Articles | Volume 15, issue 5
https://doi.org/10.5194/esd-15-1277-2024
© 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/esd-15-1277-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Sep 2024
Research article |
| 30 Sep 2024
| 30 Sep 2024
An enhanced Standardized Precipitation–Evapotranspiration Index (SPEI) drought-monitoring method integrating land surface characteristics
Department of Geography, The University of Hong Kong, Hong Kong SAR, China
Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong SAR, China
Justin Sheffield
School of Geography and Environmental Science, University of Southampton, Southampton, UK
Zhongwang Wei
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
Michael Ek
Joint Numerical Testbed, Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
Eric F. Wood
Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA
deceased
Related authors
No articles found.
Shiao Feng, Wenhong Wang, Yonggen Zhang, Zhongwang Wei, Jianzhi Dong, Lutz Weihermüller, and Harry Vereecken
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-410, https://doi.org/10.5194/essd-2025-410, 2025
Preprint under review for ESSD
Short summary
Short summary
Soil moisture is key for weather, farming, and ecosystems, but global datasets have gaps and biases. We compared three products against 1,615 stations with more than 1.9 million measured moisture, finding ERA5-Land highly correlated but biased high, and SMAP-L4 accurate but short-term. Fusing them created an enhanced dataset, improving correlation by 5%, reducing errors by 20%, and enhancing overall fit by 15%. This seamless resource aids drought tracking, water planning, and climate adaptation.
Stephen E. Darby, Ivan D. Haigh, Melissa Wood, Bui Duong, Tien Le Thuy Du, Thao Phuong Bui, Justin Sheffield, Hal Voepel, and Joël J.-M. Hirschi
EGUsphere, https://doi.org/10.5194/egusphere-2025-3506, https://doi.org/10.5194/egusphere-2025-3506, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We use model simulations to see what changes have been occurring to Mekong and Red River flows, 1970–2019, due to changes in tropical cyclone (TC)-linked precipitation. Results suggest that the highest river flows in multiple sub-catchments have been increasing over time, with coastal zones most intensely affected due to the combination of TC track and wet soils from prior rainfall. Climate change may exacerbate this TC-linked risk in the future making it a topic of strategic importance.
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci., 29, 2201–2218, https://doi.org/10.5194/hess-29-2201-2025, https://doi.org/10.5194/hess-29-2201-2025, 2025
Short summary
Short summary
We developed the first high-resolution, integrated surface water–groundwater hydrologic model of the entirety of continental China using ParFlow. The model shows good performance in terms of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision-making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibo Liu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2025-1380, https://doi.org/10.5194/egusphere-2025-1380, 2025
Short summary
Short summary
Land surface models are used for simulating earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes evaluation process more comprehensive and efficient. It stands out by incorporating various factors and working with data at any scale which enabling scientists to incorporate new types of models and measurements as our understanding of Earth’s systems evolves.
Gaosong Shi, Wenye Sun, Wei Shangguan, Zhongwang Wei, Hua Yuan, Lu Li, Xiaolin Sun, Ye Zhang, Hongbin Liang, Danxi Li, Feini Huang, Qingliang Li, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 517–543, https://doi.org/10.5194/essd-17-517-2025, https://doi.org/10.5194/essd-17-517-2025, 2025
Short summary
Short summary
In this study, we developed the second version of China's high-resolution soil information grid using legacy soil samples and advanced machine learning. This version predicts over 20 soil properties at six depths, providing accurate soil variation maps across China. It outperforms previous versions and global products, offering valuable data for hydrological and ecological analyses and Earth system modelling, enhancing our understanding of soil roles in environmental processes.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 117–134, https://doi.org/10.5194/essd-17-117-2025, https://doi.org/10.5194/essd-17-117-2025, 2025
Short summary
Short summary
Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally, with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Solomon H. Gebrechorkos, Julian Leyland, Simon J. Dadson, Sagy Cohen, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Yinxue Liu, Justin Sheffield, Daniel R. Parsons, and Stephen E. Darby
Hydrol. Earth Syst. Sci., 28, 3099–3118, https://doi.org/10.5194/hess-28-3099-2024, https://doi.org/10.5194/hess-28-3099-2024, 2024
Short summary
Short summary
This study evaluated six high-resolution global precipitation datasets for hydrological modelling. MSWEP and ERA5 showed better performance, but spatial variability was high. The findings highlight the importance of careful dataset selection for river discharge modelling due to the lack of a universally superior dataset. Further improvements in global precipitation data products are needed.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
Short summary
Short summary
Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Dominikus Heinzeller, Ligia Bernardet, Grant Firl, Man Zhang, Xia Sun, and Michael Ek
Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023, https://doi.org/10.5194/gmd-16-2235-2023, 2023
Short summary
Short summary
The Common Community Physics Package is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model’s dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and physics–dynamics coupling methods while offering capabilities for numerical weather prediction operations, for example in the upcoming implementation of the Global Forecast System (GFS) v17.
