Abstract
Abstract. Catchment-scale hydrological models frequently miss essential characteristics of what determines the functioning of catchments. The most important active agent in catchments is the ecosystem. It manipulates and partitions moisture in a way that supports the essential functions of survival and productivity: infiltration of water, retention of moisture, mobilization and retention of nutrients, and drainage. Ecosystems do this in the most efficient way, establishing a continuous, ever-evolving feedback loop with the landscape and climatic drivers. In brief, hydrological systems are alive and have a strong capacity to adjust themselves to prevailing and changing environmental conditions. Although most models take Newtonian theory at heart, as best they can, what they generally miss is Darwinian theory on how an ecosystem evolves and adjusts its environment to maintain crucial hydrological functions. In addition, catchments, such as many other natural systems, do not only evolve over time, but develop features of spatial organization, including surface or sub-surface drainage patterns, as a by-product of this evolution. Models that fail to account for patterns and the associated feedbacks miss a critical element of how systems at the interface of atmosphere, biosphere and pedosphere function. In contrast to what is widely believed, relatively simple, semi-distributed conceptual models have the potential to accommodate organizational features and their temporal evolution in an efficient way, a reason for that being that because their parameters (and their evolution over time) are effective at the modelling scale, and thus integrate natural heterogeneity within the system, they may be directly inferred from observations at the same scale, reducing the need for calibration and related problems. In particular, the emergence of new and more detailed observation systems from space will lead towards a more robust understanding of spatial organization and its evolution. This will further permit the development of relatively simple time-dynamic functional relationships that can meaningfully represent spatial patterns and their evolution over time, even in poorly gauged environments.
Highlights
“The whole is greater than the sum of the parts” and “Everything changes and nothing remains still [. . . ]” are quotes commonly attributed to the Greek philosophers Aristotle (384–322 BC) and Heraclitus (535–475 BC)
Largely not questioning the validity of model concepts themselves, it ignores the fact that a significant proportion of uncertainty in current-generation catchment-scale hydrological models – both conceptual and physically based – can be directly linked to the fact that our conceptual understanding of two of the critical aspects of the system, i.e. internal organization and the capacity of the ecosystem to manipulate the system in response to the temporal dynamics of the atmospheric drivers, as encapsulated in the above two quotes, is only insufficiently or often not at all accounted for in these models
It is clear that both model concepts, whether “top-down” conceptual or “bottom-up” physically based, have an important role to play in discovering the physics of underlying pattern formation
Summary
“The whole is greater than the sum of the parts” and “Everything changes and nothing remains still [. . . ]” are quotes commonly attributed to the Greek philosophers Aristotle (384–322 BC) and Heraclitus (535–475 BC). State-of-the-art catchment-scale hydrological models, for varying reasons depending on the model under consideration, frequently do a poor job in addressing overall system behaviour emerging from the characteristics above This results in many models being inadequate representations of real-world systems, haunted by large model and/or parameter uncertainties and unreliable predictions. One reason for that is the common absence of observations at the modelling scale of interest and our resulting inability to meaningfully characterize natural heterogeneity in the model domain This leads to the largely indispensable need for model calibration (for both, conceptual and physically based models), which in turn exacerbates our problem to meaningfully parameterize, test and constrain models. McDonnell et al (2007), motivated by Dooge’s (1986) paper on “Looking for hydrologic laws”, concluded that “In order to make continued progress in watershed hydrology and to bring greater coherence to the science, we need to move beyond the status quo of having to explicitly characterize or prescribe landscape heterogeneity in our (highly calibrated) models and in this way reproduce process complexity but instead explore the set of organizing principles that might underlie the heterogeneity and complexity.”, suggesting that we need to find the organizing principles underlying the apparent simplicity we can observe in system behaviour
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