Articles | Volume 24, issue 2
https://doi.org/10.5194/hess-24-919-2020
© 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/hess-24-919-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Feb 2020
Research article |
| 27 Feb 2020
| 27 Feb 2020
Rainfall Estimates on a Gridded Network (REGEN) – a global land-based gridded dataset of daily precipitation from 1950 to 2016
Steefan Contractor
CORRESPONDING AUTHOR
Climate Change Research Centre, UNSW Sydney, Sydney, Australia
ARC Centre of Excellence for Climate System Science, Sydney, Australia
Markus G. Donat
Climate Change Research Centre, UNSW Sydney, Sydney, Australia
ARC Centre of Excellence for Climate Extremes, Sydney, Australia
Barcelona Supercomputing Center, Barcelona, Spain
Lisa V. Alexander
Climate Change Research Centre, UNSW Sydney, Sydney, Australia
ARC Centre of Excellence for Climate Extremes, Sydney, Australia
Markus Ziese
Global Precipitation Climatology Centre, Deutscher Wetterdienst, Offenbach, Germany
Anja Meyer-Christoffer
Global Precipitation Climatology Centre, Deutscher Wetterdienst, Offenbach, Germany
Udo Schneider
Global Precipitation Climatology Centre, Deutscher Wetterdienst, Offenbach, Germany
Elke Rustemeier
Global Precipitation Climatology Centre, Deutscher Wetterdienst, Offenbach, Germany
Andreas Becker
Global Precipitation Climatology Centre, Deutscher Wetterdienst, Offenbach, Germany
Imke Durre
National Centers for Environmental Information, National Oceanic and Atmospheric Administration, Asheville, NC, USA
Russell S. Vose
National Centers for Environmental Information, National Oceanic and Atmospheric Administration, Asheville, NC, USA
Related authors
No articles found.
Rashed Mahmood, Markus G. Donat, Roberto Bilbao, Pablo Ortega, Vladimir Lapin, Etienne Tourigny, and Francisco Doblas-Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-1208, https://doi.org/10.5194/egusphere-2025-1208, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
We present 30 year long initialized climate predictions run with the EC-Earth3 model. The predictions show high skill in most regions for near-surface temperatures, with some added skill from initialization for the first decade, but only very limited added skill beyond. The predictions exhibit drift associated with a persistent slowdown in Atlantic Meridonial Overturning Circulation , leaving the initialised predictions in a different climate state than the historical climate simulations.
Alvise Aranyossy, Paolo De Luca, Carlos Delgado-Torres, Balakrishnan Solaraju-Murali, Margarida Samso Cabre, and Markus Gabriel Donat
EGUsphere, https://doi.org/10.5194/egusphere-2025-940, https://doi.org/10.5194/egusphere-2025-940, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
We investigate multi-year predictability of hot-dry compound events, and their univariate hot and dry contributions, using the CMIP6 multi-model decadal hindcast experiments, focusing on the forecast years 2–5. We find that hot-dry compound extremes and hot extremes are skillfully predicted in many regions, but lower skill is found for dry extremes. The skill is largely due to long-term trends in response to external forcing, while the added skill from initialisation is limited to a few regions.
Juan C. Acosta Navarro, Alvise Aranyossy, Paolo De Luca, Markus G. Donat, Arthur Hrast Essenfelder, Rashed Mahmood, Andrea Toreti, and Danila Volpi
EGUsphere, https://doi.org/10.5194/egusphere-2025-319, https://doi.org/10.5194/egusphere-2025-319, 2025
Short summary
Short summary
A computationally inexpensive climate model analog method yields skillful climate predictions across timescales, from seasons to multiple years, complementing existing climate prediction systems and potentially providing valuable information for sectors like agriculture and energy.
Pedro José Roldán-Gómez, Paolo De Luca, Raffaele Bernardello, and Markus G. Donat
Earth Syst. Dynam., 16, 1–27, https://doi.org/10.5194/esd-16-1-2025, https://doi.org/10.5194/esd-16-1-2025, 2025
Short summary
Short summary
Current trends in CO2 emissions increase the probability of an overshoot scenario in which temperatures exceed the targets of the Paris Agreement and are brought back afterwards with a net-negative emission strategy. This work analyses how the climate after the overshoot would differ from the climate before, linking large scale non-reversibility mechanisms to changes in regional climates and identifying those regions more impacted by changes in temperature and precipitation extremes.
Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, https://doi.org/10.5194/esd-15-1319-2024, 2024
Short summary
Short summary
We propose a number of priority areas for the international climate research community to address over the coming decade. Advances in these areas will both increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, and deliver significantly improved scientific support to international climate policy, such as future IPCC assessments and the UNFCCC Global Stocktake.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
Short summary
Short summary
We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Bradley Hall, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan P. Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Blair Trewin, Myles Allen, Robbie Andrew, Richard A. Betts, Alex Borger, Tim Boyer, Jiddu A. Broersma, Carlo Buontempo, Samantha Burgess, Chiara Cagnazzo, Lijing Cheng, Pierre Friedlingstein, Andrew Gettelman, Johannes Gütschow, Masayoshi Ishii, Stuart Jenkins, Xin Lan, Colin Morice, Jens Mühle, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Jan C. Minx, Gunnar Myhre, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, Sophie Szopa, Peter Thorne, Mahesh V. M. Kovilakam, Elisa Majamäki, Jukka-Pekka Jalkanen, Margreet van Marle, Rachel M. Hoesly, Robert Rohde, Dominik Schumacher, Guido van der Werf, Russell Vose, Kirsten Zickfeld, Xuebin Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
Short summary
Short summary
This paper tracks some key indicators of global warming through time, from 1850 through to the end of 2023. It is designed to give an authoritative estimate of global warming to date and its causes. We find that in 2023, global warming reached 1.3 °C and is increasing at over 0.2 °C per decade. This is caused by all-time-high greenhouse gas emissions.
Elsa Mohino, Paul-Arthur Monerie, Juliette Mignot, Moussa Diakhaté, Markus Donat, Christopher David Roberts, and Francisco Doblas-Reyes
Earth Syst. Dynam., 15, 15–40, https://doi.org/10.5194/esd-15-15-2024, https://doi.org/10.5194/esd-15-15-2024, 2024
Short summary
Short summary
The impact of the Atlantic multidecadal variability (AMV) on the rainfall distribution and timing of the West African monsoon is not well known. Analysing model output, we find that a positive AMV enhances the number of wet days, daily rainfall intensity, and extremes over the Sahel and tends to prolong the monsoon length through later demise. Heavy rainfall events increase all over the Sahel, while moderate ones only occur in the north. Model biases affect the skill in simulating AMV impact.
