The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset

Abstract. We introduce the first catchment data set for large sample studies in Chile (South America). The data set includes 516 catchments and provides catchment boundaries, daily streamflow records and basin-averaged time series of the following hydrometeorological variables: 1) daily precipitation retrieved from four gridded sources; 2) daily maximum, minimum and mean temperature; 3) daily potential evapotranspiration (PET); 4) 8-day accumulated PET; and 5) daily snow water equivalent. In addition to the hydro-meteorological time series, we use diverse data sets to extract key landscape attributes characterizing climatic, hydrological, topographic, geological and land cover features. We also describe the degree of anthropic intervention within the catchments by relying on publicly available water rights data for the country. The information is synthetized in 64 catchment attributes describing the landscape and water use characteristics of each catchment. To facilitate the use of the dataset presented here and promote common standards in large-sample studies, we computed most catchment attributes introduced by Addor et al., (2017) in their Catchment Attributes and MEteorology for Large-sample Studies dataset (CAMELS dataset) created for the United States, and proposed several others. Following this nomenclature, we named our dataset CAMELS-CL, which stands for CAMELS dataset in Chile. Based on the constructed dataset, we analysed the main spatial patterns of catchment attributes and the relationships between them. In general, the topographic attributes were explained by the Andes Cordillera; climatic attributes revealed the basic features of Chilean climate; and hydrological signatures revealed the leading patterns of catchment hydrologic responses, resulting from complex, non-linear process interactions across a range of spatiotemporal scales, enhanced by heterogeneities in topography, soils, vegetation, geology and other landscape properties. Further, we analysed human influence in catchment behaviour by relating hydrological signatures with a novel human intervention attribute. Our findings reveal that larger human intervention results in decreased annual flows, runoff ratios, decreased elasticity of runoff with respect to precipitation, and decreased flashiness of runoff, especially in drier catchments. CAMELS-CL provides unprecedented information in South America, a continent largely underrepresented in large-sample studies. The proximity of the Andes means that this dataset includes high-elevation catchments, which are generally poorly represented world-wide due to data-scarcity. The CAMELS-CL dataset can be used to address a myriad of applications, including catchment classification and regionalization studies, the modelling of water availability under different management scenarios, the characterisation of drought history and projections, and the exploration of climate change impacts on hydrological processes. This effort is part of an international initiative to create a multi-national large sample data sets freely available for the community.

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The CAMELS data set: catchment attributes and meteorology for large-sample studies

Hydrology and Earth System Sciences ◽

10.5194/hess-21-5293-2017 ◽

2017 ◽

Vol 21 (10) ◽

pp. 5293-5313 ◽

Cited By ~ 64

Author(s):

Nans Addor ◽

Andrew J. Newman ◽

Naoki Mizukami ◽

Martyn P. Clark

Keyword(s):

Time Series ◽

Data Sets ◽

Catchment Scale ◽

Data Set ◽

Meteorological Forcing ◽

Large Sample ◽

Source Data ◽

Daily Time Series ◽

Comparative Hydrology ◽

Daily Time

Abstract. We present a new data set of attributes for 671 catchments in the contiguous United States (CONUS) minimally impacted by human activities. This complements the daily time series of meteorological forcing and streamflow provided by Newman et al. (2015b). To produce this extension, we synthesized diverse and complementary data sets to describe six main classes of attributes at the catchment scale: topography, climate, streamflow, land cover, soil, and geology. The spatial variations among basins over the CONUS are discussed and compared using a series of maps. The large number of catchments, combined with the diversity of the attributes we extracted, makes this new data set well suited for large-sample studies and comparative hydrology. In comparison to the similar Model Parameter Estimation Experiment (MOPEX) data set, this data set relies on more recent data, it covers a wider range of attributes, and its catchments are more evenly distributed across the CONUS. This study also involves assessments of the limitations of the source data sets used to compute catchment attributes, as well as detailed descriptions of how the attributes were computed. The hydrometeorological time series provided by Newman et al. (2015b, https://doi.org/10.5065/D6MW2F4D) together with the catchment attributes introduced in this paper (https://doi.org/10.5065/D6G73C3Q) constitute the freely available CAMELS data set, which stands for Catchment Attributes and MEteorology for Large-sample Studies.

