Assessment of Climate Change Impact on Water Requirement and Rice Productivity

doi:10.1016/j.rsci.2023.03.010

Abstract

Abstract:

Assessing the impact of climate change (CC) on agricultural production systems is mainly done using crop models associated with climate model outputs. This review is one of the few, with the main objective of providing a recent compendium of CC impact studies on irrigation needs and rice yields for a better understanding and use of climate and crop models. We discuss the strengths and weaknesses of climate impact studies on agricultural production systems, with a particular focus on uncertainty and sensitivity analyses of crop models. Although the new generation global climate models (GCMs) are more robust than previous ones, there is still a need to consider the effect of climate uncertainty on estimates when using them. Current GCMs cannot directly simulate the agro-climatic variables of interest for future irrigation assessment, hence the use of intelligent climate tools. Therefore, sensitivity and uncertainty analyses must be applied to crop models, especially for their calibration under different conditions. The impacts of CC on irrigation needs and rice yields vary across regions, seasons, varieties and crop models. Finally, integrated assessments, the use of remote sensing data, climate smart tools, CO₂ enrichment experiments, consideration of changing crop management practices and multi-scale crop modeling, seem to be the approaches to be pursued for future climate impact assessments for agricultural systems.

Key words: climate change, rice production, irrigation, crop model, climate model

Konan Jean-Yves N’guessan, Botou Adahi, Arthur-Brice Konan-Waidhet, Satoh Masayoshi, Nogbou Emmanuel Assidjo. Assessment of Climate Change Impact on Water Requirement and Rice Productivity[J]. Rice Science, 2023, 30(4): 276-293.

Figures/Tables 7

References 149

[1]	Acharjee T K, Ludwig F, van Halsema G, Hellegers P, Supit I. 2017. Future changes in water requirements of Boro rice in the face of climate change in North-West Bangladesh. Agric Water Manag, 194: 172-183.
[2]	Affholder F, Tittonell P, Corbeels M, Roux S, Motisi N, Tixier P, Wery J. 2012. Ad hoc modeling in agronomy: What have we learned in the last 15 years? Agron J, 104(3): 735-748.
[3]	Ahmed K F, Wang G L, Silander J, Wilson A M, Allen J M, Horton R, Anyah R. 2013. Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast. Glob Planet Change, 100: 320-332.
[4]	Ahmed K F, Wang G L, Yu M, Koo J, You L Z. 2015. Potential impact of climate change on cereal crop yield in West Africa. Clim Change, 133(2): 321-334.
[5]	Ahmed M, Stöckle C O, Nelson R, Higgins S, Ahmad S, Raza M A. 2019. Novel multimodel ensemble approach to evaluate the sole effect of elevated CO₂ on winter wheat productivity. Sci Rep, 9(1): 7813.
[6]	Ahmed S M. 2020. Impacts of drought, food security policy and climate change on performance of irrigation schemes in Sub- Saharan Africa: The case of Sudan. Agric Water Manag, 232: 106064.
[7]	Ahuja L R, Ma L W. 2015. A synthesis of current parameterization approaches and needs for further improvements. In: Ahuja L R, Ma L W. Methods of Introducing System Models into Agricultural Research. Madison, WI, USA: American Society of Agronomy and Soil Science Society of America: 427-440.
[8]	Akinbile C O. 2013. Assessment of the CERES: Rice model for rice production in Ibadan, Nigeria. Agric Eng Int CIGR J, 15(1): 19-26.
[9]	Alexandrov V A, Hoogenboom G. 2000. Vulnerability and adaptation assessments of agricultural crops under climate change in the Southeastern USA. Theor Appl Climatol, 67(1): 45-63.
[10]	Amiri E, Rezaei M, Bannayan M, Soufizadeh S. 2013. Calibration and evaluation of CERES rice model under different nitrogen- and water-management options in semi-Mediterranean climate condition. Commun Soil Sci Plant Anal, 44(12): 1814-1830.
[11]	Ammar M E, Davies E G R. 2019. On the accuracy of crop production and water requirement calculations: Process-based crop modeling at daily, semi-weekly, and weekly time steps for integrated assessments. J Environ Manag, 238: 460-472.
[12]	Andrianandraina, Ventura A, Senga Kiessé T, Cazacliu B, Idir R, van der Werf H M G. 2015. Sensitivity analysis of environmental process modeling in a life cycle context: A case study of hemp crop production. J Ind Ecol, 19(6): 978-993.
[13]	Angulo C, Rötter R, Lock R, Enders A, Fronzek S, Ewert F. 2013. Implication of crop model calibration strategies for assessing regional impacts of climate change in Europe. Agric For Meteorol, 170: 32-46.
[14]	Asseng S, Ewert F, Rosenzweig C, Jones J W, Hatfield J L, Ruane A C, Boote K J, Thorburn P J, Rötter R P, Cammarano D, Brisson N, Basso B, Martre P, Aggarwal P K, Angulo C, Bertuzzi P, Biernath C, Challinor A J, Doltra J, Gayler S, Goldberg R, Grant R, Heng L, Hooker J, Hunt L A, Ingwersen J, Izaurralde R C, Kersebaum K C, Müller C, Naresh Kumar S, Nendel C, O’Leary G, Olesen J E, Osborne T M, Palosuo T, Priesack E, Ripoche D, Semenov M A, Shcherbak I, Steduto P, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Travasso M, Waha K, Wallach D, White J W, Williams J R, Wolf J. 2013. Uncertainty in simulating wheat yields under climate change. Nat Clim Change, 3(9): 827-832.
[15]	Barbottin A. 2004. A general probabilistic framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study. INRA de Grignon, Paris-Grignon, France. (in French)
[16]	Baroni G, Tarantola S. 2014. A general probabilistic framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study. Environ Model Softw, 51: 26-34.
[17]	Basheer A K, Lu H S, Omer A, Ali A B, Abdelgader A M S. 2016. Impacts of climate change under CMIP5 RCP scenarios on the streamflow in the Dinder River and ecosystem habitats in Dinder National Park, Sudan. Hydrol Earth Syst Sci, 20(4): 1331-1353.