Piers M. Forster, Christopher J. Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Sonia I. Seneviratne, Blair Trewin, Xuebin Zhang, Myles Allen, Robbie Andrew, Arlene Birt, Alex Borger, Tim Boyer, Jiddu A. Broersma, Lijing Cheng, Frank Dentener, Pierre Friedlingstein, José M. Gutiérrez, Johannes Gütschow, Bradley Hall, Masayoshi Ishii, Stuart Jenkins, Xin Lan, June-Yi Lee, Colin Morice, Christopher Kadow, John Kennedy, Rachel Killick, Jan C. Minx, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sophie Szopa, Peter Thorne, Robert Rohde, Maisa Rojas Corradi, Dominik Schumacher, Russell Vose, Kirsten Zickfeld, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 15, 2295–2327, https://doi.org/10.5194/essd-15-2295-2023, https://doi.org/10.5194/essd-15-2295-2023, 2023
Short summary
Short summary
This is a critical decade for climate action, but there is no annual tracking of the level of human-induced warming. We build on the Intergovernmental Panel on Climate Change assessment reports that are authoritative but published infrequently to create a set of key global climate indicators that can be tracked through time. Our hope is that this becomes an important annual publication that policymakers, media, scientists and the public can refer to.
Eva Sebok, Hans Jørgen Henriksen, Ernesto Pastén-Zapata, Peter Berg, Guillaume Thirel, Anthony Lemoine, Andrea Lira-Loarca, Christiana Photiadou, Rafael Pimentel, Paul Royer-Gaspard, Erik Kjellström, Jens Hesselbjerg Christensen, Jean Philippe Vidal, Philippe Lucas-Picher, Markus G. Donat, Giovanni Besio, María José Polo, Simon Stisen, Yvan Caballero, Ilias G. Pechlivanidis, Lars Troldborg, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci., 26, 5605–5625, https://doi.org/10.5194/hess-26-5605-2022, https://doi.org/10.5194/hess-26-5605-2022, 2022
Short summary
Short summary
Hydrological models projecting the impact of changing climate carry a lot of uncertainty. Thus, these models usually have a multitude of simulations using different future climate data. This study used the subjective opinion of experts to assess which climate and hydrological models are the most likely to correctly predict climate impacts, thereby easing the computational burden. The experts could select more likely hydrological models, while the climate models were deemed equally probable.
Rashed Mahmood, Markus G. Donat, Pablo Ortega, Francisco J. Doblas-Reyes, Carlos Delgado-Torres, Margarida Samsó, and Pierre-Antoine Bretonnière
Earth Syst. Dynam., 13, 1437–1450, https://doi.org/10.5194/esd-13-1437-2022, https://doi.org/10.5194/esd-13-1437-2022, 2022
Short summary
Short summary
Near-term climate change projections are strongly affected by the uncertainty from internal climate variability. Here we present a novel approach to reduce such uncertainty by constraining decadal-scale variability in the projections using observations. The constrained ensembles show significant added value over the unconstrained ensemble in predicting global climate 2 decades ahead. We also show the applicability of regional constraints for attributing predictability to certain ocean regions.
Vera Thiemig, Goncalo N. Gomes, Jon O. Skøien, Markus Ziese, Armin Rauthe-Schöch, Elke Rustemeier, Kira Rehfeldt, Jakub P. Walawender, Christine Kolbe, Damien Pichon, Christoph Schweim, and Peter Salamon
Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, https://doi.org/10.5194/essd-14-3249-2022, 2022
Short summary
Short summary
EMO-5 is a free and open European high-resolution (5 km), sub-daily, multi-variable (precipitation, temperatures, wind speed, solar radiation, vapour pressure), multi-decadal meteorological dataset based on quality-controlled observations coming from almost 30 000 stations across Europe, and is produced in near real-time. EMO-5 (v1) covers the time period from 1990 to 2019. In this paper, we have provided insight into the source data, the applied methods, and the quality assessment of EMO-5.
Roberto Bilbao, Simon Wild, Pablo Ortega, Juan Acosta-Navarro, Thomas Arsouze, Pierre-Antoine Bretonnière, Louis-Philippe Caron, Miguel Castrillo, Rubén Cruz-García, Ivana Cvijanovic, Francisco Javier Doblas-Reyes, Markus Donat, Emanuel Dutra, Pablo Echevarría, An-Chi Ho, Saskia Loosveldt-Tomas, Eduardo Moreno-Chamarro, Núria Pérez-Zanon, Arthur Ramos, Yohan Ruprich-Robert, Valentina Sicardi, Etienne Tourigny, and Javier Vegas-Regidor
Earth Syst. Dynam., 12, 173–196, https://doi.org/10.5194/esd-12-173-2021, https://doi.org/10.5194/esd-12-173-2021, 2021
Short summary
Short summary
This paper presents and evaluates a set of retrospective decadal predictions with the EC-Earth3 climate model. These experiments successfully predict past changes in surface air temperature but show poor predictive capacity in the subpolar North Atlantic, a well-known source region of decadal climate variability. The poor predictive capacity is linked to an initial shock affecting the Atlantic Ocean circulation, ultimately due to a suboptimal representation of the Labrador Sea density.
Mia H. Gross, Markus G. Donat, Lisa V. Alexander, and Steven C. Sherwood
Earth Syst. Dynam., 11, 97–111, https://doi.org/10.5194/esd-11-97-2020, https://doi.org/10.5194/esd-11-97-2020, 2020
Short summary
Short summary
This study explores the amplified warming of cold extremes relative to average temperatures for both the recent past and future in the Northern Hemisphere and the possible physical processes that are driving this. We find that decreases in snow cover and
warmer-than-usual winds are driving the disproportionate rates of warming in cold extremes relative to average temperatures. These accelerated warming rates in cold extremes have implications for tourism, insect longevity and human health.