Yi Nan, Zhihua He, Fuqiang Tian, Zhongwang Wei, and Lide Tian
Hydrol. Earth Syst. Sci., 26, 4147–4167, https://doi.org/10.5194/hess-26-4147-2022, https://doi.org/10.5194/hess-26-4147-2022, 2022
Short summary
Short summary
Tracer-aided hydrological models are useful tool to reduce uncertainty of hydrological modeling in cold basins, but there is little guidance on the sampling strategy for isotope analysis, which is important for large mountainous basins. This study evaluated the reliance of the tracer-aided modeling performance on the availability of isotope data in the Yarlung Tsangpo river basin, and provides implications for collecting water isotope data for running tracer-aided hydrological models.
M. G. Ziliani, M. U. Altaf, B. Aragon, R. Houborg, T. E. Franz, Y. Lu, J. Sheffield, I. Hoteit, and M. F. McCabe
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1045–1052, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, 2022
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Noemi Vergopolan, Sitian Xiong, Lyndon Estes, Niko Wanders, Nathaniel W. Chaney, Eric F. Wood, Megan Konar, Kelly Caylor, Hylke E. Beck, Nicolas Gatti, Tom Evans, and Justin Sheffield
Hydrol. Earth Syst. Sci., 25, 1827–1847, https://doi.org/10.5194/hess-25-1827-2021, https://doi.org/10.5194/hess-25-1827-2021, 2021
Short summary
Short summary
Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or (infrequent) vegetation indexes that do not always indicate the conditions farmers face in the field. Consequently, decision-making based on these indices can often be disconnected from the farmer reality. Our study focuses on smallholder farming systems in data-sparse developing countries, and it shows how field-scale soil moisture can leverage and improve crop yield prediction and drought impact assessment.
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
Short summary
Short summary
We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
Cited articles
Abeysiriwardana, H. D., Muttil, N., and Rathnayake, U.: A comparative study of potential evapotranspiration estimation by three methods with FAO Penman–Monteith method across Sri Lanka, Hydrology, 9, 206, https://doi.org/10.3390/hydrology9110206, 2022.
Acreman, M. C., Harding, R. J., Lloyd, C. R., and McNeil, D. D.: Evaporation characteristics of wetlands: experience from a wetgrassland and a reedbed using eddy correlation measurements, Hydrol. Earth Syst. Sci., 7, 11–21, https://doi.org/10.5194/hess-7-11-2003, 2003.
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, 300, D05109, 1998.
Allen, R. G., Walter, I. A., Elliott, R., Howell, T. A., Itenfisu, D., and Jensen, M.: The ASCE standardized reference evapotranspiration equation, American Society of Civil Engineers, Reston, VA, 4 pp., https://www.mesonet.org/images/site/ASCE_Evapotranspiration_Formula.pdf (last access: 23 September 2024), 2005.
Andreadis, K. M. and Lettenmaier, D. P.: Trends in 20th century drought over the continental United States, Geophys. Res. Lett., 33, L10403, https://doi.org/10.1029/2006GL025711, 2006.
Andreadis, K. M., Clark, E. A., Wood, A. W., Hamlet, A. F., and Lettenmaier, D. P.: Twentieth-Century Drought in the Conterminous United States, J. Hydrometeorol., 6, 985–1001, https://doi.org/10.1175/JHM450.1, 2005.
Barbeta, A., Ogaya, R., and Peñuelas, J.: Dampening effects of long-term experimental drought on growth and mortality rates of a Holm oak forest, Glob. Change Biol., 19, 3133–3144, https://doi.org/10.1111/gcb.12269, 2013.
Beguería, S., Vicente-Serrano, S. M., Reig, F., and Latorre, B.: Standardized 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, 2013.
Brisson, N., Itier, B., L'Hotel, J. C., and Lorendeau, J. Y.: Parameterisation of the Shuttleworth-Wallace model to estimate daily maximum transpiration for use in crop models, Ecol. Model., 107, 159–169, 1998.
Broxton, P. D.: MODIS land cover, https://www2.mmm.ucar.edu/wrf/src/wps_files/modis_landuse_20class_15s.tar.bz2, last access: 1 January 2019.
