Articles | Volume 13, issue 9
https://doi.org/10.5194/essd-13-4529-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-4529-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 Sep 2021
Data description paper |
| 16 Sep 2021
| 16 Sep 2021
LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe
Christoph Klingler
CORRESPONDING AUTHOR
Institute for Hydrology and Water Management, University of Natural
Resources and Life Sciences, Vienna, 1190, Austria
Karsten Schulz
Institute for Hydrology and Water Management, University of Natural
Resources and Life Sciences, Vienna, 1190, Austria
Mathew Herrnegger
Institute for Hydrology and Water Management, University of Natural
Resources and Life Sciences, Vienna, 1190, Austria
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Hydrol. Earth Syst. Sci., 25, 2869–2894, https://doi.org/10.5194/hess-25-2869-2021, https://doi.org/10.5194/hess-25-2869-2021, 2021
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We compared a suite of globally available meteorological and DEM data with in situ data for physically based snow hydrological modelling in a small high-alpine catchment. Although global meteorological data were less suited to describe the snowpack properly, transferred station data from a similar location in the vicinity and substituting single variables with global products performed well. In addition, using 30 m global DEM products as model input was useful in such complex terrain.
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Hydrol. Earth Syst. Sci., 24, 4463–4489, https://doi.org/10.5194/hess-24-4463-2020, https://doi.org/10.5194/hess-24-4463-2020, 2020
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The USLE is a commonly used model to estimate soil erosion by water. It quantifies soil loss as a product of six inputs representing rainfall erosivity, soil erodibility, slope length and steepness, plant cover, and support practices. Many methods exist to derive these inputs, which can, however, lead to substantial differences in the estimated soil loss. Here, we analyze the effect of different input representations on the estimated soil loss in a large-scale study in Kenya and Uganda.
Cited articles
Addor, N.: R scripts for reproducing the climatic and hydrological indices, as well as for creating the maps, GitHub [code], available at: https://github.com/naddor/camels (last access: 2 March 2020), 2017.
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017.
Addor, N., Do, H. X., Alvarez-Garreton, C., Coxon, G., Fowler, K., and
Mendoza, P. A.: Large-sample hydrology: recent progress, guidelines for new
datasets and grand challenges, Hydrolog. Sci. J., 65, 712–725,
https://doi.org/10.1080/02626667.2019.1683182, 2019.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop Evapotranspiration. Guidelines for Computing Crop Water Requirements, FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization (FAO) of the United Nations, Rome, 300 pp., ISBN 92-5-104219-5, 1998.
Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Puelma, C., Cortes, G., Garreaud, R., McPhee, J., and Ayala, A.: The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset, Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018, 2018.
Arora, V. K.: The use of the aridity index to assess climate change effect on annual runoff, J. Hydrol., 265, 164–177, https://doi.org/10.1016/S0022-1694(02)00101-4, 2002.
BAFU: Federal Office for the Environment – Hydrology Division, Bern, Switzerland (runoff data received: 23 September 2020), 2020.
Beck, H. E., Van Dijk, A. I. J. M., Miralles, D. G., De Jeu, R. A. M., Bruijnzeel, L. A., McVicar, T. R., and Schellekens, J.: Global patterns in base flow index and recession based on streamflow observations from 3394 catchments, Water Resour. Res., 49, 7843–7863, https://doi.org/10.1002/2013WR013918, 2013.
Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I. J. M., Weedon, G. P., Brocca, L., Pappenberger, F., Huffman, G. J., and Wood, E. F.: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling, Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, 2017.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van
Dijk, A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly
0.1 Precipitation: Methodology and Quantitative Assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019.
Berghuijs, W. R., Sivapalan, M., Woods, R. A., and Savenije, H. H. G.: Patterns of similarity of seasonal water balances: A window into streamflow variability over a range of time scales, Water Resour. Res., 50, 5638–5661, https://doi.org/10.1002/2014WR015692, 2014.
Bergström, S.: The HBV model – its structure and applications, SMHI, Norrköpping, Sweden, SMHI Reports Hydrology, No. 4, ISSN 0283-1104, 32 pp., 1992.
