Vol.:(0123456789) Climate Dynamics (2024) 62:3417–3430 https://doi.org/10.1007/s00382-023-07073-2 ORIGINAL ARTICLE Spatial variability in stable isotopes from Lesotho surface waters: insights into regional moisture transport Jennifer M. Fitchett1  · Jonathan A. Holmes2 · Simoné Dahms‑Verster1 · Christopher J. Curtis3 · Anson W. Mackay2 Received: 1 August 2023 / Accepted: 16 December 2023 / Published online: 1 February 2024 © The Author(s) 2024 Abstract Precipitation in Lesotho is highly spatially variable, a feature of the high altitude and rugged topography. The hydroclimate dynamics, despite being critical to the water security of Lesotho and adjacent South Africa, are poorly understood. Ratios of oxygen and hydrogen isotopes in meteoric water are excellent tracers of hydroclimatic processes. This study presents the first analysis of stable isotopes from surface waters in Lesotho, and an investigation into the moisture sources. Our results demonstrate considerable variability in isotope values. There are statistically significant relationships between both oxygen and hydrogen isotopes and the altitude of the site and source of rivers sampled, and with hydrogen isotopes and longitude. The meteoric water line for the Lesotho samples is most closely aligned with that of the Global Network of Isotopes in Precipita- tion (GNIP) station at Harare, in Zimbabwe. The meteoric water line for Windhoek is more closely aligned to the Lesotho samples than the more proximate Cape Town or Pretoria meteoric water lines, which would more closely represent the South African winter- and summer-rainfall zones respectively. HYSPLIT back-trajectory air parcel analysis supports these findings, demonstrating a frequent continental anticyclonic track through southern Zimbabwe. Deuterium excess values vary widely, although are most likely related to processes during moisture transport rather than differences in moisture source. These findings are of particular importance in the context of the future water security of both Lesotho and South Africa, especially as the poleward displacement of the westerly moisture corridor has raised concerns for winter precipitation in the region. Keywords Precipitation · Stable isotopes · HYSPLIT · Southern Africa · Surface water 1 Introduction The eastern Lesotho highlands are often referred to as the ‘water tower’ of southern Africa (UNEP 2010; Hitchcock 2015; Geppert et al. 2022). The hydroclimate of the region is crucial in supplying water to the subsistence farms of Lesotho, and to the economic and industrial hubs of adja- cent South Africa through the export of water through the Lesotho Highlands Water Project (Matete and Hassan 2006). While the region currently experiences some of the largest volumes of mean annual precipitation in southern Africa, palaeoclimate records indicate that the eastern Lesotho Highlands have experienced a reduction in surface water availability during the Holocene (Fitchett et al. 2017; Nor- ström et al. 2018). Climate change projections indicate likely reductions in both precipitation and soil moisture for the southern African region under 1.5 – 4 °C warming scenarios to 2080–2100 (Archer et al. 2018; IPCC 2021). For the east- ern Lesotho highlands, the projections are less consistent (Morris 2017; Mukwada et al. 2020), but considering both climatic change and future water demand in Lesotho and South Africa through population growth (GCRO 2019), the region is anticipated to face water scarcity before the end of the century, under all modelled climate change scenarios (Pryor et al. 2022). Western Lesotho, which serves as the economic hub of the country, experiences lower total annual rainfall, with dynamics more consistent with those of South Africa (Hydén 2002). The moisture sources of precipitation * Jennifer M. Fitchett Jennifer.Fitchett@wits.ac.za 1 School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Private Bag X3, Wits 2050, South Africa 2 Environmental Change Research Centre, Department of Geography, University College London, Gower Street, London WC1E 6BT, UK 3 Department of Geography, Environmental Management and Energy Studies, University of Johannesburg, Auckland Park, Johannesburg, South Africa http://crossmark.crossref.org/dialog/?doi=10.1007/s00382-023-07073-2&domain=pdf http://orcid.org/0000-0002-0854-1720 3418 J. M. Fitchett et al. in both eastern and western Lesotho remain poorly under- stood (Hydén 2002). Lesotho is located within the summer rainfall zone of southern Africa, with 70–80% of the mean annual pre- cipitation falling between the months of November and March (Sene et al. 1998; Fig. 1). During these months the precipitation comprises orographic rainfall along the escarpment and across the rugged topography (Sene et al. 1998; Xulu et al. 2020), and more short-lived local con- vection (Dedekind 2015) associated with the Tropical Temperate Trough (TTT) that extends from Namibia to the southeast coast of South Africa (James et al. 2020). Less than 10% of the mean annual precipitation falls during the winter months, predominantly as snowfall, induced by mid-latitude cyclones and associated Cut-off Low (CoL) systems (Stander et al. 2016; Wunderle et al. 2016; Grab et al. 2017). Although a small contribution to the mean annual precipitation, this winter precipita- tion is likely to diminish with the progressive poleward displacement of the westerlies (Sousa et al. 2018). The TTTs, influenced by ENSO and South Indian Ocean sea surface temperatures (Manhique et al. 2011), are also likely to change in their dynamics under climate change. Key to projecting the hydroclimate future of Lesotho is understanding the moisture transport associated with the synoptic features that drive precipitation in the region, and their relative contributions to seasonal and annual precipitation. The paucity of ground-based meteorologi- cal data for Lesotho, and the complexity of the rugged topography present challenges in this