Abstract
Managing FEW systems requires modeling tools to understand the merits of different decisions, policies, and investments given potential future constraints and the wider social, environmental, and economic contexts in which these are made. This chapter reviews integrated modeling tools used to support the analysis of FEW systems; especially those used for integrated planning; and the identification and evaluation of trade-offs and synergies.
Integrated FEW system models include representations of Coupled Natural-Human Systems (e.g., the energy system, agriculture and land use, water supply and use, the economy, and the climate). Through this integration, these models allow for exploration of FEW system interactions, and the interactions between these systems and other key external forces such as climate change, socioeconomic and technological change, and policy interventions. There is a clear relationship between FEW system modeling and the metrics reflecting interactions.
While several modeling frameworks are described in this book, only a small number of models and projects have been actually implemented in practice. Ongoing research and applications of FEW system modeling consist of the development of principles, algorithms, data requirements, and model formulations for understanding and evaluating the potential of implementing FEW system nexus approaches within a systems perspective. Outputs and products of these efforts are quantitative tools that focus on FEW system planning in order to identify primary opportunities and constraints to FEW system development, indicating priorities for more detailed analysis as well as providing a characterization of alternative system configurations that meet integrated FEW objectives.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Food-Centric Approaches to FEWS Modeling
Adams, D., Alig, R., McCarl, B. A., Murray, B. C., Bair, L., Depro, B., Latta, G., Lee, H.-C., Schneider, U. A., & Callaway, J. (2005). FASOMGHG conceptual structure, and specification: Documentation. Unpublished paper, Texas A&M University, Department of Agricultural Economics, College Station, TX.
Baker, D. N., Lambert, J. R., & McKinion, J. M. (1983). GOSSYM: A simulator of cotton crop growth and yield (134p). Technical Bulletin, South Carolina Agricultural Experiment Station.
Baker, D. N., & Landivar, J. A. (1990). The simulation of plant development in GOSSYM. In T. Hodges (Ed.), Predicting crop phenology (pp. 153–170). Boston, MA: CRC Press.
Beach, R. H., & McCarl, B. A. (2010). U.S. Agricultural and Forestry Impacts of the Energy Independence and Security Act: FASOM Results and Model Description (p. 178p). Research Triangle Park, NC: RTI International.
Boote, K. J., Jones, J. W., Hoogenboom, G., & Pickering, N. B. (1998). The CROPGRO model for grain legumes. In G. Tsuji, G. Hoogenboom, & P. Thornton (Eds.), Understanding options for agricultural production (Vol. 7, pp. 99–128). Berlin: Springer.
Chen, L., Liang, X.-Z., DeWitt, D., Samel, A. N., & Wang, J. X. L. (2015). Simulation of seasonal U.S. precipitation and temperature by the nested CWRF-ECHAM system. Climate Dynamics, 46(3), 1–18. https://doi.org/10.1007/s00382-015-2619-9.
Hodges, H. F., Whisler, F. D., Bridges, S. M., Reddy, K. R., & McKinion, J. M. (1997). Simulation in crop management—GOSSYM/COMAX. In R. M. Peart & R. B. Curry (Eds.), Agricultural systems modeling and simulation (pp. 235–282). New York: Marcel Dekker Inc.
Hoogenboom, G., Jones, J., & Boote, K. (1992). Modeling growth, development, and yield of grain legumes using SOYGRO, PNUTGRO, and BEANGRO: A review. Transactions of the ASAE (USA), 35(6), 2043–2056.
Jones, C. A., Kiniry, J. R., & Dyke, P. (1986). CERES-Maize: A simulation model of maize growth and development. College Station, TX: Texas A&M University Press.
Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L., Wilkens, P. W., Singh, U., Gijsman, A. J., & Ritchie, J. T. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18(3), 235–265.
Kiniry, J. R., Williams, J. R., Vanderlip, R. L., Atwood, J. D., Reicosky, D. C., Mulliken, J., Cox, W. J., Mascagni, H. J., Hollinger, S. E., & Wiebold, W. J. (1997). Evaluation of two maize models for nine U.S. locations. Agronomy Journal, 89(3), 421–426.
Larsen, S., Jaiswal, D., Bentsen, N. S., Wang, D., & Long, S. P. (2015). Comparing predicted yield and yield stability of willow and Miscanthus across Denmark. Global Change Biology Bioenergy. https://doi.org/10.1111/gcbb.12318.
Lee, H. (2002). The dynamic role for carbon sequestration by the U.S. agricultural and forest sectors in greenhouse gas emission mitigation. Unpublished Ph.D. Thesis, Texas A&M University.
Liang, X. Z., Xu, M., Gao, W., Reddy, K. R., Kunkel, K., Schmoldt, D. L., & Samel, A. N. (2012a). Physical modeling of U.S. cotton yields and climate stresses during 1979 to 2005. Agronomy Journal, 104(3), 675–683.
Liang, X. Z., Xu, M., Gao, W., Reddy, K. R., Kunkel, K., Schmoldt, D. L., & Samel, A. N. (2012b). A distributed cotton growth model developed from GOSSYM and its parameter determination. Agronomy Journal, 104(3), 661–674.
Liang, X.-Z., Xu, M., Yuan, X., Ling, T., Choi, H. I., Zhang, F., Chen, L., Liu, S., Su, S., & Qiao, F. (2012c). Regional climate-weather research and forecasting model. Bulletin of the American Meteorological Society, 93(9), 1363–1387.