[18]	Bauwens A, Sohier C, Degré A. 2013. Impacts of climate change on the hydrology and management of water resources in the Meuse basin (bibliographic summary). Biotechnol Agron Soc Environ, 17(1): 76-86. (in French)
[19]	Benke K K, Lowell K E, Hamilton A J. 2008. Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model. Math Comput Model, 47: 1134-1149.
[20]	Berg P, Feldmann H, Panitz H J. 2012. Bias correction of high resolution regional climate model data. J Hydrol, 448/449: 80-92.
[21]	Bertin N, Martre P, Génard M, Quilot B, Salon C. 2010. Under what circumstances can process-based simulation models link genotype to phenotype for complex traits? Case-study of fruit and grain quality traits. J Exp Bot, 61(4): 955-967. PMID
[22]	Boé J, Terray L, Habets F, Martin E. 2007. Statistical and dynamical downscaling of the Seine Basin climate for hydro- meteorological studies. Int J Climatol, 27(12): 1643-1655.
[23]	Bolster C H, Vadas P A. 2013. Sensitivity and uncertainty analysis for the annual phosphorus loss estimator model. J Environ Qual, 42(4): 1109-1118. PMID
[24]	Bouman B A M, Lampayan R M, Tuong T P. 2007. Water Management in Irrigated Rice: Coping with Water Scarcity. Los Baños, the Philippines: International Rice Research Institute.
[25]	Bregaglio S, Giustarini L, Suarez E, Mongiano G, de Gregorio T. 2020. Analysing the behaviour of a hazelnut simulation model across growing environments via sensitivity analysis and automatic calibration. Agric Syst, 181: 102794.
[26]	Brocca L, Tarpanelli A, Filippucci P, Dorigo W, Zaussinger F, Gruber A, Fernández-Prieto D. 2018. How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products. Int J Appl Earth Obs Geoinfor, 73: 752-766.
[27]	Buddhaboon C, Jintrawet A, Hoogenboom G. 2011. Effects of planting date and variety on flooded rice production in the deepwater area of Thailand. Field Crops Res, 124(2): 270-277.
[28]	Buddhaboon C, Jintrawet A, Hoogenboom G. 2018. Methodology to estimate rice genetic coefficients for the CSM-CERES-Rice model using GENCALC and GLUE genetic coefficient estimators. J Agric Sci, 156(4): 482-492.
[29]	Buis S, Wallach D, Guillaume S, Varella H, Lecharpentier P, Launay M, Guérif M, Bergez J E, Justes E. 2015. The STICS crop model and associated software for analysis, parameterization, and evaluation. In: Ahuja L R, Ma L W. Methods of Introducing System Models into Agricultural Research. Madison, WI, USA: American Society of Agronomy and Soil Science Society of America: 395-426.
[30]	Campbell B M, Vermeulen S J, Aggarwal P K, Corner-Dolloff C, Girvetz E, Loboguerrero A M, Ramirez-Villegas J, Rosenstock T, Sebastian L, Thornton P K, Wollenberg E. 2016. Reducing risks to food security from climate change. Glob Food Secur, 11: 34-43.
[31]	Carpani M, Bergez J E, Monod H. 2012. Sensitivity analysis of a hierarchical qualitative model for sustainability assessment of cropping systems. Environ Model Softw, 27/28: 15-22.
[32]	Charron I. 2016. Climate Scenarios Guide: Using Climate Information to Guide Adaptation Research and Decision Making. Ouranos, Montreal, Québec. (in French)
[33]	Chen J, Brissette F P, Leconte R. 2011. Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. J Hydrol, 401: 190-202.
[34]	Chen Y, Marek G W, Marek T H, Moorhead J E, Heflin K R, Brauer D K, Gowda P H, Srinivasan R. 2019. Simulating the impacts of climate change on hydrology and crop production in the Northern High Plains of Texas using an improved SWAT model. Agric Water Manag, 221: 13-24.
[35]	Cho J, Ko G, Kim K, Oh C. 2016. Climate change impacts on agricultural drought with consideration of uncertainty in CMIP5 scenarios. Irrig Drain, 65: 7-15.
[36]	Chun J A, Li S N, Wang Q G, Lee W S, Lee E J, Horstmann N, Park H, Veasna T, Vanndy L, Pros K, Vang S. 2016. Assessing rice productivity and adaptation strategies for Southeast Asia under climate change through multi-scale crop modeling. Agric Syst, 143: 14-21.
[37]	Corbeels M, Berre D, Rusinamhodzi L, Lopez-Ridaura S. 2018. Can we use crop modelling for identifying climate change adaptation options? Agric For Meteorol, 256/257: 46-52.
[38]	Cuculeanu V, Tuinea P, Bălteanu D. 2002. Climate change impacts in Romania: Vulnerability and adaptation options. GeoJournal, 57(3): 203-209.
[39]	Dahal P, Shrestha M L, Panthi J, Pradhananga D. 2020. Modeling the future impacts of climate change on water availability in the Karnali River Basin of Nepal Himalaya. Environ Res, 185: 109430.
[40]	Dalla Marta A, Eitzinger J, Kersebaum K C, Todorovic M, Altobelli F. 2018. Assessment and monitoring of crop water use and productivity in response to climate change. J Agric Sci, 156(5): 575-576.
[41]	de Wit A, Boogaard H, Fumagalli D, Janssen S, Knapen R, van Kraalingen D, Supit I, van der Wijngaart R, van Diepen K. 2019. 25 years of the WOFOST cropping systems model. Agric Syst, 168: 154-167.
[42]	Di Paola A, Valentini R, Santini M. 2016. An overview of available crop growth and yield models for studies and assessments in agriculture. J Sci Food Agric, 96(3): 709-714.
[43]	Dias M P N M, Navaratne C M, Weerasinghe K D N, Hettiarachchi R H A N. 2016. Application of DSSAT crop simulation model to identify the changes of rice growth and yield in Nilwala river basin for mid-centuries under changing climatic conditions. Procedia Food Sci, 6: 159-163.
