North American precipitation isotope (δ18O) zones revealed in time series modeling across Canada and northern United States

Delineating spatial patterns of precipitation isotopes ("isoscapes") is becoming increasingly important to understand the processes governing the modern water isotope cycle and their application to migration forensics, climate proxy interpretation, and ecohydrology of terrestrial systems. However, the extent to which these patterns can be empirically predicted across Canada and the northern United States has not been fully articulated, in part due to a lack of time series precipitation isotope data for major regions of North America. In this study, we use multiple linear regressions of CNIP, GNIP, and USNIP observations alongside climatological variables, teleconnection indices, and geographic indicators to create empirical models that predict the δ18O of monthly precipitation (δ18Oppt) across Canada and the northern United States. Five regionalization approaches are used to separate the study domain into isotope zones to explore the effect of spatial grouping on model performance. Stepwise regression-derived parameterizations quantified by permutation testing indicate the significance of precipitable water content and latitude as predictor variables. Within the Canadian Arctic and eastern portion of the study domain, models from all regionalizations capture the interannual and intraannual variability of δ18Oppt. The Pacific coast and northwestern portions of the study domain show less agreement between models and poorer model performance, resulting in higher uncertainty in simulations throughout these regions. Long-term annual average δ18Oppt isoscapes are generated, highlighting the uncertainty in the regionalization approach as it compounds over time. Additionally, monthly time series simulations are presented at various locations, and model structure uncertainty and 90% bootstrapped prediction bounds are detailed for these predictions. Key Points: Empirical models are developed to simulate 18O of monthly precipitation Precipitable water content describes the most variance in precipitation 18O Uncertainty in modeling monthly and long-term precipitation 18O is assesse

Modeling the isotopic evolution of snowpack and snowmelt : Testing a spatially distributed parsimonious approach

This work was funded by the NERC/JPI SIWA project (NE/M019896/1) and the European Research Council ERC (project GA 335910 VeWa). The Krycklan part of this study was supported by grants from the Knut and Alice Wallenberg Foundation (Branch-points), Swedish Research Council (SITES), SKB and Kempe foundation. The data and model code is available upon request. Authors declare that they have no conflict of interest. We would like to thank the three anonymous reviewers for their constructive comments that improved the manuscript.Peer reviewedPublisher PD

Predicting Spatial Patterns in Precipitation Isotope (δ2H and δ18O) Seasonality Using Sinusoidal Isoscapes

Understanding how precipitation isotopes vary spatially and temporally is important for tracer applications. We tested how well month‐to‐month variations in precipitation δ18O and δ2H were captured by sinusoidal cycles, and how well spatial variations in these seasonal cycles could be predicted, across Switzerland. Sine functions representing seasonal cycles in precipitation isotopes explained between 47% and 94% of the variance in monthly δ18O and δ2H values at each monitoring site. A significant sinusoidal cycle was also observed in line‐conditioned excess. We interpolated the amplitudes, phases, and offsets of these sine functions across the landscape, using multiple linear regression models based on site characteristics. These interpolated maps, here referred to as a sinusoidal isoscape, reproduced monthly observations with prediction errors that were smaller than or similar to those of other isoscapes. Sinusoidal isoscapes are likely broadly useful because they concisely describe seasonal isotopic behavior and can be estimated efficiently from sparse or irregular data. Plain Language Summary Naturally occurring isotopic variations in precipitation are used to trace water movement through landscapes and ecosystems. However, direct measurements are often unavailable, so many isotope‐based approaches to studying terrestrial processes require predicted isotopic inputs. We found that the isotopic composition of precipitation follows a predictable seasonal pattern. We developed a new approach for mapping precipitation isotope seasonality that will be useful in a wide range of fields

Testing a spatially distributed tracer-aided runoff model in a snow-influenced catchment : effects of multi-criteria calibration on streamwater ages

This work was funded by the European Research Council (project GA 335910 VeWa). Dr. Huaxia Yao, Chris McConnell and Tim Field were instrumental to the collection of isotope samples and for many aspects of hydrological data collection.Peer reviewedPostprin

Examining the impacts of precipitation isotope input (<i>δ</i>¹⁸O_ppt) on distributed, tracer-aided hydrological modelling

Tracer-aided hydrological models are becoming increasingly popular tools as they assist with process understanding and source separation, which facilitates model calibration and diagnosis of model uncertainty (Tetzlaff et al., 2015; Klaus and McDonnell, 2013). Data availability in high-latitude regions, however, proves to be a major challenge associated with this type of application (Tetzlaff et al., 2015). Models require a time series of isotopes in precipitation (δ18Oppt) to drive simulations, and throughout much of the world &ndash; particularly in sparsely populated high-latitude regions &ndash; these data are not widely available. Here we investigate the impact that choice of precipitation isotope product (δ18Oppt) has on simulations of streamflow, δ18O in streamflow (δ18OSF), resulting hydrograph separations, and model parameters. In a high-latitude, data-sparse, seasonal basin (Fort Simpson, NWT, Canada), we assess three precipitation isotope products of different spatial and temporal resolutions (i.e. semi-annual static, seasonal KPN43, and daily bias-corrected REMOiso), and apply them to force the isoWATFLOOD tracer-aided hydrologic model. Total simulated streamflow is not significantly impacted by choice of δ18Oppt product; however, simulated isotopes in streamflow (δ18OSF) and the internal apportionment of water (driven by model parameterization) are impacted. The highest-resolution product (REMOiso) was distinct from the two lower-resolution products (KPN43 and static), but could not be verified as correct due to a lack of daily δ18Oppt observations. The resolution of δ18Oppt impacts model parameterization and seasonal hydrograph separations, producing notable differences among simulations following large snowmelt and rainfall events when event compositions differ significantly from δ18OSF. Capturing and preserving the spatial variability in δ18Oppt using distributed tracer-aided models is important because this variability impacts model parameterization. We achieve an understanding of tracer-aided modelling and its application in high-latitude regions with limited δ18Oppt observations, and the value such models have in defining modelling uncertainty. In this study, application of a tracer-aided model is able to identify simulations with improved internal process representation, reinforcing the fact that tracer-aided modelling approaches assist with resolving hydrograph component contributions and work towards diagnosing equifinality

Le livre noir de Messieurs Delavau et Franchet, ou Répertoire alphabétique de la police politique sous le ministère déplorable. Tome 1 / ; ouvrage imprimé d'après les registres de l'administration ; précédé d'une introduction, par M. Année. Tome premier [quatrième].

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