Liang, X.-Z., & Zhang, F. (2013). The cloud–aerosol–radiation (CAR) ensemble modeling system. Atmospheric Chemistry and Physics, 13(16), 8335–8364.
Liu, S., Wang, J. X. L., Liang, X.-Z., Morris, V., & Fine, S. S. (2015). A hybrid approach to improving the skills of seasonal climate outlook at the regional scale. Climate Dynamics, 46(1–2), 483–494. https://doi.org/10.1007/s00382-015-2594-1.
McKinion, J., Baker, D., Whisler, F., & Lambert, J. (1989). Application of the GOSSYM/COMAX system to cotton crop management. Agricultural Systems, 31(1), 55–65.
Miguez, F. E., Zhu, X. G., Humphries, S., Bollero, G. A., & Long, S. P. (2009). A semimechanistic model predicting the growth and production of the bioenergy crop Miscanthus × giganteus: Description, parameterization and validation. Global Change Biology Bioenergy, 1, 282–296.
Miguez, F. E., Maughan, M., Bollero, G. A., & Long, S. P. (2012). Modeling spatial and dynamic variation in growth, yield, and yield stability of the bioenergy crops Miscanthus × giganteus and Panicum virgatum across the conterminous United States. Global Change Biology Bioenergy, 4, 509–520.
Pang, X., Letey, J., & Wu, L. (1997). Yield and nitrogen uptake prediction by CERES-Maize model under semiarid conditions. Soil Science Society of America Journal, 61(1), 254–256.
Reddy, K. R., Hodges, H. F., & McKinion, J. M. (1997). Modeling temperature effects on cotton internode and leaf growth. Crop Science, 37(2), 503–509.
Reddy, K. R., Kakani, V. G., McKinion, J. M., & Baker, D. N. (2002). Applications of a cotton simulation model, GOSSYM, for crop management, economic and policy decisions. In L. R. Ahuja, L. Ma, & T. A. Howell (Eds.), Agricultural system models in field research and technology transfer. Boston, MA: CRC Press.
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Duda, M. G., Huang, X.-Y., Wang, W., & Powers, J.G. (2008). A description of the advanced research WRF Version 3. NCAR Technical Note. NCAR/TN–475+ STR (113p).
Tsvetsinskaya, E. A., Mearns, L. O., & Easterling, W. E. (2001). Investigating the effect of seasonal plant growth and development in three-dimensional atmospheric simulations. Part I: Simulation of surface fluxes over the growing season. Journal of Climate, 14(5), 692–709.
Wang, D., Jaiswal, D., Lebauer, D. S., Wertin, T. M., Bollero, G. A., Leakey, A. D. B., & Long, S. P. (2015). A physiological and biophysical model of coppice willow (Salix spp.) production yields for the contiguous USA in current and future climate scenarios. Plant, Cell and Environment, 38(9), 1850–1865. https://doi.org/10.1111/pce.12556.
Yuan, X., & Liang, X. Z. (2011). Improving cold season precipitation prediction by the nested CWRF-CFS system. Geophysical Research Letters, 38(2). https://doi.org/10.1029/2010GL046104.
Energy-Centric Approaches to FEWS Modeling
Dale, L., Karali, N., Millstein, D., Carnall, M., Vicuña, S., Borchers, N., Bustos, E., O’Hagan, J., Purkey, D., Heaps, C., Sieber, J., Collins, W., & Sohn, M. (2015). An integrated assessment of water-energy and climate change in Sacramento, California: How strong is the nexus? Climatic Change, 132, 223–235.
Hunt, P., Miller, J., Ducey, T., Lang, M., Szogi, A., & McCarty, G. (2014). Denitrification in soils of hydrologically restored wetlands relative to natural and converted wetlands in the Mid-Atlantic coastal plain of the USA. Ecological Engineering, 71, 438–447.
Loulou, R., Goldstein, G., & Noble, K. (2004). Documentation for the MARKAL Family of Models. ETSAP.
Loulou, R., Remne, U., Kanudia, A., Lehtila, A., & Goldstein, G. (2005). Documentation for the TIMES Model—Part I (pp. 1–78). ETSAP.
Mirchi, A., Madani, K., Watkins, D., & Ahmad, S. (2012). Synthesis of system dynamics tools for holistic conceptualization of water resources problems. Water Resources Management, 26, 2421–2442.
Rodriguez, D., Delgado, A., Bazilian, M., Ahjum, F., Cullis, J., Delaquil, P., Goldstein, G., Liden, R., Merven, B., Miralles-Wilhelm, F., Sohns, A., Stone, A., & Toman, M. (2017). Water Contrains South Africa’s Energy Future: A Case Study on Integrated Energy-Water Nexus Modeling and Analysis, International Journal of Engineering Science, 6(10), 1–25.
Yates, D., & Miller, K. (2013). Integrated decision support for energy/water planning in California and the southwest. International Journal of Climate Change: Impacts and Responses, 4(1), 49–63.
Water-Centric Approaches to FEWS Modeling
Ahmad, S., & Simonovic, S. (2004). Spatial system dynamics: New approach for simulation of water resources systems. Journal of Computing in Civil Engineering ASCE, 18(4), 331–340.
Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment Part I: Model development. Journal of the American Water Resources Association, 34(1), 73–89.
ComÃn, F. A., Sorando, R., Darwiche-Criado, N., GarcÃa, M., & Masip, A. (2014). A protocol to prioritize wetland restoration and creation for water quality improvement in agricultural watersheds. Ecological Engineering, 66, 10–18.
