Articles | Volume 17, issue 13
https://doi.org/10.5194/gmd-17-5249-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-5249-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
RoGeR v3.0.5 – a process-based hydrological toolbox model in Python
Robin Schwemmle
CORRESPONDING AUTHOR
Hydrology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
Hannes Leistert
Hydrology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
Andreas Steinbrich
Hydrology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
Markus Weiler
Hydrology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
Related authors
Robin Schwemmle, Dominic Demand, and Markus Weiler
Hydrol. Earth Syst. Sci., 25, 2187–2198, https://doi.org/10.5194/hess-25-2187-2021, https://doi.org/10.5194/hess-25-2187-2021, 2021
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A better understanding of the reasons why model performance is unsatisfying represents a crucial part for meaningful model evaluation. We propose the novel diagnostic efficiency (DE) measure and diagnostic polar plots. The proposed evaluation approach provides a diagnostic tool for model developers and model users and facilitates interpretation of model performance.
Jonas Pyschik and Markus Weiler
EGUsphere, https://doi.org/10.5194/egusphere-2025-2411, https://doi.org/10.5194/egusphere-2025-2411, 2025
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This study introduces a new method of detecting how water moves quickly through certain paths in soil, bypassing the usual, slower flow. By analysing natural water markers in soil samples taken at different depths, we identified unusual flow patterns. Our method is simple and non-invasive, and can be used to cover large areas. This helps us to better understand how water travels through the ground, which is important for managing water resources and protecting the environment.
Heinke Paulsen and Markus Weiler
Hydrol. Earth Syst. Sci., 29, 2309–2319, https://doi.org/10.5194/hess-29-2309-2025, https://doi.org/10.5194/hess-29-2309-2025, 2025
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This technical note describes the development of a weighing forest floor grid lysimeter. The device is needed to investigate the dynamics of the water balance components of the organic layer in forests, quantifying precipitation, drainage, evaporation, and storage. We designed a setup that can be easily rebuilt and that is cost-effective, which allows for customized applications. Performance metrics from laboratory results and initial field data are presented.
Markus Weiler, Julia Krumm, Ingo Haag, Hannes Leistert, Max Schmit, Andreas Steinbrich, and Andreas Hänsler
EGUsphere, https://doi.org/10.5194/egusphere-2025-1519, https://doi.org/10.5194/egusphere-2025-1519, 2025
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Pluvial (flash) floods, caused by intense local rainfall, result in surface runoff and overland flow, making them different from fluvial floods. A new Pluvial Flood Index (PFI) combines precipitation, hydrological, and hydrodynamic processes to assess surface flooding hazards. The PFI, based on flood hazard areas, helps forecast flash floods and supports real-time warning systems, aiding municipal decision-making, preparedness, and planning.
Jonas Pyschik, Stefan Seeger, Barbara Herbstritt, and Markus Weiler
Hydrol. Earth Syst. Sci., 29, 525–534, https://doi.org/10.5194/hess-29-525-2025, https://doi.org/10.5194/hess-29-525-2025, 2025
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We developed a device (named VapAuSa) that automates stable water isotope analysis. Stable water isotopes are a natural tracer that many researchers use to investigate water (re-)distribution processes in environmental systems. VapAuSa helps to analyse such environmental samples by automating a formerly tedious manual process, allowing for higher sample throughput. This enables larger sampling campaigns, as more samples can be processed before reaching their limited storage time.
Barbara Herbstritt, Benjamin Gralher, Stefan Seeger, Michael Rinderer, and Markus Weiler
Hydrol. Earth Syst. Sci., 27, 3701–3718, https://doi.org/10.5194/hess-27-3701-2023, https://doi.org/10.5194/hess-27-3701-2023, 2023
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We present a method to collect water vapor samples into bags in the field without an in-field analyser, followed by isotope analysis in the lab. This new method resolves even fine-scaled natural isotope variations. It combines low-cost and lightweight components for maximum spatial and temporal flexibility regarding environmental setups. Hence, it allows for sampling even in terrains that are rather difficult to access, enabling future extended isotope datasets in soil sciences and ecohydrology.
