Hydraulic validation of two-dimensional simulations of braided river flow with spatially continuous aDcp data

Gravel‐bed braided rivers are characterized by shallow, branching flow across low relief, complex, and mobile bed topography. These conditions present a major challenge for the application of higher dimensional hydraulic models, the predictions of which are nevertheless vital to inform flood risk and ecosystem management. This paper demonstrates how high‐resolution topographic survey and hydraulic monitoring at a density commensurate with model discretization can be used to advance hydrodynamic simulations in braided rivers. Specifically, we detail applications of the shallow water model, Delft3d, to the Rees River, New Zealand, at two nested scales: a 300 m braid bar unit and a 2.5 km reach. In each case, terrestrial laser scanning was used to parameterize the topographic boundary condition at hitherto unprecedented resolution and accuracy. Dense observations of depth and velocity acquired from a mobile acoustic Doppler current profiler (aDcp), along with low‐altitude aerial photography, were then used to create a data‐rich framework for model calibration and testing at a range of discharges. Calibration focused on the estimation of spatially uniform roughness and horizontal eddy viscosity, νH, through comparison of predictions with distributed hydraulic data. Results revealed strong sensitivity to νH, which influenced cross‐channel velocity and localization of high shear zones. The high‐resolution bed topography partially accounts for form resistance, and the recovered roughness was found to scale by 1.2–1.4 D84 grain diameter. Model performance was good for a range of flows, with minimal bias and tight error distributions, suggesting that acceptable predictions can be achieved with spatially uniform roughness and νH.Field campaigns were primarily funded by NERC Grant NE/G005427/1 and NERC Geophysical Equipment Facility Loan 892 as well as NSERC and CFI (Canada) grants to Colin Rennie. Damia Vericat was supported by a Ramon y Cajal Fellowship (RYC‐2010‐06264) funded by the Spanish Ministry of Science and Innovation during the preparation of this manuscript. Numerical simulations were undertaken during a visit by Richard Williams to NIWA. This visit was funded by the British Hydrology Society and an Aberystwyth University Postgraduate Studentship. Murray Hicks and Richard Measures were funded by NIWA core funding under the Sustainable Water Allocation Programme

The effects of spatial resolution and dimensionality on modeling regional-scale hydraulics in a multichannel river

As modeling capabilities at regional and global scales improve, questions remain regarding the appropriate process representation required to accurately simulate multichannel river hydraulics. This study uses the hydrodynamic model LISFLOOD-FP to simulate patterns of water surface elevation (WSE), depth, and inundation extent across a ∼90 km, anabranching reach of the Tanana River, Alaska. To provide boundary conditions, we collected field observations of bathymetry and WSE during a 2 week field campaign in summer 2013. For the first time at this scale, we test a simple, raster-based model's capabilities to simulate 2-D, in-channel patterns of WSE and inundation extent. Additionally, we compare finer resolution (≤25 m) 2-D models to four other models of lower dimensionality and coarser resolution (100–500 m) to determine the effects of simplifying process representation. Results indicate that simple, raster-based models can accurately simulate 2-D, in-channel hydraulics in the Tanana. Also, the fine-resolution, 2-D models produce lower errors in spatiotemporal outputs of WSE and inundation extent compared to coarse-resolution, 1-D models: 22.6 cm versus 56.4 cm RMSE for WSE, and 90% versus 41% Critical Success Index values for simulating inundation extent. Incorporating the anabranching channel network using subgrid representations for smaller channels is important for simulating accurate hydraulics and lowers RMSE in spatially distributed WSE by at least 16%. As a result, better representation of the converging and diverging multichannel network by using subgrid solvers or downscaling techniques in multichannel rivers is needed to improve errors in regional to global-scale models

Chaos in a simple model of a delta network

The flux partitioning in delta networks controls how deltas build land and generate stratigraphy. Here, we study flux-partitioning dynamics in a delta network using a simple numerical model consisting of two orders of bifurcations. Previous work on single bifurcations has shown periodic behavior arising due to the interplay between channel deepening and downstream deposition. We find that coupling between upstream and downstream bifurcations can lead to chaos; despite its simplicity, our model generates surprisingly complex aperiodic yet bounded dynamics. Our model exhibits sensitive dependence on initial conditions, the hallmark signature of chaos, implying long-term unpredictability of delta networks. However, estimates of the predictability horizon suggest substantial room for improvement in delta-network modeling before fundamental limits on predictability are encountered. We also observe periodic windows, implying that a change in forcing (e.g., due to climate change) could cause a delta to switch from predictable to unpredictable or vice versa. We test our model by using it to generate stratigraphy; converting the temporal Lyapunov exponent to vertical distance using the mean sedimentation rate, we observe qualitatively realistic patterns such as upwards fining and scale-dependent compensation statistics, consistent with ancient and experimental systems. We suggest that chaotic behavior may be common in geomorphic systems and that it implies fundamental bounds on their predictability. We conclude that while delta “weather” (precise configuration) is unpredictable in the long-term, delta “climate” (statistical behavior) is predictable

Identification of high-permeability subsurface structures with multiple point geostatistics and normal score ensemble Kalman filter

Alluvial aquifers are often characterized by the presence of braided high-permeable paleo-riverbeds, which constitute an interconnected preferential flow network whose localization is of fundamental importance to predict flow and transport dynamics. Classic geostatistical approaches based on two-point correlation (i.e., the variogram) cannot describe such particular shapes. In contrast, multiple point geostatistics can describe almost any kind of shape using the empirical probability distribution derived from a training image. However, even with a correct training image the exact positions of the channels are uncertain. State information like groundwater levels can constrain the channel positions using inverse modeling or data assimilation, but the method should be able to handle non-Gaussianity of the parameter distribution. Here the normal score ensemble Kalman filter (NS-EnKF) was chosen as the inverse conditioning algorithm to tackle this issue. Multiple point geostatistics and NS-EnKF have already been tested in synthetic examples, but in this study they are used for the first time in a real-world casestudy. The test site is an alluvial unconfined aquifer in northeastern Italy with an extension of approximately 3 km2. A satellite training image showing the braid shapes of the nearby river and electrical resistivity tomography (ERT) images were used as conditioning data to provide information on channel shape, size, and position. Measured groundwater levels were assimilated with the NS-EnKF to update the spatially distributed groundwater parameters (hydraulic conductivity and storage coefficients). Results from the study show that the inversion based on multiple point geostatistics does not outperform the one with a multiGaussian model and that the information from the ERT images did not improve site characterization. These results were further evaluated with a synthetic study that mimics the experimental site. The synthetic results showed that only for a much larger number of conditioning piezometric heads, multiple point geostatistics and ERT could improve aquifer characterization. This shows that state of the art stochastic methods need to be supported by abundant and high-quality subsurface data