Daloğlu, I., Cho, K. H., & Scavia, D. (2012). Evaluating causes of trends in long-term dissolved reactive phosphorus loads to Lake Erie. Environmental Science & Technology, 46(19), 10660–10666.
Denver, J., Ator, S., Lang, M., Fisher, T., Gustafson, A., Fox, R., Clune, J., & McCarty, G. (2014). Nitrate fate and transport through current and former depressional wetlands in an agricultural landscape, Choptank Watershed, Maryland, United States. Journal of Soil and Water Conservation, 69(1), 1–16.
Ducey, T., Miller, J., Lang, M., Szogi, A., Hunt, P., Fenstermacher, D., Rabenhorst, M., & McCarty, G. (2015). Soil physicochemical conditions, denitrification rates, and abundance in North Carolina coastal plain restored wetlands. Journal of Environmental Quality, 44(3), 1011–1022.
Ficklin, D. L., Luo, Y., Stewart, I. T., & Maurer, E. P. (2012). Development and application of a hydroclimatological stream temperature model within the Soil and Water Assessment Tool. Water Resources Research, 48(1). https://doi.org/10.1029/2011WR011256.
Ficklin, D. L., Stewart, I. T., & Maurer, E. P. (2013). Effects of climate change on stream temperature, dissolved oxygen, and sediment concentration in the Sierra Nevada in California. Water Resources Research, 49(5), 2765–2782.
Garg, K. K., Bharati, L., Gaur, A., George, B., Acharya, S., Jella, K., & Narasimhan, B. (2012). Spatial mapping of agricultural water productivity using the swat model in Upper Bhima catchment, India. Irrigation and Drainage, 61(1), 60–79.
Gassman, P. W., Reyes, M. R., Green, C. H., & Arnold, J. G. (2007). The soil and water assessment tool: Historical development, applications, and future research directions. Transactions of the ASABE, 50(4), 1211–1250.
Gober, P., Wentz, E., Lant, T., Tschudi, M., & Kirkwood, C. (2011). WaterSim: A simulation model for urban water planning in Phoenix, Arizona, USA. Environment and Planning B: Urban Analytics and City Science, 38(2), 197–215.
Hejazi, M., Edmonds, J., Clarke, L., Kyle, P., Davies, E., Chaturvedi, V., Wise, M., Patel, P., Eom, J., Calvin, K., Moss, R., & Kim, S. (2014a). Long-term global water projections using six socioeconomic scenarios in an integrated assessment modeling framework. Technological Forecasting and Social Change, 81, 205–226. https://doi.org/10.1016/j.techfore.2013.05.006.
Hejazi, M. I., Edmonds, J., Clarke, L., Kyle, P., Davies, E., Chaturvedi, V., Wise, M., Patel, P., Eom, J., & Calvin, K. (2014b). Integrated assessment of global water scarcity over the 21st century under multiple climate change mitigation policies. Hydrology and Earth System Sciences, 18(8), 2859–2883. https://doi.org/10.5194/hess-18-2859-2014.
Hejazi, M. I., Voisin, N., Liu, L., Bramer, L., Fortin, D., Huang, M., Hathaway, J., Kyle, P., Leung, L. R., Li, H.-Y., Liu, Y., Patel, P., Pulsipher, T., Rice, J. S., Tesfa, T. K., Vernon, C. R., & Zhou, Y. (2015). 21st Century U.S. emissions mitigation increases water stress more than the climate change it is mitigating. Proceedings of the National Academy of Sciences, 112(34), 10635–10640.
Hively, W. D., Hapeman, C. J., McConnell, L. L., Fisher, T. R., Rice, C. P., McCarty, G. W., Sadeghi, A. M., Whitall, D. R., Downey, P. M., & de Guzmán, G. T. N. (2011). Relating nutrient and herbicide fate with landscape features and characteristics of 15 subwatersheds in the Choptank River watershed. Science of the Total Environment, 409(19), 3866–3878.
Jang, J.-H., Jung, K.-W., & Yoon, C. G. (2012). Modification of SWAT model for simulation of organic matter in Korean watersheds. Water Science & Technology, 66(11), 2355–2362.
Khoi, D. N., & Suetsugi, T. (2014). Impact of climate and land-use changes on the hydrological processes and sediment yield—A case study for the Be River Catchment, Vietnam. Hydrological Sciences Journal, 59(5), 1095–1108.
Lang, M., McCarty, G., Oesterling, R., & Yeo, I.-Y. (2013). Topographic metrics for improved mapping of forested wetlands. Wetlands, 33(1), 141–155.
Luo, Y., Ficklin, D. L., Liu, X., & Zhang, M. (2013). Assessment of climate change impacts on hydrology and water quality with a watershed modeling approach. Science of the Total Environment, 450, 72–82.
Michalak, A. M., Anderson, E. J., Beletsky, D., Boland, S., Bosch, N. S., Bridgeman, T. B., Chaffin, J. D., Cho, K., Confesor, R., & Daloğlu, I. (2013). Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions. Proceedings of the National Academy of Sciences, 110(16), 6448–6452.
Miralles-Wilhelm, F., Clarke, L., Hejazi, M., Kim, S., Gustafson, K., Muñoz-Castillo, R., & Graham, N. (2017). Physical impacts of climate change on water resources. World Bank Discussion Paper.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and Water Assessment Tool theoretical documentation: Version 2009 (618p). Texas Water Resources Institute Technical Report 406. Texas Water Resources Institute.
