scholarly journals CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil

2020 ◽  
Vol 12 (3) ◽  
pp. 2075-2096 ◽  
Author(s):  
Vinícius B. P. Chagas ◽  
Pedro L. B. Chaffe ◽  
Nans Addor ◽  
Fernando M. Fan ◽  
Ayan S. Fleischmann ◽  
...  
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Human Impacts ◽  
The United States ◽  
Meteorological Forcing ◽  
Large Sample ◽  
Spatial Features ◽  
Combining Data ◽  
The Tropics ◽  
Daily Time

Abstract. We introduce a new catchment dataset for large-sample hydrological studies in Brazil. This dataset encompasses daily time series of observed streamflow from 3679 gauges, as well as meteorological forcing (precipitation, evapotranspiration, and temperature) for 897 selected catchments. It also includes 65 attributes covering a range of topographic, climatic, hydrologic, land cover, geologic, soil, and human intervention variables, as well as data quality indicators. This paper describes how the hydrometeorological time series and attributes were produced, their primary limitations, and their main spatial features. To facilitate comparisons with catchments from other countries, the data follow the same standards as the previous CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets for the United States, Chile, and Great Britain. CAMELS-BR (Brazil) complements the other CAMELS datasets by providing data for hundreds of catchments in the tropics and the Amazon rainforest. Importantly, precipitation and evapotranspiration uncertainties are assessed using several gridded products, and quantitative estimates of water consumption are provided to characterize human impacts on water resources. By extracting and combining data from these different data products and making CAMELS-BR publicly available, we aim to create new opportunities for hydrological research in Brazil and facilitate the inclusion of Brazilian basins in continental to global large-sample studies. We envision that this dataset will enable the community to gain new insights into the drivers of hydrological behavior, better characterize extreme hydroclimatic events, and explore the impacts of climate change and human activities on water resources in Brazil. The CAMELS-BR dataset is freely available at https://doi.org/10.5281/zenodo.3709337 (Chagas et al., 2020).

scholarly journals CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in Brazil

2020 ◽  
Author(s):  
Vinícius B. P. Chagas ◽  
Pedro L. B. Chaffe ◽  
Nans Addor ◽  
Fernando M. Fan ◽  
Ayan S. Fleischmann ◽  
...  
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Human Impacts ◽  
The United States ◽  
Meteorological Forcing ◽  
Large Sample ◽  
Spatial Features ◽  
Combining Data ◽  
The Tropics ◽  
Daily Time

Abstract. We introduce a new catchment dataset for large-sample hydrological studies in Brazil. This dataset encompasses daily time series of observed streamflow from 3713 gauges, as well as meteorological forcing (precipitation, evapotranspiration and temperature) for 897 selected catchments. It also includes 63 attributes covering a range of topographic, climatic, hydrologic, land cover, geologic, soil and human intervention variables, as well as data quality indicators. This paper describes how the hydrometeorological time series and attributes were produced, their primary limitations and their main spatial features. To facilitate comparisons with catchments from other countries, the data follow the same standards as the previous CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets for the United States, Chile and Great Britain. CAMELS-BR complements the other CAMELS datasets by providing data for hundreds of catchments in the tropics and in the Amazon rainforest. Importantly, precipitation and evapotranspiration uncertainties are assessed using several gridded products and quantitative estimates of water consumption are provided to characterize human impacts on water resources. By extracting and combining data from these different data products and making CAMELS-BR publicly available, we aim to create new opportunities for hydrological research in Brazil and to facilitate the inclusion of Brazilian basins in continental to global large-sample studies. We envision that this dataset will enable the community to gain new insights into the drivers of hydrological behavior, better characterize extreme hydroclimatic events, and explore the impacts of climate change and human activities on water resources in Brazil. The CAMELS-BR dataset is freely available at https://doi.org/10.5281/zenodo.3709338 (Chagas et al., 2020).

