115 years of sediment deposition in a reservoir in Central Europe: Topographic change detection

Abstract

Reservoirs have become an important component in the worldwide river sediment flux. Reservoirs prevent downstream sediment transport and have become a major sediment sink. In this study, sediment deposition during the last 115 years in the Urft Reservoir in western Germany is reconstructed. The Urft Reservoir is the oldest reservoir in the Eifel Mountains and was almost completely drained in 2020. This enabled a detailed mapping of the lake bottom using an unmanned aerial system and the computation of a high‐resolution digital surface model. Topographic maps with a nominal resolution of 1:1000 from the time prior to the construction of the dam (around 1900) were used to construct a pre‐reservoir elevation model. A digital elevation model of difference (DoD) was calculated from these two datasets for the reservoir floor (0.72 km2). Based on the DoD, a net sediment accumulation of 1.16 × 106 m3 was calculated alongside a propagated volume error of 6.91 × 105 m3, resulting in a mean accumulation of 1.54 m. Conservative vertical error propagation results in an average level of detection (LoD) of 1.8 m. In contrast, the comparison of the DoD with 47 cores in the upper part of the reservoir showed a mean difference of −0.11 m, indicating a high, independently assessed accuracy of the DoD. Three depositional hotspots were identified in the reservoir. One is close to the Urft dam where very fine sediments are draped across the pre‐reservoir topography. Two areas are related to reservoir management. Sediment deposition in the Urft Reservoir has been comparably low in comparison to other regions globally, resulting in a 3.25% ± 1.93% loss of reservoir volume between 1905 and 2020. To analyse the effect of strong flooding events, a subset of the reservoir was analysed after an extreme event in July 2021, but accumulation did almost entirely not exceed the LoD.

Automated cleaning of tie point clouds following USGS guidelines in Agisoft Metashape professional (ver. 2.1.0)

“…We here do not report measures for e cam since the images were either not geotagged (BA18–008 and Lucia projects) or not used for camera model building due to large vertical offsets (Urft project; cf. [26] ). Furthermore, we did not adjust the tie point accuracy in Step 4 as recommended by Over et al [20] to more conveniently track the change of SEUW during the cleaning process (changing the point accuracy will affect SEUW ).…”

“…The second and third test projects represent landscape-scaled reconstruction scenarios investigated by Sanz-Ablanedo et al [13] and Stauch et al [26] . The authors kindly provided their raw data (i.e., imagery and ground control points, hereafter abbreviated GCPs).…”

“…The last project tested in this study (“Urft project”) builds on a comprehensive drone imagery and GCP dataset provided by Stauch et al [26] , who investigated spatiotemporal patterns of sediment deposition in the Urft Reservoir close to the village of Gemünd in Germany (50.6°N, 6.4°W) during the 20th and 21st century. The dataset consists raw data of their second drone survey (conducted in December 2021), including 1527 geotagged drone images and 171 GCPs (Fig.…”

“…S5). The GCPs were measured at 1–2 cm horizontal and 2–3 cm vertical localization accuracies, respectively [26] . Drone images were taken using a DJI Phantom 4 drone equipped with a FC6310S camera (1” CMOS sensor, f/2.8–11 24 mm in 35 mm film equivalent; for further details see their supplement data).…”

“…The GSD achieved was 2.49 cm (the final digital surface model can be obtained from [27] ). We obtained the original check point configuration as used by Stauch et al [26] , implying that half of the GCPs were used as control points. Image alignment (twelve flights assigned to individual camera groups but processed in a single chunk) resulted in a tie point cloud of 3897,864 points (1512 images aligned, without considering geotagging information).…”