Blöschl, G., Sivapalan, M., Savenije, H., Wagener, T., and Viglione, A. (Eds.): Runoff prediction in ungauged basins: synthesis across processes, places and scales, Cambridge University Press, Cambridge, ISBN 9781107028180, 490 pp., 2013.
Blöschl, G., Hall, J., Viglione, A., et al.: Changing climate both increases and decreases European river floods, Nature, 573, 108–111, https://doi.org/10.1038/s41586-019-1495-6, 2019a.
Blöschl, G., Bierkens, M. F. P., Chambel, A., et al.: Twenty-three unsolved problems in hydrology (UPH) – a community perspective, Hydrolog. Sci. J., 64, 1141–1158, https://doi.org/10.1080/02626667.2019.1620507, 2019b.
BMLFUW: Hydrographic Yearbook of Austria 2013, Federal Ministry of
Agriculture, Regions and Tourism – Hydrographic Central Office, Vienna,
Austria, available at:
https://info.bmlrt.gv.at/service/publikationen/wasser/Hydrographisches-Jahrbuch-von-Oesterreich-2013.html (last access: 31 August 2021), 2013.
Boer-Euser, T., McMillan, H. K., Hrachowitz, M., Winsemius, H. C., and Savenije, H. H. G.: Influence of soil and climate on root zone storage capacity, Water Resour. Res., 52, 2009–2024, https://doi.org/10.1002/2015WR018115, 2016.
Bossard, M., Feranec, J., and Otahel, J.: CORINE land cover technical guide – Addendum 2000, Technical report No. 40, European Environment Agency, Copenhagen, Denmark, 105 pp., 2000.
Brewer, C. A.: ColorBrewer 2.0, GitHub [code], available at: https://github.com/axismaps/colorbrewer/, last access: 31 August 2021.
Broxton, P. D., Zeng, X., Scheftic, W., and Troch P. A.: A MODIS-Based Global 1-km Maximum Green Vegetation Fraction Dataset, J. Appl. Meteorol. Clim., 53, 1996–2004, https://doi.org/10.1175/JAMC-D-13-0356.1, 2014.
Budyko, M. I.: Climate and Life, Academic Press, New York, NY, USA, 1974.
Büttner, G. and Maucha, G.: The thematic accuracy of Corine land cover 2000 – Assessment using LUCAS (land use/cover area frame statistical survey), Technical report No. 7/2006, European Environment Agency, Copenhagen, Denmark, ISBN 92-9167-844-9, 90 pp., 2006.
Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020.
Chapman, T.: A comparison of algorithms for stream flow recession and baseflow separation, Hydrol. Process., 13, 701–714, https://doi.org/10.1002/(SICI)1099-1085(19990415)13:5<701::AID-HYP774>3.0.CO;2-2, 1999.
CHMI: Czech Hydrometeorological Institute, Brno, Czech Republic (runoff data received: 14 December 2020), 2020.
Clausen, B. and Biggs, B. J. F.: Flow variables for ecological studies in temperate streams: groupings based on covariance, J. Hydrol., 237, 184–197, https://doi.org/10.1016/S0022-1694(00)00306-1, 2000.
COPa: European Space Agency and European Commission, Copernicus Program, Copernicus Open Access Hub, available at: https://scihub.copernicus.eu/, last access: 22 February 2021.
COPb: European Space Agency and European Commission, Copernicus Program, Copernicus Climate Data Store, available at: https://cds.climate.copernicus.eu/#!/home, last access: 22 February 2021.
CORINE: CORINE Land Cover 2012, European Environment Agency [data set], Copenhagen, Denmark, available at: https://land.copernicus.eu/pan-european/corine-land-cover (last access: 2 March 2020), 2012.
Court, A.: Measures of streamflow timing, J. Geophys. Res., 67, 4335–4339, https://doi.org/10.1029/JZ067i011p04335, 1962.
Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., Lewis, M., Robinson, E. L., Wagener, T., and Woods, R.: CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, 2020.
Do, H. X., Gudmundsson, L., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata, Earth Syst. Sci. Data, 10, 765–785, https://doi.org/10.5194/essd-10-765-2018, 2018.
Döll, P., Douville, H., Güntner, A., Schmied, H. M., and Wada, Y.: Modelling freshwater resources at the global scale: challenges and prospects, Surv. Geophys., 37, 195–221, https://doi.org/10.1007/s10712-015-9343-1, 2016.
