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 good insight in 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 arcseconds (~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. 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 correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM) and 0.52 (CGLS-SSM), respectively. 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 interannual 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 proves to be useful in assessing crop production and soil moisture at various scales and could serve as a bridge between point-based and global models.

Net irrigation requirement under different climate scenarios using AquaCrop over Europe

“…The study domain focuses on the part of the European continent with latitudes (lat) ranging from 34.75 to 59.75 • N and longitudes (long) from −10.75 to 41.25 • E. The spatial and temporal resolutions of the model simulations are set to those of the ISIMIP3 input datasets, i.e., 0.5 • lat × 0.5 • long, and daily time steps. The same spatial AquaCrop (v6.1) model structure, as described by de Roos et al (2021b), 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 croprelated parameters.…”

“…For the historical model evaluation with satellite retrievals, 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 (2021b). 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., Niu et al, 2011;Rodell et al, 2004).…”

“…C3 crops are dominant in Europe (Monfreda et al, 2008;Still et al, 2003). A detailed description of the crop characteristics is given in Table 1 of de Roos et al (2021b). For the model evaluation, no irrigation is activated, the soil fertility stress of 30 % is maintained (de Roos et al, 2021b), and the AquaCrop default record of mean annual CO 2 concentration observed at Mauna Loa (Hawaii, USA) is considered in the simulations.…”

“…AquaCrop has already been used in regional agricultural climate impact studies by Dale et al (2017), where an open-source version of AquaCrop (AquaCrop-OS;Foster et al, 2017) was used to project crop yields for a high number of GCMs under different climate scenarios at a resolution of 2 • × 2 • . In this study, the impact of climate change on the future net irrigation requirement is assessed for different emission scenarios and GCMs, using the spatial version of AquaCrop (de Roos et al, 2021b) forced with ISIMIP3 meteorological data over the European continent for the first time. First, the model performance is evaluated by comparing historical spatial AquaCrop v6.1 simulations without any irrigation, forced with (i) reanalysis data from ISIMIP3a and (ii) GCM-based meteorological data from ISIMIP3b, against satellite-based surface soil moisture (SSM) observations.…”

A Regional Version of the Aquacrop Model Evaluated with Satellite Retrievals and Backscatter Data

“…In the regional version of the AquaCrop model, the computational unit is adjusted from a single field to a 30 arc seconds (~1-km) pixel, and input and output are defined independently of other pixels [6]. To be able to do this over a large domain, great computational power is required.…”