Preprints
https://doi.org/10.5194/hess-2021-247
https://doi.org/10.5194/hess-2021-247

  25 May 2021

25 May 2021

Review status: a revised version of this preprint is currently under review for the journal HESS.

Improved Representation of Agricultural Land Use and Crop Management for Large Scale Hydrological Impact Simulation in Africa using SWAT+

Albert Nkwasa1, Celray James Chawanda1, Jonas Jägermeyr2,3,4, and Ann van Griensven1,5 Albert Nkwasa et al.
  • 1Hydrology and Hydraulic Engineering Department, Vrije Universiteit Brussel (VUB), 1050 Brussel, Belgium
  • 2NASA Goddard Institute for Space Studies, New York, NY 10025, USA
  • 3Center for Climate Systems Research, Columbia University, New York, NY 10025, USA
  • 4Climate Resilience, Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, 14412, Potsdam, Germany
  • 5Water Science & Engineering Department, IHE Delft Institute for Water Education, 2611 AX Delft, The Netherlands

Abstract. To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, harvesting). The SWAT+ model represents agricultural land by default in a generic way where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and sub-tropical regions such as the sub-Saharan Africa where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for Northeast Africa.

We evaluate the influence of the crop phenology representation on simulations of Leaf Area Index (LAI) and Evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR ET data respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural Hydrologic Response Units (HRUs) which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in regional to global hydrological models.

Albert Nkwasa et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-247', Anonymous Referee #1, 27 Jul 2021
  • RC2: 'Comment on hess-2021-247', Anonymous Referee #2, 30 Jul 2021

Albert Nkwasa et al.

Albert Nkwasa et al.

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Short summary
In this study, we propose an approach on how to incorporate crop phenology (start and end of cropping season) using global datasets of rainfed and irrigated croplands with the associated management practices (fertilizer and irrigation) through rule sets and their corresponding actions in a regional hydrological model for North Eastern Africa. Model results show improved simulations of the above plant growing (Leaf Area Index) and Evapotranspiration (ET), evaluated using remote sensing data.