1. Characterizing Errors Using Satellite Metadata for Eco‐Hydrological Model Calibration.
- Author
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Zou, Hui, Marshall, Lucy, and Sharma, Ashish
- Subjects
METADATA ,LEAF area index ,CALIBRATION ,STATISTICAL models - Abstract
Understanding the origins of errors between model predictions and catchment observations is a critical element in hydrologic model calibration and uncertainty estimation. Difficulties arise because there are a variety of error sources but only one measure of the total residual error between model predictions and catchment observations. One promising approach is to collect extra information a priori to characterize the data error before calibration. We implement here a new model calibration strategy for an ecohydrological model, using the satellite metadata information as a means to inform the model priors, to decompose data error from total residual error. This approach, referred to as Bayesian ecohydrological error model (BEEM), is first examined in a synthetic setting to establish its validity, and then applied to three real catchments across Australia. Results show that (a) BEEM is valid in a synthetic setting, as it can perfectly ascertain the true underlying error; (b) in real catchments the model error is reduced when utilizing the observation error variance as added error contributing to total error variance, while the magnitude of total residual error is more robust when utilizing metadata about the data quality proportionality as the basis for assigning total error variance; (c) BEEM improves model calibration by estimating the model error appropriately and estimating the uncertainty interval more precisely. Overall, our work demonstrates a new approach to collect prior error information in satellite metadata and reveals the potential for fully utilizing metadata about error sources in uncertainty estimation. Plain Language Summary: Ecohydrological models use climate data to make predictions about streamflow and vegetation variables, such as leaf area index (LAI). However, these models have errors that affect their accuracy. Generally, three sources of error may impair model prediction and uncertainty analysis: (a) data error; (b) parameter error; and (c) model structure error. These errors are difficult to identify, as they are only observed through the mismatch between the model output and observations. One solution is to collect independent information about the data error to help identify different sources error and to undertake uncertainty analysis. If we can estimate independently that the observations have a certain amount of error (e.g., due to the sensing equipment accuracy) then we can estimate what the remaining error is via a statistical model. Following this logic, this study collected prior info of LAI error based on satellite metadata to improve our estimates of the total model error. We call this approach BEEM (Bayesian Ecohydrological Error Model), and this study is focused on how BEEM improves the uncertainty analysis of a model. Results show that BEEM gives more accurate model prediction intervals in real case studies. Key Points: A Bayesian ecohydrological error model (BEEM) is proposed to separate data error from total residual errorThe BEEM uses error information from satellite LAI metadata and reveals the potential of utilizing prior information in uncertainty estimationThe BEEM works well in both synthetic case and real catchments [ABSTRACT FROM AUTHOR]
- Published
- 2023
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