Rémy Roca, Lisa V. Alexander, Gerald Potter, Margot Bador, Rômulo Jucá, Steefan Contractor, Michael G. Bosilovich, and Sophie Cloché
Earth Syst. Sci. Data, 11, 1017–1035, https://doi.org/10.5194/essd-11-1017-2019, https://doi.org/10.5194/essd-11-1017-2019, 2019
Short summary
Short summary
This paper presents a database that is a collection of datasets of gridded 1° × 1° daily precipitation estimates from a variety of sources. It includes observations from in situ networks, satellite-based estimations and outputs from atmospheric reanalysis. All the datasets have been formatted in the same way to ease their manipulation. This database aims at facilitating intercomparisons and validation exercises performed by the scientific community.
Stephen Blenkinsop, Hayley J. Fowler, Renaud Barbero, Steven C. Chan, Selma B. Guerreiro, Elizabeth Kendon, Geert Lenderink, Elizabeth Lewis, Xiao-Feng Li, Seth Westra, Lisa Alexander, Richard P. Allan, Peter Berg, Robert J. H. Dunn, Marie Ekström, Jason P. Evans, Greg Holland, Richard Jones, Erik Kjellström, Albert Klein-Tank, Dennis Lettenmaier, Vimal Mishra, Andreas F. Prein, Justin Sheffield, and Mari R. Tye
Adv. Sci. Res., 15, 117–126, https://doi.org/10.5194/asr-15-117-2018, https://doi.org/10.5194/asr-15-117-2018, 2018
Short summary
Short summary
Measurements of sub-daily (e.g. hourly) rainfall totals are essential if we are to understand short, intense bursts of rainfall that cause flash floods. We might expect the intensity of such events to increase in a warming climate but these are poorly realised in projections of future climate change. The INTENSE project is collating a global dataset of hourly rainfall measurements and linking with new developments in climate models to understand the characteristics and causes of these events.
Richard Wartenburger, Martin Hirschi, Markus G. Donat, Peter Greve, Andy J. Pitman, and Sonia I. Seneviratne
Geosci. Model Dev., 10, 3609–3634, https://doi.org/10.5194/gmd-10-3609-2017, https://doi.org/10.5194/gmd-10-3609-2017, 2017
Short summary
Short summary
This article analyses regional changes in climate extremes as a function of projected changes in global mean temperature. We introduce the DROUGHT-HEAT Regional Climate Atlas, an interactive tool to analyse and display a range of well-established climate extremes and water-cycle indices and their changes as a function of global warming. Readers are encouraged to use the online tool for visualization of specific indices of interest, e.g. to assess their response to 1.5 or 2 °C global warming.
Udo Schneider, Markus Ziese, Anja Meyer-Christoffer, Peter Finger, Elke Rustemeier, and Andreas Becker
Proc. IAHS, 374, 29–34, https://doi.org/10.5194/piahs-374-29-2016, https://doi.org/10.5194/piahs-374-29-2016, 2016
Short summary
Short summary
The Global Precipitation Climatology Centre (GPCC) operated by Deutscher Wetterdienst since 1989 provides reliable information on monthly and daily precipitatation since 1901 and 1988, respectively. The information is provided as DOI referenced data sets and we provide herewith an overview and orientation which product is most suitable for a number of archetype use cases. We also provide some illustration on particular use cases. The GPCC data product will be further upgraded and prolongued.
R. J. H. Dunn, M. G. Donat, and L. V. Alexander
Clim. Past, 10, 2171–2199, https://doi.org/10.5194/cp-10-2171-2014, https://doi.org/10.5194/cp-10-2171-2014, 2014
Short summary
Short summary
Observational data sets contain uncertainties, e.g. from the instrument accuracy, as well as from the fact that usually only a single method is used in processing. We have performed an assessment of the size of the uncertainties associated with choices in the method used. The largest effects come from changes which affect the station network or the gridding method used. However, for the temperature indices in places with many stations, these changes have little effect on the long-term behaviour.
K. Willett, C. Williams, I. T. Jolliffe, R. Lund, L. V. Alexander, S. Brönnimann, L. A. Vincent, S. Easterbrook, V. K. C. Venema, D. Berry, R. E. Warren, G. Lopardo, R. Auchmann, E. Aguilar, M. J. Menne, C. Gallagher, Z. Hausfather, T. Thorarinsdottir, and P. W. Thorne
Geosci. Instrum. Method. Data Syst., 3, 187–200, https://doi.org/10.5194/gi-3-187-2014, https://doi.org/10.5194/gi-3-187-2014, 2014
M. Ziese, U. Schneider, A. Meyer-Christoffer, K. Schamm, J. Vido, P. Finger, P. Bissolli, S. Pietzsch, and A. Becker
Earth Syst. Sci. Data, 6, 285–295, https://doi.org/10.5194/essd-6-285-2014, https://doi.org/10.5194/essd-6-285-2014, 2014
B. Merz, J. Aerts, K. Arnbjerg-Nielsen, M. Baldi, A. Becker, A. Bichet, G. Blöschl, L. M. Bouwer, A. Brauer, F. Cioffi, J. M. Delgado, M. Gocht, F. Guzzetti, S. Harrigan, K. Hirschboeck, C. Kilsby, W. Kron, H.-H. Kwon, U. Lall, R. Merz, K. Nissen, P. Salvatti, T. Swierczynski, U. Ulbrich, A. Viglione, P. J. Ward, M. Weiler, B. Wilhelm, and M. Nied
Nat. Hazards Earth Syst. Sci., 14, 1921–1942, https://doi.org/10.5194/nhess-14-1921-2014, https://doi.org/10.5194/nhess-14-1921-2014, 2014
R. Lorenz, A. J. Pitman, M. G. Donat, A. L. Hirsch, J. Kala, E. A. Kowalczyk, R. M. Law, and J. Srbinovsky
Geosci. Model Dev., 7, 545–567, https://doi.org/10.5194/gmd-7-545-2014, https://doi.org/10.5194/gmd-7-545-2014, 2014
K. Schamm, M. Ziese, A. Becker, P. Finger, A. Meyer-Christoffer, U. Schneider, M. Schröder, and P. Stender
Earth Syst. Sci. Data, 6, 49–60, https://doi.org/10.5194/essd-6-49-2014, https://doi.org/10.5194/essd-6-49-2014, 2014
A. Becker, P. Finger, A. Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider, and M. Ziese
Earth Syst. Sci. Data, 5, 71–99, https://doi.org/10.5194/essd-5-71-2013, https://doi.org/10.5194/essd-5-71-2013, 2013
Related subject area
Subject: Global hydrology | Techniques and Approaches: Mathematical applications
Projecting end-of-century climate extremes and their impacts on the hydrology of a representative California watershed
Integrating process-related information into an artificial neural network for root-zone soil moisture prediction
Coherence of global hydroclimate classification systems
Design flood estimation for global river networks based on machine learning models
Attributing correlation skill of dynamical GCM precipitation forecasts to statistical ENSO teleconnection using a set-theory-based approach
