Articles | Volume 17, issue 18
https://doi.org/10.5194/gmd-17-6929-2024
© 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-17-6929-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
18 Sep 2024
Model description paper |
| 18 Sep 2024
| 18 Sep 2024
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong SAR, China
Zhao Yang
Pacific Northwest National Laboratory, Richland, WA, USA
Min-Hui Lo
Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
Jina Hur
National Institute of Agricultural Sciences, Rural Development Administration, Wanju-gun, Jeollabuk-do, South Korea
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong SAR, China
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
Related authors
No articles found.
Sheng-Lun Tai, Zhao Yang, Brian Gaudet, Koichi Sakaguchi, Larry Berg, Colleen Kaul, Yun Qian, Ye Liu, and Jerome Fast
Earth Syst. Sci. Data, 17, 4587–4611, https://doi.org/10.5194/essd-17-4587-2025, https://doi.org/10.5194/essd-17-4587-2025, 2025
Short summary
Short summary
Our study created a high-resolution soil moisture dataset for the eastern US by integrating satellite data with a land surface model and advanced algorithms, achieving 1 km scale analyses. Validated against multiple in situ networks and analysis datasets, it demonstrated superior accuracy. This dataset is vital for understanding soil moisture dynamics, especially during droughts, and highlights the need to mitigate soil-type-dependent biases in the model.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Ye Liu, Yun Qian, Larry K. Berg, Zhe Feng, Jianfeng Li, Jingyi Chen, and Zhao Yang
Atmos. Chem. Phys., 24, 8165–8181, https://doi.org/10.5194/acp-24-8165-2024, https://doi.org/10.5194/acp-24-8165-2024, 2024
Short summary
Short summary
Deep convection under various large-scale meteorological patterns (LSMPs) shows distinct precipitation features. In southeastern Texas, mesoscale convective systems (MCSs) contribute significantly to precipitation year-round, while isolated deep convection (IDC) is prominent in summer and fall. Self-organizing maps (SOMs) reveal convection can occur without large-scale lifting or moisture convergence. MCSs and IDC events have distinct life cycles influenced by specific LSMPs.
Liying Qiu, Eun-Soon Im, Seung-Ki Min, Yeon-Hee Kim, Dong-Hyun Cha, Seok-Woo Shin, Joong-Bae Ahn, Eun-Chul Chang, and Young-Hwa Byun
Earth Syst. Dynam., 14, 507–517, https://doi.org/10.5194/esd-14-507-2023, https://doi.org/10.5194/esd-14-507-2023, 2023
Short summary
Short summary
This study evaluates four bias correction methods (three univariate and one multivariate) for correcting multivariate heat-stress indices. We show that the multivariate method can benefit the indirect correction that first adjusts individual components before index calculation, and its advantage is more evident for indices relying equally on multiple drivers. Meanwhile, the direct correction of heat-stress indices by the univariate quantile delta mapping approach also has comparable performance.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
Short summary
Short summary
Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Lena R. Boysen, Victor Brovkin, Julia Pongratz, David M. Lawrence, Peter Lawrence, Nicolas Vuichard, Philippe Peylin, Spencer Liddicoat, Tomohiro Hajima, Yanwu Zhang, Matthias Rocher, Christine Delire, Roland Séférian, Vivek K. Arora, Lars Nieradzik, Peter Anthoni, Wim Thiery, Marysa M. Laguë, Deborah Lawrence, and Min-Hui Lo
Biogeosciences, 17, 5615–5638, https://doi.org/10.5194/bg-17-5615-2020, https://doi.org/10.5194/bg-17-5615-2020, 2020
Short summary
Short summary
We find a biogeophysically induced global cooling with strong carbon losses in a 20 million square kilometre idealized deforestation experiment performed by nine CMIP6 Earth system models. It takes many decades for the temperature signal to emerge, with non-local effects playing an important role. Despite a consistent experimental setup, models diverge substantially in their climate responses. This study offers unprecedented insights for understanding land use change effects in CMIP6 models.
