Hydrological signature indicators

.gitignore

@@ -113,8 +113,8 @@ docs/_dynamic/indicators.json
 docs/variables.json
 
 # autogenerated BibTeX files
-docs/tmp_saved_input_references.bib
 docs/paper/tmp_saved_input_paper.bib
+docs/tmp_saved_input_references.bib
 
 # VS Code
 .vscode

.zenodo.json

@@ -88,6 +88,11 @@
       "affiliation": "Ouranos Consortium, Montréal, Québec, Canada",
       "orcid": "0000-0003-0738-3940"
     },
+    {
+      "name": "Larose, Ève",
+      "affiliation": "Polytechnique, Montréal, Québec, Canada",
+      "orcid": "0009-0005-0832-4047"
+    },
     {
       "name": "Lierhammer, Ludwig",
       "affiliation": "Deutscher Wetterdienst, Hamburg, Germany",

AUTHORS.rst

@@ -52,3 +52,4 @@ Contributors
 * Jack Kit-tai Wong <kit.tai.wong@gmail.com> `@jack-ktw <https://github.com/jack-ktw>`_
 * Jens de Bruijn <j.a.debruijn@outlook.com> `@jensdebruijn <https://github.com/jensdebruijn>`_
 * Armin Hofmann `@HofmannGeo <https://github.com/HofmannGeo>`_
+* Eve Larose <eve.larose.r@gmail.com> `@e-larose <https://github.com/e-larose>`_

CHANGELOG.rst

@@ -4,7 +4,11 @@ Changelog
 
 v0.60.0 (unreleased)
 --------------------
-Contributors to this version: Éric Dupuis (:user:`coxipi`), Trevor James Smith (:user:`Zeitsperre`).
+Contributors to this version: Éric Dupuis (:user:`coxipi`), Trevor James Smith (:user:`Zeitsperre`), David Huard (:user:`huard`), Ève Larose (:user:`e-larose`), Faisal Mahmood (:user:`faimahsho`).
+
+New indicators and features
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+* New hydrological indices added to ``xclim.indices._hydrology.py``. (:issue:`1624`, :pull:`2227`).
 
 Breaking changes
 ^^^^^^^^^^^^^^^^

CITATION.cff

@@ -48,6 +48,9 @@ authors:
 - family-names: Labonté
   given-names: Marie-Pier
   orcid: "https://orcid.org/0000-0003-0738-3940"
+- family-name: "Larose"
+  given-name: "Ève"
+  orcid: "https://orcid.org/0009-0005-0832-4047"
 - family-names: Lierhammer
   given-names: Ludwig
   orcid: "https://orcid.org/0000-0002-7207-0003"

docs/paper/tmp_saved_input_paper.bib

@@ -0,0 +1,108 @@
+@article{Hassel:2017,
+  title = {A data model of the Climate and Forecast metadata conventions (CF-1.6) with a
+    software implementation (cf-python v2.1)},
+  author = {Hassell, D. and Gregory, J. and Blower, J. and Lawrence, B. N. and Taylor, K.
+    E.},
+  doi = {10.5194/gmd-10-4619-2017},
+  journal = {Geoscientific Model Development},
+  number = {12},
+  pages = {4619--4646},
+  url = {https://gmd.copernicus.org/articles/10/4619/2017/},
+  volume = {10},
+  year = {2017}
+}
+
+
+@article{Hoyer:2017,
+  title = {xarray: {N}-{D} labeled {Arrays} and {Datasets} in {Python}},
+  author = {Hoyer, Stephan and Hamman, Joseph J.},
+  doi = {10.5334/jors.148},
+  issn = {2049-9647},
+  journal = {Journal of Open Research Software},
+  language = {en},
+  month = {apr},
+  pages = {10},
+  shorttitle = {xarray},
+  url = {http://openresearchsoftware.metajnl.com/articles/10.5334/jors.148/},
+  urldate = {2019-07-02},
+  volume = {5},
+  year = {2017}
+}
+
+
+@article{Page:2022,
+  title = {Access to Analysis and Climate Indices Tools for Climate Researchers and End
+    Users},
+  author = {Christian Pagé and Alessandro Spinuso and Lars Bärring and Klaus Zimmermann and
+    Abel Aoun},
+  doi = {10.1002/essoar.10510291.1},
+  month = {jan},
+  publisher = {Wiley},
+  url = {https://doi.org/10.1002%2Fessoar.10510291.1},
+  year = {2022}
+}
+
+
+@misc{icclim,
+  title = {Python library for climate indices calculation},
+  author = {Christian Pagé and Abel Aoun and Natalia Tatarinova},
+  journal = {GitHub repository},
+  doi = {10.5281/zenodo.7382653},
+  license = {Apache-2.0 license},
+  publisher = {GitHub},
+  url = {https://github.com/cerfacs-globc/icclim},
+  year = {2022}
+}
+
+
+@misc{metpy,
+  title = {{MetPy: A Python Package for Meteorological Data}},
+  author = {May, Ryan and Arms, Sean and Marsh, Patrick and Bruning, Eric and Leeman, John
+    and Goebbert, Kevin and Thielen, Jonathan and Bruick, Zachary and Camron, M. Drew},
+  doi = {10.5065/D6WW7G29},
+  journal = {GitHub repository},
+  license = {BSD-3-Clause},
+  publisher = {GitHub},
+  url = {https://github.com/Unidata/MetPy},
+  year = {2022}
+}
+
+
+@misc{clisops,
+  title = {clisops - climate simulation operations},
+  author = {Ag Stephens and Eleanor Smith and Carsten Ehbrecht and Trevor James Smith},
+  journal = {GitHub repository},
+  publisher = {GitHub},
+  url = {https://github.com/roocs/clisops},
+  year = {2022}
+}
+
+
+@misc{xesmf,
+  title = {xESMF: Universal Regridder for Geospatial Data},
+  author = {Jiawei Zhuang and David Huard and Pascal Bourgault and Raphael Dussin and
+    Anderson Banihirwe and Stéphane Raynaud},
+  journal = {GitHub repository},
+  publisher = {GitHub},
+  url = {https://github.com/pangeo-data/xESMF},
+  year = {2022}
+}
+
+
+@misc{finch,
+  title = {A Web Processing Service for Climate Indicators},
+  author = {David Huard and Pascal Bourgault and Trevor James Smith and David Caron and
+    Long Vu and Mathieu Provencher},
+  journal = {GitHub repository},
+  publisher = {GitHub},
+  url = {https://github.com/bird-house/finch},
+  year = {2022}
+}
+
+
+@manual{dask:2016,
+  title = {Dask: Library for dynamic task scheduling},
+  author = {Dask Development Team},
+  year = {2016},
+  url = {https://dask.org}
+}

