Abstract
Climatic changes are associated with fluctuations spanning over a period of three decades as a classic period of computing weather trends all around the world which, by studies till now, was proved to be harmful for life on earth. Natural processes going on in this earth were observed to be impacted significantly by these variations in our climate that are the result of anthropogenic activities. Rapid growth in population demands more resources for their survival that includes the basic amenities of livelihood, i.e., nutrition, energy, and housing. Limited resources in combination with the risk of climatic changes are in fact a big problem that must be solved before it results in nonreversible damage. Modeling is the advanced approach to study climate change. Right after the Second World War, predominantly in the USA, by the end of the 1960s, representatives were being presented with the model’s findings, which strongly supported the concept that the persistent intensification in greenhouse gas (GHG) emissions caused by human activities have completely changed the overall impact of global climate. With the passage of time, more advancement in modeling was observed; first of all, conceptual models were formed; those were replaced by analog models and then energy balance models were introduced by researchers. In agricultural systems, modeling as an essential tool is accomplished by scientists from different disciplines that has contributed for six decades in this field. Models have been used in ecosystem studies, hydrology, climate, crops, livestock and Hadley Climate model version 3 (HadCM3) is recently commonly used and several other Global climate models (GCMs) are in practice apart from statistical models like Statistical Downscaling Model (SDSM) are prominent among others for analytical climatic data studies. In order to study the climate changes; different climate projection scenarios have been made on the basis of previously provided data, i.e., rainfall, temperature, carbon dioxide and GHG emissions, and other components. On the basis of these scenarios, future predictions are likely to be more realistic and hopefully helpful for addressing the changing climatic situations across the globe and proactively devising mitigation practices to save the masses.
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References
Amin A, et al. 2018a. Regional climate assessment of precipitation and temperature in Southern Punjab (Pakistan) using SimCLIM climate model for different temporal scales. Theoretical and Applied Climatology. January 2018, Volume 131, Issue 1–2, pp 121–131
Amin, A. et al. 2017. Comparison of future and base precipitation anomalies by SimCLIM statistical projection through ensemble approach in Pakistan. Atmospheric Research 294:214-225.
Amin, A. et al. 2018b. Evaluation and analysis of temperature for historical (1996-2015) and projected (2030-2060) climates in Pakistan using SimCLIM climate model: Ensemble application. Atmospheric Research, 213: 422-436
Amin, A. et al. 2018c. Simulated CSM-CROPGRO-cotton yield under projected future climate by SimCLIM for southern Punjab, Pakistan. Agricultural Systems 167: 213–222
Brisson N, Gate P, Gouache D, Charmet G, Oury F-X, Huard F (2010) Why are wheat yields stagnating in Europe? A comprehensive data analysis for France. Field Crops Research 119, 201–212.
Brooks CEP (1951) Geological and historical aspects of climatic change. In: Malone TF, ed. Compendium of Meteorology. Boston: American Meteorological Society; 1951, 1004–1018.
Cairns JE, Hellin J, Sonder K, Araus JL, MacRobert JF, Thierfelder C, Prasanna B (2013) Adapting maize production to climate change in Sub-Saharan Africa. Food Security 5, 345-360
Carberry PS, Liang W, Twomlow S, Holzworth DP, Dimes JP, McClelland T, Huth NI, Chen F, Hochman Z, Keating BA. 2013. Scope for improved eco-efficiency varies among diverse cropping
Ceglar A, Kajfež-Bogataj L (2012) Simulation of maize yield in current and changed climatic conditions: addressing modelling uncertainties and the importance of bias correction in climate model simulations. European Journal of Agronomy 37, 83-95.
