analyses within groups. This allows the data to be of variable sizes without overly complicating or adding redundancies to the structure of the data. In turn, this can improve the reliability to appropriately analyze the data. Because of its efficiency and speed, being able to use data.table to work with list-columns would be beneficial in many data contexts (e.g. to reduce memory usage in large data sets). Herein, I demonstrate how one can create list-columns in a data table using the by argument in data.table and purrr::map(). This is done using an example data set containing information on professional basketball players in the United States. I compare the behavior of the data.table approach to the dplyr::group_nest() function, one of the several powerful tidyverse nesting functions. Results using bench::mark() show the speed and efficiency of using data.table to work with list-columns.

Helminth species diversity of mammals: parasite species richness is a host species attribute

Parasitology ◽  
2008 ◽  
Vol 135 (14) ◽  
pp. 1701-1705 ◽  
Author(s):  
F. BORDES ◽  
S. MORAND
Keyword(s):  
Species Richness ◽  
Host Species ◽  
Parasite Species ◽  
Regional Scale ◽  
Large Data ◽  
Helminth Species ◽  
Future Research ◽  
Data Set ◽  
Parasite Species Richness ◽  
Trade Offs

SUMMARYStudies investigating parasite diversity have shown substantial geographical variation in parasite species richness. Most of these studies have, however, adopted a local scale approach, which may have masked more general patterns. Recent studies have shown that ectoparasite species richness in mammals seems highly repeatable among populations of the same mammal host species at a regional scale. In light of these new studies we have reinvestigated the case of parasitic helminths by using a large data set of parasites from mammal populations in 3 continents. We collected homogeneous data and demonstrated that helminth species richness is highly repeatable in mammals at a regional scale. Our results highlight the strong influence of host identity in parasite species richness and call for future research linking helminth species found in a given host to its ecology, immune defences and potential energetic trade-offs.

scholarly journals Detrital zircon petrochronology of central Australia, and implications for the secular record of zircon trace element composition

Geosphere ◽  
2021 ◽  
Author(s):  
Charles Verdel ◽  
Matthew J. Campbell ◽  
Charlotte M. Allen
Keyword(s):  
Trace Element ◽  
Continental Crust ◽  
Detrital Zircon ◽  
Isotope Composition ◽  
Regional Scale ◽  
Large Data ◽  
Hf Isotope ◽  
Geological Time ◽  
Related Factors ◽  
Data Set

Hafnium (Hf) isotope composition of zircon has been integrated with U-Pb age to form a long-term (>4 b.y.) record of the evolution of the crust. In contrast, trace element compositions of zircon are most commonly utilized in local- or regional-scale petrological studies, and the most noteworthy applications of trace element studies of detrital zircon have been in “fingerprinting” potential source lithologies. The extent to which zircon trace element compositions varied globally over geological time scales (as, for example, zircon U-Pb age abundance, O isotope composition, and Hf isotope composition seem to have varied) has been little explored, and it is a topic that is well suited to the large data sets produced by detrital zircon studies. In this study we present new detrital zircon U-Pb ages and trace element compositions from a continent-scale basin system in Australia (the Centralian Superbasin) that bear directly on the Proterozoic history of Australia and which may be applicable to broader interpretations of plate-tectonic processes in other regions. U-Pb ages of detrital zircon in the Centralian Superbasin are dominated by populations of ca. 1800, 1600, 1200, and 600 Ma, and secular variations of zircon Hf isotope ratios are correlated with some trace element parameters between these major age populations. In particular, elevated εHf(i) (i.e., radiogenic “juvenile” Hf isotope composition) of detrital zircon in the Centralian Superbasin tends to correspond with relatively high values of Yb/U, Ce anomaly, and Lu/Nd (i.e., depletion of light rare earth elements). These correlations seem to be fundamentally governed by three related factors: elemental compatibility in the continental crust versus mantle, the thickness of continental crust, and the contributions of sediment to magmas. Similar trace element versus εHf(i) patterns among a global zircon data set suggest broad applicability. One particularly intriguing aspect of the global zircon data set is a late Neoproterozoic to Cambrian period during which both zircon εHf(i) and Yb/U reached minima, marking an era of anomalous zircon geochemistry that was related to significant contributions from old continental crust.

