Articles | Volume 12, issue 3
https://doi.org/10.5194/essd-12-2075-2020
© Author(s) 2020. 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-12-2075-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
08 Sep 2020
Data description paper |
| 08 Sep 2020
| 08 Sep 2020
CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil
Vinícius B. P. Chagas
Department of Sanitary and Environmental Engineering, Graduate Program
of Environmental Engineering, Federal University of Santa Catarina–UFSC,
Florianopolis, Brazil
Department of Sanitary and Environmental Engineering, Federal
University of Santa Catarina–UFSC, Florianopolis, Brazil
Nans Addor
Department of Geography, College of Life and Environmental Sciences, University of
Exeter, Exeter, UK
Fernando M. Fan
Hydraulic Research Institute, Federal University of Rio Grande do
Sul-UFRGS, Porto Alegre, Brazil
Ayan S. Fleischmann
Hydraulic Research Institute, Federal University of Rio Grande do
Sul-UFRGS, Porto Alegre, Brazil
Rodrigo C. D. Paiva
Hydraulic Research Institute, Federal University of Rio Grande do
Sul-UFRGS, Porto Alegre, Brazil
Vinícius A. Siqueira
Hydraulic Research Institute, Federal University of Rio Grande do
Sul-UFRGS, Porto Alegre, Brazil
Related authors
No articles found.
Nele Reyniers, Qianyu Zha, Nans Addor, Timothy J. Osborn, Nicole Forstenhäusler, and Yi He
Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, https://doi.org/10.5194/essd-17-2113-2025, 2025
Short summary
Short summary
We present bias-corrected UK Climate Projections 2018 (UKCP18) regional datasets for temperature, precipitation, and potential evapotranspiration (1981–2080). All 12 members of the 12 km ensemble were corrected using quantile mapping and a change-preserving variant. Both methods effectively reduce biases in multiple statistics while maintaining projected climatic changes. We provide guidance on using the bias-corrected datasets for climate change impact assessment.
Leonardo Laipelt, Fernando Mainardi Fan, Rodrigo Cauduro Dias de Paiva, Matheus Sampaio, Walter Collischonn, and Anderson Ruhoff
EGUsphere, https://doi.org/10.5194/egusphere-2025-1285, https://doi.org/10.5194/egusphere-2025-1285, 2025
Short summary
Short summary
In May 2024, southern Brazil experienced severe flooding, particularly in Porto Alegre’s Metropolitan Region. This study uses hydrodynamic modelling to analyze the event and its impacts. Results showed that the Taquari River caused the flood's peak, while the Jacuí River influenced its duration. The study also evaluated flood control measures, finding them to have limited effectiveness. These findings are important for improving flood preparedness and decision-making in the region and globally.
Olivier Delaigue, Guilherme Mendoza Guimarães, Pierre Brigode, Benoît Génot, Charles Perrin, Jean-Michel Soubeyroux, Bruno Janet, Nans Addor, and Vazken Andréassian
Earth Syst. Sci. Data, 17, 1461–1479, https://doi.org/10.5194/essd-17-1461-2025, https://doi.org/10.5194/essd-17-1461-2025, 2025
Short summary
Short summary
This dataset covers 654 rivers all flowing in France. The provided time series and catchment attributes will be of interest to those modelers wishing to analyze hydrological behavior and perform model assessments.
Christophe Cudennec, Ernest Amoussou, Yonca Cavus, Pedro L. B. Chaffe, Svenja Fischer, Salvatore Grimaldi, Jean-Marie Kileshye Onema, Mohammad Merheb, Maria-Jose Polo, Eric Servat, and Elena Volpi
Proc. IAHS, 385, 501–511, https://doi.org/10.5194/piahs-385-501-2025, https://doi.org/10.5194/piahs-385-501-2025, 2025
Claudia Färber, Henning Plessow, Simon Mischel, Frederik Kratzert, Nans Addor, Guy Shalev, and Ulrich Looser
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-427, https://doi.org/10.5194/essd-2024-427, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Large-sample datasets are essential in hydrological science to support modelling studies and advance process understanding. Caravan is a community initiative to create a large-sample hydrology dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world. This dataset is a subset of hydrological discharge data and station-based watersheds from the Global Runoff Data Centre (GRDC), which are covered by an open data policy.
Marvin Höge, Martina Kauzlaric, Rosi Siber, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Marius Günter Floriancic, Daniel Viviroli, Sibylle Wilhelm, Anna E. Sikorska-Senoner, Nans Addor, Manuela Brunner, Sandra Pool, Massimiliano Zappa, and Fabrizio Fenicia
Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, https://doi.org/10.5194/essd-15-5755-2023, 2023
Short summary
Short summary
CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in hydrologic Switzerland from 1 January 1981 to 31 December 2020. It comprises (a) daily data of river discharge and water level as well as meteorologic variables like precipitation and temperature; (b) yearly glacier and land cover data; (c) static attributes of, e.g, topography or human impact; and (d) catchment delineations. CAMELS-CH enables water and climate research and modeling at catchment level.
