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Global Isotope Hydrogeology―Review

DOI:10.1029/2018RG000627
Authors:
Scott Jasechko at University of California, Santa Barbara
Scott Jasechko
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Abstract and Figures

Groundwater ¹⁸O/¹⁶O, ²H/¹H, ¹³C/¹²C, ³H, and ¹⁴C data can help quantify molecular movements and chemical reactions governing groundwater recharge, quality, storage, flow, and discharge. Here, commonly applied approaches to isotopic data analysis are reviewed, involving groundwater recharge seasonality, recharge elevations, groundwater ages, paleoclimate conditions, and groundwater discharge. Reviewed works confirm and quantify long held tenets: (i) that recharge derives disproportionately from wet season and winter precipitation; (ii) that modern groundwaters comprise little global groundwater; (iii) that “fossil” (>12,000‐year‐old) groundwaters dominate global aquifer storage; (iv) that fossil groundwaters capture late‐Pleistocene climate conditions; (v) that surface‐borne contaminants are more common in younger groundwaters; and (vi) that groundwater discharges generate substantial streamflow. Groundwater isotope data are disproportionately common to midlatitudes and sedimentary basins equipped for irrigated agriculture, but less plentiful across high latitudes, hyperarid deserts, and equatorial rainforests. Some of these underexplored aquifer systems may be suitable targets for future field testing.
Common hydrogeological interpretations based on groundwater isotope compositions. Each of the circles is reviewed in the forthcoming subsections (see section numbers aside each circle). Circles along the top portion of the figure relate to groundwater recharge (sections 2–4); circles along the left side of the figure relate to the isotope composition of groundwater stored in aquifers and pumped from wells (sections 5–6); and circles on the lower‐right side of the figure depict groundwater discharges (sections 7 and 8).
Common hydrogeological interpretations based on groundwater isotope compositions. Each of the circles is reviewed in the forthcoming subsections (see section numbers aside each circle). Circles along the top portion of the figure relate to groundwater recharge (sections 2–4); circles along the left side of the figure relate to the isotope composition of groundwater stored in aquifers and pumped from wells (sections 5–6); and circles on the lower‐right side of the figure depict groundwater discharges (sections 7 and 8).
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Groundwater δ¹⁸O measurements made in spring and well waters around the globe. (a) Groundwater isotope measurement locations and δ¹⁸O values. High δ¹⁸O values are common at low latitudes and near coastlines; low δ¹⁸O values are common at high latitudes, high elevations, and continental interiors. Groundwater isotope data are relatively common throughout the contiguous United States, southern Canada, Europe, north and east Africa, north China, Bangladesh, the western islands of the Malay Archipelago, New Zealand, and populated regions of Australia. Groundwater isotope data are relatively sparse across Latin America, southwest Africa, central Asia, eastern Europe, central and southern China, Borneo, New Guinea, and areas of Australia. For example, relatively high‐density and nation‐wide groundwater δ¹⁸O data sets exist for Ireland (Regan et al., 2017), Costa Rica (Sánchez‐Murillo & Birkel, 2016), Uganda (Jasechko, 2014; samples collected with M. GebreEgziabher), Mexico (Wassenaar et al., 2009), India (Bhattacharya et al., 1985), South Africa (West et al., 2014), the United States of America, and Canada (Jasechko, Wassenaar & Mayer, 2017). (b) Groundwater (grey circles) and annual amount‐weighted precipitation (black squares) δ¹⁸O values and their variance with latitude. Precipitation isotope compositions are derived from the International Atomic Energy Agency (www‐naweb.iaea.org/napc/ih/IHS_resources_isohis.html), the United States Network for Isotopes in Precipitation (Welker, 2012) and the Canadian Network for Isotopes in Precipitation (e.g., Birks & Gibson, 2009; Delavau et al., 2011). Precipitation δ¹⁸O values plotted in panel b are ‘amount‐weighted’ over the entire period of record, meaning time‐steps during which more precipitation fell are weighted more than those during which less precipitation fell. Groundwater δ¹⁸O data presented here are derived from the United States' National Water Information System (data downloaded May 2018 from www.waterqualitydata.us) and from data sets compiled from the following n = 435 references (Al Faitouri & Sanford, 2015; Abid et al., 2010; Abid et al., 2011; Abid et al., 2012; Abouelmagd et al., 2014; Abu‐Jaber & Kharabsheh, 2008; Adams et al., 2001; Adiaffi et al., 2009; Adomako et al., 2011; Aeschbach‐Hertig et al., 2002; Aggarwal et al., 2000; Ahmad & Green, 1986; Ahmed et al., 2011; Ako Ako et al., 2012; Al‐Charideh & Abou‐Zakhem, 2010; Al‐Charideh, 2012; Al‐Charideh & Kattan, 2016; Al‐Charideh & Hasan, 2013; Alemayehu et al., 2011; Al‐Katheeri et al., 2009; Allen, 2003; Al‐Mashaikhi et al., 2012; Alpers & Whittemore, 1990; Al‐Ruwaih & Shehata, 2004; Alsaaran, 2005; Alyamani, 2001; Amer et al., 2012; Andre et al., 2005; Andrews et al., 1989; Andrews, Edmunds, et al., 1994; Andrews, Fontes, et al., 1994; Aravena et al., 2003; Arslan et al., 2013; Atkinson et al., 2014; Awad, 2011; Awad et al., 1994; Awad et al., 1997; Awad, 1997; Back et al., 1983; Bahati et al., 2005; Bajjali & Abu‐Jaber, 2001; Bajjali, 2006; Bakari, Aagaard, Vogt, Ruden, Brennwald, et al., 2012; Baker, 2009; Barbecot et al., 2000; Batista, Santiago, Frischkorn, Filho, & Forster, 1998; Bayari et al., 2009; Bennetts et al., 2006; Berg & Pearson, 2012; Beyerle et al., 1998; Beyerle et al., 2003; Bhatia et al., 2011; Bhattacharya et al., 1985; Blomqvist, 1999; Böhlke et al., 1998; Bouchaou et al., 2008; Bouchaou