Abstract
Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30 arcsec (∼1 km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active–Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.
Highlights
Over the past 60 years, global agricultural production has more than tripled (FAO, 2017)
A visual comparison of simulated and satellite-based biomass on different days in the year 2017 is presented in Fig. 2 and gives an indication of the spatial performance of the regional AquaCrop model against the CGLS-DMP product
Water stress in the simulations brings crop production to a halt, which is not in agreement with the CGLS-DMP. This can be caused by an overestimation of water stress by the model, unmodelled irrigation, or the CGLS-DMP product not accounting for drought stresses well
Summary
Over the past 60 years, global agricultural production has more than tripled (FAO, 2017). This became possible through productivity-enhanced technologies, industrialization, and expansion of agricultural land. To meet the future crop demand of a vastly growing population, while minimizing the ecological footprint and increasing the crop resilience for changing climatic conditions, the need to adapt to more effective and sustainable land cultivation practices is urgent (Aznar-Sánchez et al, 2019; Pingali, 2012; Raes and Vanuytrecht, 2017). To evaluate the effect of environmental conditions and different management practices on crop production, there are a variety of models that simulate the biophysiological growth.
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