regard. Hydrogen and oxygen Isotope ratios (2H/1H and 18O/16O respectively) of precipitation and resultant surface waters can serve as important tracers of moisture sources and hydrocli- matic processes (Sengupta and Sarkar 2006; Xie et al. 2011; Li et al. 2016; Ren et al. 2017; Geppert et al. 2022). Isotope ratios of oxygen and hydrogen in the water molecule are generally expressed as deviations, in parts per thousand (‰), relative to a standard and expressed in delta (δ) units: Where R = 18O/16O or 2H/1H and δ = δ18O or δ2H and the relevant standard for water is VSMOW. The relationship between δ2H and δ18O for meteoric waters worldwide is described by the Global Meteoric Water Line (GMWL), given by: (1)�(‰) = ( Rsample∕Rstandard−1 ) x 1000 (2)� 2H = 8.17 ± 0.906 x � 18O + 10.35 ± 0.65 Fig. 1 A Topography and major drainage features of Lesotho along with location of the sampling sites for this study. Inset shows loca- tion of Lesotho and of the 4 GNIP stations within southern Africa. B Major synoptic features of climate over southern Africa. (adapted from Kylander et al. 2021; Saarinen et al. 2022) 3419Spatial variability in stable isotopes from Lesotho surface waters: insights into regional… Rozanski et al. (1993). Geographical variations in the δ2H and δ18O relation- ship form Local Meteoric Water Lines (LMWLs), the slope and intercept of which are controlled by moisture source and various atmospheric processes. The intercept of the relationship is referred to as the deuterium excess (d-excess), where: Dansgaard (1964). The deuterium excess is a second-order parameter controlled by sea-surface conditions (temperature and especially humidity) at the site of primary evaporation, as well as by the evaporation of raindrops below the cloud base and any addition of ‘recycled’ moisture to the atmosphere from the land surface (Fröhlich et al. 2002). Water-isotope data are used extensively to investigate moisture transport ranging from individual rainfall events over very short periods of hours (Soderberg et al. 2013; Good et al. 2014), through to long-term rainfall dynam- ics over seasons to years (Vystavna et al. 2021; Li et al. 2022a). Many of these studies use data from the Interna- tional Atomic Energy Agency (IAEA) Global Network of Isotopes in Precipitation (GNIP) in their analyses. While this network comprises more than 1000 sites across 125 countries, it is sparse in southern Africa (Geppert et al. 2022). Only four sites (Cape Town and Pretoria in South Africa, Windhoek in Namibia, and Harare in Zimbabwe; Fig. 1) have long-term records. There are no GNIP sta- tions in Lesotho. A small dataset of surface water analy- ses from Katse Dame and other unspecified locations in Lesotho is reported as part of the study of de Wet et al. (2020) into the isotopic composition of South African tap water. Analyses of rainwater isotopes from Cape Town (Harris et al. 2010), Mossel Bay (Braun et al. 2017), Thohoyandou (Durowoju et al. 2019) and Johannesburg (Leketa and Abiye 2020), and from sites across Namibia (Kaseke et al. 2016, 2018), confirm the variability in stable isotope ratios across the different rainfall zones that characterise the subcontinent (Geppert et al. 2022), and in turn the value in tracing their moisture sources. In this study we present a spatial analysis of surface water samples obtained from rivers, dams, springs and snowpack in the northern half of Lesotho in a reconnais- sance study, coupled with HYSPLIT moisture back-tra- jectory modelling. The study has two goals (1) to assess the utility of surface-waters for tracing moisture source and (2) to provide a baseline for future, more detailed, work. (3)d − excess = � 2H − 8 x � 18O 2 Methods 2.1 Study site Lesotho is a small landlocked country, surrounded entirely by South Africa, spanning 28°34’36.923”S–30°40’56.806”S and 27°0’28.134”E–29°27’47.735”E, covering a land surface area of 30,340km2 (Mbata 2001; Fig. 1). Lesotho has the highest low point of any country in the world, at 1400 m.asl in the western Lowlands, and a maximum elevation of 3482 m.asl at Thabana Ntlenyana in the eastern Highlands (Grab and Nash 2009). Mean annual precipitation decreases with distance from the Indian Ocean, from approximately 1600 mm in the eastern Highlands along the escarpment (Sene et al. 1998), to 735 mm in the lowlands (Hydén and Sekoli 2000). While the majority of rainfall is experienced during summer months (Sene et al. 1998), snow falls approximately 8 times per annum in the highlands, and once every 2.3 years in the lowlands, predominantly during the colder months of April through September (Grab et al. 2017). Mean annual temperatures range from approximately 6 °C in the highlands to 15 °C in the lowlands, reaching maxima of 29 °C in the lowlands mid-summer, and minima of – 6.1 °C in the high- lands in winter (Grab and Nash 2010). The source of the Senqu-Orange River, the longest and one of the largest rivers in southern Africa, is located in the eastern Lesotho Highlands (Putteman et al. 2011). The Senqu basin, referring to the catchment within the political boundaries of Lesotho, covers approximately two thirds of the land surface area of the country (Pryor et al. 2022). Major tributaries of the Senqu River, including the Mohokare River, flow from the highlands to the lowlands (Chatanga et al. 2019). Despite the uniform basalt geol- ogy across the eastern Lesotho Highlands, and sandstone in the western Lesotho Lowlands, the river morphomet- rics are highly spatially variable (Knight and Grab 2018). Although mean annual precipitation is relatively high, there is only one known natural lake in Lesotho, Letšeng-la Letsie, in the central southern part of the country (Rose et al. 2020). There are, however, a number of small rock- bottom tarns and grass pans in the Sehlabathebe National Park, located along the eastern escarpment (Dunnink et al. 2016). A number of small dams have been built to provide water to cities in the western Lesotho Lowlands (Bates and Haacke 2003; Gwimbi and Selimo 2020). Major dams that were built to form part of the Lesotho Highlands Water Project, which export water to South Africa, include the Katse, Muela and Mohale Dams, while the Polihali Dam which forms part of stage II of the project is still under construction (Tanner et al. 2009; Vinti 2022). 