CRAN Task View: Hydrological Data and Modeling

Maintainer:Sam Albers, Ilaria Prosdocimi
Contact:sam.albers at
Contributions:Suggestions and improvements for this task view are very welcome and can be made through issues or pull requests on GitHub or via e-mail to the maintainer address. For further details see the Contributing guide.
Citation:Sam Albers, Ilaria Prosdocimi (2024). CRAN Task View: Hydrological Data and Modeling. Version 2024-03-08. URL
Installation:The packages from this task view can be installed automatically using the ctv package. For example, ctv::install.views("Hydrology", coreOnly = TRUE) installs all the core packages or ctv::update.views("Hydrology") installs all packages that are not yet installed and up-to-date. See the CRAN Task View Initiative for more details.

This task view contains information about packages broadly relevant to hydrology , defined as the movement, distribution and quality of water and water resources over a broad spatial scale of landscapes. Packages are broadly grouped according to their function; however, many have functionality that spans multiple categories. We also highlight other, existing resources that have related functions - for example, statistical analysis or spatial data processing. See also Riccardo Rigon’s excellent list of hydrology-related R tools and resources. Some Python-related resources can be found here and here.

If you have any comments or suggestions for additions or improvements for this task view, go to GitHub and submit an issue, or make some changes and submit a pull request. If you can’t contribute on GitHub, send Sam Albers an email. If you have an issue with one of the packages discussed below, please contact the maintainer of that package.

Data retrieval

Hydrological data sources (surface water/groundwater quantity and quality)

Meteorological data (precipitation, radiation, temperature, etc - including both measurements and reanalysis)

Data analysis

Data tidying (gap-filling, data organization, QA/QC, etc)

Meteorology (functions for working with meteorological and climate data)


Spatial data processing

The CRAN Spatial task view gives an overview of packages to be used in R to read, visualise, and analyse spatial data. See also the rOpenSci MapTools listing.


Process-based modeling (scripts for preparing inputs/outputs and running process-based models)

See also the r-hydro project on R-Forge and the Astagneau et al. (2021, HESS) paper discussing R packages for Hydrology modelling.

The Environmetrics task view gives an overview of packages used in the analysis of environmental data, encompassing hydrological data, including many statistical approaches used in the ecological sciences. Additionally, packages that help model datasets with extreme values are discussed in the ExtremeValue task view.

CRAN packages

Core:hydroGOF, hydroTSM.
Regular:agriwater, airGR, airGRdatasets, airGRdatassim, airGRiwrm, airGRteaching, baytrends, bdrc, berryFunctions, bigleaf, biotic, BLRPM, boussinesq, clifro, climatol, CoSMoS, CSHShydRology, dataRetrieval, dbhydroR, DeductiveR, dynatop, dynatopGIS, echor, Ecohydmod, ecoval, EGRET, EGRETci, epanet2toolkit, epanetReader, Evapotranspiration, FAdist, fasstr, FedData, FlowScreen, frostr, geotopbricks, grwat, GSODR, gsw, gumboot, gwavr, GWSDAT, HBV.IANIGLA, htsr, hubeau, humidity, hydraulics, hydroEvents, hydrogeo, HydroMe, hydropeak, hydroroute, hydrostats, hydrotoolbox, hyfo, IDF, ie2misc, ie2miscdata, IETD, isoWater, kitagawa, kiwisR, lakemorpho, lfstat, lmom, lmomco, lmomRFA, LPM, lulcc, LWFBrook90R, MBC, meteo, meteoland, metR, MODISTools, musica, nasapower, nhdplusTools, nhdR, noaastormevents, nsRFA, openair, PowerSDI, pRecipe, prism, qmap, Raquifer, RavenR, rdwd, reasonabletools, reservoir, RGENERATEPREC, RHMS, riverdist, rivnet, RMAWGEN, RNCEP, rnrfa, rsoi, rtop, RWDataPlyr, rwunderground, SBN, SCI, soilhypfit, soilwater, SPEI, stationaRy, streamDepletr, SWTools, synthesis, tidyhydat, topmodel, transfR, traudem, TUWmodel, VIC5, washdata, WASP, waterData, waterquality, worldmet, wql, WRSS, WRTDStidal.
Archived:iemisc, iemiscdata, NPRED, rpdo.

Related links

Other resources