A short introduction to RoGeR¶
The model architecture¶
We adapted the model architecture from [Haefner2018].
Easy to access: Python modules are simple to install, and projects like Anaconda are doing a great job in creating platform-independent environments.
Easy to use: Anyone with some experience can use their favourite Python tools to pre-process rquired data, set up, modify, and post-process simulations with RoGeR.
Easy to modify: Due to Python’s popularity, available abstractions, and dynamic nature, RoGeR can be extended and modified with relatively little effort.
However, choosing Python over a compiled language like Fortran or C usually comes at a high computational cost. We overcome this gap by using JAX, a framework that can act as a high-performance replacement for NumPy. JAX takes care of all performance optimizations in the background, and runs on CPUs and GPUs.
Available processes¶
Here, we provide a brief overview for the available processes and their underlying theories:
Soil hydraulics:
soil hydraulic parameters are approximated by the Brooks-Corey scheme ([Brooks1966]).
Interception:
rainfall and snowfall interception by vegetation ([Larsim2021])
Snow:
snow accumulation
delayed snow melt is based on degree-day approach and water retention of snow cover ([Larsim2021])
rain-on-snow
Infiltration:
matrix-driven infiltration based on Green-Ampt approach ([Peschke1985], [Weiler2005])
gravity-driven infiltration based on viscous flow approach ([Germann2018])
Evaporation:
evaporation from interception and surface storage
soil evaporation based on Stage I (i.e. energy limiting stage) and Stage II (i.e. falling rate stage) ([Torres2010]).
Transpiration:
combination of residual potential evapotranspiration and vegetation-specific coeffcient
Subsurface Runoff:
lateral subsurface runoff in the soil as described in [Steinbrich2016]
Percolation:
capillary-driven vertical drainage ([Salvucci1993])
gravity-driven vertical drainage based on viscous flow approach ([Germann2018])
Capillary rise:
capillary-driven vertical uplift ([Salvucci1993])
Groundwater flow:
spatial explicit representation of shallow groundwater follows the aproach presented in [Stoll2010]
Crop phenology and crop rotation:
time-varying crop canopy cover and crop root depth is implemented as in [Steduto2009]
Solute transport:
StorAge selection (SAS) functions ([Rinaldo2015]) are coupled with hydrologic simulations. SAS functions are used to calculate travel time distributions, residence time distribution and solute concentrations
Biogeochemical processes:
Solute specific transformation processes, for example, denitrification ([Kunkel2012]) or soil temperature ([Hillel1998])
Available pre-defined model structures¶
SVAT:
only vertical processes are considered
no lateral processes
SVAT-CROP:
same as SVAT, but crop phenology (i.e. varying rooting depth and varying canopy cover) is explicitly represented
ONED:
vertical and lateral processes are considered
ONED-EVENT:
vertical and lateral processes are considered
simulation of a single event
SVAT-OXYGEN18:
calculates offline coupled oxygen-18 transport based on the hydrologic simulations with the SVAT model
SVAT-BROMIDE:
calculates offline coupled bromide transport based on the hydrologic simulations with the SVAT model
Diagnostics¶
Diagnostics are responsible for handling all model output, sanity checks of the solution, and restart file handling. They are implemented in a modular fashion, so additional diagnostics can be implemented easily. Already implemented diagnostics handle snapshot output, aggregation of variables, and monitoring of mass balance.
For more information, see Diagnostics.
Pre-defined model setups¶
RoGeR supports a wide range of pre-configured models. Several setups are already implemented that highlight some of the capabilities of RoGeR, and that serve as a basis for users to set up their own configuration: Model gallery.
Current limitations¶
RoGeR is still in development. There are many open issues that we would like to fix later on:
A routing scheme is not implemented, yet
Simulations with biogeochemical processes have not been compared to measured data
Simulations with gravity-driven infiltration have not been compared to measured data
Sowing and harvesting of crops is time-invariant i.e. fixed dates are assumed for sowing and harvesting
References¶
Brooks, R. H., and Corey, A. T.: Properties of porous media affecting fluid flow, Journal of the Irrigation and Drainage Division, 92, 61-90, 1966.
Germann, P. F. and Prasuhn, V.: Viscous Flow Approach to Rapid Infiltration and Drainage in a Weighing Lysimeter, Vadose Zone Journal, 17, 170020, 2018.
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, 2018.
Harman, C. J.: Time-variable transit time distributions and transport: Theory and application to storage-dependent transport of chloride in a watershed, Water Resources Research, 51, 1-30, 2015.
Hillel, D.: Environmental soil physics, Academic Press, London, UK, 1998.
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, 2012.
LARSIM-Entwicklergemeinschaft: Das Wasserhaushaltsmodell LARSIM: Modellgrundlagen und Anwendungsbeispiele, LARSIM-Entwicklergemeinschaft - Hochwasserzentralen LUBW, BLfU, LfU RP, HLNUG, BAFU, 258, 2021.
Peschke, G.: Zur Bildung und Berechnung von Regenabfluss, Wissenschaftliche Zeitschrift der Technischen Universität Dresden, 34, 1985.
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 Resources Research, 51, 4840-4847, 2015.
Salvucci, G. D.: An approximate solution for steady vertical flux of moisture through an unsaturated homogeneous soil, Water Resources Research, 29, 3749-3753, 1993.
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, Agronomy Journal, 101, 426-437, 2009.
Steinbrich, A., Leistert, H., and Weiler, M.: Model-based quantification of runoff generation processes at high spatial and temporal resolution, Environmental Earth Sciences, 75, 1423, 2016.
Stoll, S. and Weiler, M.: Explicit simulations of stream networks to guide hydrological modelling in ungauged basins, Hydrol. Earth Syst. Sci., 14, 1435-1448, 2010.
Torres, E. A. and Calera, A.: Bare soil evaporation under high evaporation demand: a proposed modification to the FAO-56 model, Hydrological Sciences Journal, 55, 303-315, 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 Resources Research, 48, 2012.
Weiler, M.: An infiltration model based on flow variability in macropores: development, sensitivity analysis and applications, Journal of Hydrology, 310, 294-315, 2005.