User-friendly workflows for catchment modelling: Towards reproducible SWAT+ model studies

Chawanda, Celray James; George, Chris; Thiery, Wim; Griensven, Ann van; Tech, Jaclyn; Arnold, Jeffrey G.; Srinivasan, Raghavan

doi:10.1016/j.envsoft.2020.104812

Cited by 27 publications

(24 citation statements)

References 35 publications

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“…Several open-source packages have been proposed to facilitate automated and reproducible model building. For the Soil and Water Assessment Tool (SWAT) model (Arnold et al, 2012), the SWAT+AW package provides scripted automated workflows (AW) that are compatible with the SWAT GUI to improve reproducibility (Chawanda et al, 2020). For groundwater modeling the FloPy (Bakker et al, 2016) and iMOD-Python (Engelen et al, 2022) packages provide scripting interfaces to automatically setup instances of the groundwater model MODFLOW (Harbaugh, 2005).…”

Section: Statement Of Needmentioning

confidence: 99%

HydroMT: Automated and reproducible model building and analysis

Eilander¹,

Boisgontier²,

Bouaziz³

et al. 2023

JOSS

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Many impact assessment studies rely on hydrological and hydrodynamic (hydro) models. These models typically require a large set of parameters derived from different datasets and hence manual setup can be time consuming and hard to reproduce. HydroMT (Hydro Model Tools) is an open-source Python package that aims to make the process of building model instances and analyzing model results automated and reproducible. The package provides a common interface to data and model instances, workflows to transform data into models based on (hydrological) GIS and statistical methods, and various methods to analyze model results. The user can describe a full model instance including its forcing in a single configuration file based on a sequence of workflows, making the process reproducible, fast, and modular. The package has been designed with an iterative, data-centered modeling process in mind. First-order model schematizations can be built for any location in the world by leveraging open global datasets. These can later be improved by updating the input datasets with detailed local datasets. This iterative process enables the user to quickly get an initial result to then make informed decisions about the most relevant improvements and/or required data collection and to kick-start discussions with stakeholders. Furthermore, model parameter maps or forcing data can easily be modified for sensitivity analysis or calibration to support these robust modeling practices. HydroMT is successfully being used for several model software through a plugin infrastructure that allow for model specific functionality, such as readers and writers of model data formats, and can easily be extended to new model software. Currently supported model software include the distributed rainfall-runoff model Wflow, the hydrodynamic flood model SFINCS, the water quality models D-Water Quality and D-Emissions and the flood impact model Delft-FIAT.

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Section: Statement Of Needmentioning

confidence: 99%

HydroMT: Automated and reproducible model building and analysis

Eilander¹,

Boisgontier²,

Bouaziz³

et al. 2023

JOSS

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“…Decision tables embedded in the SWAT+ model allow for rule sets and their corresponding operations (Arnold et al, 2018). With the list of conditions considered in the decision tables, the model can efficiently represent complex, rule–based management such as volume and timing of reservoir releases (Arnold et al, 2018; Chawanda et al, 2020). This study aims at (i) investigating the capabilities of the SWAT+ model to represent dense networks of South Indian water harvesting systems (water tanks) with the help of remote sensing data, and (ii) evaluating the influence of water harvesting systems implementation on streamflow simulations.…”

Section: Introductionmentioning

confidence: 99%

Enhancing hydrologic modelling through the representation of traditional rainwater harvesting systems: A case study of water tanks in South India

