the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Wflow_sbm v0.6.1, a spatially distributed hydrologic model: from global data to local applications
Willem J. van Verseveld
Albrecht H. Weerts
Martijn Visser
Joost Buitink
Ruben O. Imhoff
Hélène Boisgontier
Laurène Bouaziz
Dirk Eilander
Mark Hegnauer
Corine ten Velden
Bobby Russell
Abstract. The wflow_sbm hydrologic model, recently released by Deltares, as part of the Wflow.jl (v0.6.1) modelling framework is being used to better understand and potentially address multiple operational and water resources planning challenges from catchment scale, national scale to continental and global scale. Wflow.jl is a free and open-source distributed hydrologic modelling framework written in the Julia programming language. The development of wflow_sbm, the model structure, equations and functionalitities are described in detail, including example applications of wflow_sbm. The wflow_sbm model aims to strike a balance between low-resolution, low-complexity and high-resolution, high-complexity hydrologic models. Most wflow_sbm parameters are based on physical characteristics or processes and at the same time wflow_sbm has a runtime performance well suited for large-scale high-resolution model applications. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets and through the use of point-scale (pedo)transfer functions and suitable upscaling rules and generally results in a satisfactory (0.4 ≥ Kling-Gupta Efficiency (KGE) < 0.7) to good (KGE ≥ 0.7) performance a-priori (without further tuning). Wflow_sbm includes relevant hydrologic processes as glacier and snow processes, evapotranspiration processes, unsaturated zone dynamics, (shallow) groundwater and surface flow routing including lakes and reservoirs. Further planned developments include improvements on the computational efficiency and flexibility of the routing scheme, implementation of a water demand and allocation module for water resources modelling, the addition of a deep groundwater concept and distributed computing with a focus on multi-node parallelism.
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Willem J. van Verseveld et al.
Status: final response (author comments only)
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RC1: 'Comment on gmd-2022-182', Anonymous Referee #1, 04 Oct 2022
General comments
The manuscript is a model description paper that presents the Wflow_sbm v0.6.1 hydrological model developed by Deltares. The model structure and equations are presented in detail followed by case studies of its application in various catchments across the world. The presented model has a great potential in contributing to large scale and high resolution hydrological modelling. Overall, the paper is well written, the model is presented in detail and the applications demonstrate the capability of the model to simulate major hydrological processes in different regions. I appreciate the effort of the authors to make it public and provide transparency in the model functioning. I have enjoyed the paper, but I am missing a key component when it comes to “spatially fully distributed” hydrological models, which concerns the Wflow_sbm ability to be spatially calibrated and evaluated with gridded data (not catchment average) and its performance in representing the spatial patterns, which is the major feature of grid-based models, as compared to lumped or semi-distributed models. Therefore, I urge the authors to demonstrate the performance of their model in reproducing the spatial patterns of major hydrological processes like actual evapotranspiration, soil moisture, and terrestrial water storage and snow accumulation as global data exist to do so.
Specific comments
Major Comments
The key strength for spatially distributed hydrological models is the ability to simulate hydrological processes in space and provide their spatial variations. I strongly recommend demonstrating that your model can be calibrated and evaluated on spatial patterns as it is becoming the state-of-the-art in this field (e.g. Dembele et al. 2020, Demirel et al. 2018, Zink et al 2018).
L672-674: Why the use of ERA5 for Europe and other products for other regions (e.g. CHIRPS for Oueme in Africa)? Were these datasets evaluated or previously found suitable for hydrological modelling in these regions?
Minor comments
What are the available objective functions for model calibration? Is multivariate calibration supported by the model?
Be consistent with the use of the term “hydrological” or “hydrologic” (see e.g. lines 1 and 783). Choose one and keep it throughout the paper.
L10-11: Mention clearly that this is the model performance for discharge.
L511: A variable name should not have several meanings. Here P is defined as the wetted perimeter while it refers to precipitation in Table A1. Please correct this.
L382: is f_canopygap time dependent? There is no exponent t in the name in Table A2.
Technical corrections
L73: “most gauging” ---> “most discharge gauging”
L127: “then” ---> “than”
L135: “water when” ---> “water occurs when”
L136: “in” ---> “from”
L137: “river” ---> “river routing”
L155: “extendig” ---> “extending”
L203: delete the duplicated “is”
L348, 366, 383, 388, 396: “bucket” ---> “unsaturated soil bucket ”
L564: “expresses” ---> “expressed”
L569: In the second part of the equation 103 (i.e. if SI_lake <=…), “A” should be “A_lake” in the numerator of the fraction.
L585: Give the definition of “I/O”.
L607: “orginal” ---> “original”
L681, 704, 726, 755: “avalance” ---> “avalanche”
L720: “hydrometeorlogical” ---> “hydrometeorological”
L733: “1630” ---> “station 1630”
L774: “yearly average rainfall” ---> “annual average daily rainfall”
L776: “disables” ---> “disabled”
L776: “scores” ---> “scores of discharge”
L800: “function” ---> “functions”
Figure 11: In the caption: “average” ---> “catchment-average”
Figure 13: In the caption: “scores” ---> “scores of discharge”
Table A1: No Wflow.jl name for E_act,soil?
