Input uncertainty in CSM-CERES-wheat modeling: Dry farming and irrigated conditions using alternative weather and soil data

Abstract

In the current study, the uncertainties of wheat modeling using gridded soil and weather datasets were analyzed under dry farming and irrigated conditions. In this regard, the performance of the CSM-CERES-Wheat model forced with different weather-soil data combinations was studied in some dryland regions in Iran based on normalized Root Mean Square Error (nRMSE), Kling-Gupta Efficiency (KGE), and Percent Bias (PBIAS). The data combination scenarios were W_S-S_O: soil observations and gridded weather datasets including ERA5-Land (W_E-S_O) and the combinations of non-precipitation ERA5-Land forcings with CHIRPS (W_CE-S_O) and PERSIANN-CDR (W_PE-S_O), SoilGrids250m gridded soil data and weather observations (W_O-S_S), and soil and weather observations (W_O-S_O). Although the CHIRPS-ERA5L improved simulations relative to ERA5-Land and PERSIANN-CDR-ERA5-Land, there was still an nRMSE greater than 30 %, a KGE below 0.50, and an absolute PBIAS exceeding 25 % for dry farming yield in most drylands under W_S-S_S and W_S-S_O, indicating significant input uncertainties. The high uncertainty in dry farming wheat yield under W_S-S_S and W_S-S_O can be attributed to the uncertainties in simulating the water stress index in CSM-CERES-Wheat. The dry farming wheat yield was, however, simulated satisfactorily with SoilGrids250m products for W_O-S_S. The dry farming wheat yield showed the largest sensitivity to the uncertainty in precipitation forcing. The notable uncertainty in water stress simulation, and therefore in dry farming yield, appears to stem from the high uncertainty in precipitation products. These findings demonstrate that dry farming modeling is subject to notable input uncertainty when reliable meteorological records are lacking in our study area. SoilGrids250m can be reliably used to model wheat yield under dry farming conditions in the study area when weather observations are available. However, the applicability of SoilGrids250m largely depends on the availability of regional soil observations. Irrigated wheat yield was successfully simulated due to the reduced uncertainty in water stress. Therefore, using alternate weather-soil data provides a robust solution to data unavailability when wheat water requirements are sufficiently met. Nonetheless, caution is needed when using gridded weather datasets to force the CSM-CERES-Wheat model for dry farming.