et al., 2009; Bouragba et al., 2011; Boutin, 2009; Bowen et al., 2012; Branchu & Bergonzini, 2004; Bretzler et al., 2011; Brown et al., 2011; Buck et al., 2005; Burg et al., 2013; Bwire Ojiambo et al., 2001; Calmels et al., 2008; Capaccioni et al., 2003; Carneiro et al., 1998; Carreira et al., 2011; Carreira, Marques & Nunes, 2014; Carrillo‐Rivera et al., 1992; Cartwright et al., 2012; Cartwight & Morgenstern, 2012; Cartwright & Weaver, 2005; Castany et al., 1974; Celle‐Jeanton et al., 2009; Cheikh et al., 2012; Chen et al., 2003; Chen et al., 2005; Chen et al., 2011; Chen et al., 2012; Chen et al., 2014; Cheung & Mayer, 2009; Choi et al., 2005; Ciezkowski et al., 1992; Cidu & Bahaj, 2000; Clark et al., 1987; Clark et al., 1997; Clark et al., 1998; Clark et al., 2000; Condesso de Melo et al., 2001; Corcho Alvarado et al., 2013; Cortecci et al., 2001; Cresswell, Wischusen, et al., 1999; Cronin et al., 2005; Cruz et al., 2005; Cunningham et al., 1998; Currell et al., 2010; Currell et al., 2013; Dafny et al., 2006; Dakin et al., 1983; Darling et al., 1997; Das et al., 1988; Delalande et al., 2008; Dellepere, 1994; Demlie et al., 2007; Dennis et al., 1997; Deng et al., 2009; Derwich et al., 2012; Deshpande et al., 2003; Diamond & Harris, 2000; Diédhiou et al., 2012; Dodo & Zuppi, 1997; Dodo & Zuppi, 1999; Dogramaci et al., 2012; Douglas et al., 2007; Dowgiallo et al., 1990; Dupalová et al., 2012; Dutton, 1995; Eastoe & Rodney, 2014; Eastoe et al., 2014; Edmunds et al., 2002; Edmunds et al., 2003; Edmunds et al., 2006; Edmunds & Wright, 1979; Ellins, 1992; Elliot et al., 1999; Ettayfi et al., 2012; Everdingen et al., 1985; Fadlelmawla et al., 2008; Fang et al., 2015; Farah et al., 2000; Fass et al., 2007; Fernandez‐Chacon et al., 2010; Fernandes & Carreira, 2008; Fontes et al., 1991; Foriz et al., 2005; Fortin et al., 1991; Foto et al., 2012; Fritz et al., 1981; Garfias et al., 2010; Gastmans et al., 2010; Gat & Issar, 1974; Gat & Galai, 1982; Gates, Edmunds, Darling, et al., 2008; van Geldern et al., 2014; Gerber & Howard, 2001; Geyh & Ploethner, 2008; Geyh & Sofner, 1989; Gherardi et al., 2002; Girard et al., 1997; Gonfiantini et al., 1974; Gonfiantini & Simonot, 1987; Goni, 2006; Gomez et al., 2014; Gonzalez Hita et al., 1994; Gouvea da Silva, 1983; Gorgni et al., 1982; Grasby et al., 2010; Grassi & Cortecci, 2005; Greene et al., 2008; Guendouz et al., 2003; Guendouz et al., 2006; Gupta et al., 2005; Han et al., 2011; Haggaz & Kheirallah, 1988; Harrington et al., 2011; Hatipoglu et al., 2009; Heaton et al., 1986; Hedley et al., 2009; He et al., 2012; Heilweil et al., 2009; Helstrup, 2006; Herrera et al., 2006; Hinsby et al., 2001; Horst, 2006; Horst et al., 2007; Horst et al., 2011; Houben et al., 2014; Hoque & Burgess, 2012; Howard & Mullings, 1996; Huang & Pang, 2010; Huff et al., 2012; Hui et al., 2007; Huneau et al., 2011; Hussien, 2010; Iacumin et al., 2009; Ingraham & Matthews, 1988; Iriarte et al., 2006; Iwatsuki et al., 2000; Janik et al., 1991; Janik et al., 1992; Jasechko, 2014; Jasechko, Wassenaar, & Mayer, 2017; Jessen et al., 2008; Jiráková et al., 2009; Jiráková et al., 2010; Jones et al., 2000; Joseph et al., 2011; Jurgens et al., 2008; Kagabu et al., 2011; Kagabu et al., 2013; Kamel et al., 2006; Kamdee et al., 2013; Karro et al., 2004; Kattan, 2001; Kattan, 2006; Keatings et al., 2007; Kebede et al., 2005; Kebede et al., 2007; Kennedy & Genereux, 2007; Khaska et al., 2013; King et al., 2014; Kloppmann et al., 1998; Klump et al., 2008; Koh et al., 2012; Kohfahl et al., 2008; Kulongoski et al., 2004; Kulongoski et al., 2008; Kulongoski et al., 2009; Kortelainen & Karhu, 2004; Külls, 2000; Kumar et al., 2009; Kumar et al., 2011; Kpegli et al., 2015; Kusano et al., 2014; Kreuzer et al., 2009; Lanza, 2009; Lapworth et al., 2012; Larsen et al., 2002; Larsen et al., 2003; Leaney & Allison, 1986; Le Gal La Salle, Fontes, et al., 1995; Le Gal La Salle, Marlin, et al., 1995; Le Gal La Salle et al., 2001; Lehmann et al., 2003; Lemkademe et al., 2011; Leontiadis et al., 1996; Leybourne et al., 2009; J. Li, et al., 2015; Li et al., 2008; Liu et al., 2008; Lorenzen et al., 2012; Lu et al., 2007; Ma & Edmunds, 2006; Ma et al., 2004; Ma et al., 2009; Ma et al., 2010; Ma et al., 2013; Machida et al., 2013; Mack et al., 2014; Madioune et al., 2014; Maduabuchi et al., 2006; Magaritz et al., 1989; Mahlknecht et al., 2006; Maldonado Astudillo et al., 1994; Mandal et al., 2011; Marfia et al., 2004; Margane et al., 2005; Martinelli et al., 2014; Mashiatullah et al., 2000; Matter et al., 2006; Maulé et al., 1994; Mayr et al., 2007; Mažeika et al., 2013; Mazor & Verhagen, 1983; Mazor et al., 1974; Mazor et al., 1995; McKenzie et al., 2001; McKenzie et al., 2010; McMahon et al., 2004; McMahon et al., 2006; Mehta et al., 2000; Mendonca et al., 2005; Mendoza et al., 2006; Michel et al., 2002; Min et al., 2007; Mischke et al., 2010; Mohammadi et al., 2012; Moulla et al., 2012; Möller et al., 2007; Monjerezi et al., 2011; Moore et al., 2006; Moran & Rose, 2003; Morrissey et al., 2010; Mostapa et al., 2011; Moussa et al., 2010; Mukherjee et al., 2007; Mulligan et al., 2011; Murad & Krishnamurthy, 2008; Murad, Garamoon, et al., 2011; Murad, Hussein, et al., 2011; Mutlu et al., 2011; Ndembo et al., 2007; Nieva et al., 1997; Njitchoua & Ngounou Ngatcha, 1997; Njitchoua et al., 1995; Nkotagu, 1996; Noseck et al., 2009; Nurmi et al., 1988; Oberhänsli et al., 2009; Otálvaro et al., 2006; Osborn & McIntosh, 2010; Pang et al., 2013; Panichi et al., 1974; Parizi & Samani, 2014; Pelig‐Ba, 2009; Peng et al., 2012; Phillips et al., 1986; Pilla et al., 2006; Plummer et al., 2004; Plummer et al., 2012; Povinec, Burnett, et al., 2012; Povinec, Zenisova, et al., 2012; Porowski, 2004: Praamsma et al., 2009; Puig et al., 2012; Purdy et al., 1996; Qin et al., 2012; Rachid et al., 2014; Raco et al., 2013; Rahube, 2003; Raidla et al., 2009; Raidla et al., 2012; Rango et al., 2010; Rauert