Palao, L. K. M., Dorado, M. M., Anit, K. P. A., & Lasco, R. D. (2013). Using the Soil and Water Assessment Tool (SWAT) to assess material transfer in the Layawan watershed, Mindanao, Philippines and its implications on payment for ecosystem services. Journal of Sustainable Development, 6(6), 73–88.
SEI (Stockholm Environment Institute). (2001). WEAP: Water evaluation and planning system—User guide. Boston, MA: SEI.
Sieber, J., Yates, D., Purkey, D., & Huber Lee, A. (2004). WEAP: A demand, priority and preference driven water planning model: Part 1: Model characteristics. Water International. https://doi.org/10.1080/02508060508691893.
White, M., Santhi, C., Kannan, N., Arnold, J., Harmel, D., Norfleet, L., Allen, P., DiLuzio, M., Wang, X., & Atwood, J. (2014). Nutrient delivery from the Mississippi River to the Gulf of Mexico and effects of cropland conservation. Journal of Soil and Water Conservation, 69(1), 26–40.
Yepsen, M., Baldwin, A. H., Whigham, D. F., McFarland, E., LaForgia, M., & Lang, M. (2014). Agricultural wetland restorations on the USA Atlantic coastal plain achieve diverse native wetland plant communities but differ from natural wetlands. Agriculture, Ecosystems & Environment, 197, 11–20.
Zhanxue, Z., Broersma, K., & Mazumder, A. (2012). Impacts of land use, fertilizer and manure application on the stream nutrient loadings in the Salmon River watershed, south-central British Columbia, Canada. Journal of Environmental Protection, 3, 809–822.
Integrated FEWS Modeling Approaches
Blanc, E., Strzepek, K., Schlosser, A., Jacoby, H., Gueneau, A., Fant, C., Rausch, S., & Reilly, J. (2014). Modeling U.S. water resources under climate change. Earth’s Future, 2(4), 197–224. https://doi.org/10.1002/2013EF000214.
Chaturvedi, V., Hejazi, M., Edmonds, J., Clarke, L., Kyle, P., Davies, E., & Wise, M. (2015). Climate mitigation policy implications for global irrigation water demand. Mitigation and Adaptation Strategies for Global Change., 20(3), 389–407. https://doi.org/10.1007/s11027-013-9497-4.
Crona, B. I., & Parker, J. N. (2011). Network determinants of knowledge utilization preliminary lessons from a boundary organization. Science Communication, 33(4), 448–471.
Fawcett, A., Iyer, G. C., Clarke, L. E., Edmonds, J. A., Hultman, N. E., McJeon, H. C., Rogelj, J., Schuler, R., Alsalam, J., Asrar, G. R., Creason, J., Jeong, M., McFarland, J., Mundra, A., & Shi, W. (2015). Can Paris pledges avert severe climate change? Science, 350(6265), 1168–1169. https://doi.org/10.1126/science.aad5761.
Hibbard, K. A., & Janetos, A. C. (2013). The regional nature of global challenges: A need and strategy for integrated regional modeling. Climatic Change, 118(3–4), 565–577.
Kim, S. H., Hejazi, M. I., Liu, L., Calvin, K. V., Clarke, L. E., Edmonds, J., Kyle, P., Patel, P. L., & Wise, M. A. (2016). Balancing global water availability and use at basin scale in an integrated assessment model. Climatic Change. https://doi.org/10.1007/s10584-016-1604-6.
Kraucunas, I., et al. (2015). Investigating the nexus of climate, energy, water, and land at decision-relevant scales: The Platform for Regional Integrated Modeling and Analysis (PRIMA). Climatic Change, 129(3–4), 573–588.
Liu, L., Hejazi, M., Patel, P., Kyle, P., Davies, E., Zhou, Y., Clarke, L., & Edmonds, J. (2015a). Water demands for electricity generation in the U.S.: Modeling different scenarios for the water–energy nexus. Technological Forecasting and Social Change, 94, 318–334. https://doi.org/10.1016/j.techfore.2014.11.004.
Schlosser, A. P., Kicklighter, D., Dutkiewicz, S., Reilly, J., Wang, C., Felzer, B., Melillo, J. M., & Jacoby, H. D. (2009). Probabilistic forecast for twenty-first-century climate based on uncertainties in emissions (without policy) and climate parameters. Journal of Climate, 22(19), 5175–5204. https://doi.org/10.1175/2009JCLI2863.1.
Strachan, N., Fais, B., & Daly, H. (2016). Reinventing the energy modelling–policy interface. Nature Energy, 1, 16012.
Zhou, Y., Clarke, L., Eom, J., Kyle, P., Patel, P., Kim, S. H., Dirks, J., Jensen, E., Liu, Y., Rice, J., Schmidt, L., & Seiple, T. (2014). Modeling the effect of climate change on U.S. state-level buildings energy demands in an integrated assessment framework. Applied Energy, 113, 1077–1088. https://doi.org/10.1016/j.apenergy.2013.08.034.
Food-Energy (Bioenergy)
Anderson, C. J., Anex, R. P., Arritt, R. W., Gelder, B. K., Khanal, S., Herzmann, D. E., & Gassman, P. W. (2013). Regional climate impacts of a biofuels policy projection. Geophysical Research Letters, 40(6), 1217–1222.
Barton, B., & Clark, S. E. (2014). Water & climate risks facing U.S. corn production: How companies and investors can cultivate sustainability (p. 71pp). Austin, TX: CERES.
