Abstract
Starting with an elaborate global CGE model, we investigate three simplifications: (1) tackling global questions in a national level model; (2) collapsing irrigated and rainfed crop production into a single sector; and (3) removing river basin boundaries within a country. In each case, we compare their performance in predicting the impacts of future irrigation scarcity on international trade, crop output, land use change and welfare , relative to the full scale model. We find that, if the research question has to do with changes in national-scale trade, production and welfare changes, it may be sufficient to ignore the sub-national hydrological boundaries in global economic analysis of water scarcity . However, when decision makers have an interest in the distribution of inputs and outputs within a region, preserving the river basin and sectoral detail in the model brings considerable added value to the analysis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
For example, land supply to crop sectors is determined by crop output, the price of cropland relative to managed land, and the elasticity of transformation σ1. In GTAP coding, qocropland(i, r) = qo(i, r) − endwslack(i, r) + σ1 * [pmland(i, r) − pmcropland(i, r)], where i indicates agro-ecological zone (AEZ) and r indicates region.
- 2.
The full model or benchmark model refers to the GTAP-BIO-W model with rainfed -irrigated split and basin boundaries within regions.
- 3.
All models contain 19 regions. In some tables, we aggregate the original 19 regions into 11 regions (based on weighted summation) for the ease of reporting.
- 4.
SSA is not heavily reliant on irrigation. For Brazil , irrigation condition will improve in one of the major agricultural production areas.
- 5.
We also conducted the Welch two-sample t-test to examine whether the results obtained from different models are significantly different. Each sample contains 114 observations (19 regions, 6 crops). We consider two pairs of results: full model versus combined I&R model (t-statistic = 1.63, p-value = 0.11), and full model versus unified basin model (t-statistic = −0.04, p-value = 0.97). In both tests, the crop output changes in each pair are not significantly different from each other at the 5% level, although the combined I&R model produces more similar results (to the full model results) than does its competitor model.
- 6.
Here the weight is the value share of irrigated land θr, which is less than one. For example, qfe(r) = θrqfe(irr, r) + (1 − θr)qfe(rdf, r). Contraction of total cropland area in region r will be less pronounced due to the weight or even flipped as the expansion of rainfed cropland becomes strong.
References
Berrittella M, Hoekstra AY, Rehdanz K, Roson R, Tol RSJ (2007) The economic impact of restricted water supply: a computable general equilibrium analysis. Water Res 41:1799–1813
Cai X, Rosegrant MW (2002) Global water demand and supply projections part 1: a modeling approach. Water Int 27:159–169
Cakmak E, Dudu H, Saracoglu S (2009) Climate change and agriculture in Turkey: a CGE modeling approach. In: EconAnadolu 2009: Anadolu international conference in economics
Calzadilla A, Rehdanz K, Tol RSJ (2010) The economic impact of more sustainable water use in agriculture: a computable general equilibrium analysis. J Hydrol 384:292–305
Decaluwe B, Patry A, Savard L (1999) When water is no longer heaven sent: comparative pricing analysis in a AGE model. Département d’économique, Université Laval Working Paper 9908
Diao X, Roe T (2003) Can a water market avert the “double-whammy” of trade reform and lead to a “win–win” outcome? J Environ Econ Manag 45:708–723
Dinar A (2014) Water and economy-wide policy interventions. Found Trends R Microecon 10:85–165
Dixon PB, Rimmer MT, Wittwer G (2011) Saving the Southern Murray-darling basin: the economic effects of a buyback of irrigation water. Econ Rec 87:153–168
Dudu H, Chumi S (2008) Economics of irrigation water management: a literature survey with focus on partial and general equilibrium models. SSRN Scholarly Paper No. ID 1106504. Rochester, NY
Falkenmark M, Lannerstad M (2005) Consumptive water use to feed humanity - curing a blind spot. Hydrol Earth Syst Sci 9:15–28
Garrick D, Whitten SM, Coggan A (2013) Understanding the evolution and performance of water markets and allocation policy: a transaction costs analysis framework. Ecol Econ 88:195–205
Gomez CM, Tirado D, Rey-Maquieira J (2004) Water exchanges versus water works: insights from a computable general equilibrium model for the Balearic Islands. Water Resour Res 40
Griffith M (2012) Water resources modeling: a review. In: Wittwer G (ed) Economic modeling of water: the Australian CGE experience. Springer, Dordrecht, pp 59–77
Hassan R, Thurlow J (2011) Macro–micro feedback links of water management in South Africa: CGE analyses of selected policy regimes. Agric Econ 42:235–247. https://doi.org/10.1111/j.1574-0862.2010.00511.x
Hertel TW (1997) Global trade analysis: modeling and applications. Cambridge University Press, New York
Hertel TW, Golub AA, Jones AD, O’Hare M, Plevin RJ, Kammen DM (2010) Effects of US Maize ethanol on global land use and greenhouse gas emissions: estimating market-mediated responses. Bioscience 60:223–231