Broxton, P. D., Zeng, X., Sulla-Menashe, D., and Troch, P. A.: A Global Land Cover Climatology Using MODIS Data, J. Appl. Meteorol. Clim., 53, 1593–1605, https://doi.org/10.1175/JAMC-D-13-0270.1, 2014.
Brutsaert, W.: The Surface Roughness Parameterization, in: Evaporation into the Atmosphere, edited by: Davenport, A. J., Hicks, B. B., Hilst, G. R., Munn, R. E., and Smith, J. D., Springer Netherlands, Dordrecht, 113–127, https://doi.org/10.1007/978-94-017-1497-6_5, 1982.
Brutsaert, W. and Stricker, H.: An advection-aridity approach to estimate actual regional evapotranspiration, Water Resour. Res., 15, 443–450, https://doi.org/10.1029/WR015i002p00443, 1979.
Campbell, G. S. and Norman, J. M.: Wind, in: An Introduction to Environmental Biophysics, Springer, New York, NY, 68–70, https://doi.org/10.1007/978-1-4612-1626-1, 1998.
Clark, J. S., Iverson, L., Woodall, C. W., Allen, C. D., Bell, D. M., Bragg, D. C., D'Amato, A. W., Davis, F. W., Hersh, M. H., Ibanez, I., Jackson, S. T., Matthews, S., Pederson, N., Peters, M., Schwartz, M. W., Waring, K. M., and Zimmermann, N. E.: The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States, Glob. Change Biol., 22, 2329–2352, https://doi.org/10.1111/gcb.13160, 2016.
Dai, A., Trenberth, K. E., and Qian, T.: A Global Dataset of Palmer Drought Severity Index for 1870–2002: Relationship with Soil Moisture and Effects of Surface Warming, J. Hydrometeorol., 5, 1117–1130, https://doi.org/10.1175/JHM-386.1, 2004.
Daly, C., Neilson, R. P., and Phillips, D. L.: A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain, J. Appl. Meteorol., 33, 140–158, https://doi.org/10.1175/1520-0450(1994)033<0140:astmfm>2.0.co;2, 1994.
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States, Int. J. Climatol., 28, 2031–2064, https://doi.org/10.1002/joc.1688, 2008.
Dewes, C. F., Rangwala, I., Barsugli, J. J., Hobbins, M. T., and Kumar, S.: Drought risk assessment under climate change is sensitive to methodological choices for the estimation of evaporative demand, PLoS One, 12, e0174045, https://doi.org/10.1371/journal.pone.0174045, 2017.
Dong, C., MacDonald, G. M., Willis, K., Gillespie, T. W., Okin, G. S., and Williams, A. P.: Vegetation Responses to 2012–2016 Drought in Northern and Southern California, Geophys. Res. Lett., 46, 3810–3821, https://doi.org/10.1029/2019GL082137, 2019.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003.
Ershadi, A., McCabe, M. F., Evans, J. P., and Wood, E. F.: Impact of model structure and parameterization on Penman–Monteith type evaporation models, J. Hydrol., 525, 521–535, https://doi.org/10.1016/j.jhydrol.2015.04.008, 2015.
European Space Agency (ESA): CCI Soil Moisture project, https://www.esa-soilmoisture-cci.org/node/145, last access: 1 January 2019.
Federer, C. A., Vörösmarty, C., and Fekete, B.: Intercomparison of methods for calculating potential evaporation in regional and global water balance models, Water Resour. Res., 32, 2315–2321, https://doi.org/10.1029/96WR00801, 1996.
Feng, S., Trnka, M., Hayes, M., and Zhang, Y.: Why Do Different Drought Indices Show Distinct Future Drought Risk Outcomes in the U.S. Great Plains?, J. Climate, 30, 265–278, https://doi.org/10.1175/JCLI-D-15-0590.1, 2017.
Forzieri, G., Miralles, D. G., Ciais, P., Alkama, R., Ryu, Y., Duveiller, G., Zhang, K., Robertson, E., Kautz, M., Martens, B., Jiang, C., Arneth, A., Georgievski, G., Li, W., Ceccherini, G., Anthoni, P., Lawrence, P., Wiltshire, A., Pongratz, J., Piao, S., Sitch, S., Goll, D. S., Arora, V. K., Lienert, S., Lombardozzi, D., Kato, E., Nabel, J. E. M. S., Tian, H., Friedlingstein, P., and Cescatti, A.: Increased control of vegetation on global terrestrial energy fluxes, Nat. Clim. Change, 10, 356–362, https://doi.org/10.1038/s41558-020-0717-0, 2020.