Gelfand, I., Sahajpal, R., Zhang, X., Izaurralde, R. C., Gross, K. L., & Robertson, G. P. (2013). Sustainable bioenergy production from marginal lands in the U.S. Midwest. Nature, 493(7433), 514–517.
Hamilton, S. K., Hussain, M. Z., Bhardwaj, A. K., Basso, B., & Robertson, G. P. (2015a). Comparative water use by maize, perennial crops, restored prairie, and poplar trees in the U.S. Midwest. Environmental Research Letters, 10(6), 064015.
Johnston, C. A. (2014). Agricultural expansion: Land use shell game in the U.S. Northern Plains. Landscape Ecology, 29(1), 81–95.
Lark, T. J., Salmon, J. M., & Gibbs, H. K. (2015). Cropland expansion outpaces agricultural and biofuel policies in the United States. Environmental Research Letters, 10(4), 044003.
Le, P. V., Kumar, P., & Drewry, D. T. (2011). Implications for the hydrologic cycle under climate change due to the expansion of bioenergy crops in the Midwestern United States. Proceedings of the National Academy of Sciences of the United States of America, 108(37), 15085–15090.
Mladenoff, D. J., Sahajpal, R., Johnson, C. P., & Rothstein, D. E. (2016). Recent land use change to agriculture in the U.S. lake states: Impacts on cellulosic biomass potential and natural lands. PLoS One, 11(2), e0148566. https://doi.org/10.1371/journal.pone.0148566.
Nonhebel, S. (2005). Renewable energy and food supply: Will there be enough land? Renewable and Sustainable Energy Reviews, 9, 191–201.
Plourde, J. D., Pijanowski, B. C., & Pekin, B. K. (2013). Evidence for increased monoculture cropping in the Central United States. Agriculture, Ecosystems & Environment, 165(2013), 50–59.
Qin, Z., Zhuang, Q., & Cai, X. (2015). Bioenergy crop productivity and potential climate change mitigation from marginal lands in the United States: An ecosystem modeling perspective. GCB Bioenergy, 7, 1211–1221.
Robertson, G. P., Hamilton, S. K., Del Grosso, S. J., & Parton, W. J. (2011). The biogeochemistry of bioenergy landscapes: Carbon, nitrogen, and water considerations. Ecological Applications, 21, 1055–1067.
Tilman, D., Hill, J., & Lehman, C. (2006). Carbon-negative biofuels from low-input high-diversity grassland biomass. Science, 314, 1598–1600.
Werling, B. P., Dickson, T. L., Isaacs, R., Gaines, H., Gratton, C., Gross, K. L., Liere, H., Malmstrom, C. M., Meehan, T. D., Ruan, L., & Robertson, B. A. (2014). Perennial grasslands enhance biodiversity and multiple ecosystem services in bioenergy landscapes. Proceedings of the National Academy of Sciences of the United States of America, 111, 1652–1657.
Energy-Water
Environmental Protection Agency. (2013). National rivers and streams assessment, 2008–2009 results. Retrieved from http://www.epa.gov/national-aquatic-resource-surveys/nrsa.
Hamilton, S. H., ElSawah, S., Guillaume, J. H., Jakeman, A. J., & Pierce, S. A. (2015b). Integrated assessment and modelling: Overview and synthesis of salient dimensions. Environmental Modelling & Software, 64, 215–229.
Schornagel, J., Niele, F., Worrell, E., & Boggermann, M. (2012). Water accounting for (agro) industrial operations and its application to energy pathways. Resources, Conservation and Recycling., 61(2012), 1–15.
World Bank. (2013). Thirsty energy, water papers. Water Partnership Program.
World Energy Council. (2010). Water for energy. London, UK: World Energy Council.
World Water Assessment Program (WWAP). (2012). The United Nations World Water Development Report 4. Paris: UNESCO.
Yimam, Y. T., Ochsner, T. E., Kakani, V. G., & Warren, J. G. (2014). Soil water dynamics and evapotranspiration under annual and perennial bioenergy crops. Soil Science Society of America Journal, 78(5), 1584–1593.
Climate-FEWS
Alter, R. E., Fan, Y., Lintner, B. R., & Weaver, C. P. (2015). Observational evidence that Great Plains irrigation has enhanced summer precipitation intensity and totals in the Midwestern US. Journal of Hydrometeorology, 16(4), 1717–1735.
Boucher, O., Myhre, G., & Myhre, A. (2004). Direct human influence of irrigation on atmospheric water vapour and climate. Climate Dynamics, 22(6–7), 597–603.
DeAngelis, A., Dominguez, F., Fan, Y., Robock, A., Kustu, M. D., & Robinson, D. (2010). Evidence of enhanced precipitation due to irrigation over the Great Plains of the United States. Journal of Geophysical Research-Atmospheres, 115. https://doi.org/10.1029/2010JD013892.
Dunbar, J. A., Allen, P. M., & Bennett, S. J. (2010). Effect of multiyear drought on upland sediment yield and subsequent impacts on flood control reservoir storage. Water Resources Research, 46, W05526.
Field, C. B., Lobell, D. B., Peters, H. A., & Chiariello, N. R. (2007). Feedbacks of terrestrial ecosystems to climate change. Annual Review of Environment and Resources, 32, 1–29.
Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., & Toulmin, C. (2010). Food security: The challenge of feeding 9 billion people. Science, 327(5967), 812–818.
