Evaluation of Regional-Scale River Depth Simulations Using Various Routing Schemes within a Hydrometeorological Modeling Framework for the Preparation of the SWOT Mission

Abstract

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 15734 ABSTRACTThe Surface Water and Ocean Topography (SWOT) mission will provide free water surface elevations, slopes, and river widths for rivers wider than 50 m. Models must be prepared to use this new finescale information by explicitly simulating the link between runoff and the river channel hydraulics. This study assesses one regional hydrometeorological model's ability to simulate river depths. The Garonne catchment in southwestern France (56 000 km 2 ) has been chosen for the availability of operational gauges in the river network and finescale hydraulic models over two reaches of the river. Several routing schemes, ranging from the simple Muskingum method to time-variable parameter kinematic and diffusive waves schemes, are tested. The results show that the variable flow velocity schemes are advantageous for discharge computations when compared to the original Muskingum routing method. Additionally, comparisons between river depth computations and in situ observations in the downstream Garonne River led to root-mean-square errors of 50-60 cm in the improved Muskingum method and 40-50 cm in the kinematic-diffusive wave method. The results also highlight SWOT's potential to improve the characterization of hydrological processes for subbasins larger than 10 000 km 2 , the importance of an accurate digital elevation model, and the need for spatially varying hydraulic parameters.

Impact of river water levels on the simulation of stream–aquifer exchanges over the Upper Rhine alluvial aquifer (France/Germany)

“…This study also points out the need for data to better estimate the river levels at regional scale, both for parameterizing the models and evaluating the simulated hydrological variables. Such need could be fulfilled by the future Surface Water and Ocean Topography satellite mission, which will provide estimations of land surface water with a spatial resolution of about 50-100 m (Häfliger et al 2015;Biancamaria et al 2016).…”

“…Thierion et al (2012) also performed a detailed sensitivity analyses on the T p and Q lim parameters for this case study: different values of Q lim (0, −0.025, −0.050 and -0.1 m has three options for calculating river level in each river gridcell: (1) fixed river level, (2) river level estimated from observed rating curves and (3) river level estimated from the inversion of the Manning Formula. For the two last options, observations can be used to derive the associated parameters locally, and a method has to be used to interpolate the value along the reach between two observations (Saleh et al 2011;Häfliger et al 2015). This step is not straightforward, and can lead to some errors that are sensitive when the module is coupled to groundwater modeling.…”

“…This coupling is explicit since the river-aquifer exchanges used for the calculation of river discharges and piezometric heads are based on river levels calculated beforehand by QtoZ. Here, the river level impacts only the river-aquifer exchanges, although it could also affect the river flow velocity (Häfliger et al 2015).…”

“…The fluctuations of river levels are commonly simulated either by using a variable velocity algorithm in a hydrological routing model (Saleh et al 2011;Dai et al 2015;Häfliger et al 2015) or by using a hydrodynamic model based on the Saint-Venant equations (Thompson et al 2004;Saleh et al 2013;Barthel and Banzhaf 2016). Simulating variable river levels makes the representation of the river geometry essential for studying both flood inundation and stream-aquifer exchanges-for example, any deviation from a vertical representation of banks will increase the area of exchange during a stream flow event (Doble et al 2012).…”

Improvement of surface run‐off in the hydrological model ParFlow by a scale‐consistent river parameterization

“…In order to scale the hydraulic parameters via Equations and for each river grid cell, the true river widths ( W 1 ) are needed, which we set to an effective river width W 1 , estimated from known upstream discharge values via an empirical relationship by Leopold and Maddock () as a function of the mean annual discharge Q, with k = 7.12 and m = 0.53. The parameters k and m were fitted, tested, and validated by Häfliger et al () for the Garonne catchment (55,846 km 2 ), whose catchment area is close to the simulated domain (57,850 km 2 ), covering all the Rhine‐Neckar area. Figure shows the resulting effective river width (spatial and cumulative distribution) for the Rhine‐Neckar area, which varies between 10 and 150 m, with a mean width of about 34.5 m and a standard deviation of 31.5 m. River widths below 10 m (contained in 96.6% of all surface grid cells) are not taken into account, because rivers narrower than 10 m will show up in a 400 m resolution topography as gridcells with overland flow only during and shortly after rain events, but not, for example, during dry spells.…”