Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3691-2021
https://doi.org/10.5194/hess-25-3691-2021
Research article
 | 
30 Jun 2021
Research article |  | 30 Jun 2021

Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape

Vince P. Kaandorp, Hans Peter Broers, Ype van der Velde, Joachim Rozemeijer, and Perry G. B. de Louw

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Cited articles

Ali, G., Birkel, C., Tetzlaff, D., Soulsby, C., McDonnell, J. J., and Tarolli, P.: A comparison of wetness indices for the prediction of observed connected saturated areas under contrasting conditions, Earth Surf. Proc. Land., 39, 399–413, https://doi.org/10.1002/esp.3506, 2014. 
Anderson, T. R., Groffman, P. M., Kaushal, S. S., and Walter, M. T.: Shallow Groundwater Denitrification in Riparian Zones of a Headwater Agricultural Landscape, J. Environ. Qual., 43, 732–744, https://doi.org/10.2134/jeq2013.07.0303, 2014. 
Aquilina, L., Vergnaud-Ayraud, V., Labasque, T., Bour, O., Molénat, J., Ruiz, L., de Montety, V., De Ridder, J., Roques, C., and Longuevergne, L.: Nitrate dynamics in agricultural catchments deduced from groundwater dating and long-term nitrate monitoring in surface- and groundwaters, Sci. Total Environ., 435–436, 167–178, https://doi.org/10.1016/j.scitotenv.2012.06.028, 2012. 
Benettin, P., Rinaldo, A., and Botter, G.: Tracking residence times in hydrological systems: Forward and backward formulations, Hydrol. Process., 29, 5203–5213, https://doi.org/10.1002/hyp.10513, 2015. 
Benettin, P., Soulsby, C., Birkel, C., Tetzlaff, D., Botter, G., and Rinaldo, A.: Using SAS functions and high-resolution isotope data to unravel travel time distributions in headwater catchments, Water Resour. Res., 53, 1864–1878, https://doi.org/10.1002/2016WR020117, 2017. 
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We reconstructed historical and present-day tritium, chloride, and nitrate concentrations in stream water of a catchment using land-use-based input curves and calculated travel times of groundwater. Parameters such as the unsaturated zone thickness, mean travel time, and input patterns determine time lags between inputs and in-stream concentrations. The timescale of the breakthrough of pollutants in streams is dependent on the location of pollution in a catchment.
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