3420 J. M. Fitchett et al. 2.2 Field water sampling Fieldwork was conducted in Lesotho during the winter sea- son between 2 and 5 June 2022. Surface water samples were collected from snowpack, springs, rivers and dams along an approximate E-W transect spanning the eastern Lesotho Highlands and the western Lesotho Lowlands, and a N-S transect along the western border of the country (Fig. 1). Due to the rugged topography and the sparse road network, a convenience sampling approach was used, traversing the major road networks of Lesotho, the majority of which are untarred. Permission for the study was granted by the Leso- tho Ministry for Tourism, Environment and Culture. Water samples for isotope analysis were collected in 50mL polyethylene sample bottles, which were completely filled, sealed and stored in a cool box to prevent evaporation. In the case of snowpack, sample bottles were filled with compacted snow, which later melted out. All samples were later subsampled into 20mL duplicate bottles, and re-sealed for transportation. At each site, GPS coordinates and alti- tude were recorded using a Garmin eTrex handheld device. For record-keeping, air temperatures were recorded using a Kestrel 5000 handheld device. For samples obtained from rivers, springs and dams, water quality variables including water temperature, pH, electrical conductivity (EC) and dis- solved oxygen (DO) were measured using a calibrated YSI multiparameter probe. For the river samples, the river source altitude was obtained from Google Earth, tracing each river to its source, and identifying the altitude from overlaid con- tours at 20 m intervals (Table 1) following the approach of Lachniet and Patterson (2002). Water samples were transported to the University College London Bloomsbury Environmental Isotope Facility (BEIF) for analysis. Prior to analysis, aliquots of each sample were filtered and transferred to 2 mL glass Thermo vials. Sam- ples were then analysed for oxygen and hydrogen isotopes by Cavity RingDown Spectroscopy (CRDS) using a Picarro L2130-i analyzer. Results are expressed in δ notation rela- tive to the VSMOW standard. The errors, based on multiple analyses of an in-house standard and the variability on the repeat analyses of the 9 injections for each sample, were better than 0.17 and 1.1‰ for δ18O and δ2H respectively. 2.3 Isotopes in southern African precipitation For evaluation of the surface-waters from Lesotho we used GNIP data from the four southern African stations mentioned previously. For each station, we extracted the weighted mean and annual average δ18O, δ2H and deuterium excess values as well as the respective local meteoric water lines (LMWLs) determined using penalised weighted least squares regression (PWLSR; Table 1). Ta bl e 1 D et ai ls o f t he fo ur lo ng -te rm G N IP si te s i n so ut he rn A fr ic a (I A EA /W M O (2 02 3) G lo ba l N et w or k of Is ot op es in P re ci pi ta tio n. T he G N IP D at ab as e. A cc es si bl e at : h ttp s: // n uc le us . ia ea . o rg / w is er ) G N IP S ite C ou nt ry La tit ud e Lo ng itu de A lti tu de (m .a sl ) D at a ra ng e δ18 O (‰ ) w ei gh te d m ea n δ18 O (‰ ) av er ag e δ2 H (‰ ) w ei gh te d m ea n δ2 H (‰ ) av er ag e d- ex ce ss (‰ ) w ei gh te d m ea n d- ex ce ss (‰ ) a ve r- ag e Lo ca l M et eo ric W at er Li ne a Lo ca l M et e- or ic w at er Li ne b Lo ca l M et e- or ic W at er Li ne r2 Pr et or ia So ut h A fr ic a – 25 .7 3 28 .1 8 13 30 19 58 – 19 97 – 3. 85 ± 1. 1 – 2. 68 ± 0. 91 – 16 .8 ± 7. 0 – 8. 8 ± 6. 4 14 .1 ± 4. 3 12 .1 ± 4. 8 6. 81 ± 0. 19 9. 1 ± 0. 87 0. 88 C ap e To w n A irp or t So ut h A fr ic a – 33 .9 7 18 .6 44 19 61 – 20 13 – 3. 51 ± 0. 74 – 2. 57 ± 0. 66 – 12 .9 ± 4. 4 – 8. 2 ± 3. 7 14 .1 ± 2. 8 11 .8 ± 4. 0 5. 86 ± 0. 17 6. 74 ± 0. 64 0. 83 W in dh oe k N am ib ia – 22 .9 5 17 .1 5 16 85 19 61 – 20 01 – 4. 99 ± 2. 39 – 2. 59 ± 1. 73 – 24 .3 ± 14 .4 – 11 .3 ± 11 .4 12 .6 ± 3. 7 10 .1 ± 3. 5 7. 30 ± 0. 17 9. 36 ± 1. 0 0. 96 H ar ar e Zi m ba bw e – 17 .8 3 31 .0 2 14 71 19 60 – 20 03 – 6. 18 ± 1. 31 – 3. 78 ± 1. 12 – 34 .3 ± 12 .7 – 15 .9 ± 9. 5 13 .4 ± 3. 1 11 .8 ± 3. 5 7. 87 ± 0. 11 12 .9 7 ± 0. 72 0. 97 https://nucleus.iaea.org/wiser 3421Spatial variability in stable isotopes from Lesotho surface waters: insights into regional… 2.3.1 Identifying moisture sources of precipitation in Lesotho To identify the various moisture sources and air-mass path- ways of precipitation in Lesotho, the National Oceano- graphic and Atmospheric Association (NOAA) Hybrid Sin- gle Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Stein et al. 2015) was used. Following the meth- odology detailed by Li et al. (2022a) for moisture trajec- tory analysis in alpine river watersheds, back trajectories of all air parcels were modelled in HYSPLIT from the central field-sampling GPS coordinates of 29.11°S, 28.47°E for the 72-hours preceding each day of precipitation at the same coordinates, at 6-hourly resolution, and at three levels of 500 m, 1000 and 2000 m above ground level. Because the iso- topic composition of streams and rivers often approximates the mean weighted precipitation isotope value (Mook 2006), we conducted this analysis for each precipitation event in the centre of our sampling region for a full calendar year prior to sampling. As there is a paucity of meteorological data in Lesotho (Sene et al. 1998), rainfall days were estimated using the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) gridded rainfall data (Funk et al. 2015). Days with snowfall were identified from Snow Report South Africa. Input data for HYSPLIT include cloud height, wind