Stefan Seeger and Markus Weiler
Hydrol. Earth Syst. Sci., 27, 3393–3404, https://doi.org/10.5194/hess-27-3393-2023, https://doi.org/10.5194/hess-27-3393-2023, 2023
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This study proposes a low-budget method to quantify the radial distribution of water transport velocities within trees at a high spatial resolution. We observed a wide spread of water transport velocities within a tree stem section, which were on average 3 times faster than the flux velocity. The distribution of transport velocities has implications for studies that use water isotopic signatures to study root water uptake and usually assume uniform or even implicitly infinite velocities.
Andreas Hänsler and Markus Weiler
Hydrol. Earth Syst. Sci., 26, 5069–5084, https://doi.org/10.5194/hess-26-5069-2022, https://doi.org/10.5194/hess-26-5069-2022, 2022
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Spatially explicit quantification of design storms is essential for flood risk assessment and planning. However, available datasets are mainly based on spatially interpolated station-based design storms. Since the spatial interpolation of the data inherits a large potential for uncertainty, we develop an approach to be able to derive spatially explicit design storms on the basis of weather radar data. We find that our approach leads to an improved spatial representation of design storms.
Anne Hartmann, Markus Weiler, Konrad Greinwald, and Theresa Blume
Hydrol. Earth Syst. Sci., 26, 4953–4974, https://doi.org/10.5194/hess-26-4953-2022, https://doi.org/10.5194/hess-26-4953-2022, 2022
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Analyzing the impact of soil age and rainfall intensity on vertical subsurface flow paths in calcareous soils, with a special focus on preferential flow occurrence, shows how water flow paths are linked to the organization of evolving landscapes. The observed increase in preferential flow occurrence with increasing moraine age provides important but rare data for a proper representation of hydrological processes within the feedback cycle of the hydro-pedo-geomorphological system.
Nils Hinrich Kaplan, Theresa Blume, and Markus Weiler
Hydrol. Earth Syst. Sci., 26, 2671–2696, https://doi.org/10.5194/hess-26-2671-2022, https://doi.org/10.5194/hess-26-2671-2022, 2022
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This study is analyses how characteristics of precipitation events and soil moisture and temperature dynamics during these events can be used to model the associated streamflow responses in intermittent streams. The models are used to identify differences between the dominant controls of streamflow intermittency in three distinct geologies of the Attert catchment, Luxembourg. Overall, soil moisture was found to be the most important control of intermittent streamflow in all geologies.
Benjamin Gralher, Barbara Herbstritt, and Markus Weiler
Hydrol. Earth Syst. Sci., 25, 5219–5235, https://doi.org/10.5194/hess-25-5219-2021, https://doi.org/10.5194/hess-25-5219-2021, 2021
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We scrutinized the quickest currently available method for stable isotope analysis of matrix-bound water. Simulating common procedures, we demonstrated the limits of certain materials currently used and identified a reliable and cost-efficient alternative. Further, we calculated the optimum proportions of important protocol aspects critical for precise and accurate analyses. Our unifying protocol suggestions increase data quality and comparability as well as the method's general applicability.
Jan Greiwe, Markus Weiler, and Jens Lange
Biogeosciences, 18, 4705–4715, https://doi.org/10.5194/bg-18-4705-2021, https://doi.org/10.5194/bg-18-4705-2021, 2021
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We analyzed variability in diel nitrate patterns at three locations in a lowland stream. Comparison of time lags between monitoring sites with water travel time indicated that diel patterns were created by in-stream processes rather than transported downstream from an upstream point of origin. Most of the patterns (70 %) could be explained by assimilatory nitrate uptake. The remaining patterns suggest seasonally varying dominance and synchronicity of different biochemical processes.
Stefan Seeger and Markus Weiler
Biogeosciences, 18, 4603–4627, https://doi.org/10.5194/bg-18-4603-2021, https://doi.org/10.5194/bg-18-4603-2021, 2021
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We developed a setup for fully automated in situ measurements of stable water isotopes in soil and the stems of fully grown trees. We used this setup in a 12-week field campaign to monitor the propagation of a labelling pulse from the soil up to a stem height of 8 m.
We could observe trees shifting their main water uptake depths multiple times, depending on water availability.
The gained knowledge about the temporal dynamics can help to improve water uptake models and future study designs.
Andreas Hänsler and Markus Weiler
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-366, https://doi.org/10.5194/hess-2021-366, 2021
Manuscript not accepted for further review
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Spatially explicit quantification on design storms are essential for flood risk assessment. However this information can be only achieved from substantially long records of rainfall measurements, usually only available for a few stations. Hence, design storms estimates from these few stations are then spatially interpolated leading to a major source of uncertainty. Therefore we defined a methodology to extend spatially explicit weather radar data to be used for the estimation of design storms.