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

2017 ◽  
Vol 21 (10) ◽  
pp. 5293-5313 ◽  
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.

scholarly journals LamaH | <i>La</i>rge-Sa<i>m</i>ple D<i>a</i>ta for <i>H</i>ydrology and Environmental Sciences for Central Europe

2021 ◽  
Author(s):  
Christoph Klingler ◽  
Karsten Schulz ◽  
Mathew Herrnegger
Keyword(s):  
Time Series ◽  
Central Europe ◽  
Hydrological Cycle ◽  
Human Impacts ◽  
Small Scale ◽  
High Temporal Resolution ◽  
Large Sample ◽  
Potential Applications ◽  
The Individual ◽  
Headwater Basins

Abstract. Very large and comprehensive datasets are increasingly used in the field of hydrology. Large-sample studies provide insights into the hydrological cycle that might not be available with small-scale studies. LamaH (Large-Sample Data for Hydrology) is a new dataset for large-sample studies and comparative hydrology in Central Europe. It covers the entire upper Danube to the state border Austria/Slovakia, as well as all other Austrian catchments including their foreign upstream areas. LamaH covers an area of 170 000 km2 in 9 different countries, ranging from lowland regions characterized by a continental climate to high alpine zones dominated by snow and ice. Consequently, a wide diversity of properties is present in the individual catchments. We represent this variability in 859 observed catchments with over 60 catchment attributes, covering topography, climatology, hydrology, land cover, vegetation, soil and geological properties. LamaH further contains a collection of runoff time series as well as meteorological time series. These time series are provided with daily and also hourly resolution. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses, also with a high temporal resolution. The runoff time series are classified by over 20 different attributes including information about human impacts and indicators for data quality and completeness. The structure of LamaH is based on the well-known CAMELS datasets. In contrast, however, LamaH does not only consider headwater basins. Intermediate catchments are also covered, allowing, for the first time within a hydrological large sample dataset, to consider the hydrological network and river topology in applications. We discuss not only the data basis and the methodology of data preparation, but also focus on possible limitations and uncertainties. Potential applications of LamaH are also outlined, since it is intended to serve as a uniform basis for further research. LamaH is available at https://doi.org/10.5281/zenodo.4525244 (Klingler et al., 2021).

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

2018 ◽  
Author(s):  
Camila Alvarez-Garreton ◽  
Pablo A. Mendoza ◽  
Juan Pablo Boisier ◽  
Nans Addor ◽  
Mauricio Galleguillos ◽  
...  
Keyword(s):  
Time Series ◽  
South America ◽  
The United States ◽  
Data Sets ◽  
Human Intervention ◽  
Data Set ◽  
Large Sample ◽  
Daily Streamflow ◽  
The Andes ◽  
Hydrological Signatures

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.

scholarly journals Extreme species density of bees (Apiformes, Hymenoptera) in the warm deserts of North America

2021 ◽  
Vol 82 ◽  
pp. 317-345
Author(s):  
Robert L. Minckley ◽  
William R. Radke
Keyword(s):  
United States ◽  
North America ◽  
Chihuahuan Desert ◽  
Human Impacts ◽  
The United States ◽  
Limited Area ◽  
Bee Diversity ◽  
The Tropics ◽  
Intensive Sampling

Despite the long intertwined evolutionary histories of bees and plants, bee diversity peaks in the xeric areas of the eastern and western hemispheres and not the tropics, where plant diversity is greatest. Intensive sampling in the northeast Chihuahuan Desert of Mexico and the United States provide the first quantitative estimate of bee species richness where high diversity had been predicted in North America from museum records. We find that the density of bee species in a limited area of 16 km2 far exceeds any other site in the world and amounts to approximately 14% of the bee species described from the United States. Long-term studies of bees and other pollinators from areas that are minimally impacted by humans provide much-needed baseline data for studies of bees where human impacts are more severe and as climate change accelerates.

scholarly journals ERA-CLIM: Historical Surface and Upper-Air Data for Future Reanalyses