et al., 1993; Reboucas & Santiago, 1989; Reddy et al., 2011; Riddell et al., 2016; Rissmann et al., 2015; Robinson & Gunatilaka, 1991; Rock & Mayer, 2009; Röper et al., 2012; Rosenthal et al., 2007; Sader et al., 2013; Saha et al., 2011; Saka et al., 2013; Salati et al., 1974; van Sambeck et al., 2000; Samborska et al., 2012; Sami, 1992; Sanford & Buapeng, 1996; Santiago et al., 1997; Satrio et al., 2012; Scheiber et al., 2015;Schlegel et al., 2009; Seal & Shanks, 1998; Séguis et al., 2011; Shehata & Al‐Ruwaih, 1999; Shi et al., 2000; Shivanna et al., 2004; Shouakar‐Stash et al., 2007; Siegel, 1991; Sikdar & Sahu, 2009; Silva et al., 1989; Sklash & Mwangi, 1991; Smellie & Wikberg, 1991; Sokolovskii et al., 2012; Solis & Araguas‐Araguas, 1995; Soro & Goula Bi Tie, 1997; de Souza et al., 2015; Squeo et al., 2006; Stadler et al., 2010; Stadler et al., 2012; Stewart, 2012; Stimson et al., 2001; Stoecker et al., 2013; Stotler et al., 2009; Sturchio et al., 1996; Stute & Deák, 1989; Stute & Talma, 1998; Su et al., 2009; Subyani, 2004; Sukhija et al., 2006; Sultan et al., 1997; Sultan et al., 2000; Sultan et al., 2007; Sultan et al., 2008; Sultan, Metwally, et al., 2011; Sultan, Yousef, et al., 2011; Sveinbjornsdottir et al., 1995; Swarzenski et al., 2013; Szocs et al., 2013; Tan et al., 2011; Tantawi et al., 1998; Taupin et al., 2009; Taylor & Evans, 1999; Taylor & Howard, 1996; Taylor, 1994; Taylor, Trompetter, et al., 2001; Tenu et al., 1975; Tenu et al., 1981; Thomas et al., 2003; Tijani et al., 1996; Tosaki, Tase, Kimikazu, et al., 2011; Tosaki, Tase, Kondoh, et al., 2011; Trabelsi et al., 2012; Truesdell et al., 1986; Vaikmäe, Vallner, et al., 2001; Varsányi et al., 1999; Varsányi et al., 2011; Vengosh et al., 2007; Visser et al., 2013; Wallick, 1981; Walraevens, 1990; Wang & Jiao, 2012; Wang et al., 2012; Wang, Hu, et al., 2013; Warrier et al., 2012; Wassenaar et al., 2009; Weyhenmeyer et al., 2000; Weyhenmeyer et al., 2002; Winckel et al., 2002; Wischusen et al., 2004; Wood, 2011; Xie, Wang, Li, et al., 2012; Xie, Wang, Su, et al., 2012; Yangui et al., 2011; Yangui, Abidi, et al., 2012; Yangui, Zouari, et al., 2012; Yechieli et al., 2008; Yin, Hou, Dou, et al., 2011; Yitbarek et al., 2012; Yuan et al., 2011; Zammouri et al., 2007; Zhang et al., 2005; Zhang et al., 2014; Zhu et al., 2007; Zuber et al., 1997; Zuber et al., 2000; Zuber et al., 2004; Zuber, 1983; Дублянский et al., 2012). SMOW = standard mean ocean water.
Groundwater δ¹⁸O measurements made in spring and well waters around the globe. (a) Groundwater isotope measurement locations and δ¹⁸O values. High δ¹⁸O values are common at low latitudes and near coastlines; low δ¹⁸O values are common at high latitudes, high elevations, and continental interiors. Groundwater isotope data are relatively common throughout the contiguous United States, southern Canada, Europe, north and east Africa, north China, Bangladesh, the western islands of the Malay Archipelago, New Zealand, and populated regions of Australia. Groundwater isotope data are relatively sparse across Latin America, southwest Africa, central Asia, eastern Europe, central and southern China, Borneo, New Guinea, and areas of Australia. For example, relatively high‐density and nation‐wide groundwater δ¹⁸O data sets exist for Ireland (Regan et al., 2017), Costa Rica (Sánchez‐Murillo & Birkel, 2016), Uganda (Jasechko, 2014; samples collected with M. GebreEgziabher), Mexico (Wassenaar et al., 2009), India (Bhattacharya et al., 1985), South Africa (West et al., 2014), the United States of America, and Canada (Jasechko, Wassenaar & Mayer, 2017). (b) Groundwater (grey circles) and annual amount‐weighted precipitation (black squares) δ¹⁸O values and their variance with latitude. Precipitation isotope compositions are derived from the International Atomic Energy Agency (www‐naweb.iaea.org/napc/ih/IHS_resources_isohis.html), the United States Network for Isotopes in Precipitation (Welker, 2012) and the Canadian Network for Isotopes in Precipitation (e.g., Birks & Gibson, 2009; Delavau et al., 2011). Precipitation δ¹⁸O values plotted in panel b are ‘amount‐weighted’ over the entire period of record, meaning time‐steps during which more precipitation fell are weighted more than those during which less precipitation fell. Groundwater δ¹⁸O data presented here are derived from the United States' National Water Information System (data downloaded May 2018 from www.waterqualitydata.us) and from data sets compiled from the following n = 435 references (Al Faitouri & Sanford, 2015; Abid et al., 2010; Abid et al., 2011; Abid et al., 2012; Abouelmagd et al., 2014; Abu‐Jaber & Kharabsheh, 2008; Adams et al., 2001; Adiaffi et al., 2009; Adomako et al., 2011; Aeschbach‐Hertig et al., 2002; Aggarwal et al., 2000; Ahmad & Green, 1986; Ahmed et al., 2011; Ako Ako et al., 2012; Al‐Charideh & Abou‐Zakhem, 2010; Al‐Charideh, 2012; Al‐Charideh & Kattan, 2016; Al‐Charideh & Hasan, 2013; Alemayehu et al., 2011; Al‐Katheeri et al., 2009; Allen, 2003; Al‐Mashaikhi et al., 2012; Alpers & Whittemore, 1990; Al‐Ruwaih & Shehata, 2004; Alsaaran, 2005; Alyamani, 2001; Amer et al., 2012; Andre et al., 2005; Andrews et al., 1989; Andrews, Edmunds, et al., 1994; Andrews, Fontes, et al., 1994; Aravena et al., 2003; Arslan et al., 2013; Atkinson et al., 2014; Awad, 2011; Awad et al., 1994; Awad et al., 1997; Awad, 1997; Back et al., 1983; Bahati et al., 2005; Bajjali & Abu‐Jaber, 2001; Bajjali, 2006; Bakari, Aagaard, Vogt, Ruden, Brennwald, et al., 2012; Baker, 2009; Barbecot et al., 2000; Batista, Santiago, Frischkorn, Filho, & Forster, 1998; Bayari et al., 2009; Bennetts et al., 2006; Berg & Pearson, 2012; Beyerle et al., 1998; Beyerle et al., 2003; Bhatia et al., 2011; Bhattacharya et al., 1985; Blomqvist, 1999; Böhlke et al., 1998; Bouchaou et al., 2008; Bouchaou et al., 2009; Bouragba et al., 2011; Boutin, 2009; Bowen et al., 2012; Branchu & Bergonzini, 2004; Bretzler et al., 2011; Brown