Anne Hartmann, Markus Weiler, Konrad Greinwald, and Theresa Blume
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-242, https://doi.org/10.5194/hess-2021-242, 2021
Manuscript not accepted for further review
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Our field observation-based examination of flow path evolution, soil formation and vegetation succession across ten millennia on calcareous parent material shows how water flow paths and subsurface water storage are linked to the organization of evolving landscapes. We provide important but rare data and observations for a proper handling of hydrologic processes and their role within the feedback cycle of the hydro-pedo-geomorphological system.
Axel Schaffitel, Tobias Schuetz, and Markus Weiler
Geosci. Model Dev., 14, 2127–2142, https://doi.org/10.5194/gmd-14-2127-2021, https://doi.org/10.5194/gmd-14-2127-2021, 2021
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This paper presents FluSM, an algorithm to derive the water balance from soil moisture and metrological measurements. This data-driven water balance framework uses soil moisture as an input and therefore is applicable for cases with unclear processes and lacking parameters. In a case study, we apply FluSM to derive the water balance of 15 different permeable pavements under field conditions. These findings are of special interest for urban hydrology.
Robin Schwemmle, Dominic Demand, and Markus Weiler
Hydrol. Earth Syst. Sci., 25, 2187–2198, https://doi.org/10.5194/hess-25-2187-2021, https://doi.org/10.5194/hess-25-2187-2021, 2021
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A better understanding of the reasons why model performance is unsatisfying represents a crucial part for meaningful model evaluation. We propose the novel diagnostic efficiency (DE) measure and diagnostic polar plots. The proposed evaluation approach provides a diagnostic tool for model developers and model users and facilitates interpretation of model performance.
Michael Rinderer, Jaane Krüger, Friederike Lang, Heike Puhlmann, and Markus Weiler
Biogeosciences, 18, 1009–1027, https://doi.org/10.5194/bg-18-1009-2021, https://doi.org/10.5194/bg-18-1009-2021, 2021
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We quantified the lateral and vertical subsurface flow (SSF) and P concentrations of three beech forest plots with contrasting soil properties during sprinkling experiments. Vertical SSF was 2 orders of magnitude larger than lateral SSF, and both consisted mainly of pre-event water. P concentrations in SSF were high during the first 1 to 2 h (nutrient flushing) but nearly constant thereafter. This suggests that P in the soil solution was replenished fast by mineral or organic sources.
Merle Koelbing, Tobias Schuetz, and Markus Weiler
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-24, https://doi.org/10.5194/hess-2021-24, 2021
Revised manuscript not accepted
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Based on a unique and comprehensive data set of urban micro-meteorological variables, which were observed with a mobile climate station, we developed a new method to transfer mesoscale reference potential evapotranspiration to the urban microscale in street canyons. Our findings can be transferred easily to existing urban hydrologic models to improve modelling results with a more precise estimate of potential evapotranspiration on street level.
Anne Hartmann, Markus Weiler, and Theresa Blume
Earth Syst. Sci. Data, 12, 3189–3204, https://doi.org/10.5194/essd-12-3189-2020, https://doi.org/10.5194/essd-12-3189-2020, 2020
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Our analysis of soil physical and hydraulic properties across two soil chronosequences of 10 millennia in the Swiss Alps provides important observation of the evolution of soil hydraulic behavior. A strong co-evolution of soil physical and hydraulic properties was revealed by the observed change of fast-draining coarse-textured soils to slow-draining soils with a high water-holding capacity in correlation with a distinct change in structural properties and organic matter content.
Daniel Beiter, Markus Weiler, and Theresa Blume
Hydrol. Earth Syst. Sci., 24, 5713–5744, https://doi.org/10.5194/hess-24-5713-2020, https://doi.org/10.5194/hess-24-5713-2020, 2020
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We investigated the interactions between streams and their adjacent hillslopes in terms of water flow. It could be revealed that soil structure has a strong influence on how hillslopes connect to the streams, while the groundwater table tells us a lot about when the two connect. This observation could be used to improve models that try to predict whether or not hillslopes are in a state where a rain event will be likely to produce a flood in the stream.