Benjamin M. Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Frederic Frappart, Stephane Calmant, Omid Elmi, Ayan Santos Fleischmann, Melanie Becker, Mohammad J. Tourian, Rômulo A. Jucá Oliveira, and Sly Wongchuig
Earth Syst. Sci. Data, 15, 2957–2982, https://doi.org/10.5194/essd-15-2957-2023, https://doi.org/10.5194/essd-15-2957-2023, 2023
Short summary
Short summary
The surface water storage (SWS) in the Congo River basin (CB) remains unknown. In this study, the multi-satellite and hypsometric curve approaches are used to estimate SWS in the CB over 1992–2015. The results provide monthly SWS characterized by strong variability with an annual mean amplitude of ~101 ± 23 km3. The evaluation of SWS against independent datasets performed well. This SWS dataset contributes to the better understanding of the Congo basin’s surface hydrology using remote sensing.
Nele Reyniers, Timothy J. Osborn, Nans Addor, and Geoff Darch
Hydrol. Earth Syst. Sci., 27, 1151–1171, https://doi.org/10.5194/hess-27-1151-2023, https://doi.org/10.5194/hess-27-1151-2023, 2023
Short summary
Short summary
In an analysis of future drought projections for Great Britain based on the Standardised Precipitation Index and the Standardised Precipitation Evapotranspiration Index, we show that the choice of drought indicator has a decisive influence on the resulting projected changes in drought characteristics, although both result in increased drying. This highlights the need to understand the interplay between increasing atmospheric evaporative demand and drought impacts under a changing climate.
Benjamin Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Stephane Calmant, Ayan Santos Fleischmann, Frederic Frappart, Melanie Becker, Mohammad J. Tourian, Catherine Prigent, and Johary Andriambeloson
Hydrol. Earth Syst. Sci., 26, 1857–1882, https://doi.org/10.5194/hess-26-1857-2022, https://doi.org/10.5194/hess-26-1857-2022, 2022
Short summary
Short summary
This study presents a better characterization of surface hydrology variability in the Congo River basin, the second largest river system in the world. We jointly use a large record of in situ and satellite-derived observations to monitor the spatial distribution and different timings of the Congo River basin's annual flood dynamic, including its peculiar bimodal pattern.
Pablo Borges de Amorim and Pedro Luiz Borges Chaffe
Geosci. Commun., 4, 527–554, https://doi.org/10.5194/gc-4-527-2021, https://doi.org/10.5194/gc-4-527-2021, 2021
Short summary
Short summary
Climate change is one of the major challenges of our society, and therefore we present a climate risk training for tertiary students and practitioners. The training uses a hands-on method and was tested with five independent groups in Brazil. We find that the application of a mapping exercise supports learning about climate risk, as well as the development of problem-solving skills. The proposed training enables the teaching of climate risk in stand-alone courses and professional development.
Andrew J. Newman, Amanda G. Stone, Manabendra Saharia, Kathleen D. Holman, Nans Addor, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 5603–5621, https://doi.org/10.5194/hess-25-5603-2021, https://doi.org/10.5194/hess-25-5603-2021, 2021
Short summary
Short summary
This study assesses methods that estimate flood return periods to identify when we would obtain a large flood return estimate change if the method or input data were changed (sensitivities). We include an examination of multiple flood-generating models, which is a novel addition to the flood estimation literature. We highlight the need to select appropriate flood models for the study watershed. These results will help operational water agencies develop more robust risk assessments.
Peter T. La Follette, Adriaan J. Teuling, Nans Addor, Martyn Clark, Koen Jansen, and Lieke A. Melsen
Hydrol. Earth Syst. Sci., 25, 5425–5446, https://doi.org/10.5194/hess-25-5425-2021, https://doi.org/10.5194/hess-25-5425-2021, 2021
Short summary
Short summary
Hydrological models are useful tools that allow us to predict distributions and movement of water. A variety of numerical methods are used by these models. We demonstrate which numerical methods yield large errors when subject to extreme precipitation. As the climate is changing such that extreme precipitation is more common, we find that some numerical methods are better suited for use in hydrological models. Also, we find that many current hydrological models use relatively inaccurate methods.
John P. Bloomfield, Mengyi Gong, Benjamin P. Marchant, Gemma Coxon, and Nans Addor
Hydrol. Earth Syst. Sci., 25, 5355–5379, https://doi.org/10.5194/hess-25-5355-2021, https://doi.org/10.5194/hess-25-5355-2021, 2021
Short summary
Short summary
Groundwater provides flow, known as baseflow, to surface streams and rivers. It is important as it sustains the flow of many rivers at times of water stress. However, it may be affected by water management practices. Statistical models have been used to show that abstraction of groundwater may influence baseflow. Consequently, it is recommended that information on groundwater abstraction is included in future assessments and predictions of baseflow.
