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The GLES Open Science Challenge 2021 was a pilot project aimed at demonstrating that registered reports are an appropriate and beneficial publication format in quantitative political science that helps to increase transparency and replicability in the research process and thus yields substantial and relevant contributions to our discipline. The project resulted in the publication of this special issue, which includes seven registered reports based on data from the German Longitudinal Election Study (GLES) collected in the context of the 2021 German federal election. This concluding article of the special issue brings together the perspectives of the participating authors, reviewers, organizers, and editors in order to take stock of the different experiences gained and lessons learned in the course of the project. We are confident that future projects of a similar nature in political science, as well as authors, reviewers, and editors of registered reports, will benefit from these reflections. Die GLES Open Science Challenge 2021 ist ein Pilotprojekt, das zeigt, dass Registered Reports ein geeignetes und gewinnbringendes Publikationsformat in der quantitativen Politikwissenschaft sind, die dazu beitragen können, die Transparenz und Replizierbarkeit im Forschungsprozess zu erhöhen und somit substanzielle und relevante Beiträge für unsere Disziplin zu liefern. Das Ergebnis ist die Veröffentlichung dieses Sonderheftes mit sieben Registered Reports, die auf Daten der German Longitudinal Election Study (GLES) basieren, die im Rahmen der Bundestagswahl 2021 erhoben wurden. Dieser abschließende Artikel des Sonderheftes bringt die Perspektiven von Autor*innen, Gutachter*innen, Organisator*innen und Herausgeber*innen zusammen, um eine Bilanz der verschiedenen Erfahrungen und Lehren zu ziehen, die im Laufe dieses Projektes gewonnen wurden.

Shallow landslides pose a risk to infrastructure and residential areas. Therefore, we developed SlideforMAP, a probabilistic model that allows for a regional assessment of shallow-landslide probability while considering the effect of different scenarios of forest cover, forest management and rainfall intensity. SlideforMAP uses a probabilistic approach by distributing hypothetical landslides to uniformly randomized coordinates in a 2D space. The surface areas for these hypothetical landslides are derived from a distribution function calibrated on observed events. For each generated landslide, SlideforMAP calculates a factor of safety using the limit equilibrium approach. Relevant soil parameters are assigned to the generated landslides from log-normal distributions based on mean and standard deviation values representative of the study area. The computation of the degree of soil saturation is implemented using a stationary flow approach and the topographic wetness index. The root reinforcement is computed by root proximity and root strength derived from single-tree-detection data. The ratio of unstable landslides to the number of generated landslides, per raster cell, is calculated and used as an index for landslide probability. We performed a calibration of SlideforMAP for three test areas in Switzerland with a reliable landslide inventory by randomly generating 1000 combinations of model parameters and then maximizing the area under the curve (AUC) of the receiver operation curve. The test areas are located in mountainous areas ranging from 0.5–7.5 km2 with mean slope gradients from 18–28∘. The density of inventoried historical landslides varies from 5–59 slides km−2. AUC values between 0.64 and 0.93 with the implementation of single-tree detection indicated a good model performance. A qualitative sensitivity analysis indicated that the most relevant parameters for accurate modelling of shallow-landslide probability are the soil thickness, soil cohesion and the precipitation intensity / transmissivity ratio. Furthermore, we show that the inclusion of single-tree detection improves overall model performance compared to assumptions of uniform vegetation. In conclusion, our study shows that the approach used in SlideforMAP can reproduce observed shallow-landslide occurrence at a catchment scale.