Duan, Q., Schaake, J., Andréassian, V., Franks, S., Goteti, G., Gupta, H. V., Gusev, Y. M., Habets, F., Hall, A., Hay, L., Hogue, T., Huang, M., Leavesley, G., Liang, X., Nasonova, O. N., Noilhan, J., Oudin, L., Sorooshian, S., Wagener, T., and Wood, E. F.: Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops, J. Hydrol., 320, 3–17, https://doi.org/10.1016/j.jhydrol.2005.07.031, 2006.
Eckhardt, K.: A comparison of baseflow indices, which were calculated with seven different baseflow separation methods, J. Hydrol., 352, 168–173, https://doi.org/10.1016/j.jhydrol.2008.01.005, 2008.
Eder, G., Fuchs, M., Nachtnebel, H. P., and Loibl, W.: Semidistributed modelling of the monthly water balance in an alpine catchment, Hydrol. Process., 19, 2339–2360, https://doi.org/10.1002/hyp.5888, 2005.
EEA: EU-Hydro – River Network Database, Version 1.2, European Environment Agency under the framework of the Copernicus program [data set], available at: https://land.copernicus.eu/imagery-in-situ/eu-hydro/eu-hydro-river-network-database (last access: 22 October 2020), 2019.
Enzinger, P. A.: Modelling the hydrological cycle in a Siberian
catchment: application of the COSERO model, Master thesis, Institute for Water Management, Hydrology and Hydraulic Engineering, University of Natural
Resources and Life Science, Vienna, Austria, 142 pp., available at:
https://epub.boku.ac.at/obvbokhs/content/titleinfo/1127291?lang=en (last access: 31 August 2021), 2009.
ESDB: The European Soil Database distribution version 2.0, European Commission and the European Soil Bureau Network [data set], CD-ROM, EUR 19945 EN, 2004.
Falkenmark, M. and Chapman, T.: Comparative hydrology: An ecological approach to land and water resources Unesco, UNESCO, Paris, 1989.
Fan, Y.: Groundwater in the Earth's critical zone: Relevance to large-scale patterns and processes: Groundwater at large scales, Water Resour. Res., 51, 3052–3069, https://doi.org/10.1002/2015WR017037, 2015.
Fan, Y., Clark, M., Lawrence, D. M., Swenson, S., Band, L. E., Brantley, S. L., Brooks, P. D., Dietrich, W. E., Flores, A., Grant, G., Kirchner, J. W., Mackay, D. S., McDonnell, J. J., Milly, P. C. D., Sullivan, P. L., Tague, C., Ajami, H., Chaney, N., Hartmann, A., Hazenberg, P., McNamara, J., Pelletier, J., Perket, J., Rouholahnejad-Freund, E., Wagener, T., Zeng, X., Beighley, E., Buzan, J., Huang, M., Livneh, B., Mohanty, B. P., Nijssen, B., Safeeq, M., Shen, C., Verseveld, W., Volk, J., and Yamazaki, D.: Hillslope Hydrology in Global Change Research and Earth System Modeling, Water Resour. Res., 55, 1737–1772, https://doi.org/10.1029/2018WR023903, 2019.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D. E.: The shuttle radar topography mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Frey, S. and Holzmann, H.: A conceptual, distributed snow redistribution model, Hydrol. Earth Syst. Sci., 19, 4517–4530, https://doi.org/10.5194/hess-19-4517-2015, 2015.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes, Scientific Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66, 2015.
GEE: Google Earth Engine Platform, available at: https://earthengine.google.com/platform/, last access: 22 February 2021a.
GEE: Google Earth Engine Data Catalog, available at: https://developers.google.com/earth-engine/datasets, last access: 22 January 2021b.
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019.
GKD: Bavarian State Office for the Environment – Hydrographic Service, Munich, Germany, available at: https://www.gkd.bayern.de/en/rivers/discharge/tables (runoff data downloaded: 15 September 2020), 2020.
Gleeson, T., Moosdorf, N., Hartmann, J., and van Beek, L. P. H.: A glimpse
beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability
and porosity, Geophys. Res. Lett., 41, 3891–3898,
https://doi.org/10.1002/2014GL059856, 2014.