Harding, K. J., & Snyder, P. K. (2012a). Modeling the atmospheric response to irrigation in the great plains. Part II: The precipitation of irrigated water and changes in precipitation recycling. Journal of Hydrometeorology, 13(6), 1687–1703.
Harding, K. J., & Snyder, P. K. (2012b). Modeling the atmospheric response to irrigation in the great plains. Part I: General impacts on precipitation and the energy budget. Journal of Hydrometeorology, 13(6), 1667–1686.
Hatfield, J., Takle, G., Grotjahn, R., Holden, P., Izaurralde, R. C., Mader, T., Marshall, E., & Liverman, D. (2014). Chapter 6: Agriculture. In J. M. Melillo, T. C. Richmond, & G. W. Yohe (Eds.), Climate change impacts in the United States: The third National Climate Assessment (pp. 150–174). Washington, DC: U.S. Global Change Research Program, 150–174.
Im, E. S., Marcella, M. P., & Eltahir, E. A. B. (2014). Impact of potential large-scale irrigation on the West African monsoon and its dependence on location of irrigated area. Journal of Climate, 27(3), 994–1009.
IPCC. (2013). Climate change 2013: The physical science basis. In T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, & P. M. Midgley (Eds.), Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (p. 1535pp). Cambridge, UK: Cambridge University Press.
Karlen, D. L., Lal, R., Follett, R. F., Kimble, J. M., Hatfield, J. L., Miranowski, J. M., Cambardella, C. A., Manale, A., Anex, R. P., & Rice, C. W. (2009). Crop residues: The rest of the story. Environmental Science & Technology, 43(21), 8011–8015.
Kustu, M. D., Fan, Y., & Rodell, M. (2011). Possible link between irrigation in the U.S. High Plains and increased summer streamflow in the Midwest. Water Resources Research, 47(3). https://doi.org/10.1029/2010WR010046.
Lo, M. H., & Famiglietti, J. S. (2013). Irrigation in California’s Central Valley strengthens the southwestern U.S. water cycle. Geophysical Research Letters, 40(2), 301–306.
Lobell, D., Bala, G., Bonfils, C., & Duffy, P. (2006). Potential bias of model projected greenhouse warming in irrigated regions. Geophysical Research Letters, 33(13), 2–5.
Lobell, D., Bala, G., Mirin, A., Phillips, T., Maxwell, R., & Rotman, D. (2009). Regional differences in the influence of irrigation on climate. Journal of Climate, 22(8), 2248–2255.
Lobell, D. B., Roberts, M. J., Schlenker, W., Braun, N., Little, B. B., Rejesus, R. M., & Hammer, G. L. (2014). Greater sensitivity to drought accompanies maize yield increase in the U.S. Midwest. Science, 344(6183), 516–519.
McGuire, V. L. (2009). Water-level changes in the High Plains aquifer, predevelopment to 2007, 2005–06, and 2006–07 (9pp). U.S. Geological Survey Scientific Investigations Report 2009–5019. Retrieved from http://pubs.usgs.gov/sir/2009/5019/.
Melillo, J. M., Richmond, T. C., & Yohe, G. W. (Eds.). (2014). Climate Change Impacts in the United States: The Third National Climate Assessment (p. 841pp). Washington, DC: U.S. Global Change Research Program.
Ort, D. R., & Long, S. P. (2014). Limits on yields in the corn belt. Science, 344, 483–484.
Osborne, T., Slingo, J., Lawrence, D., & Wheeler, T. (2009). Examining the interaction of growing crops with local climate using a coupled crop-climate model. Journal of Climate, 22(6), 1393–1411.
Ozdogan, M., Rodell, M., Beaudoing, H. K., & Toll, D. L. (2010). Simulating the effects of irrigation over the United States in a land surface model based on satellite-derived agricultural data. Journal of Hydrometeorology, 11(1), 171–184.
Porter, J. R., & Semenov, M. A. (2005). Crop responses to climatic variation. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1463), 2021–2035.
Pryor, S. C., Scavia, D., Downer, C., Gaden, M., Iverson, L., Nordstrom, R., Patz, J., & Robertson, G. P. (2014). Chapter 18: Midwest. In J. M. Melillo, T. C. Richmond, & G. W. Yohe (Eds.), Climate change impacts in the United States: The third National Climate Assessment (pp. 418–440). Washington, DC: U.S. Global Change Research Program.
Qian, Y., Huang, M., Yang, B., & Berg, L. K. (2013). A modeling study of irrigation effects on surface fluxes and land–air–cloud interactions in the Southern Great Plains. Journal of Hydrometeorology, 14(3), 700–721.
Roberts, M., Long, S., Tieszen, L., & Beadle, C. (1993). Measurement of plant biomass and net primary production of herbaceous vegetation. In Photosynthesis and production in a changing environment (pp. 1–21). Berlin: Springer.
Seager, R., Ting, M., Held, I., Kushnir, Y., Lu, H., Vecchi, G., Huang, H.-P., Harnik, N., Leetmaa, A., Lau, N.-C., Li, C., Velez, J., & Naik, N. (2007). Model projections of an imminent transition to a more arid climate in southern North America. Science, 316, 1181–1184.
Shafer, M., Ojima, D., Antle, J. M., Kluck, D., McPherson, R. A., Petersen, S., Scanlon, B., & Sherman, K. (2014a). Chapter 19: Great plains. In J. M. Melillo, T. C. Richmond, & G. W. Yohe (Eds.), Climate change impacts in the United States: The third National Climate Assessment (pp. 441–461). Washington, DC: U.S. Global Change Research Program.
