Articles | Volume 17, issue 22
https://doi.org/10.5194/gmd-17-8115-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-8115-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
18 Nov 2024
Methods for assessment of models |
| 18 Nov 2024
| 18 Nov 2024
Generalised drought index: a novel multi-scale daily approach for drought assessment
João António Martins Careto
CORRESPONDING AUTHOR
IDL – Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Rita Margarida Cardoso
IDL – Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Ana Russo
IDL – Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
CEF – Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, Lisbon, Portugal
Daniela Catarina André Lima
IDL – Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Pedro Miguel Matos Soares
IDL – Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Related authors
No articles found.
Óscar Mirones, Joaquín Bedia, Pedro M. M. Soares, José M. Gutiérrez, and Jorge Baño-Medina
EGUsphere, https://doi.org/10.5194/egusphere-2025-2349, https://doi.org/10.5194/egusphere-2025-2349, 2025
Short summary
Short summary
We explored how to better estimate wildfire risk using advanced computer models in a region that often experiences fires. Traditional methods rely on detailed weather data that is not always available, so we tested deep learning tools to fill this gap. Our approach produced more accurate results, especially for predicting extreme fire danger. This can help improve future climate impact studies and support better planning and decisions related to fire safety and climate adaptation.
Florian Knutzen, Paul Averbeck, Caterina Barrasso, Laurens M. Bouwer, Barry Gardiner, José M. Grünzweig, Sabine Hänel, Karsten Haustein, Marius Rohde Johannessen, Stefan Kollet, Mortimer M. Müller, Joni-Pekka Pietikäinen, Karolina Pietras-Couffignal, Joaquim G. Pinto, Diana Rechid, Efi Rousi, Ana Russo, Laura Suarez-Gutierrez, Sarah Veit, Julian Wendler, Elena Xoplaki, and Daniel Gliksman
Nat. Hazards Earth Syst. Sci., 25, 77–117, https://doi.org/10.5194/nhess-25-77-2025, https://doi.org/10.5194/nhess-25-77-2025, 2025
Short summary
Short summary
Our research, involving 22 European scientists, investigated drought and heat impacts on forests in 2018–2022. Findings reveal that climate extremes are intensifying, with central Europe being most severely impacted. The southern region showed resilience due to historical drought exposure, while northern and Alpine areas experienced emerging or minimal impacts. The study highlights the need for region-specific strategies, improved data collection, and sustainable practices to safeguard forests.
Jan Wohland, Peter Hoffmann, Daniela C. A. Lima, Marcus Breil, Olivier Asselin, and Diana Rechid
Earth Syst. Dynam., 15, 1385–1400, https://doi.org/10.5194/esd-15-1385-2024, https://doi.org/10.5194/esd-15-1385-2024, 2024
Short summary
Short summary
We evaluate how winds change when humans grow or cut down forests. Our analysis draws from climate model simulations with extreme scenarios where Europe is either fully forested or covered with grass. We find that the effect of land use change on wind energy is very important: wind energy potentials are twice as high above grass as compared to forest in some locations. Our results imply that wind profile changes should be better incorporated in climate change assessments for wind energy.
João P. A. Martins, Sara Caetano, Carlos Pereira, Emanuel Dutra, and Rita M. Cardoso
Nat. Hazards Earth Syst. Sci., 24, 1501–1520, https://doi.org/10.5194/nhess-24-1501-2024, https://doi.org/10.5194/nhess-24-1501-2024, 2024
Short summary
Short summary
Over Europe, 2022 was truly exceptional in terms of extreme heat conditions, both in terms of temperature anomalies and their temporal and spatial extent. The satellite all-sky land surface temperature (LST) is used to provide a climatological context to extreme heat events. Where drought conditions prevail, LST anomalies are higher than 2 m air temperature anomalies. ERA5-Land does not represent this effect correctly due to a misrepresentation of vegetation anomalies.
Amélie Simon, Coline Poppeschi, Sandra Plecha, Guillaume Charria, and Ana Russo
Ocean Sci., 19, 1339–1355, https://doi.org/10.5194/os-19-1339-2023, https://doi.org/10.5194/os-19-1339-2023, 2023
Short summary
Short summary
In the coastal northeastern Atlantic and for three subregions (the English Channel, Bay of Brest and Bay of Biscay) over the period 1982–2022, marine heatwaves are more frequent and longer and extend over larger areas, while the opposite is seen for marine cold spells. This result is obtained with both in situ and satellite datasets, although the satellite dataset underestimates the amplitude of these extremes.
Margarida L. R. Liberato, Irene Montero, Célia Gouveia, Ana Russo, Alexandre M. Ramos, and Ricardo M. Trigo
Earth Syst. Dynam., 12, 197–210, https://doi.org/10.5194/esd-12-197-2021, https://doi.org/10.5194/esd-12-197-2021, 2021
Short summary
Short summary
Extensive, long-standing dry and wet episodes are frequent climatic extreme events (EEs) in the Iberian Peninsula (IP). A method for ranking regional extremes of persistent, widespread drought and wet events is presented, using different SPEI timescales. Results show that there is no region more prone to EE occurrences in the IP, the most extreme extensive agricultural droughts evolve into hydrological and more persistent extreme droughts, and widespread wet and dry EEs are anti-correlated.