The spatial extent of hydrological and landscape changes across the mountains and prairies of Canada in the Mackenzie and Nelson River basins based on data from a warm-season time window
Averaging over spatiotemporal heterogeneity substantially biases evapotranspiration rates in a mechanistic large-scale land evaporation model
A framework for deriving drought indicators from the Gravity Recovery and Climate Experiment (GRACE)
Hydrological effects of climate variability and vegetation dynamics on annual fluvial water balance in global large river basins
Spatial patterns and characteristics of flood seasonality in Europe
Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate
Effects of different reference periods on drought index (SPEI) estimations from 1901 to 2014
The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis
Global trends in extreme precipitation: climate models versus observations
A global water cycle reanalysis (2003–2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble
A generic method for hydrological drought identification across different climate regions
Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria
Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 2: Generalization in time and space
Fadji Z. Maina, Alan Rhoades, Erica R. Siirila-Woodburn, and Peter-James Dennedy-Frank
Hydrol. Earth Syst. Sci., 26, 3589–3609, https://doi.org/10.5194/hess-26-3589-2022, https://doi.org/10.5194/hess-26-3589-2022, 2022
Short summary
Short summary
In this work, we assess the effects of end-of-century extreme dry and wet conditions on the hydrology of California. Our results, derived from cutting-edge and high-resolution climate and hydrologic models, highlight that (1) water storage will be larger and increase earlier in the year, yet the summer streamflow will decrease as a result of high evapotranspiration rates, and that (2) groundwater and lower-order streams are very sensitive to decreases in snowmelt and higher evapotranspiration.
Roiya Souissi, Mehrez Zribi, Chiara Corbari, Marco Mancini, Sekhar Muddu, Sat Kumar Tomer, Deepti B. Upadhyaya, and Ahmad Al Bitar
Hydrol. Earth Syst. Sci., 26, 3263–3297, https://doi.org/10.5194/hess-26-3263-2022, https://doi.org/10.5194/hess-26-3263-2022, 2022
Short summary
Short summary
In this study, we investigate the combination of surface soil moisture information with process-related features, namely, evaporation efficiency, soil water index and normalized difference vegetation index, using artificial neural networks to predict root-zone soil moisture. The joint use of process-related features yielded more accurate predictions in the case of arid and semiarid conditions. However, they have no to little added value in temperate to tropical conditions.
Kathryn L. McCurley Pisarello and James W. Jawitz
Hydrol. Earth Syst. Sci., 25, 6173–6183, https://doi.org/10.5194/hess-25-6173-2021, https://doi.org/10.5194/hess-25-6173-2021, 2021
Short summary
Short summary
Climate classification systems divide the Earth into zones of similar climates. We compared the within-zone hydroclimate similarity and zone shape complexity of a suite of climate classification systems, including new ones formed in this study. The most frequently used system had high similarity but high complexity. We propose the Water-Energy Clustering framework, which also had high similarity but lower complexity. This new system is therefore proposed for future hydroclimate assessments.
Gang Zhao, Paul Bates, Jeffrey Neal, and Bo Pang
Hydrol. Earth Syst. Sci., 25, 5981–5999, https://doi.org/10.5194/hess-25-5981-2021, https://doi.org/10.5194/hess-25-5981-2021, 2021
Short summary
Short summary
Design flood estimation is a fundamental task in hydrology. We propose a machine- learning-based approach to estimate design floods anywhere on the global river network. This approach shows considerable improvement over the index-flood-based method, and the average bias in estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. This approach is a valid method to estimate design floods globally, improving our prediction of flood hazard, especially in ungauged areas.
Tongtiegang Zhao, Haoling Chen, Quanxi Shao, Tongbi Tu, Yu Tian, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 5717–5732, https://doi.org/10.5194/hess-25-5717-2021, https://doi.org/10.5194/hess-25-5717-2021, 2021
Short summary
Short summary
This paper develops a novel approach to attributing correlation skill of dynamical GCM forecasts to statistical El Niño–Southern Oscillation (ENSO) teleconnection using the coefficient of determination. Three cases of attribution are effectively facilitated, which are significantly positive anomaly correlation attributable to positive ENSO teleconnection, attributable to negative ENSO teleconnection and not attributable to ENSO teleconnection.
Paul H. Whitfield, Philip D. A. Kraaijenbrink, Kevin R. Shook, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 25, 2513–2541, https://doi.org/10.5194/hess-25-2513-2021, https://doi.org/10.5194/hess-25-2513-2021, 2021
Short summary
Short summary
Using only warm season streamflow records, regime and change classifications were produced for ~ 400 watersheds in the Nelson and Mackenzie River basins, and trends in water storage and vegetation were detected from satellite imagery. Three areas show consistent changes: north of 60° (increased streamflow and basin greenness), in the western Boreal Plains (decreased streamflow and basin greenness), and across the Prairies (three different patterns of increased streamflow and basin wetness).
Elham Rouholahnejad Freund, Massimiliano Zappa, and James W. Kirchner
Hydrol. Earth Syst. Sci., 24, 5015–5025, https://doi.org/10.5194/hess-24-5015-2020, https://doi.org/10.5194/hess-24-5015-2020, 2020
Short summary
Short summary
Evapotranspiration (ET) is the largest flux from the land to the atmosphere and thus contributes to Earth's energy and water balance. Due to its impact on atmospheric dynamics, ET is a key driver of droughts and heatwaves. In this paper, we demonstrate how averaging over land surface heterogeneity contributes to substantial overestimates of ET fluxes. We also demonstrate how one can correct for the effects of small-scale heterogeneity without explicitly representing it in land surface models.