Cited articles
Ahmed, K. F., Wang, G., Yu, M., Koo, J., and You, L.: Potential impact of climate change on cereal crop yield in West Africa, Climatic Change, 133, 321–334, https://doi.org/10.1007/s10584-015-1462-7, 2015.
Amanullah: Specific Leaf Area and Specific Leaf Weight in Small Grain Crops Wheat, Rye, Barley, and Oats Differ at Various Growth Stages and NPK Source, J. Plant Nutr., 38, 1694–1708, https://doi.org/10.1080/01904167.2015.1017051, 2015.
An, L., Wang, J., Huang, J., Pokhrel, Y., Hugonnet, R., Wada, Y., Cáceres, D., Müller Schmied, H., Song, C., Berthier, E., Yu, H., and Zhang, G.: Divergent Causes of Terrestrial Water Storage Decline Between Drylands and Humid Regions Globally, Geophys. Res. Lett., 48, e2021GL095035, https://doi.org/10.1029/2021GL095035, 2021.
Asmus, C., Hoffmann, P., Pietikäinen, J.-P., Böhner, J., and Rechid, D.: Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization, Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, 2023.
Bou-Zeid, E., Parlange, M. B., and Meneveau, C.: On the Parameterization of Surface Roughness at Regional Scales, J. Atmos. Sci., 64, 216–227, https://doi.org/10.1175/JAS3826.1, 2007.
Chen, F. and Xie, Z.: Effects of crop growth and development on land surface fluxes, Adv. Atmos. Sci., 28, 927–944, https://doi.org/10.1007/s00376-010-0105-1, 2011.
Choi, Y., Gim, H., Ho, C., Jeong, S., Park, S. K., and Hayes, M. J.: Climatic influence on corn sowing date in the Midwestern United States, Int. J. Climatol., 37, 1595–1602, https://doi.org/10.1002/joc.4799, 2017.
Cook, B. I., Puma, M. J., and Krakauer, N. Y.: Irrigation induced surface cooling in the context of modern and increased greenhouse gas forcing, Clim. Dynam., 37, 1587–1600, https://doi.org/10.1007/s00382-010-0932-x, 2011.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., Van De Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., De Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
DeJonge, K. C., Ascough, J. C., Andales, A. A., Hansen, N. C., Garcia, L. A., and Arabi, M.: Improving evapotranspiration simulations in the CERES-Maize model under limited irrigation, Agric. Water Manage., 115, 92–103, https://doi.org/10.1016/j.agwat.2012.08.013, 2012.
Dudhia, J.: Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model, J. Atmos. Sci., 46, 3077–3107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2, 1989.
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, 2002JD003296, https://doi.org/10.1029/2002JD003296, 2003.
Famiglietti, J. S.: The global groundwater crisis, Nat. Clim. Change, 4, 945–948, https://doi.org/10.1038/nclimate2425, 2014.
Fan, Y.: Source code for double-cropping and interactive irrigation using WRF4.5, Zenodo [code], https://doi.org/10.5281/zenodo.10729554, 2024.
Fan, Y., Im, E.-S., Lan, C.-W., and Lo, M.-H.: An increase in precipitation driven by irrigation over the North China Plain based on RegCM and WRF simulations, J. Hydrometeorol., 24, 1155–1173, https://doi.org/10.1175/JHM-D-22-0131.1, 2023.
Fang, J., Piao, S., Tang, Z., Peng, C., and Ji, W.: Interannual Variability in Net Primary Production and Precipitation, Science, 293, 1723–1723, https://doi.org/10.1126/science.293.5536.1723a, 2001.
FAO: Food and Agriculture Organization Statistic Data, https://www.fao.org/faostat/en/#compare (last access: 24 December 2021), 2019.
Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., Mueller, N. D., O'Connell, C., Ray, D. K., West, P. C., Balzer, C., Bennett, E. M., Carpenter, S. R., Hill, J., Monfreda, C., Polasky, S., Rockström, J., Sheehan, J., Siebert, S., Tilman, D., and Zaks, D. P. M.: Solutions for a cultivated planet, Nature, 478, 337–342, https://doi.org/10.1038/nature10452, 2011.