Keirnan J. A. Fowler, Suwash Chandra Acharya, Nans Addor, Chihchung Chou, and Murray C. Peel
Earth Syst. Sci. Data, 13, 3847–3867, https://doi.org/10.5194/essd-13-3847-2021, https://doi.org/10.5194/essd-13-3847-2021, 2021
Short summary
Short summary
This paper presents the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS comprises data for 222 unregulated catchments with long-term monitoring, combining hydrometeorological time series (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. It is freely downloadable from https://doi.pangaea.de/10.1594/PANGAEA.921850.
Peter Uhe, Daniel Mitchell, Paul D. Bates, Nans Addor, Jeff Neal, and Hylke E. Beck
Geosci. Model Dev., 14, 4865–4890, https://doi.org/10.5194/gmd-14-4865-2021, https://doi.org/10.5194/gmd-14-4865-2021, 2021
Short summary
Short summary
We present a cascade of models to compute high-resolution river flooding. This takes meteorological inputs, e.g., rainfall and temperature from observations or climate models, and takes them through a series of modeling steps. This is relevant to evaluating current day and future flood risk and impacts. The model framework uses global data sets, allowing it to be applied anywhere in the world.
Gemma Coxon, Nans Addor, John P. Bloomfield, Jim Freer, Matt Fry, Jamie Hannaford, Nicholas J. K. Howden, Rosanna Lane, Melinda Lewis, Emma L. Robinson, Thorsten Wagener, and Ross Woods
Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, https://doi.org/10.5194/essd-12-2459-2020, 2020
Short summary
Short summary
We present the first large-sample catchment hydrology dataset for Great Britain. The dataset collates river flows, catchment attributes, and catchment boundaries for 671 catchments across Great Britain. We characterise the topography, climate, streamflow, land cover, soils, hydrogeology, human influence, and discharge uncertainty of each catchment. The dataset is publicly available for the community to use in a wide range of environmental and modelling analyses.
Cited articles
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., 1–14,
https://doi.org/10.1080/02626667.2019.1683182, 2019.
Alfieri, L., Lorini, V., Hirpa, F. A., Harrigan, S., Zsoter, E., Prudhomme,
C., and Salamon, P.: A global streamflow reanalysis for 1980–2018, J. Hydrol., 6, 100049, https://doi.org/10.1016/j.hydroa.2019.100049, 2020.
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.
ANA – Brazilian National Water Agency: Relatorio de Seguranca de Barragens
2017, 2018.
ANA – Brazilian National Water Agency, HIDROWEB:
available at: http://www.snirh.gov.br/hidroweb (last access: 15 June 2019), 2019a.
ANA – Brazilian National Water Agency: Levantamento Da Agricultura Irrigada
Por Pivôs Centrais No Brasil (1985–2017), 2a edição, 2019b.
ANA – Brazilian National Water Agency: Manual De Usos Consuntivos Da
Água No Brasil, 2019c.
Archfield, S. A., Hirsch, R. M., Viglione, A. and Blöschl, G.:
Fragmented patterns of flood change across the United States, Geophys. Res.
Lett., 43, 10232–10239, https://doi.org/10.1002/2016GL070590, 2016.
Arino, O., Perez, J. J. R., Kalogirou, V., Bontemps, S., Defourny, P., and
Bogaert, E. V.: Global Land Cover Map for 2009 (GlobCover 2009), PANGAEA, https://doi.org/10.1594/PANGAEA.787668,
2012.
Bartiko, D., Oliveira, D. Y., Bonumá, N. B., and Chaffe, P. L. B.:
Spatial and seasonal patterns of flood change across Brazil, Hydrolog.
Sci. J., 64, 1071–1079, https://doi.org/10.1080/02626667.2019.1619081,
2019.
Beck, H. E., van Dijk, A. I., De Roo, A., Miralles, D. G., McVicar, T. R.,
Schellekens, J., and Bruijnzeel, L. A.: Global-scale regionalization of
hydrologic model parameters, Water Resour. Res., 52, 3599–3622,
2016.
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Dutra, E., Fink, G., Orth, R., and Schellekens, J.: Global evaluation of runoff from 10 state-of-the-art hydrological models, Hydrol. Earth Syst. Sci., 21, 2881–2903, https://doi.org/10.5194/hess-21-2881-2017, 2017a.
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G., Martens, B., and de Roo, A.: MSWEP: 3-hourly 0.25∘ global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci., 21, 589–615, https://doi.org/10.5194/hess-21-589-2017, 2017b.
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, 2017c.
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.
Bierkens, M. F.: Global hydrology 2015: State, trends, and directions, Water
Resour. Res., 51, 4923–4947, 2015.