Helena Gerdener, Olga Engels, and Jürgen Kusche
Hydrol. Earth Syst. Sci., 24, 227–248, https://doi.org/10.5194/hess-24-227-2020, https://doi.org/10.5194/hess-24-227-2020, 2020
Short summary
Short summary
GRACE-derived drought indicators enable us to detect hydrological droughts based on changes observed in all storages. By performing synthetic experiments, we find that droughts identified by existing and modified indicators are biased by trends and GRACE-based spatial noise. A modified version of the Zhao et al. (2017) indicator is found to be particularly robust against spatial noise and is therefore applied to real GRACE data over South Africa.
Jianyu Liu, Qiang Zhang, Vijay P. Singh, Changqing Song, Yongqiang Zhang, Peng Sun, and Xihui Gu
Hydrol. Earth Syst. Sci., 22, 4047–4060, https://doi.org/10.5194/hess-22-4047-2018, https://doi.org/10.5194/hess-22-4047-2018, 2018
Short summary
Short summary
Considering effective precipitation (Pe), the Budyko framework was extended to the annual water balance analysis. To reflect the mismatch between water supply (precipitation, P) and energy (potential evapotranspiration,
E0), a climate seasonality and asynchrony index (SAI) were proposed in terms of both phase and amplitude mismatch between P and E0.
Julia Hall and Günter Blöschl
Hydrol. Earth Syst. Sci., 22, 3883–3901, https://doi.org/10.5194/hess-22-3883-2018, https://doi.org/10.5194/hess-22-3883-2018, 2018
Sanaa Hobeichi, Gab Abramowitz, Jason Evans, and Anna Ukkola
Hydrol. Earth Syst. Sci., 22, 1317–1336, https://doi.org/10.5194/hess-22-1317-2018, https://doi.org/10.5194/hess-22-1317-2018, 2018
Short summary
Short summary
We present a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009 and 0.5 grid cell size. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. We confirm that point-based estimates of flux towers provide information at the grid scale of these products. We also show that the weighted product performs better than 10 different existing global ET datasets in a range of metrics.
Myoung-Jin Um, Yeonjoo Kim, Daeryong Park, and Jeongbin Kim
Hydrol. Earth Syst. Sci., 21, 4989–5007, https://doi.org/10.5194/hess-21-4989-2017, https://doi.org/10.5194/hess-21-4989-2017, 2017
Short summary
Short summary
This study aims to understand how different reference periods (i.e., calibration periods) of climate data for estimating the drought index influence regional drought assessments. Specifically, we investigate the influence of different reference periods on historical drought characteristics such as trends, frequency, intensity and spatial extents using the Standard Precipitation Evapotranspiration Index (SPEI) estimated from the two widely used global datasets.
Lorenzo Mentaschi, Michalis Vousdoukas, Evangelos Voukouvalas, Ludovica Sartini, Luc Feyen, Giovanni Besio, and Lorenzo Alfieri
Hydrol. Earth Syst. Sci., 20, 3527–3547, https://doi.org/10.5194/hess-20-3527-2016, https://doi.org/10.5194/hess-20-3527-2016, 2016
Short summary
Short summary
The climate is subject to variations which must be considered
studying the intensity and frequency of extreme events.
We introduce in this paper a new methodology
for the study of variable extremes, which consists in detecting
the pattern of variability of a time series, and applying these patterns
to the analysis of the extreme events.
This technique comes with advantages with respect to the previous ones
in terms of accuracy, simplicity, and robustness.
B. Asadieh and N. Y. Krakauer
Hydrol. Earth Syst. Sci., 19, 877–891, https://doi.org/10.5194/hess-19-877-2015, https://doi.org/10.5194/hess-19-877-2015, 2015
Short summary
Short summary
We present a systematic comparison of changes in historical extreme precipitation in station observations (HadEX2) and 15 climate models from the CMIP5 (as the largest and most recent sets of available observational and modeled data sets), on global and continental scales for 1901-2010, using both parametric (linear regression) and non-parametric (the Mann-Kendall as well as Sen’s slope estimator) methods, taking care to sample observations and models spatially and temporally in comparable ways.
A. I. J. M. van Dijk, L. J. Renzullo, Y. Wada, and P. Tregoning
Hydrol. Earth Syst. Sci., 18, 2955–2973, https://doi.org/10.5194/hess-18-2955-2014, https://doi.org/10.5194/hess-18-2955-2014, 2014
M. H. J. van Huijgevoort, P. Hazenberg, H. A. J. van Lanen, and R. Uijlenhoet
Hydrol. Earth Syst. Sci., 16, 2437–2451, https://doi.org/10.5194/hess-16-2437-2012, https://doi.org/10.5194/hess-16-2437-2012, 2012
D. Brochero, F. Anctil, and C. Gagné
Hydrol. Earth Syst. Sci., 15, 3307–3325, https://doi.org/10.5194/hess-15-3307-2011, https://doi.org/10.5194/hess-15-3307-2011, 2011
D. Brochero, F. Anctil, and C. Gagné
Hydrol. Earth Syst. Sci., 15, 3327–3341, https://doi.org/10.5194/hess-15-3327-2011, https://doi.org/10.5194/hess-15-3327-2011, 2011
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The Version-2 Global Precipitation Climatology
Project (GPCP) Monthly Precipitation Analysis (1979–Present), J. Hydrometeorol., 4, 1147–1167,
https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003. a
Alexander, L. V., Zhang, X., Peterson, T. C., Caesar, J., Gleason, B.,
Klein Tank, A. M. G., Haylock, M., Collins, D., Trewin, B., Rahimzadeh, F.,
Tagipour, A., Rupa Kumar, K., Revadekar, J., Griffiths, G., Vincent, L.,
Stephenson, D. B., Burn, J., Aguilar, E., Brunet, M., Taylor, M., New, M.,
Zhai, P., Rusticucci, M., and Vazquez-Aguirre, J. L.: Global observed