[44]	Dibaba W T, Miegel K, Demissie T A. 2019. Evaluation of the CORDEX regional climate models performance in simulating climate conditions of two catchments in Upper Blue Nile Basin. Dyn Atmos Oceans, 87: 101104.
[45]	Ding Y M, Wang W G, Song R M, Shao Q X, Jiao X Y, Xing W Q. 2017. Modeling spatial and temporal variability of the impact of climate change on rice irrigation water requirements in the middle and lower reaches of the Yangtze River, China. Agric Water Manag, 193: 89-101.
[46]	Ding Y M, Wang W G, Zhuang Q L, Luo Y F. 2020. Adaptation of paddy rice in China to climate change: The effects of shifting sowing date on yield and irrigation water requirement. Agric Water Manag, 228: 105890.
[47]	Dorchies D, Thirel G, Jay-Allemand M, Chauveau M, Dehay F, Bourgin P Y, Perrin C, Jost C, Rizzoli J L, Demerliac S, Thépot R. 2014. Climate change impacts on multi-objective reservoir management: Case study on the Seine River Basin, France. Int J River Basin Manag, 12(3): 265-283.
[48]	Ehret U, Zehe E, Wulfmeyer V, Warrach-Sagi K, Liebert J. 2012. HESS Opinions ‘Should we apply bias correction to global and regional climate model data?’ Hydrol Earth Syst Sci, 16(9): 3391-3404.
[49]	Eyring V, Bony S, Meehl G A, Senior C A, Stevens B, Stouffer R J, Taylor K E. 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Dev, 9(5): 1937-1958.
[50]	Famien A M, Janicot S, Ochou A D, Vrac M, Defrance D, Sultan B, Noël T. 2018. A bias-corrected CMIP5 dataset for Africa using the CDF-t method: A contribution to agricultural impact studies. Earth Syst Dynam, 9(1): 313-338.
[51]	Fischer G, Tubiello F N, van Velthuizen H, Wiberg D A. 2007. Climate change impacts on irrigation water requirements: Effects of mitigation, 1990-2080. Technol Forecast Soc Change, 74(7): 1083-1107.
[52]	Fowler H J, Blenkinsop S, Tebaldi C. 2007. Linking climate change modelling to impacts studies: Recent advances in downscaling techniques for hydrological modelling. Int J Climatol, 27(12): 1547-1578.
[53]	Franchito S H, Rao V B. 2015. Studies of climate change with statistical-dynamical models: A review. Am J Clim Change, 4(1): 57-68.
[54]	Gallardo M, Elia A, Thompson R B. 2020. Decision support systems and models for aiding irrigation and nutrient management of vegetable crops. Agric Water Manag, 240: 106209.
[55]	García-Ruiz J M, López-Moreno J I, Vicente-Serrano S M, Lasanta- Martínez T, Beguería S. 2011. Mediterranean water resources in a global change scenario. Earth Sci Rev, 105: 121-139.
[56]	Gilardelli C, Confalonieri R, Alessandro Cappelli G, Bellocchi G. 2018. Sensitivity of WOFOST-based modelling solutions to crop parameters under climate change. Ecol Model, 368: 1-14.
[57]	Guo D X, Zhao R H, Xing X G, Ma X Y. 2020. Global sensitivity and uncertainty analysis of the AquaCrop model for maize under different irrigation and fertilizer management conditions. Arch Agron Soil Sci, 66(8): 1115-1133.
[58]	Hakala K, Addor N, Teutschbein C, Vis M, Dakhlaoui H, Seibert J. 2019. Hydrological modeling of climate change impacts. Human Dimens, 1-20.
[59]	Haro-Monteagudo D, Palazón L, Beguería S. 2020. Long-term sustainability of large water resource systems under climate change: A cascade modeling approach. J Hydrol, 582: 124546.
[60]	Hasan M M, Rahman Md M. 2019. Simulating climate change impacts on T. aman (BR-22) rice yield: A predictive approach using DSSAT model. Water Environ J, 34: 250-262.
[61]	Hasegawa T, Sakai H, Tokida T, Usui Y, Yoshimoto M, Fukuoka M, Nakamura H, Shimono H, Okada M. 2016. Rice free-air carbon dioxide enrichment studies to improve assessment of climate change effects on rice agriculture. In: Hatfield J L, Fleisher D. Advances in Agricultural Systems Modeling. Madison, WI, USA: American Society of Agronomy and Soil Science Society of America: 45-68.
[62]	Hatfield J L, Boote K J, Kimball B A, Ziska L H, Izaurralde R C, Ort D, Thomson A M, Wolfe D. 2011. Climate impacts on agriculture: Implications for crop production. Agron J, 103(2): 351-370.
[63]	He J Q, Jones J W, Graham W D, Dukes M D. 2010a. Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method. Agric Syst, 103(5): 256-264.
[64]	He J Q, Porter C, Wilkens P, Marin F, Hu H, Jones J W. 2010b. Generalized likelihood uncertainty analysis tool for genetic parameter estimation (GLUE Tool). In: Jones J W, Hoogenboom G, Wilkens P W, Porter C H, Tsuji G Y. Decision Support System for Agrotechnology Transfer. Version 4.5. Volume 3: ICASA Tools. Honolulu, Hawai: 11.
[65]	Holzkämper A, Calanca P, Honti M, Fuhrer J. 2015. Uncertainties in climate impacts on grain maize in Switzerland: Does the choice of crop modelling approach matter? Procedia Environ Sci, 29: 152-153.
[66]	Holzkämper A. 2017. Adapting agricultural production systems to climate change: What’s the use of models? Agriculture, 7(10): 86.
[67]	Hong E M, Nam W H, Choi J Y, Pachepsky Y A. 2016. Projected irrigation requirements for upland crops using soil moisture model under climate change in South Korea. Agric Water Manag, 165: 163-180.
[68]	Hoogenboom G, Porter C H, Boote K J, Shelia V, Wilkens P W, Singh U, White J W, Asseng S, Lizaso J I, Moreno L P, Pavan W, Ogoshi R, Hunt L A, Tsuji G Y, Jones J W. 2019. The DSSAT crop modeling ecosystem. In: Boote K. Advances in Crop Modeling for a Sustainable Agriculture: Burleigh Dodds Series in Agricultural Science. Cambridge, United Kingdom: Burleigh Dodds Science Publishing: 173-216.
