references.bib

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@article{gariano_landslides_2016,
	title = {Landslides in a changing climate},
	volume = {162},
	issn = {0012-8252},
	url = {http://www.sciencedirect.com/science/article/pii/S0012825216302458},
	doi = {10.1016/j.earscirev.2016.08.011},
	abstract = {Warming of the Earth climate system is unequivocal. That climate changes affect the stability of natural and engineered slopes and have consequences on landslides, is also undisputable. Less clear is the type, extent, magnitude and direction of the changes in the stability conditions, and on the location, abundance, activity and frequency of landslides in response to the projected climate changes. Climate and landslides act at only partially overlapping spatial and temporal scales, complicating the evaluation of the climate impacts on landslides. We review the literature on landslide-climate studies, and find a bias in their geographical distribution, with large parts of the world not investigated. We recommend to fill the gap with new studies in Asia, South America, and Africa. We examine advantages and limits of the approaches adopted to evaluate the effects of climate variations on landslides, including prospective modelling and retrospective methods that use landslide and climate records. We consider changes in temperature, precipitation, wind and weather systems, and their direct and indirect effects on the stability of single slopes, and we use a probabilistic landslide hazard model to appraise regional landslide changes. Our review indicates that the modelling results of landslide-climate studies depend more on the emission scenarios, the Global Circulation Models, and the methods to downscale the climate variables, than on the description of the variables controlling slope processes. We advocate for constructing ensembles of projections based on a range of emissions scenarios, and to use carefully results from worst-case scenarios that may over/under-estimate landslide hazards and risk. We further advocate that uncertainties in the landslide projections must be quantified and communicated to decision makers and the public. We perform a preliminary global assessment of the future landslide impact, and we present a global map of the projected impact of climate change on landslide activity and abundance. Where global warming is expected to increase the frequency and intensity of severe rainfall events, a primary trigger of rapid-moving landslides that cause many landslide fatalities, we predict an increase in the number of people exposed to landslide risk. Finally, we give recommendations for landslide adaptation and risk reduction strategies in the framework of a warming climate.},
	urldate = {2019-05-02},
	journal = {Earth-Science Reviews},
	author = {Gariano, Stefano Luigi and Guzzetti, Fausto},
	month = nov,
	year = {2016},
	keywords = {Climate change, Climate variables, Hazard, Landslide, Modelling, Risk},
	pages = {227--252},
	file = {Landslides in a changing climate _ Elsevier Enhanced Reader.pdf:C\:\\Users\\cband\\Zotero\\storage\\L2THG6HK\\Landslides in a changing climate _ Elsevier Enhanced Reader.pdf:application/pdf;ScienceDirect Full Text PDF:C\:\\Users\\cband\\Zotero\\storage\\PZP5KD2U\\Gariano and Guzzetti - 2016 - Landslides in a changing climate.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\cband\\Zotero\\storage\\F73EGP9X\\S0012825216302458.html:text/html}
}

@article{strauch_hydroclimatological_2018,
	title = {A hydroclimatological approach to predicting regional landslide probability using {Landlab}},
	volume = {6},
	issn = {2196-6311},
	url = {https://www.earth-surf-dynam.net/6/49/2018/esurf-6-49-2018.html},
	doi = {https://doi.org/10.5194/esurf-6-49-2018},
	abstract = {{\textless}p{\textgreater}{\textless}strong{\textgreater}Abstract.{\textless}/strong{\textgreater} We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km$^{\textrm{2}}$. The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at mid-elevations (1400 to 2400 m), and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.{\textless}/p{\textgreater}},
	language = {English},
	number = {1},
	urldate = {2020-04-16},
	journal = {Earth Surface Dynamics},
	author = {Strauch, Ronda and Istanbulluoglu, Erkan and Nudurupati, Sai Siddhartha and Bandaragoda, Christina and Gasparini, Nicole M. and Tucker, Gregory E.},
	month = feb,
	year = {2018},
	note = {Publisher: Copernicus GmbH},
	pages = {49--75},
	file = {Full Text PDF:C\:\\Users\\cband\\Zotero\\storage\\YWSJH4IA\\Strauch et al. - 2018 - A hydroclimatological approach to predicting regio.pdf:application/pdf;Snapshot:C\:\\Users\\cband\\Zotero\\storage\\GI2FA8C9\\esurf-6-49-2018.html:text/html}
}

