2021
DOI: 10.1002/hyp.14085
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Evaporation in Mediterranean conditions: Estimations based on isotopic approaches at the watershed scale

Abstract: In Mediterranean regions, the marked climatic seasonality and uneven precipitation distribution complicate the application of isotope mass balances to obtain meaningful basin-wide annual average evaporation rates. In the present study, a mass balance approach carried out on the Tavignanu River watershed in Corsica (France), showed unrealistic evaporation rate estimates:

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Cited by 11 publications
(11 citation statements)
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“…The δ 2 H and δ 18 O models had significant differences in terms of interflow and groundwater proportions, which were not related to the interflow parameter bounds. δ 2 H has a better precision than δ 18 O when using laser spectroscopy and these result support the move by isotope hydrologists to use δ 2 H in isotope mixing, where δ 18 O is used for comparison (Mattei et al, 2021;Vystavna et al, 2020). Likewise this study supports the need to integrate and simulate isotope mass fluxes (e.g.…”
Section: Isotope Mixing Considering Data Scarcitysupporting
confidence: 78%
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“…The δ 2 H and δ 18 O models had significant differences in terms of interflow and groundwater proportions, which were not related to the interflow parameter bounds. δ 2 H has a better precision than δ 18 O when using laser spectroscopy and these result support the move by isotope hydrologists to use δ 2 H in isotope mixing, where δ 18 O is used for comparison (Mattei et al, 2021;Vystavna et al, 2020). Likewise this study supports the need to integrate and simulate isotope mass fluxes (e.g.…”
Section: Isotope Mixing Considering Data Scarcitysupporting
confidence: 78%
“…This includes hydrological modelling applications as more temporal and spatially comprehensive datasets become available. However, the challenge to compensate for a single missing tracer component, such as soil‐water isotopes, (which are difficult to measure), has, in the past, reduced the application of more robust tertiary mixing relationships between rain, groundwater and soil‐water, as part of stream water in tropical and sub‐tropical climates where snow has limited or no impact on hydrological processes (Mattei et al, 2021; Watson, Miller, Kralisch, et al, 2022).…”
Section: Introductionmentioning
confidence: 99%
“…To highlight the dominant water reservoirs contributing to the flooding and water maintenance in the pond through time, an end‐member mixing analysis (EMMA) approach based on the major element composition of the pond water was used to constrain the proportional contribution of the three different possible water sources: (1) groundwater, (2) subsurface and (3) direct precipitation (Equation ). We assumed that the heterogeneous landscape and geological structures result in marked physico‐chemical variability amongst individual hydrological pathways that are not always detectable using only isotope‐based investigations (Mattei et al, 2021; Vystavna et al, 2020) and propose using major element data as tracers: 1goodbreak=f1()tgoodbreak+f2()tgoodbreak+f3()t As()tgoodbreak=f1()tA1()tgoodbreak+f2()tA2()tgoodbreak+f3()tA3()t Bs()tgoodbreak=f1()tB1()tgoodbreak+f2()tB2()tgoodbreak+f3()tB3()t where f 1 , f 2 and f 3 are the mixing proportions and A 1 , A 2 and A 3 (respectively B 1 , B 2 and B 3 ) the end‐member concentrations for tracer A (respectively, tracer B ) for the groundwater, subsurface and surface water reservoirs varying with time ( t ). A s (respectively B s ) is the sample concentration measured in the pond for tracer A varying with time ( t ).…”
Section: Methodsmentioning
confidence: 99%
“…(1) groundwater, (2) subsurface and (3) direct precipitation (Equation 2). We assumed that the heterogeneous landscape and geological structures result in marked physico-chemical variability amongst individual hydrological pathways that are not always detectable using only isotope-based investigations (Mattei et al, 2021;Vystavna et al, 2020) and propose using major element data as tracers:…”
Section: End Member Mixing Analysismentioning
confidence: 99%
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