[69]	Hunt L A, Pararajasingham S, Jones J W, Hoogenboom G, Imamura D T, Ogoshi R M. 1993. GENCALC: Software to facilitate the use of crop models for analyzing field experiments. Agron J, 85(5): 1090-1094.
[70]	Ibarra S, Romero R, Poulin A N, Glaus M, Cervantes E, Bravo J, Pérez R, Castillo E. 2016. Sensitivity analysis in hydrological modeling for the Gulf of México. Procedia Eng, 154: 1152-1162.
[71]	IPCC. 2013. Summary for policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. New York, USA: Intergovernmental Panel on Climate Change.
[72]	Ishida K, Ercan A, Trinh T, Kavvas M L, Ohara N, Carr K, Anderson M L. 2018. Analysis of future climate change impacts on snow distribution over mountainous watersheds in Northern California by means of a physically-based snow distribution model. Sci Total Environ, 645: 1065-1082.
[73]	Ismail H, Kamal M R, bin Abdullah A F, bin Mohd M S F. 2020. Climate-smart agro-hydrological model for a large scale rice irrigation scheme in Malaysia. Appl Sci, 10(11): 3906.
[74]	Jha R K, Kalita P K, Jat R. 2020. Development of production management strategies for a long-duration rice variety: Rajendra Mahsuri: Using crop growth model, DSSAT, for the state of Bihar, India. Paddy Water Environ, 18: 531-545.
[75]	Jones J W, He J Q, Boote K J, Wilkens P, Porter C H, Hu Z. 2015. Estimating DSSAT cropping system cultivar-specific parameters using Bayesian techniques. In: Ahuja L W, Ma L M. Methods of Introducing System Models into Agricultural Research. Madison, WI, USA: American Society of Agronomy and Soil Science Society of America: 365-393.
[76]	Kaini S, Nepal S, Pradhananga S, Gardner T, Sharma A K. 2020. Representative general circulation models selection and downscaling of climate data for the transboundary Koshi River Basin in China and Nepal. Int J Climatol, 40(9): 4131-4149.
[77]	Kaini S, Harrison M T, Gardner T, Nepal S, Sharma A K. 2022. The impacts of climate change on the irrigation water demand, grain yield, and biomass yield of wheat crop in Nepal. Water, 14(17): 2728.
[78]	Kersebaum K C, Boote K J, Jorgenson J S, Nendel C, Bindi M, Frühauf C, Gaiser T, Hoogenboom G, Kollas C, Olesen J E, Rötter R P, Ruget F, Thorburn P J, Trnka M, Wegehenkel M. 2015. Analysis and classification of data sets for calibration and validation of agro-ecosystem models. Environ Model Softw, 72: 402-417.
[79]	Kihara J, Fatondji D, Jones J W, Hoogenboom G, Tabo R, Bationo A. 2012. Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT). Dordrecht: Springer Netherlands.
[80]	Kim Y H, Min S K, Zhang X B, Sillmann J, Sandstad M. 2020. Evaluation of the CMIP6 multi-model ensemble for climate extreme indices. Weather Clim Extrem, 29: 100269.
[81]	Knutti R, Masson D, Gettelman A. 2013. Climate model genealogy: Generation CMIP5 and how we got there. Geophys Res Lett, 40(6): 1194-1199.
[82]	Kontgis C, Schneider A, Ozdogan M, Kucharik C, Dang Tri V P, Duc N H, Schatz J. 2019. Climate change impacts on rice productivity in the Mekong River Delta. Appl Geogr, 102: 71-83.
[83]	Krishnan P, Aggarwal P. 2018. Global sensitivity and uncertainty analyses of a web based crop simulation model (web InfoCrop wheat) for soil parameters. Plant Soil, 423(1): 443-463.
[84]	Kumar N, Tischbein B, Kusche J, Laux P, Beg M K, Bogardi J J. 2017. Impact of climate change on water resources of upper Kharun Catchment in Chhattisgarh, India. J Hydrol Reg Stud, 13: 189-207.
[85]	Leenhardt D, Wallach D, Moigne P L, Guérif M, Bruand A, Casterad M A. 2006. Using crop models for multiple fields. In: Wallach D, Makowski D, Jones J W. Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization and Application. Amsterdam, the Netherland and London, UK: Elsevier: 209-248.
[86]	Li T, Hasegawa T, Yin X Y, Zhu Y, Boote K, Adam M, Bregaglio S, Buis S, Confalonieri R, Fumoto T, Gaydon D, Marcaida M 3rd, Nakagawa H, Oriol P, Ruane A C, Ruget F, Singh B, Singh U, Tang L, Tao F L, Wilkens P, Yoshida H, Zhang Z, Bouman B. 2015. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Glob Chang Biol, 21(3): 1328-1341.
[87]	Li W J, Tang H J, Qin Z H, You F, Wang X F, Chen C L, Ji J H, Liu X M. 2014. Climate change impact and its contribution share to paddy rice production in Jiangxi, China. J Integr Agric, 13(7): 1565-1574.
[88]	Liang H, Qi Z M, DeJonge K C, Hu K L, Li B G. 2017. Global sensitivity and uncertainty analysis of nitrate leaching and crop yield simulation under different water and nitrogen management practices. Comput Electron Agric, 142: 201-210.
[89]	Liersch S, Cools J, Kone B, Koch H, Diallo M, Reinhardt J, Fournet S, Aich V, Hattermann F F. 2013. Vulnerability of rice production in the Inner Niger Delta to water resources management under climate variability and change. Environ Sci Policy, 34: 18-33.
[90]	Liu L, Hong Y, Hocker J E, Shafer M A, Carter L M, Gourley J J, Bednarczyk C N, Yong B, Adhikari P. 2012. Analyzing projected changes and trends of temperature and precipitation in the southern USA from 16 downscaled global climate models. Theor Appl Climatol, 109(3): 345-360.
