Errors in temporal disaggregation of temperature can lead to non-negligible biases in agroecosystem risk assessment

• Published in: Agricultural and forest meteorology Vol. 349; p. 109952
• Main Authors: Savalkar, Supriya, Khan, Md. Redwan Ahmad, Singh, Bhupinderjeet, Pruett, Matt, Peters, R. Troy, Stöckle, Claudio O, Hill, Sean E., Rajagopalan, Kirti
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.04.2024
• Subjects: agroecosystems; Agroecosystems modeling; decision making; forests; Input error propagation into models; meteorological data; meteorology; Radiation disaggregation; risk estimate; solar radiation; sunburn; temperature; Temperature disaggregation; Temperature disaggregation error adjustment; uncertainty; Temperature disaggregation error adjustment; Agroecosystems modeling; Temperature disaggregation; Radiation disaggregation; Input error propagation into models
• ISSN: 0168-1923; 1873-2240
• DOI: 10.1016/j.agrformet.2024.109952

•Temporal temperature disaggregation can lead to large agroecosystem model errors.•Change assessments can be sensitive to temporal temperature disaggregation errors.•Simple monthly adjustments based on observations can substantially reduce errors. Models are crucial for simulating complex systems and decision-making, but they have uncertainties that must be characterized and understood. One uncertainty that has been overlooked in agroecosystem assessments is that arising from the temporal disaggregation of temperature and solar radiation. Our study used data from an agricultural weather station network to investigate (a) the errors associated with hourly temporal disaggregation of daily temperatures and solar radiation, (b) how these input errors impact two agroecosystem models, (c) the sensitivity of change assessments to disaggregation errors, and (d) how high-temporal-resolution weather station networks can be leveraged to correct disaggregation errors in daily gridded meteorological data products. Our findings demonstrate that temporal temperature disaggregation errors can have a significant impact on agroecosystem model output, with large errors in sunburn risk estimation (>100% median deviation percentage) but minimal effects on chill accumulation (<5% median deviation percentage). However, we were able to achieve significant reductions in error (>75% error reduction in sunburn risk assessment in majority of cases) by integrating simple monthly statistics from station observations into the disaggregation process. Our study highlights the importance of understanding uncertainties in agroecosystem models stemming from temporal disaggregation of temperature, and the potential benefits of utilizing simple adjustments leveraging weather station networks to improve model accuracy and applicability for decision-making.