Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+

Abstract

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, and harvesting). The SWAT+ (Soil and Water Assessment Tool) 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 subtropical regions such as 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 northeastern 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 (Water Productivity through Open access of Remotely sensed derived data) 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 areas where they occur using regional to global hydrological models.

Regionalization of the SWAT+ model for projecting climate change impacts on sediment yield: An application in the Nile basin

“…In turn, affecting the estimation of the cover and management factor ( ) in the MUSLE for cultivated areas. As an alternative, Nkwasa et al (2022) proposed an approach to incorporate crop phenology and associated management practices at a large scale using global datasets adopted in this study ( Fig. 2 ).…”

“…Despite the efforts, assessing sediment yield at large spatial scales remains one of the main challenges in sediment yield modeling, not the least due to high computational demands, especially for process-based models ( Eekhout et al, 2018 ). As a compromise between accuracy and practicality, models often utilize input data of low spatial resolution, restrict the number of simulated model units while adopting simplifications that ignore physical landscape processes ( Vigiak et al, 2015 , Nkwasa et al, 2022 ). Subsequently, research is continuously needed to improve modeling approaches for estimating sediment yield at large spatial scales.…”

“…According to Morgan and Nearing (2011) and Zhao et al (2013) , the cover management factor is probably among the different sediment yield factors, the most important with crop cover playing an important role in controlling soil erosion ( Gyssels et al, 2005 , Bosco et al, 2015 ; Panagos et al, 2015 ). However, simplified approaches in representing agricultural land use are usually adopted at a large scale in regional hydrological models ( Srivastava et al, 2020 , Nkwasa et al, 2022 ). Previous applications of the SWAT model ( Schuol and Abbaspour, 2006 , Schuol et al, 2008 ) and SWAT+ model ( Chawanda et al, 2020 ) at a regional scale in Africa have utilized the default generic way of representing agricultural land use without management practices affecting the parametrization of the cover management factor for cultivated areas and subsequent sediment yields.…”

“…Previous applications of the SWAT model ( Schuol and Abbaspour, 2006 , Schuol et al, 2008 ) and SWAT+ model ( Chawanda et al, 2020 ) at a regional scale in Africa have utilized the default generic way of representing agricultural land use without management practices affecting the parametrization of the cover management factor for cultivated areas and subsequent sediment yields. However, Nkwasa et al (2022) suggested a methodological approach that utilizes global crop phenology datastes and decision tables to incorporate crop phenology and the associated management practices of planting, harvesting, irrigation and fertilization in regional SWAT+ model applications. This approach, coupled with the MUSLE equation, could improve crop cover estimation for cultivated areas by accounting for the correct temporal variation.…”

“…This paper proposes a methodological framework using the SWAT+ model ( Bieger et al, 2017 , Arnold et al, 2018 ) to improve regional scale predictions of sediment yield in a data scare region and where relatively coarse spatial data is utilized. More specifically, the framework involves; (1) assessing and adopting an approach to utilize topographic factors extracted from medium/fine resolution DEMs and resampled to a coarser SWAT+ model resolution, (2) incorporating and assessing the approach proposed by Vigiak et al (2015) for reducing the sensitivity of sediment yields to the HRU area in the MUSLE, (3) adopting the approach proposed by Nkwasa et al (2022) for incorporating crop phenology and agricultural management in regional SWAT+ model applications with respect to sediment yield estimates, (4) applying the regional hydrological mass balance calibration approach ( Chawanda et al, 2020 ) to improve model surface runoff prediction. We compare the influence of the methodological framework on sediment yield estimates and river sediment loads against measured and reported local estimates in the region.…”