Maria Staudinger, Stefan Seeger, Barbara Herbstritt, Michael Stoelzle, Jan Seibert, Kerstin Stahl, and Markus Weiler
Earth Syst. Sci. Data, 12, 3057–3066, https://doi.org/10.5194/essd-12-3057-2020, https://doi.org/10.5194/essd-12-3057-2020, 2020
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The data set CH-IRP provides isotope composition in precipitation and streamflow from 23 Swiss catchments, being unique regarding its long-term multi-catchment coverage along an alpine–pre-alpine gradient. CH-IRP contains fortnightly time series of stable water isotopes from streamflow grab samples complemented by time series in precipitation. Sampling conditions, catchment and climate information, lab standards and errors are provided together with areal precipitation and catchment boundaries.
Nils Hinrich Kaplan, Theresa Blume, and Markus Weiler
Hydrol. Earth Syst. Sci., 24, 5453–5472, https://doi.org/10.5194/hess-24-5453-2020, https://doi.org/10.5194/hess-24-5453-2020, 2020
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In recent decades the demand for detailed information of spatial and temporal dynamics of the stream network has grown in the fields of eco-hydrology and extreme flow prediction. We use temporal streamflow intermittency data obtained at various sites using innovative sensing technology as well as spatial predictors to predict and map probabilities of streamflow intermittency. This approach has the potential to provide intermittency maps for hydrological modelling and management practices.
Michael Stoelzle, Maria Staudinger, Kerstin Stahl, and Markus Weiler
Proc. IAHS, 383, 43–50, https://doi.org/10.5194/piahs-383-43-2020, https://doi.org/10.5194/piahs-383-43-2020, 2020
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The role of recharge and catchment storage is crucial to understand streamflow drought sensitivity. Here we introduce a model experiment with recharge stress tests as complement to climate scenarios to quantify the streamflow drought sensitivities of catchments in Switzerland. We identified a pre-drought period of 12 months as maximum storage-memory for the study catchments. From stress testing, we found up to 200 days longer summer streamflow droughts and minimum flow reductions of 50 %–80 %.
Cited articles
Allen, S. T., Kirchner, J. W., and Goldsmith, G. R.: Predicting Spatial Patterns in Precipitation Isotope (δ2H and δ18O) Seasonality Using Sinusoidal Isoscapes, Geophys. Res. Lett., 45, 4859-4868, https://doi.org/10.1029/2018GL077458, 2018.
Asadollahi, M., Stumpp, C., Rinaldo, A., and Benettin, P.: Transport and Water Age Dynamics in Soils: A Comparative Study of Spatially Integrated and Spatially Explicit Models, Water Resour. Res., 56, e2019WR025539, https://doi.org/10.1029/2019wr025539, 2020.
Bakker, M., Post, V., Langevin, C. D., Hughes, J. D., White, J. T., Starn, J. J., and Fienen, M. N.: Scripting MODFLOW Model Development Using Python and FloPy, Groundwater, 54, 733–739, https://doi.org/10.1111/gwat.12413, 2016.
Bartos, M.: pysheds: simple and fast watershed delineation in python, Zenodo, https://doi.org/10.5281/zenodo.3822494, 2020.
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.
Beven, K. J.: Rainfall-Runoff Modelling, The Primer, John Wiley & Sons, Chichester, England, https://doi.org/10.1002/9781119951001, 2011.
Blöschl, G. and Sivapalan, M.: Scale issues in hydrological modelling: A review, Hydrol. Process., 9, 251–290, https://doi.org/10.1002/hyp.3360090305, 1995.
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q.: JAX: composable transformations of Python+NumPy programs, available at: http://github.com/google/jax (last access: 20 January 2023), 2018.
Burek, P., Satoh, Y., Kahil, T., Tang, T., Greve, P., Smilovic, M., Guillaumot, L., Zhao, F., and Wada, Y.: Development of the Community Water Model (CWatM v1.04) – a high-resolution hydrological model for global and regional assessment of integrated water resources management, Geosci. Model Dev., 13, 3267–3298, https://doi.org/10.5194/gmd-13-3267-2020, 2020.
Clark, M. P. and Kavetski, D.: Ancient numerical daemons of conceptual hydrological modeling: 1. Fidelity and efficiency of time stepping schemes, Water Resour. Res., 46, W10510, https://doi.org/10.1029/2009wr008894, 2010.
Collenteur, R. A., Bakker, M., Caljé, R., Klop, S. A., and Schaars, F.: Pastas: Open Source Software for the Analysis of Groundwater Time Series, Groundwater, 57, 877–885, https://doi.org/10.1111/gwat.12925, 2019.
