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 a typical hydrological workflow based on reproducible principles and packages for retrieval of hydro-meteorological data, spatial analysis, hydrological modelling, statistics, and the design of static and dynamic visualizations 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. Lastly, 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.

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.

Mechanistic understanding of tree-ring formation and its modelling requires a cellularbased and spatially organized characterization of a tree ring, moving from whole rings, to intraannual growth zones and individual cells. A tracheidogram is a radial profile of conifer anatomical features, such as lumen area and cell wall thickness, of sequentially-and positionally-ranked tracheids. However, its construction is tedious and time-consuming since image-analysis-based measurements do not recognize the position of cells within a radial file, and present-day tracheidograms must be constructed manually.Here we present the R-program library RAPTOR that complements tracheid anatomical data obtained from quantitative wood anatomy software (e.g., ROXAS, WinCELL, ImageJ), with the specific positional information necessary for the automatic construction of tracheidograms. The package includes functions to read and visualize tracheid anatomical data, and uses local search algorithms to ascribe a ranked position to each tracheid in identified radial files. The package also provides functions to ensure that tracheids are adequately aligned for identifying the first tracheid in each radial file, and obtaining the correct ranking of tracheids along each radial file. Additional functions allow automating the analyses for multiple samples and rings (batch mode) and exporting data and plots for quality control.RAPTOR allows tracheidogram users to take advantage of the latest generation of cell anatomical measuring systems. With this R-package we aim at facilitating the construction of more robust and versatile tracheidograms for the benefit of the research community.

1. A key ecophysiological measurement is the flow of water (or sap) along the tree's water-transport system, which is an essential process for maintaining the hydraulic connection within the soil-plant-atmosphere continuum. The thermal dissipation method (TDM) is widespread in the scientific community for measuring sap flow and has provided novel insights into water use and its environmental sensitivity, from the tree-to the forest-stand level. Yet, methodological approaches to determine sap flux density (SFD) from raw TDM measurements remain case-specific, introducing uncertainties and hampering data syntheses and meta-analyses. 2. Here, we introduce the r package TREX (TRee sap flow EXtractor), incorporating a wide range of sap flow data-processing procedures to quantify SFD from raw TDM measurements. TREX provides functions for (a) importing and assimilating raw measurements, (b) data quality control and filtering and (c) calculating standardized SFD outputs and their associated uncertainties according to different data-processing methods. 3. A case study using a Norway spruce tree illustrates TREX's functionalities, featuring interactive data curation and generating outputs in a reproducible and transparent way. The calculations of SFD in TREX can, for instance, use the original TDM calibration coefficients, user-supplied calibration parameters or calibration data from a recently compiled database of 22 studies and 37 species. Moreover, the package includes an automatic procedure for quantifying the sensitivity and uncertainty of the obtained results to user-defined assumptions and parameter values, by means of a state-of-the-art global sensitivity analysis. 4. Time series of plant ecophysiological measurements are becoming increasingly available and enhance our understanding of climate change impacts on tree functioning. TREX allows for establishing a baseline for data processing of TDM measurements and supports comparability between case studies, facilitating