et al., 2011; Buck et al., 2005; Burg et al., 2013; Bwire Ojiambo et al., 2001; Calmels et al., 2008; Capaccioni et al., 2003; Carneiro et al., 1998; Carreira et al., 2011; Carreira, Marques & Nunes, 2014; Carrillo‐Rivera et al., 1992; Cartwright et al., 2012; Cartwight & Morgenstern, 2012; Cartwright & Weaver, 2005; Castany et al., 1974; Celle‐Jeanton et al., 2009; Cheikh et al., 2012; Chen et al., 2003; Chen et al., 2005; Chen et al., 2011; Chen et al., 2012; Chen et al., 2014; Cheung & Mayer, 2009; Choi et al., 2005; Ciezkowski et al., 1992; Cidu & Bahaj, 2000; Clark et al., 1987; Clark et al., 1997; Clark et al., 1998; Clark et al., 2000; Condesso de Melo et al., 2001; Corcho Alvarado et al., 2013; Cortecci et al., 2001; Cresswell, Wischusen, et al., 1999; Cronin et al., 2005; Cruz et al., 2005; Cunningham et al., 1998; Currell et al., 2010; Currell et al., 2013; Dafny et al., 2006; Dakin et al., 1983; Darling et al., 1997; Das et al., 1988; Delalande et al., 2008; Dellepere, 1994; Demlie et al., 2007; Dennis et al., 1997; Deng et al., 2009; Derwich et al., 2012; Deshpande et al., 2003; Diamond & Harris, 2000; Diédhiou et al., 2012; Dodo & Zuppi, 1997; Dodo & Zuppi, 1999; Dogramaci et al., 2012; Douglas et al., 2007; Dowgiallo et al., 1990; Dupalová et al., 2012; Dutton, 1995; Eastoe & Rodney, 2014; Eastoe et al., 2014; Edmunds et al., 2002; Edmunds et al., 2003; Edmunds et al., 2006; Edmunds & Wright, 1979; Ellins, 1992; Elliot et al., 1999; Ettayfi et al., 2012; Everdingen et al., 1985; Fadlelmawla et al., 2008; Fang et al., 2015; Farah et al., 2000; Fass et al., 2007; Fernandez‐Chacon et al., 2010; Fernandes & Carreira, 2008; Fontes et al., 1991; Foriz et al., 2005; Fortin et al., 1991; Foto et al., 2012; Fritz et al., 1981; Garfias et al., 2010; Gastmans et al., 2010; Gat & Issar, 1974; Gat & Galai, 1982; Gates, Edmunds, Darling, et al., 2008; van Geldern et al., 2014; Gerber & Howard, 2001; Geyh & Ploethner, 2008; Geyh & Sofner, 1989; Gherardi et al., 2002; Girard et al., 1997; Gonfiantini et al., 1974; Gonfiantini & Simonot, 1987; Goni, 2006; Gomez et al., 2014; Gonzalez Hita et al., 1994; Gouvea da Silva, 1983; Gorgni et al., 1982; Grasby et al., 2010; Grassi & Cortecci, 2005; Greene et al., 2008; Guendouz et al., 2003; Guendouz et al., 2006; Gupta et al., 2005; Han et al., 2011; Haggaz & Kheirallah, 1988; Harrington et al., 2011; Hatipoglu et al., 2009; Heaton et al., 1986; Hedley et al., 2009; He et al., 2012; Heilweil et al., 2009; Helstrup, 2006; Herrera et al., 2006; Hinsby et al., 2001; Horst, 2006; Horst et al., 2007; Horst et al., 2011; Houben et al., 2014; Hoque & Burgess, 2012; Howard & Mullings, 1996; Huang & Pang, 2010; Huff et al., 2012; Hui et al., 2007; Huneau et al., 2011; Hussien, 2010; Iacumin et al., 2009; Ingraham & Matthews, 1988; Iriarte et al., 2006; Iwatsuki et al., 2000; Janik et al., 1991; Janik et al., 1992; Jasechko, 2014; Jasechko, Wassenaar, & Mayer, 2017; Jessen et al., 2008; Jiráková et al., 2009; Jiráková et al., 2010; Jones et al., 2000; Joseph et al., 2011; Jurgens et al., 2008; Kagabu et al., 2011; Kagabu et al., 2013; Kamel et al., 2006; Kamdee et al., 2013; Karro et al., 2004; Kattan, 2001; Kattan, 2006; Keatings et al., 2007; Kebede et al., 2005; Kebede et al., 2007; Kennedy & Genereux, 2007; Khaska et al., 2013; King et al., 2014; Kloppmann et al., 1998; Klump et al., 2008; Koh et al., 2012; Kohfahl et al., 2008; Kulongoski et al., 2004; Kulongoski et al., 2008; Kulongoski et al., 2009; Kortelainen & Karhu, 2004; Külls, 2000; Kumar et al., 2009; Kumar et al., 2011; Kpegli et al., 2015; Kusano et al., 2014; Kreuzer et al., 2009; Lanza, 2009; Lapworth et al., 2012; Larsen et al., 2002; Larsen et al., 2003; Leaney & Allison, 1986; Le Gal La Salle, Fontes, et al., 1995; Le Gal La Salle, Marlin, et al., 1995; Le Gal La Salle et al., 2001; Lehmann et al., 2003; Lemkademe et al., 2011; Leontiadis et al., 1996; Leybourne et al., 2009; J. 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SMOW = standard mean ocean water.
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Global precipitation and groundwater isotope compositions. Panel (a) presents global precipitation δ¹⁸O and δ²H data (n = 68,382 samples). Panel (b) is a schematic of some of the processes that may alter stable O and H isotopic compositions of groundwaters. High‐temperature water‐rock interactions may increase δ¹⁸O values more so than δ²H values—yielding low deuterium excess values (Giggenbach, 1992). Low temperature water‐rock interactions may decrease δ¹⁸O values—in some cases leading to high deuterium excess values (examples limited mostly to deep crystalline basement brines; Kloppmann et al., 2002). Methanogensis may increase δ²H values with minimal impact on δ¹⁸O values—leading to high deuterium excess values. Partial evaporation may increase both δ²H and δ¹⁸O values along δ²H/δ¹⁸O slopes of ~3 to ~6—leading to low deuterium excess values. δ²H/δ¹⁸O slopes tend to be lower under low‐humidity conditions and where evaporation takes place from soils (e.g., δ²H/δ¹⁸O slopes of ~2 to ~5); δ²H/δ¹⁸O evaporation slopes tend to be higher for open water evaporation under humid condi‐tions (e.g., δ²H/δ¹⁸O slopes of ~5 to ~8). The great majority (90%) of compiled groundwater isotope compositions have deuterium excess values of between 0‰ and 20‰. A global regression of precipitation isotope compositions is labeled “meteoric waters” and follows δ²H = 8 × δ¹⁸O + 10 (the global meteoric water line; Craig, 1961). Deuterium excess (d) is calculated, following d = δ²H − 8 × δ¹⁸O (Dansgaard, 1964). Panel (b) is based partly on schematic presented by Horita (2005). Last, panel (c) presents compiled groundwater isotope compositions (n = 44,948 samples). A linear regression describing groundwater δ²H variations with groundwater δ¹⁸O is similar for the linear regression describing precipitation δ²H and δ¹⁸O values (from panel a). SMOW = standard mean ocean water.