Dal Molin, M., Kavetski, D., and Fenicia, F.: SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models, Geosci. Model Dev., 14, 7047–7072, https://doi.org/10.5194/gmd-14-7047-2021, 2021.
Dalcin, L. D., Paz, R. R., Kler, P. A., and Cosimo, A.: Parallel distributed computing using Python, Adv. Water Resour., 34, 1124–1139, https://doi.org/10.1016/j.advwatres.2011.04.013, 2011.
Demand, D. and Weiler, M.: Potential of a Gravity-Driven Film Flow Model to Predict Infiltration in a Catchment for Diverse Soil and Land Cover Combinations, Water Resour. Res., 57, e2019WR026988, https://doi.org/10.1029/2019WR026988, 2021.
Fenicia, F., Kavetski, D., and Savenije, H. H. G.: Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development, Water Resour. Res., 47, W11510, https://doi.org/10.1029/2010WR010174, 2011.
Förster, K., Hanzer, F., Winter, B., Marke, T., and Strasser, U.: An open-source MEteoroLOgical observation time series DISaggregation Tool (MELODIST v0.1.1), Geosci. Model Dev., 9, 2315–2333, https://doi.org/10.5194/gmd-9-2315-2016, 2016.
Germann, P. F. and Prasuhn, V.: Viscous Flow Approach to Rapid Infiltration and Drainage in a Weighing Lysimeter, Vadose Zone J., 17, 170020, https://doi.org/10.2136/vzj2017.01.0020, 2018.
Häfner, D. and Vicentini, F.: mpi4jax: Zero-copy MPI communication of JAX arrays, J. Open Source Softw., 6, 3419, https://doi.org/10.21105/joss.03419, 2021.
Häfner, D., Jacobsen, R. L., Eden, C., Kristensen, M. R. B., Jochum, M., Nuterman, R., and Vinter, B.: Veros v0.1 – a fast and versatile ocean simulator in pure Python, Geosci. Model Dev., 11, 3299–3312, https://doi.org/10.5194/gmd-11-3299-2018, 2018.
Häfner, D., Nuterman, R., and Jochum, M.: Fast, Cheap, and Turbulent – Global Ocean Modeling With GPU Acceleration in Python, J. Adv. Model. Earth Syst., 13, e2021MS002717, https://doi.org/10.1029/2021MS002717, 2021.
Hall, C. A., Saia, S. M., Popp, A. L., Dogulu, N., Schymanski, S. J., Drost, N., van Emmerik, T., and Hut, R.: A hydrologist's guide to open science, Hydrol. Earth Syst. Sci., 26, 647–664, https://doi.org/10.5194/hess-26-647-2022, 2022.
Hallouin, T., Ellis, R. J., Clark, D. B., Dadson, S. J., Hughes, A. G., Lawrence, B. N., Lister, G. M. S., and Polcher, J.: UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python, Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, 2022.
Harman, C. J.: Time-variable transit time distributions and transport: Theory and application to storage-dependent transport of chloride in a watershed, Water Resour. Res., 51, 1–30, https://doi.org/10.1002/2014WR015707, 2015.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array programming with NumPy, Nature, 585, 357–362, https://doi.org/10.1038/s41586-020-2649-2, 2020.
Helmus, J. J. and Collis, S. M.: The Python ARM Radar Toolkit (Py-ART), a library for working with weather radar data in the Python programming language, J. Open Res. Softw., 4, https://doi.org/10.5334/jors.119, 2016.
Heße, F., Zink, M., Kumar, R., Samaniego, L., and Attinger, S.: Spatially distributed characterization of soil-moisture dynamics using travel-time distributions, Hydrol. Earth Syst. Sci., 21, 549–570, https://doi.org/10.5194/hess-21-549-2017, 2017.
Hrachowitz, M., Benettin, P., van Breukelen, B. M., Fovet, O., Howden, N. J. K., Ruiz, L., van der Velde, Y., and Wade, A. J.: Transit times – the link between hydrology and water quality at the catchment scale, Wiley Interdisciplinary Reviews: Water, 3, 629–657, https://doi.org/10.1002/wat2.1155, 2016.