Berghuijs, W. R. and Woods, R. A.: A simple framework to quantitatively
describe monthly precipitation and temperature climatology, Int. J.
Climatol., 36, 3161–3174, https://doi.org/10.1002/joc.4544, 2016.
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.
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, UK, 465 pp., 2013.
Blöschl, G., Hall, J., Viglione, A., Perdigão, R. A. P., Parajka,
J., Merz, B., Lun, D., Arheimer, B., Aronica, G. T., Bilibashi, A.,
Boháč, M., Bonacci, O., Borga, M., Čanjevac, I., Castellarin,
A., Chirico, G. B., Claps, P., Frolova, N., Ganora, D., Gorbachova, L.,
Gül, A., Hannaford, J., Harrigan, S., Kireeva, M., Kiss, A., Kjeldsen,
T. R., Kohnová, S., Koskela, J. J., Ledvinka, O., Macdonald, N.,
Mavrova-Guirguinova, M., Mediero, L., Merz, R., Molnar, P., Montanari, A.,
Murphy, C., Osuch, M., Ovcharuk, V., Radevski, I., Salinas, J. L., Sauquet,
E., Šraj, M., Szolgay, J., Volpi, E., Wilson, D., Zaimi, K., and
Živković, N.: 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., Cudennec, C., Destouni,
G., Fiori, A., Kirchner, J. W., McDonnell, J. J., Savenije, H. H. G.,
Sivapalan, M., Stumpp, C., Toth, E., Volpi, E., Carr, G., Lupton, C.,
Salinas, J., Széles, B., Viglione, A., Aksoy, H., Allen, S. T., Amin,
A., Andréassian, V., Arheimer, B., Aryal, S. K., Baker, V., Bardsley,
E., Barendrecht, M. H., Bartosova, A., Batelaan, O., Berghuijs, W. R.,
Beven, K., Blume, T., Bogaard, T., Borges de Amorim, P., Böttcher, M.
E., Boulet, G., Breinl, K., Brilly, M., Brocca, L., Buytaert, W.,
Castellarin, A., Castelletti, A., Chen, X., Chen, Y., Chen, Y., Chifflard,
P., Claps, P., Clark, M. P., Collins, A. L., Croke, B., Dathe, A., David, P.
C., de Barros, F. P. J., de Rooij, G., Di Baldassarre, G., Driscoll, J. M.,
Duethmann, D., Dwivedi, R., Eris, E., Farmer, W. H., Feiccabrino, J.,
Ferguson, G., Ferrari, E., Ferraris, S., Fersch, B., Finger, D., Foglia, L.,
Fowler, K., Gartsman, B., Gascoin, S., Gaume, E., Gelfan, A., Geris, J.,
Gharari, S., Gleeson, T., Glendell, M., Gonzalez Bevacqua, A.,
González-Dugo, M. P., Grimaldi, S., Gupta, A. B., Guse, B., Han, D.,
Hannah, D., Harpold, A., Haun, S., Heal, K., Helfricht, K., Herrnegger, M.,
Hipsey, M., Hlaváčiková, H., Hohmann, C., Holko, L., Hopkinson,
C., Hrachowitz, M., Illangasekare, T. H., Inam, A., Innocente, C.,
Istanbulluoglu, E., Jarihani, B., 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.
Börker, J., Hartmann, J., Amann, T., and Romero-Mujalli, G.: Terrestrial
Sediments of the Earth: Development of a Global Unconsolidated Sediments Map
Database (GUM), Geochem. Geophy. Geosy., 19, 997–1024,
https://doi.org/10.1002/2017GC007273, 2018.
Budyko, M. I.: Climate and life, Academic press New York, 1974.
Carvalho, L. M. V., Jones, C., Silva, A. E., Liebmann, B., and Silva Dias, P.
L.: The South American Monsoon System and the 1970s climate transition, Int.
J. Climatol., 31, 1248–1256, https://doi.org/10.1002/joc.2147, 2011.
Chagas, V. B. P. and Chaffe, P. L. B.: The Role of Land Cover in the
Propagation of Rainfall Into Streamflow Trends, Water Resour. Res., 54,
5986–6004, https://doi.org/10.1029/2018WR022947, 2018.
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 – link to
files, Zenodo, https://doi.org/10.5281/zenodo.3709337, 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.
Collischonn,
W., Tucci, C. E. M., and Clarke, R. T.: Further evidence of changes in the
hydrological regime of the River Paraguay: part of a wider phenomenon of
climate change?, J. Hydrol., 245, 218–238,
https://doi.org/10.1016/S0022-1694(01)00348-1, 2001.
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 Discuss., https://doi.org/10.5194/essd-2020-49, in review, 2020.