@article{hobley_creative_2017,
	title = {Creative computing with {Landlab}: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of {Earth}-surface dynamics},
	volume = {5},
	issn = {2196-6311},
	shorttitle = {Creative computing with {Landlab}},
	url = {https://www.earth-surf-dynam.net/5/21/2017/esurf-5-21-2017.html},
	doi = {https://doi.org/10.5194/esurf-5-21-2017},
	abstract = {{\textless}p{\textgreater}{\textless}strong{\textgreater}Abstract.{\textless}/strong{\textgreater} The ability to model surface processes and to couple them to both subsurface and atmospheric regimes has proven invaluable to research in the Earth and planetary sciences. However, creating a new model typically demands a very large investment of time, and modifying an existing model to address a new problem typically means the new work is constrained to its detriment by model adaptations for a different problem. Landlab is an open-source software framework explicitly designed to accelerate the development of new process models by providing (1) a set of tools and existing grid structures – including both regular and irregular grids – to make it faster and easier to develop new process components, or numerical implementations of physical processes; (2) a suite of stable, modular, and interoperable process components that can be combined to create an integrated model; and (3) a set of tools for data input, output, manipulation, and visualization. A set of example models built with these components is also provided. Landlab's structure makes it ideal not only for fully developed modelling applications but also for model prototyping and classroom use. Because of its modular nature, it can also act as a platform for model intercomparison and epistemic uncertainty and sensitivity analyses. Landlab exposes a standardized model interoperability interface, and is able to couple to third-party models and software. Landlab also offers tools to allow the creation of cellular automata, and allows native coupling of such models to more traditional continuous differential equation-based modules. We illustrate the principles of component coupling in Landlab using a model of landform evolution, a cellular ecohydrologic model, and a flood-wave routing model.{\textless}/p{\textgreater}},
	language = {English},
	number = {1},
	urldate = {2020-04-16},
	journal = {Earth Surface Dynamics},
	author = {Hobley, Daniel E. J. and Adams, Jordan M. and Nudurupati, Sai Siddhartha and Hutton, Eric W. H. and Gasparini, Nicole M. and Istanbulluoglu, Erkan and Tucker, Gregory E.},
	month = jan,
	year = {2017},
	note = {Publisher: Copernicus GmbH},
	pages = {21--46},
	file = {Full Text PDF:C\:\\Users\\cband\\Zotero\\storage\\ULNX64AN\\Hobley et al. - 2017 - Creative computing with Landlab an open-source to.pdf:application/pdf;Snapshot:C\:\\Users\\cband\\Zotero\\storage\\J2LXMGLR\\esurf-5-21-2017.html:text/html}
}

@misc{stephan_hoyer_pydataxarray_2020,
	title = {pydata/xarray: v0.16.0},
	shorttitle = {pydata/xarray},
	url = {https://zenodo.org/record/3940662#.Xxtk55uSmiM},
	abstract = {This release adds xarray.cov \& xarray.corr for covariance \& correlation respectively; the idxmax \& idxmin methods, the polyfit method \& xarray.polyval for fitting polynomials, as well as a number of documentation improvements, other features, and bug fixes. Many thanks to all 44 contributors who contributed to this release.},
	urldate = {2020-07-24},
	publisher = {Zenodo},
	author = {Stephan Hoyer and Joe Hamman and Maximilian Roos and Deepak Cherian and Clark Fitzgerald and Keisuke Fujii and Fabien Maussion and keewis and crusaderky and Alex Kleeman and Spencer Clark and Thomas Kluyver and James Munroe and Tom Nicholas and Zac Hatfield-Dodds and Mathias Hauser and Ryan Abernathey and MaximilianR and Phillip J. Wolfram and Julia Signell and Yohai Bar Sinai and Jonathan J. Helmus and Gregory Gundersen and Markel and Pete Cable and Benoit Bovy and Andrew Barna and Gerardo Rivera and Matthew Rocklin and johnomotani},
	month = jul,
	year = {2020},
	doi = {10.5281/zenodo.3940662},
	file = {Zenodo Snapshot:C\:\\Users\\cband\\Zotero\\storage\\GE6I2IMQ\\3940662.html:text/html}
}

@article{hoyer_xarray_2017,
	title = {xarray: {N}-{D} labeled {Arrays} and {Datasets} in {Python}},
	volume = {5},
	copyright = {Authors who publish with this journal agree to the following terms:    Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a  Creative Commons Attribution License  that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.  Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.  Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See  The Effect of Open Access ).  All third-party images reproduced on this journal are shared under Educational Fair Use. For more information on  Educational Fair Use , please see  this useful checklist prepared by Columbia University Libraries .   All copyright  of third-party content posted here for research purposes belongs to its original owners.  Unless otherwise stated all references to characters and comic art presented on this journal are ©, ® or ™ of their respective owners. No challenge to any owner’s rights is intended or should be inferred.},
	issn = {2049-9647},
	shorttitle = {xarray},
	url = {http://openresearchsoftware.metajnl.com/articles/10.5334/jors.148/},
	doi = {10.5334/jors.148},
	abstract = {xarray is an open source project and Python package that provides a toolkit and data structures for N-dimensional labeled arrays. Our approach combines an application programing interface (API) inspired by pandas with the Common Data Model for self-described scientific data. Key features of the xarray package include label-based indexing and arithmetic, interoperability with the core scientific Python packages (e.g., pandas, NumPy, Matplotlib), out-of-core computation on datasets that don’t fit into memory, a wide range of serialization and input/output (I/O) options, and advanced multi-dimensional data manipulation tools such as group-by and resampling. xarray, as a data model and analytics toolkit, has been widely adopted in the geoscience community but is also used more broadly for multi-dimensional data analysis in physics, machine learning and finance.},
	language = {en},
	number = {1},
	urldate = {2020-07-24},
	journal = {Journal of Open Research Software},
	author = {Hoyer, Stephan and Hamman, Joe},
	month = apr,
	year = {2017},
	note = {Number: 1
Publisher: Ubiquity Press},
	keywords = {data analysis, data, data handling, multidimensional, netCDF, pandas, Python},
	pages = {10},
	file = {Full Text PDF:C\:\\Users\\cband\\Zotero\\storage\\Z3NAP8FT\\Hoyer and Hamman - 2017 - xarray N-D labeled Arrays and Datasets in Python.pdf:application/pdf;Snapshot:C\:\\Users\\cband\\Zotero\\storage\\78LMZUU7\\jors.148.html:text/html}
}