Landslides alter the morphology and sediment dynamics of mountainous terrain. Here, we evaluate how the spatial and temporal variability of landslide triggering events adjust this footprint. We use the HyLands landscape evolution model that explicitly simulates the occurrence of landslide events as well as fluvial incision and sediment dynamics. Both existing landscapes as well as synthetically produced landscapes that evolve over geological timescales are considered. This enables us to identify the required magnitude and frequency of extreme events for them to be recorded in landscape morphology. Moreover, we compare the relative contribution of long-term tectonic processes versus spatially clustered extreme events in shaping mountainous terrain. Finally, we evaluate if and how the temporal occurrence of landslide-triggering events alter morphology. Here we compare two scenarios: a first one evaluates how a landslide-prone landscape responds to events that are uniformly spread through time, a second one tests how such a landscape responds to regionally synchronous events. This contribution aims to clarify the distinctive role of landsliding in shaping mountainous terrain, which will in turn contribute to understanding how landslide prone regions respond to spatial and temporal changes in extreme events.

How to cite: Campforts, B., Duvall, A., Shobe, C., Tucker, G., and Overeem, I.: Are spatial and temporal patterns of landslide triggering events reflected in topography and sediment dynamics?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3684, https://doi.org/10.5194/egusphere-egu23-3684, 2023.

Timely and systematic collection of landslide information after a triggering event is essential for the definition of landslide trends in response to climate change. On September 15, 2022 Marche and Umbria regions, in Central Italy, were struck by an anomalous rainfall event that showed characteristics of a persistent convective system. An extraordinary cumulated rainfall of 419 mm was recorded by a rain gauge in the area in only 9 hours. It was carried out a systematic reconnaissance field survey to prepare an event landslide inventory map in an area of 550 km² that includes a large neighbourhood of the area that recorded the highest rainfall intensity. The rainfall triggered 1687 landslides in the area affected by the peak rainfall intensity. Landslide area spans from a few tens of square meters to 105 m², with a median value of 87 m². We describe the characteristics of the landslides identified during a field survey conducted immediately after the event. Most of the mass movements are shallow, many are rapid (i.e., debris flows, earth flows) and widely affecting the road network. Many national and local roads were interrupted, mostly by earth and rock slides; national and local railways were interrupted at several points; extensive damage was registered to structures and infrastructures. Furthermore, field evidence revealed that a vast proportion of landslides occurred in the immediate vicinity of roads, mostly affecting road embankments and that a large number of landslides initiated within natural and semi-natural areas and hit the road network and, locally, affected houses and activities. Field surveys also revealed diffuse residual risk conditions, being a large proportion of landslides located in the immediate vicinity of infrastructures. Besides reporting the spatial distribution of landslides triggered by an extreme rainfall event, the data collected on landslides can be used to make comparisons with the distribution of landslides in the past, validation of landslide susceptibility models, definition of the general interaction between landslides and structures/infrastructures.

How to cite: Fiorucci, F., Donnini, M., Santangelo, M., Gariano, S., Bucci, F., Cardinali, M., Ardizzone, F., Marchesini, I., Melillo, M., Bornaetxea, T., Salvati, P., Alvioli, M., Peruccacci, S., Brunetti, M. T., Esposito, G., Althuwaynee, O., Yazdani, M., Cinzia, B., and Grita, S.: Characteristics of the landslides triggered by the extraordinary rainfall event occurred in Central Italy on September 15, 2022, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7184, https://doi.org/10.5194/egusphere-egu23-7184, 2023.

Rivers within sub-tropical and temperate regions serve several purposes, including agricultural irrigation, hydro-power generation, and drivers of civilization. The impacts of six large-scale oscillation indices on river flow at three stations within Humber catchments (Ontario and Labrador) between 1970 and 2020 were investigated using sensitivity and wavelet analyses. Results showed that the discharge at East Humber River near Pine has the highest statistically significant sensitivity of 0.304 and 0.394 units per month to the Dipole Mode Index (DMI) and Tropical North Atlantic (TNA), respectively. Monthly significance analysis also showed the varied influence of large-scale oscillations on the river flow at these locations. Wavelet analysis reveals significant active multidecadal oscillations for the North Atlantic Oscillation (NAO) at East Humber River near Pine with high spectral power. This study has identified the contributions of different climatic indices to river flow within the Humber catchments. The results will be helpful in environmental planning and effective water management within the basin.