Frolking, S., Wisser, D., Grogan, D., Proussevitch, A., and Glidden, S.: GAEZ+_2015 Crop Yield, V2, Harvard Dataverse [data set], https://doi.org/10.7910/DVN/XGGJAV, 2020.
Goldewijk, K. K.: Estimating global land use change over the past 300 years: The HYDE Database, Global Biogeochem. Cy., 15, 417–433, https://doi.org/10.1029/1999GB001232, 2001.
Grogan, D., Frolking, S., Wisser, D., Prusevich, A., and Glidden, S.: Global gridded crop harvested area, production, yield, and monthly physical area data circa 2015, Sci. Data, 9, 15, https://doi.org/10.1038/s41597-021-01115-2, 2022.
Han, J. and Pan, H.-L.: Revision of Convection and Vertical Diffusion Schemes in the NCEP Global Forecast System, Weather Forecast., 26, 520–533, https://doi.org/10.1175/WAF-D-10-05038.1, 2011.
Harding, K. J., Twine, T. E., and Lu, Y.: Effects of Dynamic Crop Growth on the Simulated Precipitation Response to Irrigation, Earth Interact., 19, 1–31, https://doi.org/10.1175/EI-D-15-0030.1, 2015.
He, C., Valayamkunnath, P., Barlage, M., Chen, F., Gochis, D., Cabell, R., Schneider, T., Rasmussen, R., Niu, G., and Yang, Z.: The Community Noah-MP Land Surface Modeling System Technical Description Version 5.0, NCAR Technical Note NCAR/TN-575+ STR, https://doi.org/10.5065/ew8g-yr95, 2023.
Hong, S., Park, S. K., and Yu, X.: Scheme-Based Optimization of Land Surface Model Using a Micro-Genetic Algorithm: Assessment of Its Performance and Usability for Regional Applications, SOLA, 11, 129–133, https://doi.org/10.2151/sola.2015-030, 2015.
Hong, S.-Y., Dudhia, J., and Chen, S.-H.: A Revised Approach to Ice Microphysical Processes for the Bulk Parameterization of Clouds and Precipitation, Mon. Weather Rev., 132, 103–120, https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2, 2004.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006.
Huang, H., Huang, J., Li, X., Zhuo, W., Wu, Y., Niu, Q., Su, W., and Yuan, W.: A dataset of winter wheat aboveground biomass in China during 2007–2015 based on data assimilation, Sci. Data, 9, 200, https://doi.org/10.1038/s41597-022-01305-6, 2022.
Huang, Y., Huang, X., Xie, M., Cheng, W., and Shu, Q.: A study on the effects of regional differences on agricultural water resource utilization efficiency using super-efficiency SBM model, Sci. Rep., 11, 9953, https://doi.org/10.1038/s41598-021-89293-2, 2021.
Im, E.-S., Marcella, M. P., and Eltahir, E. A. B.: Impact of Potential Large-Scale Irrigation on the West African Monsoon and Its Dependence on Location of Irrigated Area, J. Climate, 27, 994–1009, https://doi.org/10.1175/JCLI-D-13-00290.1, 2014.
Jeong, S.-J., Ho, C.-H., Piao, S., Kim, J., Ciais, P., Lee, Y.-B., Jhun, J.-G., and Park, S. K.: Effects of double cropping on summer climate of the North China Plain and neighbouring regions, Nat. Clim. Change, 4, 615–619, https://doi.org/10.1038/nclimate2266, 2014.
Jiang, Y., Yin, X., Wang, X., Zhang, L., Lu, Z., Lei, Y., Chu, Q., and Chen, F.: Impacts of global warming on the cropping systems of China under technical improvements from 1961 to 2016, Agron. J., 113, 187–199, https://doi.org/10.1002/agj2.20497, 2021.
Kang, S. and Eltahir, E. A. B.: North China Plain threatened by deadly heatwaves due to climate change and irrigation, Nat. Commun., 9, 2894, https://doi.org/10.1038/s41467-018-05252-y, 2018.