Kahsay TN, Kuik O, Brouwer R, van der Zaag P (2015) Estimation of the transboundary economic impacts of the Grand Ethiopia Renaissance Dam: a computable general equilibrium analysis. Water Resour Econ 10:14–30
Keeney R, Hertel TW (2009) The indirect land use impacts of United States biofuel policies: the importance of acreage, yield, and bilateral trade responses. Am J Agric Econ 91:895–909
Koopman JF, Kuik O, Tol RSJ, Brouwer R (2015) Water scarcity from climate change and adaptation response in an international river basin context. Clim Change Econ 06:1550004
Liu J, Hertel TW, Taheripour F, Zhu T, Ringler C (2014) International trade buffers the impact of future irrigation shortfalls. Glob Environ Change 29:22–31
Luckmann J, Grethe H, McDonald S, Orlov A, Siddig K (2014) An integrated economic model of multiple types and uses of water. Water Resour Res 50:3875–3892
Maupin MA, Kenny JF, Hutson SS, Lovelace JK, Barber NL, Linsey KS (2014) Estimated use of water in the United States in 2010. USGS No. 1405. U.S. Geological Survey, Reston, VA
Monfreda C, Ramankutty N, Foley JA (2008) Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob Biogeochem Cycles 22
Olmstead SM (2014) Climate change adaptation and water resource management: a review of the literature. Energy Econ 46:500–509
Ponce R, Bosello F, Giupponi C (2012) Integrating water resources into computable general equilibrium models - a survey. Fondazione Eni Enrico Mattei Work, Pap
Portmann FT, Siebert S, Döll P (2010) MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Glob Biogeochem Cycles 24
Ramankutty N, Evan AT, Monfreda C Foley JA (2008) Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Glob Biogeochem Cycles 22
Rosegrant MW, Ringler C, Zhu T, Tokgoz S, Bhandary P (2013) Water and food in the bioeconomy—challenges and opportunities for development. Agric Econ 44:139–150
Siebert S, Döll P (2010) Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation. J Hydrol 384:198–217
Taheripour F, Hertel T, Liu J (2013) Introducing water by river basin into the GTAP-BIO model: GTAP-BIO-W. GTAP Work. Pap. No 77. http://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=4304. Accessed 27 Sept 2018
Wittwer G (ed) (2012) Economic modeling of water: the Australian CGE experience. Springer, Dordrecht, New York
Young RA (1986) Why are there so few transactions among water users? Am J Agric Econ 68(5):1143–1151
Zhu T, Ringler C, Iqbal MM, Sulser TB, Goheer MA (2013) Climate change impacts and adaptation options for water and food in Pakistan: scenario analysis using an integrated global water and food projections model. Water Int 38(5), pp.651-669.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
In the IMPACT modeling suite, natural water availability and supply for irrigation are determined with the Global Hydrologic Model (IGHM) and the Water Simulation Model (IWSM), respectively, as illustrated in Fig. 3.9. The IGHM is a semi-distributed hydrological model that simulates evapotranspiration, surface runoff and base flow on 0.5° latitude × 0.5° longitude grid cells over global land surfaces, except for Antarctica. It uses a temperature-index method adapted from NOAA’s SNOW-17 model to simulate snowpack accumulation and ablation. Gridded hydrological output is spatially aggregated to the Food Production Units (FPUs), weighted by grid cell areas, for use by the IWSM model.
The IWSM uses monthly runoff and potential evapotranspiration from the IGHM to simulate water management and allocation processes for river basins, using FPUs as the fundamental unit of water balance. It simulates reservoir regulation of natural flow and abstraction of surface and groundwater based on projected total water demand for domestic, industrial, livestock and irrigation sectors. Irrigation water demand is estimated using effective rainfall and potential evapotranspiration generated by the IGHM, plus irrigated areas, cropping patterns, crop characteristics, and basin irrigation efficiency. With projected sectoral water demand, the IWSM optimizes water supply according to demand, subject to water availability and capacity constraints of water infrastructure . Sequentially, the model first calculates total monthly water supply; second, it allocates the total supply to water-use sectors on a priority-based manner, assuming domestic water demand is the first priority, industrial and livestock demand is the second priority, and the remaining water is available for irrigation. Total irrigation water supply is further allocated to crops according to crop water requirements. As a water scarcity indicator, irrigation water supply reliability is determined as the ratio of total irrigation water supply to demand, on an annual basis.
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Liu, J., Hertel, T., Taheripour, F. (2019). Analyzing Future Water Scarcity in Computable General Equilibrium Models. In: Wittwer, G. (eds) Economy-Wide Modeling of Water at Regional and Global Scales. Advances in Applied General Equilibrium Modeling. Springer, Singapore. https://doi.org/10.1007/978-981-13-6101-2_3
Download citation
DOI: https://doi.org/10.1007/978-981-13-6101-2_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6100-5
Online ISBN: 978-981-13-6101-2
eBook Packages: Economics and FinanceEconomics and Finance (R0)