direction, temperature and surface pressure (Bagheri et al. 2019), which were obtained from the Global Data Assimilation System (GDAS) global 1° resolution meteor- ology data. Dominant air-mass pathways were identified, and their proportional representation calculated, through cluster analysis of all moisture tracks calculated from each of the model runs (Rose et al. 2020; Li et al. 2022a). A second set of HYSPLIT runs were modelled for 72-hour back trajec- tories of the same set of precipitation dates, with the over- lay of relative humidity from the GDAS global 1° database (Rapolaki et al. 2020), to identify those trajectories that had air parcels saturated with moisture along or in the terminal phase of their path. 3 Results A total of 35 samples were collected from 33 sites (Table 2) including rivers, snowpack (LS3, LS5, LS11, LS12), springs (LS7, LS8) and dams (LS27, LS28, LS32). Snow samples were collected at a maximum altitude of 3205 m. asl (LS3), while river sample sites ranged in altitude from 3063 m. asl (LS4) to 1461 m.asl (LS33; Table 1). River source alti- tudes ranged from 3350 m.asl (LS14) to 1690 m.asl (LS29; Table 1). For the river samples, DO ranged from 86.6% (7.98 mg/l, LS21) to 127.8% (12.51 mg/l, LS26), while generally higher levels were recorded for the dam samples (Table 2). The EC was generally much lower in the alpine regions, as would be expected for first order streams, increas- ing in western Lesotho lowlands, with values ranging from 191.1 µS/cm (LS21) to 317.5 µS/cm (LS32, Table 2). The EC for the dams was representative of that of the nearby riv- ers, and considerably higher than the high-altitude streams (Table 2). Both air and water temperature varied both by altitude and time of day (Table 2). The isotope values for the dataset as a whole range from + 0.40 to – 18.53‰ and + 1.5 to – 138.3‰ for δ18O and δ2H respectively (Table 3). The lowest δ18O and δ2H values are recorded for the samples from the snowpacks, ranging from − 18.53‰ (LS11-2) to -5.85 (LS3-2) for δ18O: correspond- ing values for δ2H are − 138.3 (LS12) and − 28.9‰ (LS3-2). This snow remained on the land surface following a snowfall event on 23 May 2022 and there had been no further pre- cipitation events that date until the time of sampling. The d-excess values for snowpack varied from 17.9‰ (LS3-2) to 9.4‰ (LS5). For the 24 river samples, the mean δ18O is – 3.78‰ (σ = 0.84‰), with individual values ranging from − 2.29‰ (LS15) to − 2.25‰ (LS21). For δ2H, the mean is -20.0‰ (σ = 4.9‰) and the individual values range from − 13.8‰ (LS26) to − 2.29.1‰ (LS10). The mean value for d-excess is 10.2‰ (σ = 3.0‰), with a range of 17.4‰ (LS21)−2.2.9‰ (LS16). Comparing the mean isotope values for samples from the eastern Lesotho Highlands (samples LS2-LS21, > 2000 m.asl) to those from the western Lowlands (sam- ples LS1, LS22-33) using the two-tailed t-test, statistically significant differences are calculated for δ18O (t = 2.23, p = 0.0361) and δ2H (t = 3.44, p = 0.0023). For d-excess, the difference in means between samples from the High- lands and Lowlands is not statistically significant (t = 0.044, p = 0.9656). The isotopic values for the two samples from springs did not differ markedly from those of the river samples (δ18O values of – 4.11‰ – 4.70‰, δ2H values of – 23.9‰ and − 26.5‰ and d- excess values of 9.2‰ and 11.1‰ were obtained for samples LS-7 and LS-8 respectively) whereas the dam samples show slight heavy-isotope enrichment, with δ18O and δ2H values ranging from + 0.40‰ and + 1.5‰ respectively (LS28) to – 2.76‰ and − 13.6‰ respectively (LS32), with wide variation in d-excess. The δ18O and δ2H values for all water and snow-pack samples are strongly correlated and described by an ordi- nary-least squares regression given by: for the entire dataset excluding the lake samples, which appear to lie on an evaporation line. (4)� 2H = 7.8 x � 18O + 9.9 ( R2 = 0.99 ) 3422 J. M. Fitchett et al. Ta bl e 2 L oc at io n, sa m pl e de ta ils a nd p hy si ca l v ar ia bl es o f w at er sa m pl es Si te La tit ud e Lo ng itu de Si te A lti - tu de (m .a sl ) So ur ce a lti - tu de (m .a sl ) A ir te m p (° C ) W at er te m p (° C ) D O % D O m g/ l EC µ S/ cm pH D at e (2 02 2) Ti m e Sa m pl e Ty pe LS 1 28 °4 2’ 29 .7 ”S 28 °1 9’ 41 .9 ”E 14 43 16 50 20 .8 13 .3 11 0 9. 9 23 3. 1 7. 81 02 /0 6 14 :4 9 R iv er LS 2 28 °5 5’ 53 .8 ”S 28 °3 5’ 00 .0 0” E 23 57 27 10 19 .4 11 .1 12 0. 7 10 .0 7 68 .3 8. 17 02 /0 6 15 :2 6 R iv er LS 3 28 °4 8’ 52 .2 ”S 28 °4 3’ 15 .9 ”E 32 05 – 10 .1 – – – – – 02 /0 6 16 :0 5 *S no w LS 4 28 °5 1’ 27 .6 ”S 28 °4 6’ 13 .6 ”E 30 63 32 00 10 .4 5. 6 11 5. 3 10 .1 9 31 .9 7. 58 02 /0 6 16 :2 6 R iv er LS 5 28 °5 1’ 37 .6 ”S 28 °4 6’ 13 .6 ”E 27 91 – 2 – – – – – 03 /0 6 08 :5 0 Sn ow LS 6 29 °3 5’ 01 .4 ”S 29 °1 6’ 08 .7 ”E 27 85 29 20 7. 4 0. 8 10 7. 1 11 .0 2 84 .3 7. 18 03 /0 6 09 :2 4 R iv er LS 7 29 °3 5’ 07 .5 ”S 29 °1 5’ 57 .5 ”E 27 91 – 7. 3 – – – – – 03 /0 6 09 :3 5 Sp rin g LS 8 29 °3 3’ 48 .7 ”S 29 °1 4’ 49 .9 ”E 27 44 – 10 .2 7. 8 11 3 9. 87 98 .7 6. 82 03 /0 6 10 :2 3 Sp rin g LS 9 29 °3 3’ 48 .3 ”S 29 °1 4’ 50 .2 ”E 27 12 29 20 10 .1 4. 6 11 2. 9 10 .5 7 87 .2 7. 09 03 /0 6 10 :2 8 R iv er LS 10 29 °3 3’ 18 .1 ”S 29 °1 4’ 05 .5 ”E 26 93 30 00 14 .7 1. 3 11 9. 7 12 .2 7 49 .6 7. 51 03 /0 6 10 :4 4 R iv er LS 11 29 °3 1’ 36 .7 ”S 29 °1 2’ 24 .1 ”E 31 59 – 5. 4 – – – – – 03 /0 6 10 :5 8 *S no w LS 12 29 °3 1’ 19 .3 ”S 29 °1 1’ 26 .4 ”E 31 44 – 7. 2 – – – – – 03 /0 6 11 :1 2 Sn ow LS 13 29 °2 5’ 22 .8 ”S 29 °0 7’ 08 .1 ”E 23 05 32 60 12 .7 4. 3 10 2. 1 10 .1 8 69 .6 7. 8 03 /0 6 11 :4 1 R iv er LS 14 29 °1 7’ 25 .0 ”S 28 °5 9’ 23 .1 ”E 20 06 33 50 20 .5 5. 4 11 6. 9 11 .7 6 91 .2 7. 78 03 /0 6 12 :4 1 R iv er LS 15 29 °1 7’ 07 .0 ”S 28 °5 9’ 02 .1 ”E 21 17 23 00 19 .1 13 .5 96 .7 8. 06 22 7. 5 7. 73 03 /0 6 12 :5 5 R iv er LS 16 29 °1 4’ 15 .9 ”S 28 °5 3’ 36 .6 ”E 20 54 21 60 21 .2 14 10 5. 4 8. 56 24 4. 9 8. 