Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations

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.

Net irrigation requirement under different climate scenarios using AquaCrop over Europe

“…Even though a conservative screening was used, this target may be possibly exceeded at some times and locations. The findings of the evaluation against SMAP SSM are comparable to the results found by de Roos et al (2021). The latter study, however, showed a slightly higher performance between the simulated SSM of the regional AquaCrop model and SMAP SSM.…”

“…0.5° lat x 0.5° lon, and daily time steps. The same spatial AquaCrop (v6.1) model structure as described by de Roos et al (2021) is used for this study, but adaptations are made to the spatial resolution, input datasets, and simulated periods. Simulations are performed from 1985 through 2100, either with or without considering irrigation, and with the respective associated crop-related parameters.…”

“…To assess the irrigation water requirement over time, the choice is made to use a general C3-type of crop with a 1 m rooting depth to describe the vegetation component, similar to de Roos et al (2021). This choice is motivated by the coarse spatial resolution and the high uncertainty in crop modifications over time and follows the methodology of well-known hydrological and land surface models, that also make use of general vegetation descriptions (e.g.…”

“…For the historical model evaluation with satellite retrievals, no irrigation is activated, the soil fertility stress of 30% is maintained (de Roos et al, 2021), and the AquaCrop default record of mean annual CO2 concentration observed at Mauna Loa (Hawaii, USA) is considered in the simulations. By contrast, the simulations with irrigation follow the yearly CO2 concentrations of the emission scenarios from ISIMIP3 and assume near-optimal soil fertility.…”

“…Earlier studies suggest that the contrast between satellite observations and model simulation could identify unmodeled processes (Brocca et al, 2018). In a separate analysis (not shown), this effect was assessed by evaluating the correlation values with regard to irrigation areas that are equipped for irrigation (AEI; similar to de Roos et al, 2021). By using the FAO global maps of irrigated areas version 5 (Siebert et al, 2013) aggregated to ISIMIP resolution, pixels were divided into two groups: (1) less than 10% of the area is equipped for irrigation, and (2) more than 10% of the pixel is equipped.…”