Modeling the isotopic evolution of snowpack and snowmelt: Testing a spatially distributed parsimonious approach. Issue 7 (20th July 2017)

• Record Type: Journal Article
• Authors: Ala‐aho, Pertti; Tetzlaff, Doerthe; McNamara, James P.; Laudon, Hjalmar; Kormos, Patrick; Soulsby, Chris
• Is Part Of: Water resources research. Volume 53:Issue 7(2017)
• Page Start: 5813
• Page End: 5830
• Publication Date: 2017-07-20
• Subjects: snowmelt runoff -- stable water isotopes -- isotope fractionation -- spatially distributed -- parsimonious modeling -- tracer‐aided modeling; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2017WR020650
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Use of stable water isotopes has become increasingly popular in quantifying water flow paths and travel times in hydrological systems using tracer‐aided modeling. In snow‐influenced catchments, snowmelt produces a traceable isotopic signal, which differs from original snowfall isotopic composition because of isotopic fractionation in the snowpack. These fractionation processes in snow are relatively well understood, but representing their spatiotemporal variability in tracer‐aided studies remains a challenge. We present a novel, parsimonious modeling method to account for the snowpack isotope fractionation and estimate isotope ratios in snowmelt water in a fully spatially distributed manner. Our model introduces two calibration parameters that alone account for the isotopic fractionation caused by sublimation from interception and ground snow storage, and snowmelt fractionation progressively enriching the snowmelt runoff. The isotope routines are linked to a generic process‐based snow interception‐accumulation‐melt model facilitating simulation of spatially distributed snowmelt runoff. We use a synthetic modeling experiment to demonstrate the functionality of the model algorithms in different landscape locations and under different canopy characteristics. We also provide a proof‐of‐concept model test and successfully reproduce isotopic ratios in snowmelt runoff sampled with snowmelt lysimeters in two long‐term experimental catchment with contrasting winter conditions. To our knowledge, the method is the first such tool to allow estimation of the spatially distributed nature of isotopic fractionation in snowpacks and the resulting isotope ratios in snowmelt runoff. The method can thus provide a useful tool for tracer‐aided modeling to better understand the integrated nature of flow, mixing, and transport processes in snow‐influenced catchments. Key Points: We present a novel modeling method for snowpack isotope fractionation to estimate spatiotemporal variability of isotope ratios in snowmelt Our parsimonious model uses two calibration parameters that alone account for the isotopic fractionation caused by snow sublimation and melt We provide a modeling experiment to demonstrate the model functionality and a proof‐of‐concept comparison using snowmelt isotope data