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The CAMELS data set: catchment attributes and meteorology for large-sample studies

10.5194/hess-2017-169 ◽

2017 ◽

Cited By ~ 2

Author(s):

Nans Addor ◽

Andrew J. Newman ◽

Naoki Mizukami ◽

Martyn P. Clark

Keyword(s):

Time Series ◽

Essential Feature ◽

Data Sets ◽

Catchment Scale ◽

Data Set ◽

Large Sample ◽

Quantitative Estimates ◽

Comparative Hydrology ◽

Vegetation Characteristics ◽

Landscape Attributes

Abstract. We present a new data set of attributes for 671 catchments in the contiguous USA (CONUS). This complements the daily hydrometeorological time series provided by Newman et al. (2015b) and opens new opportunities to explore how the interplay between landscape attributes shapes hydrological processes and catchment behavior. To produce this extension, we synthesized diverse and complementary data sets to describe topography, climate, hydrology, soil and vegetation characteristics at the catchment scale. The spatial variations among basins over the CONUS are discussed and compared using a series of maps. The large number of catchments, combined with the diversity of their geophysical characteristics, makes this new data well suited for large-sample studies and comparative hydrology. An essential feature, that differentiates this data set from similar ones, is that it both provides quantitative estimates of diverse catchment attributes, and involves assessments of the limitations of the data and methods used to compute those attributes. This data set will be publicly available and we encourage the community to further extend it. The hydrometeorological time series provided by Newman et al. (2015b) together with the catchment attributes introduced in this paper constitute the CAMELS data set: Catchment Attributes and MEteorology for Large-sample Studies.

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Origins and Destinations – Social Security Claimant Dynamics

Journal of Social Policy ◽

10.1017/s0047279498005327 ◽

1998 ◽

Vol 27 (3) ◽

pp. 351-369 ◽

Cited By ~ 5

Author(s):

MICHAEL NOBLE ◽

SIN YI CHEUNG ◽

GEORGE SMITH

Keyword(s):

The United States ◽

Data Sets ◽

Income Support ◽

Data Set ◽

Family Structures ◽

Highly Educated ◽

Lone Parents ◽

Lone Mothers ◽

Time Period ◽

Short Time

This article briefly reviews American and British literature on welfare dynamics and examines the concepts of welfare dependency and ‘dependency culture’ with particular reference to lone parents. Using UK benefit data sets, the welfare dynamics of lone mothers are examined to explore the extent to which they inform the debates. Evidence from Housing Benefits data show that even over a relatively short time period, there is significant turnover in the benefits-dependent lone parent population with movement in and out of income support as well as movement into other family structures. Younger lone parents and owner-occupiers tend to leave the data set while older lone parents and council tenants are most likely to stay. Some owner-occupier lone parents may be relatively well off and on income support for a relatively short time between separation and a financial settlement being reached. They may also represent a more highly educated and highly skilled group with easier access to the labour market than renters. Any policy moves paralleling those in the United States to time limit benefit will disproportionately affect older lone parents.

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US academic libraries' staffing and expenditure trends (1996–2016)

Library Management ◽

10.1108/lm-12-2019-0093 ◽

2020 ◽

Vol 41 (4/5) ◽

pp. 247-268 ◽

Cited By ~ 1

Author(s):

Starr Hoffman ◽

Samantha Godbey

Keyword(s):

Academic Libraries ◽

The United States ◽

Data Sets ◽

Size Category ◽

Data Set ◽

Content Type ◽

Education Statistics ◽

Trends Over Time ◽

Doctoral Institutions ◽

Over Time

PurposeThis paper explores trends over time in library staffing and staffing expenditures among two- and four-year colleges and universities in the United States.Design/methodology/approachResearchers merged and analyzed data from 1996 to 2016 from the National Center for Education Statistics for over 3,500 libraries at postsecondary institutions. This study is primarily descriptive in nature and addresses the research questions: How do staffing trends in academic libraries over this period of time relate to Carnegie classification and institution size? How do trends in library staffing expenditures over this period of time correspond to these same variables?FindingsAcross all institutions, on average, total library staff decreased from 1998 to 2012. Numbers of librarians declined at master’s and doctoral institutions between 1998 and 2016. Numbers of students per librarian increased over time in each Carnegie and size category. Average inflation-adjusted staffing expenditures have remained steady for master's, baccalaureate and associate's institutions. Salaries as a percent of library budget decreased only among doctoral institutions and institutions with 20,000 or more students.Originality/valueThis is a valuable study of trends over time, which has been difficult without downloading and merging separate data sets from multiple government sources. As a result, few studies have taken such an approach to this data. Consequently, institutions and libraries are making decisions about resource allocation based on only a fraction of the available data. Academic libraries can use this study and the resulting data set to benchmark key staffing characteristics.