Hut, R., Drost, N., van de Giesen, N., van Werkhoven, B., Abdollahi, B., Aerts, J., Albers, T., Alidoost, F., Andela, B., Camphuijsen, J., Dzigan, Y., van Haren, R., Hutton, E., Kalverla, P., van Meersbergen, M., van den Oord, G., Pelupessy, I., Smeets, S., Verhoeven, S., de Vos, M., and Weel, B.: The eWaterCycle platform for open and FAIR hydrological collaboration, Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, 2022.
Hutton, C., Wagener, T., Freer, J., Han, D., Duffy, C., and Arheimer, B.: Most computational hydrology is not reproducible, so is it really science?, Water Resour. Res., 52, 7548–7555, https://doi.org/10.1002/2016WR019285, 2016.
Hutton, E. W., Piper, M. D., and Tucker, G. E.: The Basic Model Interface 2.0: A standard interface for coupling numer ical models in the geosciences, J. Open Source Softw., 5, 2317, https://doi.org/10.21105/joss.02317, 2020.
IEEE Spectrum: Top Programming Languages 2022, available at: https://spectrum.ieee.org/top-programming-languages-2022 (last access: 12 January 2023), 2022.
Ivanov, V. Y., Vivoni, E. R., Bras, R. L., and Entekhabi, D.: Catchment hydrologic response with a fully distributed triangulated irregular network model, Water Resour. Res., 40, W11102, https://doi.org/10.1029/2004WR003218, 2004a.
Ivanov, V. Y., Vivoni, E. R., Bras, R. L., and Entekhabi, D.: Preserving high-resolution surface and rainfall data in operational-scale basin hydrology: a fully-distributed physically-based approach, J. Hydrol., 298, 80–111, https://doi.org/10.1016/j.jhydrol.2004.03.041, 2004b.
Knoben, W. J. M., Freer, J. E., Fowler, K. J. A., Peel, M. C., and Woods, R. A.: Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations, Geosci. Model Dev., 12, 2463–2480, https://doi.org/10.5194/gmd-12-2463-2019, 2019.
Koutsoyiannis, D. and Onof, C.: Rainfall disaggregation using adjusting procedures on a Poisson cluster model, J. Hydrol., 246, 109–122, https://doi.org/10.1016/S0022-1694(01)00363-8, 2001.
Kratzert, F., Gauch, M., Nearing, G., and Klotz, D.: NeuralHydrology – A Python library for Deep Learning research in hydro logy, J. Open Source Softw., 7, 4050, https://doi.org/10.21105/joss.04050, 2022.
Kumar, R., Samaniego, L., and Attinger, S.: Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations, Water Resour. Res., 49, 360–379, https://doi.org/10.1029/2012WR012195, 2013.
Kumar, R., Heße, F., Rao, P. S. C., Musolff, A., Jawitz, J. W., Sarrazin, F., Samaniego, L., Fleckenstein, J. H., Rakovec, O., Thober, S., and Attinger, S.: Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe, Nat. Commun., 11, 6302, https://doi.org/10.1038/s41467-020-19955-8, 2020.
Kumaraswamy, P.: A generalized probability density function for double-bounded random processes, J. Hydrol., 46, 79–88, https://doi.org/10.1016/0022-1694(80)90036-0, 1980.
Kunkel, R. and Wendland, F.: Diffuse Nitrateinträge in die Grund- und Oberflächengewässer von Rhein und Ems – Ist-Zustands- und Maßnahmenanalysen, Forschungszentrum Jülich, Jülich, Germany, 143 pp., http://hdl.handle.net/2128/2524 (last access: 11 June 2024), 2006.
Kuppel, S., Tetzlaff, D., Maneta, M. P., and Soulsby, C.: EcH2O-iso 1.0: water isotopes and age tracking in a process-based, distributed ecohydrological model, Geosci. Model Dev., 11, 3045–3069, https://doi.org/10.5194/gmd-11-3045-2018, 2018a.
Kuppel, S., Tetzlaff, D., Maneta, M. P., and Soulsby, C.: What can we learn from multi-data calibration of a process-based ecohydrological model?, Environ. Modell. Softw., 101, 301–316, https://doi.org/10.1016/j.envsoft.2018.01.001, 2018b.
Lam, S. K., Pitrou, A., Florisson, M., Seibert, S., Markall, G., Anderson, T. A., Leobas, G., Collison, M., Bourque, J., Meurer, A., Oliphant, T. E., Riasanovsky, N., Wang, M., Pronovost, E., Totoni, E., Wieser, E., Seefeld, S., Grecco, H., Peterson, P., Virshup, I., Matty, G., Turner-Trauring, I., and Bourbeau, J.: numba/numba: Version 0.56.4, https://doi.org/10.5281/zenodo.7289231, 2023.