[91]	Liu S L, Pu C, Ren Y X, Zhao X L, Zhao X, Chen F, Xiao X P, Zhang H L. 2016. Yield variation of double-rice in response to climate change in Southern China. Eur J Agron, 81: 161-168.
[92]	Martre P, Wallach D, Asseng S, Ewert F, Jones J W, Rötter R P, Boote K J, Ruane A C, Thorburn P J, Cammarano D, Hatfield J L, Rosenzweig C, Aggarwal P K, Angulo C, Basso B, Bertuzzi P, Biernath C, Brisson N, Challinor A J, Doltra J, Gayler S, Goldberg R, Grant R F, Heng L E, Hooker J, Hunt L A, Ingwersen J, Izaurralde R C, Kersebaum K C, Müller C, Kumar S N, Nendel C, O'Leary G, Olesen J E, Osborne T M, Palosuo T, Priesack E, Ripoche D, Semenov M A, Shcherbak I, Steduto P, Stöckle C O, Stratonovitch P, Streck T, Supit I, Tao F L, Travasso M, Waha K, White J W, Wolf J. 2015. Multimodel ensembles of wheat growth: Many models are better than one. Glob Chang Biol, 21(2): 911-925.
[93]	Mashnik D, Jacobus H, Barghouth A, Wang E J, Blanchard J, Shelby R. 2017. Increasing productivity through irrigation: Problems and solutions implemented in Africa and Asia. Sustain Energy Technol Assess, 22: 220-227.
[94]	Mbaye M L, Hagemann S, Haensler A, Stacke T, Gaye A T, Afouda A. 2015. Assessment of climate change impact on water resources in the upper Senegal Basin (West Africa). Am J Clim Change, 4(1): 77-93.
[95]	Mdemu M V, Rodgers C, Vlek P L G, Borgadi J J. 2009. Water productivity (WP) in reservoir irrigated schemes in the upper east region (UER) of Ghana. Phys Chem Earth Parts A/B/C, 34(4/5): 324-328.
[96]	Misra A K. 2014. Climate change and challenges of water and food security. Int J Sustain Built Environ, 3(1): 153-165.
[97]	Montesino-San Martin M, Wallach D, Olesen J E, Challinor A J, Hoffman M P, Koehler A K, Rötter R P, Porter J R. 2018. Data requirements for crop modeling: Applying the learning curve approach to the simulation of winter wheat flowering time under climate change. Eur J Agron, 95: 33-44.
[98]	Moss R H, Edmonds J A, Hibbard K A, Manning M R, Rose S K, van Vuuren D P, Carter T R, Emori S, Kainuma M, Kram T, Meehl G A, Mitchell J F B, Nakicenovic N, Riahi K, Smith S J, Stouffer R J, Thomson A M, Weyant J P, Wilbanks T J. 2010. The next generation of scenarios for climate change research and assessment. Nature, 463: 747-756.
[99]	Muerth M J, Gauvin St-Denis B, Ricard S, Velázquez J A, Schmid J, Minville M, Caya D, Chaumont D, Ludwig R, Turcotte R. 2013. On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff. Hydrol Earth Syst Sci, 17(3): 1189-1204.
[100]	Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde R C, Jones C D, Khabarov N, Lawrence P, Liu W F, Olin S, Pugh T A M, Reddy A, Rosenzweig C, Ruane A C, Sakurai G, Schmid E, Skalsky R, Wang X H, de Wit A, Yang H. 2019. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset. Sci Data, 6(1): 50. PMID
[101]	Ndhlovu G, Woyessa Y. 2020. Modelling impact of climate change on catchment water balance, Kabompo River in Zambezi River Basin. J Hydrol Reg Stud, 27: 100650.
[102]	Nover D M, Witt J W, Butcher J B, Johnson T E, Weaver C P. 2016. The effects of downscaling method on the variability of simulated watershed response to climate change in five U.S. basins. Earth Interact, 20(11): 1-27. PMID
[103]	Ogle S M, Breidt F J, Easter M, Williams S, Killian K, Paustian K. 2010. Scale and uncertainty in modeled soil organic carbon stock changes for US croplands using a process-based model. Glob Change Biol, 16(2): 810-822.
[104]	Peng B, Guan K Y, Tang J Y, Ainsworth E A, Asseng S, Bernacchi C J, Cooper M, Delucia E H, Elliott J W, Ewert F, Grant R F, Gustafson D I, Hammer G L, Jin Z N, Jones J W, Kimm H, Lawrence D M, Li Y, Lombardozzi D L, Marshall-Colon A, Messina C D, Ort D R, Schnable J C, Vallejos C E, Wu A, Yin X Y, Zhou W. 2020. Towards a multiscale crop modelling framework for climate change adaptation assessment. Nat Plants, 6(4): 338-348. PMID
[105]	Ramarohetra J, Roudier P, Sultan B. 2012. Gaps and filling in rainfall measurements: What impact for the simulation of agricultural yields in the Sahelian zone? In: Regional Climates: Observation and Modeling. Presented at the 25th Colloquium of the International Association of Climatology. Grenoble, France: 649-654. (in French)
[106]	Razavi S, Gupta H V. 2016. A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory. Water Resour Res, 52(1): 423-439.
[107]	Robles-Morua A, Che D, Mayer A S, Vivoni E R. 2015. Hydrological assessment of proposed reservoirs in the Sonora River Basin, Mexico, under historical and future climate scenarios. Hydrol Sci J, 60: 50-66.
[108]	Roudier P, Sultan B, Quirion P, Berg A. 2011. The impact of future climate change on West African crop yields: What does the recent literature say? Glob Environ Change, 21(3): 1073-1083.
[109]	Rowshon M K, Dlamini N S, Mojid M A, Adib M N M, Amin M S M, Lai S H. 2019. Modeling climate-smart decision support system (CSDSS) for analyzing water demand of a large-scale rice irrigation scheme. Agric Water Manag, 216: 138-152.
[110]	Sachindra D A, Huang F, Barton A, Perera B J C. 2014. Statistical downscaling of general circulation model outputs to precipitation: Part 1. Calibration and validation. Int J Climatol, 34(11): 3264-3281.
