Modeling streamflow variability at the regional scale: (2) Development of a bespoke distributed conceptual model

Regional scale distributed conceptual models are typically developed with a bottom-up approach, which is process-inclusive but prone to over-parameterization. Here we demonstrate a proof of concept top-down approach for distributed conceptual model development, intended to emphasize dominant streamflow generating processes and to fulfill the principle of model parsimony. A key challenge in applying the top-down approach to distributed model development is devising a model comparison experiment that is both informative and limited to a few model alternatives. Here, we show how such model comparisons can be informed by a perceptual model of key processes that control streamflow response variability at the regional scale. We demonstrate our approach for the 27,100 km2 Moselle catchment, using the perceptual model developed in Part 1 of this two-part paper. We develop 5 distributed model structures for simulating daily streamflow at 26 subcatchments, and validate them on subcatchments that are not used during the calibration process. Our model comparisons illustrate how the spatial distribution of precipitation, lithology and topography affect the simulation of key signatures of streamflow response variability in the Moselle catchment, providing a basis to justify model decisions. Our analyses show how a minimally parameterized distributed model, with 12 calibration parameters, matches signatures of streamflow average (r = 0.96), baseflow index (r = 0.86), and hydrograph lag time (correct at 22 out of 26 subcatchments). Our proposed top-down approach contributes to improving distributed model development strategies, and can be used to develop parsimonious process based regional models elsewhere.

Automated summary

In this paper, a top-down approach for distributed conceptual model development is demonstrated, emphasizing dominant streamflow generating processes and model parsimony. A model comparison experiment informed by a perceptual model is applied to the Moselle catchment, showing how the spatial distribution of precipitation, lithology, and topography affect streamflow response variability. The proposed top-down approach leads to a minimally parameterized distributed model that matches key signatures of streamflow response variability.