Falsification‐Oriented Signature‐Based Evaluation for Guiding the Development of Land Surface Models and the Enhancement of Observations

Abstract We develop a novel framework for rigorously evaluating land surface models (LSMs) against observations by recognizing the asymmetry between verification‐ and falsification‐oriented approaches. The former approach cannot completely verify LSMs even though it exhausts every case of consistency between the model predictions and observations, whereas the latter only requires a single case of inconsistency to reveal that there must be something wrong. We argue that it is such an inconsistency that stimulates further development of the models and enhancement of the observations. We therefore propose a falsification‐oriented signature‐based evaluation framework to identify cases of inconsistency between model predictions and observations by extracting signatures based on a set of key assumptions. We apply this framework to evaluate an ensemble of simulations from the Noah‐MP LSM against observations over the continental United States under the three assumptions of water mass conservation, no lateral water flow, and a sufficiently long period of time. Regions showing inconsistencies between the Noah‐MP ensemble simulations and the observations are located in the western mountainous areas, the Yellowstone river basin, the lower Floridan aquifer, the Niobrara river basin at the north tip of the Ogallala aquifer, and the basins downstream of the Balcones fault zones in Texas. These regions coincide with the sites where both advances in theoretical modeling and new observational data (e.g., from the Critical Zone Observatories) have emerged.

Abstract licence: CC BY-NC

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