Welcome to wflow’s documentation!¶
There will be no further developments in the Python wflow framework (bugfixes are possible), and the documentation is no longer updated. Developments continue in the Julia package Wflow, available here, including documentation.
This documentation was generated on Mar 10, 2021
Documentation for the development version: https://wflow.readthedocs.org/en/latest/
Documentation for the stable version: https://wflow.readthedocs.org/en/stable/
wflow is released under version 3 of the GPL
wflow uses PCRaster/Python (see http://www.pcraster.eu) as its calculation engine
This document describes the wflow distributed hydrological modelling platform.
wflow is part of the Deltares’
OpenStreams project. 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
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 and wflow_w3 models (implementations and adaptations of the Australian Water Resources Assessment Landscape model (AWRA-L))
the wflow_topoflex model (a distributed version of the FLEX-Topo model)
the wflow_pcrglobwb model (PCR-GLOBWB (PCRaster Global Water Balance, v2.1.0_beta_1))
the wflow_sphy model (SPHY (Spatial Processes in HYdrology, version 2.1))
the wflow_stream model (STREAM (Spatial Tools for River Basins and Environment and Analysis of Management Options))
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 wflow_sediment model (an experimental erosion and sediment dynamics model that uses the output of the wflow_sbm model).
the wflow_lintul model (rice crop growth model LINTUL (Light Interception and Utilization))
The low level api and links to other frameworks allow the models to be linked as part of larger modelling systems:
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.
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 PCRaster/Python.
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 (Köhler et al., 2006) 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 in 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.
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, withinitial 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