Articles | Volume 28, issue 2
https://doi.org/10.5194/hess-28-321-2024
© 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/hess-28-321-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jan 2024
Research article |
| 19 Jan 2024
| 19 Jan 2024
Using statistical models to depict the response of multi-timescale drought to forest cover change across climate zones
Institute of Meteorology, Freie Universität Berlin, 12165 Berlin, Germany
Department of Energy and Process Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway
Henning W. Rust
Institute of Meteorology, Freie Universität Berlin, 12165 Berlin, Germany
Related authors
Yan Li, Bo Huang, Chunping Tan, Xia Zhang, Francesco Cherubini, and Henning W. Rust
Hydrol. Earth Syst. Sci., 29, 1637–1658, https://doi.org/10.5194/hess-29-1637-2025, https://doi.org/10.5194/hess-29-1637-2025, 2025
Short summary
Short summary
Deforestation has a significant impact on climate, yet its effects on drought remain less understood. This study investigates how deforestation affects drought across various climate zones and timescales. Findings indicate that deforestation leads to drier conditions in tropical regions and wetter conditions in arid areas, with minimal effects in temperate zones. Long-term drought is more affected than short-term drought, offering valuable insights into vegetation–climate interactions.
Ahmed Homoudi, Henning W. Rust, Klemens Barfus, Christian Bernhofer, and Matthias Mauder
EGUsphere, https://doi.org/10.5194/egusphere-2026-3288, https://doi.org/10.5194/egusphere-2026-3288, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
As climate warms, extremes events over the Arabian Peninsula is projected to increase, raising flood risk. We identify favourable atmospheric conditions for rainfall in the current and future climates using machine learning. These conditions are currently underestimated by climate models and will increase at moderate warming, but decrease at higher warming. Despite this, a moister and more unstable atmosphere suggests more intense rainfall and greater flash flood risk across the region.
Andreas Trojand, Henning W. Rust, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 25, 2331–2350, https://doi.org/10.5194/nhess-25-2331-2025, https://doi.org/10.5194/nhess-25-2331-2025, 2025
Short summary
Short summary
The study investigates how the intensity of previous windstorm events and the time between two events affect the vulnerability of residential buildings in Germany. By analyzing 23 years of data, it was found that higher intensity of previous events generally reduces vulnerability in subsequent storms, while shorter intervals between events increase vulnerability. The results emphasize the approach of considering vulnerability in risk assessments as temporally dynamic.
Yan Li, Bo Huang, Chunping Tan, Xia Zhang, Francesco Cherubini, and Henning W. Rust
Hydrol. Earth Syst. Sci., 29, 1637–1658, https://doi.org/10.5194/hess-29-1637-2025, https://doi.org/10.5194/hess-29-1637-2025, 2025
Short summary
Short summary
Deforestation has a significant impact on climate, yet its effects on drought remain less understood. This study investigates how deforestation affects drought across various climate zones and timescales. Findings indicate that deforestation leads to drier conditions in tropical regions and wetter conditions in arid areas, with minimal effects in temperate zones. Long-term drought is more affected than short-term drought, offering valuable insights into vegetation–climate interactions.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
Short summary
Short summary
The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Madlen Peter, Henning W. Rust, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 24, 1261–1285, https://doi.org/10.5194/nhess-24-1261-2024, https://doi.org/10.5194/nhess-24-1261-2024, 2024
Short summary
Short summary
The paper introduces a statistical modeling approach describing daily extreme precipitation in Germany more accurately by including changes within the year and between the years simultaneously. The changing seasonality over years is regionally divergent and mainly weak. However, some regions stand out with a more pronounced linear rise of summer intensities, indicating a possible climate change signal. Improved modeling of extreme precipitation is beneficial for risk assessment and adaptation.
Johannes Riebold, Andy Richling, Uwe Ulbrich, Henning Rust, Tido Semmler, and Dörthe Handorf
Weather Clim. Dynam., 4, 663–682, https://doi.org/10.5194/wcd-4-663-2023, https://doi.org/10.5194/wcd-4-663-2023, 2023
Short summary
Short summary
Arctic sea ice loss might impact the atmospheric circulation outside the Arctic and therefore extremes over mid-latitudes. Here, we analyze model experiments to initially assess the influence of sea ice loss on occurrence frequencies of large-scale circulation patterns. Some of these detected circulation changes can be linked to changes in occurrences of European temperature extremes. Compared to future global temperature increases, the sea-ice-related impacts are however of secondary relevance.