Gao, G., Feng, Q., Liu, X., and Zhao, Y.: Measuring and modeling evapotranspiration of a Populus euphratica forest in northwestern China, J. Forest Res., 32, 1963–1977, https://doi.org/10.1007/s11676-020-01228-1, 2021.
Gruber, A., Dorigo, W. A., Crow, W., and Wagner, W.: Triple collocation-based merging of satellite soil moisture retrievals, IEEE T. Geosci. Remote, 55, 6780–6792, https://doi.org/10.1109/TGRS.2017.2734070, 2017.
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019.
Heim, R. R.: A Review of Twentieth-Century Drought Indices Used in the United States, B. Am. Meteor. Soc., 83, 1149–1166, https://doi.org/10.1175/1520-0477-83.8.1149, 2002.
Hobbins, M. T., Wood, A., Streubel, D., and Werner, K.: What Drives the Variability of Evaporative Demand across the Conterminous United States?, J. Hydrometeorol., 13, 1195–1214, https://doi.org/10.1175/JHM-D-11-0101.1, 2012.
Hoerling, M., Eischeid, J., Kumar, A., Leung, R., Mariotti, A., Mo, K., Schubert, S., and Seager, R.: Causes and Predictability of the 2012 Great Plains Drought, B. Am. Meteor. Soc., 95, 269–282, https://doi.org/10.1175/BAMS-D-13-00055.1, 2014.
Katul, G. G., Oren, R., Manzoni, S., Higgins, C., and Parlange, M. B.: Evapotranspiration: A process driving mass transport and energy exchange in the soil-plant-atmosphere-climate system, Rev. Geophys., 50, RG3002, https://doi.org/10.1029/2011rg000366, 2012.
Kelliher, F. M., Leuning, R., Raupach, M. R., and Schulze, E.-D.: Maximum conductances for evaporation from global vegetation types, Agr. Forest Meteorol., 73, 1–16, https://doi.org/10.1016/0168-1923(94)02178-m, 1995.
Kogan, F. N.: Droughts of the Late 1980s in the United States as Derived from NOAA Polar-Orbiting Satellite Data, B. Am. Meteor. Soc., 76, 655–668, https://doi.org/10.1175/1520-0477(1995)076<0655:dotlit>2.0.co;2, 1995.
Kustas, W. P., Choudhury, B. J., Moran, M. S., Reginato, R. J., Jackson, R. D., Gay, L. W., and Weaver, H. L.: Determination of sensible heat flux over sparse canopy using thermal infrared data, Agr. Forest Meteorol., 44, 197–216, https://doi.org/10.1016/0168-1923(89)90017-8, 1989.
Lang, N., Jetz, W., Schindler, K., and Wegner, J. D.: A high-resolution canopy height model of the Earth, Nat. Ecol. Evol., 7, 1778–1789, 2023.
Leuning, R., Zhang, Y. Q., Rajaud, A., Cleugh, H., and Tu, K.: A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation, Water Resour. Res., 44, 1872, https://doi.org/10.1029/2007WR006562, 2008.
Lhomme, J.-P., Troufleau, D., Monteny, B., Chehbouni, A., and Bauduin, S.: Sensible heat flux and radiometric surface temperature over sparse Sahelian vegetation II. A model for the kB-1 parameter, J. Hydrol., 188-189, 839–854, https://doi.org/10.1016/s0022-1694(96)03173-3, 1997.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res.-Atmos., 99, 14415–14428, https://doi.org/10.1029/94JD00483, 1994.
Liu, Q., Wang, L., Qu, Y., Liu, N., Liu, S., Tang, H., and Liang, S.: Preliminary evaluation of the long-term GLASS albedo product, Int. J. Digit. Earth, 6, 69–95, https://doi.org/10.1080/17538947.2013.804601, 2013.
Liu, Y. Y., Dorigo, W. A., Parinussa, R. M., de Jeu, R. A. M., Wagner, W., McCabe, M. F., and van Dijk, A. I. J. M.: Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sens. Environ., 123, 280–297, https://doi.org/10.1016/j.rse.2012.03.014, 2012.
Loon, A. F. V.: Hydrological drought explained, Wiley Interdiscip. Rev. Water, 2, 359–392, https://doi.org/10.1002/wat2.1085, 2015.
Lorenz, R., Davin, E. L., Lawrence, D. M., Stöckli, R., and Seneviratne, S. I.: How important is vegetation phenology for European climate and heat waves?, J. Climate, 26, 10077–10100, https://doi.org/10.1175/JCLI-D-13-00040.1, 2013.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Max-Planck-Institute for Meteorology: Climate Data Operators (CDO), https://code.zmaw.de/projects/cdo, last access: 1 January 2019.