Crochemore, L., Isberg, K., Pimentel, R., Pineda, L., Hasan, A., and
Arheimer, B.: Lessons learnt from checking the quality of openly accessible
river flow data worldwide, Hydrolog. Sci. J., 65, 699–711, https://doi.org/10.1080/02626667.2019.1659509, 2019.
Dias, L. C. P., Pimenta, F. M., Santos, A. B., Costa, M. H., and Ladle, R.
J.: Patterns of land use, extensification, and intensification of Brazilian
agriculture, Glob. Change Biol., 22, 2887–2903, https://doi.org/10.1111/gcb.13314,
2016.
Di Baldassarre, G., Wanders, N., AghaKouchak, A., Kuil, L., Rangecroft, S.,
Veldkamp, T. I. E., Garcia, M., van Oel, P. R., Breinl, K., and Van Loon, A.
F.: Water shortages worsened by reservoir effects, Nat. Sustain., 1,
617–622, https://doi.org/10.1038/s41893-018-0159-0, 2018.
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, 2016.
Ehret, U., Gupta, H. V., Sivapalan, M., Weijs, S. V., Schymanski, S. J., Blöschl, G., Gelfan, A. N., Harman, C., Kleidon, A., Bogaard, T. A., Wang, D., Wagener, T., Scherer, U., Zehe, E., Bierkens, M. F. P., Di Baldassarre, G., Parajka, J., van Beek, L. P. H., van Griensven, A., Westhoff, M. C., and Winsemius, H. C.: Advancing catchment hydrology to deal with predictions under change, Hydrol. Earth Syst. Sci., 18, 649–671, https://doi.org/10.5194/hess-18-649-2014, 2014.
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., Li, H., and Miguez-Macho, G.: Global Patterns of Groundwater Table
Depth, Science, 339, 1–5, https://doi.org/10.1126/science.1229881, 2013.
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.
Feng, X., Thompson, S. E., Woods, R., and Porporato, A.: Quantifying
Asynchronicity of Precipitation and Potential Evapotranspiration in
Mediterranean Climates, Geophys. Res. Lett., 46, 14692–14701,
https://doi.org/10.1029/2019GL085653, 2019.
Fleig, A. K., Tallaksen, L. M., Hisdal, H., and Demuth, S.: A global evaluation of streamflow drought characteristics, Hydrol. Earth Syst. Sci., 10, 535–552, https://doi.org/10.5194/hess-10-535-2006, 2006.
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, Sci. Data, 2, 1–21, https://doi.org/10.1038/sdata.2015.66,
2015.
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.
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.
GRDC – Global Runoff Data Centre, GRDC, available at:
https://www.bafg.de/GRDC/EN/Home/homepage_node.html, last
access: 24 December 2019.
Grimm, A. M.: Interannual climate variability in South America: impacts on
seasonal precipitation, extreme events, and possible effects of climate
change, Stoch. Env. Res. Risk A., 25, 537–554,
https://doi.org/10.1007/s00477-010-0420-1, 2011.
Gudmundsson, L., Wagener, T., Tallaksen, L. M., and Engeland, K.:
Evaluation of nine large-scale hydrological models with respect to the
seasonal runoff climatology in Europe, Water Resour. Res., 48, W11504, https://doi.org/10.1029/2011WR010911,
2012.
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., 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.
Haddeland, I., Clark, D. B., Franssen, W., Ludwig, F., Voß, F., Arnell,
N. W., and Gomes, S.: Multimodel estimate of the global terrestrial
water balance: setup and first results, J. Hydrometeorol., 12,
869–884, 2011.
Hall, J., Arheimer, B., Borga, M., Brázdil, R., Claps, P., Kiss, A., Kjeldsen, T. R., Kriaučiūnienė, J., Kundzewicz, Z. W., Lang, M., Llasat, M. C., Macdonald, N., McIntyre, N., Mediero, L., Merz, B., Merz, R., Molnar, P., Montanari, A., Neuhold, C., Parajka, J., Perdigão, R. A. P., Plavcová, L., Rogger, M., Salinas, J. L., Sauquet, E., Schär, C., Szolgay, J., Viglione, A., and Blöschl, G.: Understanding flood regime changes in Europe: a state-of-the-art assessment, Hydrol. Earth Syst. Sci., 18, 2735–2772, https://doi.org/10.5194/hess-18-2735-2014, 2014.
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.
Haylock, M. and Nicholls, N.: Trends in extreme rainfall indices for an
updated high quality data set for Australia, 1910–1998, Int. J. Climatol.,
20, 1533–1541, https://doi.org/10.1002/1097-0088(20001115)20:13<1533::AID-JOC586>3.0.CO;2-J, 2000.
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.
Hirpa, F. A., Salamon, P., Beck, H. E., Lorini, V., Alfieri, L., Zsoter, E.,
and Dadson, S. J.: Calibration of the Global Flood Awareness System
(GloFAS) using daily streamflow data, J. Hydrol., 566, 595–606,
2018.