Collier, M.A., Jeffrey, S.J., Rotstayn, L.D., Wong, K.K., Dravitzki, S.M., Moseneder, C., Hamalainen, C., Syktus, J.I., Suppiah, R., Antony, J., El Zein, A., 2011. The CSIROMk3.6.0Atmosphere-Ocean GCM: participation in CMIP5 and data publication. In: 19th International Congress on Modelling and Simulation. Perth, Australia, 12–16 December 2011. http://mssanz.org.au/modsim2011
de Wit, C.T. (1958). Transpiration and crop yields. Volume 64 of Agricultural research report/Netherlands Volume 59 of Mededeling (Instituut voor Biologisch en Scheikundig Onderzoek va Landbouwgewasses) Verslagen van landbouwkundige onderzoekingen. Institute of Biological and Chemical Research on Field Crops and Herbage (1958).
Edwards PN (2011) History of climate modeling. Climate change vol 2, 128-139.
Ghamghami M, Ghahreman N, Olya H (2019) Comparison of three multi-site models in stochastic reconstruction of winter daily rainfall over Iran. Model. Earth Syst. Environ. 5, 1319–1332 doi: https://doi.org/10.1007/s40808-019-00599-7
Hussain, S. et al. (2020). Study of land use/land cover changes using RS and GIS: A case study of Multan district, Pakistan. Environmental Monitoring and Assessment (2020) 192: 2
IPCC (2012). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp 582
Jalbert J, Murphy OA., Genset C, Neˇslehov´a JG. 2019. Modelling extreme rain accumulation with an application to the 2011 Lake Champlain flood. Appl. Statist 68:831-856
Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. European Journal of Agronomy, 18(3–4), 235–265.
Jones JW, Naab J, Fatondji D, Dzotsi K, Adiku S, He J (2012) Uncertainties in simulating crop performance in degraded soils and low input production systems. In Improving soil fertility recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT) (pp. 43–59). Springer, Dordrecht.
Jones JW, Antle JM, Basso BO, Boote KJ, Conant RT, Foster I, Godfray HCJ, Herrero M, Howitt RE, Janssen S, Keating BA, Munoz-Carpena R, Porter CH, Rosenzweig C, Wheeler TR, (2016) Towards a new generation of agricultural system models, data, and knowledge products: state of agricultural systems science. Agric.Syst. 155:269–288 (in this issue).
Jørgensen SE (2010) A review of recent developments in lake modelling. Ecol Modell 221:689–692
Kastner T, Rivas MJI, Koch W, Nonhebel S (2012) Global changes in diets and the consequences for land requirements for food. Proceedings of the National Academy of Sciences U S A 109:6868–6872.a
Kottas A, Fellingham GW (2012) Bayesian semiparametric modeling and inference with mixtures of symmetric distributions. Stat Comput 22:93–106
Mares C, Mares I, Huebener H, Mihailescu M, Cubasch U, Stanciu P (2014) A Hidden Markov Model Applied to the Daily Spring Precipitation over the Danube Basin. Advances in Meteorology Volume 2014, Article ID 237247, 11 pages
Marin, F.R., Jones, J.W., Royce, F., Suguitani, C., Donzeli, J.L., Filho, W.J.P. and Nassif, D.S., 2011. Parameterization and evaluation of predictions of DSSAT/CANEGRO for Brazilian sugarcane. Agronomy Journal, 103(2):304–315.
Mooij, W.M., Trolle, D., Jeppesen, E., Arhonditsis, G., Belolipetsky, P.V., Chitamwebwa, D.B., Degermendzhy, A.G., DeAngelis, D.L., Domis, L.N.D.S., Downing, A.S. and Elliott, J.A., 2010. Challenges and opportunities for integrating lake ecosystem modelling approaches. Aquatic Ecology, 44(3), pp. 633–667.
Mubeen M. et al. 2016. Application of CSM-CERES-Maize Model in Optimizing Irrigated conditions. Outlook on Agriculture. 45(3) 173–184
Mubeen, M. et al. 2019. Evaluating the climate change impact on crop water requirement of cotton- wheat in semi-arid conditions using DSSAT model. Accepted in Journal of Water and Climate Change. doi: https://doi.org/10.2166/wcc.2019.179
Müller, C., Cramer, W., Hare, W.L., Lotze-Campen, H., 2011. Climate change risks for African agriculture. Proceedings of the National Academy of Sciences 108, 4313-4315.
