Articles | Volume 21, issue 1
Hydrol. Earth Syst. Sci., 21, 549–570, 2017
https://doi.org/10.5194/hess-21-549-2017
Hydrol. Earth Syst. Sci., 21, 549–570, 2017
https://doi.org/10.5194/hess-21-549-2017
Research article
27 Jan 2017
Research article | 27 Jan 2017

Spatially distributed characterization of soil-moisture dynamics using travel-time distributions

Falk Heße et al.

Related authors

GSTools v1.3: a toolbox for geostatistical modelling in Python
Sebastian Müller, Lennart Schüler, Alraune Zech, and Falk Heße
Geosci. Model Dev., 15, 3161–3182, https://doi.org/10.5194/gmd-15-3161-2022,https://doi.org/10.5194/gmd-15-3161-2022, 2022
Short summary
Predicting the impact of spatial heterogeneity on microbially mediated nutrient cycling in the subsurface
Swamini Khurana, Falk Heße, Anke Hildebrandt, and Martin Thullner
Biogeosciences, 19, 665–688, https://doi.org/10.5194/bg-19-665-2022,https://doi.org/10.5194/bg-19-665-2022, 2022
Short summary
Assessing the response of groundwater quantity and travel time distribution to 1.5, 2, and 3 °C global warming in a mesoscale central German basin
Miao Jing, Rohini Kumar, Falk Heße, Stephan Thober, Oldrich Rakovec, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 24, 1511–1526, https://doi.org/10.5194/hess-24-1511-2020,https://doi.org/10.5194/hess-24-1511-2020, 2020
Short summary
Influence of input and parameter uncertainty on the prediction of catchment-scale groundwater travel time distributions
Miao Jing, Falk Heße, Rohini Kumar, Olaf Kolditz, Thomas Kalbacher, and Sabine Attinger
Hydrol. Earth Syst. Sci., 23, 171–190, https://doi.org/10.5194/hess-23-171-2019,https://doi.org/10.5194/hess-23-171-2019, 2019
Short summary
Stochastic hydrogeology's biggest hurdles analyzed and its big blind spot
Yoram Rubin, Ching-Fu Chang, Jiancong Chen, Karina Cucchi, Bradley Harken, Falk Heße, and Heather Savoy
Hydrol. Earth Syst. Sci., 22, 5675–5695, https://doi.org/10.5194/hess-22-5675-2018,https://doi.org/10.5194/hess-22-5675-2018, 2018
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise
Wouter J. M. Knoben and Diana Spieler
Hydrol. Earth Syst. Sci., 26, 3299–3314, https://doi.org/10.5194/hess-26-3299-2022,https://doi.org/10.5194/hess-26-3299-2022, 2022
Short summary
Effects of spatial and temporal variability in surface water inputs on streamflow generation and cessation in the rain–snow transition zone
Leonie Kiewiet, Ernesto Trujillo, Andrew Hedrick, Scott Havens, Katherine Hale, Mark Seyfried, Stephanie Kampf, and Sarah E. Godsey
Hydrol. Earth Syst. Sci., 26, 2779–2796, https://doi.org/10.5194/hess-26-2779-2022,https://doi.org/10.5194/hess-26-2779-2022, 2022
Short summary
Quantifying multi-year hydrological memory with Catchment Forgetting Curves
Alban de Lavenne, Vazken Andréassian, Louise Crochemore, Göran Lindström, and Berit Arheimer
Hydrol. Earth Syst. Sci., 26, 2715–2732, https://doi.org/10.5194/hess-26-2715-2022,https://doi.org/10.5194/hess-26-2715-2022, 2022
Short summary
On constraining a lumped hydrological model with both piezometry and streamflow: results of a large sample evaluation
Antoine Pelletier and Vazken Andréassian
Hydrol. Earth Syst. Sci., 26, 2733–2758, https://doi.org/10.5194/hess-26-2733-2022,https://doi.org/10.5194/hess-26-2733-2022, 2022
Short summary
Influences of land use changes on the dynamics of water quantity and quality in the German lowland catchment of the Stör
Chaogui Lei, Paul D. Wagner, and Nicola Fohrer
Hydrol. Earth Syst. Sci., 26, 2561–2582, https://doi.org/10.5194/hess-26-2561-2022,https://doi.org/10.5194/hess-26-2561-2022, 2022
Short summary

Cited articles

Almorox, J., Quej, V. H., and Martí, P.: Global performance ranking of temperature-based approaches for evapotranspiration estimation considering Köppen climate classes, J. Hydrol., 528, 514–522, https://doi.org/10.1016/j.jhydrol.2015.06.057, 2015.
Benettin, P., Rinaldo, A., and Botter, G.: Kinematics of age mixing in advection-dispersion models, Water Resour. Res., 49, 8539–8551, https://doi.org/10.1002/2013WR014708, 2013.
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, 2015a.
Benettin, P., Kirchner, J. W., Rinaldo, A., and Botter, G.: Modeling chloride transport using travel time distributions at plynlimon, wales, Water Resour. Res., 51, 3259–3276, https://doi.org/10.1002/2014WR016600, 2015b.
Bergström, S.: Computer Models of Watershed Hydrology, in: The HBV Model, edited by: Singh, V. P., Water Resources Publications, LLC, USA, 443–476, 1995.
Download
Short summary
Travel-time distributions are a comprehensive tool for the characterization of hydrological systems. In our study, we used data that were simulated by virtue of a well-established hydrological model. This gave us a very large yet realistic dataset, both in time and space, from which we could infer the relative impact of different factors on travel-time behavior. These were, in particular, meteorological (precipitation), land surface (land cover, leaf-area index) and subsurface (soil) properties.