Kang, S. and Eltahir, E. A. B.: Impact of Irrigation on Regional Climate Over Eastern China, Geophys. Res. Lett., 46, 5499–5505, https://doi.org/10.1029/2019GL082396, 2019.
Koch, J., Zhang, W., Martinsen, G., He, X., and Stisen, S.: Estimating Net Irrigation Across the North China Plain Through Dual Modeling of Evapotranspiration, Water Resour. Res., 56, e2020WR027413, https://doi.org/10.1029/2020WR027413, 2020.
Kwon, Y. C. and Hong, S.-Y.: A Mass-Flux Cumulus Parameterization Scheme across Gray-Zone Resolutions, Mon. Weather Rev., 145, 583–598, https://doi.org/10.1175/MWR-D-16-0034.1, 2017.
Li, J. and Zeng, Q.: A unified monsoon index, Geophys. Res. Lett., 29, 115-1–115-4, https://doi.org/10.1029/2001GL013874, 2002 (data available at: http://lijianping.cn/dct/page/65577, last access: 3 September 2023).
Liu, J., Ding, Y., Zhou, X., and Li, Y.: A parameterization scheme for regional average runoff over heterogeneous land surface under climatic rainfall forcing, Acta Meteorol. Sin., 24, 116–122, 2010.
Liu, W., Wang, G., Yu, M., Chen, H., Jiang, Y., Yang, M., and Shi, Y.: Projecting the future vegetation–climate system over East Asia and its RCP-dependence, Clim. Dynam., 55, 2725–2742, https://doi.org/10.1007/s00382-020-05411-2, 2020.
Liu, X., Chen, F., Barlage, M., Zhou, G., and Niyogi, D.: Noah-MP-Crop: Introducing dynamic crop growth in the Noah-MP land surface model: Noah-MP-Crop, J. Geophys. Res.-Atmos., 121, 13953–13972, https://doi.org/10.1002/2016JD025597, 2016.
Livneh, B. and Lettenmaier, D. P.: Regional parameter estimation for the unified land model, Water Resour. Res., 49, 100–114, https://doi.org/10.1029/2012WR012220, 2013.
Lo, M.-H., Wey, H.-W., Im, E.-S., Tang, L. I., Anderson, R. G., Wu, R.-J., Chien, R.-Y., Wei, J., AghaKouchak, A., and Wada, Y.: Intense agricultural irrigation induced contrasting precipitation changes in Saudi Arabia, Environ. Res. Lett., 16, 064049, https://doi.org/10.1088/1748-9326/ac002e, 2021.
Lombardozzi, D. L., Lu, Y., Lawrence, P. J., Lawrence, D. M., Swenson, S., Oleson, K. W., Wieder, W. R., and Ainsworth, E. A.: Simulating Agriculture in the Community Land Model Version 5, J. Geophys. Res.-Biogeo., 125, e2019JG005529, https://doi.org/10.1029/2019JG005529, 2020.
Lu, Y., Jin, J., and Kueppers, L. M.: Crop growth and irrigation interact to influence surface fluxes in a regional climate-cropland model (WRF3.3-CLM4crop), Clim. Dynam., 45, 3347–3363, https://doi.org/10.1007/s00382-015-2543-z, 2015.
Luo, Y., Zhang, Z., Chen, Y., Li, Z., and Tao, F.: ChinaCropPhen1km: A high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on LAI products, Figshare [data set], https://doi.org/10.6084/m9.figshare.8313530.v5, 2019.
Luo, Y., Zhang, Z., Chen, Y., Li, Z., and Tao, F.: ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products, Earth Syst. Sci. Data, 12, 197–214, https://doi.org/10.5194/essd-12-197-2020, 2020.
McDermid, S., Nocco, M., Lawston-Parker, P., Keune, J., Pokhrel, Y., Jain, M., Jägermeyr, J., Brocca, L., Massari, C., Jones, A. D., Vahmani, P., Thiery, W., Yao, Y., Bell, A., Chen, L., Dorigo, W., Hanasaki, N., Jasechko, S., Lo, M.-H., Mahmood, R., Mishra, V., Mueller, N. D., Niyogi, D., Rabin, S. S., Sloat, L., Wada, Y., Zappa, L., Chen, F., Cook, B. I., Kim, H., Lombardozzi, D., Polcher, J., Ryu, D., Santanello, J., Satoh, Y., Seneviratne, S., Singh, D., and Yokohata, T.: Irrigation in the Earth system, Nat. Rev. Earth Environ., 4, 435–453, https://doi.org/10.1038/s43017-023-00438-5, 2023.