Edmund P. Meredith, Uwe Ulbrich, and Henning W. Rust
Geosci. Model Dev., 16, 851–867, https://doi.org/10.5194/gmd-16-851-2023, https://doi.org/10.5194/gmd-16-851-2023, 2023
Short summary
Short summary
Cell-tracking algorithms allow for the study of properties of a convective cell across its lifetime and, in particular, how these respond to climate change. We investigated whether the design of the algorithm can affect the magnitude of the climate-change signal. The algorithm's criteria for identifying a cell were found to have a strong impact on the warming response. The sensitivity of the warming response to different algorithm settings and cell types should thus be fully explored.
Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
Short summary
Short summary
In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Robert Polzin, Annette Müller, Henning Rust, Peter Névir, and Péter Koltai
Nonlin. Processes Geophys., 29, 37–52, https://doi.org/10.5194/npg-29-37-2022, https://doi.org/10.5194/npg-29-37-2022, 2022
Short summary
Short summary
In this study, a recent algorithmic framework called Direct Bayesian Model Reduction (DBMR) is applied which provides a scalable probability-preserving identification of reduced models directly from data. The stochastic method is tested in a meteorological application towards a model reduction to latent states of smaller scale convective activity conditioned on large-scale atmospheric flow.
Noelia Otero, Oscar E. Jurado, Tim Butler, and Henning W. Rust
Atmos. Chem. Phys., 22, 1905–1919, https://doi.org/10.5194/acp-22-1905-2022, https://doi.org/10.5194/acp-22-1905-2022, 2022
Short summary
Short summary
Surface ozone and temperature are strongly dependent and their extremes might be exacerbated by underlying climatological drivers, such as atmospheric blocking. Using an observational data set, we measure the dependence structure between ozone and temperature under the influence of atmospheric blocking. Blocks enhanced the probability of occurrence of compound ozone and temperature extremes over northwestern and central Europe, leading to greater health risks.
Felix S. Fauer, Jana Ulrich, Oscar E. Jurado, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6479–6494, https://doi.org/10.5194/hess-25-6479-2021, https://doi.org/10.5194/hess-25-6479-2021, 2021
Short summary
Short summary
Extreme rainfall events are modeled in this study for different timescales. A new parameterization of the dependence between extreme values and their timescale enables our model to estimate extremes on very short (1 min) and long (5 d) timescales simultaneously. We compare different approaches of modeling this dependence and find that our new model improves performance for timescales between 2 h and 2 d without affecting model performance on other timescales.
Jana Ulrich, Felix S. Fauer, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6133–6149, https://doi.org/10.5194/hess-25-6133-2021, https://doi.org/10.5194/hess-25-6133-2021, 2021
Short summary
Short summary
The characteristics of extreme precipitation on different timescales as well as in different seasons are relevant information, e.g., for designing hydrological structures or managing water supplies. Therefore, our aim is to describe these characteristics simultaneously within one model. We find similar characteristics for short extreme precipitation at all considered stations in Germany but pronounced regional differences with respect to the seasonality of long-lasting extreme events.
Carola Detring, Annette Müller, Lisa Schielicke, Peter Névir, and Henning W. Rust
Weather Clim. Dynam., 2, 927–952, https://doi.org/10.5194/wcd-2-927-2021, https://doi.org/10.5194/wcd-2-927-2021, 2021
Short summary
Short summary
Stationary, long-lasting blocked weather patterns can lead to extreme conditions. Within this study the temporal evolution of the occurrence probability is analyzed, and the onset, decay and transition probabilities of blocking within the past 30 years are modeled. Using Markov models combined with logistic regression, we found large changes in summer, where the probability of transitions to so-called Omega blocks increases strongly, while the unblocked state becomes less probable.
