Impact of Meteorological Data Variability on Budyko Parameter Estimations

Over time, many hydrologic models have been developed, ranging from physical-based to system-theoretic approaches. A simplified system-level understanding of the hydrologic process can be represented using conceptual models. Budyko framework, a lumped first-order representation of precipitation partitioning, has been widely applied to evaluate water balance. The Budyko equations are characterized by specific parameters representing the climatic and catchment characteristics. Therefore, the reliability of the framework is highly influenced by the accurate estimation of the parameters. The different input data sources can lead to varied estimates of the model parameter. The study examines the parametric uncertainties arising from various meteorological data sources. With the uncertainty attributed to precipitation, temperature, and potential evapotranspiration data sources, the study highlights the need to select and validate data sources carefully. In addition, the study highlights the challenges in parameter estimation and in capturing the underlying hydrologic processes within the Budyko framework.