How to cite: Olusola, A., Ogunjo, S., and Olusegun, C.: Imprints of large-scale oscillations on river flow in selected Canadian river catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1104, https://doi.org/10.5194/egusphere-egu23-1104, 2023.

The geometry of stream networks varies systematically with climate [1,2]. In humid regions diffusive processes seem to dominate the branching geometry of stream networks, resulting in wider branching angles close to 72 degrees, which is the theoretical angle for growth in a diffusive field [1,3]. In arid climates, on the other hand, channel networks display much narrower angles [1,2].

Here we show that the narrower angles in arid regions can be related to the higher frequency of extreme runoff events, which are more common in arid landscapes than in humid ones [4]. Erosion due to overland flow leads to incision which is more focused in the direction of regional topographic gradients and thus resulting in narrower branching angles as the influence of diffusive processes becomes weaker and weaker. Our analysis is based on flow frequency distributions derived from USGS gauging stations across the United States [4] and branching angles obtained from the USGS medium resolution National Hydrographic Dataset [1]. Our measurements show, that the tails of the flow frequency distributions become systematically heavier with aridity in the same way as branching angles become narrower.

This result suggests that the relative impact of diffusive network growth systematically decreases with increasing aridity as the landscape's Peclet number changes across a landscape with varying climate.

References:

[1] H. J. Seybold et al., Climate's watermark in the geometry of stream networks, GRL (2017)

[2]  A. Getraer & A. C. Maloof, Climate-Driven Variability in Runoff Erosion Encoded in Stream Network Geometry, GRL (2021)

[3] O. Devauchelle et al., Ramification of stream networks, PNAS (2012)

[4] M. W. Rossi et al., Precipitation and evapotranspiration controls on daily runoff variability in the contiguous United States and Puerto Rico, JGR (2016)

How to cite: Seybold, H., Li, M., and Kirchner, J.: Runoff controls on stream network branching, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8219, https://doi.org/10.5194/egusphere-egu23-8219, 2023.

The magnitude and frequency of extreme precipitation events are expected to increase in central Peruvian Andes for this century, which will pose a significant challenge on water resources management and flood risk mitigation. The present study focuses on assessing the possible flood hazard under two different climate change scenarios (SSP 4.5 and SSP 8.5) in the lower part of the Lurin River watershed (~ 1642.5 Km²) by using a distributed physically-based hydrologic and erosional model (e.g. TREX) and a 2-D depth-averaged hydraulic and sediment transport model (e.g. BASEMENT-2D). The models were calibrated using hydrometeorological data corresponding to the extreme flood events of 2015 and 2017 and satellite-based and UAV-derived inundation maps. Future climate scenarios are going to be constructed from bias-corrected outputs of CMIP6 global climate models, while the rainfall temporal patterns for different return periods will be obtained from observed precipitation events corresponding to extreme flood events of El Niño 2017. Results are expected to provide important data needed to make policy changes to mitigate the negative impacts of climate change in the Lurin River basin. 

How to cite: Asencios, H., Lavado, W., Sabino, E., Qquenta, J., and Felipe, O.: Climate change effects on flooding at Lurin river basin, Peru, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9028, https://doi.org/10.5194/egusphere-egu23-9028, 2023.

The recent catastrophic flooding across the world’s driest inhabited continent – Australia – has highlighted an urgent need to understand the climatic (atmospheric) and hydrological (land surface) mechanisms comprising hydroclimate. Records of past hydrologic change may help in this endeavor by informing us about different hydroclimate states and their manifestation on the land surface. By virtue of its antiquity, aridity and relative paucity of available sediment, however, the Australian continent preserves few records of long-term hydroclimate. As a result, we know little about long-term water availability and the drivers of surface hydrology and climate circulation, particularly for the dry inland regions where water resources and sensitive land surfaces need to be carefully managed.