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Loi and Gong Low-Degree Rotational Splittings

Symposium - International Astronomical Union ◽

10.1017/s0074180900238515 ◽

1998 ◽

Vol 185 ◽

pp. 167-168

Author(s):

T. Appourchaux ◽

M.C. Rabello-Soares ◽

L. Gizon

Keyword(s):

Time Series ◽

The Other ◽

Data Sets ◽

Data Set ◽

Low Degree ◽

Fourier Spectra

Two different data sets have been used to derive low-degree rotational splittings. One data set comes from the Luminosity Oscillations Imager of VIRGO on board SOHO; the observation starts on 27 March 96 and ends on 26 March 97, and are made of intensity time series of 12 pixels (Appourchaux et al, 1997, Sol. Phys., 170, 27). The other data set was kindly made available by the GONG project; the observation starts on 26 August 1995 and ends on 21 August 1996, and are made of complex Fourier spectra of velocity time series for l = 0 − 9. For the GONG data, the contamination of l = 1 from the spatial aliases of l = 6 and l = 9 required some cleaning. To achieve this, we applied the inverse of the leakage matrix of l = 1, 6 and 9 to the original Fourier spectra of the same degrees; cleaning of all 3 degrees was achieved simultaneously (Appourchaux and Gizon, 1997, these proceedings).

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An assessment of Bayesian bias estimator for numerical weather prediction

Nonlinear Processes in Geophysics ◽

10.5194/npg-15-1013-2008 ◽

2008 ◽

Vol 15 (6) ◽

pp. 1013-1022 ◽

Cited By ~ 2

Author(s):

J. Son ◽

D. Hou ◽

Z. Toth

Keyword(s):

Time Series ◽

Numerical Weather Prediction ◽

Sampling Error ◽

Weather Prediction ◽

Training Data ◽

Statistical Characteristics ◽

Forecast Errors ◽

Data Sets ◽

Data Set ◽

Numerical Weather

Abstract. Various statistical methods are used to process operational Numerical Weather Prediction (NWP) products with the aim of reducing forecast errors and they often require sufficiently large training data sets. Generating such a hindcast data set for this purpose can be costly and a well designed algorithm should be able to reduce the required size of these data sets. This issue is investigated with the relatively simple case of bias correction, by comparing a Bayesian algorithm of bias estimation with the conventionally used empirical method. As available forecast data sets are not large enough for a comprehensive test, synthetically generated time series representing the analysis (truth) and forecast are used to increase the sample size. Since these synthetic time series retained the statistical characteristics of the observations and operational NWP model output, the results of this study can be extended to real observation and forecasts and this is confirmed by a preliminary test with real data. By using the climatological mean and standard deviation of the meteorological variable in consideration and the statistical relationship between the forecast and the analysis, the Bayesian bias estimator outperforms the empirical approach in terms of the accuracy of the estimated bias, and it can reduce the required size of the training sample by a factor of 3. This advantage of the Bayesian approach is due to the fact that it is less liable to the sampling error in consecutive sampling. These results suggest that a carefully designed statistical procedure may reduce the need for the costly generation of large hindcast datasets.