Menefee, D., Rajan, N., Cui, S., Bagavathiannan, M., Schnell, R., and West, J.: Simulation of dryland maize growth and evapotranspiration using DSSAT-CERES-Maize model, Agron. J., 113, 1317–1332, https://doi.org/10.1002/agj2.20524, 2021.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997.
National Bureau of Statistics of China: China Statistical Yearbook, Beijing, https://www.stats.gov.cn/sj/ndsj/2005/indexeh.htm (last access: 16 January 2024), 2005.
National Ecosystem Research Network of China and National Science and Technology Infrastructure of China: The crop dynamic of leaf mass index and biomass [data set], http://rs.cern.ac.cn/data/meta?id=16969, last access: 9 June 2023.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Oleson, K., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C., Levis, S., Li, F., Riley, W., Subin, Z., Swenson, S., Thornton, P., Bozbiyik, A., Fisher, R., Heald, C., Kluzek, E., Lamarque, J.-F., Lawrence, P., Leung, L., Lipscomb, W., Muszala, S., Ricciuto, D., Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L.: Technical description of version 4.5 of the Community Land Model (CLM), UCAR/NCAR, https://doi.org/10.5065/D6RR1W7M, 2013.
Ozdogan, M., Rodell, M., Beaudoing, H. K., and Toll, D. L.: Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data, J. Hydrometeorol., 11, 171–184, https://doi.org/10.1175/2009JHM1116.1, 2010.
Park, S. and Park, S. K.: A micro-genetic algorithm (GA v1.7.1a) for combinatorial optimization of physics parameterizations in the Weather Research and Forecasting model (v4.0.3) for quantitative precipitation forecast in Korea, Geosci. Model Dev., 14, 6241–6255, https://doi.org/10.5194/gmd-14-6241-2021, 2021.
Pei, L., Moore, N., Zhong, S., Kendall, A. D., Gao, Z., and Hyndman, D. W.: Effects of irrigation on summer precipitation over the United States, J. Climate, 29, 3541–3558, 2016.
Pielke, R. A., Adegoke, J. O., Chase, T. N., Marshall, C. H., Matsui, T., and Niyogi, D.: A new paradigm for assessing the role of agriculture in the climate system and in climate change, Agr. Forest Meteorol., 142, 234–254, https://doi.org/10.1016/j.agrformet.2006.06.012, 2007.
Pokhrel, Y., Hanasaki, N., Koirala, S., Cho, J., Yeh, P. J.-F., Kim, H., Kanae, S., and Oki, T.: Incorporating Anthropogenic Water Regulation Modules into a Land Surface Model, J. Hydrometeorol., 13, 255–269, https://doi.org/10.1175/JHM-D-11-013.1, 2012.
Porter, J. R. and Semenov, M. A.: Crop responses to climatic variation, Philos. T. R. Soc. B, 360, 2021–2035, https://doi.org/10.1098/rstb.2005.1752, 2005.
Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling: MONTHLY IRRIGATED AND RAINFED CROP AREAS, Global Biogeochem. Cy., 24, GB1011, https://doi.org/10.1029/2008GB003435, 2010.
Puma, M. J. and Cook, B. I.: Effects of irrigation on global climate during the 20th century, J. Geophys. Res., 115, D16120, https://doi.org/10.1029/2010JD014122, 2010.
Qian, Y., Huang, M., Yang, B., and Berg, L. K.: A Modeling Study of Irrigation Effects on Surface Fluxes and Land–Air–Cloud Interactions in the Southern Great Plains, J. Hydrometeorol., 14, 700–721, https://doi.org/10.1175/JHM-D-12-0134.1, 2013.