Gleeson, T.: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity, Scholars Portal Dataverse, V1 [data set], https://doi.org/10.5683/SP2/DLGXYO, 2018.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Gudmundsson, L., Do, H. X., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment, Earth Syst. Sci. Data, 10, 787–804, https://doi.org/10.5194/essd-10-787-2018, 2018.
Gudmundsson, L., Leonard, M., Do, H. X., Westra, S., and Seneviratne, S. I.: Observed Trends in Global Indicators of Mean and Extreme Streamflow, Geophys. Res. Lett., 46, 756–766, https://doi.org/10.1029/2018GL079725, 2019.
Gupta, H. V. and Kling, H.: On typical range, sensitivity, and normalization of Mean Squared Error and Nash–Sutcliffe Efficiency type metrics, Water Resour. Res., 47, W10601, https://doi.org/10.1029/2011WR010962, 2011.
Gupta, H. V., Perrin, C., Blöschl, G., Montanari, A., Kumar, R., Clark, M., and Andréassian, V.: Large-sample hydrology: a need to balance depth with breadth, Hydrol. Earth Syst. Sci., 18, 463–477, https://doi.org/10.5194/hess-18-463-2014, 2014.
HAO: Hydrological Atlas of Austria (digHAO), 3. Delivery, Federal Ministry of Agriculture, Regions and Tourism – Hydrographic Central Office [data set], Vienna, Austria, ISBN 3-85437-250-7, 2007.
Hargreaves, G. H.: Defining and Using Reference Evapotranspiration, J. Irrig. Drain. Eng., 120, 1132–1139, https://doi.org/10.1061/(ASCE)0733-9437(1994)120:6(1132), 1994.
Hartmann, J. and Moosdorf, N.: The new global lithological map database GLiM: A representation of rock properties at the Earth surface, Geochem. Geophy. Geosy., 13, 1–37, https://doi.org/10.1029/2012GC004370, 2012.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global gridded soil information based on machine learning, edited by: Bond-Lamberty, B., PLoS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Hennermann, K. and Guillory, A.: ERA5: uncertainty estimation, CDS dataset documentation, European Centre for Medium-Range Weather Forecasts (ECMWF), available at: https://confluence.ecmwf.int/display/CKB/ERA5{%}3A+uncertainty+estimation, last access: 30 November 2020.
Herrnegger, M., Nachtnebel, H. P., and Haiden, T.: Evapotranspiration in high alpine catchments – an important part of the water balance!, Hydrol. Res., 43, 460–475, https://doi.org/10.2166/nh.2012.132, 2012.
Herrnegger, M., Nachtnebel, H. P., and Schulz, K.: From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution, Hydrol. Earth Syst. Sci., 19, 4619–4639, https://doi.org/10.5194/hess-19-4619-2015, 2015.
Herrnegger, M., Senoner, T., and Nachtnebel, H. P.: Adjustment of spatio-temporal precipitation patterns in a high Alpine environment, J. Hydrol., 556, 913–921, https://doi.org/10.1016/j.jhydrol.2016.04.068, 2018.
Hersbach, H., Bell, B., Berrisford, P., et al.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hiederer, R.: Mapping Soil Properties for Europe – Spatial Representation
of Soil Database Attributes, Publications Office of the European Union, Luxembourg, EUR26082EN Scientific and Technical Research series, ISSN 1831-9424, 47 pp., https://doi.org/10.2788/94128, 2013a.
Hiederer, R.: Mapping Soil Typologies – Spatial Decision Support Applied to
European Soil Database, Publications Office of the European Union,
Luxembourg, EUR25932EN Scientific and Technical Research series, ISSN
1831-9424, 147 pp., https://doi.org/10.2788/87286, 2013b.
Hoedt, P. J., Kratzert, F., Klotz, D., Halmich, C., Holzleitner, M., Nearing, G., Hochreiter, S., and Klambauer, G.: MC-LSTM: Mass-Conserving LSTM, arXiv [preprint], arXiv:2101.05186, 10 June 2021.
Horn, B. K. P.: Hill shading and the reflectance map, P. IEEE,, 69, 14–47, https://doi.org/10.1109/PROC.1981.11918, 1981.
