Diagnostic Framework for Evaluating How Parametric Uncertainty Influences Agro‐Hydrologic Model Projections of Crop Yields Under Climate Change. Issue 6 (26th May 2022)

• Record Type: Journal Article
• Authors: Karimi, Tina; Reed, Patrick; Malek, Keyvan; Adam, Jennifer
• Is Part Of: Water resources research. Volume 58:Issue 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-26
• Subjects: parametric uncertainty -- global sensitivity analysis -- model diagnostic -- yield projection -- agro‐hydrologic coupled model -- climate change; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2021WR031249
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Despite the prevalence of climate change assessments of crop yields, there are significant limits to our understanding of how parametric uncertainty in the underlying agro‐hydrologic models as well as the stationarity assumptions tacit to their commonly employed calibration procedures are influencing projections. This study addresses this knowledge gap by clarifying how parametric uncertainty in agro‐hydrologic models influences yield projections under changing future climate. We focus on rain‐fed winter wheat systems in the drylands of United States Pacific Northwest. We use a tightly coupled agro‐hydrologic model, VIC‐CropSyst, as a representative of this class of models. Our contributed diagnostic global sensitivity analysis framework identifies differences in how influential factors (e.g., temperature during early growth stages or the growing degree‐day required to reach peak leaf area index) vary across zones during historical and future periods. Our results show that the dominant parametric controls for yield projection and their sensitivities change subject to agro‐climatic zones and differences in the specific temperature‐precipitation trends in future climate scenarios. Our results also indicate that the stationarity assumptions tacit to using historical observations to calibrate agro‐hydrologic model parameters and their subsequent use in future yield projections may introduce significant bias. Employing the stationarity assumption in future projections problematically ignores how shifts in climate influence the relative dominance of underlying agro‐hydrologic processes in the model. This study's contributed diagnostic exploratory modeling framework has promise for advancing our understanding of how calibration, parametric uncertainties, and climate induced changes in the dominance of model biophysical processes shape crop yield projections. Key Points: Climate change impacts on crop yields are influenced by the degree that dominant parameterized agro‐hydrologic processes changes in future Global sensitivity analysis clarifies how history‐focused model calibration can neglect climate change induced shifts in dominant processes Contribute a novel diagnostic modeling framework for understanding how parametric uncertainties and calibration influence model projections