Hoekstra, A. Y. and Mekonnen, M. M.: The water footprint of humanity,
P. Natl. Acad. Sci. USA, 109, 3232–3237,
https://doi.org/10.1073/pnas.1109936109, 2012.
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.
IBGE – Brazilian Institute of Geography and Statistics: Censo
Agropecuário 2006, 2007.
Knoben, W. J. M., Woods, R. A., and Freer, J. E.: A Quantitative Hydrological
Climate Classification Evaluated With Independent Streamflow Data, Water
Resour. Res., 54, 5088–5109, https://doi.org/10.1029/2018WR022913, 2018.
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.
Latrubesse, E. M., Arima, E. Y., Dunne, T., Park, E., Baker, V. R., d'Horta,
F. M., Wight, C., Wittmann, F., Zuanon, J., Baker, P. A., Ribas, C. C.,
Norgaard, R. B., Filizola, N., Ansar, A., Flyvbjerg, B., and Stevaux, J. C.:
Damming the rivers of the Amazon basin, Nature, 546, 363–369,
https://doi.org/10.1038/nature22333, 2017.
Lehner, B.: Derivation of watershed boundaries for GRDC gauging stations
based on the HydroSHEDS drainage network, Global Runoff Data Centre in the
Federal Institute of Hydrology (BFG), 2012.
Lehner, B., Liermann, C. R., 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.
Leite, C. C., Costa, M. H., Soares-Filho, B. S., and de Barros Viana Hissa,
L.: Historical land use change and associated carbon emissions in Brazil
from 1940 to 1995, Global Biogeochem. Cy., 26, 1–13,
https://doi.org/10.1029/2011GB004133, 2012.
Levy, M. C., Lopes, A. V., Cohn, A., Larsen, L. G., and Thompson, S. E.: Land
Use Change Increases Streamflow Across the Arc of Deforestation in Brazil,
Geophys. Res. Lett., 45, 3520–3530, https://doi.org/10.1002/2017GL076526, 2018.
Lima, C. H. R., AghaKouchak, A., and Lall, U.: Classification of mechanisms, climatic context, areal scaling, and synchronization of floods: the hydroclimatology of floods in the Upper Paraná River basin, Brazil, Earth Syst. Dynam., 8, 1071–1091, https://doi.org/10.5194/esd-8-1071-2017, 2017.
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, Sci. Data, 6, 1–15,
https://doi.org/10.1038/s41597-019-0300-6, 2019.
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.
Marengo, J. A. and Espinoza, J. C.: Extreme seasonal droughts and floods in
Amazonia: causes, trends and impacts, Int. J. Climatol., 36, 1033–1050,
https://doi.org/10.1002/joc.4420, 2016.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Martinez, J. A. and Dominguez, F.: Sources of Atmospheric Moisture for the
La Plata River Basin, J. Climate, 27, 6737–6753,
https://doi.org/10.1175/JCLI-D-14-00022.1, 2014.
McMillan, H., Westerberg, I., and Branger, F.: Five guidelines for selecting
hydrological signatures, Hydrol. Process., 31, 4757–4761,
https://doi.org/10.1002/hyp.11300, 2017.
Melo, D. D. C. D., Scanlon, B. R., Zhang, Z., Wendland, E., and Yin, L.: Reservoir storage and hydrologic responses to droughts in the Paraná River basin, south-eastern Brazil, Hydrol. Earth Syst. Sci., 20, 4673–4688, https://doi.org/10.5194/hess-20-4673-2016, 2016.
Merz, B., Vorogushyn, S., Uhlemann, S., Delgado, J., and Hundecha, Y.: HESS Opinions “More efforts and scientific rigour are needed to attribute trends in flood time series”, Hydrol. Earth Syst. Sci., 16, 1379–1387, https://doi.org/10.5194/hess-16-1379-2012, 2012.
Milliman, J. D., Farnsworth, K. L., Jones, P. D., Xu, K. H., and Smith, L.
C.: Climatic and anthropogenic factors affecting river discharge to the
global ocean, 1951–2000, Global Planet. Change, 62, 187–194,
https://doi.org/10.1016/j.gloplacha.2008.03.001, 2008.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Montanari, A.: What do we mean by “uncertainty”? The need for a consistent
wording about uncertainty assessment in hydrology, Hydrol. Process., 21,
841–845, https://doi.org/10.1002/hyp.6623, 2007.
Montanari, A., Young, G., Savenije, H. H. G., Hughes, D., Wagener, T., Ren,
L. L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl,
G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer,
B., Boegh, E., Schymanski, S. J., Di Baldassarre, G., Yu, B., Hubert, P.,
Huang, Y., Schumann, A., Post, D. A., Srinivasan, V., Harman, C., Thompson,
S., Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., and
Belyaev, V.: “Panta Rhei – Everything Flows”: Change in hydrology and
society – The IAHS Scientific Decade 2013–2022, Hydrolog. Sci.