Mahmoodi,

Wagner,

Lei

et al. 2024

Hydrological Processes

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Water tanks as traditional rainwater harvesting systems for agriculture are widely distributed in South India. They have a strong impact on hydrological processes, affecting streamflow in rivers as well as evapotranspiration. This study aims at an accurate representation of water harvesting systems in a hydrologic model to improve model performance and assessment of the catchment water balance. To this end, spatio‐temporal variations of water bodies between the years 2016 and 2018 and the months of January and May 2017 were derived from Sentinel‐2 satellite data to parameterize the water tanks (reservoir) parameters in the Soil and Water Assessment Tool (SWAT+) model of the Adyar basin, Chennai, India. Approximately 16% of the basin is covered by water tanks. The initial model performance was evaluated for two model setups, with and without water tanks. The best model run was selected with a multi‐metric approach comparing observed and modelled monthly streamflow for 5000 model runs. The final model evaluation was carried out by comparing estimated water body areas by the model and remote sensing observations for January to May 2017. The results showed that representing water tanks in the hydrologic model led to an improvement in the representation of the seasonal variations of streamflow for the whole simulation period (2004–2018). The model performance was classified as good and very good for the calibration (2004–2011) and validation (2012–2018) periods as NSE varies between 0.67 and 0.85, KGE varies between 0.65 and 0.72, PBIAS varies between −24.1 and −23.6, and RSR varies between 0.57 and 0.39. The best fit was shown for the high and middle flow segments of the hydrograph where the coefficient of determination (R2) ranges from 0.81 to 0.97 and 0.75 to 0.81, respectively. The monthly variation of water body areas in 2017 estimated by the hydrologic model was consistent with changes observed in remote sensing surveys. In summary, the water tank parametrization using remote sensing techniques enhanced the hydrologic model's efficiency and applicability for future studies.

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“…SWAT+ applications in Africa focused on improving model performance through hydrological mass balance calibration and better representation of reservoir and irrigation management in Southern Africa (Chawanda, Arnold, et al, 2020), on introducing a user‐friendly software for generating reproducible SWAT+ model setups using the Upper Blue Nile as an example (Chawanda, George, et al, 2020), on an improved representation of seasonal land use dynamics in Tanzania (Nkwasa et al, 2020), and on using high resolution gridded data to analyse the surface runoff response to land use and climate change in Kenya (Kiprotich et al, 2021). In Europe, Senent‐Aparicio et al (2021) developed a post‐processing tool for calculating environmental flows that is included in the QGIS interface of SWAT+ and tested it in the Eo River basin in Spain.…”

Section: Introductionmentioning

confidence: 99%

Representation of hydrological processes in a rural lowland catchment in Northern Germany usingSWATandSWAT+

Wagner

Bieger

Arnold

et al. 2022

Hydrological Processes

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The latest version of the Soil and Water Assessment Tool (SWAT+) features several improvements compared with previous versions of the model, for example, the definition of landscape units that allow for a better representation of spatio‐temporal dynamics. To evaluate the new model capabilities in lowland catchments characterized by near‐surface groundwater tables and extensive tile drainage, we assess the performance of two SWAT+ model setups in comparison to a setup based on a previous SWAT model version (SWAT3S with a modified three groundwater storage model) in the Kielstau catchment in Northern Germany. The Kielstau catchment has an area of about 50 km2, is dominated by agricultural land use, and has been thoroughly monitored since 2005. In both SWAT+ setups, the catchment is divided into upland areas and floodplains, but in the first SWAT+ model setup, runoff from the hydrologic response units is summed up at landscape unit level and added directly to the stream. In the second SWAT+ model setup, runoff is routed across the landscape before it reaches the streams. Model results are compared with regard to (i) model performance for stream flow at the outlet of the catchment and (ii) aggregated as well as temporally and spatially distributed water balance components. All three model setups show a very good performance at the catchment outlet. In comparison to a previous version of the SWAT model that produced more groundwater flow, the SWAT+ model produced more tile drainage flow and surface runoff. Results from the new SWAT+ model confirm that the representation of routing processes from uplands to floodplains in the model further improved the representation of hydrological processes. Particularly, the stronger spatial heterogeneity that can be related to characteristics of the landscape, is very promising for a better understanding and model representation of hydrological fluxes in lowland areas. The outcomes of this study are expected to further prove the applicability of SWAT+ and provide useful information for future model development.

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User-friendly workflows for catchment modelling: Towards reproducible SWAT+ model studies

Cited by 27 publications

References 35 publications

HydroMT: Automated and reproducible model building and analysis

HydroMT: Automated and reproducible model building and analysis

Enhancing hydrologic modelling through the representation of traditional rainwater harvesting systems: A case study of water tanks in South India

Representation of hydrological processes in a rural lowland catchment in Northern Germany usingSWATandSWAT+

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