the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical Note: Testing the Connection Between Hillslope Scale Runoff Fluctuations and Streamflow Hydrographs at the Outlet of Large River Basins
Ricardo Mantilla
Morgan Fonley
Nicolas Velasquez
Abstract. A series of numerical experiments were conducted to test the connection between streamflow hydrographs at the outlet of large watersheds and the time-series of hillslope-scale runoff yield. We used a distributed hydrological routing model that discretizes a large watershed (~17,000 km2) into small hillslope units (~0.1 km2) and applied distinct surface runoff time-series to each unit that deliver the same volume of water into the river network. The numerical simulations show that distinct runoff delivery time-series at the hillslope scale result in indistinguishable streamflow hydrographs at large scales. This limitation is imposed by space-time averaging of input flows into the river network that are draining the landscape. The results of the simulations presented in this paper show that under very general conditions of streamflow routing (i.e., nonlinear variable velocities in space and time), the streamflow hydrographs at the outlet of basins with Horton-Strahler (H-S) order five or above (larger than 100 km2 in our set up) contain very little information about the temporal variability of runoff production at the hillslope scale and therefore the processes from which they originate. In addition, our results indicate that the rate of convergence to a common hydrograph shape at larger scales (above H-S order 5) is directly proportional to how different the input signals are to each other at the hillslope scale. We conclude that the ability of a hydrological model to replicate outlet hydrographs does not imply that a correct and meaningful description of small-scale rainfall-runoff processes has been provided. Furthermore, our results provide context for other studies that demonstrate how the physics of runoff generation cannot be inferred from output signals in commonly used hydrological models.
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Ricardo Mantilla et al.
Status: open (until 07 Oct 2023)
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RC1: 'Comment on hess-2023-187', Keith Beven, 08 Aug 2023
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This is a very nice study of the effects of network dissipation on hillslope hydrographs with catchment scale but I do feel it needs some modification to reflect earlier contributions that have come to the same conclusions but which are not cited.
L48. But see arguments for model invalidation in Beven and Lane HP 2022
L54/55. OK that is the conclusion of a specific study but why do you think this type of criticism started in 2020? Discussions about identifiability started at least as far back as Dawdy and O’Donnell in 1965, and the issues of uncertainty and equifinality on inference from models in the 1990s.
L60. Papers describing different optima for different objective criteria also date back to at least the 1980s, together with the discussion of Pareto optimisation for multiple criteria in the Sorooshian group at UA.
L73/74. “This result implies that our ability to reproduce hydrographs at the outlet of a large basin is not a reliable indicator that we have correctly described small-scale processes controlling runoff production”. But you do not need a model to understand that the catchment is a dissipative integrative system that will necessarily make disaggregation to smaller scales highly uncertain. This was understood back in the 1970s e.g. in Kirkby, 1976, Tests of the random network model, and its application to basin hydrology, Earth Surface Processes 1 (3), 197-212 who built on the earlier work of Surkan, A. J. (1968). ‘Synthetic hydrographs: effects of network geometry’, Water Resources Research, 5, pp. 112–128. There was further work on this in the 1990s, e.g. Beven, K.J. and E.F. Wood (1993), Flow routing and the hydrological response of channel networks, in K.J. Beven and M.J.Kirkby (eds), Channel Network Hydrology, Wiley, 99-128. None of this earlier work is referenced in this paper.
L85/86. Is it not celerity rather than velocity that you need for the routing (and you can have a constant celerity with a highly nonlinear velocity for certain functions – see Beven, 1979, 'On the generalised kinematic routing method'. Water Resources Research, 15(5), 1238-1242. Also for routing on hillslopes – see the misconception of time of concentration in Beven, K. J. 2020, A history of the concept of time of concentration, Hydrology and Earth System Sciences, 24: 2655–2670, doi:10.5194/hess-24-2655-2020. The manuscript should clearly differentiate between the relationship between velocities and celerities in their routing.
L161. See Kirkby 1976 or Beven and Wood, 1993 again for same conclusions.
L199. See Beven and Lane HP 2022 for discussions of this point given uncertainties in data as well as the disaggregation problem. The equifinality concept has long suggested that you cannot differentiate between “successful/acceptable/behavioural” models but you have not referenced that either. So it does appear as if you are somewhat reinventing the wheel in both methods and conclusions.
Citation: https://doi.org/10.5194/hess-2023-187-RC1 -
RC2: 'Comment on hess-2023-187', Warrick Dawes, 08 Sep 2023
reply
hess-2023-187 “Technical Note: Testing the Connection Between Hillslope Scale Runoff Fluctuations and Streamflow Hydrographs at the Outlet of Large River Basins” Ricardo Mantilla, Morgan Fonley and Nicolás Velasquez
This article deserves more prominence than being seen as simply technical. It addresses a fundamental issue that arises from increasing computing power and remote data collection toward highly distributed modelling systems with extremely fine spatial and temporal resolution that are sadly, and inevitably, over parameterised. Hydrological modelling results are routinely described with many decimal places and far too many significant digits given the errors inherent in even measuring the target variable, added to errors in model structure and parameter estimation or measurement.
I was reminded immediately of an article by Kirchner (2006) from nearly 20-years ago whose title completely embodies the spirit of this article: that we should strive for the correct answer for the correct reason. Advances in AI/ML along with a plethora of physical and empirical models at various levels of complexity show that any answer can be arrived at by many different means without anyone knowing of any of them is doing it correctly.
I agree with every word in this manuscript and can only paraphrase: more powerful computers fuelled by higher resolution data are a good servant but a poor master.
Kirchner, J. W. (2006) Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology. Water Resources Research, 42, W03S04, doi:10.1029/2005WR004362
Citation: https://doi.org/10.5194/hess-2023-187-RC2
Ricardo Mantilla et al.
Ricardo Mantilla et al.
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