Welcome to wflow’s 2018.1 documentation!

Note

This documentation is for release 2018.1 of wflow This documentation was generated Dec 07, 2018

Latest version documentation (development):

http://wflow.readthedocs.org/en/latest/

Latest release (stable) version documentation

http://wflow.readthedocs.org/en/stable/

Note

wflow is released under version 3 of the GPL

wflow uses pcraster/python (see http://www.pcraster.eu) as it’s calculation engine.

Introduction

This document describes the wflow distributed hydrological modelling platform. wflow is part of the Deltares’ OpenStreams project (http://www.openstreams.nl). Wflow consists of a set of python programs that can be run on the command line and perform hydrological simulations. The models are based on the PCRaster python framework (www.pcraster.eu). In wflow this framework is extended (the wf_DynamicFramework class) so that models build using the framework can be controlled using the API. Links to BMI and OpenDA (www.openda.org) have been established. All code is available at github (https://github.com/openstreams/wflow/) and distributed under the GPL version 3.0.

The wflow distributed hydrological model platform currently includes the following models:

  • the wflow_sbm model (derived from topog_sbm )
  • the wflow_hbv model (a distributed version of the HBV96 model).
  • the wflow_gr4 model (a distributed version of the gr4h/d models).
  • the wflow_W3RA model (a global hydrological model)
  • the wflow_routing model (a kinematic wave model that can run on the output of one of the hydrological models optionally including a floodplain for more realistic simulations in areas that flood).
  • the wflow_wave model (a dynamic wave model that can run on the output of the wflow_routing model).
  • the wflow_floodmap model (a flood mapping model that can use the output of the wflow_wave model or de wflow_routing model).

The low level api and links to other frameworks allow the models to be linked as part of larger modelling systems:

digraph Linking {
WFLOW_HBV -> WFLOWAPI;
WFLOW_SBM -> WFLOWAPI;
WFLOWAPI -> "PI"  [dir=both];
"Data and Models" -> "PI";
WFLOWAPI -> OpenMI  [dir=both];
ModelX -> OpenMI;
ModelY -> OpenMI;
ModelY -> BMI;
BMI -> OpenDA  [dir=both];
WFLOWAPI -> BMI  [dir=both];
Calibration -> OpenDA;
Assimilation -> OpenDA;
WFLOWAPI [shape=square];
OpenDA [shape=square];
OpenMI [shape=square];
BMI [shape=square];
"PI" [shape=square];
dpi=69;
}

Note

wflow is part of the Deltares OpenStreams project (http://www.openstreams.nl). The OpenStreams project is a work in progress. Wflow functions as a toolkit for distributed hydrological models within OpenStreams.

Note

As part of the eartH2Observe project global dataset of forcing data has been compiled that can also be used with the wflow models. A set of tools is available that can work with wflow (the wflow_dem.map file) to extract data from the server and downscale these for your wflow model. Check https://github.com/earth2observe/downscaling-tools for the tools. A description of the project can be found at http://www.earth2observe.eu and the data server can be access via http://wci.earth2observe.eu

The different wflow models share the same structure but are fairly different with respect to the conceptualisation. The shared software framework includes the basic maps (dem, landuse, soil etc) and the hydrological routing via the kinematic wave. The Python class framework also exposes the models as an API and is based on the PCRaster/Python version 4.0 Beta (www.pcraster.eu).

The wflow_sbm model maximises the use of available spatial data. Soil depth, for example, is estimated from the DEM using a topographic wetness index . The model is derived from the [CQFLOW] model that has been applied in various countries, most notably in Central America. The wflow_hbv model is derived from the HBV-96 model but does not include the routing functions, instead it uses the same kinematic wave routine as the wflow_sbm model to route the water downstream.

The models are programmed python using the pcraster python extension. As such, the structure of the model is transparent, can be changed by other modellers easily, and the system allows for rapid development. In order to run the model both PCRaster 4.* and Python 2.7 are needed. At the moment only 64 bit versions are supported.

Adding a new model yourself using the framework

Indices and tables

References

[CQFLOW]Köhler, L., Mulligan, M., Schellekens, J., Schmid, S. and Tobón, C.: Final Technical Report DFID-FRP Project no. R7991 Hydrological impacts of converting tropical montane cloud forest to pasture, with initial reference to northern Costa Rica.,, 2006.