changes in daily climate extremes of temperature and precipitation, J. Geophys. Rese.-Atmos., 111, D05109, https://doi.org/10.1029/2005JD006290, 2006. a
Allen, M. R. and Ingram, W. J.: Constraints on future changes in climate and
the hydrologic cycle, Nature, 419, 228–232, https://doi.org/10.1038/nature01092, 2002. a
Ashouri, H., Hsu, K.-L. L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R.,
Cecil, L. D., Nelson, B. R., and Prat, O. P.: PERSIANN-CDR: Daily
Precipitation Climate Data Record from Multisatellite Observations for
Hydrological and Climate Studies, B. Am. Meteorol. Soc., 96, 69–83, https://doi.org/10.1175/BAMS-D-13-00068.1, 2014. a
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. a
Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K.,
Schneider, U., and Ziese, M.: A description of the global land-surface
precipitation data products of the Global Precipitation Climatology Centre
with sample applications including centennial (trend) analysis from
1901–present, Earth Syst. Sci. Data, 5, 71–99, https://doi.org/10.5194/essd-5-71-2013, 2013. a, b
Bytheway, J. L. and Kummerow, C. D.: Inferring the uncertainty of satellite
precipitation estimates in data-sparse regions over land, J. Geophys. Res.-Atmos., 118, 9524–9533, https://doi.org/10.1002/jgrd.50607, 2013. a
Chen, M., Shi, W., Xie, P., Silva, V. B. S., Kousky, V. E., Wayne Higgins, R., and Janowiak, J. E.: Assessing objective techniques for gauge-based analyses of global daily precipitation, J. Geophys. Res.-Atmos., 113, D04110, https://doi.org/10.1029/2007JD009132, 2008. a, b, c, d
Compo, G. P., Whitaker, J. S., and Sardeshmukh, P. D.: Feasibility of a
100-year reanalysis using only surface pressure data, B. Am. Meteorol. Soc., 87, 175–190, https://doi.org/10.1175/BAMS-87-2-175, 2006. a
Contractor, S., Alexander, L. V., Donat, M. G., and Herold, N.: How Well Do
Gridded Datasets of Observed Daily Precipitation Compare over Australia?,
Adv. Meteorol., 2015, 1–15, https://doi.org/10.1155/2015/325718, 2015. a
Contractor, S., Donat, M. G., Alexander, L. V., Ziese, M., Meyer-Christoffer, A., Schneider, U., Rustemeier, E., Becker, A., Durre, I., and Vose, R. S.: Rainfall Estimates on a Gridded Network based on all station data v1-2019, NCI National Research Data Collection, https://doi.org/10.25914/5ca4c380b0d44, 2019a. a
Contractor, S., Donat, M. G., Alexander, L. V., Ziese, M., Meyer-Christoffer, A., Schneider, U., Rustemeier, E., Becker, A., Durre, I., and Vose, R. S.: Rainfall Estimates on a Gridded Network based on long-term station data v1-2019, NCI National Research Data Collection, https://doi.org/10.25914/5ca4c2c6527d2, 2019b. a
Contractor, S., Donat, M. G., Alexander, L. V., Ziese, M., Meyer-Christoffer, A., Schneider, U., Rustemeier, E., Becker, A., Durre, I., and Vose, R. S.: Rainfall Estimates on a Gridded Network based on all station data v1.0, NCI National Research Data Collection, https://doi.org/10.25914/5b9fa55a8298c, 2019c. a
Contractor, S., Donat, M. G., Alexander, L. V., Ziese, M., Meyer-Christoffer, A., Schneider, U., Rustemeier, E., Becker, A., Durre, I., and Vose, R. S.: Rainfall Estimates on a Gridded Network based on long-term station data v1.0, NCI National Research Data Collection, https://doi.org/10.25914/5b9fa67fce5d6, 2019d. a
Donat, M. G., Alexander, L. V., Yang, H., Durre, I., Vose, R., and Caesar, J.: Global land-based datasets for monitoring climatic extremes, B. Am. Meteorol. Soc., 94, 997–1006, https://doi.org/10.1175/BAMS-D-12-00109.1, 2013a. a
Donat, M. G., Alexander, L. V., Yang, H., Durre, I., Vose, R., Dunn, R. J. H., Willett, K. M., Aguilar, E., Brunet, M., Caesar, J., Hewitson, B., Jack, C., Klein Tank, A. M. G., Kruger, A. C., Marengo, J., Peterson, T. C., Renom, M., Oria Rojas, C., Rusticucci, M., Salinger, J., Elrayah, A. S., Sekele, S. S., Srivastava, A. K., Trewin, B., Villarroel, C., Vincent, L. A., Zhai, P.,
Zhang, X., and Kitching, S.: Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century:
The HadEX2 dataset, J. Geophys. Res.-Atmos., 118, 2098–2118, https://doi.org/10.1002/jgrd.50150, 2013b. a
Donat, M. G., Lowry, A. L., Alexander, L. V., O'Gorman, P. A., and Maher, N.:
More extreme precipitation in the world's dry and wet regions, Nat. Clim. Change, 6, 508–513, https://doi.org/10.1038/nclimate2941, 2016. a
Frei, C. and Schär, C.: A precipitation climatology of the Alps from
high-resolution rain-gauge observations, Int. J. Climatol., 18, 873–900,
https://doi.org/10.1002/(SICI)1097-0088(19980630)18:8<873::AID-JOC255>3.0.CO;2-9, 1998. a
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, Scient. Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66, 2015. a, b
Groisman, P. Y., Knight, R. W., Easterling, D. R., Karl, T. R., Hegerl, G. C., and Razuvaev, V. N.: Trends in Intense Precipitation in the Climate Record, J. Climate, 18, 1326–1350, https://doi.org/10.1175/JCLI3339.1, 2005. a
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated
high-resolution grids of monthly climatic observations – the CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014. a
Hartmann, D. L., Klein Tank, A. M., Rusticucci, M., Alexander, L. V.,
Brönnimann, S., Charabi, Y. A. R., Dentener, F. J., Dlugokencky, E. J.,
Easterling, D. R., Kaplan, A., Soden, B. J., Thorne, P. W., Wild, M., and
Zhai, P.: Observations: Atmosphere and surface, in: Climate Change 2013 the
Physical Science Basis: Working Group I Contribution to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change, vol. 9781107057, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 159–254, https://doi.org/10.1017/CBO9781107415324.008, 2013. a, b, c, d