Table A1: Should the unit of R_input be mm/t?
Table A1: Should the unit of Q_in,res be m3/t?
Table A2: It would be better to separate the list of the parameters from the forcing.
Table A2: Is this mean air temperature? Say it explicitly.
Table A2: For f_paved, should read “compacted” in the description.
Table A2: Add f_river to the table.
References
Dembélé, M., Ceperley, N., Zwart, S. J., Salvadore, E., Mariethoz, G., & Schaefli, B. (2020). Potential of satellite and reanalysis evaporation datasets for hydrological modelling under various model calibration strategies. Advances in Water Resources, 143, 103667.
Demirel, M. C., J. Mai, G. Mendiguren, J. Koch, L. Samaniego, and S. Stisen (2018), Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model, Hydrology and Earth System Sciences, 22(2), 1299-1315, https://doi.org/10.5194/hess-22-1299-2018.
Zink, M., J. Mai, M. Cuntz, and L. Samaniego (2018), Conditioning a Hydrologic Model Using Patterns of Remotely Sensed Land Surface Temperature, Water Resources Research, 54(4), 2976-2998, https://doi.org/10.1002/2017wr021346.
Citation: https://doi.org/10.5194/gmd-2022-182-RC1 -
AC1: 'Reply on RC1', Willem van Verseveld, 20 Dec 2022
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2022-182/gmd-2022-182-AC1-supplement.pdf
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AC1: 'Reply on RC1', Willem van Verseveld, 20 Dec 2022
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RC2: 'Comment on gmd-2022-182', Anonymous Referee #2, 24 Jul 2023
This manuscript presents an open-source hydrologic modeling framework, wflow_sbm, and demonstrates its applicability through multiple case studies under various hydro-meteorological conditions. The new features of hydrological processes in the current version resulted in substantial improvements. Moreover, the computational efficiency of the Julia code has been enhanced to meet the requirements for large-scale, high-resolution modeling. Here are some comments which may be helpful in further improving the manuscript before possible publication.
1. wflow_sbm simulates multiple hydrological processes with different time scales using flexible temporal discretization. Modeling time steps can vary from daily to sub-daily, depending on the size and characteristics of the catchment of interest (and that’s good). However, without proper disaggregation or aggregation of temporal variables, a potential problem may arise, where changing time steps can lead to unreliable model responses. Except for the sub-daily Rutter interception model, it remains unclear whether varying model time steps are available or not for most of the other hydrological components. In particular, temperature-related variables, such as soil temperature, infiltration and snow melting, may be sensitive to the selection of the model time step. For instance, the infiltration through multiple soil layers described in sub-sections 2.4.1 and 2.4.2 is a sub-daily process, which may lead to over- or under-estimation of the process if a daily time step is applied. Therefore, it is recommended to specify how different hydrological processes with varying temporal scales can be integrated within wflow_sbm and to outline the precautions that should be taken to avoid potential drawbacks.
2. In most of the example simulations, especially in Fig. 12, significant mismatches between observations and simulations are found in the timing of flood peaks. Although not clearly evident since hydrographs are drawn for several months or a year, the peak timing differences seem to exceed several days, which is a drawback for a hydrologic model. It is uncertain whether the multiplication parameters, f_Kh0 and f_v0, are properly calibrated for example cases. In my view, inadequate integration of hydrologic processes with different time scales may also affect the timing errors of flood peaks.
3. While improving overall model performance, considering constant groundwater loss seems to significantly reduce the volume of small to mid-size runoff events, potentially causing another problem. As shown in Fig. 14, different from observations, the runoff simulation with groundwater loss for small events before Aug 2010 is nearly zero.
4. While using a global data set, wflow_sbm seems to target local applications. The size of application cases ranges between 434 and 28,000 square kilometers. For those local applications, a typical spatial resolution of 1 km may not be considered high resolution. Even for small-sized catchments, wflow_sbm seems not to be tested for a spatial grid finer than 1 km. Please describe the potential challenges of the model when applying the high-resolution grid finer than 1 km.
5. Please check “groundwater loss” in the sub-section 4.2.5 is the correct expression. When the water is lost in the stream to the groundwater, it is usually called channel (transmission) loss or river water leakage.
6. typos
Line 812: 1) improve -> 1) to improve
Citation: https://doi.org/10.5194/gmd-2022-182-RC2 -
AC2: 'Reply on RC2', Willem van Verseveld, 22 Aug 2023
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2022-182/gmd-2022-182-AC2-supplement.pdf
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AC2: 'Reply on RC2', Willem van Verseveld, 22 Aug 2023
Willem J. van Verseveld et al.
Willem J. van Verseveld et al.
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