HYPERstream: a multi-scale framework for streamflow routing in large-scale hydrological model

Abstract

Abstract. We present HYPERstream, an innovative streamflow routing scheme based on the width function instantaneous unit hydrograph (WFIUH) theory, which is specifically designed to facilitate coupling with weather forecasting and climate models. The proposed routing scheme preserves geomorphological dispersion of the river network when dealing with horizontal hydrological fluxes, irrespective of the computational grid size inherited from the overlaying climate model providing the meteorological forcing. This is achieved by simulating routing within the river network through suitable transfer functions obtained by applying the WFIUH theory to the desired level of detail. The underlying principle is similar to the block-effective dispersion employed in groundwater hydrology, with the transfer functions used to represent the effect on streamflow of morphological heterogeneity at scales smaller than the computational grid. Transfer functions are constructed for each grid cell with respect to the nodes of the network where streamflow is simulated, by taking advantage of the detailed morphological information contained in the digital elevation model (DEM) of the zone of interest. These characteristics make HYPERstream well suited for multi-scale applications, ranging from catchment up to continental scale, and to investigate extreme events (e.g., floods) that require an accurate description of routing through the river network. The routing scheme enjoys parsimony in the adopted parametrization and computational efficiency, leading to a dramatic reduction of the computational effort with respect to full-gridded models at comparable level of accuracy. HYPERstream is designed with a simple and flexible modular structure that allows for the selection of any rainfall-runoff model to be coupled with the routing scheme and the choice of different hillslope processes to be represented, and it makes the framework particularly suitable to massive parallelization, customization according to the specific user needs and preferences, and continuous development and improvements.

Combining Hydrologic Simulations and Stream‐network Models to Reveal Flow‐ecology Relationships in a Large Alpine Catchment

“…Hydrological simulations were performed at the daily time scale with the HYPERstreamHS model (Avesani et al., 2021; Laiti et al., 2018), which couples the HYPERstream routing scheme (recently proposed by Piccolroaz et al., 2016) with a continuous Soil Conservation Service (SCS) module for surface flow generation (Michel et al., 2005). Subsurface return flow was modeled by a nonlinear reservoir (Majone et al., 2010).…”

“…HYPERstream inherits the computational grid of the climatic model, or of the gridded product providing the meteorological forcing, and preserves geomorphological dispersion due to the structure of the river network (Rinaldo et al, 1991), regardless of grid resolution. In previous studies, the SCS runoff module was successfully applied to two tributaries of the Adige River (Bellin et al, 2016;Piccolroaz et al, 2016). For a detailed description of the hydrologic modeling framework see Laiti et al, (2018) and Avesani et al (2021).…”

Analysis of high streamflow extremes in climate change studies: How do we calibrate hydrological models?

“…Hydrological simulations were performed at the daily time scale with the HYPERstreamHS model [Avesani et al, 2021;Laiti et al, 2018;Larsen et al, 2021] which couples the HYPERstream routing scheme, recently proposed by Piccolroaz et al, [2016], with a continuous SCS-CN module for surface and subsurface flow generation [Michel et al, 2005].…”

Evolution of river-routing schemes in macro-scale models and their potential for watershed management

“…Two recent models, mizuRoute (Mizukami et al 2016) and HYPERstream (Piccolroaz et al 2016), attempt to provide "general" flow routing models that can couple to any GM runoff results available. Additionally, they can leverage both raster and vector data with the computationally efficient IRF type methods of the early noted "source-to-sink" methods.…”