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Bed-load measurements on large, sand-bed rivers in the United States

E3S Web of Conferences ◽

10.1051/e3sconf/20184006021 ◽

2018 ◽

Vol 40 ◽

pp. 06021

Author(s):

David Abraham ◽

Tate McAlpin ◽

Keaton Jones

Keyword(s):

United States ◽

Sand Dunes ◽

Measurement Techniques ◽

The United States ◽

Transport Rate ◽

Bed Load ◽

Data Sets ◽

Data Set ◽

Load Transport ◽

Section Surface

The movement of bed forms (sand dunes) in large sand-bed rivers is being used to determine the transport rate of bed load. The ISSDOTv2 (Integrated Section Surface Difference Over Time version 2) methodology uses time sequenced differences of measured bathymetric surfaces to compute the bed-load transport rate. The method was verified using flume studies [1]. In general, the method provides very consistent and repeatable results, and also shows very good fidelity with most other measurement techniques. Over the last 7 years we have measured, computed and compiled what we believe to be the most extensive data set anywhere of bed-load measurements on large, sand bed rivers. Most of the measurements have been taken on the Mississippi, Missouri, Ohio and Snake Rivers in the United States. For cases where multiple measurements were made at varying flow rates, bed-load rating curves have been produced. This paper will provide references for the methodology, but is intended more to discuss the measurements, the resulting data sets, and current and potential uses for the bed-load data.

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Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-1573-2018 ◽

2018 ◽

Vol 18 (3) ◽

pp. 1573-1592 ◽

Cited By ~ 48

Author(s):

Gerrit de Leeuw ◽

Larisa Sogacheva ◽

Edith Rodriguez ◽

Konstantinos Kourtidis ◽

Aristeidis K. Georgoulias ◽

...

Keyword(s):

Time Series ◽

European Space Agency ◽

Mainland China ◽

Satellite Observations ◽

Data Sets ◽

Data Set ◽

Modis Aod ◽

The Difference ◽

Along Track ◽

Aerosol Properties

Abstract. The retrieval of aerosol properties from satellite observations provides their spatial distribution over a wide area in cloud-free conditions. As such, they complement ground-based measurements by providing information over sparsely instrumented areas, albeit that significant differences may exist in both the type of information obtained and the temporal information from satellite and ground-based observations. In this paper, information from different types of satellite-based instruments is used to provide a 3-D climatology of aerosol properties over mainland China, i.e., vertical profiles of extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a lidar flying aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the column-integrated extinction (aerosol optical depth – AOD) available from three radiometers: the European Space Agency (ESA)'s Along-Track Scanning Radiometer version 2 (ATSR-2), Advanced Along-Track Scanning Radiometer (AATSR) (together referred to as ATSR) and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, together spanning the period 1995–2015. AOD data are retrieved from ATSR using the ATSR dual view (ADV) v2.31 algorithm, while for MODIS Collection 6 (C6) the AOD data set is used that was obtained from merging the AODs obtained from the dark target (DT) and deep blue (DB) algorithms, further referred to as the DTDB merged AOD product. These data sets are validated and differences are compared using Aerosol Robotic Network (AERONET) version 2 L2.0 AOD data as reference. The results show that, over China, ATSR slightly underestimates the AOD and MODIS slightly overestimates the AOD. Consequently, ATSR AOD is overall lower than that from MODIS, and the difference increases with increasing AOD. The comparison also shows that neither of the ATSR and MODIS AOD data sets is better than the other one everywhere. However, ATSR ADV has limitations over bright surfaces which the MODIS DB was designed for. To allow for comparison of MODIS C6 results with previous analyses where MODIS Collection 5.1 (C5.1) data were used, also the difference between the C6 and C5.1 merged DTDB data sets from MODIS/Terra over China is briefly discussed. The AOD data sets show strong seasonal differences and the seasonal features vary with latitude and longitude across China. Two-decadal AOD time series, averaged over all of mainland China, are presented and briefly discussed. Using the 17 years of ATSR data as the basis and MODIS/Terra to follow the temporal evolution in recent years when the environmental satellite Envisat was lost requires a comparison of the data sets for the overlapping period to show their complementarity. ATSR precedes the MODIS time series between 1995 and 2000 and shows a distinct increase in the AOD over this period. The two data series show similar variations during the overlapping period between 2000 and 2011, with minima and maxima in the same years. MODIS extends this time series beyond the end of the Envisat period in 2012, showing decreasing AOD.