McDowell, N., Allen, C. D., Anderson-Teixeira, K., Brando, P., Brienen, R., Chambers, J., Christoffersen, B., Davies, S., Doughty, C., Duque, A., Espirito-Santo, F., Fisher, R., Fontes, C. G., Galbraith, D., Goodsman, D., Grossiord, C., Hartmann, H., Holm, J., Johnson, D. J., Kassim, A. R., Keller, M., Koven, C., Kueppers, L., Kumagai, T., Malhi, Y., McMahon, S. M., Mencuccini, M., Meir, P., Moorcroft, P., Muller-Landau, H. C., Phillips, O. L., Powell, T., Sierra, C. A., Sperry, J., Warren, J., Xu, C., and Xu, X.: Drivers and mechanisms of tree mortality in moist tropical forests, New Phytol., 219, 851–869, https://doi.org/10.1111/nph.15027, 2018.
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, 17, 179–183, American Meteorological Society, Boston, MA, https://climate.colostate.edu/pdfs/relationshipofdroughtfrequency.pdf (last access: 23 September 2024), 1993.
Monteith, J. L.: Evaporation and environment, Symp. Soc. Exp. Biol., 19, 205–234, 1965.
Monteith, J. and Unsworth, M.: Micrometeorology, in: Principles of environmental physics: plants, animals, and the atmosphere, Academic Press, 301–310, ISBN 978-0-12-386910-4, 2013.
Moran, M. S., Rahman, A. F., Washburne, J. C., Goodrich, D. C., Weltz, M. A., and Kustas, W. P.: Combining the Penman-Monteith equation with measurements of surface temperature and reflectance to estimate evaporation rates of semiarid grassland, Agr. Forest Meteorol., 80, 87–109, https://doi.org/10.1016/0168-1923(95)02292-9, 1996.
Nadeau, D. F., Brutsaert, W., Parlange, M. B., Bou-Zeid, E., Barrenetxea, G., Couach, O., and Vetterli, M.: Estimation of urban sensible heat flux using a dense wireless network of observations, Environ. Fluid Mech., 9, 635–653, https://doi.org/10.1007/s10652-009-9150-7, 2009.
Or, D. and Lehmann, P.: Surface Evaporative Capacitance: How Soil Type and Rainfall Characteristics Affect Global-Scale Surface Evaporation, Water Resour. Res., 55, 519–539, https://doi.org/10.1029/2018wr024050, 2019.
Palmer, W. C.: Meteorological drought, U.S. Weather Bureau, Washington, DC, Res. Pap. No. 45, 58 pp., https://www.ncei.noaa.gov/monitoring-content/temp-and-precip/drought/docs/palmer.pdf (last access: 23 September 2024), 1965.
Paulo, A. A., Rosa, R. D., and Pereira, L. S.: Climate trends and behaviour of drought indices based on precipitation and evapotranspiration in Portugal, Nat. Hazards Earth Syst. Sci., 12, 1481–1491, https://doi.org/10.5194/nhess-12-1481-2012, 2012.
Pederson, N., Bell, A. R., Knight, T. A., Leland, C., Malcomb, N., Anchukaitis, K. J., and Riddle, J.: A long-term perspective on a modern drought in the American Southeast, Environ. Res. Lett., 7, 014034, https://doi.org/10.1088/1748-9326/7/1/014034, 2012.
Peng, L.: spei-pet-evaluation, GitHub [code], https://github.com/pitcheverlasting/spei-pet-evaluation/, last access: 23 September 2024.
Peng, L., Li, D., and Sheffield, J.: Drivers of Variability in Atmospheric Evaporative Demand: Multiscale Spectral Analysis Based on Observations and Physically Based Modeling, Water Resour. Res., 54, 3510–3529, https://doi.org/10.1029/2017WR022104, 2018.
Peng, L., Zeng, Z., Wei, Z., Chen, A., Wood, E. F., and Sheffield, J.: Determinants of the ratio of actual to potential evapotranspiration, Glob. Change Biol., 25, 1326–1343, https://doi.org/10.1111/gcb.14577, 2019.
Peng, L., Sheffield, J., Wei, Z., Ek, M., and Wood, E. F.: An enhanced Standardized Precipitation-Evapotranspiration Index (SPEI) drought-monitoring method integrating land surface characteristics, figshare [data set], https://doi.org/10.6084/m9.figshare.12132696, 2024.
Penman, H. L.: Natural evaporation from open water, bare soil and grass, P. R. Soc. Lond. A, 193, 120–145, 1948.
