Global investigation of impacts of PET methods on simulating crop-water relations for maize

Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration ​– Guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56. FAO, Rome. Anothai, 2013, Evaluation of two evapotranspiration approaches simulated with the CSM–CERES–Maize model under different irrigation strategies and the impact on maize growth, development and soil moisture content for semi-arid conditions, Agric. For. Meteorol., 176, 64, 10.1016/j.agrformet.2013.03.001 Baier, 1965, Estimation of latent evaporation from simple weather observations, Can. J. Plant Sci., 45, 276, 10.4141/cjps65-051 Balkovič, 2013, Pan-European crop modelling with EPIC: implementation, up-scaling and regional crop yield validation, Agrofor. Syst., 120, 61, 10.1016/j.agsy.2013.05.008 Balkovič, 2014, Global wheat production potentials and management flexibility under the representative concentration pathways, Global Planet. C, 122, 107, 10.1016/j.gloplacha.2014.08.010 Bassu, 2014, How do various maize crop models vary in their responses to climate change factors?, Global Change Biol., 20, 2301, 10.1111/gcb.12520 Batjes, N.H., 2006. ISRIC-WISE derived soil properties on a 5 by 5 arc-minutes global grid (version 1.1), ISRIC—World Soil Infromation, Wageningen. Benson, 1992, Nitrogen leaching sensitivity to evapotranspiration and soil water storage estimates in EPIC, J. Soil Water Conserv., 47, 334 Chapagain, A.K., Hoekstra, A.Y., 2004. Water footprints of nations. Research Report Series No. 16. UNESCO-IHE, Delft. Doorenbos, 1977, Guidelines for predicting crop water requirements, FAO Irrig. Drain. Pap., 24, 15 Elliott, 2014, Constraints and potentials of future irrigation water availability on agricultural production under climate change, Proc. Natl. Acad. Sci. U. S. A., 111, 3239, 10.1073/pnas.1222474110 Elliott, 2015, The global gridded crop model intercomparison: data and modeling protocols for phase 1 (v1.0), Geosci. Model Dev., 8, 261, 10.5194/gmd-8-261-2015 FAO, 1995 FAO, 2007 Fader, 2011, Internal and external green-blue agricultural water footprints of nations, and related water and land savings through trade, Hydrol. Earth Syst. Sci., 15, 1641, 10.5194/hess-15-1641-2011 Folberth, 2012, Regionalization of a large-scale crop growth model for sub-Saharan Africa: model setup, evaluation, and estimation of maize yields, Agric. Ecosyst. Environ., 151, 21, 10.1016/j.agee.2012.01.026 Folberth, 2013, Modeling maize yield responses to improvement in nutrient, water and cultivar inputs in sub-Saharan Africa, Agric. Syst., 119, 22, 10.1016/j.agsy.2013.04.002 Folberth, 2014, Effects of ecological and conventional agricultural intensification practices on maize yields in sub-Saharan Africa under potential climate change, Environ. Res. Lett., 9, 044004, 10.1088/1748-9326/9/4/044004 Gassman, P.W. et al., 2005. Historical development and applications of the EPIC and APEX Models Iowa State University, Center for Agricultural and Rural Development. Working Paper 05-WP 397, Ames, Iowa. Hargreaves, 2003, History and evaluation of Hargreaves evapotranspiration equation, J. Irrig. Drain. Eng., 129, 53, 10.1061/(ASCE)0733-9437(2003)129:1(53) Hargreaves, 1985, Reference crop evapotranspiration from temperature, Appl. Eng. Agric., 1, 96, 10.13031/2013.26773 Hempel, 2013, A trend-preserving bias correction ​– the ISI-MIP approach, Earth Syst. Dyn., 4, 219, 10.5194/esd-4-219-2013 Jensen, M.E., Burman, R.D., Allen, R.G., 1990. Evapotranspiration and Irrigation Water Requirements. ASCE Manual of Practice No. 70. ASCE, New York. Jones, 2003, The DSSAT cropping system model, Eur. J. Agron., 18, 235, 10.1016/S1161-0301(02)00107-7 Kiniry, 1992, A general, process-oriented model for two competing plant species, Trans. ASABE, 35, 801, 10.13031/2013.28665 Knisel, W.G., 1980. CREAMS: a field-scale model for chemicals, runoff and erosion from agricultural management systems. Conservation Research Report No. 26, Washington D.C. Leonard, 1987, GLEAMS: groundwater loading effects of agricultural management systems, Trans. ASABE, 30, 1403, 10.13031/2013.30578 Liu, 2007, GEPIC—modelling wheat yield and crop water productivity with high