2014
DOI: 10.1016/j.techfore.2013.05.006
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Long-term global water projections using six socioeconomic scenarios in an integrated assessment modeling framework

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Cited by 188 publications
(196 citation statements)
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“…As a consequence, the majority of studies of the current generation of IAMs assessing water demand trends at a global scale (e.g. Fricko et al, 2016;Hejazi et al, 2014;Kyle et al, 2013) still lack a representation of water quantity and quality constraints. The incorporation of such features in our framework is work in progress.…”
Section: Limitations Of the Modelling Frameworkmentioning
confidence: 99%
See 1 more Smart Citation
“…As a consequence, the majority of studies of the current generation of IAMs assessing water demand trends at a global scale (e.g. Fricko et al, 2016;Hejazi et al, 2014;Kyle et al, 2013) still lack a representation of water quantity and quality constraints. The incorporation of such features in our framework is work in progress.…”
Section: Limitations Of the Modelling Frameworkmentioning
confidence: 99%
“…Chaturvedi et al, 2015) or the water-energy interactions Kyle et al, 2013;Fricko et al, 2016), yet the implications of combined socioeconomic and mitigation assumptions across the full nexus is underexplored. One exception is the study of Hejazi et al (2014), which investigates changes in water demand across the nexus under different socio-economic conditions, but does not explore the implications of such conditions under stringent mitigation scenarios. This paper adds to the literature by providing a systematic exploration of the impact of socioeconomic assumptions and selected water policies on the effects of climate change mitigation on water demand, using an IAM approach across the water-energy-land-climate nexus.…”
Section: Introductionmentioning
confidence: 99%
“…Kaldellis e Kondili (2007) relatam que o aumento da demanda para consumo humano e irrigação, redução das precipitações e aumento do uso de água subterrânea têm gerado escassez em determinadas regiões na Grécia, situação essa similar à verificada na RMSP, embora a população não tenha plena informação sobre isso (Ribeiro, 2011). Essa situação tem afetado de maneira indiscriminada os mais diversos países, como China Wang, 2010); Austrália (Tapsuwan et al, 2014); Arábia Saudita (Ouda, 2014); Espanha (Milano et al, 2013); Estados Unidos (Gelcer et al, 2013); Chile (Meza, 2013;Núñez et al, 2013); Taiwan (Tsai;Elsberry, 2013), além de outras áreas na Europa e América do Norte (Hejazi et al, 2014).…”
Section: Revisão Da Literaturaunclassified
“…Embora essa escassez reflita um fenômeno que vem ocorrendo há alguns anos em outras regiões (Hadas;Gal, 2014;Zaimes;Emmanouloudis, 2012;Kummu et al, 2010), ela pode ser reduzida com um planejamento estratégico adequado que leve em consideração informações climáticas na concepção de ações. Há exemplos que ilustram essa possibilidade, como o estudo de cenários estratégicos empreendido por Hejazi et al (2014) e que verificou a tendência de um aumento da escassez hídrica em diversos países, ou o estudo de Bolson e Broad (2013) sobre o gerenciamento de recursos hídricos na Flórida. Há também os estudos de Justes, Barberán e Farizo (2014) e Martin-Carrasco et al (2013) na Espanha; Chun, Wheater e Onof (2013) no Reino Unido; e Wu e Wang (2010) no norte da China, para citar alguns exemplos, considerando diferentes cenários e escalas de abordagem.…”
Section: Introductionunclassified
“…A water resources management model has been developed and coupled to a routing model called Model for Scale Adaptive River Transport (MOSART) (Li et al, 2013a). The coupled model, MOSART-WM, takes as input the daily runoff and baseflow generated by a land surface hydrology model, a subbasin implementation of the Community Land Model (SCLM) (Lawrence et al, 2011;Li et al, 2011), and the total consumptive water demand provided by a water demand model of the Global Change Assessment Model (GCAM) (Hejazi et al, 2013a;Wise et al, 2009;Kim et al, 2006;Clarke et al, 2007a, b;Brenkert et al, 2003). The land surface scheme SCLM is forced by meteorological data 3 Fig.…”
Section: Models and Datasetsmentioning
confidence: 99%