05 03 /0 6 13 :2 8 R iv er LS 17 29 °1 4’ 24 .7 ”S 28 °5 3’ 31 .9 ”E 20 31 31 20 18 .2 7. 9 11 0. 8 10 .4 9 13 0. 9 7. 9 03 /0 6 13 :4 0 R iv er LS 18 29 °1 6’ 40 .6 ”S 28 °4 4’ 56 .6 ”E 24 45 27 95 18 .4 9. 9 11 4 9. 71 14 8. 4 7. 82 03 /0 6 14 :5 7 R iv er LS 19 29 °1 6’ 10 .8 ”S 28 °3 5’ 08 .2 ”E 21 62 31 50 17 .7 7. 5 12 4. 1 11 .7 13 4 7. 9 03 /0 6 16 :1 8 R iv er LS 20 29 °0 6’ 45 .6 ”S 28 °2 8’ 16 .8 ”E 21 50 26 50 5. 1 4. 7 10 0. 6 10 .1 10 0. 9 8. 26 04 /0 6 08 :3 3 R iv er LS 21 29 °0 4’ 24 .1 ”S 28 °2 2’ 54 .7 ”E 26 13 28 10 3. 8 6 86 .6 7. 98 19 .1 8. 36 04 /0 6 09 :0 4 R iv er LS 22 29 °0 2’ 15 .0 ”S 28 °1 6’ 44 .8 ”E 18 29 26 60 15 .2 5. 6 10 4. 1 10 .0 7 10 2. 2 7. 38 04 /0 6 09 :3 1 R iv er LS 23 28 °5 3’ 46 .2 ”S 28 °0 6’ 51 .8 ”E 16 30 18 50 18 6. 7 12 0. 2 12 .2 8 22 8. 9 7. 5 04 /0 6 10 :2 8 R iv er LS 24 28 °5 3’ 34 .4 ”S 28 °0 2’ 31 .7 ”E 15 59 20 70 22 7. 5 10 7. 6 10 .8 7 13 6. 1 7. 63 04 /0 6 11 :1 2 R iv er LS 25 28 °5 7’ 18 .8 ”S 27 °4 8’ 08 .3 ”E 15 74 17 19 19 .6 8. 6 10 7. 5 10 .5 3 17 6. 3 7. 69 04 /0 6 12 :0 0 R iv er LS 26 29 °1 1’ 28 .6 ”S 27 °3 7’ 21 .0 ”E 15 31 17 30 18 .9 8. 9 12 7. 8 12 .5 1 24 6. 7 7. 78 04 /0 6 12 :5 6 R iv er LS 27 29 °4 8’ 39 .9 ”S 27 °1 5’ 00 .9 ”E 16 36 – 18 .1 12 .2 14 3. 3 12 .7 6 25 3. 9 8. 16 04 /0 6 15 :1 4 D am LS 28 29 °4 8’ 22 .1 ”S 27 °1 4’ 03 .2 ”E 16 41 – 20 .7 13 .5 10 0. 4 8. 77 14 6. 5 7. 92 04 /0 6 15 :2 9 D am LS 29 29 °4 7’ 51 .5 ”S 27 °1 5’ 31 .4 ”E 16 36 16 90 18 .2 12 .3 12 7. 3 11 .3 3 29 1. 6 7. 98 04 /0 6 16 :2 1 R iv er LS 30 29 °4 1’ 20 .0 ”S 27 °2 6’ 13 .7 ”E 15 52 18 20 17 .3 13 .1 10 3. 6 9. 16 21 9. 5 8. 02 04 /0 6 16 :4 6 R iv er LS 31 29 °3 1’ 23 .2 ”S 27 °3 1’ 19 .2 ”E 15 58 17 40 11 .6 9. 7 10 8. 1 10 .3 2 20 2. 2 8. 08 04 /0 6 17 :1 0 R iv er LS 32 29 °1 8’ 00 .2 ”S 27 °3 0’ 48 .4 ”E 14 99 – 7. 1 7. 2 51 .5 5. 28 31 7. 5 7. 85 05 /0 6 08 :3 0 D am LS 33 39 °2 2’ 39 .6 ”S 27 °2 4’ 33 .4 ”E 14 61 23 10 12 .8 7. 4 93 .6 9. 61 21 4. 7 7. 93 05 /0 6 09 :4 7 R iv er 3423Spatial variability in stable isotopes from Lesotho surface waters: insights into regional… Following the analysis of Lachniet and Patterson (2002), the correlations between the isotopic ratios for the river sam- ples against a range of geographical characteristics were cal- culated using Pearson’s correlation coefficient. For both δ18O and δ2H, statistically significant correlations are calculated for the relationships between isotope values and the altitudes of both the sample site and of the river source (Table 4; Fig. 2 – shown for δ18O only). The strongest correlation is calculated for δ2H and site altitude (r = 0.71, p = 0.0001). Notably, for both δ18O and δ2H, stronger correlations are calculated for the site altitudes than for the source altitudes of the rivers. For δ2H, a statistically-significant correlation is also calculated between the isotope values and longitude of the site. Given the position of Lesotho, longitude addition- ally serves as a proxy for distance from the Indian Ocean. No statistically significant correlations are calculated between isotope values and latitude (Table 4). No statistically signifi- cant relationships are calculated between d-excess and any of the geographical variables (Table 4). However, d-excess shows variability with δ18O. For the snowpack samples, albeit few in number, there is an increase in d excess with δ18O whereas for the other samples, d-excess decreases sharply with increase in δ18O (Fig. 3). 4 Discussion 4.1 Local Meteoric Water Lines As the slope and y-axis intercept are indicative of moisture source, physical setting and climatic conditions, and because the isotopic composition of surface waters can reflect weighted annual precipitation in some circumstances, local meteoric water lines can be used to investigate the source of surface water. As there are no GNIP stations in Lesotho, there is no published meteoric water line based on precipita- tion for comparison. We therefore present the local meteoric water line for our samples, and the meteoric water lines for each of the four southern African GNIP stations at Harare, Windhoek, Cape Town and Pretoria (Fig. 4). We note that LMWL from a two-year-long daily rainfall isotope record from Johannesburg (Lakete and Abiye 2020) is very similar to that from the GNIP station at near-by (~ 60 km) Preto- ria, as would be expected. We also note the close similarity between our local meteoric water line for Lesotho and a best-fit line through oxygen and hydrogen isotope values for Lesotho surface waters reported in de Wet et al. (2020; their Fig. 11). The two GNIP stations closest to Lesotho are Cape Town Airport and Pretoria. Notably, Cape Town is located within the contemporary winter-rainfall-zone of southern Africa, with the westerlies forming the primary moisture source (Roffe et al. 2022). Pretoria and Johannesburg are located within the summer-rainfall-zone, and similar to Lesotho, the majority of rainfall is convective in nature, associated with the TTT (Todd and Washington 1999; Crétat et al. 2012). Based both on proximity and on the synoptic conditions, we would anticipate that the Lesotho MWL would sit between the Pretoria and Cape Town MWLs, likely with a slope more similar to that of Pretoria. However, both the slope and the y-intercept are most similar to the Harare MWL, and are more similar to Windhoek than to either Pretoria or Cape Town (Fig. 4). Notably, even the snow samples, which are from a single, known precipitation event, and which are understood to be a feature of the CoL system associated with the westerlies, predominantly sit on the Harare and Table 3 Isotope values Sample Number Sample Type δ18O δ2H d-excess LS-1 River – 3.04 – 15.2 9.2 LS-2 River – 4.54 – 22.9 13.4 LS-3-1 Snow – 8.40 – 49.5 17.7 LS-3-2 Snow – 5.85 – 28.9 17.9 LS-4 River – 4.64 – 24.0 13.2 LS-5 Snow – 15.53 – 114.8 9.4 LS-6 River – 4.97 – 28.1 11.6 LS-7 Spring – 4.11 – 23.9 9.0 LS-8 Spring – 4.70 – 26.5 11.1 LS-9 River – 3.82 – 24.0 6.6 LS-10 River – 5.02 – 29.1 11.1 