Papers/reports using wflow

Arnal, L., 2014. An intercomparison of flood forecasting models for the Meuse River basin (MSc Thesis). Vrije Universiteit, Amsterdam.

Azadeh Karami Fard, 2015. Modeling runoff of an Ethiopian catchment with WFLOW (MSc thesis). Vrije Universiteit, Amsterdam.

de Boer-Euser, T., Bouaziz, L., De Niel, J., Brauer, C., Dewals, B., Drogue, G., Fenicia, F., Grelier, B., Nossent, J., Pereira, F., Savenije, H., Thirel, G., Willems, P., 2017. Looking beyond general metrics for model comparison – lessons from an international model intercomparison study. Hydrol. Earth Syst. Sci. 21, 423–440. doi:10.5194/hess-21-423-2017

Emerton, R.E., Stephens, E.M., Pappenberger, F., Pagano, T.C., Weerts, A.H., Wood, A.W., Salamon, P., Brown, J.D., Hjerdt, N., Donnelly, C., Baugh, C.A., Cloke, H.L., 2016. Continental and global scale flood forecasting systems. WIREs Water 3, 391–418. doi:10.1002/wat2.1137

Hally, A., Caumont, O., Garrote, L., Richard, E., Weerts, A., Delogu, F., Fiori, E., Rebora, N., Parodi, A., Mihalović, A., Ivković, M., Dekić, L., van Verseveld, W., Nuissier, O., Ducrocq, V., D’Agostino, D., Galizia, A., Danovaro, E., Clematis, A., 2015. Hydrometeorological multi-model ensemble simulations of the 4 November 2011 flash flood event in Genoa, Italy, in the framework of the DRIHM project. Nat. Hazards Earth Syst. Sci. 15, 537–555. doi:10.5194/nhess-15-537-2015

Hassaballah, K., Mohamed, Y., Uhlenbrook, S., Biro, K., 2017. Analysis of streamflow response to land use land cover changes using satellite data and hydrological modelling: case study of Dinder and Rahad tributaries of the Blue Nile. Hydrol. Earth Syst. Sci. Discuss. 2017, 1–22. doi:10.5194/hess-2017-128

Jeuken, A., Bouaziz, L., Corzo, G., Alfonso, L., 2016. Analyzing Needs for Climate Change Adaptation in the Magdalena River Basin in Colombia, in: Filho, W.L., Musa, H., Cavan, G., O’Hare, P., Seixas, J. (Eds.), Climate Change Adaptation, Resilience and Hazards, Climate Change Management. Springer International Publishing, pp. 329–344.

López López, P., Wanders, N., Schellekens, J., Renzullo, L.J., Sutanudjaja, E.H., Bierkens, M.F.P., 2016. Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations. Hydrol. Earth Syst. Sci. 20, 3059–3076. doi:10.5194/hess-20-3059-2016

Maat, W.H., 2015. Simulating discharges and forecasting floods using a conceptual rainfall-runoff model for the Bolivian Mamoré basin (MSc thesis). University of Twente, Enschede.

Research paper: HYDROLOGIC MODELING OF PRINCIPAL SUB-BASINS OF THE MAGDALENA-CAUCA LARGE BASIN USING WFLOW MODEL [WWW Document], n.d. . ResearchGate. URL https://www.researchgate.net/publication/280293861_HYDROLOGIC_MODELING_OF_PRINCIPAL_SUB-BASINS_OF_THE_MAGDALENA-CAUCA_LARGE_BASIN_USING_WFLOW_MODEL (accessed 4.4.17).

Tangdamrongsub, N., Steele-Dunne, S.C., Gunter, B.C., Ditmar, P.G., Weerts, A.H., 2015. Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin. Hydrol. Earth Syst. Sci. 19, 2079–2100. doi:10.5194/hess-19-2079-2015

Tretjakova, D., 2015. Investigating the effect of using fully-distributed model and data assimilation on the performance of hydrological forecasting in the Karasu catchment, Turkey (MSc thesis). Wageningen University.

Wang, X., Zhang, J., Babovic, V., 2016. Improving real-time forecasting of water quality indicators with combination of process-based models and data assimilation technique. Ecological Indicators 66, 428–439. doi:10.1016/j.ecolind.2016.02.016

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