Andreia Filipa Silva Ribeiro, Ana Russo, Célia Marina Gouveia, Patrícia Páscoa, and Jakob Zscheischler
Biogeosciences, 17, 4815–4830, https://doi.org/10.5194/bg-17-4815-2020, https://doi.org/10.5194/bg-17-4815-2020, 2020
Short summary
Short summary
This study investigates the impacts of compound dry and hot extremes on crop yields, namely wheat and barley, over two regions in Spain dominated by rainfed agriculture. We provide estimates of the conditional probability of crop loss under compound dry and hot conditions, which could be an important tool for responsible authorities to mitigate the impacts magnified by the interactions between the different hazards.
Cited articles
Abramowitz, M. and Stegun, I. A. (Eds.): Handbook of mathematical functions with formulas, graphs, and mathematical tables, vol. 55, US Government printing office, https://books.google.pt/books?hl=pt-PT&lr=&id=ZboM5tOFWtsC&oi=fnd&pg=PR3&ots=dWhHSE67Vt&sig=mpXMahEU9bNMBD7sBTAWdMtA_3o&redir_esc=y#v=onepage&q&f=false (last access: 21 October 2024). 1968.
Ahmad, M. I., Sinclair, C. D., and Werritty, A.: Log-logistic flood frequency analysis, J. Hydrol., 98, 205–224, https://doi.org/10.1016/0022-1694(88)90015-7, 1988.
Akhtari, R., Morid, S., Mahdian, M. H., and Smakhtin, V.: Assessment of areal interpolation methods for spatial analysis of SPI and EDI drought indices, Int. J. Climatol., 29, 135–145, https://doi.org/10.1002/joc.1691, 2009.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: FAO Irrigation and drainage paper No 56 Rome: Food and Agriculture Organisation of the United Nations 56 156, http://www.climasouth.eu/sites/default/files/FAO\,%2056.pdf (last access: 29 July 2023), 1998.
Alley, W. M.: The Palmer drought severity index: limitations and assumptions, J. Appl. Meteorol. Clim., 23, 1100–1109, https://doi.org/10.1175/1520-0450(1984)023<1100:TPDSIL>2.0.CO;2, 1984.
Ban, N., Caillaud, C., Coppola, E., Pichelli, E., Sobolowski, S., Adinolfi, M., Ahrens, B., Alias, A., Anders, I., Bastin, S., Belušić, D., Berthou, S., Brisson, E., Cardoso, R. M., Chan, S. C., Christensen, O. B., Fernández, J., Fita, L., Frisius, T., Gašparac, G., Giorgi, F., Goergen, K., Haugen, J, E., Hodnebrog, Ø., Kartsios, S., Katragkou, E., Kendon, E. J., Keuler, K., Lavin-Gullon, A., Lenderink, G., Leutwyler, D., Lorenz, T., Maraun, D., Mercogliano, P., Milovac, J., Panitz, H. J., Raffa, M, Remedio, A. R., Schär, C., Soares, P. M. M., Srnec, L., Steensen, B. MM., Stocchi, Tölle, Truhetz, Temprado, J. V., de Vries, H., Warrach.-Sagi, K., Wulfmeyer, V., and Zander, M. J. The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: evaluation of precipitation, Clim. Dynam., 57, 275–302, https://doi.org/10.1007/S00382-021-05708-W, 2021.
Beguería, S., Vicente-Serrano, S. M., Reig, F., and Latorre, B.: Standardised precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring, Int. J. Climatol., 34, 3001–3023, https://doi.org/10.1002/joc.3887, 2014.
Cardoso, R. M., Soares, P. M., Lima, D. C., and Miranda, P. M.: Mean and extreme temperatures in a warming climate: EURO CORDEX and WRF regional climate high-resolution projections for Portugal, Clim. Dynam., 52, 129–157, https://doi.org/10.1007/s00382-018-4124-4, 2019.
Careto, J. A. M., Soares, P. M. M., Cardoso, R. M., Herrera, S., and Gutiérrez, J. M.: Added value of EURO-CORDEX high-resolution downscaling over the Iberian Peninsula revisited – Part 1: Precipitation, Geosci. Model Dev., 15, 2635–2652, https://doi.org/10.5194/gmd-15-2635-2022, 2022a.
Careto, J. A. M., Soares, P. M. M., Cardoso, R. M., Herrera, S., and Gutiérrez, J. M.: Added value of EURO-CORDEX high-resolution downscaling over the Iberian Peninsula revisited – Part 2: Max and min temperature, Geosci. Model Dev., 15, 2653–2671, https://doi.org/10.5194/gmd-15-2653-2022, 2022b.
