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Spatially distributed parametrization of the Total Runoff Integrating Pathways (TRIP) scheme for improved river routing at the global scale

Tsilimigkras Athanasios

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Summary

Land Surface Models (LSMs) simulate various processes of the terrestrial land surface related to the energy balance, the hydrological cycle, the carbon cycle, etc. LSMs have been developed for a variety of applications, including assessing the impact of modifying a particular process on the ecosystem as a whole, e.g., the impact of climate change on hydrology, and studying potential feedback.Due to their great complexity, the development of these models is a continuous and laborious process. For example, the JULES (Joint UK Land Environment Simulator) model is developed by a broad community of inter-disciplinary researchers. It is a fact that despite the high level of model development, some processes face parsimonious parameterization. One of these processes is the routing of surface runoff as simulated by the TRIP (Total Runoff Integrating Pathways) scheme. In its current global parameterization, TRIP uses uniform velocity and meandering characteristics for the entire land surface regardless of the actual physiographic characteristics of each river basin.This work aims to improve the routing of the surface runoff through the optimization of the effective velocity and meandering ratio parameters. In a sample of 360 global river basins, these parameters are correlated with physiographic characteristics to derive a method of extrapolation at the global scale. The development and application of the method were based on observed river discharge from the global GRDC database and basin-scale physiographic attributes from the HydroATLAS database.A factorial experiment was performed from a combination of 20 setups of effective velocity values and 12 meandering ratios, resulting in a total of 198 simulations. The Nash-Sutcliffe Efficiency (NSE) was employed to assess the model performance for each set of routing parameters. Two optimization methods were developed; in the first method, the optimum routing parameters are defined for the best NSE improvement with the least deviation from the default routing parameters, whereas in the second method a uniform parameter set was assigned based on a categorization of the basins. Neural Networks were used for regression and classification, respectively for each method, correlating the optimal routing parameters with physiographic attributes at the river basin scale. The trained neural networks were applied to the HydroATLAS attributes to extrapolate the routing parameters at the global scale.The spatially distributed optimal routing parameters from each method are available as input into the TRIP scheme, enabling the improved routing of the global runoff and the overall representation of the hydrological cycle in the JULES model.

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