Spatial prediction of topsoil salinity in the Chelif Valley, Algeria, using local ordinary kriging with local variograms versus whole-area variogram

Soil Research ◽  
2001 ◽  
Vol 39 (2) ◽  
pp. 259 ◽  
Author(s):  
Christian Walter ◽  
Alex B. McBratney ◽  
Abdelkader Douaoui ◽  
Budiman Minasny
Keyword(s):  
Ordinary Kriging ◽  
Regional Scale ◽  
Large Data ◽  
Spatial Prediction ◽  
Uncertainty Assessment ◽  
Prediction Errors ◽  
Spatial Structures ◽  
Data Set ◽  
Salinity Hazard ◽  
Validation Set

A novel form of ordinary kriging, involving the local estimation and modelling of the variogram at each prediction site (OKLV), is tested at a regional scale on a large data set, in order to adapt to non-uniform spatial structures and improve the assessment of the salinity hazard in the lower Chelif Valley, Algeria. The spatial variability study was carried out on a 38000 ha area using 5141 topsoil electrical conductivity (EC) measurements systematically sampled on a 250 m by 250 m grid. Variography analysis confirmed the existence of large trends in the EC variability with differing spatial structures between sub-areas. OKLV performed better than ordinary kriging with a whole-area variogram (OKWV) in predicting the proportion of high saline soils in large blocks, but the predictions appeared mostly similar. In contrast, the estimation variance maps revealing the uncertainties of the spatial predictions were markedly different between the 2 methods. OKLV integrates the local spatial structure in the uncertainty assessment, whereas kriging with a whole-area variogram only considers the sampling intensity. Comparison with prediction errors on a validation set confirmed the consistency of the OKLV prediction variance. This appears to be a major improvement for decision-making procedures such as delineating areas where remediation should take place.

scholarly journals Deriving global flood hazard maps of fluvial floods through a physical model cascade

2012 ◽  
Vol 16 (11) ◽  
pp. 4143-4156 ◽  
Author(s):  
F. Pappenberger ◽  
E. Dutra ◽  
F. Wetterhall ◽  
H. L. Cloke
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Flood Hazard ◽  
Regional Scale ◽  
Surface Model ◽  
Return Periods ◽  
Hazard Maps ◽  
Data Set ◽  
Flood Hazard Maps ◽  
River Routing

Abstract. Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979–2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25 × 25 km grid, which is then reprojected onto a 1 × 1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25 × 25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.

scholarly journals Narrowband-to-Broadband Conversions for Top-of-Atmosphere Reflectance from the Advanced Very High Resolution Radiometer (AVHRR)

2020 ◽  
Vol 12 (2) ◽  
pp. 305 ◽  
Author(s):  
Tom Akkermans ◽  
Nicolas Clerbaux
Keyword(s):  
High Resolution ◽  
Global Climate ◽  
Regional Scale ◽  
Radiant Energy ◽  
Large Data ◽  
Energy System ◽  
Climate Data ◽  
Data Set ◽  
Regional Analyses ◽  
Very High

The current lack of a long, 30+ year, global climate data record of reflected shortwave top-of-atmosphere (TOA) radiation could be tackled by relying on existing narrowband records from the Advanced Very High Resolution Radiometer (AVHRR) instruments, and transform these measurements into broadband quantities like provided by the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents the methodology of an AVHRR-to-CERES narrowband-to-broadband conversion for shortwave TOA reflectance, including the ready-to-use results in the form of scene-type dependent regression coefficients, allowing a calculation of CERES-like shortwave broadband reflectance from AVHRR channels 1 and 2. The coefficients are obtained using empirical relations in a large data set of collocated, coangular and simultaneous AVHRR-CERES observations, requiring specific orbital conditions for the AVHRR- and CERES-carrying satellites, from which our data analysis uses all available data for an unprecedented observation matching between both instruments. The multivariate linear regressions were found to be robust and well-fitting, as demonstrated by the regression statistics on the calibration subset (80% of data): adjusted R 2 higher than 0.9 and relative RMS residual mostly below 3%, which is a significant improvement compared to previous regressions. Regression models are validated by applying them on a validation subset (20% of data), indicating a good performance overall, roughly similar to the calibration subset, and a negligible mean bias. A second validation approach uses an expanded data set with global coverage, allowing regional analyses. In the error analysis, instantaneous accuracy is quantified at regional scale between 1.8 Wm − 2 and 2.3 Wm − 2 (resp. clear-sky and overcast conditions) at 1 standard deviation (RMS bias). On daily and monthly time scales, these errors correspond to 0.7 and 0.9 Wm − 2 , which is compliant with the GCOS requirement of 1 Wm − 2 .