2014 ◽  
Vol 95 (9) ◽  
pp. 1419-1430 ◽  
Author(s):  
A. Stickler ◽  
S. Brönnimann ◽  
M. A. Valente ◽  
J. Bethke ◽  
A. Sterin ◽  
...  
Keyword(s):  
United States ◽  
Twentieth Century ◽  
Time Series ◽  
Global Climate ◽  
Meteorological Data ◽  
The United States ◽  
Polar Regions ◽  
Surface Data ◽  
Weather And Climate ◽  
The Tropics

Future reanalyses might profit from assimilating additional historical surface as well as upper-air data. In the framework of the European Reanalysis of Global Climate Observations (ERACLIM; www.era-clim.eu) project, significant amounts of pre-1957 upper-air and surface data have been cataloged (&gt;2.5 million station days), imaged (&gt;450,000 images), and digitized (&gt;1.25 million station days) to prepare new input datasets for upcoming reanalyses. These data cover large parts of the globe, focusing henceforth on less well-covered regions such as the tropics, the polar regions, and the oceans and on very early twentieth-century upper-air data from Europe and the United States. The total numbers of digitized/inventoried records (i.e., time series of meteorological data at fixed stations or from moving observational platforms) are 80/214 (surface), 735/1,783 (upper air), and 61/101 [moving upper-air (i.e., data from ships, etc.)]. Here, the authors give an overview of the data rescue activities, the data, and the applied quality checking procedures and demonstrate their usefulness for analyzing past weather and climate. The data will be made available online (at www.era-clim.eu). The upper-air data will be included in the next version of the Comprehensive Historical Upper-Air Network (CHUAN) and are also available online (http://doi.pangaea.de/10.1594/PANGAEA.821222).

scholarly journals LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe

2021 ◽  
Vol 13 (9) ◽  
pp. 4529-4565
Author(s):  
Christoph Klingler ◽  
Karsten Schulz ◽  
Mathew Herrnegger
Keyword(s):  
Time Series ◽  
Central Europe ◽  
Hydrological Cycle ◽  
Human Impacts ◽  
Small Scale ◽  
High Temporal Resolution ◽  
Environmental Sciences ◽  
Large Sample ◽  
Sample Data ◽  
The Individual

Abstract. Very large and comprehensive datasets are increasingly used in the field of hydrology. Large-sample studies provide insights into the hydrological cycle that might not be available with small-scale studies. LamaH-CE (LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, LamaH for short; the geographical extension “-CE” is omitted in the text and the dataset) is a new dataset for large-sample studies and comparative hydrology in Central Europe. It covers the entire upper Danube to the state border of Austria–Slovakia, as well as all other Austrian catchments including their foreign upstream areas. LamaH covers an area of about 170 000 km2 in nine countries, ranging from lowland regions characterized by a continental climate to high alpine zones dominated by snow and ice. Consequently, a wide diversity of properties is present in the individual catchments. We represent this variability in 859 gauged catchments with over 60 catchment attributes, covering topography, climatology, hydrology, land cover, vegetation, soil and geological properties. LamaH further contains a collection of runoff time series as well as meteorological time series. These time series are provided with a daily and hourly resolution. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with a high temporal resolution. The runoff time series are classified by over 20 attributes including information about human impacts and indicators for data quality and completeness. The structure of LamaH is based on the well-known CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets. In contrast, however, LamaH does not only consider independent basins, covering the full upstream area. Intermediate catchments are covered as well, which allows together with novel attributes the considering of the hydrological network and river topology in applications. We not only describe the basic datasets used and methodology of data preparation but also focus on possible limitations and uncertainties. LamaH contains additionally results of a conceptual hydrological baseline model for checking plausibility of the inputs as well as benchmarking. Potential applications of LamaH are outlined as well, since it is intended to serve as a uniform data basis for further research. LamaH is available at https://doi.org/10.5281/zenodo.4525244 (Klingler et al., 2021).

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

Arsenic in Ground-Water Resources of the United States

Fact Sheet ◽  
2000 ◽  
Author(s):  
Alan H. Welch ◽  
Sharon A. Watkins ◽  
Dennis R. Helsel ◽  
Michael J. Focazio
Keyword(s):  
United States ◽  
Water Resources ◽  
Ground Water ◽  
The United States
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