Hötzl, H.: Origin of the Danube-Aach system, Environ. Geol., 27, 87–96, https://doi.org/10.1007/BF01061676, 1996.
Huscroft, J., Gleeson, T., Hartmann, J., and Börker, J.: Compiling and Mapping Global Permeability of the Unconsolidated and Consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0), Geophys. Res. Lett., 45, 1897–1904, https://doi.org/10.1002/2017GL075860, 2018.
HZB: Federal Ministry of Agriculture, Regions and Tourism – Hydrographic Central Office, Vienna, Austria (runoff data received: 8 September 2020), 2020.
ICPDR: Danube Basin Facts and Figures, available at: https://www.icpdr.org/flowpaper/viewer/default/files/nodes/documents/icpdr_facts_figures.pdf, last access: 21 September 2020.
Jain, S. K. and Sudheer, K. P.: Fitting of Hydrologic Models: A Close Look at
the Nash–Sutcliffe Index, J. Hydrol. Eng., 13, 981–986, 2008.
Kling, H. and Nachtnebel, H. P.: A method for the regional estimation of runoff separation parameters for hydrological modelling, J. Hydrol., 364, 163–174, https://doi.org/10.1016/j.jhydrol.2008.10.015, 2009a.
Kling, H. and Nachtnebel, H. P.: A spatio-temporal comparison of water balance modelling in an Alpine catchment, Hydrol. Process., 23, 997–1009, https://doi.org/10.1002/hyp.7207, 2009b.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
Kling, H., Stanzel, P., Fuchs, M., and Nachtnebel, H. P.: Performance of the COSERO precipitation – runoff model under non-stationary conditions in basins with different climates, Hydrolog. Sci. J., 60, 1374–1393, https://doi.org/10.1080/02626667.2014.959956, 2015.
Klingler, C., Bernhardt, M., Wesemann, J., Schulz, K., and Herrnegger, M.: Lokale hydrologische Modellierung mit globalen, alternativen Datensätzen, Hydrol. Wasserbewirts., 64, 166–187, https://doi.org/10.5675/HyWa_2020.4_1, 2020.
Klingler, C., Kratzert, F., Schulz, K., and Herrnegger, M.: LamaH-CE Central Europe – files, Zenodo [data set], https://doi.org/10.5281/zenodo.4525244, 2021.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Koboltschnig, G. R. and Schöner, W.: The relevance of glacier melt in the water cycle of the Alps: the example of Austria, Hydrol. Earth Syst. Sci., 15, 2039–2048, https://doi.org/10.5194/hess-15-2039-2011, 2011.
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., and Nearing, G.: Toward improved predictions in ungauged basins: Exploiting the power of machine learning, Water Resour. Res., 55, 11344–11354, https://doi.org/10.1029/2019WR026065, 2019a.
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019b.
Kuhn, M.: The Reaction of Austrian Glaciers and their Runoff to Changes in Temperature and Precipitation Levels, Österreichische Wasser- und Abfallwirtschaft, 56, 1–7, 2004.
Kuentz, A., Arheimer, B., Hundecha, Y., and Wagener, T.: Understanding hydrologic variability across Europe through catchment classification, Hydrol. Earth Syst. Sci., 21, 2863–2879, https://doi.org/10.5194/hess-21-2863-2017, 2017.
Ladson, A., Brown, R., Neal, B., and Nathan, R.: A standard approach to
baseflow separation using the Lyne and Hollick filter, Australian Journal of
Water Resources, 17, 25–34, 2013.
Lambrecht, A. and Kuhn, M.: Glacier changes in the Austrian Alps during the last three decades, derived from the new Austrian glacier inventory, Ann. Glaciol., 46, 177–184, https://doi.org/10.3189/172756407782871341, 2007.
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from
spaceborne elevation data, EOS T. Am. Geophys. Un., 89, 93–94, https://doi.org/10.1029/2008EO100001, 2008.
Lehner, B., Reidy Liermann, C., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., and Wisser, D.: High-resolution mapping of the world's reservoirs and dams for sustainable river-flow management, Front. Ecol. Environ., 9, 494–502, https://doi.org/10.1890/100125, 2011.