Cited articles
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56, Fao, Rome, 300, D05109, ISBN 9251042195, 1998. a
Anderson, R. G., Canadell, J. G., Randerson, J. T., Jackson, R. B., Hungate, B. A., Baldocchi, D. D., Ban-Weiss, G. A., Bonan, G. B., Caldeira, K., Cao, L., Diffenbaugh, N. S., Gurney, K. R., Kueppers, L. M., Law, B. E., Luyssaert, S., and O'Halloran, T. L.: Biophysical considerations in forestry for climate protection, Front. Ecol. Environ., 9, 174–182, https://doi.org/10.1890/090179, 2011. a
Anscombe, F. J.: The validity of comparative experiments, J. R. Stat. Soc. A-G, 111, 181–211, 1948. a
Bagley, J. E., Desai, A. R., Harding, K. J., Snyder, P. K., and Foley, J. A.: Drought and Deforestation: Has Land Cover Change Influenced Recent Precipitation Extremes in the Amazon?, J. Climate, 27, 345–361, https://doi.org/10.1175/Jcli-D-12-00369.1, 2014. a
Bala, G., Caldeira, K., Wickett, M., Phillips, T. J., Lobell, D. B., Delire, C., and Mirin, A.: Combined climate and carbon-cycle effects of large-scale deforestation, P. Natl. Acad. Sci. USA, 104, 6550–6555, https://doi.org/10.1073/pnas.0608998104, 2007. a, b
Barichivich, J., Osborn, T., Harris, I., Van Der Schrier, G., and Jones, P.: Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in “State of the Climate in 2020”], B. Am. Meteorol. Soc., 102, S68–S70, 2021. a
Beringer, J., Chapin, F. S., Thompson, C. C., and McGuire, A. D.: Surface energy exchanges along the tundra-forest transition and feedbacks to climate, Agr. Forest Meteorol., 131, 143–161, https://doi.org/10.1016/j.agrformet.2005.05.006, 2005. a
Boulton, C. A., Lenton, T. M., and Boers, N.: Pronounced loss of Amazon rainforest resilience since the early 2000s, Nat. Clim. Change, 12, 271–278, 2022. a
Bryan, B. A., Gao, L., Ye, Y. Q., Sun, X. F., Connor, J. D., Crossman, N. D., Stafford-Smith, M., Wu, J. G., He, C. Y., Yu, D. Y., Liu, Z. F., Li, A., Huang, Q. X., Ren, H., Deng, X. Z., Zheng, H., Niu, J. M., Han, G. D., and Hou, X. Y.: China's response to a national land-system sustainability emergency, Nature, 559, 193–204, https://doi.org/10.1038/s41586-018-0280-2, 2018. a
Chambers, J. and Hastie, T.: Linear models, Chap. 4 in: Statistical models in S, Wadsworth & Brooks/Cole, 1992, https://doi.org/10.1201/9780203738535, ISBN 9780203738535, 1992. a, b
Cherubini, F., Huang, B., Hu, X., Tölle, M. H., and Strømman, A. H.: Quantifying the climate response to extreme land cover changes in Europe with a regional model, Environ. Res. Lett., 13, 074002, https://doi.org/10.1088/1748-9326/aac794, 2018. a, b
Cook, B. I., Smerdon, J. E., Seager, R., and Coats, S.: Global warming and 21 st century drying, Clim. Dynam., 43, 2607–2627, 2014. a
Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A.: Mapping tree density at a global scale, Nature, 525, 201–205, https://doi.org/10.1038/nature14967, 2015. a
Dai, A.: Drought under global warming: a review, Wires Clim. Change, 2, 45–65, 2011. a
Dai, A.: Increasing drought under global warming in observations and models, Nat. Clim. Change, 3, 52, https://doi.org/10.1038/nclimate1633, 2013. a
ESA: Land Cover CCI Product User Guide Version 2, https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 27 May 2022), 2017. a
Field, C. B., Lobell, D. B., Peters, H. A., and Chiariello, N. R.: Feedbacks of Terrestrial Ecosystems to Climate Change, Annu. Rev. Env. Resour., 32, 1–29, https://doi.org/10.1146/annual.energy.32.053006.141119, 2007. a, b
Findell, K. L., Berg, A., Gentine, P., Krasting, J. P., Lintner, B. R., Malyshev, S., Santanello, J. A., and Shevliakova, E.: The impact of anthropogenic land use and land cover change on regional climate extremes, Nat. Commun., 8, 989, https://doi.org/10.1038/s41467-017-01038-w, 2017. a, b