Shallow landslides pose a risk to infrastructure and residential areas. Therefore, we developed SlideforMAP, a probabilistic model that allows for a regional assessment of shallow-landslide probability while considering the effect of different scenarios of forest cover, forest management and rainfall intensity. SlideforMAP uses a probabilistic approach by distributing hypothetical landslides to uniformly randomized coordinates in a 2D space. The surface areas for these hypothetical landslides are derived from a distribution function calibrated on observed events. For each generated landslide, SlideforMAP calculates a factor of safety using the limit equilibrium approach. Relevant soil parameters are assigned to the generated landslides from log-normal distributions based on mean and standard deviation values representative of the study area. The computation of the degree of soil saturation is implemented using a stationary flow approach and the topographic wetness index. The root reinforcement is computed by root proximity and root strength derived from single-tree-detection data. The ratio of unstable landslides to the number of generated landslides, per raster cell, is calculated and used as an index for landslide probability. We performed a calibration of SlideforMAP for three test areas in Switzerland with a reliable landslide inventory by randomly generating 1000 combinations of model parameters and then maximizing the area under the curve (AUC) of the receiver operation curve. The test areas are located in mountainous areas ranging from 0.5–7.5 km2 with mean slope gradients from 18–28∘. The density of inventoried historical landslides varies from 5–59 slides km−2. AUC values between 0.64 and 0.93 with the implementation of single-tree detection indicated a good model performance. A qualitative sensitivity analysis indicated that the most relevant parameters for accurate modelling of shallow-landslide probability are the soil thickness, soil cohesion and the precipitation intensity transmissivity ratio. Furthermore, we show that the inclusion of single-tree detection improves overall model performance compared to assumptions of uniform vegetation. In conclusion, our study shows that the approach used in SlideforMAP can reproduce observed shallow-landslide occurrence at a catchment scale.

Gully formation has been associated to groundwater seepage in unconsolidated sand- to gravel-sized sediments. Our understanding of gully evolution by groundwater seepage mostly relies on experiments and numerical simulations, and these rarely take into consideration contrasts in lithology and permeability. In addition, process-based observations and detailed instrumental analyses are rare. As a result, we have a poor understanding of the temporal scale of gully formation by groundwater seepage and the influence of geological heterogeneity on their formation. This is particularly the case for coastal gullies, where the role of groundwater in their formation and evolution has rarely been assessed. We address these knowledge gaps along the Canterbury coast of the South Island (New Zealand) by integrating field observations, luminescence dating, multi-temporal unoccupied aerial vehicle and satellite data, time domain electromagnetic data and slope stability modelling. We show that gully formation is a key process shaping the sandy gravel cliffs of the Canterbury coastline. It is an episodic process associated to groundwater flow that occurs once every 227 d on average, when rainfall intensities exceed 40mmd􀀀1. The majority of the gullies in a study area southeast (SE) of Ashburton have undergone erosion, predominantly by elongation, during the last 11 years, with the most recent episode occurring 3 years ago. Gullies longer than 200m are relict features formed by higher groundwater flow and surface erosion >2 ka ago. Gullies can form at rates of up to 30md􀀀1 via two processes, namely the formation of alcoves and tunnels by groundwater seepage, followed by retrogressive slope failure due to undermining and a decrease in shear strength driven by excess pore pressure development. The location of gullies is determined by the occurrence of hydraulically conductive zones, such as relict braided river channels and possibly tunnels, and of sand lenses exposed across sandy gravel cliffs.We also show that the gully planform shape is generally geometrically similar at consecutive stages of evolution. These outcomes will facilitate the reconstruction and prediction of a prevalent erosive process and overlooked geohazard along the Canterbury coastline., peer-reviewed

Modelling paleo-glacier networks in mountain ranges on the millennial timescales requires ice flow approximations. Hybrid models calculating ice flow by combining vertical shearing (shallow ice approximation) and longitudinal stretching (shallow shelf approximation) have been applied to model paleo-glacier networks on steep terrain, yet their validity has not yet been assessed quantitatively. Moreover, hybrid models consistently yield higher ice thicknesses than Last Glacial Maximum geomorphological reconstructions in the European Alps. Here, we compare results based on the hybrid Parallel Ice Sheet Model (PISM) and the Stokes model Elmer/Ice on the Rhine Glacier, a catchment of the former European Alpine Icefield. For PISM, we also test two magnitudes of flux limitation in a scheme that reduces shearing velocities. We find that the flux limitation typically used in PISM yields significantly reduced shearing speeds and increases ice thicknesses by up to 500 m, partly explaining previous overestimations. However, reducing the ice flux limitation allows the hybrid model to minimize this mismatch and captures sliding speeds, ice thicknesses, ice extent and basal temperatures in close agreement with those obtained with the Stokes model., Journal of Glaciology, 65 (254), ISSN:0022-1430, ISSN:1727-5652