LS-11-1 Snow – 9.67 – 61.4 15.9 LS-11-2 Snow – 16.75 – 123.3 10.7 LS-12 Snow – 18.53 – 138.3 9.9 LS-13 River – 4.84 – 27.8 10.9 LS-14 River – 4.68 – 26.7 10.8 LS-15 River – 2.29 – 14.8 3.5 LS-16 River – 2.37 – 16.1 2.9 LS-17 River – 3.81 – 22.0 8.5 LS-18 River – 4.03 – 20.9 11.3 LS-19 River – 3.64 – 18.3 10.8 LS-20 River – 3.12 – 15.1 9.9 LS-21 River – 5.25 – 24.4 17.6 LS-22 River – 3.33 – 15.8 10.8 LS-23 River –3.58 – 16.9 11.8 LS-24 River – 3.74 – 19.1 10.8 LS-25 River – 3.19 – 14.6 10.9 LS-26 River – 2.86 – 13.8 9.0 LS-27 Dam – 2.51 – 12.6 7.5 LS-28 Dam 0.40 1.5 -1.8 LS-29 River – 3.29 – 17.1 9.2 LS-30 River – 3.30 – 16.8 9.6 LS-31 River – 4.15 – 22.4 10.8 LS-32 Dam – 2.76 – 13.6 8.4 LS-33 River – 3.10 – 14.8 10.0 3424 J. M. Fitchett et al. Windhoek MWL, rather than the Cape Town MWL. The isotopic composition of the spring samples from Lesotho is consistent with that of streams, although since only two springs were sampled, we cannot draw further conclusions based on spring composition. 4.2 Moisture source and air transport modelling Averaging the 72-hour back trajectories of all air parcels for the 70 precipitation days, 11 dominant air parcel trans- port pathways are evident. Approximately 13.5% of the airmasses originate from south of the southern African subcontinent, tracking east and then north over the Indian Ocean, before recurving west to Lesotho (Fig. 5a). A further 12.9% originate further north over the Indian Ocean in the Mozambique Channel, curving northwest, before recurv- ing south to Lesotho. The largest proportion of tracks, at 19.6%, originate over central Mozambique, and move west through southern Zimbabwe before recurving southwards to Lesotho. This dominant pathway is consistent with the alignment in the isotope records with the MWL for Harare. A similar anticlockwise track path is followed by air parcels Fig. 2 δ18O (A and B) and δ2H (C and D) versus altitude and latitude for river samples only Fig. 3 Deuterium excess (d-excess) values versus δ18O for all water samples 3425Spatial variability in stable isotopes from Lesotho surface waters: insights into regional… originating over southeastern Botswana, tracking across Bot- swana (6.9%); central south Botswana, tracking due south to Lesotho (9.6%); western Botswana, tracking south along the Botswana-Namibia border (4.8%); and central Namibia, tracking southeast to Lesotho (4.1%). The latter two cor- respond with the alignment of the isotope values with the MWL for Windhoek. Approximately 17.7% of the tracks originate over the Atlantic Ocean and follow a due-easterly path to Lesotho. A further 2.7% originate south of southern Africa, tracking west before recurving north and then east over the Atlantic Ocean. A small proportion of tracks are very short in length, originating over land in close proximity to Lesotho from the south (2.0%), and north (6.2%) respec- tively (Fig. 5a). Inspecting the individual 72-hour back-trajectories of all air masses for each storm event, there are no notable differences between the tracks, and in turn the broad cli- mate dynamics, of snow and rain events collectively, nor any distinct shifts in the different types of tracks between seasons. There are, however, more frequent due-east tracks off the Atlantic Ocean during the winter months, and more common anticyclonic tracks off the Indian Ocean during the summer months. It is important to note that these refer to all air parcel trajectories, not all of which would be moisture bearing, or indeed sufficiently moisture bearing to contrib- ute to a precipitation event along their trajectory or in the region of interest, Lesotho (Rapolaki et al. 2020). Explor- ing only those air masses that are close to (> 70% relative humidity) or at saturation (100% relative humidity), there are broad similarities in the distribution of clustered air masses (Fig. 5b). Following the work of Li et al. (2022a) in an alpine setting, we argue that an analysis of all air masses is likely to provide a more comprehensive overview of both the syn- optic climate systems responsible for precipitation events, and of the pathways of moisture transport. This is because the rugged topography induces orographic saturation over Table 4 Correlations between stable isotope ratios for river samples and geographical variables. * indicates statistically significant correlations Latitude Longitude Site Altitude Source Altitude δ18O r = 0.08, p = 0.7102 r= – 0.40, p = 0.0528 *r= – 0.63, p = 0.0001 *r= – 0.62, p = 0.0012 δ2H r = 0.27, p = 0.2020 *r= – 0.62, p = 0.0012 *r= – 0.71, p = 0.0001 *r= – 0.70, p = 0.0001 d-xs r = 0.27, p = 0.2020 r= – 0.12, p = 0.5765 r = 0.24, p = 0.2586 r = 0.25, p = 0.2387 Fig. 4 δ18O v δ2H for A. The entire set of water and snowpack samples and B for all samples excluding snowpack. The regression line for this study describes all data except samples from lakes. Local mete- oric water lines from the four southern African GNIP stations are also shown. For explanation, see text 3426 J. M. Fitchett et al. much shorter timescales than the 6-hourly trajectories, and air masses that are at comparatively low relative humidity along their track to Lesotho may reach saturation due to the considerable uplift. This is demonstrated in particular by the precipitation events in Lesotho for which none of the air mass back trajectories have a modelled relative humidity of > 50% for the 72-hour period prior to precipitation. 