Ge, J., Guo, W. D., Pitman, A. J., De Kauwe, M. G., Chen, X. L., and Fu, C. B.: The Nonradiative Effect Dominates Local Surface Temperature Change Caused by Afforestation in China, J. Climate, 32, 4445–4471, https://doi.org/10.1175/Jcli-D-18-0772.1, 2019. a, b
Hansen, M. C., Stehman, S. V., and Potapov, P. V.: Quantification of global gross forest cover loss, P. Natl. Acad. Sci. USA, 107, 8650–8655, 2010. a
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R. G.: High-resolution global maps of 21st-century forest cover change, Science, 342, 850–853, 2013. a, b, c
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, 2014. a
Hoek van Dijke, A. J., Herold, M., Mallick, K., Benedict, I., Machwitz, M., Schlerf, M., Pranindita, A., Theeuwen, J. J. E., Bastin, J.-F., and Teuling, A. J.: Shifts in regional water availability due to global tree restoration, Nat. Geosci., 15, 363–368, https://doi.org/10.1038/s41561-022-00935-0, 2022. a
Hu, X., Huang, B., Verones, F., Cavalett, O., and Cherubini, F.: Overview of recent land‐cover changes in biodiversity hotspots, Front. Ecol. Environ., 19, 91–97, https://doi.org/10.1002/fee.2276, 2020. a
Huang, B., Hu, X., Fuglstad, G.-A., Zhou, X., Zhao, W., and Cherubini, F.: Predominant regional biophysical cooling from recent land cover changes in Europe, Nat. Commun., 11, 1–13, 2020. a
Huang, B., Li, Y., Liu, Y., Hu, X., Zhao, W., and Cherubini, F.: A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia, Agr. Forest Meteorol., 332, 109362, https://doi.org/10.1016/j.agrformet.2023.109362, 2023. a
Keyantash, J. and Dracup, J. A.: The quantification of drought: an evaluation of drought indices, B. Am. Meteorol. Soc., 83, 1167–1180, 2002. a
Kim, D.-H., Sexton, J. O., and Townshend, J. R.: Accelerated deforestation in the humid tropics from the 1990s to the 2000s, Geophys. Res. Lett., 42, 3495–3501, 2015. a
Kimball, J. W.: Biology, 6th Edition, Published by William C Brown Pub, 800 pp., ISBN 13 9780697142573, 1993. a
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of the Köppen– Geiger climate classification updated, Meteorol. Z., 15, 259–263, 2006. a
Kriticos, D. J., Webber, B. L., Leriche, A., Ota, N., Macadam, I., Bathols, J., and Scott, J. K.: CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling, Meth. Ecol. Evol., 3, 53–64, 2012. a
Leite-Filho, A. T., Soares-Filho, B. S., Davis, J. L., Abrahão, G. M., and Börner, J.: Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon, Nat. Commun., 12, 2591, https://doi.org/10.1038/s41467-021-22840-7, 2021. a
Li, Y., Zhao, M., Motesharrei, S., Mu, Q., Kalnay, E., and Li, S.: Local cooling and warming effects of forests based on satellite observations, Nat. Commun., 6, 6603, https://doi.org/10.1038/ncomms7603, 2015. a
Li, Y., Zhao, M., Mildrexler, D. J., Motesharrei, S., Mu, Q., Kalnay, E., Zhao, F., Li, S., and Wang, K.: Potential and Actual impacts of deforestation and afforestation on land surface temperature, J. Geophys. Res.-Atmos., 121, 14372–14386, https://doi.org/10.1002/2016JD024969, 2016. a
Liang, Y., Xu, X., and Jia, G.: Deforestation drives desiccation in global monsoon region, Earth's Future, 10, e2022EF002863, https://doi.org/10.1029/2022EF002863, 2022. a
Luo, X., Ge, J., Guo, W., Fan, L., Chen, C., Liu, Y., and Yang, L.: The biophysical impacts of deforestation on precipitation: results from the CMIP6 model intercomparison, J. Climate, 35, 3293–3311, 2022. a
Mahmood, R., Pielke, R. A., Hubbard, K. G., Niyogi, D., Dirmeyer, P. A., McAlpine, C., Carleton, A. M., Hale, R., Gameda, S., Beltrán-Przekurat, A., Baker, B., McNider, R., Legates D. R., Shepherd, M., Du, J., Blanken, P. D., Frauenfeld, O. W., Nair, U. S., and Fall, S.: Land cover changes and their biogeophysical effects on climate, Int. J. Climatol., 34, 929–953, 2014. a, b