Lannelongue, L., Grealey, J., and Inouye, M.: Green Algorithms: Quantifying the Carbon Footprint of Computation, Adv. Sci., 8, 2100707, https://doi.org/10.1002/advs.202100707, 2021.
LARSIM-Entwicklergemeinschaft: Das Wasserhaushaltsmodell LARSIM: Modellgrundlagen und Anwendungsbeispiele, LARSIM-Entwicklergemeinschaft – Hochwasserzentralen LUBW, BLfU, LfU RP, HLNUG, BAFU, 258 pp., https://larsim.info/dokumentation/LARSIM-Dokumentation.pdf (last access: 11 June 2024), 2021.
Mälicke, M.: SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python, Geosci. Model Dev., 15, 2505–2532, https://doi.org/10.5194/gmd-15-2505-2022, 2022.
May, R. M., Goebbert, K. H., Thielen, J. E., Leeman, J. R., Camron, M. D., Bruick, Z., Bruning, E. C., Manser, R. P., Arms, S. C., and Marsh, P. T.: MetPy: A Meteorological Python Library for Data Analysis and Visualization, B. Am. Meteorol. Soc., 103, E2273–E2284, https://doi.org/10.1175/bams-d-21-0125.1, 2022.
Or, D., Lehmann, P., Shahraeeni, E., and Shokri, N.: Advances in Soil Evaporation Physics – A Review, Vadose Zone J., 12, vzj2012.0163, https://doi.org/10.2136/vzj2012.0163, 2013.
PYPL: PYPL PopularitY of Programming Language index, available at: https://pypl.github.io/PYPL.html (last access: 12 January 2023), 2022.
Reinecke, R., Trautmann, T., Wagener, T., and Schüler, K.: The critical need to foster computational reproducibility, Environ. Res. Lett., 17, 041005, https://doi.org/10.1088/1748-9326/ac5cf8, 2022.
Rinaldo, A., Benettin, P., Harman, C. J., Hrachowitz, M., McGuire, K. J., van der Velde, Y., Bertuzzo, E., and Botter, G.: Storage selection functions: A coherent framework for quantifying how catchments store and release water and solutes, Water Resour. Res., 51, 4840–4847, https://doi.org/10.1002/2015WR017273, 2015.
Rose, B. E.: CLIMLAB: a Python toolkit for interactive, process-oriented climate modeling, J. Open Source Softw., 3, 659, https://doi.org/10.21105/joss.00659, 2018.
Salvucci, G. D.: An approximate solution for steady vertical flux of moisture through an unsaturated homogeneous soil, Water Resour. Res., 29, 3749–3753, https://doi.org/10.1029/93wr02068, 1993.
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, W05523, https://doi.org/10.1029/2008WR007327, 2010.
Schmit, M.: WeatherDB, https://apps.hydro.uni-freiburg.de/de/weatherdb (last access: 20 January 2023), WeatherDB [software], 2022.
Schwemmle, R.: RoGeR – a process-based hydrological toolbox model in Python, https://doi.org/10.5281/zenodo.7633362 and available at: https://github.com/Hydrology-IFH/roger (last access: 9 March 2023), 2023a.
Schwemmle, R.: Calculating energy usage of RoGeR using Green Algorithms (v1.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.8095094, 2023b.
Schwemmle, R.: Application example of the GMD publication “RoGeR v3.0.5 – a process-based hydrological toolbox model in Python”, (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.11562349, 2024.
Schwemmle, R. and Weiler, M.: Consistent modelling of transport processes and travel times – coupling soil hydrologic processes with StorAge Selection functions, Water Resour. Res., 60, e2023WR034441, https://doi.org/10.1029/2023WR034441, 2023.
Schwemmle, R., Demand, D., and Weiler, M.: Technical note: Diagnostic efficiency – specific evaluation of model performance, Hydrol. Earth Syst. Sci., 25, 2187–2198, https://doi.org/10.5194/hess-25-2187-2021, 2021.
Schwemmle, R., Leistert, H., Steinbrich, A., and Weiler, M.: RoGeR (v3.0.5), Zenodo [code], https://doi.org/10.5281/zenodo.10948254, 2024.