Shafer, M., Ojima, D., Antle, J. M., Kluck, D., McPherson, R. A., Petersen, S., Scanlon, B., & Sherman, K. (2014b). Chapter 19: Great plains. In J. M. Melillo, T. C. Richmond, & G. W. Yohe (Eds.), Climate change impacts in the United States: The third National Climate Assessment (pp. 441–461). Washington, DC: U.S. Global Change Research Program.
Sollenberger, L. E., Srygley, R., Stöckle, C., Takle, E. S., Timlin, D., White, J. W., Winfree, R., Wright-Morton, L., & Ziska, L. H. (2010). Climate change and agriculture in the United States: Effects and adaptation (196pp). USDA, Technical Bulletin 1935.
Sorooshian, S., AghaKouchak, A., & Li, J. (2014). Influence of irrigation on land hydrological processes over California. Journal of Geophysical Research: Atmospheres, 119(23), 13137–13152.
Teuling, A. J., Van Loon, A. F., Seneviratne, S. I., Lehner, I., Aubinet, M., Heinesch, B., Bernhofer, C., Grunwald, T., Prasse, H., & Spank, U. (2013). Evapotranspiration amplifies European summer drought. Geophysical Research Letters, 40(10), 2071–2075.
Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences of the United States of America, 108(50), 20260–20264.
Tuinenburg, O. A., Hutjes, R. W. A., Stacke, T., Wiltshire, A., & Lucas-Picher, P. (2014). Effects of irrigation in India on the atmospheric water budget. Journal of Hydrometeorology, 15(3), 1028–1050.
Vose, R. S., Applequist, S., Bourassa, M. A., Pryor, S. C., Barthelmie, R. J., Blanton, B., Bromirski, P. D., Brooks, H. E., DeGaetano, A. T., Dole, R. M., & Easterling, D. R. (2014). Monitoring and understanding changes in extremes: Extratropical storms, winds, and waves. Bulletin of the American Meteorological Society, 95(3), 377–386.
Walthall, C. L., Hatfield, J., Backlund, P., Lengnick, L., Marshall, E., Walsh, M., Adkins, S., Aillery, M., Ainsworth, E. A., Ammann, C., Anderson, C. J., Bartomeus, I., Baumgard, L. H., Booker, F., Bradley, B., Blumenthal, D. M., Bunce, J., Burkey, K., Dabney, S. M., Delgado, J. A., Dukes, J., Funk, A., Garrett, K., Glenn, M., Grantz, D. A., Goodrich, D., Hu, S., Izaurralde, R.C., Jones, R. A. C., Kim, S.-H., Leaky, A. D. B., Lewers, K., Mader, T. L., McClung, A., Morgan, J., Muth, D. J., Nearing, M., Oosterhuis, D. M., Ort, D., Parmesan, C., Pettigrew, W. T., Polley, W., Rader, R., Rice, C., Rivington, M., Rosskopf, E., Salas, W. A., Sollenberger, L. E., Srygley, R., Stöckle, C., Takle, E. S., Timlin, D., White, J. W., Winfree, R., Wright-Morton, L., & Ziska, L. H. (2010). Climate change and agriculture in the United States: Effects and adaptation (196pp). USDA, Technical Bulletin 1935.
Wei, J., Dirmeyer, P. A., Wisser, D., Bosilovich, M. G., & Mocko, D. M. (2013). Where does the irrigation water go? An estimate of the contribution of irrigation to precipitation using MERRA. Journal of Hydrometeorology, 14(1), 275–289.
Wuebbles, D. J., Meehl, G., Hayhoe, K., Karl, T. R., Kunkel, K., Santer, B., Wehner, M., Colle, B., Fischer, E. M., Fu, R., Goodman, A., Janssen, E., Kharin, V., Lee, H., Li, W., Long, L. N., Olsen, S., Seth, A., Sheffield, J., Tao, Z., & Sun, L. (2014). CMIP5 climate model analyses: Climate extremes in the United States. Bulletin of the American. Meteorological Society. https://doi.org/10.1175/BAMS-D-12-00172.1.
Integrated Assessment Models
Edmonds, J., & Reilly, J. (1983). A long-term global energy—Economic model of carbon dioxide release from fossil fuel use. Energy Economics, 5(2), 74–88. https://doi.org/10.1016/0140-9883(83)90014-2.
Clarke, L., Jiang, K., Akimoto, K., Babiker, M., Blanford, G., Fisher-Vanden, K., Hourcade, J.-C., Krey, V., Kriegler, E., Löschel, A., McCollum, D., Paltsev, S., Paltsev, S., Rose, Shukla, P. R., Tavoni, M., van der Zwaan, B. C. C., & van Vuuren, D. P. (2014). Assessing transformation pathways. In O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, & J. C. Minx (Eds.), Climate change 2014: Mitigation of climate change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.
Davies, E. G. R., Kyle, P., & Edmonds, J. A. (2013). An integrated assessment of global and regional water demands for electricity generation to 2095. Advances in Water Resources, 52, 296–313. https://doi.org/10.1016/j.advwatres.2012.11.020.
Edmonds, J., & Reilly, J. (1983b). Global energy and CO2 to the year 2050. The Energy Journal, 21–47.
Kyle, P., Müller, C., Calvin, K., & Thomson, A. (2014). Meeting the radiative forcing targets of the representative concentration pathways in a world with agricultural climate impacts. Earth’s Future, 2(2), 83–98. https://doi.org/10.1002/2013EF000199.