Carvalho, D., Cardoso Pereira, S., and Rocha, A.: Future surface temperature changes for the Iberian Peninsula according to EURO-CORDEX climate projections, Clim. Dynam., 56, 123–138, https://doi.org/10.1007/s00382-020-05472-3, 2021.
Chen, M., Shi, W., Xie, P., Silva, V. B., 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, 1–13, https://doi.org/10.1029/2007JD009132, 2008.
Christian, J. I., Martin, E. R., Basara, J. B., Furtado, J. C., Otkin, J. A., Lowman, L. E., Hunt, E. D., Mishra, V., and Xiao, X.: Global projections of flash drought show increased risk in a warming climate, Commun. Earth Environ., 4, 165, https://doi.org/10.1038/s43247-023-00826-1, 2023.
Coppola, E., Sobolowski, S., Pichelli, E., Raffaele, F., Ahrens, B., Anders, I., Ban, N., Bastin, S., Belda, M., Belusic, D., Caldas-Alvarez, A., Cardoso, R. M., Davolio, S., Dobler, A., Fernadez, J., Fita, L., Fumiere, Q., Giorgi, G., Goergen, K., Güttler, I., Halenka, T., Heinzeller, D., Hodnebrog, Ø., Jacob, D., Kartsios, S., Katragkou, E., Kendon, E., Khodayar, S., Kunstmann, H., Knist, S., Lavín-Gullón, A., Lind, P., Lorenz, T., Maraun, D., Marelle, L., van Meijgaard, E., Milovac, J., Myhre, G., Panitz, H.-J., Piazza, M., Raffa, M., Raub, T, Rockel, B., Schär, C., Sieck., Soares, P. M. M., Somot, S., Srnec, L., Stocchi, P., Tölle, M. H., Truhertz, Vautard, R., de Vries, H., and Warrach-Sagi, K.: A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean, Clim. Dynam., 55, 3–34, https://doi.org/10.1007/s00382-018-4521-8, 2020.
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J., and Jones, P. D.: An ensemble version of the E-OBS temperature and precipitation data sets, J. Geophys. Res.-Atmos., 123, 9391–9409, https://doi.org/10.1029/2017JD028200, 2018.
Dai, A.: Drought under global warming: a review, Wires Clim. Change, 2, 45–65, https://doi.org/10.1002/wcc.81, 2011.
Droogers, P., and Allen, R. G.: Estimating reference evapotranspiration under inaccurate data conditions, Irrig. Drain., 16, 33–45, https://doi.org/10.1023/A:1015508322413, 2002.
Edwards, D. C.: Characteristics of 20th Century drought in the United States at multiple timescales, Air Force Inst of Tech Wright-Patterson Afb Oh, https://apps.dtic.mil/sti/pdfs/ADA325595.pdf (last access: 16 October 2023), 1997.
Farmer, W., Strzepek, K., Schlosser, C. A., Droogers, P., and Gao, X.: A method for calculating reference evapotranspiration on daily timescales, MIT Joint Program on the Science and Policy of Global Change, https://dspace.mit.edu/bitstream/handle/1721.1/61773/MITJPSPGC_Rpt195.pdf?sequence=_1&isAllowed=_y (last access: 16 October 2023), 2011.
FCiênciasID Associação para a Investigação e Desenvolvimento de Ciências (Fciências.ID): Land Use Changes and Atmosphere Feedbacks in a Changing Climate, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/2021.01280.CEECIND/CP1650/CT0006, 2022.
FCiênciasID Associação para a Investigação e Desenvolvimento de Ciências (Fciências.ID): COMPLEX – Compound Extreme Events and their relevance for human life and the environment under a climate change perspective, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/2022.01167.CEECIND/CP1722/CT0006, 2023.
FCiênciasID Associação para a Investigação e Desenvolvimento de Ciências: Análise de eventos secos, quentes e fogos combinados ou consecutivos e dos seus impactos na qualidade do ar ao nível Europeu numa perspetiva de alterações climáticas, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/2022.09185.PTDC, 2023–2024.
Fonseca, D., Carvalho, M. J., Marta-Almeida, M., Melo-Gonçalves, P., and Rocha, A.: Recent trends of extreme temperature indices for the Iberian Peninsula, Phys. Chem. Earth, 94, 66–76, https://doi.org/10.1016/j.pce.2015.12.005, 2016.
Fritsch, F. N. and Butland, J.: A method for constructing local monotone piecewise cubic interpolants, SIAM J. Sci. Stat. Comp., 5, 300–304, https://doi.org/10.1137/0905021, 1984.
García-Herrera, R., Hernández, E., Barriopedro, D., Paredes, D., Trigo, R. M., Trigo, I. F., and Mendes, M. A.: The outstanding 2004/05 drought in the Iberian Peninsula: associated atmospheric circulation, J. Hydrometeorol., 8, 483–498, https://doi.org/10.1175/JHM578.1, 2007.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., and Wargan, K.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Giorgi, F., Jones, C., and Asrar, G. R.: Addressing climate information needs at the regional level: the CORDEX framework, World Meteorological Organisation (WMO) Bulletin, 58, 175, http://wcrp.ipsl.jussieu.fr/cordex/documents/CORDEX_giorgi_WMO.pdf (last access: 29 July 2023), 2009.