Nassif D.S.P., Marin F.R., Pallone Filho W.J., Resende R.S., Pellegrino G.Q., 2012. Parametrização e avaliação do modelo DSSAT/Canegro para variedades brasileiras de cana-de-açúcar. Pesquisa Agropecuária Brasileira, 47, 311–318.
Nasim, W., et al. 2018. Future risk assessment by estimating historical heat wave trends with projected heat accumulation using SimCLIM climate model in Pakistan. Atmospheric Research 205 (2018) 118–133.
Nguyen-Huy T., Deo R.C., Mushtaq S., Khan S., 2020. Probabilistic seasonal rainfall forecasts using semiparametric d-vine copula-based quantile regression. Handbook of Probabilistic Models 203-227https://doi.org/10.1016/B978-0-12-816514-0.00008-4
Nikahd A, Hashim M, Mirzaie AA, Ghosiraie ZN (2015) Advanced of Mathematics-Statistics Methods to Radar Calibration for Rainfall Estimation; A Review. International Journal on Recent and Innovation Trends in Computing and Communication 3(1):96-105
Ray DK, Ramankutty N, Mueller ND, West PC, Foley JA (2012) Recent patterns of crop yield growth and stagnation. Nature Communications 3, 1293.
Rötter RP, Carter TR, Olesen JE, Porter JR (2012) Crop-climate models need an overhaul. Nature Climate Change 1, 175-177.
Schlenker W, Lobell DB (2010) Robust negative impacts of climate change on African agriculture. Environmental Research Letters 5: 014010
Semenov MA, Stratonovitch P, Alghabari F, Gooding MJ (2014) Adapting wheat in Europe for climate change. Journal of Cereal Science 59:245−256.
Tariq M et al.2018. The impact of climate warming and crop management on phenology of sunflower-based cropping systems in Punjab, Pakistan. Agricultural and Forest Meteorology 256–257 (2018) 270–282
Tencaliec P, Favre A-C, Naveau P, Prieur C, Nicolet G(2020) Flexible semiparametric generalized Pareto modeling of the entire range of rainfall amount Environ metrics 31(2) https://doi.org/10.1002/env.2582
Tester M, Langridge P (2010) Breeding technologies to increase crop production in a changing world. Science 327:818–822.
Thornton PK, Jones PG, Alagarswamy G, Andresen J, Herrero M (2010) Adapting to climate change: agricultural system and household impacts in East Africa. Agricultural Systems 103:73-82.
Thornton PK, Jones PG, Ericksen PJ, Challinor AJ (2011) Agriculture and food systems in Sub-Saharan Africa in a 4oC+ world. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 369:117-136
Tilman D, Balzer C, Hill J, Befort BL (2011) Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences U S A 108(50):20260–20264.
Trnka M, Rötter RP, Ruiz-Ramos M, Kersebaum KC, Olesen JE, Zalud Z, Semenov MA (2014) Adverse weather conditions for European Wheat production will become more frequent with climate change. Nature Climate Change 4, 637–643.
Tyralis H, Langousis A (2018) Modelling of rainfall maxima at different durations using max-stable processes. European Geosciences Union General Assembly 2018 Geophysical Research Abstracts Vol. 20 https://www.researchgate.net/publication/325908779
Wheeler T, von Braun J (2013) Climate change impacts on global food security. Science 341 (6145):508–513.
White JW, Hoogenboom G, Kimball BA, Wall GW (2011) Methodologies for simulating impacts of climate change on crop production. Field Crops Research 124:357-368
Wu J (2013) An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression Book chapter accessible at https://www.igi-global.com/chapter/content/74935
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Rasul, F. et al. (2022). Role of Modeling in Assessing Climate Change. In: Jatoi, W.N., Mubeen, M., Ahmad, A., Cheema, M.A., Lin, Z., Hashmi, M.Z. (eds) Building Climate Resilience in Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-030-79408-8_18
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