4.3 Spatial variations in surface‑water isotope composition The stable isotope values from the sampled surface water and snowpack in Lesotho demonstrate considerable spatial variation. The greatest variation is between the snow and water samples, as snow forms in clouds under non-equi- librium conditions (Lamb et al. 2017; Bagheri et al. 2019). Moreover, snow typically forms at low temperature and is thus heavy-isotope depleted in contrast to river waters, which tend to approximate the isotopic composition of weighted annual precipitation. There is notably less variation between the river, spring and dam samples than has been found else- where in southern Africa (Geppert et al. 2022). Despite the small number of samples, the dam waters may lie on a line with lower gradient than the rivers (Fig. 4), which is consist- ent with evaporative enrichment, commonly seen in lakes. The spatial variability in stable isotope values is consistent with the spatial heterogeneity in surface moisture and pre- cipitation across Lesotho (Armstrong et al. 1993), a result of the rugged topography, particularly in the eastern Highlands (Sene et al. 1998). The negative correlation between both δ18O and δ2H and altitude (Fig. 2) is an expression of the well-known altitude effect, which arises from heavy-isotope depletion as air is forced to rise by the topography, leading to adiabatic cooling. The relationship with longitude, which is similar to that for altitude (although only statistically sig- nificant for δ2H) (Fig. 2), is also a reflection of the altitude effect, because the topography rises from west to east. These relationships are stronger than those found for Costa Rican surface waters (Lachniet and Patterson 2002) in a similar topopgraphic setting, where the strongest relationship was found between δ18O values and altitude (r = 0.38). Lachniet and Patterson (2002) interpret the weak correlations found in their study to represent complex and multi-variate influences of geographical variables. In the case of Lesotho, there likely are multivariate influences, and these statistically signifi- cant relationships for altitude and longitude do not preclude this. Moreover, as the difference in isotopic values between the Highlands and Lowlands (as defined by a 2000 m.asl threshold altitude) is weak but statistically significant, it is also likely that the relationships are not linear. Thus, while linear statistical tests have been employed in the interests of comparability of data, future analysis should explore more complex statistical models. Deuterium excess in the Lesotho surface waters is likely to be strongly related to the d-excess in precipitation, which itself is controlled by conditions at the source of vapour formation (sea-surface temperature and, especially, relative humidity: Pfahl and Sodemann 2014) and modifications during vapour transport (Fröhlich et al. 2002). Given that the surface waters in our samples from Lesotho reflect the Fig. 5 A all proportional airmass trajectories, and B all saturated airmass trajectories, for Lesotho for each precipitation event over the period 30 May 2021–1 June 2022, modelled using HYSPLIT 3427Spatial variability in stable isotopes from Lesotho surface waters: insights into regional… averages of moisture from several different sources, varia- tions in source are unlikely to explain the range of d-excess values in Fig. 3. Instead, the negative correlation between δ18O and d excess may be explained by enhanced evapora- tive loss of raindrops at higher temperature (higher δ18O val- ues) possibly associated with lower relative humidity. Possi- ble evaporative enrichment of dam (lake) samples seems not to have modified these, so that they fall within the general trend of the surface waters. The snow samples, by contrast, show a positive correlation between δ18O and d-excess. As noted, the snow packs samples accumulated during a sin- gle event and the variations may simply reflect variations in atmospheric temperature during that event. The working understanding of the hydroclimate dynamics of Lesotho has been that the summer precipitation derives predominantly from the Indian Ocean, transported by the easterlies, and frequently takes the form of convective storms (Sene et al. 1998; Hydén 2002). Winter precipita- tion, often in the form of snowfalls in the eastern Lesotho Highlands, is understood to be associated with mid-latitude cyclones and associated CoLs, originating from the Southern Ocean and South Atlantic (Nel and Sumner 2008; Mulder and Grab 2009). On the basis of the dichotomy between easterly-derived summer precipitation, and westerly-derived winter precipitation, and of proximity to the sites, it was hypothesised that the meteoric water line for the Lesotho surface water samples would sit between that of Pretoria, representing the southern African easterly-derived summer- rainfall zone, and Cape Town, representing the westerly- derived winter rainfall zone, with a likely closer affinity to that of Pretoria. The results demonstrate a meteoric water line and d-excess that is most similar to that of the Harare GNIP station, and with greater similarity to the Wind- hoek station than to Pretoria or Cape Town, indicative of a primary moisture source north of South Africa. This is important when considering the impact that the progressive southward displacement of the westerlies (Sousa et al. 2018) may have on precipitation and water security in Lesotho in the future. The role of TTT cloud bands, stretching from Namibia through to the southeast coast of South Africa, in induc- ing rainfall in subtropical southern Africa during summer months, is increasingly well understood as a synoptic phe- nomenon (e.g. Vigaud et al. 2012; Ratna et al. 2013; James et al. 2020). Moreover, the role of the TTTs in explaining convective rainfall in Lesotho has been documented (Crétat et al. 2012; Hart et al. 2013), but little interpretation has been conducted on the dynamics of this feature across the country. The TTTs and the South Indian Ocean Conver- gence Zone (SICZ) interact with a range of synoptic features including the Mascarene and St Helena Highs, the Angolan Low, the Congo Air Boundary, and the westerly storm tracks (James et al. 2020). Notably, a greater number of TTTs occur during La Niña years (Ratna et al. 2013);the year prior to sampling, which arguably represents the reservoir period for the surface water samples, followed a strong ‘triple’ La Niña (Jones 2022; Li et al. 