Limitations Posed by Free DEMs in Watershed Studies: The Case of River Tanaro in Italy

Abstract

Topography is a critical element in the hydrological response of a drainage basin and its availability in the form of digital elevation models (DEMs) has advanced the modeling of hydrological and hydraulic processes. However, progress experienced in these fields may stall, as intrinsic characteristics of free DEMs may limit new findings, while at the same time new releases of free, high-accuracy, global digital terrain models are still uncertain. In this paper, the limiting nature of free DEMs is dissected in the context of hydrogeomorphology. Ten sets of terrain data are analyzed: the SRTM GL1 and GL3, HydroSHEDS, TINITALY, ASTER GDEM, EU DEM, VFP, ALOS AW3D30, MERIT and the TDX. In specific, the influence of three parameters are investigated, i.e., spatial resolution, hydrological reconditioning and vertical accuracy, on four relevant geomorphic terrain descriptors, namely the upslope contributing area, the local slope, the elevation difference and the flow path distance to the nearest stream, H and D, respectively. The Tanaro river basin in Italy is chosen as the study region and the newly released LiDAR for the Italian territory is used as benchmark to reassess vertical accuracies. In addition, the EU-Hydro photo-interpreted river network is used to compare DEM-based river networks. Most DEMs approximate well the frequency curve of elevations of the LiDAR, but this is not necessarily reflected in the representation of geomorphic features. For example, DEMs with finer spatial resolution present larger contributing areas; differences in the slope can reach 10%; between 5 m and 12 m H, none of the considered DEMs can faithfully represent the LiDAR; D presents significant variability between DEMs; and river network extraction can be problematic in flatter terrain. It is also found that the lowest mean absolute error (MAE) is given by the MERIT, 2.85 m, while the lowest root mean square error (RMSE) is given by the SRTM GL3, 4.83 m. Practical implications of choosing a DEM over another may be expected, as the limitations of any particular DEM in faithfully reproducing critical geomorphic terrain features may hinder our ability to find satisfactory answers to some pressing problems.

Predictive Modeling of Envelope Flood Extents Using Geomorphic and Climatic‐Hydrologic Catchment Characteristics

“…Limitations to this methodological approach are known. For instance, the GFI itself, as detailed by Manfreda et al (2014, 2015), Samela et al (2017), Tavares da Costa, Manfreda, et al (2019), and Tavares da Costa, Mazzoli, and Bagli (2019), is found to be less than optimal in identifying flood‐prone areas in flat terrain, which may explain the high FDR and overestimation. In other words, in a more incised fluvial valley, there are more independent GFI contours to choose from, which allows for a better representation of flood‐prone areas.…”

“…However, such cases should be completely withheld from the performance analysis, as they might artificially influence the performance. Additional tests using the EU‐DEM, not reported here, have revealed that the use of a DEM to compute the GFI that is different from the DEM used in the modeling of the benchmark flood maps negatively influences the results. Caution should, thus, be exercised in the selection of the DEM, as in this study (a consistent use of the RMS‐DEM), but also in its processing (e.g., terrain analysis, river network, and catchment delineation; Tavares da Costa, Mazzoli, & Bagli, 2019). …”

“…Terrain analysis employed in this work follows a simple workflow (see Figure S2 for an illustration of this workflow) using the TauDEM toolbox. For more details, the reader can refer to Tavares da Costa, Manfreda, et al (2019) and Tavares da Costa, Mazzoli, and Bagli (2019).…”

“…Bottlenecks, such as the ones presented above, motivate a number of authors to produce alternative low‐complexity solutions that rely on data‐driven methods (Caprario & Finotti, 2019; Giovannettone et al, 2018; Schumann et al, 2014; Tang et al, 2018; Zhao et al, 2019). Some authors take advantage of the causality between historical floods and the floodplain hydraulic geometry (e.g., Bhowmik, 1984; Dodov & Foufoula‐Georgiou, 2006; McGlynn & Seibert, 2003) and make use of digital elevation models (DEMs) that are data sets representing the Earth's surface, distributed as gridded values of local terrain elevations (Tavares da Costa, Mazzoli, & Bagli, 2019). For example, Nardi et al (2006, 2013, 2019), Morrison et al (2018), and Annis et al (2019) employed a flat‐water approach (i.e., intersection of a water level with the surrounding DEM, or a variation of it, such as the HAND—Height Above the Nearest Drainage; Rennó et al, 2008; Nobre et al, 2016) to delineate floodplains.…”

Predictive modelling of envelope flood extents using geomorphic and climatic-hydrologic catchment characteristics

“…Bottlenecks, such as the ones presented above, motivated a number of authors to produce alternative low-complexity solutions that rely on data-driven methods (Schumann et al, 2014a;Tang et al, 2018;Giovannettone et al, 2018;Caprario & Finotti, 2019;Zhao et al, 2019). Some take advantage of the causality between historical floods and the floodplain hydraulic geometry (e.g., Bhowmik, 1984;McGlynn and Seibert, 2003;Dodov and Foufola-Georgiou, 2006) and make use of digital elevation models (DEMs) that are datasets representing the Earth's surface, distributed as gridded values of local terrain elevations (Tavares da Costa et al, 2019b). For example, Nardi et al (2006Nardi et al ( , 2013Nardi et al ( , 2019, Morrison et al (2018) and Annis et al (2019) employed a flat-water approach (i.e., intersection of a water level with the surrounding DEM, or a variation of it, such as the HAND -Height Above the Nearest Drainage; Rennó et al, 2008;Nobre et al, 2016) to delineate floodplains.…”