Linke, S., Lehner, B., Ouellet Dallaire, C., Ariwi, J., Grill, G., Anand, M., Beames, P., Burchard-Levine, V., Maxwell, S., Moidu, H., Tan, F., and Thieme, M.: Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution, Scientific Data, 6, 283, https://doi.org/10.1038/s41597-019-0300-6, 2019.
LUBW: State Agency for the Environment Baden-Württemberg – Hydrographic Service, Karlsruhe, Germany, available at: http://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer (runoff data downloaded: 4 September 2020), 2020.
Luke, A., Vrugt, J. A., AghaKouchak, A., Matthew, R., and Sanders, B. F.: Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States, Water Resour. Res., 53, 5469–5494, https://doi.org/10.1002/2016WR019676, 2017.
McCuen, R. H., Knight, Z., and Cutter, G.: Evaluation of the Nash–Sutcliffe Efficiency Index, J. Hydrol. Eng., 11, 597–602, https://doi.org/10.1061/(ASCE)1084-0699(2006)11:6(597), 2006.
McMillan, H., Krueger, T., and Freer, J.: Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality, Hydrol. Process., 26, 4078–4111, https://doi.org/10.1002/hyp.9384, 2012.
Mehdi, B., Dekens, J., and Herrnegger, M.: Climatic impacts on water resources in a tropical catchment in Uganda and adaptation measures proposed by resident stakeholders, Climatic Change, 164, 10, https://doi.org/10.1007/s10584-021-02958-9, 2021.
Mulligan, M., van Soesbergen, A., and Saenz, L.: GOODD, a global dataset of more than 38,000 georeferenced dams, Scientific Data, 7, 31, https://doi.org/10.1038/s41597-020-0362-5, 2020.
Muñoz Sabater, J.: ERA5-Land hourly data from 1981 to present, version CY45r1, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019a.
Muñoz Sabater, J.: First ERA5-Land dataset to be released this spring, ECMWF newsletter, number 159 – spring 2019, available at: https://www.ecmwf.int/en/newsletter/159/news/first-era5-land-dataset-be-released-spring (last access: 30 November 2020), 2019b.
Muñoz Sabater, J., Dutra, E., Balsamo, G., Hersbach, H.,
Boussetta, S., Dee, D., and Hirahara, S.: ERA5-Land: A new state-of-the-art
Global Land Surface Reanalysis Dataset, 31st Conference on Hydrology – 2017
AMS annual meeting, Seattle, US, 25 January 2017.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Myneni, R., Knyazikhin, Y., and Park, T.: MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006 [data set], NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MCD15A3H.006, 2015.
Nachtnebel, H. P. and Fuchs, M.: Assessment of hydrological changes in Austria
due to possible climate change, Österreichische Wasser- und
Abfallwirtschaft, 56, 79–92, 2004 (in German).
Nachtnebel, H. P., Baumung, S., and Lettl, W.: Abflussprognosemodell für
das Einzugsgebiet der Enns und Steyr, Report, Institute for Water Management,
Hydrology and Hydraulic Engineering, University of Natural Resources and
Applied Life Sciences, Vienna, Austria, 1993 (in German).
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D.,
Frame, J. M., Prieto, C., and Gupta, H. V.: What Role Does Hydrological
Science Play in the Age of Machine Learning?, Water Resour. Res., 57,
e2020WR028091, https://doi.org/10.1029/2020wr028091, 2020.
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., and Duan, Q.: 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, Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, 2015.
Olden, J. D. and Poff, N. L.: Redundancy and the choice of hydrologic indices for characterizing streamflow regimes, River Res. Appl., 19, 101–121, https://doi.org/10.1002/rra.700, 2003.
Oerlemans, J., Anderson, B., Hubbard, A., Huybrechts, P., Johannesson, T., Knap, W. H., Schmeits, M., Stroeven, A. P., van de Wal, R. S. W., and Wallinga, J.: Modelling the response of glaciers to climate warming, Clim. Dynam., 14, 267–274, https://doi.org/10.1007/s003820050222, 1998.
Panagos, P.: The European soil database, GEO: connexion, 5, 32–33, 2006.
Panagos, P., Van Liedekerke, M., Jones, A., and Montanarella L.: European Soil Data Centre: Response to European policy support and public data requirements, Land Use Policy, 29, 329–338, https://doi.org/10.1016/j.landusepol.2011.07.003, 2012.