One of the few areas in dryland Australia which preserves semi-continuous deposition of hydrologic change is the Willandra Lakes system. The Willandra Lakes are located on the semi-arid desert margin of southeastern Australia, yet its headwaters lie in the temperate eastern highlands. Long-term lake filling and drying is consequently driven by rainfall in the headwaters and hydrologic connectivity both across the catchment and between the lakes. These environmental changes – both long and short in duration – are recorded in the sediments of the downwind transverse dunes (lunettes). In this study we investigate long-term hydrologic connectivity across the catchment and between the lakes. Our approach uses a novel integration of both classical lake-level reconstruction based on lunette sedimentology, stratigraphy and luminescence geochronology, with hydrologic modelling of key event time slices over the last 60 ky, fed into a palaeoclimate model. We characterize the land-surface response to various hydroclimate states, so improving our understanding of dryland atmosphere-hydrosphere interactions.

How to cite: Fitzsimmons, K., Fischer, M., Nowatzki, M., Lauer, T., Mishra, K., and Stern, N.: Long-term hydrologic connectivity on the Australian dryland margins: evidence from the Willandra Lakes World Heritage Area over the last 60 ky, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15791, https://doi.org/10.5194/egusphere-egu23-15791, 2023.

The climate history of wind-blown dune fields is commonly determined by dune morphology, stratigraphy and age. These properties in the Rub’ al Khali have been used to interpret climate history relevant to human dispersal and monsoon variability during the glacial cycles. An underlying assumption of some of these interpretations is that the time it takes dunes to respond to a change in climate is shorter than the time over which climate changes. Here we show that this assumption does not always hold. We do this by comparing the bedform reconstitution time T_r (i.e., the time taken for sand to be completely reworked within a dune) to a climate persistence time T_c (i.e., how long dune-relevant wind properties stay the same). T_r is found using modern wind reanalysis and topography data, and T_c using paleoclimate simulations. Where T_r>T_c, climate varies too fast to be recorded in dune properties. In some areas of the Rub’ al Khali, T_r is longer than the time between glacial cycles, so dune properties and modern climate are decoupled. We extend this case study to a general theory to assess if wind-blown dunes properties can be used to interpret past climate.

How to cite: Gunn, A., Ewing, R., and Brown, J.: Decoupling of Rub’ al Khali Quaternary dune record and paleoclimate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10832, https://doi.org/10.5194/egusphere-egu23-10832, 2023.

The Đurđevac Sands constitute a wide area of extraordinary small-scale dune relief in the Podravina (northeastern Croatia), along the central part of the southern Drava river valley. They are thought to have been formed by reworking of fluvial material due to strong northern winds. Their significance is evident from the geometry of the dunes (shape, orientation, thickness), and the presence of intra- and post-formational alteration (pedogenesis). In addition, the elevated heavy mineral content puts the sands in the position of potential ore deposit.

The objective of this study is to explore this aeolian archive in an attempt to extract relevant palaeo-environmental information and to compare it with similar landscapes across Europe. The lithology (grain-size) and intra-formational alteration (palaeosoils) as well as geochemical signatures are investigated from outcrops in an abandoned sand pit to define phases of sand movement and landscape stability. Radiocarbon dating of charcoal, optically stimulated luminescence (OSL) dating of quartz, and historical archives are used to develop a geochronological framework. The heavy and light mineral fractions of the sands are used to determine their composition, provenance and detailed sedimentological context at the time of deposition. A digital elevation model of the region is used to gain insight into the geometry of the dunes, while geo-electric soundings and mechanical coring are applied to investigate the vertical and lateral variations in sand lithology and thickness, as well as intraformational soils.

At first sight, the dune landscape seems to have a chaotic nature, showing an irregular alignment of smaller parabolic, linear and domal shaped dunes. Although, larger structures may also be classified as complex long-walled transgressive dunes or compound en-echelon parabolic dunes. The thickness of the dune sand can clearly be traced on geo-electrical profiles, where the dry dune sand appears to generate a different signal than the underlying water-saturated fluvial material. Furthermore, the results show that phases of sand movement occurred before and after the Bølling-Allerød (B-A) interstadial, as well as during the early Holocene and up to the 19^th century. Phases of stability are witnessed by the presence of slightly altered parent material (presence of organic carbon, slightly finer grain size, and decalcified) and are dated to the B-A interstadial, and several episodes in the Holocene. The heavy mineral content is dominated by garnet, while muscovite is strikingly more present in the Holocene sediments. This may be due to either a change in source material (new Holocene Drava river sediment) and/or changing aeolian dynamics. Overall, these new findings obtained from the Đurđevac Sands area correlate rather well with other regions in the Pannonian Basin as well as the North European Plain, especially in terms of the timing of events.