docs/references.bib

@@ -4216,3 +4216,198 @@ @incollection{ecwmf_physical_2016
   year = {2016},
   url = {    https://www.ecmwf.int/sites/default/files/elibrary/2016/17117-part-iv-physical-processes.pdf}
 }
+
+
+@article{alonso_gonzalez_2022,
+  title = {Combined influence of maximum accumulation and melt rates on the duration of the
+    seasonal snowpack over temperate mountains},
+  journal = {Journal of Hydrology},
+  volume = {608},
+  pages = {127574},
+  year = {2022},
+  issn = {0022-1694},
+  doi = {https://doi.org/10.1016/j.jhydrol.2022.127574},
+  url = {https://www.sciencedirect.com/science/article/pii/S0022169422001494},
+  author = {Esteban Alonso-González and Jesús Revuelto and Steven R. Fassnacht and Juan
+    {Ignacio López-Moreno}},
+  abstract = {The duration of the seasonal snowpack determines numerous aspects of the
+    water cycle, ecology and the economy in cold and mountainous regions, and is a balance
+    between the magnitude of accumulated snow and the rate of melt. The contribution of
+    each component has not been well quantified under contrasting topography and
+    climatological conditions although this may provide useful insights into how snow cover
+    duration could respond to climate change. Here, we examined the contribution of the
+    annual peak snow water equivalent (SWE) and the seasonal melt rate to define the
+    duration of the snowpack over temperate mountains, using snow data for mountain areas
+    with different climatological characteristics across the Iberian Peninsula. We used a
+    daily snowpack database for the period 1980--2014 over Iberia to derive the seasonal
+    peak SWE, melt rate and season snow cover duration. The influence of peak SWE and melt
+    rates on seasonal snow cover duration was estimated using a stepwise linear model
+    approach. The stepwise linear models showed high R-adjusted values (average R-adjusted
+    = 0.7), without any clear dependence on the elevation or geographical location. In
+    general, the peak SWE influenced the snow cover duration over all of the mountain areas
+    analysed to a greater extent than the melt rates (89.1\%, 89.2\%, 81.6\%, 93.2\% and
+    95.5\% in the areas for the Cantabrian, Central, Iberian, Pyrenees and Sierra Nevada
+    mountain ranges, respectively). At these colder sites, the melt season occurs mostly in
+    the spring and tends to occur very fast. In contrast, the areas where the melt rates
+    dominated snow cover duration were located systematically at lower elevations, due to
+    the high interannual variability in the occurrence of annual peak SWE (in winter or
+    early spring), yielding highly variable melt rates. However, in colder sites the melt
+    season occurs mostly in spring and it is very fast in most of the years. The results
+    highlight the control that the seasonal precipitation patterns, in combination with
+    temperature, exert on the seasonal snow cover duration by influencing the peak SWE and
+    suggest a future increased importance of melt rates as temperatures increase. Despite
+    the high climatological variability of the Iberian mountain ranges, the results showed
+    a consistent behaviour along the different mountain ranges, indicating that the methods
+    and results may be transferrable to other temperate mountain areas of the world.}
+}
+
+
+@article{sauquet_2025,
+  AUTHOR = {Sauquet, E. and Evin, G. and Siauve, S. and Aissat, R. and Arnaud, P. and
+    B\'erel, M. and Bonneau, J. and Branger, F. and Caballero, Y. and Coll\'eoni, F. and
+    Ducharne, A. and Gailhard, J. and Habets, F. and Hendrickx, F. and H\'eraut, L. and
+    Hingray, B. and Huang, P. and Jaouen, T. and Jeantet, A. and Lanini, S. and Le Lay, M.
+    and Magand, C. and Mimeau, L. and Monteil, C. and Munier, S. and Perrin, C. and
+    Robelin, O. and Rousset, F. and Soubeyroux, J.-M. and Strohmenger, L. and Thirel, G.
+    and Tocquer, F. and Tramblay, Y. and Vergnes, J.-P. and Vidal, J.-P.},
+  TITLE = {A large transient multi-scenario multi-model ensemble of future streamflow and
+    groundwater projections in France},
+  JOURNAL = {EGUsphere},
+  VOLUME = {2025},
+  YEAR = {2025},
+  PAGES = {1--41},