Gutowski Jr., W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O'Rourke, E., Rixen, M., Solman, S., Stephenson, T., and Tangang, F.: WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6, Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, 2016.
Guttman, N. B.: Comparing the palmer drought index and the Standardised precipitation index 1, JAWRA J. Am. Water Resour. Assoc., 34, 113–121, https://doi.org/10.1111/j.1752-1688.1998.tb05964.x, 1998.
Guttman, N. B.: Accepting the Standardised precipitation index: a calculation algorithm 1, JAWRA J. Am. Water Resour. Assoc., 35, 311–322, https://doi.org/10.1111/j.1752-1688.1999.tb03592.x, 1999.
Hayes, M. J., Svoboda, M. D., Wiihite, D. A., and Vanyarkho, O. V.: Monitoring the 1996 drought using the Standardised precipitation index, B. Am. Meteorol. Soc., 80, 429–438, https://doi.org/10.1175/1520-0477(1999)080<0429:MTDUTS>2.0.CO;2, 1999.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., and Simmons, A.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Herrera, S., Cardoso, R. M., Soares, P. M., Espírito-Santo, F., Viterbo, P., and Gutiérrez, J. M.: Iberia01: a new gridded dataset of daily precipitation and temperatures over Iberia, Earth Syst. Sci. Data, 11, 1947–1956, https://doi.org/10.5194/essd-11-1947-2019, 2019a.
Herrera, S., Cardoso, R. M., Soares, P. M. M., Espírio-Santo, F., Viterbo, P., and Gutiérrez, J. M.: Iberia01: Daily gridded (0.1° resolution) dataset of precipitation and temperatures over the Iberian Peninsula, DIGITAL.CSIC [data set], https://doi.org/10.20350/digitalCSIC/8641, 2019b.
Herrera, S., Soares, P. M., Cardoso, R. M., and Gutiérrez, J. M.: Evaluation of the EURO-CORDEX regional climate models over the Iberian Peninsula: Observational uncertainty analysis, J. Geophys. Res.-Atmos., 125, e2020JD032880, https://doi.org/10.1029/2020JD032880, 2020.
Hosking, J. R.: The theory of probability weighted moments, pp. 3–16, New York, USA, IBM Research Division, TJ Watson Research Center, https://dominoweb.draco.res.ibm.com/reports/RC12210.pdf (last access: 13 March 2023), 1986.
Hosking, J. R.: L-moments: analysis and estimation of distributions using linear combinations of order statistics, J. Roy. Stat. Soc. B, 52, 105–124, https://doi.org/10.1111/j.2517-6161.1990.tb01775.x, 1990.
Hu, Q. and Willson, G. D.: Effect of temperature anomalies on the Palmer drought severity index in the central United States, Int. J. Climatol., 20, 1899–1911, https://doi.org/10.1002/1097-0088(200012)20:15<1899::AID-JOC588>3.0.CO;2-M, 2000.
Hunt, E. D., Svoboda, M., Wardlow, B., Hubbard, K., Hayes, M., and Arkebauer, T.: Monitoring the effects of rapid onset of drought on non-irrigated maise with agronomic data and climate-based drought indices, Agr. Forest Meteorol., 191, 1–11, https://doi.org/10.1016/j.agrformet.2014.02.001, 2014.
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colettem A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menit, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner N., Kotlarski, S., Kriiegsmann, A., Martin, E., van Mejigaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K, Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014.
Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda, M., Benestad, R., Boberg, F., Buonomo, E., Cardoso, R. M., Casanueva, A., Christensen, O. B., Christensen, J. H., Coppola, E., De Cruz, L., Davin, E. L., Dobler, A., Domínguez, M., Fealy, R., Fernandez, J., Gaertner, M. A., García-Díez, M., Giorgi, F., Gobiet, A., Goergen, K., Gómez-Navarro, J. J., Alemán, J. J. G., Gutiérrez, C., Gutiérrez, J. M., Güttler, I., Haensler, A., Halenka, T., Jerez, S., Jiménez-Guerrero, P., Jones, R. G., Keuler, K., Kjellström, E., Knist, S., Kotlarski, S., Maraun, D., van Meijgaard, E., Mercogliano, P., Montávez, J. P., Navarra, A., Nikulin, G., de Noblet-Ducoudré, N., Panitz, H.-J., Pfeifer, S., Piazza, M., Pichelli, E., Pietikäinen, J.-P., Prein, A. F., Preuschmann, S., Rechid, D., Rockel, B., Romera, R., Sánchez, E., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L., Lund, Sørland, S. L., Termonia, P., Truhetz, H., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community, Reg. Environ. Change, 20, 1–20, https://doi.org/10.1007/s10113-020-01606-9, 2020.