J., 58, 1256–1275, https://doi.org/10.1080/02626667.2013.809088, 2013.
Müller Schmied, H., Eisner, S., Franz, D., Wattenbach, M., Portmann, F. T., Flörke, M., and Döll, P.: Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration, Hydrol. Earth Syst. Sci., 18, 3511–3538, https://doi.org/10.5194/hess-18-3511-2014, 2014.
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.
NOAA: CPC Global Temperature, available at: https://www.esrl.noaa.gov/psd/ (last access 15
June 2019), 2019a.
NOAA: CPC Global Unified Precipitation, available at: https://www.esrl.noaa.gov/psd/ (last
access: 15 June 2019), 2019b.
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.
ONS – National Electrical System Operator: SIGEL – Sistema Geográfico de
Informações do Sistema Elétrico, available at: https://sigel.aneel.gov.br/,
last access: 10 December 2019.
Paiva, R. C. D., Buarque, D. C., Collischonn, W., Bonnet, M.-P., Frappart,
F., Calmant, S., and Bulhões Mendes, C. A.: Large-scale hydrologic and
hydrodynamic modeling of the Amazon River basin, Water Resour. Res., 49,
1226–1243, https://doi.org/10.1002/wrcr.20067, 2013.
Pasquini, A. I. and Depetris, P. J.: Discharge trends and flow dynamics of
South American rivers draining the southern Atlantic seaboard: An overview,
J. Hydrol., 333, 385–399,
https://doi.org/10.1016/j.jhydrol.2006.09.005, 2007.
Pfister, L. and Kirchner, J. W.: Debates-Hypothesis testing in hydrology:
Theory and practice, Water Resour. Res., 53, 1792–1798,
https://doi.org/10.1002/2016WR020116, 2017.
Raia, A. and Cavalcanti, I. F. A.: The Life Cycle of the South American
Monsoon System, J. Climate, 21, 6227–6246, https://doi.org/10.1175/2008JCLI2249.1,
2008.
Salio, P., Nicolini, M., and Zipser, E. J.: Mesoscale convective systems over
southeastern South America and their relationship with the South American
low-level jet, Mon. Weather Rev., 135, 1290–1309, 2007.
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.
Sawicz, K. A., Kelleher, C., Wagener, T., Troch, P., Sivapalan, M., and Carrillo, G.: Characterizing hydrologic change through catchment classification, Hydrol. Earth Syst. Sci., 18, 273–285, https://doi.org/10.5194/hess-18-273-2014, 2014.
Schewe, J., Heinke, J., Gerten, D., Haddeland, I., Arnell, N. W., Clark, D.
B., and Gosling, S. N.:
Multimodel assessment of water scarcity under
climate change, P. Natl. Acad. Sci. USA, 111,
3245–3250, 2014.
Schobbenhaus, C., Gonçalves, J., Santos, J., Abram, M., Leão Neto,
R., Matos, G., Vidotti, R., Ramos, M., and de Jesus, J.: Carta geológica
do Brasil ao milionésimo: Sistema de Informações
Heográficas, CPRM – Serviço Geológico do Brasil, Brasília,
2004.
Seager, R., Naik, N., Baethgen, W., Robertson, A., Kushnir, Y., Nakamura, J.,
and Jurburg, S.: Tropical Oceanic Causes of Interannual to Multidecadal
Precipitation Variability in Southeast South America over the Past Century,
J. Climate, 23, 5517–5539, https://doi.org/10.1175/2010JCLI3578.1, 2010.
Shangguan, W., Hengl, T., Mendes de Jesus, J., Yuan, H., and Dai, Y.: Mapping
the global depth to bedrock for land surface modeling, J. Adv. Model. Earth
Sy., 9, 65–88, https://doi.org/10.1002/2016MS000686, 2017.
Shen, X., Anagnostou, E. N., Mei, Y., and Hong, Y.: A global distributed
basin morphometric dataset, Sci. Data, 4, 160124,
https://doi.org/10.1038/sdata.2016.124, 2017.
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.
Siqueira, V. A., Paiva, R. C. D., Fleischmann, A. S., Fan, F. M., Ruhoff, A. L., Pontes, P. R. M., Paris, A., Calmant, S., and Collischonn, W.: Toward continental hydrologic–hydrodynamic modeling in South America, Hydrol. Earth Syst. Sci., 22, 4815–4842, https://doi.org/10.5194/hess-22-4815-2018, 2018.
Slater, L. J., Singer, M. B., and Kirchner, J. W.: Hydrologic versus
geomorphic drivers of trends in flood hazard, Geophys. Res. Lett., 42,
370–376, https://doi.org/10.1002/2014GL062482, 2015.