+  URL = {https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1788/},
+  DOI = {10.5194/egusphere-2025-1788}
+}
+
+
+@article{burn_2010,
+  author = {Burn, Donald H. and Sharif, Mohammed and Zhang, Kan},
+  title = {Detection of trends in hydrological extremes for Canadian watersheds},
+  journal = {Hydrological Processes},
+  volume = {24},
+  number = {13},
+  pages = {1781-1790},
+  keywords = {flood analysis, low flow events, climate change, trend analysis, Canada},
+  doi = {https://doi.org/10.1002/hyp.7625},
+  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.7625},
+  eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.7625},
+  abstract = {Abstract The potential impacts of climate change can alter the risk to
+    critical infrastructure resulting from changes to the frequency and magnitude of
+    extreme events. As well, the natural environment is affected by the hydrologic regime,
+    and changes in high flows or low flows can have negative impacts on ecosystems. This
+    article examines the detection of trends in extreme hydrological events, both high and
+    low flow events, for streamflow gauging stations in Canada. The trend analysis involves
+    the application of the Mann–Kendall non-parametric test. A bootstrap resampling process
+    has been used to determine the field significance of the trend results. A total of 68
+    gauging stations having a nominal record length of at least 50 years are analysed for
+    two analysis periods of 50 and 40 years. The database of Canadian rivers investigated
+    represents a diversity of hydrological conditions encompassing different extreme flow
+    generating processes and reflects a national scale analysis of trends. The results
+    reveal more trends than would be expected to occur by chance for most of the measures
+    of extreme flow characteristics. Annual and spring maximum flows show decreasing trends
+    in flow magnitude and decreasing trends in event timing (earlier events). Low flow
+    magnitudes exhibit both decreasing and increasing trends. Copyright © 2010 John Wiley
+    \& Sons, Ltd.},
+  year = {2010}
+}
+
+
+@article{zomer_2022,
+  title = {Version 3 of the global aridity index and potential evapotranspiration database},
+  author = {Zomer, Robert J and Xu, Jianchu and Trabucco, Antonio},
+  journal = {Scientific Data},
+  volume = {9},
+  number = {1},
+  pages = {409},
+  year = {2022},
+  publisher = {Nature Publishing Group UK London}
+}
+
+
+@article{knoben_2024,
+  title = {Setting expectations for hydrologic model performance with an ensemble of simple
+    benchmarks},
+  author = {Knoben, Wouter JM},
+  journal = {Hydrological Processes},
+  volume = {38},
+  number = {10},
+  pages = {e15288},
+  year = {2024},
+  publisher = {Wiley Online Library}
+}
+
+
+@article{singh_2019,
+  title = {Towards baseflow index characterisation at national scale in New Zealand},
+  journal = {Journal of Hydrology},
+  volume = {568},
+  pages = {646-657},
+  year = {2019},
+  issn = {0022-1694},
+  doi = {https://doi.org/10.1016/j.jhydrol.2018.11.025},
+  url = {https://www.sciencedirect.com/science/article/pii/S0022169418308801},
+  author = {Shailesh Kumar Singh and Markus Pahlow and Doug J. Booker and Ude Shankar and
+    Alejandro Chamorro},
+  keywords = {Baseflow, Quickflow, Prediction, BFI, Random forests technique},
+  abstract = {Streamflow is typically divided into two components for hydrograph
+    separation, quickflow and baseflow. Baseflow is the portion of streamflow that contains
+    groundwater flow and flow from other delayed sources and is of key importance for river
+    basin ecology and water resources planning and management. The BaseFlow Index (BFI) is
+    defined as the ratio of long-term mean baseflow to total streamflow. Knowledge of the
+    BFI is not directly available for ungauged catchments and hence for most of the
+    terrestrial land surface. In this study, the BFI was determined for all river reaches
+    in New Zealand. First a recursive digital filtering technique was applied to separate
+    baseflow from total streamflow for 482 gauged sites across New Zealand, whereby an