resolution on a global scale, Agric. Syst., 94, 478, 10.1016/j.agsy.2006.11.019 Liu, 2009, Global consumptive water use for crop production: the importance of green water and virtual water, Water Resour. Res., 45, 10.1029/2007WR006051 Liu, 2013, A global and spatially explicit assessment of climate change impacts on crop production and consumptive water use, PLoS One, 8, e57750, 10.1371/journal.pone.0057750 Liu, 2009, A GIS-based tool for modelling large-scale crop-water relations, Environ. Modell. Software, 24, 411, 10.1016/j.envsoft.2008.08.004 Mekonnen, 2011, The green, blue and grey water footprint of crops and derived crop products, Hydrol. Earth Syst. Sci., 15, 1577, 10.5194/hess-15-1577-2011 Mitchell, 2005, An improved method of constructing a database of monthly climate observations and associated high-resolution grids, Int. J. Climatol., 25, 693, 10.1002/joc.1181 Monteith, 1965, Evaporation and environment, Symp. Soc. Exp. Biol., 19, 205 Palosuo, 2011, Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models, Eur. J. Agron., 35, 103, 10.1016/j.eja.2011.05.001 Parton, 1994, A general model for soil organic matter dynamics: sensitivity to litter chemistry, texture and management, Soil Sci. Soc. Am. Inc., Minneap. Minn. U. S. A., 147 Peel, 2007, Updated world map of the Köppen–Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633, 10.5194/hess-11-1633-2007 Penman, 1948, Natural evaporation from open water, bare soil and grass, Proc. R. Soc. Lond. Ser. A, 193, 120, 10.1098/rspa.1948.0037 Portmann, 2010, MIRCA2000 ​– Global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling, Global Biogeochem. Cycles, 24 Priestley, 1972, On the assessment of surface heat flux and evaporation using large-scale parameters, Mon. Weather Rev., 100, 81, 10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2 Ritchie, 1972, Model for predicting evaporation from a row crop with incomplete cover, Water Resour. Res., 8, 1204, 10.1029/WR008i005p01204 Roloff, 1998, Estimating spring wheat yield variability with EPIC, Can. J. Soil Sci., 78, 541, 10.4141/S97-063 Rosenzweig, 2013, The agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studies, Agric. For. Meteorol., 170, 166, 10.1016/j.agrformet.2012.09.011 Rosenzweig, 2014, Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison, Proc. Natl. Acad. Sci. U. S. A., 111, 3268, 10.1073/pnas.1222463110 Sacks, 2010, Crop planting dates: an analysis of global patterns, Global Change Biol., 19, 607 Saghravani, 2009, Comparison of daily and monthly results of three evapotranspiration models in tropical zone: a case study, Am. J. Environ. Sci., 5, 698, 10.3844/ajessp.2009.698.705 Sau, 2004, Testing and improving evapotranspiration and soil water balance of the DSSAT crop models, Agron. J., 96, 1243, 10.2134/agronj2004.1243 Shiklomanov, 2003 Siebert, 2010, Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation, J. Hydrol., 384, 198, 10.1016/j.jhydrol.2009.07.031 Tan, 2003, Global estimation of crop productivity and the impacts of global warming by GIS and EPIC integration, Ecol. Modell., 168, 357, 10.1016/S0304-3800(03)00146-7 Trajkovic, 2007, Hargreaves versus Penman–Monteith under humid conditions, J .Irrig. Drain. Eng., 133, 38, 10.1061/(ASCE)0733-9437(2007)133:1(38) Wang, 2012, A review of global terrestrial evapotranspiration: observation, modeling climatology, and climatic variability, Rev. Geophys., 50 Weedon, 2014, The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-Interim reanalysis data, Water Resour. Res., 50, 7505, 10.1002/2014WR015638 Williams, 1984, A modeling approach to determining the relationship between erosion and soil productivity, Trans. ASABE, 27, 129, 10.13031/2013.32748 Williams, 1995, The EPIC model Xiong, 2014, A calibration procedure to improve global rice yield simulations with EPIC, Ecol. Modell., 273, 128, 10.1016/j.ecolmodel.2013.10.026 Yin, 2014, GEPIC-V-R model: a GIS-based tool for regional crop drought risk assessment, Agric. Water Manage., 144, 107, 10.1016/j.agwat.2014.05.017 Yoder, 2005, Evaluation of methods for estimating daily reference crop evapotranspiration at a site in the humid Southeast United States, Appl. Eng. Agric., 21, 197, 10.13031/2013.18153