Sensitivity of habitat hydraulic model outputs to DTM and computational mesh resolution

Abstract

In this study, a state‐of‐the‐art approach in modelling fish habitats, using high‐resolution topographical data, obtained from unmanned aerial vehicle, was applied. Habitat Suitability Indices are used to predict how changes in discharge affect instream fish habitats. Habitat Suitability Indices regarding depth and velocity for two size classes (small sized fish 5–15 cm total length and large sized >15 cm total length) of Salmo pelagonicus and Barbus balcanicus were used, in combination with a two‐dimensional hydraulic‐hydrodynamic model, for the estimation of the weighted usable area (WUA) in a mountainous stream. Computational mesh and/or digital terrain model (DTM) resolution selection may influence the accuracy of WUA results, especially in boulder and cobble‐bed streams with complex habitat structures. The aim of the study is to examine the sensitivity of various hydraulic‐hydrodynamic modelling geometry configurations on WUA at ungauged or poorly gauged streams. Comparisons of three different geometry configurations: (1) identical computational mesh and DTM resolution (SensComb); (2) finest computational mesh resolution combined with different DTM resolutions (SensDTM); (3) finest DTM resolution combined with different computational mesh resolutions, as part of two‐dimensional hydrodynamic modelling, were applied to test the differences in WUA (SensMesh). WUA maps were generated for both fish species and class sizes for each modelling geometry configuration and compared with each other for assessing the sensitivity of the two‐input data (computational mesh and DTM). Results provided by both indices and their spatial distribution indicated the optimal DTM and computational mesh resolution as well as the sensitivity of a specific hydraulic‐habitat model on them.

Systematic comparison of 1D and 2D hydrodynamic models for the assessment of hydropeaking alterations

“…The assessment of hydropeaking alterations via ERHPs or habitat modelling is sensitive to the accuracy in the simulated flow variables. Model performance in turn may be affected by model dimensionality (e.g., Brown & Pasternack, 2009), resolution of the domain discretization (e.g., Papaioannou et al, 2020), but also by river morphological complexity or discharge conditions (e.g., Hauer et al, 2013; Legleiter, Kyriakidis, McDonald, & Nelson, 2011). We found that for a given morphology and flow condition, the mean absolute errors (MAE) of flow velocities simulated with the 1D models are larger than those simulated with any of the considered 2D models.…”

“…Recommendations regarding the required model dimensionality and spatial resolutions are established for different ERHPs and with respect to the morphological complexity of the river reach under consideration. When comparing 1D and 2D hydrodynamic models as input for a hydropeaking impact analysis we hypothesize that the hydrodynamic modelling results (flow velocities and flow depths) exhibit increasing accuracy by increasing the (a) model dimensionality (1D/2D); (Pasternack et al, 2006) and (b) spatial resolution of the computational domain (Ghamry & Katopodis, 2017; Horritt, Bates, & Mattinson, 2006; Papaioannou, Papadaki, & Dimitriou, 2020). Therefore, we assume that the finest resolved 2D model provides the most accurate solution and use this as the reference scenario (benchmark).…”

Linking SVM based habitat model and evolutionary optimisation for managing environmental impacts of hydropower plants

“…This model has been successfully utilised to simulate hydraulic habitats, which demonstrated its efficiency for simulating physical habitats. More details on using HEC-RAS 2D to simulate habitat hydraulic addresses have been in the literature (Papaioannou et al, 2020). However, Figure 3 shows the workflow of HEC-RAS 2D for simulating depth and velocity distribution in this study.…”

Efficiency of coupled invasive weed optimization-adaptive neuro fuzzy inference system method to assess physical habitats in streams

“…Another useful and applicable index to measure system performance was mean absolute error (MAE) which is displayed in Eq. (4) [32] Moreover, we used NSE that is initially used to assess the predictive power of hydrological models [33]. Equation (5) displays definition of NSE in the present study.…”