+    individual filter parameter was determined for each catchment. Based on the baseflow
+    and total streamflow data the long-term BFI for each gauged site was determined, as
+    well as seasonal values of BFI. BFI varies between 0.20 and 0.96 with an average of
+    0.53, which indicates that 53% of long-term streamflow in New Zealand is likely to
+    originate from groundwater discharge and other delayed sources. Long-term BFI values
+    for all river reaches that comprise the New Zealand river network were predicted using
+    the random forest technique. Furthermore, the winter to summer BFI for all river
+    reaches in New Zealand were also determined. Distinct spatial patterns of the BFI were
+    identified. While the spatial distribution and the magnitude of the BFI was determined
+    by a combination of factors, certain patterns can be attributed to geological
+    formations in New Zealand, namely the volcanic plateau region and the Southern Alps.
+    While the dataset determined in this work can support work specifically pertaining to
+    water resources planning and management in New Zealand, in particular water supply,
+    stream ecology and pollution risk, the methodology devised to calculate the BFI for
+    gauged sites and to predict the BFI for ungauged sites is applicable to any region
+    around the world.}
+}
+
+
+@article{jaffres_2021,
+  title = {Hydrological characteristics of Australia: relationship between surface flow,
+    climate and intrinsic catchment properties},
+  journal = {Journal of Hydrology},
+  volume = {603},
+  pages = {126911},
+  year = {2021},
+  issn = {0022-1694},
+  doi = {https://doi.org/10.1016/j.jhydrol.2021.126911},
+  url = {https://www.sciencedirect.com/science/article/pii/S0022169421009616},
+  author = {Jasmine B.D. Jaffrés and Ben Cuff and Chris Cuff and Iain Faichney and Matthew
+    Knott and Cecily Rasmussen},
+  keywords = {Climate variability, Non-perennial streams, Surface hydrology, Topography,
+    Soil field capacity, Water infiltration},
+  abstract = {Streamflow and baseflow dynamics are driven by complex, interconnected
+    catchment properties. A national study was conducted to assess the relationship between
+    surface flow, climate and intrinsic catchment attributes in Australia. Subcatchments
+    were delineated based on Horton's 5th stream order and were characterised by
+    identifying parameters that influence streamflow and flood behaviour. Because
+    observational datasets like rainfall and streamflow commonly have a non-normal
+    distribution, the method of L-moments was applied to several time series. Surface
+    hydrology and baseflow patterns were represented by twenty indices, which were
+    statistically summarised via principal component (PC) analysis, yielding six PCs. Forty
+    catchment descriptors from the themes of climate, topography, surface condition and
+    hydrogeology were used to investigate their link with runoff patterns. Among these is
+    the land surface value, a newly defined index incorporating soil properties and land
+    use to estimate the capacity for water infiltration. All metrics were explored via
+    correlation and regression analysis against the surface hydrology PCs and their
+    influence on runoff discussed. The predictive skill of the regression models is
+    improved when non-perennial waterways are excluded. Although rainfall characteristics
+    dominate streamflow behaviour, topographical and surface conditions also greatly impact
+    on runoff, especially during low-flow periods.}
+}

pyproject.toml

@@ -108,7 +108,7 @@ docs = [
   "sphinxcontrib-bibtex",
   "sphinxcontrib-svg2pdfconverter[Cairosvg]"
 ]
-extras = ["flox >=0.9", "lmoments3 >=1.0.7", "numbagg >=0.8", "xsdba >=0.4.0"]
+extras = ["flox >=0.9", "lmoments3 >=1.0.7", "numbagg >=0.8", "pymannkendall >=1.4.0", "xsdba >=0.4.0"]
 all = ["xclim[dev]", "xclim[docs]", "xclim[extras]"]
 
 [project.scripts]
@@ -170,6 +170,7 @@ pep621_dev_dependency_groups = ["all", "dev", "docs"]
 "pyyaml" = "yaml"
 
 [tool.deptry.per_rule_ignores]
+DEP001 = ["pymannkendall"]
 DEP002 = ["bottleneck", "h5netcdf", "lmoments3", "numbagg", "pyarrow"]
 DEP004 = ["matplotlib", "pooch", "pytest", "pytest_socket"]