Peters, M. P., Iverson, L. R., and Matthews, S. N.: Spatio-temporal trends of drought by forest type in the conterminous United States, 1960–2013, U.S. Department of Agriculture Forest Service, Northern Research Station, https://doi.org/10.2737/nrs-rmap-7, 2014.
Piao, S., Zhang, X., Chen, A., Liu, Q., Lian, X., Wang, X., Peng, S., and Wu, X.: The impacts of climate extremes on the terrestrial carbon cycle: A review, Sci. China Earth Sci., 62, 1551–1563, https://doi.org/10.1007/s11430-018-9363-5, 2019.
Pimentel, R., Arheimer, B., Crochemore, L., Andersson, J. C. M., Pechlivanidis, I. G., and Gustafsson, D.: Which potential evapotranspiration formula to use in hydrological modeling world-wide?, Water Resour. Res., 59, e2022WR033447, https://doi.org/10.1029/2022WR033447, 2023.
Potop, V.: Evolution of drought severity and its impact on corn in the Republic of Moldova, Theor. Appl. Climatol., 105, 469–483, https://doi.org/10.1007/s00704-011-0403-2, 2011.
Potop, V., Možný, M., and Soukup, J.: Drought evolution at various time scales in the lowland regions and their impact on vegetable crops in the Czech Republic, Agr. Forest Meteorol., 156, 121–133, https://doi.org/10.1016/j.agrformet.2012.01.002, 2012.
Priestley, C. H. B. and Taylor, R. J.: On the assessment of surface heat flux and evaporation using large-scale parameters, Mon. Weather Rev., 100, 81–92, 1972.
PRISM Climate Group: Parameter-elevation Regressions on Independent Slopes Model (PRISM) Datasets, Oregon State University, https://www.prism.oregonstate.edu/downloads/ (last access: 1 January 2019), 2014.
Qu, Y., Liu, Q., Liang, S., Wang, L., Liu, N., and Liu, S.: Direct-Estimation Algorithm for Mapping Daily Land-Surface Broadband Albedo From MODIS Data, IEEE T. Geosci. Remote, 52, 907–919, https://doi.org/10.1109/tgrs.2013.2245670, 2014.
Rigden, A., Li, D., and Salvucci, G.: Dependence of thermal roughness length on friction velocity across land cover types: A synthesis analysis using AmeriFlux data, Agr. Forest Meteorol., 249, 512–519, https://doi.org/10.1016/j.agrformet.2017.06.003, 2018.
Ross, T. and Lott, N.: A climatology of 1980-2003 extreme weather and climate events, National Climatic Data Center Technical Report, 1, 14, https://www.ncdc.noaa.gov/monitoring-content/billions/docs/lott-and-ross-2003.pdf (last access: 23 September 2024), 2003.
Seager, R., Tzanova, A., and Nakamura, J.: Drought in the Southeastern United States: Causes Variability over the Last Millennium, and the Potential for Future Hydroclimate Change, J. Climate, 22, 5021–5045, https://doi.org/10.1175/2009jcli2683.1, 2009.
Sellers, P. J., Randall, D. A., Collatz, G. J., Berry, J. A., Field, C. B., Dazlich, D. A., Zhang, C., Collelo, G. D., and Bounoua, L.: A revised land surface parameterization (SiB2) for atmospheric GCMs.1. Model formulation, J. Climate, 9, 676–705, https://doi.org/10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2, 1996.
Seneviratne, S. I.: Historical drought trends revisited, Nature, 491, 338–339, https://doi.org/10.1038/491338a, 2012.
Sheffield, J. and Wood, E. F.: Characteristics of global and regional drought, 1950–2000: Analysis of soil moisture data from off-line simulation of the terrestrial hydrologic cycle, J. Geophys. Res.-Atmos., 112, D17115, https://doi.org/10.1029/2006JD008288, 2007.
Sheffield, J., Wood, E. F., and Roderick, M. L.: Little change in global drought over the past 60 years, Nature, 491, 435–438, https://doi.org/10.1038/nature11575, 2012.
Shuttleworth, W. J.: Chapter 4 Evaporation, in: Handbook of hydrology, edited by: Maidment, D. R., McGraw-Hill, Sydney, 9780070, ISBN-10 0070397325, 1993.
Shuttleworth, W. J. and Wallace, J. S.: Evaporation from sparse crops-an energy combination theory, Q. J. Roy. Meteor. Soc., 111, 839–855, 1985.
Shuttleworth, W. J. and Gurney, R. J.: The theoretical relationship between foliage temperature and canopy resistance in sparse crops, Q. J. Roy. Meteor. Soc., 116, 497–519, 1990.