Maidment, D. R.: Handbook of hydrology, vol. 9780070, McGraw-Hill New York, ISBN 9780070397323, 1993. a
McCullagh, P. and Nelder, J.: Generalized Linear Models, CRC Press, Boca Raton, Fla, 2 edn., ISBN 9780412317606, 1989. a
McKee, T. B., Doesken, N. J., and Kleist, J.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology, vol. 17, American Meteorological Society Boston, Anaheim, Californiam 17-22 January 1993, 179–183, https://climate.colostate.edu/pdfs/relationshipofdroughtfrequency.pdf (last access: 27 December 2023), 1993. a, b
Mishra, A. K. and Singh, V. P.: A review of drought concepts, J. Hydrol., 391, 202–216, 2010. a
Naumann, G., Alfieri, L., Wyser, K., Mentaschi, L., Betts, R., Carrao, H., Spinoni, J., Vogt, J., and Feyen, L.: Global changes in drought conditions under different levels of warming, Geophys. Res. Lett., 45, 3285–3296, 2018. a
Nepstad, D., Soares-Filho, B. S., Merry, F., Lima, A., Moutinho, P., Carter, J., Bowman, M., Cattaneo, A., Rodrigues, H., Schwartzman, S., McGrath, D. G., Stickler, C. M., Lubowski, R., Piris-Cabezas, P., Rivero, S., Alencar, A., Almeida, O., and Stella, O.: Environment. The end of deforestation in the Brazilian Amazon, Science, 326, 1350–1351, https://doi.org/10.1126/science.1182108, 2009. a
Nepstad, D. C., Tohver, I. M., Ray, D., Moutinho, P., and Cardinot, G.: Mortality of large trees and lianas following experimental drought in an Amazon forest, Ecology, 88, 2259–2269, 2007. a
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. a
Peng, S. S., Piao, S., Zeng, Z., Ciais, P., Zhou, L., Li, L. Z., Myneni, R. B., Yin, Y., and Zeng, H.: Afforestation in China cools local land surface temperature, P. Natl. Acad. Sci. USA, 111, 2915–2919, https://doi.org/10.1073/pnas.1315126111, 2014. 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, 1997. a
Peterson, T. C., Heim Jr., R. R., Hirsch, R., Kaiser, D. P., Brooks, H., Diffenbaugh, N. S., Dole, R. M., Giovannettone, J. P., Guirguis, K., Karl, T. R., Katz, R. W., Kunkel, K., Lettenmaier, D., McCabe, G. J., Paciorek, C. J., Ryberg, K. R., Schubert, S., Silva, V. B. S., Stewart, B. C., Vecchia, A. V., Villarini, G., Vose, R. S., Walsh, J., Wehner, M., Wolock, D., Wolter, K., Woodhouse, C. A., and Wuebbles, D.: Monitoring and understanding changes in heat waves, cold waves, floods, and droughts in the United States: state of knowledge, B. Am. Meteorol. Soc., 94, 821–834, 2013. a
Pitman, A. J., Avila, F. B., Abramowitz, G., Wang, Y. P., Phipps, S. J., and de Noblet-Ducoudre, N.: Importance of background climate in determining impact of land-cover change on regional climate, Nat. Clim. Change, 1, 472–475, https://doi.org/10.1038/Nclimate1294, 2011. a
Popp, A., Calvin, K., Fujimori, S., Havlik, P., Humpenöder, F., Stehfest, E., Bodirsky, B. L., Dietrich, J. P., Doelmann, J. C., Gusti, M., Hasegawa, T., Kyle, P., Obersteiner, M., Tabeau, A., Takahashi, K., Valin, H., Waldhoff, S., Weindl, I., Wise, M., Kriegler, E., Lotze-Campen, H., Fricko, O., Riahi, K., and Vuuren, D. P.: Land-use futures in the shared socio-economic pathways, Global Environ. Chang., 42, 331–345, 2017. a
Poulter, B., MacBean, N., Hartley, A., Khlystova, I., Arino, O., Betts, R., Bontemps, S., Boettcher, M., Brockmann, C., Defourny, P., Hagemann, S., Herold, M., Kirches, G., Lamarche, C., Lederer, D., Ottlé, C., Peters, M., and Peylin, P.: Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative, Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, 2015. a
Salati, E. and Nobre, C. A.: Possible climatic impacts of tropical deforestation, Climatic Change, 19, 177–196, https://doi.org/10.1007/BF00142225, 1991. a