Jain, S. K., Keshri, R., Goswami, A., and Sarkar, A.: Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India, Nat. Hazards, 54, 643–656, https://doi.org/10.1007/s11069-009-9493-x, 2010.
Jain, V. K., Pandey, R. P., Jain, M. K., and Byun, H. R.: Comparison of drought indices for appraisal of drought characteristics in the Ken River Basin, Weather and Climate Extremes, 8, 1–11, https://doi.org/10.1016/j.wace.2015.05.002, 2015.
Jia, Y., Zhang, B., and Ma, B.: Daily SPEI reveals long-term change in drought characteristics in Southwest China, Chinese Geogr. Sci., 28, 680–693, https://doi.org/10.1007/s11769-018-0973-3, 2018.
Klein-Tank, A. M., Wijngaard, J. B., Können, G. P., Böhm, R., Demarée, G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C., and Heino, R.: Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment, Int. J. Climatol., 22, 1441–1453, https://doi.org/10.1002/joc.773, 2002.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., and Miyaoka, K.: The JRA-55 reanalysis: General specifications and basic characteristics, J. Meteorol. Soc. Jpn. Ser. II, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015.
Lai, C., Zhong, R., Wang, Z., Wu, X., Chen, X., Wang, P., and Lian, Y.: Monitoring hydrological drought using long-term satellite-based precipitation data, Sci. Total Environ., 649, 1198–1208, https://doi.org/10.1016/j.scitotenv.2018.08.245, 2019.
Li, J., Wang, Z., Wu, X., Xu, C. Y., Guo, S., and Chen, X.: Toward monitoring short-term droughts using a novel daily scale, Standardised antecedent precipitation evapotranspiration index, J. Hydrometeorol., 21, 891–908, https://doi.org/10.1175/JHM-D-19-0298.1, 2020.
Lima, D. C., Lemos, G., Bento, V. A., Nogueira, M., and Soares, P. M.: A multi-variable constrained ensemble of regional climate projections under multi-scenarios for Portugal–Part I: An overview of impacts on means and extremes, Climate Services, 30, 100351, https://doi.org/10.1016/j.cliser.2023.100351, 2023a.
Lima, D. C., Bento, V. A., Lemos, G., Nogueira, M., and Soares, P. M.: A multi-variable constrained ensemble of regional climate projections under multi-scenarios for Portugal – Part II: Sectoral climate indices. Climate Services, 30, 100377, https://doi.org/10.1016/j.cliser.2023.100377, 2023b.
Ma, B., Zhang, B., Jia, L., and Huang, H.: Conditional distribution selection for SPEI-daily and its revealed meteorological drought characteristics in China from 1961 to 2017, Atmos. Res., 246, 105108, https://doi.org/10.1016/j.atmosres.2020.105108, 2020.
McKee, T. B., Doesken, N. J., and Kleist, J.: The relationship of drought frequency and duration to timescales, in: Proceedings of the 8th Conference on Applied Climatology, Vol. 17, 179–183, https://climate.colostate.edu/pdfs/relationshipofdroughtfrequency.pdf (last access: 30 July 2023) 1993.
Meyer, S. J., Hubbard, K. G., and Wilhite, D. A.: A crop-specific drought index for corn: I. Model development and validation, Agronomy J., 85, 388–395, https://doi.org/10.2134/agronj1993.00021962008500020040x, 1993.
Mishra, A. K. and Singh, V. P.: A review of drought concepts, J. Hydrol., 391, 202–216, https://doi.org/10.1016/j.jhydrol.2010.07.012, 2010.
Moemken, J., Koerner, B., Ehmele, F., Feldmann, H., and Pinto, J. G.: Recurrence of drought events over Iberia. Part II: Future changes using regional climate projections, Tellus, 74, 262, https://doi.org/10.16993/tellusa.52, 2022.
Monish, N. T. and Rehana, S.: Suitability of distributions for standard precipitation and evapotranspiration index over meteorologically homogeneous zones of India, J. Earth Syst. Sci., 129, 1–19, https://doi.org/10.1007/s12040-019-1271-x, 2020.
Onuşluel Gül, G., Gül, A., and Najar, M.: Historical evidence of climate change impact on drought outlook in river basins: analysis of annual maximum drought severities through daily SPI definitions, Nat. Hazards, 110, 1389–1404, https://doi.org/10.1007/s11069-021-04995-0, 2021.
Palmer, W. C.: Meteorological drought, Vol. 30, US Department of Commerce, Weather Bureau, https://books.google.pt/books?hl=en&lr=&id=kyYZgnEk-L8C&oi=fnd&pg=PA6&dq=Palmer,+W.+C.,+(1965)+Meteorological+drought&ots=U4hAdh1Fom&sig=b_uhe6PC7FvMeofO1D9S59q7l64&redir_esc=y#v=onepage&q=Palmer%2C%20W.%20C.%2C%20(1965)%20Meteorological%20drought&f=false (last access: 16 October 2023), 1965.