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy height globally with spaceborne lidar, J. Geophys. Res.-Biogeo., 116, G4, https://doi.org/10.1029/2011JG001708, 2011.
Spanish National Research Council: SPEI: Calculation of the Standardized Precipitation-Evapotranspiration Index, https://cran.r-project.org/web/packages/SPEI/, last access: 1 January 2019.
Stewart, J. B., Kustas, W. P., Humes, K. S., Nichols, W. D., Moran, M. S., and de Bruin, H. A. R.: Sensible Heat Flux-Radiometric Surface Temperature Relationship for Eight Semiarid Areas, J. Appl. Meteorol., 33, 1110–1117, https://doi.org/10.1175/1520-0450(1994)033<1110:shfrst>2.0.co;2, 1994.
Sun, S., Bi, Z., Xiao, J., Liu, Y., Sun, G., Ju, W., Liu, C., Mu, M., Li, J., Zhou, Y., Li, X., Liu, Y., and Chen, H.: A global 5 km monthly potential evapotranspiration dataset (1982–2015) estimated by the Shuttleworth–Wallace model, Earth Syst. Sci. Data, 15, 4849–4876, https://doi.org/10.5194/essd-15-4849-2023, 2023.
Thornthwaite, C. W.: An Approach toward a Rational Classification of Climate, Geogr. Rev., 38, 55–94, 1948.
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, 2013.
Troufleau, D., Lhomme, J. P., Monteny, B., and Vidal, A.: Sensible heat flux and radiometric surface temperature over sparse Sahelian vegetation. I. An experimental analysis of the kB-1 parameter, J. Hydrol., 188–189, 815–838, https://doi.org/10.1016/s0022-1694(96)03172-1, 1997.
University of Maryland: Global LAnd Surface Satellite (GLASS) Albedo Product, http://www.glass.umd.edu/Albedo/MODIS/0.05D, last access: 1 January 2019.
Verhoef, A., De Bruin, H. A. R., and Van Den Hurk, B. J. J. M.: Some Practical Notes on the Parameter kB-1 for Sparse Vegetation, J. Appl. Meteorol., 36, 560–572, https://doi.org/10.1175/1520-0450(1997)036<0560:spnotp>2.0.co;2, 1997.
Verma, S. B.: Aerodynamic resistances to transfers of heat, mass and momentum, in: Estimation of Areal Evapotranspiration, edited by: Black, T. A., Spittlehouse, D. L., Novak, M. D., and Price, D. T., IAHS Press, 13–20, https://digitalcommons.unl.edu/natrespapers/1211 (last access: 23 September 2024), 1989.
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, J. Climate, 23, 1696–1718, https://doi.org/10.1175/2009jcli2909.1, 2010.
Vicente-Serrano, S. M., Beguería, S., Lorenzo-Lacruz, J., Camarero, J. J., López-Moreno, J. I., Azorin-Molina, C., Revuelto, J., Morán-Tejeda, E., and Sanchez-Lorenzo, A.: Performance of Drought Indices for Ecological Agricultural, and Hydrological Applications, Earth Interact., 16, 1–27, https://doi.org/10.1175/2012ei000434.1, 2012.
Vicente-Serrano, S. M., Gouveia, C., Camarero, J. J., Beguería, S., Trigo, R., López-Moreno, J. I., Azorín-Molina, C., Pasho, E., Lorenzo-Lacruz, J., Revuelto, J., and Morán-Tejeda, E.: 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.
Vicente-Serrano, S. M., van der Schrier, G., Beguería, S., Azorin-Molina, C., and Lopez-Moreno, J.-I.: Contribution of precipitation and reference evapotranspiration to drought indices under different climates, J. Hydrol., 526, 42–54, https://doi.org/10.1016/j.jhydrol.2014.11.025, 2015.
Wei, Z., Yoshimura, K., Wang, L., G Miralles, D., Jasechko, S., and Lee, X.: Revisiting the contribution of transpiration to global terrestrial evapotranspiration, Geophys. Res. Lett., 44, 2792–2801, https://doi.org/10.1002/2016GL072235, 2017.
Wilhite, D. A.: Drought as a natural hazard: Concepts and definitions, in: Drought: A Global Assessment, Routledge, London, 3–18, ISBN 9780415168335, 2000.
Wilhite, D. A., Sivakumar, M. V. K., and Pulwarty, R.: Managing drought risk in a changing climate: The role of national drought policy, Weather Clim. Extrem., 3, 4–13, https://doi.org/10.1016/j.wace.2014.01.002, 2014.