Schlesinger, W. H. and Jasechko, S.: Transpiration in the global water cycle, Agr. Forest Meteorol., 189, 115–117, 2014. a
Seneviratne, S. I.: Climate science: Historical drought trends revisited, Nature, 491, 338, https://doi.org/10.1038/491338a, 2012. a
Silva Dias, M. A., Avissar, R., and Silva Dias, P.: Modeling the regional and remote climatic impact of deforestation, Washington DC American Geophysical Union, Geophysical Monograph Series, 186, 251–260, https://doi.org/10.1029/2008GM000817, 2009. a
Smith, C., Baker, J., and Spracklen, D.: Tropical deforestation causes large reductions in observed precipitation, Nature, 615, 270–275, 2023. a
Snyder, P. K., Delire, C., and Foley, J. A.: Evaluating the influence of different vegetation biomes on the global climate, Clim. Dynam., 23, 279–302, https://doi.org/10.1007/s00382-004-0430-0, 2004. a, b
Staal, A., Flores, B. M., Aguiar, A. P. D., Bosmans, J. H., Fetzer, I., and Tuinenburg, O. A.: Feedback between drought and deforestation in the Amazon, Environ. Res. Lett., 15, 044024, https://doi.org/10.1088/1748-9326/ab738e, 2020. a
Thornthwaite, C. W.: An approach toward a rational classification of climate, Geogr. Rev., 38, 55–94, 1948. a
Tian, L., Zhang, B. Q., Wang, X. J., Chen, S. Y., and Pan, B. T.: Large-Scale Afforestation Over the Loess Plateau in China Contributes to the Local Warming Trend, J. Geophys. Res.-Atmos., 127, e2021JD035730, https://doi.org/10.1029/2021JD035730, 2022. a, b
Trenberth, K. E., Dai, A., Van Der Schrier, G., Jones, P. D., Barichivich, J., Briffa, K. R., and Sheffield, J.: Global warming and changes in drought, Nat. Clim. Change, 4, 17–22, https://doi.org/10.1038/nclimate2067, 2014. a
van der Schrier, G., Barichivich, J., Briffa, K., and Jones, P.: A scPDSI-based global data set of dry and wet spells for 1901–2009, J. Geophys. Res.-Atmos., 118, 4025–4048, 2013. a
Vicente-Serrano, S. M., Beguería, S., López-Moreno, J. I., Angulo, M., and El Kenawy, A.: A new global 0.5 gridded dataset (1901–2006) of a multiscalar drought index: comparison with current drought index datasets based on the Palmer Drought Severity Index, J. Hydrometeorol., 11, 1033–1043, 2010b. a
Vicente-Serrano, S. M., Lopez-Moreno, J.-I., Beguería, S., Lorenzo-Lacruz, J., Sanchez-Lorenzo, A., García-Ruiz, J. M., Azorin-Molina, C., Morán-Tejeda, E., Revuelto, J., Trigo, R., Coelho, F., and Espejo, F.: Evidence of increasing drought severity caused by temperature rise in southern Europe, Environ. Res. Lett., 9, 044001, https://doi.org/10.1088/1748-9326/9/4/044001, 2014. a
Wayne, C. P.: Meteorological drought, US weather bureau research paper, 58, https://books.google.de/books?hl=en&lr=&id=kyYZgnEk-L8C&oi=fnd&pg=PA6&dq=Meteorological+drought&ots=U58wfl-Eki&sig=8PQ8kJ4kWT2LGepZQbEH7QoT6vE&redir_esc=y#v=onepage&q=Meteorological drought&f=false (last access: 27 May 2022), 1965. a
Wilks, D. S.: Statistical methods in the atmospheric sciences, Elsevier, Amsterdam, NL, 4th edn., ISBN 9780128158234, 2019. a
Zhao, T. and Dai, A.: The magnitude and causes of global drought changes in the twenty-first century under a low–moderate emissions scenario, J. Climate, 28, 4490–4512, 2015. a
Short summary
The inconsistent changes in temperature and precipitation induced by forest cover change are very likely to affect drought condition. We use a set of statistical models to explore the relationship between forest cover change and drought change in different timescales and climate zones. We find that the influence of forest cover on droughts varies under different precipitation and temperature quantiles. Forest cover also could modulate the impacts of precipitation and temperature on drought.
The inconsistent changes in temperature and precipitation induced by forest cover change are...