Global precipitation and groundwater isotope compositions. Panel (a) presents global precipitation δ¹⁸O and δ²H data (n = 68,382 samples). Panel (b) is a schematic of some of the processes that may alter stable O and H isotopic compositions of groundwaters. High‐temperature water‐rock interactions may increase δ¹⁸O values more so than δ²H values—yielding low deuterium excess values (Giggenbach, 1992). Low temperature water‐rock interactions may decrease δ¹⁸O values—in some cases leading to high deuterium excess values (examples limited mostly to deep crystalline basement brines; Kloppmann et al., 2002). Methanogensis may increase δ²H values with minimal impact on δ¹⁸O values—leading to high deuterium excess values. Partial evaporation may increase both δ²H and δ¹⁸O values along δ²H/δ¹⁸O slopes of ~3 to ~6—leading to low deuterium excess values. δ²H/δ¹⁸O slopes tend to be lower under low‐humidity conditions and where evaporation takes place from soils (e.g., δ²H/δ¹⁸O slopes of ~2 to ~5); δ²H/δ¹⁸O evaporation slopes tend to be higher for open water evaporation under humid condi‐tions (e.g., δ²H/δ¹⁸O slopes of ~5 to ~8). The great majority (90%) of compiled groundwater isotope compositions have deuterium excess values of between 0‰ and 20‰. A global regression of precipitation isotope compositions is labeled “meteoric waters” and follows δ²H = 8 × δ¹⁸O + 10 (the global meteoric water line; Craig, 1961). Deuterium excess (d) is calculated, following d = δ²H − 8 × δ¹⁸O (Dansgaard, 1964). Panel (b) is based partly on schematic presented by Horita (2005). Last, panel (c) presents compiled groundwater isotope compositions (n = 44,948 samples). A linear regression describing groundwater δ²H variations with groundwater δ¹⁸O is similar for the linear regression describing precipitation δ²H and δ¹⁸O values (from panel a). SMOW = standard mean ocean water.
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Some scenarios that may lead to the condition: groundwater δ¹⁸O > amount‐weighted annual precipitation δ¹⁸O. These include (a) high‐temperature alteration of the parent groundwater δ¹⁸O values (Giggenbach, 1992; Giggenbach & Soto, 1992); (b) higher summer recharge/precipitation ratios than winter recharge/precipitation ratios; (c) partial evaporation of meteoric water prior to, or during, infiltration (e.g., Cartwright et al., 2004; Huang & Pang, 2010; Njitchoua & Ngounou Ngatcha, 1997; Salem & El‐Sayed, 2015); (d) replenishment of coastal or (sub)tropical aquifers during older, different climate conditions than present (e.g., Clark et al., 1997; Plummer, 1993; Stute & Talma, 1998); and/or (e) transport of meteoric water over long distances before it recharges the aquifer either naturally (e.g., losing reach of a river; Darling et al., 1987; Baskaran et al., 2009) or through diversion to meet human water demands (e.g., Criss & Davisson, 1996; Plummer et al., 2000).
Some scenarios that may lead to the condition: groundwater δ¹⁸O > amount‐weighted annual precipitation δ¹⁸O. These include (a) high‐temperature alteration of the parent groundwater δ¹⁸O values (Giggenbach, 1992; Giggenbach & Soto, 1992); (b) higher summer recharge/precipitation ratios than winter recharge/precipitation ratios; (c) partial evaporation of meteoric water prior to, or during, infiltration (e.g., Cartwright et al., 2004; Huang & Pang, 2010; Njitchoua & Ngounou Ngatcha, 1997; Salem & El‐Sayed, 2015); (d) replenishment of coastal or (sub)tropical aquifers during older, different climate conditions than present (e.g., Clark et al., 1997; Plummer, 1993; Stute & Talma, 1998); and/or (e) transport of meteoric water over long distances before it recharges the aquifer either naturally (e.g., losing reach of a river; Darling et al., 1987; Baskaran et al., 2009) or through diversion to meet human water demands (e.g., Criss & Davisson, 1996; Plummer et al., 2000).
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Some scenarios that may lead to the condition: groundwater δ¹⁸O < amount‐weighted annual precipitation δ¹⁸O. These include (a) groundwater aquifers replenished in part by precipitation that fell at higher elevations than the land surface at the location that the sample was collected (e.g., Gonfiantini et al., 1976; Payne & Yurtsever, 1974); (b) recharge of surface waters diverted for agricultural, domestic or industrial uses (e.g., Williams & Rodoni, 1997); (c) disproportionate recharge from intensive rainfall, in places where precipitation rates and δ¹⁸O values correlate inversely (e.g., Geirnaert et al., 1984; Vogel & Van Urk, 1975); (d) higher recharge/precipitation ratios for cold‐season precipitation relative to warm‐season precipitation (e.g., Brinkmann et al., 1963; Simpson et al., 1970); (e) retention of groundwater derived from precipitation during the late‐Pleistocene, when global atmospheric temperatures were as much as ~4°C cooler‐than present (e.g., Gonfiantini et al., 1974; Phillips et al., 1986; Vogel & Ehhalt, 1963); and (f) transport of waters from a place where precipitation δ¹⁸O values are relatively low to another place where precipitation δ¹⁸O values are relatively high before the water recharges (e.g., Liu & Yamanaka, 2012). These processes are not mutually exclusive; more than one may affect groundwater isotope compositions (e.g., Liu & Yamanaka, 2012). Decoupling these various processes can be challenging and requires consideration of local hydroclimate and hydrogeologic conditions (read, e.g., Uliana et al., 2007).
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Some scenarios that may lead to the condition: groundwater δ¹⁸O < amount‐weighted annual precipitation δ¹⁸O. These include (a) groundwater aquifers replenished in part by precipitation that fell at higher elevations than the land surface at the location that the sample was collected (e.g., Gonfiantini et al., 1976; Payne & Yurtsever, 1974); (b) recharge of surface waters diverted for agricultural, domestic or industrial uses (e.g., Williams & Rodoni, 1997); (c) disproportionate recharge from intensive rainfall, in places where precipitation rates and δ¹⁸O values correlate inversely (e.g., Geirnaert et al., 1984; Vogel & Van Urk, 1975); (d) higher recharge/precipitation ratios for cold‐season precipitation relative to warm‐season precipitation (e.g., Brinkmann et al., 1963; Simpson et al., 1970); (e) retention of groundwater derived from precipitation during the late‐Pleistocene, when global atmospheric temperatures were as much as ~4°C cooler‐than present (e.g., Gonfiantini et al., 1974; Phillips et al., 1986; Vogel & Ehhalt, 1963); and (f) transport of waters from a place where precipitation δ¹⁸O values are relatively low to another place where precipitation δ¹⁸O values are relatively high before the water recharges (e.g., Liu & Yamanaka, 2012). These processes are not mutually exclusive; more than one may affect groundwater isotope compositions (e.g., Liu & Yamanaka, 2012). Decoupling these various processes can be challenging and requires consideration of local hydroclimate and hydrogeologic conditions (read, e.g., Uliana et al., 2007).
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... The stable isotopes of water, i.e., δ 2 H and δ 18 O and tritium ( 3 H) were analyzed using Isotope Ratio Mass Spectrometer (IRMS). The stable isotopes were calculated relative to the standard, i.e., Vienna Standard Mean Ocean Water (SMOW) [(R SAMPLE /R STANDARD )-1] and denoted as per mil (‰) (Ali, 2022;Jasechko, 2019). The Local Meteoric Water Line (LMWL) for Kabul was adopted from IAEA (http:// www. ...
... In general, the lowest values were found in deep aquifer, while the highest values were found in the shallow aquifers and surface water. This is in accordance with the earlier study carried out by Jasechko (2019). This suggests higher proportion of recharge from recent water in shallow aquifers than the deep aquifer (Ali, 2022). ...
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... For this purpose, we converted the obtained values from VPDB to VSMOW following Coplen (1988), therefore all δ 18 O dw are expressed in VSMOW. The obtained δ 18 O dw were compared to the available isoscapes for South America showing the δ 18 O values for rainfall and river water, as well as the expected differences for the late Pleistocene (Terzer et al., 2013;Jasechko, 2019;Nan et al., 2019). ...