Williams, A. P., Allen, C. D., Macalady, A. K., Griffin, D., Woodhouse, C. A., Meko, D. M., Swetnam, T. W., Rauscher, S. A., Seager, R., Grissino-Mayer, H. D., Dean, J. S., Cook, E. R., Gangodagamage, C., Cai, M., and McDowell, N. G.: Temperature as a potent driver of regional forest drought stress and tree mortality, Nat. Clim. Change, 3, 292–297, https://doi.org/10.1038/nclimate1693, 2012.
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V., Duan, Q., Mo, K., Fan, Y., and Mocko, D.: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products, J. Geophys. Res.-Atmos., 117, D03109, https://doi.org/10.1029/2011jd016048, 2012.
Xu, H., Lian, X., Slette, I. J., Yang, H., Zhang, Y., Chen, A., and Piao, S.: Rising ecosystem water demand exacerbates the lengthening of tropical dry seasons, Nat. Commun., 13, 4093, https://doi.org/10.1038/s41467-022-31826-y, 2022.
Yan, H. A., Wang, S. Q., Billesbach, D., Oechel, W., Zhang, J. H., Meyers, T., Martin, T. A., Matamala, R., Baldocchi, D., Bohrer, G., Dragoni, D., and Scott, R.: Global estimation of evapotranspiration using a leaf area index-based surface energy and water balance model, Remote Sens. Environ., 124, 581–595, https://doi.org/10.1016/j.rse.2012.06.004, 2012.
Yang, H., Munson, S. M., Huntingford, C., Carvalhais, N., Knapp, A. K., Li, X., Peñuelas, J., Zscheischler, J., and Chen, A.: The detection and attribution of extreme reductions in vegetation growth across the global land surface, Glob. Change Biol., 29, 2351–2362, https://doi.org/10.1111/gcb.16595, 2023.
Yang, K., Koike, T., Ishikawa, H., Kim, J., Li, X., Liu, H., Liu, S., Ma, Y., and Wang, J.: Turbulent Flux Transfer over Bare-Soil Surfaces: Characteristics and Parameterization, J. Appl. Meteorol. Clim., 47, 276–290, https://doi.org/10.1175/2007jamc1547.1, 2008.
Yang, R. and Friedl, M. A.: Determination of Roughness Lengths for Heat and Momentum Over Boreal Forests, Bound.-Lay. Meteorol., 107, 581–603, https://doi.org/10.1023/a:1022880530523, 2003.
Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R., and Donohue, R. J.: Hydrologic implications of vegetation response to elevated CO2 in climate projections, Nat. Clim. Change, 9, 44–48, https://doi.org/10.1038/s41558-018-0361-0, 2019.
Zhang, L., Jiao, W., Zhang, H., Huang, C., and Tong, Q.: Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices, Remote Sens. Environ., 190, 96–106, https://doi.org/10.1016/j.rse.2016.12.010, 2017.
Zhao, H., Gao, G., An, W., Zou, X., Li, H., and Hou, M.: Timescale differences between SC-PDSI and SPEI for drought monitoring in China, Phys. Chem. Earth, 102, 48–58, https://doi.org/10.1016/j.pce.2015.10.022, 2017.
Zhou, M. C., Ishidaira, H., Hapuarachchi, H. P., Magome, J., Kiem, A. S., and Takeuchi, K.: Estimating potential evapotranspiration using Shuttleworth–Wallace model and NOAA-AVHRR NDVI data to feed a distributed hydrological model over the Mekong River basin, J. Hydrol., 327, 151–173, https://doi.org/10.1016/j.jhydrol.2005.11.013, 2006.
Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., Samanta, A., Piao, S., Nemani, R. R., and Myneni, R. B.: Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011, Remote Sens., 5, 927–948, https://doi.org/10.3390/rs5020927, 2013 (data available at: https://drive.google.com/open?id=0BwL88nwumpqYaFJmR2poS0d1ZDQ, last access: 1 January 2019).
Zilitinkevich, S. S., Grachev, A. A., and Fairall, C. W.: Scaling Reasoning and Field Data on the Sea Surface Roughness Lengths for Scalars, J. Atmos. Sci., 58, 320–325, https://doi.org/10.1175/1520-0469(2001)058<0320:nacraf>2.0.co;2, 2001.
Short summary
Integrating evaporative demand into drought indicators is effective, but the choice of method and the effectiveness of surface features remain undocumented. We evaluate various methods and surface features for predicting soil moisture dynamics. Using minimal ancillary information alongside meteorological and vegetation data, we develop a simple land-cover-based method that improves soil moisture drought predictions, especially in forests, showing promise for better real-time drought forecasting.
Integrating evaporative demand into drought indicators is effective, but the choice of method...
Altmetrics
Final-revised paper
Preprint