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Abstract. Understanding the variability of the chemical composition of surface waters is a major issue for the scientific community. To date, the study of concentration–discharge relations has been intensively used to assess the spatiotemporal variability of the water chemistry at watershed scales. However, the lack of independent estimations of the water transit times within catchments limits the ability to model and predict the water chemistry with only geochemical approaches. In this study, a dimensionally reduced hydrological model coupling surface flow with subsurface flow (i.e., the Normally Integrated Hydrological Model, NIHM) has been used to constrain the distribution of the flow lines in a headwater catchment (Strengbach watershed, France). Then, hydrogeochemical simulations with the code KIRMAT (i.e., KInectic Reaction and MAss Transport) are performed to calculate the evolution of the water chemistry along the flow lines. Concentrations of dissolved silica (H4SiO4) and in basic cations (Na+, K+, Mg2+, and Ca2+) in the spring and piezometer waters are correctly reproduced with a simple integration along the flow lines. The seasonal variability of hydraulic conductivities along the slopes is a key process to understand the dynamics of flow lines and the changes of water transit times in the watershed. The covariation between flow velocities and active lengths of flow lines under changing hydrological conditions reduces the variability of water transit times and explains why transit times span much narrower variation ranges than the water discharges in the Strengbach catchment. These findings demonstrate that the general chemostatic behavior of the water chemistry is a direct consequence of the strong hydrological control of the water transit times within the catchment. Our results also show that a better knowledge of the relations between concentration and mean transit time (C–MTT relations) is an interesting new step to understand the diversity of C–Q shapes for chemical elements. The good match between the measured and modeled concentrations while respecting the water–rock interaction times provided by the hydrological simulations also shows that it is possible to capture the chemical composition of waters using simply determined reactive surfaces and experimental kinetic constants. The results of our simulations also strengthen the idea that the low surfaces calculated from the geometrical shapes of primary minerals are a good estimate of the reactive surfaces within the environment.

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From the study of the Strengbach and Ringelbach watersheds we propose to illustrate the interest of combining the geochemical tracing and geochemical modeling approaches on surface and deep borehole waters, to decipher the diversity of the water flow and the associated water-rock interactions in such elementary mountainous catchments. The results point to a clear geochemical typology of waters depending on the water circulations (deep vs. hypodermic) within the substratum.

In numerical codes for reactive transport modeling, systems of nonlinear chemical equations are often solved through the Newton Raphson method (NR). NR is an iterative procedure that results in a sequential solution of linear systems. The algorithm is known for its effectiveness in the vicinity of the solution but also for its lack of robustness otherwise. Therefore, inaccurate initial conditions can lead to non‐convergence or excessive numbers of iterations, which significantly increase the computational cost. In this work, we show that inaccurate initial conditions can lead to very ill‐conditioned system matrices, which makes NR inefficient. This efficiency is improved by preconditioning techniques and/or by coupling the NR method with a zero‐order method called the positive continuous fraction method. Numerical experiments that are based on seven different test cases show that the ill‐conditioned linear systems within NR represent a problem and that coupling NR with a method that bypasses the computation of the Jacobian matrix significantly improves the robustness and efficiency of the algorithm. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1246–1262, 2017

Vegetal cover and the water cycle are closely linked. The climate controls the distribution and productivity of terrestrial vegetation, and the vegetal cover type is a key determinant for evapotranspiration and global runoff. In this study, a dynamic vegetation model (LPJ) has been coupled with a 3D hydrogeological model (MODFLOW) to estimate, for the first time, the impact of climate change on a small forested temperate watershed (Strengbach, Vosges, France). The model, calibrated with monthly hydrological and climate data, is able to globally reproduce the observed vegetal cover distribution and the water cycle over the 1987-2009 period. The discrepancies between calculated and observed intra-annual discharge variations highlight the importance of processes such as snow formation, snowmelt, and catchment recharge after drought periods, as well as the impact of evapotranspiration. Longterm simulations extending up to the year 2100 have been performed with climatic output from the Meteo-France climate model ARPEGE/Climate (IPCC, 2007 scenario A1B). With a predicted increase in temperature of 2.6°C and a rise in atmospheric CO 2 concentration of 80%, the mean annual precipitation decreases by 4.5% in the Strengbach watershed over the course of the century. The models cascade predicts a limited decrease of evapotranspiration (by 2.5%), an impact on discharge (11% decrease) at the watershed outlet over the 21st century, and a significant change in vegetation distribution starting in approximately 2085. The response of land plants to climate change in the future seems to only slightly affect the water resources in the Strengbach catchment. This study also highlights existing shortcomings and limitations of simulations for measuring the impacts of climate change.