Reilly, J., Stone, P. H., Forest, C. E., Webster, M. D., Jacoby, H. D., & Prinn, R. G. (2001). Uncertainty and climate change assessments. Science, 293, 430–433.
Reilly, J., Paltsev, S., Strzepek, K., Selin, N. E., Cai, Y., Nam, K.-M., Monier, E., Dutkiewicz, S., Scott, J., Webster, M., & Sokolov, A. (2013). Valuing climate impacts in integrated assessment models: The MIT IGSM. Climatic Change, 117(3), 561–573. https://doi.org/10.1007/s10584-012-0635-x.
Schlosser, C. A., Strzepek, K., Gao, X., Fant, C., Blanc, É., Paltsev, S., Jacoby, H., Reilly, J., & Gueneau, A. (2014). The future of global water stress: An integrated assessment. Earth’s Future, 2(8), 341–361. https://doi.org/10.1002/2014EF000238.
Scott, M. J., Daly, D. S., Hejazi, M. I., Kyle, G. P., Liu, L., McJeon, H. C., Mundra, A., Patel, P. L., Rice, J. S., & Voisin, N. (2016). Sensitivity of future U.S. Water shortages to socioeconomic and climate drivers: a case study in Georgia using an integrated human-earth system modeling framework, Climatic Change 136:233–246.
Sokolov, A.P., Stone, P. H., Forest, C. E., Prinn, R., Sarofim, M. C., Webster, M., Paltsev, S., Schlosser, C. A., Kicklighter, D., Dutkiewicz, S. Reilly, J., Wang, C., Felzer, B., & Jacoby, H. D. (2009). Probabilistic Forecast for 21st Century Climate Based on Uncertainties in Emissions (without Policy) and Climate Parameters, Report No. 169, MIT Joint Program on the Science and Policy of Global Change, Feb 2009.
Voisin, N., Liu, L., Hejazi, M., Tesfa, T., Li, H., Huang, M., Liu, Y., & Leung, L. R. (2013). One-way coupling of an integrated assessment model and a water resources model: Evaluation and implications of future changes over the U.S. Midwest. Hydrology and Earth System Sciences, 17(11), 4555–4575. https://doi.org/10.5194/hess-17-4555-2013.
Von Lampe, M., Willenbockel, D., Ahammad, H., Blanc, E., Cai, Y., Calvin, K., Fujimori, S., Hasegawa, T., Havlik, P., Heyhoe, E., Kyle, P., Lotze-Campen, H., Mason d’Croz, D., Nelson, G. C., Sands, R. D., Schmitz, C., Tabeau, A., Valin, H., van der Mensbrugghe, D., & van Meijl, H. (2014). Why do global long-term scenarios for agriculture differ? An overview of the AgMIP global economic model intercomparison. Agricultural Economics, 45(1), 3–20. https://doi.org/10.1111/agec.12086.
Webster, M. D., Babiker, M., Mayer, M., Reilly, J. M., Harnisch, J., Hyman, R., Sarofim, M. C., & Wang, C. (2002). Uncertainty in emissions projections for climate models. Atmospheric Environment, 36(22), 3659–3670. https://doi.org/10.1016/S1352-2310(02)00245-5.
Weyant, J. P., de la Chesnaye, F. C., & Blanford, G. J. (2006). Overview of EMF-21: Multigas mitigation and climate policy. The Energy Journal, 27, 1–32.
Weyant, J. P. (2004). Special issue-EMF 19 Alternative technology strategies for climate change policy. Energy Economics, 26, 501–755.
Wigley, T. M. L., Richels, R., & Edmonds, J. A. (1996). Economic and environmental choices in the stabilization of atmospheric CO2 concentrations. Nature, 379(6562), 240–243. https://doi.org/10.1038/379240a0.
Further Reading
Graham, N. T., et al. (2018). Water sector assumptions for the shared socioeconomic pathways in an integrated modeling framework. Water Resources Research, 54, 6423–6440 Retrieved from https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018WR023452.
Motesharrei, S., et al. (2014). Human and nature dynamics (HANDY): Modeling inequality and use of resources in the collapse or sustainability of societies. Ecological Economics, 101, 90–102 Retrieved from https://www.sciencedirect.com/science/article/pii/S0921800914000615.
Motesharrei, S., et al. (2016). Modeling sustainability: Population, inequality, consumption, and bidirectional coupling of the earth and human systems. National Science Review, 3(4), 470–494 Retrieved from https://academic.oup.com/nsr/article/3/4/470/2669331.
Muñoz-Castillo, R., et al. (2017). Uncovering the green, blue, and grey water footprint and virtual water of biofuel production in brazil: A nexus perspective. Sustainability, 9(11), 2049 Retrieved from https://www.mdpi.com/2071-1050/9/11/2049.
National Science Foundation (NSF). (2014). Food, energy and water transformative research opportunities in the mathematical and physical sciences.
Perrone, D., & Hornberger, G. (2014). Water, food, and energy security: Scrambling for resources or solutions? WIREs Water, 1, 49–68. https://doi.org/10.1002/wat2.1004.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Miralles-Wilhelm, F.R. (2020). Modeling. In: Saundry, P., Ruddell, B. (eds) The Food-Energy-Water Nexus. AESS Interdisciplinary Environmental Studies and Sciences Series. Springer, Cham. https://doi.org/10.1007/978-3-030-29914-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-29914-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29913-2
Online ISBN: 978-3-030-29914-9
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)