hydroscoper: R interface to the Greek National Data Bank for Hydrological and Meteorological Information

Vantas, Konstantinos

doi:10.21105/joss.00625

Cited by 11 publications

(13 citation statements)

References 4 publications

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“…• Pluviograph data for 87 meteorological stations that were taken from the Greek National Bank of Hydrological and Meteorological Information (NBHM) [52]. The time series…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

“…The data importing, analysis and presentation were done using the open source R language for statistical computing and graphics [82] using the packages: hydroscoper [52], hyetor [85], ranger [67], tuneRanger [83], raster [86] and ggplot2 [87].…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…Pluviograph data for 87 meteorological stations that were taken from the Greek National Bank of Hydrological and Meteorological Information (NBHM) [52]. The time series comprised a total of 2273 years of 30-min-records and 394 years of five-min-records for the time period from 1953 to 1997, with a mean length of 30 years per station.…”

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confidence: 99%

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Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests

Vantas

Sidiropoulos

Λουκάς

2020

Water

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A future variation of precipitation characteristics, due to climate change, will affect the ability of rainfall to precipitate soil loss. In this paper, the monthly and annual values of rainfall erosivity (R) in Greece are calculated, for the historical period 1971–2000, using precipitation records that suffer from a significant volume of missing values. In order to overcome the data limitations, an intermediate step is applied using the calculation of monthly erosivity density, which is more robust to the presence of missing values. Spatial Quantile Regression Forests, a data driven algorithm that imitates kriging without the need of strict statistical assumptions, was utilized and validated, in order to create maps of R and its uncertainty using error propagation. The monthly average precipitation for the historical period 1971–2000 estimated by five (5) Global Circulation Models-Regional Climatic Models were validated against observed values and the one with the best performance was used to estimate projected changes of R in Greece for the future time period 2011–2100 and two different greenhouse gases concentration scenarios. The main findings of this study are: (a) the mean annual R in Greece is 1039 MJ·mm/ha/h/y, with a range between 405.1 and 3160.2 MJ·mm/ha/h/y. The highest values are calculated at the mountain range of Pindos and the lowest at central Greece; (b) the monthly R maps adhere to the spatiotemporal characteristics of precipitation depth and intensities over the country; (c) the projected R values, as an average over Greece, follow the projected changes of precipitation of climatic models, but not in a spatially homogenous way.

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“…• Pluviograph data for 87 meteorological stations that were taken from the Greek National Bank of Hydrological and Meteorological Information (NBHM) [52]. The time series…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

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Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests

Vantas

Sidiropoulos

Λουκάς

2020

Water

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“…The data used in the analysis were taken from the Greek National Bank of Hydrological and Meteorological Information [33] for 108 meteorological stations across Greece ( Figure 2). The time series comprised a total of 2447 years of 30-minute-records and 478 years of five-minute-records for the time period from 1953 to 1997, with a mean length of 26.6 years per station.…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

Robustness Spatiotemporal Clustering and Trend Detection of Rainfall Erosivity Density in Greece

Vantas

Sidiropoulos

Λουκάς

2019

Water

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Soil erosion is affected by rainfall, among other factors, and it is likely to increase in the future due to climate change impacts, resulting in higher rainfall intensities. This paper evaluates the impact of the missing values ratio on the computation of the rainfall erosivity factor, R, and erosivity density, ED. The paper also investigates the temporal trends and defines regions of Greece with a similar monthly distribution of ED using an unsupervised method. Preprocessed and free from noise and errors rainfall data from 108 stations across Greece were extracted from the Greek National Bank of Hydrological and Meteorological Information. The rainfall data were analyzed and erosive rainfalls were identified, their return period was determined using intensity–duration–frequency curves and R and ED values were computed. The impact of missing data in the computation of annual values of R and ED was investigated using a Monte Carlo simulation. The findings indicated that missing rainfall data resulted in a linear underestimation of R, while ED is more robust. The trends in ED timeseries were evaluated using the Kendall’s Tau test and their autocorrelation and partial autocorrelation were computed for a small subset of stations using criteria based on the quality of data. Furthermore, cluster analysis was applied to a larger subset of stations to define regions of Greece with similar monthly distribution of ED. The findings of this study indicate that: (a) ED should be preferred for the assessment of erosivity in Greece over the direct computation of R, (b) ED timeseries are found to be stationary for the majority of the selected stations, in contrast to reported precipitation trends for the same time period, (c) Greece is divided into three clusters/areas of stations with distinct monthly distributions of ED.

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“…Retrieve USGS and EPA hydrologic and water quality data (Hirsch and De Cicco, 2015) daymetr Interface to the Daymet web services: NASA daily surface weather and climatological summaries over North America, Hawaii, Puerto Rico (Hufkens et al, 2018) ecmwfr Interface to the public ECMWF API web services (Hufkens, 2018) getMet Get meteorological data for hydrologic models (Sommerlot et al, 2016) hddtools Hydrological data discovery tools (Vitolo, 2017) hydroscoper Interface to the Greek national data bank for hydrometeorological information (Vantas, 2018) prism Access the Oregon State Prism climate data using the web service API data (Hart and Bell, 2015) rnoaa Interface to NOAA weather data (Chamberlain, 2019) rnrfa Retrieve, filter and visualize data from the UK National River Flow Archive (Vitolo et al, 2016a) tidyhydat Extract and tidy Canadian hydrometric data (Albers, 2017) waterData Retrieve, analyze, and calculate anomalies of daily hydrologic time series data (Ryberg and Vecchia, 2017) Once data have been retrieved or downloaded, a broad range of packages are available for reading different types of data and their associated metadata. However, these packages are not specifically hydrological and so will be discussed here with 5 brevity.…”

Section: Package Description Dataretrievalmentioning

confidence: 99%

Using R in hydrology: a review of recent developments and future directions

Slater¹,

Thirel²,

Harrigan³

et al. 2019

Preprint

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Abstract. The open-source programming language R has gained a central place in the hydrological sciences over the last decade, driven by the availability of diverse hydro-meteorological data archives and the development of open-source computational tools. The growth of R's usage in hydrology is reflected in the number of newly published hydrological packages, the strengthening of online user communities, and the popularity of training courses and events. In this paper, we explore the benefits and advantages of R's usage in hydrology, such as: the democratization of data science and numerical literacy, the enhancement of reproducible research and open science, the access to statistical tools, the ease of connecting R to and from other languages, and the support provided by a growing community. This paper provides an overview of important packages at every step of the hydrological workflow, from the retrieval of hydro-meteorological data, to spatial analysis and cartography, hydrological modelling, statistics, and the design of static and dynamic visualizations, presentations and documents. We discuss some of the challenges that arise when using R in hydrology and useful tools to overcome them, including the use of hydrological libraries, documentation and vignettes (long-form guides that illustrate how to use packages); the role of Integrated Development Environments (IDEs); and the challenges of Big Data and parallel computing in hydrology. Last, this paper provides a roadmap for R's future within hydrology, with R packages as a driver of progress in the hydrological sciences, Application Programming Interfaces (APIs) providing new avenues for data acquisition and provision, enhanced teaching of hydrology in R, and the continued growth of the community via short courses and events.

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hydroscoper: R interface to the Greek National Data Bank for Hydrological and Meteorological Information

Cited by 11 publications

References 4 publications

Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests

Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests

Robustness Spatiotemporal Clustering and Trend Detection of Rainfall Erosivity Density in Greece

Using R in hydrology: a review of recent developments and future directions

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