A window into a late Pleistocene megafauna community: Stable isotopes show niche partitioning among herbivorous taxa at the Arroyo del Vizcaíno site (Uruguay)
Article
The analysis of stable isotopes on fossil mammals has become a widely used tool for understanding the paleoecology and paleodiet of these organisms. In this work, we study the stable isotope composition of collagen (δ13C and δ15N) and bioapatite (δ13C and δ18O) of fossil bones from Arroyo del Vizcaíno, a fossiliferous site in southern Uruguay dated to ∼32 ka cal BP. Fourteen taxa were analyzed: the ground sloths Lestodon armatus, Glossotherium robustum, Mylodon darwinii, Valgipes bucklandi and Nothrotheriops sp.; the glyptodonts Glyptodon reticulatus, Panochthus tuberculatus, and Doedicurus clavicaudatus; the equids Hippidion principale and Equus neogeus; the proboscidean Notiomastodon platensis; the notoungulate Toxodon platensis; the saber-tooth felid Smilodon populator; and an indeterminate cervid. In general, the percentage of nitrogen in collagen and the C:N ratio were within the limits expected for collagen preservation in fossil bone. The δ13C and δ15N results of the herbivores indicated that most taxa consumed high proportions of C3 plants in open, relatively dry environments, but significant variations were observed. Furthermore, relatively high values of δ18O in bone bioapatite, which approximately tracks local drinking water, indicated low precipitation and/or high evaporation. Our results show that the herbivorous taxa present at the site covered a considerable range of the grazer-browser spectrum and support the existence of niche partitioning among closely related taxa. Overall, these kinds of approaches are indispensable to better understand how these communities thrived during the Pleistocene in the region, supporting an outstanding number of giant species, before their extinction at the Pleistocene-Holocene transition.
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... The stable isotopic composition of hydrogen (δ 2 H) and oxygen (δ 18 O) in precipitation is globally recognized as a powerful natural tracer in the water cycle (Bowen et al., 2019;Clark and Fritz, 1997;Dansgaard, 1964;Gat, 1996;Gat et al., 2001) and represents the starting point to investigate hydrological processes. The precipitation isotope composition is also widely adopted to characterize the regional atmospheric patterns (Baldini et al., 2008;Comas-Bru et al., 2016;Zhao et al., 2022), to understand groundwater recharge mechanisms and dynamics (Jasechko, 2019), and to investigate water uptake and movement in natural ecosystems and agroecosystems (Amin et al., 2020;Penna et al., 2018). In the Mediterranean region, few comprehensive investigations have been performed to analyze the statistical relationships between stable isotopes of oxygen and hydrogen in precipitation and climatic variables (Giustini et al., 2016;Longinelli and Selmo, 2003;Natali et al., 2021;Rindsberger et al., 1983). ...
Moisture sources and climatic effects controlling precipitation stable isotope composition in a western Mediterranean island (Pianosa, Italy)
Article
The Mediterranean basin is indicated as a hot spot of climate change, which is an area whose climate is especially responsive to variations. The insular environment is one of the most threatened by the current climate change, especially in terms of drought events, with serious consequences for water scarcity and water stress. This issue is even enhanced in small islands, whose ecosystems are among more sensitive to climatic changes and water availability. The stable isotope composition of hydrogen (δ2H) and oxygen (δ18O) in precipitation is globally recognized as a powerful natural tracer in the water cycle and represents the starting point to investigate hydrological processes. The understanding of the prevailing factors that drive the isotopic variability of precipitation in the Mediterranean is therefore essential to unravel the hydrological processes and to ensure proper and sustainable management of potentially vulnerable resources to climate change. Here, we discuss the results of multi-year isotopic monitoring in the period 2014-2021 of monthly precipitation collected on Pianosa Island (Italy), a small island located in the northern Tyrrhenian (western Mediterranean). The lower slope and intercept of the Local Meteoric Water Line of the island compared to the Global Meteoric Water Line indicated warmer and drier climatic conditions, suggesting the existence of sub-cloud evaporation processes of raindrops during precipitation, especially in summer. The mean δ18O of precipitation was lower with respect to other sites placed at higher elevation in this Mediterranean region, due to the lack of summer precipitation which were generally enriched in heavy isotopes. Temperature and amount effects may explain part of the δ18O variability observed at the monthly and seasonal scale. An HYSPLIT-based moisture uptake analysis indicated the area between the western Mediterranean basin, Italy, and the Adriatic Sea as the region that supplied most of the humidity associated with monthly precipitation samples on Pianosa Island. Less moisture was picked from the northwestern areas of Europe, the North Atlantic Ocean, the proximal Atlantic Ocean, the Iberian Peninsula and North Africa. Consistently with the rainout effect, the higher the moisture fraction picked from the more proximal regions, the more positive the δ18O of precipitation occurring on Pianosa Island; conversely, the higher the percentage of moisture sourced from more distal regions, the more negative the δ18O. A multiple linear model was proposed to predict the δ18O of monthly precipitation from temperature, precipitation amount and moisture origin data, which explained 45% of the δ18O variability. The deuterium excess variability on the island was partly controlled by the local climatic variables, whose effect potentially modifies the original d-excess signature imprinted at the moisture source. No relationship was found between the precipitation deuterium excess and moisture sources, suggesting that more attention should be paid when using the deuterium excess as a tracer of moisture origin, especially in the Mediterranean.
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... Seasonal signals in precipitation, with isotopically heavier precipitation in summer and lighter precipitation in winter, allow us to track the relative abundance of precipitation from each season in groundwaters (Jasechko, 2019;Jasechko et al. , 2014), streamflow (Allen et al. , 2019a) and plants (Martin et al. , 2018;Allen et al. , 2019b;Goldsmith et al. , 2022;Sprengeret al. , 2022). Soils carry the isotopic signature of many previous precipitation events in any given layer, and trees may take up water from multiple soil layers in different proportions (Warrenet al. , 2007). ...
Isotopic evidence for seasonal water sources in tree xylem and forest soils
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  • Aug 2023
Forest trees greatly influence both the routing of water downward into the subsurface and the re-routing of water upward through water uptake and transpiration. To reveal how the subsurface soil water pools used by trees change across seasons, we analyzed two years of stable isotope ratios of precipitation, soil water from different depths (using both bulk sampling and suction-cup lysimeters), and xylem in a mixed beech and spruce forest. Precipitation as well as mobile and bulk soil waters all showed a distinct seasonal signature; the seasonal amplitude decreased with depth, and mobile soil waters varied less than bulk soil waters. Xylem water signatures in both tree species were similar to the bulk soil water signatures and rather different from the mobile soil water signatures. The beech and spruce trees had different isotope ratios suggesting use of different water sources, and these differences were larger under dry antecedent conditions than wet antecedent conditions. Despite these differences, both species predominantly transpired waters with a winter-precipitation isotopic signature throughout the summer, including during wet conditions when more recent precipitation was available. Over most of the sampling dates, the fraction of recent precipitation (i.e., from the preceding 30 days) in xylem water was low, despite both species typically demonstrating use of both shallow and deeper soil waters. These results provide evidence that the soil water storages used by these trees are largely filled in winter and bypassed by recent precipitation, implying long residence times.
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... Finally, TTD uncertainty can have an impact on the quantification of the modern groundwater age, i.e. groundwater younger than 50 years (Bethke and Johnson, 2008). According to Jasechko (2019), the correct identification of modern groundwater abundance and distribution can help determine its renewal (Le Gal La Salle et al., 2001;Huang et al., 2017), groundwater wells and depths most likely to contain contaminants (Visser et al., 2013;Opazo et al., 2016), and the part of the aquifer flushed more rapidly. ...