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G., Williams, Z. C., Brunke, M. A., and Gochis, D.: Global 1-km Gridded Thickness of Soil, Regolith, and Sedimentary Deposit Layers, ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1304, 2016.
Prohaska, S., Brilly, M., and Kryžanowski, A.: Cooperation of hydrologists from the Danube River Basin, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2020-66, 2020.
Python Software Foundation: Python Language Reference, available at:
https://www.python.org (last access: 31 August 2021), 2020.
QGIS Development Team: QGIS Geographic Information System, Open Source Geospatial Foundation Project, available at: https://qgis.org
(last access: 31 August 2021), 2020.
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, available at: https://www.r-project.org (last access: 31 August 2021), 2020.
Sankarasubramanian, A., Vogel, R. M., and Limbrunner, J. F.: Climate elasticity of streamflow in the United States, Water Resour. Res., 37, 1771–1781, https://doi.org/10.1029/2000WR900330, 2001.
Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., and Carrillo, G.: Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA, Hydrol. Earth Syst. Sci., 15, 2895–2911, https://doi.org/10.5194/hess-15-2895-2011, 2011.
Schaake, J. C., Hamill, T. M., Buizza, R., and Clark, M.: HEPEX: the hydrological ensemble prediction experiment, B. Am. Meteorol. Soc., 88, 1541–1548, https://doi.org/10.1175/BAMS-88-10-1541, 2007.
Schaefli, B. and Gupta, H. V.: Do Nash values have value?, Hydrol. Process., 21, 2075–2080, https://doi.org/10.1002/hyp.6825, 2007.
Schulz, K., Herrnegger, M., Wesemann, J., Klotz, D., and Senoner, T:
Kalibrierung COSERO-Mur für ProVis, Final report, Institute for Water
Management, Hydrology and Hydraulic Engineering, University of Natural
Resources and Life Science, Vienna, Austria, 2016 (in German).
Schumm, S. A.: Evolution of drainage systems and slopes in Badlands at Perth Amboy, New Jersey, GSA Bulletin, 67, 597–646, https://doi.org/10.1130/0016-7606(1956)67[597:EODSAS]2.0.CO;2, 1956.
Singh, R., Archfield, S. A., and Wagener, T.: Identifying dominant controls on hydrologic parameter transfer from gauged to ungauged catchments – A comparative hydrology approach, J. Hydrol., 517, 985–996, https://doi.org/10.1016/j.jhydrol.2014.06.030, 2014.
Sit, M., Demiray, B. Z., Xiang, Z., Ewing, G. J., Sermet, Y., and Demir, I.: A comprehensive review of deep learning applications in hydrology and water resources, Water Sci. Technol., 82, 2635–2670, https://doi.org/10.2166/wst.2020.369, 2020.
Smith, M. B., Seo, D. J., Koren, V. I., Reed, S. M., Zhang, Z., Duan, Q., Moreda, F., and Cong, S.: The Distributed Model Intercomparison Project (DMIP): motivation and experiment design, J. Hydrol., 298, 4–26, https://doi.org/10.1016/j.jhydrol.2004.03.040, 2004.
Stanzel, P. and Nachtnebel, H. P.: Potential climate change impact on
Austria's water balance and hydro-power industry, Österreichische Wasser-
und Abfallwirtschaft, 62, 180–187, https://doi.org/10.1007/s00506-010-0234-x, 2010 (in German).
Stanzel, P., Kahl, B., Haberl, U., Herrnegger M., and Nachtnebel, H. P.: Continuous hydrological modelling in the context of real time flood forecasting in alpine Danube tributary catchments, IOP Conference Series: Earth and Environmental Science, 4, 012005, https://doi.org/10.1088/1755-1307/4/1/012005, 2008.
Tallaksen, L. and Van Lanen, H. A. J.: Hydrological drought, Processes and
estimation methods for streamflow and groundwater, Developments in Water
Science, 48, ISSN: 0167-5648, Elsevier, Amsterdam, 579 pp., 2004.
Thornthwaite, C. W. and Mather, J. R.: Instructions and tables for computing potential evapotranspiration and the water balance, Publications in Climatology, Laboratory of Climatology, Drexel Institute of Technology, New Jersey, 10(3), 311 pp., 1957.