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D.,
and New, M.: A European daily high-resolution gridded data set of surface
temperature and precipitation for 1950–2006, J. Geophys. Res., 113, D20119, https://doi.org/10.1029/2008JD010201, 2008. a, b, c, d
Herold, N., Alexander, L. V., Donat, M. G., Contractor, S., and Becker, A.:
How much does it rain over land?, Geophys. Res. Lett., 43, 341–348, https://doi.org/10.1002/2015GL066615, 2016. a, b
Herrera, S., Gutiérrez, J. M., Ancell, R., Pons, M. R., Frías, M. D., and Fernández, J.: Development and analysis of a 50-year high-resolution daily gridded precipitation dataset over Spain (Spain02), Int. J. Climatol., 32, 74–85, https://doi.org/10.1002/joc.2256, 2012. a, b, c, d
Hofstra, N., Haylock, M., New, M., Jones, P., and Frei, C.: Comparison of six
methods for the interpolation of daily, European climate data, J. Geophys. Res.-Atmos., 113, D21110, https://doi.org/10.1029/2008JD010100, 2008. a
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D.,
Kojima, M., Oki, R., Nakamura, K., and Iguchi, T.: The global precipitation
measurement mission, B. Am. Meteorol. Soc., 95, 701–722, https://doi.org/10.1175/BAMS-D-13-00164.1, 2014. a
Huffman, G. J., Adler, R. F., Arkin, P., Chang, A., Ferraro, R., Gruber, A.,
Janowiak, J., McNab, A., Rudolf, B., and Schneider, U.: The Global
Precipitation Climatology Project (GPCP) Combined Precipitation Dataset, B. Am. Meteorol. Soc., 78, 5–20,
https://doi.org/10.1175/1520-0477(1997)078<0005:TGPCPG>2.0.CO;2, 1997. a
Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S.,
Joyce, R., McGavock, B., and Susskind, J.: Global Precipitation at One-Degree Daily Resolution from Multisatellite Observations, J. Hydrometeorol., 2,
36–50, https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2, 2001. a
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu,
G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor
Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/JHM560.1, 2007. a
IRIN: Half million potential flood victims: WHO, available at:
http://www.irinnews.org/news/2008/09/03 (last access: 29 January 2020), 2008. a
Isotta, F. A., Frei, C., Weilguni, V., Perčec Tadić, M., Lassègues, P., Rudolf, B., Pavan, V., Cacciamani, C., Antolini, G., Ratto, S. M., Munari, M., Micheletti, S., Bonati, V., Lussana, C., Ronchi, C., Panettieri, E., Marigo, G., Vertačnik, G. A. I. F., Christoph, F., Viktor, W., Melita, P. T., Pierre, L., Bruno, R., Valentina, P., Carlo, C., Gabriele, A. M. R. S., Michela, M., Stefano, M., Veronica, B., Cristian, L., Christian, R., Elvio, P., Gianni, M., and Gregor, V.: The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data, Int. J. Climatol., 34, 1657–1675, https://doi.org/10.1002/joc.3794, 2013. a
Jackson, M. C.: Mesoscale and Small-Scale Motions As Revealed By Hourly
Rainfall Maps of an Outstanding Rainfall Event: 14–16 September 1968, Weather, 32, 2–17, https://doi.org/10.1002/j.1477-8696.1977.tb04471.x, 1977. a
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang,
J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996. a
Kidd, C. and Levizzani, V.: Status of satellite precipitation retrievals,
Hydrol. Earth Syst. Sci., 15, 1109–1116, https://doi.org/10.5194/hess-15-1109-2011, 2011. a
Krajewski, W. F., Villarini, G., and Smith, J. A.: RADAR-Rainfall
Uncertainties, B. Am. Meteorol. Soc., 91, 87–94, https://doi.org/10.1175/2009BAMS2747.1, 2010. a
Lorenz, C. and Kunstmann, H.: The Hydrological Cycle in Three State-of-the-Art Reanalyses: Intercomparison and Performance Analysis, J. Hydrometeorol., 13, 1397–1420, https://doi.org/10.1175/JHM-D-11-088.1, 2012. a
Lussana, C., Saloranta, T., Skaugen, T., Magnusson, J., Einar Tveito, O., and
Andersen, J.: SeNorge2 daily precipitation, an observational gridded dataset
over Norway from 1957 to the present day, Earth Syst. Sci. Data, 10,
235–249, https://doi.org/10.5194/essd-10-235-2018, 2018. a
McKee, J. L. and Binns, A. D.: A review of gauge–radar merging methods for
quantitative precipitation estimation in hydrology, Can. Water Resour. J./Revue canadienne des ressources hydriques, 41, 186–203,
https://doi.org/10.1080/07011784.2015.1064786, 2016. a
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. a
Menendez, C. G., De Castro, M., Sorensson, A., and Boulanger, J. P.: CLARIS
project: Towards climate downscaling in South America, Meteorol. Z., 19, 357–362, https://doi.org/10.1127/0941-2948/2010/0459, 2010. a
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An
Overview of the Global Historical Climatology Network-Daily Database, J. Atmos. Ocean. Tech., 29, 897–910, https://doi.org/10.1175/JTECH-D-11-00103.1, 2012. a
Mitchell, T. D. and Jones, P. D.: An improved method of constructing a database of monthly climate observations and associated high-resolution
grids, Int. J. Climatol., 25, 693–712, https://doi.org/10.1002/joc.1181, 2005. a, b
Osborn, T. J. and Hulme, M.: Evaluation of the European daily precipitation
characteristics from the atmospheric model intercomparison project,
Int. J. Climatol., 18, 505–522,
https://doi.org/10.1002/(SICI)1097-0088(199804)18:5<505::AID-JOC263>3.0.CO;2-7, 1998. a
Perry, M. and Hollis, D.: The generation of monthly gridded datasets for a
range of climatic variables over the UK, Int. J. Climatol., 25, 1041–1054, https://doi.org/10.1002/joc.1161, 2005. a
Peterson, T. C. and Vose, R. S.: An Overview of the Global Historical Climatology Network Temperature Database, B. Am. Meteorol. Soc., 78, 2837–2850, https://doi.org/10.1175/1520-0477(1997)078<2837:AOOTGH>2.0.CO;2, 1997. a, b