[111]	Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S. 2007. Global Sensitivity Analysis: The Primer. Chichester, UK: John Wiley & Sons.
[112]	Sar K, Mahdi S S. 2017. Calibration and validation of DSSAT model v4.6 for Kharif rice in agro-climatic zone (IIIB) of Bihar. Int J Pure App Biosci, 5: 459-463.
[113]	Seidel S J, Palosuo T, Thorburn P, Wallach D. 2018. Towards improved calibration of crop models: Where are we now and where should we go? Eur J Agron, 94: 25-35.
[114]	Semenov M A, Stratonovitch P. 2010. Use of multi-model ensembles from global climate models for assessment of climate change impacts. Clim Res, 41: 1-14.
[115]	Shamir E, Halper E, Modrick T, Georgakakos K P, Chang H I, Lahmers T M, Castro C. 2019. Statistical and dynamical downscaling impact on projected hydrologic assessment in arid environment: A case study from Bill Williams River Basin and Alamo Lake, Arizona. J Hydrol X, 2: 100019.
[116]	Sippel S, Otto F E L, Forkel M, Allen M R, Guillod B P, Heimann M, Reichstein M, Seneviratne S I, Thonicke K, Mahecha M D. 2016. A novel bias correction methodology for climate impact simulations. Earth Syst Dynam, 7(1): 71-88.
[117]	Skaggs T H, Suarez D L, Corwin D L. 2014. Global sensitivity analysis for UNSATCHEM simulations of crop production with degraded waters. Vadose Zone J, 13: 2454.
[118]	Smith W, Grant B, Qi Z M, He W T, Qian B D, Jing Q, VanderZaag A, Drury C F, St Luce M, Wagner-Riddle C. 2020. Towards an improved methodology for modelling climate change impacts on cropping systems in cool climates. Sci Total Environ, 728: 138845.
[119]	Song X M, Zhang J Y, Zhan C S, Xuan Y Q, Ye M, Xu C G. 2015. Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications. J Hydrol, 523: 739-757.
[120]	Specka X, Nendel C, Wieland R. 2015. Analysing the parameter sensitivity of the agro-ecosystem model MONICA for different crops. Eur J Agron, 71: 73-87.
[121]	Sun S K, Li C, Wu P T, Zhao X N, Wang Y B. 2018. Evaluation of agricultural water demand under future climate change scenarios in the Loess Plateau of Northern Shaanxi, China. Ecol Indic, 84: 811-819.
[122]	Tao F L, Rötter R P, Palosuo T, Díaz-Ambrona C G H, Semenov M A, Kersebaum K C, Nendel C, Specka X, Hoffmann H, Ewert F, Dambreville A, Martre P, Rodríguez L, Ruiz-Ramos M, Gaiser T, Höhn J G, Salo T, Ferrise R, Bindi M, Cammarano D, Schulman A H. 2018. Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Glob Chang Biol, 24(3): 1291-1307.
[123]	Tao F L, Palosuo T, Rötter R P, Díaz-Ambrona C G H, Inés Mínguez M, Semenov M A, Kersebaum K C, Cammarano D, Specka X, Nendel C, Srivastava A K, Ewert F, Padovan G, Ferrise R, Martre P, Rodríguez L, Ruiz-Ramos M, Gaiser T, Höhn J G, Salo T, Dibari C, Schulman A H. 2020. Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models. Agric For Meteorol, 281: 107851.
[124]	Tapiador F J, Moreno R, Navarro A, Sánchez J L, García-Ortega E. 2019. Climate classifications from regional and global climate models: Performances for present climate estimates and expected changes in the future at high spatial resolution. Atmos Res, 228: 107-121.
[125]	Teutschbein C, Seibert J. 2013. Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions? Hydrol Earth Syst Sci, 17(12): 5061-5077.
[126]	van Oort P A J, Zwart S J. 2018. Impacts of climate change on rice production in Africa and causes of simulated yield changes. Glob Chang Biol, 24(3): 1029-1045.
[127]	Vermeulen S J, Aggarwal P K, Ainslie A, Angelone C, Campbell B M, Challinor A J, Hansen J W, Ingram J S I, Jarvis A, Kristjanson P, Lau C, Nelson G C, Thornton P K, Wollenberg E. 2012. Options for support to agriculture and food security under climate change. Environ Sci Policy, 15(1): 136-144.
[128]	Vilayvong S, Banterng P, Patanothai A, Pannangpetch K. 2015. CSM-CERES-Rice model to determine management strategies for lowland rice production. Sci Agric, 72(3): 229-236.
[129]	Vrac M, Friederichs P. 2015. Multivariate: Intervariable, spatial, and temporal: Bias correction. J Clim, 28(1): 218-237.
[130]	Vrac M, Noël T, Vautard R. 2016. Bias correction of precipitation through singularity stochastic removal: Because occurrences matter. J Geophys Res Atmos, 121(10): 5237-5258.
[131]	Wallach D. 2011. Crop model calibration: A statistical perspective. Agron J, 103(4): 1144-1151.
[132]	Wallach D, Thorburn P, Asseng S, Challinor A J, Ewert F, Jones J W, Rotter R, Ruane A. 2016. Estimating model prediction error: Should you treat predictions as fixed or random? Environ Model Softw, 84: 529-539.
[133]	Wallach D, Thorburn P J. 2017. Estimating uncertainty in crop model predictions: Current situation and future prospects. Eur J Agron, 88: A1-A7.
[134]	Wang T L, Hamann A, Spittlehouse D, Carroll C. 2016. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS One, 11(6): e0156720.
[135]	Wang W G, Yu Z B, Zhang W, Shao Q X, Zhang Y W, Luo Y F, Jiao X Y, Xu J Z. 2014. Responses of rice yield, irrigation water requirement and water use efficiency to climate change in China: Historical simulation and future projections. Agric Water Manag, 146: 249-261.