Pascoa, P., Russo, A., Gouveia, C. M., Soares, P. M., Cardoso, R. M., Careto, J. A., and Ribeiro, A. F.: A high-resolution view of the recent drought trends over the Iberian Peninsula, Weather and Climate Extremes, 32, 100320, https://doi.org/10.1016/j.wace.2021.100320, 2021.
Patel, N. R., Chopra, P., and Dadhwal, V. K.: Analyzing spatial patterns of meteorological drought using Standardised precipitation index, Meteorol. Appl., 14, 329–336, https://doi.org/10.1002/met.33, 2007.
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007.
Perkins, S. E., Pitman, A. J., Holbrook, N. J., and McAneney, J.: Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions, J. Climate, 20, 4356–4376, https://doi.org/10.1175/JCLI4253.1, 2007.
Pichelli, E., Coppola, E., Sobolowski, S., Ban, N., Giorgi, F., Stocchi, P., Alias, A., Belušić, D., Berthou, S., Caillaud, C. Cardoso, R.M. Chan, S., Christensen, O. B., Dobler, A., de Vries, H., Goergen, K., Kendon, E. J., Keuler, K., Lenderink, G., Lorenz, T., Mishra, A. N., Panitz, H.-J., Schär, C., Soares, P. M. M., Truhetz, H., and Vergara-Temprado, J.: The first multi-model ensemble of regional climate simulations at kilometer-scale resolution part 2: historical and future simulations of precipitation, Clim. Dynam., 56, 3581–3602, https://doi.org/10.1007/s00382-021-05657-4, 2021.
Pohl, F., Rakovec, O., Rebmann, C., Hildebrandt, A., Boeing, F., Hermanns, F., Attinger, S., Samaniego, L., and Kumar, R.: Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS sites, Sci. Data, 10, 281, https://doi.org/10.1038/s41597-023-02192-1, 2023.
Rhee, J., Im, J., and Carbone, G. J.: Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data, Remote Sens. Environ., 114, 2875–2887, https://doi.org/10.1016/j.rse.2010.07.005, 2010.
Rios-Entenza, A., Soares, P. M., Trigo, R. M., Cardoso, R. M., and Miguez-Macho, G.: Moisture recycling in the Iberian Peninsula from a regional climate simulation: Spatiotemporal analysis and impact on the precipitation regime, J. Geophys. Res.-Atmos., 119, 5895–5912, https://doi.org/10.1002/2013JD021274, 2014.
Sánchez, E., Domínguez, M., Romera, R., López de la Franca, N., Gaertner, M.A., Gallardo, C. and Castro, M.: Regional modeling of dry spells over the Iberian Peninsula for present climate and climate change conditions: A letter, Climatic Change, 107, 625–634, https://doi.org/10.1007/s10584-011-0114-9, 2011.
Santos, J., Corte-Real, J., and Leite, S.: Atmospheric large-scale dynamics during the 2004/2005 winter drought in portugal, Int. J. Climatol., 27, 571–586, https://doi.org/10.1002/joc.1425, 2007.
Seguí, P. Q., Martin, E., Sánchez, E., Zribi, M., Vennetier, M., Vicente-Serrano, S. M., and Vidal, J. P.: Drought: observed trends, future projections. The Mediterranean under climate change, IRD Editions, 123–131, hal-01401386, 2016.
Shan, B., Verhoest, N. E. C., and De Baets, B.: Identification of compound drought and heatwave events on a daily scale and across four seasons, Hydrol. Earth Syst. Sci., 28, 2065–2080, https://doi.org/10.5194/hess-28-2065-2024, 2024.
Soares, P. M. and Cardoso, R. M.: A simple method to assess the added value using high-resolution climate distributions: application to the EURO-CORDEX daily precipitation, Int. J. Climatol., 38, 1484–1498, https://doi.org/10.1002/joc.5261, 2018.
Soares, P. M. and Lima, D. C.: Water scarcity down to earth surface in a Mediterranean climate: The extreme future of soil moisture in Portugal, J. Hydrol., 615, 128731, https://doi.org/10.1016/j.jhydrol.2022.128731, 2022.
Soares, P. M., Cardoso, R. M., Lima, D. C., and Miranda, P. M.: Future precipitation in Portugal: high-resolution projections using WRF model and EURO-CORDEX multi-model ensembles, Clim. Dynam., 49, 2503–2530, https://doi.org/10.1007/s00382-016-3455-2, 2017.
Soares, P. M., Lemos, G., and Lima, D. C.: Critical analysis of CMIPs past climate model projections in a regional context: The Iberian climate, Int. J. Climatol., 43, 2250–2270, https://doi.org/10.1002/joc.7973, 2022.
Soares, P. M., Careto, J. A., Russo, A., and Lima, D. C.: The future of Iberian droughts: a deeper analysis based on multi-scenario and a multi-model ensemble approach, Nat. Hazards, 117, 2001–2028, https://doi.org/10.1007/s11069-023-05938-7, 2023.