Uncertainty in water transit time estimation with StorAge Selection functions and tracer data interpolation
Article
Full-text available
Transit time distributions (TTDs) of streamflow are useful descriptors for understanding flow and solute transport in catchments. Catchment-scale TTDs can be modeled using tracer data (e.g. oxygen isotopes, such as δ18O) in inflow and outflows by employing StorAge Selection (SAS) functions. However, tracer data are often sparse in space and time, so they need to be interpolated to increase their spatiotemporal resolution. Moreover, SAS functions can be parameterized with different forms, but there is no general agreement on which one should be used. Both of these aspects induce uncertainty in the simulated TTDs, and the individual uncertainty sources as well as their combined effect have not been fully investigated. This study provides a comprehensive analysis of the TTD uncertainty resulting from 12 model setups obtained by combining different interpolation schemes for δ18O in precipitation and distinct SAS functions. For each model setup, we found behavioral solutions with satisfactory model performance for in-stream δ18O (KGE > 0.55, where KGE refers to the Kling–Gupta efficiency). Differences in KGE values were statistically significant, thereby showing the relevance of the chosen setup for simulating TTDs. We found a large uncertainty in the simulated TTDs, represented by a large range of variability in the 95 % confidence interval of the median transit time, varying at the most by between 259 and 1009 d across all tested setups. Uncertainty in TTDs was mainly associated with the temporal interpolation of δ18O in precipitation, the choice between time-variant and time-invariant SAS functions, flow conditions, and the use of nonspatially interpolated δ18O in precipitation. We discuss the implications of these results for the SAS framework, uncertainty characterization in TTD-based models, and the influence of the uncertainty for water quality and quantity studies.
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Hydrogeochemical and isotopic evolution of groundwater in shallow and deep aquifers of the Kabul Plain, Afghanistan
Article
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geochemical evolution of entire groundwater system of the Kabul Plain including river and dam water. The results of this study show that shallow and deep aqui-fers are dominantly of Mg-(Ca)-HCO 3 and Na-Cl water type, respectively. We observed that (1) water-rock interaction is the major contributing factor to the chemical compositions of groundwater in the Kabul Plain; (2) groundwater in deep aquifer is mainly influenced by silicate weathering, and dissolution of evaporitic and carbonate minerals and reverse cation exchange; (3) dissolution of carbonates and silicate weathering plays a pivotal role in the groundwater chemistry of shallow aquifer; (4) the stable isotopes of groundwater display that the shallow aquifer is principally recharged by river water and local precipitation; (5) the tritium analysis exhibited that groundwater of shallow aquifer was primarily recharged recently, whereas groundwater of deep aquifer is the mixture of pre 1953 with post 1953 groundwater. This study Abstract Groundwater from shallow and deep aqui-fers are widely used for drinking, agricultural and industrial use in Kabul, the capital of Afghanistan. However, unplanned urbanization and rapid population growth has led to the installation of numerous unlicensed wells to meet the public demand. This has caused to extraction of huge amounts of groundwater from the subsurface and further deterioration of groundwater quality. Therefore, understanding the hydrogeochemical characteristics of groundwater in shallow aquifers and deep aquifers is imperative for sustainable management of the groundwater resource in Kabul Plain. Thus, in this study, we used a multi-parameter approach, involving hydrochemi-cal and environmental isotopes to understand the Electronic supplementary material The online version of this article (https:// doi.
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Tritium and trees: A bomb peak perspective on soil water dynamics in semi-arid apple orchards
Article
Full-text available
Understanding the relationship between agroforest age and soil water dynamics is crucial for effective land and water resources management. However, the complexities of these dynamics, such as soil water recharge and depletion, hamper in-depth understanding, particularly in water-scarce regions. In this study, we examined soil water recharge and depletion in relation to the stand age of apple trees, a widely planted and representative deep-rooted agroforest, over four years in a semi-arid region on China's Loess Plateau (CLP). We collected soil cores to >20 m depth from four apple orchards (referred to as 'agroforests') with variable stand ages (established in 2008, 2005, 1998, and 1994). For comparison, we selected adjacent cropland as land use prior to agroforestry practices ('control'). We measured soil water content and tritium distributions to model soil water dynamics and estimate water ages across different soil profiles. Our results show that recharge amounts (and depths) in shallow soils were 298.4 mm (7 m), 303.4 mm (6.6 m), 300.6 mm (5.4 m), and 483.1 mm (7.6 m), whereas deep soils had net depletions of 111.1 mm, 391.9 mm, 192.8 mm, and 108.9 mm for AP2008, AP2005, AP1998, and AP1994, respectively. The tritium peak depths, which indicate the 1963 bomb peak depth, significantly differed between agroforested and non-agroforested plots. In particular, agroforestation reduced the seepage velocity of soil water over 20 years. Furthermore, our tritium tracer water age model suggests that the age of transpired deep soil water exceeded 200 years in the oldest orchard. These findings highlight a complex interaction between newly infiltrated water and existing water, possibly due to variations in soil pore size distributions. The results of this study offer valuable insights into the ecohydrological impacts of agroforestation on the CLP and in similar climatic regions.
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Hydrogeochemical similarities and groundwater-surface water interactions for the karst hydrological system of northwest Rwanda
Article
  • Jul 2023
The groundwater of karst environments is vulnerable to pollution due to its heterogeneous nature and can be completely depleted due to its strong connection to surface water when predominantly driven by natural and anthropogenic factors. This particular landscape is the main source of drinking water in different parts of the world. Karst Hydrological of Rwanda hosts surface and groundwater resources. Moreover, groundwater is the main source of domestic water use in that area. The surface water is threatened by drying of crater lakes, changes of other lakes, and obstruction of sinkholes swallowing water from streams and runoff. Those problems may have direct and long-term impacts on groundwater recharge. The information on the hydrogeological characteristics of surface and groundwater, groundwater-surface water interaction, was limited. This study investigated the hydrogeochemical characteristics, similarities, and interactions of surface groundwater. To understand long-term impacts of surface water challenges on groundwater when are connected, statistical analyses and Piper diagram were used to achieve the objectives. The results showed a strong correlation among spring waters, reflecting similarity in the water origins. The Piper diagram classified the water as bicarbonate water (\({\mathrm{HCO}}_{3}^{-}\), Ca2+, Mg2+). The analysis of variance between surface water and groundwater did not show significant differences at the 0.05 level, which explains a relationship. The results showed a strong similarity and interaction between surface and groundwater. The findings of this study are important for water managers in consideration of future management since current problems on surface water may affect groundwater and community depending on that resource.
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IntCal13 and Marine13 Radiocarbon Age Calibration Curves 0–50,000 Years cal BP
Article
Full-text available
The IntCal09 and Marine09 radiocarbon calibration curves have been revised utilizing newly available and updated data sets from 14C measurements on tree rings, plant macrofossils, speleothems, corals, and foraminifera. The calibration curves were derived from the data using the random walk model (RWM) used to generate IntCal09 and Marine09, which has been revised to account for additional uncertainties and error structures. The new curves were ratified at the 21st International Radiocarbon conference in July 2012 and are available as Supplemental Material at www.radiocarbon.org. The database can be accessed at http://intcal.qub.ac.uk/intcal13/.