[136]	Wang X H, Ciais P, Li L, Ruget F, Vuichard N, Viovy N, Zhou F, Chang J F, Wu X C, Zhao H F, Piao S L. 2017. Management outweighs climate change on affecting length of rice growing period for early rice and single rice in China during 1991-2012. Agric For Meteorol, 233: 1-11.
[137]	Waongo M, Laux P, Kunstmann H. 2015. Adaptation to climate change: The impacts of optimized planting dates on attainable maize yields under rainfed conditions in Burkina Faso. Agric For Meteorol, 205: 23-39.
[138]	Webber H, Hoffmann M, Rezaei E E. 2018. Crop models as tools for agroclimatology. In: Hatfield J L, Sivakumar M V K, Prueger J H. Agronomy Monographs. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America: 519-546.
[139]	Wesseling J, Kroes J, Campos Oliveira T, Damiano F. 2020. The impact of sensitivity and uncertainty of soil physical parameters on the terms of the water balance: Some case studies with default R packages. Part I: Theory, methods and case descriptions. Comput Electron Agric, 170: 105054.
[140]	White J W, Hoogenboom G, Kimball B A, Wall G W. 2011. Methodologies for simulating impacts of climate change on crop production. Field Crops Res, 124(3): 357-368.
[141]	Wilby R L, Hay L E, Gutowski W J Jr, Arritt R W, Takle E S, Pan Z T, Leavesley G H, Clark M P. 2000. Hydrological responses to dynamically and statistically downscaled climate model output. Geophys Res Lett, 27(8): 1199-1202.
[142]	Wilby R L, Charles S P, Zorita E, Timbal B, Whetton P, Mearns L O. 2004. Guidelines for use of climate scenarios developed from statistical downscaling methods. Analysis, 27: 1-27.
[143]	Xu Q, Fox G, McKenney D W, Parkin G, Li Z Y. 2020. A bio-economic crop yield response (BECYR) model for corn and soybeans in Ontario, Canada for 1959-2013. Sci Rep, 10(1): 7006. PMID
[144]	Ye Q, Yang X G, Dai S W, Chen G S, Li Y, Zhang C X. 2015. Effects of climate change on suitable rice cropping areas, cropping systems and crop water requirements in southern China. Agric Water Manag, 159: 35-44.
[145]	Zhai J Q, Mondal S K, Fischer T, Wang Y J, Su B D, Huang J L, Tao H, Wang G J, Ullah W, Uddin M J. 2020. Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia. Atmos Res, 246: 105111.
[146]	Zhang J H, Yao F M, Hao C, Boken V. 2015. Impacts of temperature on rice yields of different rice cultivation systems in Southern China over the past 40 years. Phys Chem Earth Parts A/B/C, 87/88: 153-159.
[147]	Zhang J T, Feng L P, Zou H P, Liu D L. 2015. Using ORYZA2000 to model cold rice yield response to climate change in the Heilongjiang Province, China. Crop J, 3(4): 317-327.
[148]	Zhao C, Liu B, Xiao L J, Hoogenboom G, Boote K J, Kassie B T, Pavan W, Shelia V, Kim K S, Hernandez-Ochoa I M, Wallach D, Porter C H, Stockle C O, Zhu Y, Asseng S. 2019. A SIMPLE crop model. Eur J Agron, 104: 97-106.
[149]	Zheng J Z, Wang W G, Ding Y M, Liu G S, Xing W Q, Cao X C, Chen D. 2020. Assessment of climate change impact on the water footprint in rice production: Historical simulation and future projections at two representative rice cropping sites of China. Sci Total Environ, 709: 136190.

Evaluation criterion	Description	Reference
RMSE	Root mean square error (RMSE) is used to compare the difference between observed and simulated data. An RMSE that tends towards 0 indicates a good performance of the model	Amiri et al, 2013; Zhang et al, 2015; Wang et al, 2017; Buddhaboon et al, 2018; Jha et al, 2020; Zheng et al, 2020
nRMSE	Normalized root mean square error (nRMSE) gives the percentage difference between the observed and simulated data. The lower the nRMSE value, the better the model performs	Amiri et al, 2013; Buddhaboon et al, 2018; Jha et al, 2020; Zheng et al, 2020
MAE	Mean absolute error (MAE) is an error index that can be interpreted in the same way as the RMSE	Sar and Mahdi, 2017
d-index	d-index has values between 0 and 1, with 1 as the value expressing perfect agreement between the observed and simulated data	Buddhaboon et al, 2018; Jha et al, 2020
PBIAS	Percentage of bias (PBIAS) measures the average tendency of measured variables to be under-estimated or over-estimated by the model. The optimal value for PBIAS is 0. A positive value indicates a bias of the model toward under-estimation, while a negative value indicates a bias toward over-estimation	Sar and Mahdi, 2017; Kaini et al, 2022
NSE	Nash-Sutcliffe efficiency coefficient (NSE) expresses the relative magnitude of the residual variance as a function of the variance of the observations. An NSE equal to 1 indicates a perfect match between the measured and simulated values	Jha et al, 2020; Kaini et al, 2022
ME	Modeling efficiency (ME) varies between minus infinity to 1.0. A negative ME means the mean value of the experimental data is a better predictor than the model, whereas an ME of 1.0 signifies a perfect model agreement with observations	Hasan and Rahman, 2019; Jha et al, 2020
r²	Coefficient of determination (r²) expresses the degree of collinearity between the simulated and observed data. It describes the proportion of variance in the measured data that is explained by the model, with higher values revealing less error variance. As a general rule, an r² > 0.5 is considered acceptable	Amiri et al, 2013; Buddhaboon et al, 2018; Kaini et al, 2022
P(t)	P(t) is the significance of paired t-test	Zheng et al, 2020
α, β	α is the intercept of linear relation between simulated and measured values. β is the slope of linear relation between simulated and measured values	Amiri et al, 2013; Zheng et al, 2020
SD	Standard deviation (SD) is the mean of measured or simulated values in whole population	Amiri et al, 2013; Zheng et al, 2020; Kaini et al, 2022

Evaluation criterion	Description	Reference