Qiu, B., Hu, X., Chen, C., Tang, Z., Yang, P., Zhu, X., Yan, C., and Jian, Z.: Maps of cropping patterns in China during 2015–2021, Figshare [data set], https://doi.org/10.6084/m9.figshare.14936052.v12, 2021.
Qiu, B., Hu, X., Chen, C., Tang, Z., Yang, P., Zhu, X., Yan, C., and Jian, Z.: Maps of cropping patterns in China during 2015–2021, Sci. Data, 9, 479, https://doi.org/10.1038/s41597-022-01589-8, 2022.
Ramankutty, N., Delire, C., and Snyder, P.: Feedbacks between agriculture and climate: An illustration of the potential unintended consequences of human land use activities, Global Planet. Change, 54, 79–93, https://doi.org/10.1016/j.gloplacha.2005.10.005, 2006.
Siebert, S., Burke, J., Faures, J. M., Frenken, K., Hoogeveen, J., Döll, P., and Portmann, F. T.: Groundwater use for irrigation – a global inventory, Hydrol. Earth Syst. Sci., 14, 1863–1880, https://doi.org/10.5194/hess-14-1863-2010, 2010.
Siebert, S., Henrich, V., Frenken, K., and Burke, J.: Global Map of Irrigation Areas version 5, MyGeoHub [data set], https://doi.org/10.13019/M20599, 2013.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A Description of the Advanced Research WRF Model Version 4, UCAR/NCAR, https://doi.org/10.5065/1DFH-6P97, 2019.
Song, L. and Jin, J.: Improving CERES-Maize for simulating maize growth and yield under water stress conditions, Eur. J. Agron., 117, 126072, https://doi.org/10.1016/j.eja.2020.126072, 2020.
Song, W., Tang, H., Sun, X., Xiang, Y., Ma, X., and Zhang, H.: Developing a New Parameterization Scheme of Temperature Lapse Rate for the Hydrological Simulation in a Glacierized Basin Based on Remote Sensing, Remote Sensing, 14, 4973, https://doi.org/10.3390/rs14194973, 2022.
Tuinenburg, O. A., Hutjes, R. W. A., Stacke, T., Wiltshire, A., and Lucas-Picher, P.: Effects of Irrigation in India on the Atmospheric Water Budget, J. Hydrometeorol., 15, 1028–1050, https://doi.org/10.1175/JHM-D-13-078.1, 2014.
Valayamkunnath, P., Chen, F., Barlage, M. J., Gochis, D. J., Franz, K. J., and Cosgrove, B. A.: Impact of Agriculture Management Practices on the National Water Model Simulated Streamflow, 101st American Meteorological Society Annual Meeting, online, https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/383317 (last access: 6 June 2023), 2021.
Vira, J., Hess, P., Melkonian, J., and Wieder, W. R.: An improved mechanistic model for ammonia volatilization in Earth system models: Flow of Agricultural Nitrogen version 2 (FANv2), Geosci. Model Dev., 13, 4459–4490, https://doi.org/10.5194/gmd-13-4459-2020, 2020.
Wang, D., Wang, G., and Anagnostou, E. N.: Evaluation of canopy interception schemes in land surface models, J. Hydrol., 347, 308–318, https://doi.org/10.1016/j.jhydrol.2007.09.041, 2007.
Wang, E., Yu, Q., Wu, D., and Xia, J.: Climate, agricultural production and hydrological balance in the North China Plain, Int. J. Climatol., 28, 1959–1970, https://doi.org/10.1002/joc.1677, 2008.
Wang, G.: Agricultural drought in a future climate: results from 15 global climate models participating in the IPCC 4th assessment, Clim. Dynam., 25, 739–753, https://doi.org/10.1007/s00382-005-0057-9, 2005.
Wang, S., Yongguang Zhang, and Weimin Ju: Long-term (1982–2018) global gross primary production dataset based on NIRv, Figshare [data set], https://doi.org/10.6084/M9.FIGSHARE.12981977.V2, 2020.