Šimůnek, J., van Genuchten, M. T., and Šejna, M.: Recent Developments and Applications of the HYDRUS Computer Software Packages, Vadose Zone J., 15, vzj2016.2004.0033, https://doi.org/10.2136/vzj2016.04.0033, 2016.
Stack Overflow: Stack Overflow Developer Survey, available at: https://insights.stackoverflow.com/survey/2021#technology-most-popular-technologies, (last access: 12 January 2023), 2021.
Steduto, P., Hsiao, T. C., Raes, D., and Fereres, E.: AquaCrop – The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles, Agron. J., 101, 426–437, https://doi.org/10.2134/agronj2008.0139s, 2009.
Steinbrich, A., Leistert, H., and Weiler, M.: Model-based quantification of runoff generation processes at high spatial and temporal resolution, Environ. Earth Sci., 75, 1423, https://doi.org/10.1007/s12665-016-6234-9, 2016.
Steinbrich, A., Leistert, H., and Weiler, M.: RoGeR – ein bodenhydrologisches Modell für die Beantwortung einer Vielzahl hydrologischer Fragen, Korrespondenz Wasserwirtschaft, 14, 2, https://doi.org/10.3243/kwe2021.02.004, 2021.
Stoll, S. and Weiler, M.: Explicit simulations of stream networks to guide hydrological modelling in ungauged basins, Hydrol. Earth Syst. Sci., 14, 1435–1448, https://doi.org/10.5194/hess-14-1435-2010, 2010.
van der Velde, Y., Torfs, P. J. J. F., van der Zee, S. E. A. T. M., and Uijlenhoet, R.: Quantifying catchment-scale mixing and its effect on time-varying travel time distributions, Water Resour. Res., 48, W06536, https://doi.org/10.1029/2011WR011310, 2012.
van Gompel, M., Noordzij, J., de Valk, R., and Scharnhorst, A.: Guidelines for Software Quality, Common Lab Research Infrastructure for the Arts and Humanities, Amsterdam, Netherlands, 1–42 pp., https://pure.knaw.nl/portal/en/publications/guidelines-for-software-quality-clariah-task-54100 (last access: 11 June 2024), 2016.
Vivoni, E. R., Ivanov, V. Y., Bras, R. L., and Entekhabi, D.: Generation of Triangulated Irregular Networks Based on Hydrological Similarity, J. Hydrol. Eng., 9, 288–302, https://doi.org/10.1061/(ASCE)1084-0699(2004)9:4(288), 2004.
Wagener, T. and Gupta, H. V.: Model identification for hydrological forecasting under uncertainty, Stoch. Env. Res. Risk A., 19, 378–387, https://doi.org/10.1007/s00477-005-0006-5, 2005.
Weiler, M.: An infiltration model based on flow variability in macropores: development, sensitivity analysis and applications, J. Hydrol., 310, 294–315, https://doi.org/10.1016/j.jhydrol.2005.01.010, 2005.
Weiler, M. and Beven, K.: Do we need a Community Hydrological Model?, Water Resour. Res., 51, 7777–7784, https://doi.org/10.1002/2014WR016731, 2015.
Wigmosta, M. S., Vail, L. W., and Lettenmaier, D. P.: A distributed hydrology-vegetation model for complex terrain, Water Resour. Res., 30, 1665–1679, https://doi.org/10.1029/94WR00436, 1994.
Yang, X., Jomaa, S., Zink, M., Fleckenstein, J. H., Borchardt, D., and Rode, M.: A New Fully Distributed Model of Nitrate Transport and Removal at Catchment Scale, Water Resour. Res., 54, 5856–5877, https://doi.org/10.1029/2017WR022380, 2018.
Yatheendradas, S., Wagener, T., Gupta, H., Unkrich, C., Goodrich, D., Schaffner, M., and Stewart, A.: Understanding uncertainty in distributed flash flood forecasting for semiarid regions, Water Resour. Res., 44, W05S19, https://doi.org/10.1029/2007wr005940, 2008.
Short summary
The new process-based hydrological toolbox model, RoGeR (https://roger.readthedocs.io/), can be used to estimate the components of the hydrological cycle and the related travel times of pollutants through parts of the hydrological cycle. These estimations may contribute to effective water resources management. This paper presents the toolbox concept and provides a simple example of providing estimations to water resources management.
The new process-based hydrological toolbox model, RoGeR (https://roger.readthedocs.io/), can be...