|Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+
|Nkwasa, A., C. J. Chawanda, J. Jägermeyr, J. and A. van Griensven
|Hydrology and Earth System Sciences
|URL (non-DOI journals):
|Broad Application Category:
|Primary Application Category:
|crop, forest and/or vegetation growth/yield and/or parameters
|Secondary Application Category:
|4,489,000 km^2 Nile River, which includes multiple major tributaries (Blue Nile, White Nile, Atbara, Baro–Akobo–Sobat, Bahr El Jebel, and Bahr El Ghazal) which drain parts or all of several countries (Uganda, Kenya, Tanzania, Rwanda,
Burundi, Sudan, South Sudan, Ethiopia, and Egypt) in northeast Africa.
|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.