Singh, V. P., Guo, H., and Yu, F. X.: Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome, Stoch. Hydrol. Hydraul., 7, 163–177, https://doi.org/10.1007/BF01585596, 1993.
Spinoni, J., Vogt, J. V., Naumann, G., Barbosa, P., and Dosio, A.: Will drought events become more frequent and severe in Europe?, Int. J. Climatol., 38, 1718–1736, https://doi.org/10.1002/joc.5291, 2018.
Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F., and Stahl, K.: Candidate distributions for climatological drought indices (SPI and SPEI), Int. J. Climatol., 35, 4027–4040, https://doi.org/10.1002/joc.4267, 2015.
Svoboda, M. D. and Fuchs, B. A.: Handbook of drought indicators and indices (Vol. 2), Geneva, Switzerland: World Meteorological Organisation, https://www.droughtmanagement.info/literature/GWP_Handbook_of_Drought_Indicators_and_Indices_2016.pdf (last access: 16 October 2023), 2016.
Thiemig, V., Gomes, G. N., Skøien, J. O., Ziese, M., Rauthe-Schöch, A., Rustemeier, E., Rehfeldt, K., Walawender, J. P., Kolbe, C., Pichon, D., Schweim, C., and Salamon, P.: EMO-5: a high-resolution multi-variable gridded meteorological dataset for Europe, Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, 2022.
Thornthwaite, C. W.: An approach toward a rational classification of climate, Geograph. Rev., 38, 55–94, https://doi.org/10.2307/210739, 1948.
Tsakiris, G. and Vangelis, H. J. E. W.: Establishing a drought index incorporating evapotranspiration, European water, 9, 3–11, 2005.
Umran Komuscu, A.: Using the SPI to analyze spatial and temporal patterns of drought in Turkey, Drought Network News (1994–2001), 49, https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=_1048&context=_droughtnetnews (last access: 30 July 2023), 1999.
Universidade de Lisboa Faculdade de Ciências (FCUL): Atmosphere-land-ocean interactions over southwestern Africa under a changing climate, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/2022.03183.CEECIND/CP1715/CT0004, 2023.
Universidade de Lisboa Instituto Dom Luiz: 6817 – DCRRNI ID Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) – Financiamento Base, Universidade de Lisboa Instituto Dom Luiz, Portugal, UIDB/50019/2020, https://doi.org/10.54499/UIDB/50019/2020, 2020–2024a.
Universidade de Lisboa Instituto Dom Luiz: 6817 – DCRRNI ID Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) – Financiamento Programático, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/UIDP/50019/2020, 2020–2024b.
Universidade de Lisboa Instituto Superior de Agronomia: 6817 – DCRRNI ID Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) – Financiamento Base, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/UIDB/00239/2020, 2020–2024a.
Universidade de Lisboa Instituto Superior de Agronomia: 6817 – DCRRNI ID Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) – Financiamento Programático, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/UIDP/00239/2020, 2020–2024b.
Universidade de Lisboa Instituto Superior de Agronomia and Universidade de Lisboa Centro de Estudos Florestais: 6817 – DCRRNI ID Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) – Financiamento Base, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/UIDB/00239/2020, 2020–2024a.
Universidade de Lisboa Instituto Superior de Agronomia and Universidade de Lisboa Centro de Estudos Florestais: 6817 – DCRRNI ID Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) – Financiamento Programático, Fundação para a Ciência e a Tecnologia (FCT), Portugal, https://doi.org/10.54499/UIDP/00239/2020, 2020–2024b.
Van der Schrier, G., Jones, P. D., and Briffa, K. R.: The sensitivity of the PDSI to the Thornthwaite and Penman-Monteith parameterisations for potential evapotranspiration, J. Geophys. Res.-Atmos., 116, 1–16, https://doi.org/10.1029/2010JD015001, 2011.
Vicente-Serrano, S. M.: Differences in spatial patterns of drought on different timescales: an analysis of the Iberian Peninsula, Water Resour. Manage., 20, 37–60, https://doi.org/10.1007/s11269-006-2974-8, 2006.
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A multiscalar drought index sensitive to global warming: the Standardised precipitation evapotranspiration index, J. Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2010.
Vicente-Serrano, S. M., Gouveia, C., Camarero, J. J., Beguería, S., Trigo, R., López-Moreno, J. I., Azorín-Molna, C., Poasho, E., Lorenzo-Lacruz, J., Revuelto, J., Morán-Tejeda, E., and Sanchez-Lorenzo, A.: Response of vegetation to drought time-scales across global land biomes, P. Natl. Acad. Sci. USA, 110, 52–57, https://doi.org/10.1073/pnas.1207068110, 2013.
Wan, L., Bento, V. A., Qu, Y., Qiu, J., Song, H., Zhang, R., Wu, X., Xu, F., Lu, J., and Wang, Q.: Drought characteristics and dominant factors across China: Insights from high-resolution daily SPEI dataset between 1979 and 2018, Sci. Total Environ., 901, 166362, https://doi.org/10.1016/j.scitotenv.2023.166362, 2023.