Rajeevan, M., Bhate, J., Kale, J. D., and Lal, B.: High resolution daily
gridded rainfall data for the Indian region: Analysis of break and active
monsoon spells, Current Science, 91, 296–306, 2006. a
Rauthe, M., Steiner, H., Riediger, U., Mazurkiewicz, A., and Gratzki, A.: A
Central European precipitation climatology Part I: Generation and validation
of a high-resolution gridded daily data set (HYRAS), Meteorol. Z., 22, 235–256, https://doi.org/10.1127/0941-2948/2013/0436, 2013. a
Schamm, K., Ziese, M., Becker, A., Finger, P., Meyer-Christoffer, A.,
Schneider, U., Schröder, M., and Stender, P.: Global gridded
precipitation over land: a description of the new GPCC First Guess Daily
product, Earth Syst. Sci. Data, 6, 49–60, https://doi.org/10.5194/essd-6-49-2014, 2014. a, b, c, d, e
Schamm, K., Ziese, M., Raykova, K., Becker, A., Finger, P., Meyer-Christoffer, A., and Schneider, U.: GPCC Full Data Daily Version 1.0 at 1.0∘: Daily Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data, Global Precipitation Climatology Centre (GPCC, Deutscher Wetterdienst, https://doi.org/10.5676/DWD_GPCC/FD_D_V1_100, 2015. a, b, c
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., and
Rudolf, B.: GPCC's new land surface precipitation climatology based on
quality-controlled in situ data and its role in quantifying the global water
cycle, Theore. Appl. Climatol., 115, 15–40, https://doi.org/10.1007/s00704-013-0860-x, 2014. a
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., and
Ziese, M.: GPCC Full Data Reanalysis Version 7.0 at 1.0∘: Monthly
Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic
Data, Global Precipitation Climatology Centre, Deutscher Wetterdienst, https://doi.org/10.5676/DWD_GPCC/FD_M_V7_100, 2015. a, b, c
Schneider, U., Finger, P., Meyer-Christoffer, A., Rustemeier, E., Ziese, M.,
and Becker, A.: Evaluating the hydrological cycle over land using the
newly-corrected precipitation climatology from the Global Precipitation
Climatology Centre (GPCC), Atmosphere, 8, 52, https://doi.org/10.3390/atmos8030052, 2017. a
Shen, Y., Zhao, P., Pan, Y., and Yu, J.: A high spatiotemporal gauge-satellite merged precipitation analysis over China, J. Geophys. Res.-Atmos., 119, 3063–3075, https://doi.org/10.1002/2013JD020686, 2014. a
Smith, T. M., Arkin, P. A., Ren, L., and Shen, S. S. P.: Improved
Reconstruction of Global Precipitation since 1900, J. Atmos. Ocean. Tech., 29, 1505–1517, https://doi.org/10.1175/JTECH-D-12-00001.1, 2012. a
Tian, Y. and Peters-Lidard, C. D.: A global map of uncertainties in
satellite-based precipitation measurements, Geophys. Res. Lett., 37, 1–6, https://doi.org/10.1029/2010GL046008, 2010. a, b
Trenberth, K. E.: Changes in precipitation with climate change, Clim. Res., 47, 123–138, https://doi.org/10.3354/cr00953, 2011. a
Trewin, B.: Exposure, instrumentation, and observing practice effects on land
temperature measurements, Wiley Interdisciplin. Rev.: Clim. Change, 1, 490–506, https://doi.org/10.1002/wcc.46, 2010. a
van den Besselaar, E. J. M., van der Schrier, G., Cornes, R. C., Iqbal, A. S., and Klein Tank, A. M. G.: SA-OBS: A Daily Gridded Surface Temperature and
Precipitation Dataset for Southeast Asia, J. Climate, 30, 5151–5165, https://doi.org/10.1175/JCLI-D-16-0575.1, 2017. a
Villarini, G. and Krajewski, W. F.: Sensitivity Studies of the Models of
Radar-Rainfall Uncertainties, J. Appl. Meteorol. Clim., 49, 288–309, https://doi.org/10.1175/2009JAMC2188.1, 2010.
a
Westra, S., Alexander, L. V., and Zwiers, F. W.: Global Increasing Trends in
Annual Maximum Daily Precipitation, J. Climate, 26, 3904–3918,
https://doi.org/10.1175/JCLI-D-12-00502.1, 2013. a
WHO: WHO|Floods in West Africa raise major health risks, available at:
http://www.who.int/mediacentre/news/releases/2008/pr28/en/#.WweHryUc8nM.mendeley (last access: 29 January 2020), 2010. a
Xie, P., Chen, M., Yang, S., Yatagai, A., Hayasaka, T., Fukushima, Y., and Liu, C.: A Gauge-Based Analysis of Daily Precipitation over East Asia, J.
Hydrometeorol., 8, 607–626, https://doi.org/10.1175/JHM583.1, 2007. a, b, c, d
Yamamoto, J. K.: An Alternative Measure of the Reliability of Ordinary Kriging Estimates, Math. Geol., 32, 489–509, https://doi.org/10.1023/A:1007577916868, 2000. a, b
Yatagai, A., Xie, P., and Alpert, P.: Development of a daily gridded precipitation data set for the Middle East, Adv. Geosci., 12, 165–170, https://doi.org/10.5194/adgeo-12-165-2008, 2008. a
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and Kitoh,
A.: APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset
for Asia Based on a Dense Network of Rain Gauges, B. Am. Meteorol. Soc., 93, 1401–1415, https://doi.org/10.1175/BAMS-D-11-00122.1, 2012. a, b
Ziese, M., Rauthe-Schöch, A., Becker, A., Finger, P., Meyer-Christoffer,
A., and Schneider, U.: GPCC Full Data Daily Version.2018 at 1.0∘:
Daily Land-Surface Precipitation from Rain-Gauges built on GTS-based and
Historic Data, Global Precipitation Climatology Centre, Deutscher Wetterdienst, https://doi.org/10.5676/DWD_GPCC/FD_D_V2018_100, 2018. a
Short summary
This paper provides the documentation of the REGEN dataset, a global land-based daily observational precipitation dataset from 1950 to 2016 at a gridded resolution of 1° × 1°. REGEN is currently the longest-running global dataset of daily precipitation and is expected to facilitate studies looking at changes and variability in several aspects of daily precipitation distributions, extremes and measures of hydrological intensity.
This paper provides the documentation of the REGEN dataset, a global land-based daily...
