Hydrological process knowledge in catchment modelling – Lessons and perspectives from 60 years development. Issue 1 (24th January 2022)

• Record Type: Journal Article
• Authors: Refsgaard, Jens Christian; Stisen, Simon; Koch, Julian
• Is Part Of: Hydrological processes. Volume 36:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-24
• Subjects: big data -- catchment model -- hydrological process understanding -- machine learning -- model evaluation -- spatial discretization; Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.14463
• Languages: English
• ISSNs: 0885-6087

Abstract

Abstract: Hydrological process knowledge has advanced significantly during the past six decades. During the same period catchment models have undergone major developments including simple black box models, lumped conceptual models, hydrological response unit models, spatially distributed process‐based models and, recently, the emergence of machine learning hybrid models. This development has been enabled by improved understanding of hydrological processes together with ever increasing computer power and improved availability and accessibility of data. During the first couple of decades, a key assumption motivating the development towards increasing complexity of model codes was that more detailed process description would lead to more accurate model simulations and enable prediction of impacts from human activities that previous models were not able to provide. Subsequently, scientific tests showed that this is very often not the case, leading towards a recognition of the importance of careful model evaluation accounting for key uncertainties in data, model parameters and model conceptual understanding. We have reviewed 54 model studies from the past 60 years and characterized them with respect to model type, spatial discretization and model evaluation techniques. This showed clear development trends and different strategies for enhancing hydrological process knowledge in models. In addition, we present a case study, where we use two models for the same catchment. The models are identical except for the spatial discretization of 100 m and 500 m, respectively. The two models have an apparent equal performance measured against standard calibration metrics, but nevertheless show large differences when considering detailed process information such as partitioning of streamflow components and water table depth patterns, that was not considered during the model calibration process. The paper discusses perspectives for enhancing hydrological process knowledge in future catchment modelling concluding that the emergence of big data is likely to become a major game changer. Abstract : Catchment models have become more complex due to incorporation of more detailed process descriptions and ever finer spatial discretization. Hydrological process knowledge has improved due to a combination of better and more complex models and increased focus on model evaluation accounting for key uncertainties in data, parameters, and model conceptual understanding. The emergence of big data exploited via combined use of machine learning techniques and process based hydrological models is likely to become a game changer.