Analysis of Differential Item Functioning (DIF) Using Hierarchical Logistic Regression Models

2002 ◽  
Vol 27 (1) ◽  
pp. 53-75 ◽  
Author(s):  
David B. Swanson ◽  
Brian E. Clauser ◽  
Susan M. Case ◽  
Ronald J. Nungester ◽  
Carol Featherman
Keyword(s):  
Logistic Regression ◽  
Differential Item Functioning ◽  
Empirical Bayes ◽  
Predictive Accuracy ◽  
Large Data ◽  
The United States ◽  
Related Item ◽  
Data Set ◽  
Hierarchical Logistic Regression ◽  
Item Functioning

Over the past 25 years a range of parametric and nonparametric methods have been developed for analyzing Differential Item Functioning (DIF). These procedures are typically performed for each item individually or for small numbers of related items. Because the analytic procedures focus on individual items, it has been difficult to pool information across items to identify potential sources of DIF analytically. In this article, we outline an approach to DIF analysis using hierarchical logistic regression that makes it possible to combine results of logistic regression analyses across items to identify consistent sources of DIF, to quantify the proportion of explained variation in DIF coefficients, and to compare the predictive accuracy of alternate explanations for DIF. The approach can also be used to improve the accuracy of DIF estimates for individual items by applying empirical Bayes techniques, with DIF-related item characteristics serving as collateral information. To illustrate the hierarchical logistic regression procedure, we use a large data set derived from recent computer-based administrations of Step 2, the clinical science component of the United States Medical Licensing Examination (USMLE®). Results of a small Monte Carlo study of the accuracy of the DIF estimates are also reported.

Laptop or Bust: How Lack of Technology Affects Student Achievement

2020 ◽  
Vol 64 (7) ◽  
pp. 927-949 ◽  
Author(s):  
Bianca C. Reisdorf ◽  
Whisnu Triwibowo ◽  
Aleksandr Yankelevich
Keyword(s):  
Academic Performance ◽  
Information And Communication Technologies ◽  
Large Data ◽  
The United States ◽  
Socioeconomic Background ◽  
Data Set ◽  
College Performance ◽  
Digital Divides ◽  
Incoming Freshmen ◽  
Information And Communication

Research shows that digital divides and inequalities are related to lower socioeconomic status and detrimental to social and economic capital acquisition. Other studies show that use of information and communication technologies in the classroom can lead to worse academic performance. Nevertheless, many universities require that students own or buy a laptop, and many offer financial aid for students who cannot afford to buy one. As such, laptop ownership may be crucially tied to academic performance. Based on a large data set of incoming freshmen at a large public university in the United States, this article shows that not owning a laptop is negatively associated with overall college performance, even when controlling for socioeconomic background. Whereas we find that laptop ownership is not necessarily responsible for the higher performance of individuals in our broader sample, it could be beneficial to nonowners, which has implications for university policies seeking to provide institution-wide access to laptops and for universities’ broader interactions with students who do not own a laptop.

scholarly journals A Global Drought and Flood Catalogue from 1950 to 2016

2020 ◽  
Vol 101 (5) ◽  
pp. E508-E535 ◽  
Author(s):  
Xiaogang He ◽  
Ming Pan ◽  
Zhongwang Wei ◽  
Eric F. Wood ◽  
Justin Sheffield
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Large Scale ◽  
Climate Model ◽  
Water Cycle ◽  
Consistent Estimate ◽  
Drought And Flood ◽  
Hydrological Extremes ◽  
Wide Range ◽  
Droughts And Floods

Abstract Hydrological extremes, in the form of droughts and floods, have impacts on a wide range of sectors including water availability, food security, and energy production. Given continuing large impacts of droughts and floods and the expectation for significant regional changes projected in the future, there is an urgent need to provide estimates of past events and their future risk, globally. However, current estimates of hydrological extremes are not robust and accurate enough, due to lack of long-term data records, standardized methods for event identification, geographical inconsistencies, and data uncertainties. To tackle these challenges, this article presents the development of the first Global Drought and Flood Catalogue (GDFC) for 1950–2016 by merging the latest in situ and remote sensing datasets with state-of-the-art land surface and hydrodynamic modeling to provide a continuous and consistent estimate of the terrestrial water cycle and its extremes. This GDFC also includes an unprecedented level of detailed analysis of drought and large-scale flood events using univariate and multivariate risk assessment frameworks, which incorporates regional spatial–temporal characteristics (i.e., duration, spatial extent, severity) and global hazard maps for different return periods. This Catalogue forms a basis for analyzing the changing risk of droughts and floods and can underscore national and international climate change assessments and provide a key reference for climate change studies and climate model evaluations. It also contributes to the growing interests in multivariate and compounding risk analysis.

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