Wang, Q., Wu, J., Lei, T., He, B., Wu, Z., Liu, M., Mo, X., Geng, G., Li, X., Zhou, H., and Liu, D.: Temporal-spatial characteristics of severe drought events and their impact on agriculture on a global scale, Quatern. Int., 349, 10–21, https://doi.org/10.1016/j.quaint.2014.06.021, 2014.
Wang, Q., Shi, P., Lei, T., Geng, G., Liu, J., Mo, X., Li X, Zhou, H., and Wu, J.: The alleviating trend of drought in the Huang-Huai-Hai Plain of China based on the daily SPEI, Int. J. Climatol., 35, 3760–3769, https://doi.org/10.1002/joc.4244, 2015.
Wang, Q., Wu, J., Li, X., Zhou, H., Yang, J., Geng, G., An, X., Liu, L., and Tang, Z.: A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model, Int. J. Biometeorol., 61, 685–699, https://doi.org/10.1007/s00484-016-1246-4, 2017.
Wang, Q., Zeng, J., Qi, J., Zhang, X., Zeng, Y., Shui, W., Xu, Z., Zhang, R., Wu, X., and Cong, J.: A multi-scale daily SPEI dataset for drought characterization at observation stations over mainland China from 1961 to 2018, Earth Syst. Sci. Data, 13, 331–341, https://doi.org/10.5194/essd-13-331-2021, 2021.
Wang, Q., Zhang, R., Qi, J., Zeng, J., Wu, J., Shui, W., Wu, X., and Li, J.: An improved daily Standardised precipitation index dataset for mainland China from 1961 to 2018, Sci. Data, 9, 124, doi:s41597-022-01201-z, 2022.
Wells, N., Goddard, S., and Hayes, M. J.: A self-calibrating Palmer drought severity index, J. Climate, 17, 2335–2351, https://doi.org/10.1175/1520-0442(2004)017<2335:ASPDSI>2.0.CO;2, 2004.
Wilhite, D. A.: Drought as a natural hazard: concepts and definitions, Drought Mitigation Center Faculty Publications, University of Nebraska, Lincoln, https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=_1068&context=_droughtfacpub (last access: 30 July 2023), 2000.
Wilhite, D. A. and Glantz, M. H.: Understanding: the drought phenomenon: the role of definitions, Water Int., 10, 111–120, https://doi.org/10.1080/02508068508686328, 1985.
Williams, D. N., Taylor, K. E., Cinquini, L., Evans, B., Kawamiya, M., Lautenschlager, M., Lawrencce, B., Middleton, D., and Contributors, E. S. G. F.: The Earth System Grid Federation: Software framework supporting CMIP5 data analysis and dissemination, ClIVAR Exchanges, 56, 40–42, https://centaur.reading.ac.uk/25732/1/WilEA11_CE.pdf (last access: 21 October 2024), 2011.
Wu, H., Hayes, M. J., Weiss, A., and Hu, Q. I.: An evaluation of the Standardised Precipitation Index, the China-Z Index and the statistical Z-Score, Int. J. Climatol., 21, 745–758, https://doi.org/10.1002/joc.658, 2001.
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.
Zhang, Y. and Li, Z.: Uncertainty analysis of Standardised precipitation index due to the effects of probability distributions and parameter errors, Front. Earth Sci., 8, 76, https://doi.org/10.3389/feart.2020.00076, 2020.
Zhang, R., Bento, V. A., Qi, J., Xu, F., Wu, J., Qiu, J., Li, J., Shui, W., and Wang, Q.: The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018, Big Earth Data, 7, 860–885, https://doi.org/10.1080/20964471.2022.2148331, 2023.
Zhang, X., Duan, Y., Duan, J., Jian, D., and Ma, Z.: A daily drought index based on evapotranspiration and its application in regional drought analyses, Sci. China Earth Sci., 65, 317–336, https://doi.org/10.1007/s11430-021-9822-y, 2022a.
Zhang, X., Duan, Y., Duan, J., Chen, L., Jian, D., Lv, M., Yang, Q., and Ma, Z.: A daily drought index-based regional drought forecasting using the Global Forecast System model outputs over China, Atmos. Res., 273, 106166, https://doi.org/10.1016/j.atmosres.2022.106166, 2022b.
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha, M. D., Maraun, D., Ramos, A., Ridder, N. N., Thiery, W., and Vignotto, E.: A typology of compound weather and climate events, Nature Rev. Earth Environ., 1, 333–347, 2020.
Short summary
This study proposes a new daily drought index, the generalised drought index (GDI). The GDI not only identifies the same events as established indices but is also capable of improving their results. The index is empirically based and easy to compute, not requiring fitting the data to a probability distribution. The GDI can detect flash droughts and longer-term events, making it a versatile tool for drought monitoring.
This study proposes a new daily drought index, the generalised drought index (GDI). The GDI not...