RMSE	Root mean square error (RMSE) is used to compare the difference between observed and simulated data. An RMSE that tends towards 0 indicates a good performance of the model	Amiri et al, 2013; Zhang et al, 2015; Wang et al, 2017; Buddhaboon et al, 2018; Jha et al, 2020; Zheng et al, 2020
nRMSE	Normalized root mean square error (nRMSE) gives the percentage difference between the observed and simulated data. The lower the nRMSE value, the better the model performs	Amiri et al, 2013; Buddhaboon et al, 2018; Jha et al, 2020; Zheng et al, 2020
MAE	Mean absolute error (MAE) is an error index that can be interpreted in the same way as the RMSE	Sar and Mahdi, 2017
d-index	d-index has values between 0 and 1, with 1 as the value expressing perfect agreement between the observed and simulated data	Buddhaboon et al, 2018; Jha et al, 2020
PBIAS	Percentage of bias (PBIAS) measures the average tendency of measured variables to be under-estimated or over-estimated by the model. The optimal value for PBIAS is 0. A positive value indicates a bias of the model toward under-estimation, while a negative value indicates a bias toward over-estimation	Sar and Mahdi, 2017; Kaini et al, 2022
NSE	Nash-Sutcliffe efficiency coefficient (NSE) expresses the relative magnitude of the residual variance as a function of the variance of the observations. An NSE equal to 1 indicates a perfect match between the measured and simulated values	Jha et al, 2020; Kaini et al, 2022
ME	Modeling efficiency (ME) varies between minus infinity to 1.0. A negative ME means the mean value of the experimental data is a better predictor than the model, whereas an ME of 1.0 signifies a perfect model agreement with observations	Hasan and Rahman, 2019; Jha et al, 2020
r²	Coefficient of determination (r²) expresses the degree of collinearity between the simulated and observed data. It describes the proportion of variance in the measured data that is explained by the model, with higher values revealing less error variance. As a general rule, an r² > 0.5 is considered acceptable	Amiri et al, 2013; Buddhaboon et al, 2018; Kaini et al, 2022
P(t)	P(t) is the significance of paired t-test	Zheng et al, 2020
α, β	α is the intercept of linear relation between simulated and measured values. β is the slope of linear relation between simulated and measured values	Amiri et al, 2013; Zheng et al, 2020
SD	Standard deviation (SD) is the mean of measured or simulated values in whole population	Amiri et al, 2013; Zheng et al, 2020; Kaini et al, 2022

Crop model	Calibrated parameter
Oryza2000	Development rate in juvenile, photo-sensitive, panicle and reproductive phases; parameters of a junction to calculate specific leaf area; fraction of shoot dry matter portioned to leaves, stem and panicles; leaf death coefficient as a function of development stage
Aquacrop FAO	Maximum canopy growth; maximum and minimum root length; recovery time after transplanting; time from transplanting to flowering, senescence, and maturity; length of flowering stage; reference harvest index; water productivity normalized for reference evapotranspiration and CO₂
DSSAT/CERES-Rice	Measured plant parameters to estimate rice genetic coefficients: dates of key phenological stages (panicle initiation, heading date, flowering and physiological maturity date and maturity days after planting), yield and yield components

Crop model	Calibrated parameter
Oryza2000	Development rate in juvenile, photo-sensitive, panicle and reproductive phases; parameters of a junction to calculate specific leaf area; fraction of shoot dry matter portioned to leaves, stem and panicles; leaf death coefficient as a function of development stage
Aquacrop FAO	Maximum canopy growth; maximum and minimum root length; recovery time after transplanting; time from transplanting to flowering, senescence, and maturity; length of flowering stage; reference harvest index; water productivity normalized for reference evapotranspiration and CO₂
DSSAT/CERES-Rice	Measured plant parameters to estimate rice genetic coefficients: dates of key phenological stages (panicle initiation, heading date, flowering and physiological maturity date and maturity days after planting), yield and yield components

Reference	Main result	Method	Study period
Reference	Main result	Method	Baseline	Horizon
Liersch et al, 2013	Decrease in floodable area	RCM, SRES, hydrological model SWIM	1971-2000	2011-2050
Robles-Morua et al, 2015	Increase in availability of irrigation water	RCM, SRES, downscaling approach, hydrological model HEC-HMS	1990-2000	2031-2040
Ye et al, 2015	Increase in CWR & IWR	RCM, SRES, water balance calculation	1951-1980	2011-2040, 2071-2100
Cho et al, 2016	Increase in IWR; increase in availability of irrigation water	GCM multimodel, RCP 4.5 & 8.5, reservoir model HOMWRS	1976-2005	2011-2040
Hong et al, 2016	Decrease in IWR; increase in cET and NIR	RCM, RCP 4.5 and 8.5, water balance calculation	1981-2010	2011-2040 (2025) 2041-2070 (2055) 2071-2100 (2085)
Acharjee et al, 2017	Decrease in ETc; increase in IWR	GCM, RCP 4.5 and 8.5, Crop model FAO-CropWat	1980-2013	2050, 2080
Ding et al, 2017	Increase in IWR	GCM, RCP 2.6, 4.5 and 8.5, water balance model	1961-2012	2020, 2050, 2080
Sun et al, 2018	Decrease in IWR	GCM, RCP 2.6, 4.5 and 8.5, water balance calculation	1996-2005	2020, 2030, 2040
Rowshon et al, 2019; Ismail et al, 2020	Increase in IWR; decrease in irrigation water availability	GCM, RCP 4.5, 6.0 and 8.5, graphical user interface development environment/modèle agrohydrologique intelligent utilisant visual basic for applications	1976-2005	2020, 2050, 2080
Ahmed, 2020	Decrease in the irrigation water availability	RCM, RCP 4.5 and 8.5, crop model FAO-Aquacrop	1960-1990	2040-2070
Ding et al, 2020	Increase in NIR	GCM, RCP 8.5, Crop model ORYZA v3		2011-2040, 2041-2070, 2071-2100
Zheng et al, 2020	Increase in blue water footprint; decrease in water productivity	GCM, RCP 2.6, 4.5, 6.0 and 8.5, crop model ORYZA2000	1961-2010	2020, 2050, 2080