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LamaH datasets always consist of a combination of meteorological time series (e.g., precipitation, temperature) and hydrologically relevant catchment attributes (e.g., elevation, slope, forest area, soil, bedrock) aggregated over the respective catchment as well as associated hydrological time series
82:). By evaluating the large and heterogeneous sample (large-sample) of catchments, it is possible to gain insights into the hydrological cycle that would probably not be achievable with local and small-scale studies. The structure of the dataset allows an evaluation based on
325:
Alvarez-Garreton, Camila; Mendoza, Pablo A.; Boisier, Juan Pablo; Addor, Nans; Galleguillos, Mauricio; Zambrano-Bigiarini, Mauricio; Lara, Antonio; Puelma, Cristóbal; Cortes, Gonzalo; Garreaud, Rene; McPhee, James (2018-11-13).
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Further basin delineations (based on intermediate catchments) and attributes (e.g. flow distance and altitude difference between two topologically adjacent discharge gauges), enabling the setup of a interconnected hydrological
278:"Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance"
444:
Coxon, Gemma; Addor, Nans; Bloomfield, John P.; Freer, Jim; Fry, Matt; Hannaford, Jamie; Howden, Nicholas J. K.; Lane, Rosanna; Lewis, Melinda; Robinson, Emma L.; Wagener, Thorsten (2020-10-12).
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Central Europe (Austria and its hydrological upstream areas in
Germany, Czech Republic, Switzerland, Slovakia, Italy, Liechtenstein, Slovenia and Hungary) / 859 catchments
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Chagas, Vinícius B. P.; Chaffe, Pedro L. B.; Addor, Nans; Fan, Fernando M.; Fleischmann, Ayan S.; Paiva, Rodrigo C. D.; Siqueira, Vinícius A. (2020-09-08).
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Newman, A. J.; Clark, M. P.; Sampson, K.; Wood, A.; Hay, L. E.; Bock, A.; Viger, R. J.; Blodgett, D.; Brekke, L.; Arnold, J. R.; Hopson, T. (2015-01-14).
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ydrology and
Environmental Sciences) is a cross-state initiative for unified data preparation and collection in the field of catchment
90:). The accompanying paper explains not only the data preparation but also any limitations, uncertainties and possible applications.
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Attributes for classifying catchments and runoff gauges according to the degree and type of (anthropogenic) influence
154:
493:
Fowler, Keirnan J. A.; Acharya, Suwash
Chandra; Addor, Nans; Chou, Chihchung; Peel, Murray C. (2021-08-06).
446:"CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain"
79:
495:"CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia"
328:"The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset"
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387:"CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil"
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180:"LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe"
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Addor, Nans; Newman, Andrew J.; Mizukami, Naoki; Clark, Martyn P. (2017-10-20).
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65:. Hydrological datasets, for example, are an integral component for creating
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Klingler, Christoph; Schulz, Karsten; Herrnegger, Mathew (2021-09-16).
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Cross-state initiative for unified data preparation and collection
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Contiguous USA (exclusive Alaska and Hawaii) / 671 catchments
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and are therefore available barrier-free for the public.
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datasets are available for (ranked by publication date):
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Both the CAMELS and LamaH datasets are licensed with
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98:The LamaH datasets are quite similar to the
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282:Hydrology and Earth System Sciences
233:Hydrology and Earth System Sciences
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146:Great Britain / 671 catchments
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563:Datasets in machine learning
27:For people named Lamah, see
149:Australia / 222 catchments
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23:Logo of the LamaH-datasets
520:10.5194/essd-13-3847-2021
499:Earth System Science Data
471:10.5194/essd-12-2459-2020
450:Earth System Science Data
412:10.5194/essd-12-2075-2020
391:Earth System Science Data
353:10.5194/hess-22-5817-2018
254:10.5194/hess-21-5293-2017
205:10.5194/essd-13-4529-2021
184:Earth System Science Data
78:at the catchment outlet (
303:10.5194/hess-19-209-2015
143:Brazil / 897 catchments
140:Chile / 516 catchments
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94:Difference to CAMELS
511:2021ESSD...13.3847F
462:2020ESSD...12.2459C
403:2020ESSD...12.2075C
363:20.500.11850/305909
344:2018HESS...22.5817A
294:2015HESS...19..209N
245:2017HESS...21.5293A
196:2021ESSD...13.4529K
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63:hydrology
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