How to cite: Beerten, K., Hećej, N., Pandurov, M., Kordić, B., Stejić, P., Gajić, R., Šorša, A., and Galović, L.: Lithology, mineralogy, geochemistry and chronostratigraphy of heavy-mineral bearing dune sands in the Podravina, northeastern Croatia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14873, https://doi.org/10.5194/egusphere-egu23-14873, 2023.

Dune field landscape patterns serve as an important signs of aeolian processes, such as wind conditions, sediment supply, and so on. A novel framework was proposed and evaluated for automatic dune detection and classification with remotely sensed images. The framework consists of two main steps: (1) The first step is to detect sand dunes from remote sensing images by SandUnet, which is firstly proposed in this paper. SandUnet, a Convolutional Neural Network (CNN), has a similar network structure with Attention U-net but modifies its attention gate module. In SandUnet, the input signals' information is not compressed as in the Attention U-net, therefore, the nuanced color and texture information of dunes are preserved. This paper demonstrated that SandUnet has better detection accuracy than other popular CNNs such as FCN, U-net, U-net++, and Attention U-net. (2) The second step is to compute the image similarity scores through MobileNet between each dune detection result image and the representative images of 6 different types of dunes. Then, each dune detection result image is classified into a dune type automatically. This paper applied the proposed framework to Taklimkan Desert in China. The average classification accuracy rate is around 80%, which proves the usefulness of this framework in automatic, no-cost, and accurate sand dune classification.

How to cite: Tang, Y. and Wang, Z.: Automatic Sand Dune Detection and Classification Framework Using Remote Sensing Images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2232, https://doi.org/10.5194/egusphere-egu23-2232, 2023.

Dune mapping is a traditional task for aeolian geomorphologists and has made use of satellite imagery since the 1970s (Breed & Grow, 1979). Labour-intensive manual mapping approaches are increasingly substituted by (semi-)automated ones that apply progressive Machine Learning algorithms (Zheng et al., 2022). Advanced techniques such as neural networks enable the creation of powerful computational models to automatically map dune fields (Shumack et al., 2020; Rubanenko et al., 2021). Globally available satellite imagery datasets and the progression of computational infrastructure and power facilitate the operation of increasingly elaborate models and their application to spatially extensive regions. A lack of training and validation datasets for such dune mapping models and the subjective and time-consuming nature of their creation, however, remains a challenge.

We present a framework that uses Deep Learning and different types of satellite imagery to map dune crests. It comprises automated modules to (1) retrieve and pre-process training and prediction data, (2) train a neural network (U-Net; Ronneberger et al., 2015), and (3) identify dune crests in unlabelled target areas applying the trained model. The framework has shown good performance mapping linear dunefields in the Kalahari Desert using a small training and validation dataset (130 labelled 960mx960m tiles).

Addressing the lack of global training data, we use our model to explore the possibilities of transfer learning and the universality of regional training datasets. In our main case study, we assess whether a model trained on satellite data of linear dunes in the Kalahari can be applied to map linear dunes in regions containing morphologically similar dunes in the Australian deserts.

Breed, C. S., & Grow, T. (1979). Morphology and distribution of dunes in sand seas observed by remote sensing. A study of global sand seas, 1052, 253-302.

Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.

Rubanenko, L., Pérez-López, S., Schull, J., & Lapôtre, M. G. (2021). Automatic Detection and Segmentation of Barchan Dunes on Mars and Earth Using a Convolutional Neural Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 9364-9371.

Shumack, S., Hesse, P., & Farebrother, W. (2020). Deep learning for dune pattern mapping with the AW3D30 global surface model. Earth Surface Processes and Landforms, 45(11), 2417-2431.

Zheng, Z., Du, S., Taubenböck, H., & Zhang, X. (2022). Remote sensing techniques in the investigation of aeolian sand dunes: A review of recent advances. Remote Sensing of Environment, 271, 112913.

How to cite: Nowatzki, M., Thomas, D., and Bailey, R.: Application of a deep learning framework to explore transfer learning for dune mapping across regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16209, https://doi.org/10.5194/egusphere-egu23-16209, 2023.