Wey, H.-W., Lo, M.-H., Lee, S.-Y., Yu, J.-Y., and Hsu, H.-H.: Potential impacts of wintertime soil moisture anomalies from agricultural irrigation at low latitudes on regional and global climates: Remote Impact of Low-Latitude Irrigation, Geophys. Res. Lett., 42, 8605–8614, https://doi.org/10.1002/2015GL065883, 2015.
Wu, D., Wang, C., Wang, F., Jiang, C., Huo, Z., and Wang, P.: Uncertainty in Simulating the Impact of Cultivar Improvement on Winter Wheat Phenology in the North China Plain, J. Meteorol. Res., 32, 636–647, https://doi.org/10.1007/s13351-018-7139-1, 2018.
Wu, L., Feng, J., and Miao, W.: Simulating the Impacts of Irrigation and Dynamic Vegetation Over the North China Plain on Regional Climate, J. Geophys. Res.-Atmos., 123, 8017–8034, https://doi.org/10.1029/2017JD027784, 2018.
Wu, W., Yang, P., Tang, H., Zhou, Q., Chen, Z., and Shibasaki, R.: Characterizing Spatial Patterns of Phenology in Cropland of China Based on Remotely Sensed Data, Agr. Sci. China, 9, 101–112, https://doi.org/10.1016/S1671-2927(09)60073-0, 2010.
Xie, Z., Yuan, F., Duan, Q., Zheng, J., Liang, M., and Chen, F.: Regional Parameter Estimation of the VIC Land Surface Model: Methodology and Application to River Basins in China, J. Hydrometeorol., 8, 447–468, https://doi.org/10.1175/JHM568.1, 2007.
Xu, X., Chen, F., Barlage, M., Gochis, D., Miao, S., and Shen, S.: Lessons Learned From Modeling Irrigation From Field to Regional Scales, J. Adv. Model. Earth Sy., 11, 2428–2448, https://doi.org/10.1029/2018MS001595, 2019.
Yan, H., Xiao, X., Huang, H., Liu, J., Chen, J., and Bai, X.: Multiple cropping intensity in China derived from agro-meteorological observations and MODIS data, Chinese Geogr. Sci., 24, 205–219, https://doi.org/10.1007/s11769-013-0637-2, 2014.
Yang, B., Zhang, Y., Qian, Y., Tang, J., and Liu, D.: Climatic effects of irrigation over the Huang-Huai-Hai Plain in China simulated by the weather research and forecasting model: Simulated Irrigation Effects in China, J. Geophys. Res.-Atmos., 121, 2246–2264, https://doi.org/10.1002/2015JD023736, 2016.
Yang, K. and He, J.: China meteorological forcing dataset (1979–2015), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.3972/westdc.002.2014.db, 2016.
Yang, M. and Wang, G.: Heat stress to jeopardize crop production in the US Corn Belt based on downscaled CMIP5 projections, Agric. Syst., 211, 103746, https://doi.org/10.1016/j.agsy.2023.103746, 2023.
Yang, Z., Dominguez, F., Zeng, X., Hu, H., Gupta, H., and Yang, B.: Impact of Irrigation over the California Central Valley on Regional Climate, J. Hydrometeorol., 18, 1341–1357, https://doi.org/10.1175/JHM-D-16-0158.1, 2017.
Yang, Z., Qian, Y., Liu, Y., Berg, L. K., Hu, H., Dominguez, F., Yang, B., Feng, Z., Gustafson, W. I., Huang, M., and Tang, Q.: Irrigation Impact on Water and Energy Cycle During Dry Years Over the United States Using Convection-Permitting WRF and a Dynamical Recycling Model, J. Geophys. Res.-Atmos., 124, 11220–11241, https://doi.org/10.1029/2019JD030524, 2019.
Yang, Z., Qian, Y., Liu, Y., Berg, L. K., Gustafson, W. I., Feng, Z., Sakaguchi, K., Fast, J. D., Tai, S.-L., Yang, B., Huang, M., and Xiao, H.: Understanding irrigation impacts on low-level jets over the Great Plains, Clim. Dynam., 55, 925–943, https://doi.org/10.1007/s00382-020-05301-7, 2020.