Smakhtin, V. U.: Low flow hydrology: a review, J. Hydrol.,
240, 147–186, https://doi.org/10.1016/S0022-1694(00)00340-1, 2001.
Song, X.-P., Hansen, M. C., Stehman, S. V., Potapov, P. V., Tyukavina, A.,
Vermote, E. F., and Townshend, J. R.: Global land change from 1982 to 2016,
Nature, 560, 639–643, https://doi.org/10.1038/s41586-018-0411-9, 2018.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.: A
Review of Global Precipitation Data Sets: Data Sources, Estimation, and
Intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2017RG000574,
2018.
Tedeschi, R. G., Cavalcanti, I. F. A., and Grimm, A. M.: Influences of two
types of ENSO on South American precipitation, Int. J. Climatol., 33,
1382–1400, https://doi.org/10.1002/joc.3519, 2013.
Tomasella, J., Borma, L. S., Marengo, J. A., Rodriguez, D. A., Cuartas, L.
A., A. Nobre, C., and Prado, M. C. R.: The droughts of 1996–1997 and
2004–2005 in Amazonia: hydrological response in the river main-stem, Hydrol.
Process., 25, 1228–1242, https://doi.org/10.1002/hyp.7889, 2011.
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.
Van Loon, A. F.: Hydrological drought explained, WIREs Water, 2,
359–392, https://doi.org/10.1002/wat2.1085, 2015.
Veldkamp, T. I. E., Zhao, F., Ward, P. J., De Moel, H., Aerts, J. C.,
Schmied, H. M., and Satoh, Y.: Human impact parameterizations in global
hydrological models improve estimates of monthly discharges and hydrological
extremes: a multi-model validation study, Environ. Res. Lett.,
13, 055008, https://doi.org/10.1088/1748-9326/aab96f, 2018.
Villarini, G.: On the seasonality of flooding across the continental United
States, Adv. Water Resour., 87, 80–91,
https://doi.org/10.1016/j.advwatres.2015.11.009, 2016.
Wada, Y., Wisser, D., and Bierkens, M. F. P.: Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources, Earth Syst. Dynam., 5, 15–40, https://doi.org/10.5194/esd-5-15-2014, 2014.
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.
Wohl, E., Barros, A., Brunsell, N.,
Chappell, N. A., Coe, M., Giambelluca, T., Goldsmith, S., Harmon, R.,
Hendrickx, J. M. H., Juvik, J., McDonnell, J., and Ogden, F.: The hydrology
of the humid tropics, Nat. Clim. Change, 2, 655–662,
https://doi.org/10.1038/nclimate1556, 2012.
Woldemeskel, F. and Sharma, A.: Should flood regimes change in a warming
climate? The role of antecedent moisture conditions, Geophys. Res. Lett.,
43, 7556–7563, https://doi.org/10.1002/2016GL069448, 2016.
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.
Wongchuig, S. C., de Paiva, R. C. D., Siqueira, V., and Collischonn, W.:
Hydrological reanalysis across the 20th century: A case study of the Amazon
Basin, J. Hydrol., 570, 755–773, 2019.
Xavier, A. C., King, C. W., and Scanlon, B. R.: Daily gridded meteorological
variables in Brazil (1980–2013), Int. J. Climatol., 36, 2644–2659,
https://doi.org/10.1002/joc.4518, 2016.
Yadav, M., Wagener, T., and Gupta, H.: Regionalization of constraints on
expected watershed response behavior for improved predictions in ungauged
basins, Adv. Water Resour., 30, 1756–1774,
https://doi.org/10.1016/j.advwatres.2007.01.005, 2007.
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.
Zhao, F., Veldkamp, T. I., Frieler, K., Schewe, J., Ostberg, S., Willner,
S., and Leng, G.: The critical role of the routing scheme in simulating
peak river discharge in global hydrological models, Environ. Res.
Lett., 12, 075003, https://doi.org/10.1088/1748-9326/aa7250, 2017.
Zscheischler, J., Westra, S., van den Hurk, B. J. J. M., Seneviratne, S. I.,
Ward, P. J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl,
T., and Zhang, X.: Future climate risk from compound events, Nat. Clim.
Change, 8, 469–477, https://doi.org/10.1038/s41558-018-0156-3, 2018.
Short summary
We present a new dataset for large-sample hydrological studies in Brazil. The dataset encompasses daily observed streamflow from 3679 gauges, as well as meteorological forcing for 897 selected catchments. It also includes 65 attributes covering topographic, climatic, hydrologic, land cover, geologic, soil, and human intervention variables. CAMELS-BR is publicly available and will enable new insights into the hydrological behavior of catchments in Brazil.
We present a new dataset for large-sample hydrological studies in Brazil. The dataset...
Altmetrics
Final-revised paper
Preprint