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TANK VESSEL PETROLEUM TRANSPORTATION AS A PREDICTOR OF MARINE OIL SPILLS

International Oil Spill Conference Proceedings ◽

10.7901/2169-3358-2005-1-143 ◽

2005 ◽

Vol 2005 (1) ◽

pp. 143-147

Author(s):

Daniel R. Norton

Keyword(s):

Marine Environment ◽

Oil Spills ◽

Strong Relationship ◽

The United States ◽

Coast Guard ◽

Army Corps ◽

Data Sets ◽

Data Set ◽

Annual Volume ◽

The U.S

ABSTRACT The annual volume of oil spilled into the marine environment by tank vessels (tank barges and tanks hips) is analyzed against the total annual volume of oil transported by tank vessels in order to determine any correlational relationship. U.S. Coast Guard data was used to provide the volume of oil (petroleum) spilled into the marine environment each year by tank vessels. Data from the U.S. Army Corps of Engineers and the U.S. Department of Transportation's (US DOT) National Transportation Statistics (NTS) were used for the annual volume of oil transported via tank vessels in the United States. This data is provided in the form of tonnage and ton-miles, respectively. Each data set has inherent benefits and weaknesses. For the analysis the volume of oil transported was used as the explanatory variable (x) and the volume of oil spilled into the marine environment as the response variable (y). Both data sets were tested for correlation. A weak relationship, r = −0.38 was found using tonnage, and no further analysis was performed. A moderately strong relationship, r = 0.79, was found using ton-miles. Further analysis using regression analysis and a plot of residuals showed the data to be satisfactory with no sign of lurking variables, but with the year 1990 being a possible outlier.

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Uncertainty of Climatol adjustment algorithm for daily time series of additive climate variables

10.5194/egusphere-egu2020-5365 ◽

2020 ◽

Author(s):

Oleg Skrynyk ◽

Enric Aguilar ◽

José A. Guijarro ◽

Sergiy Bubin

Keyword(s):

Time Series ◽

Climate Model ◽

Data Sets ◽

Climate Variables ◽

The European Union ◽

Data Set ◽

Raw Data ◽

Climate Signal ◽

Daily Time Series ◽

Daily Time

Before using climatological time series in research studies, it is necessary to perform their quality control and homogenization in order to remove possible artefacts (inhomogeneities) usually present in the raw data sets. In the vast majority of cases, the homogenization procedure allows to improve the consistency of the data, which then can be verified by means of the statistical comparison of the raw and homogenized time series. However, a new question then arises: how far are the homogenized data from the true climate signal or, in other words, what errors could still be present in homogenized data?The main objective of our work is to estimate the uncertainty produced by the adjustment algorithm of the widely used Climatol homogenization software when homogenizing daily time series of the additive climate variables. We focused our efforts on the minimum and maximum air temperature. In order to achieve our goal we used a benchmark data set created by the INDECIS* project. The benchmark contains clean data, extracted from an output of the Royal Netherlands Meteorological Institute Regional Atmospheric Climate Model (version 2) driven by Hadley Global Environment Model 2 - Earth System, and inhomogeneous data, created by introducing realistic breaks and errors.The statistical evaluation of discrepancies between the homogenized (by means of Climatol with predefined break points) and clean data sets was performed using both a set of standard parameters and a metrics introduced in our work. All metrics used clearly identifies the main features of errors (systematic and random) present in the homogenized time series. We calculated the metrics for every time series (only over adjusted segments) as well as their averaged values as measures of uncertainties in the whole data set.In order to determine how the two key parameters of the raw data collection, namely the length of time series and station density, influence the calculated measures of the adjustment error we gradually decreased the length of the period and number of stations in the area under study. The total number of cases considered was 56, including 7 time periods (1950-2005, 1954-2005, &#8230;, 1974-2005) and 8 different quantities of stations (100, 90, &#8230;, 30). Additionally, in order to find out how stable are the calculated metrics for each of the 56 cases and determine their confidence intervals we performed 100 random permutations in the introduced inhomogeneity time series and repeated our calculations With that the total number of homogenization exercises performed was 5600 for each of two climate variables.Lastly, the calculated metrics were compared with the corresponding values, obtained for raw time series. The comparison showed some substantial improvement of the metric values after homogenization in each of the 56 cases considered (for the both variables).-------------------*INDECIS is a part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). The work has been partially supported by the Ministry of Education and Science of Kazakhstan (Grant BR05236454) and Nazarbayev University (Grant 090118FD5345).

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