Yao, Y., Vanderkelen, I., Lombardozzi, D., Swenson, S., Lawrence, D., Jägermeyr, J., Grant, L., and Thiery, W.: Implementation and Evaluation of Irrigation Techniques in the Community Land Model, J. Adv. Model. Earth Sy., 14, e2022MS003074, https://doi.org/10.1029/2022MS003074, 2022.
Yin, X. and van Laar, H. H.: Crop Systems Dynamics: An ecophysiological simulation model of genotype-by-environment interactions, Wageningen Academic Publishers, 169 pp., ISBN 9789076998558, 2005.
Yin, Z., Wang, X. H., Ottlé, C., Zhou, F., Guimberteau, M., Polcher, J., Peng, S. S., Piao, S. L., Li, L., Bo, Y., Chen, X. L., Zhou, X. D., Kim, H., and Ciais, P.: Improvement of the Irrigation Scheme in the ORCHIDEE Land Surface Model and Impacts of Irrigation on Regional Water Budgets Over China, J. Adv. Model. Earth Sy., 12, e2019MS001770, https://doi.org/10.1029/2019MS001770, 2020.
Yu, L., Liu, Y., Liu, T., Yu, E., Bu, K., Jia, Q., Shen, L., Zheng, X., and Zhang, S.: Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China, Comput. Electron. Agric., 201, 107323, https://doi.org/10.1016/j.compag.2022.107323, 2022.
Yuan, H., Dai, Y., and Li, S.: Reprocessed MODIS Version 6 Leaf Area Index data sets for land surface and climate modelling, http://globalchange.bnu.edu.cn/research (last access: 27 December 2023), 2020.
Zhang, K., Li, X., Zheng, D., Zhang, L., and Zhu, G.: Estimation of Global Irrigation Water Use by the Integration of Multiple Satellite Observations, Water Resour. Res., 58, e2021WR030031, https://doi.org/10.1029/2021WR030031, 2022.
Zhang, Y., Tao, B., and Tang, Z.: A Simulation Model for the Growth and Development of Winter Wheat, Trans. Atmos. Sci., 14, 113–121, http://dqkxxb.cnjournals.org/dqkxxb/article/abstract/19910114 (last access: 17 August 2023), 1991.
Zhang, Z., Barlage, M., Chen, F., Li, Y., Helgason, W., Xu, X., Liu, X., and Li, Z.: Joint Modeling of Crop and Irrigation in the central United States Using the Noah-MP Land Surface Model, J. Adv. Model. Earth Sy., 12, e2020MS002159, https://doi.org/10.1029/2020MS002159, 2020.
Zhang, Z., Li, Y., Chen, F., Harder, P., Helgason, W., Famiglietti, J., Valayamkunnath, P., He, C., and Li, Z.: Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress , Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, 2023.
Zhe, Y., Denghua, Y. a. N., Zhiyong, Y., Jun, Y. I. N., and Yong, Y.: Research on temporal and spatial change of 400 mm and 800 mm rainfall contours of China in 1961–2000, Adv. Water Sci., 25, 494–502, 2014.
Zhou, H., Zhou, G., He, Q., Zhou, L., Ji, Y., and Zhou, M.: Environmental explanation of maize specific leaf area under varying water stress regimes, Environ. Exp. Bot., 171, 103932, https://doi.org/10.1016/j.envexpbot.2019.103932, 2020.
Zhu, X., Shi, P., and Pan, Y.: Development of a gridded dataset of annual irrigation water withdrawal in China, in: 2012 First International Conference on Agro- Geoinformatics (Agro-Geoinformatics), 2012 First International Conference on Agro-Geoinformatics, Shanghai, China, 2–4 August 2012, 1–6, https://doi.org/10.1109/Agro-Geoinformatics.2012.6311667, 2012.
Zhu, X., Zhu, W., Zhang, J., and Pan, Y.: Mapping Irrigated Areas in China From Remote Sensing and Statistical Data, IEEE J. Sel. Top. Appl., 7, 4490–4504, https://doi.org/10.1109/JSTARS.2013.2296899, 2014.
Short summary
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local...