Tolson, B. A. and Shoemaker, C. A.: Dynamically dimensioned search algorithm for computationally efficient watershed model calibration, Water Resour. Res., 43, W01413, https://doi.org/10.1029/2005WR004723, 2007.
Toth, B., Weynants, M., Nemes, A., Makó, A., Bilas, G., and Toth, G.: New generation of hydraulic pedotransfer functions for Europe, Eur. J. Soil Sci., 66, 226–238, https://doi.org/10.1111/ejss.12192, 2015.
Toth, B., Weynants, M., Pasztor, L., and Hengl, T.: 3D soil hydraulic database of Europe at 250 m resolution, Hydrol. Process., 31, 2662–2666, https://doi.org/10.1002/hyp.11203, 2017.
Trabucco, A. and Zomer, R.: Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2, CGIAR Consortium for Spatial Information (CGIAR-CSI) [data set], https://doi.org/10.6084/m9.figshare.7504448.v3, 2019.
TYROL: Catalog Water Network Tyrol, Government of the Austrian federal state Tyrol [data set], Innsbruck, available at: https://www.data.gv.at/katalog, last access: 17 October 2020.
Van Lanen, H. A. J., Wanders, N., Tallaksen, L. M., and Van Loon, A. F.: Hydrological drought across the world: impact of climate and physical catchment structure, Hydrol. Earth Syst. Sci., 17, 1715–1732, https://doi.org/10.5194/hess-17-1715-2013, 2013.
Vermote, E.: MOD09Q1 MODIS/Terra Surface Reflectance 8-Day L3 Global 250m SIN Grid V006 [data set], NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD09Q1.006, 2015.
Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., and Schewe, J.: The inter-sectoral impact model intercomparison project (ISI–MIP): project framework, P. Natl. Acad. Sci. USA, 111, 3228–3232, https://doi.org/10.1073/pnas.1312330110, 2014.
Wesemann, J., Herrnegger, M., and Schulz, K.: Hydrological modelling in the anthroposphere: predicting local runoff in a heavily modified high-alpine catchment, J. Mt. Sci., 15, 921–938, https://doi.org/10.1007/s11629-017-4587-5, 2018.
Westerberg, I. K. and McMillan, H. K.: Uncertainty in hydrological signatures, Hydrol. Earth Syst. Sci., 19, 3951–3968, https://doi.org/10.5194/hess-19-3951-2015, 2015.
Westerberg, I. K., Wagener, T., Coxon, G., McMillan, H. K., Castellarin, A., Montanari, A., and Freer, J.: Uncertainty in hydrological signatures for gauged and ungauged catchments, Water Resour. Res., 52, 1847–1865, https://doi.org/10.1002/2015WR017635, 2016.
WGMS (World Glacier Monitoring Service): Glacier Mass Balance Bulletin, No. 8
(2002–2003), edited by: Haeberli, W., Noetzli, J., Zemp, M., Baumann, S.,
Frauenfelder, R., and Hoelzle, M., Department of Geography, University of Zürich, 100 pp., 2005.
Woods, R. A.: Analytical model of seasonal climate impacts on snow hydrology: Continuous snowpacks, Adv. Water Resour., 32, 1465–1481, https://doi.org/10.1016/j.advwatres.2009.06.011, 2009.
Yang, X. and Giusti, M.: ERA5-Land: data documentation, CDS dataset documentation, European Centre for Medium-Range Weather Forecasts (ECMWF), available at: https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation, last access: 30 November 2020.
Yokoo, Y. and Sivapalan, M.: Towards reconstruction of the flow duration curve: development of a conceptual framework with a physical basis, Hydrol. Earth Syst. Sci., 15, 2805–2819, https://doi.org/10.5194/hess-15-2805-2011, 2011.
Short summary
LamaH-CE is a large-sample catchment hydrology dataset for Central Europe. The dataset contains hydrometeorological time series (daily and hourly resolution) and various attributes for 859 gauged basins. Sticking closely to the CAMELS datasets, LamaH includes additional basin delineations and attributes for describing a large interconnected river network. LamaH further contains outputs of a conceptual hydrological baseline model for plausibility checking of the inputs and for benchmarking.
LamaH-CE is a large-sample catchment hydrology dataset for Central Europe. The dataset contains...
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