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Pollution vulnerability of the Quaternary aquifer near Cairo, Egypt, as indicated by isotopes and hydrochemistry
Article
Full-text available
  • Dec 2000
The present study was conducted to delineate the pollution vulnerability of the Quaternary aquifer in two areas, Imbaba and Shobra El-Khima, near Cairo, Egypt. Environmental isotopes combined with hydro-chemistry were used for this purpose. The groundwater in the Imbaba area (average total dissolved solids about 900 mg/L; sodium/chloride, sulfate, and bicarbonate water types) is more mineralized than groundwater in the Shobra El-Khima area (average total dissolved solids 500 mg/L; calcium and sodium/bicarbonate water type). A high nitrate content and significant mineralization in the groundwater are probably due to contamination of recharge to the aquifer by irrigation drainage, deteriorated sewage networks, and septic tanks. The deuterium and oxygen-18 compositions of the groundwater are depleted compared to Nile River water, which is the main source of aquifer recharge. This less isotopically enriched water probably represents older Nile water recharge that flooded the region before construction of the Aswan High Dam in 1963, or it is a mixture of a young water and originally deposited paleowater that was in deeper horizons at a time of cooler and more humid climate. Intensive pumping has moved the paleowater higher in the aquifer. Groundwater in the Shobra El-Khima area has higher residence time, based on the tritium concentration , than groundwater in the Imbaba area. The percentage of the isotopically depleted water equals 75% in the Shobra El-Khima and 35% in Imbaba, and the thickness of the clay cap above the aquifer is 38 m in Shobra El-Khima and 20 m in Imbaba. These factors are indicative of the rate of recharge to the aquifer and were used to evaluate the pollution vulnerability in the two areas.
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Understanding the role of fog in forest hydrology: stable isotopes as tools for determining input and partitioning of cloud water in montane forests
Chapter
  • Jan 2011
This volume represents a uniquely comprehensive overview of our current knowledge on tropical montane cloud forests. 72 chapters cover a wide spectrum of topics including cloud forest distribution, climate, soils, biodiversity, hydrological processes, hydrochemistry and water quality, climate change impacts, and cloud forest conservation, management, and restoration. The final chapter presents a major synthesis by some of the world's leading cloud forest researchers, which summarizes our current knowledge and considers the sustainability of these forests in an ever-changing world. This book presents state-of-the-art knowledge concerning cloud forest occurrence and status, as well as the biological and hydrological value of these unique forests. The presentation is academic but with a firm practical emphasis. It will serve as a core reference for academic researchers and students of environmental science and ecology, as well as practitioners (natural resources management, forest conservation) and decision makers at local, national, and international levels.
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H-O isotopic and chemical characteristics of a precipitation-lake water-groundwater system in a desert area
Article
The recharge mechanism of groundwater in the Badain Jaran Desert, North China has been a focus of research and still disputable in the past two decades. In this study, the chemical and hydrogen (H) and oxygen (O) isotopic characteristics of shallow groundwater, lake water and local precipitation in the Badain Jaran Desert and neighboring areas were investigated to reveal the relationships between various water bodies and the recharge source of shallow groundwater. Isotopic and hydrogeochemical results show that (1) shallow groundwater was associated with local precipitation in the Ayouqi and Yabulai regions, (2) lake water was mainly recharged by groundwater in the desert hinterland, (3) shallow groundwater of the desert hinterland, Yabulai Mountain and Gurinai Grassland had a common recharge source. Shallow groundwater of the desert hinterland had a mean recharge elevation of 1869 m a.s.l. on the basis of the isotope-altitude relationship and thus originated chiefly from lateral infiltration of precipitation in the Yabulai Mountain. It is further concluded that shallow groundwater flowed towards the Gurinai Grassland according to the groundwater table contour map. Along the flow pathway, the H-O isotopic variations were primarily caused by the evaporation effect but chemical variations of shallow groundwater were affected by multiple factors, e.g., evaporation effect, dilution effect of occasional heavy-precipitation and dissolution of aquifer evaporites. Our findings provide new insight into the groundwater cycle and benefit the management of the limited water resources in the arid desert area.
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Hydrogeochemical and isotopic characteristics of groundwater in Mbanga, Njombe and Penja (Banana Plain) – Cameroon
Article
Hydrochemical and environmental isotopic (2H, 18O and 3H) data were used to investigate the hydrogeochemical and isotopic characteristics of groundwater within Mbanga, Njombe and Penja (Banana Plain). Hydrogeochemically, the groundwaters are mainly Ca–HCO3, Ca–Mg–HCO3, Ca–Na–HCO3, and Ca–Na–NO3–Cl–HCO3 water types. The groundwater chemistry may be adequately explained by the incongruent dissolution of silicate and alumino-silicate minerals, impact of anthropogenic activities and cation exchange between the groundwaters and clay minerals. The isotopic contents of groundwaters ranged from −4.2‰ to −2.1‰ for δ18O, from −23.4‰ to −10.6‰ for δD and from 0.6 to 1.4 TU for tritium. In the conventional δD–δ18O diagram, the distribution of data points indicates that the groundwaters are of meteoric origin and have not been affected by evaporation. Environmental isotopes (18O, 2H and 3H) indicate mixing between recent and old groundwaters; the latter were recharged under more humid climatic conditions than that at present or from higher elevations. Groundwater in Mbanga, Njombe and Penja are of meteoric origin, young and are still in the early stage of geochemical evolution.
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Identifying the recharge sources and age of groundwater in the Songnen Plain (Northeast China) using environmental isotopes
Article
  • Feb 2011
The recharge sources and groundwater age in the Songnen Plain, Northeast China, were confirmed using environmental isotopes. The isotopic signatures of the unconfined aquifers in the southeast elevated plain and the north and west piedmont, cluster along local meteoric water lines (LMWLs) with a slope of about 5. The signature of source water was obtained by the intersection of these LMWLs with the regional meteoric water line (RMWL). This finding provides evidence that the recharge water for these areas originate from the Changbai Mountains and the Low and High Hingan Mountains, respectively. Groundwater in the unconfined aquifer in the low plain yields a LMWL with a slope of 4.4; its nitrate concentration indicates the admixture of irrigation return flow. The δ-values of the unconfined aquifer in the east elevated plain plot along the RMWL, reflecting recharge by local precipitation. The mean residence time of groundwater in these aquifers is less than 50 years. However, the ¹⁴C age of the groundwater in the confined Quaternary aquifer ranges from modern to 19,500 years, and in the Tertiary confined aquifer from 3,100 to 24,900 years. Modern groundwater is mainly recharged to the Quaternary confined aquifer on the piedmont by local precipitation and lateral subsurface flow.
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Environmental isotopic study on the recharge and residence time of groundwater in the Heihe River Basin, northwestern China
Article
  • Dec 2006
The recharge and origin of groundwater and its residence time were studied using environmental isotopic measurements in samples from the Heihe River Basin, China. δ18O and δD values of both river water and groundwater were within the same ranges as those found in the alluvial fan zone, and lay slightly above the local meteoric water line (δD=6.87δ18O+3.54). This finding indicated that mountain rivers substantially and rapidly contribute to the water resources in the southern and northern sub-basins. δ18O and δD values of groundwater in the unconfined aquifers of these sub-basins were close to each other. There was evidence of enrichment of heavy isotopes in groundwater due to evaporation. The most pronounced increase in the δ18O value occurred in agricultural areas, reflecting the admixture of irrigation return flow. Tritium results in groundwater samples from the unconfined aquifers gave evidence for ongoing recharge, with mean residence times of: less than 36 years in the alluvial fan zone; about 12–16 years in agricultural areas; and about 26 years in the Ejina oasis. In contrast, groundwater in the confined aquifers had 14C ages between 0 and 10 ka BP.
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