2022b). Three dominant moisture sources have been identified for TTTs, namely a northerly flux from the Indian Ocean, a northwesterly flux from equa- torial southern Africa and the far eastern equatorial Atlantic, and a southerly cyclonic flow around the TTT (Todd and Washington 1999: 941). These three fluxes align relatively well with the outputs of the HYSPLIT back-trajectory mod- elling for precipitation days in Lesotho. The dominance of the anticyclonic circulation track, which originates within the 72-hour window over the northeastern interior, likely originally from the Indian Ocean, and tracks through Zimba- bwe, explains the strong coherence with the meteoric water line for Harare. Likewise, circulation through the TTT of moisture from the Atlantic Ocean would explain the simi- larities with Windhoek. The advection of air parcels in a south-easterly direction, along the SICZ, has been referred to in the case of smoke transport as the “river of smoke” (Swap et al. 2003: 1), and air-parcel trajectory analyses of air pollutants is consistent with those modelled for this study (Zunckel et al. 2000; Diab et al. 2004; Rose et al. 2020; Kok et al. 2021). To the best of our knowledge however, moisture transport along this “river of smoke” has not been explored in detail. As there is no GNIP station, nor known rainfall isotope records, for Durban or similar sites along the east coast of southern Africa, it was not possible to identify a direct Indian Ocean rainfall isotope signal. This forms an impor- tant direction both for future research on surface water and rainfall isotopes in South Africa and Lesotho, and for higher resolution rainfall isotope data collection through GNIP. It will also be valuable to establish regular rainfall isotope monitoring within Lesotho, to contribute to the relatively sparse network of GNIP stations across southern Africa. Further analyses of the isotope record to investigate the source water of moisture in Lesotho would also benefit from a more extensive sampling of surface waters across Lesotho, repeated across different seasons. 5 Conclusion This study presents the first analysis of hydrogen and oxy- gen isotopes from surface waters in Lesotho. The analysis of these isotopes reveals considerable spatial variability, with strong correlations between the isotope values and the site and source altitude of the river samples. For δ2H strong relationships are also found with longitude, which due to the position of Lesotho and the Great Escarpment of southern Africa, represents a proxy for distance from the Indian Ocean and is also related to altitude. The meteoric 3428 J. M. Fitchett et al. water line for the Lesotho samples aligns most closely with that of the isotope data from the Harare GNIP station. This can be explained through the back-trajectory analysis of air parcel movement for the 72-hours prior to each precipita- tion event over the year leading up to sampling, with the greatest proportion of air mass trajectories circulating in an anticyclonic motion over continental southern Africa, pass- ing through Zimbabwe. The alignment with the meteoric water line for Windhoek is less clearly supported by the results from HYSPLIT, but likely relates to the role of the TTT cloud band, and moisture transport that may be associ- ated with the “river of smoke”. More detailed analyses of these isotope records would benefit from a higher resolution rainfall isotope network in southern Africa, and in Leso- tho. More detailed sampling of surface waters in Lesotho should focus on investigating any seasonal variations. This work is of value in understanding the contemporary synop- tic systems responsible for precipitation in Lesotho, which would inform climate modelling and hydroclimate projec- tion efforts in determining the future water security of both Lesotho, and South Africa’s economic hub. Supplementary Information The online version contains supplemen- tary material available at https:// doi. org/ 10. 1007/ s00382- 023- 07073-2. Acknowledgements We thank Dr Mauro Lourenco for assistance in obtaining data on rainfall days for Lesotho. We thanks Dr Anne-Lise Jourdan for assistance in the Bloomsbury Environmental Isotope Facil- ity. Permission for this study was granted by the Lesotho Ministry for Tourism, Environment and Culture. Author Contributions JMF, JAH and AWM conceptualized the study. All authors were involved in the fieldwork and data collection. JMF and JAH led the data analysis and the first draft of the manuscript. All authors were involved in the revisions of the manuscript, and read and approved the final manuscript. Funding Open access funding provided by University of the Witwa- tersrand. This work was supported by funding from the WITS-UCL Strategic Partnership Seed Funding granted to JMF, JAH and AWM. Data Availability All data produced in this study are available within the dataset and supplementary files. Declarations Conflict of Interest The authors have no relevant financial or non-fi- nancial interests to disclose. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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Atmos Envi- ron 34:2797–2808 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. https://doi.org/10.1029/2003JD003747 https://doi.org/10.1029/2003JD003747 https://cdn.gcro.ac.za/media/documents/GCR_Water_Security_Perspective_for_web_2019.pdf https://cdn.gcro.ac.za/media/documents/GCR_Water_Security_Perspective_for_web_2019.pdf Spatial variability in stable isotopes from Lesotho surface waters: insights into regional moisture transport Abstract 1 Introduction 2 Methods 2.1 Study site 2.2 Field water sampling 2.3 Isotopes in southern African precipitation 2.3.1 Identifying moisture sources of precipitation in Lesotho 3 Results 4 Discussion 4.1 Local Meteoric Water Lines 4.2 Moisture source and air transport modelling 4.3 Spatial variations in surface-water isotope composition 5 Conclusion Acknowledgements References