Standing on the shoulder of a giant landslide:A six-year long InSAR look at a slow-moving hillslope in the western Karakoram

In this work, we investigate a slow-moving, large landslide (∼20 km2) in the Chitral district in Northern Pakistan, near several villages. The slow-moving landslide was reported more than four decades ago but has never been examined afterward. Interferometric Synthetic Aperture Radar (InSAR) analyses, using Sentinel-1 data that span a period of six years, allowed us to retrieve the spatio-temporal pattern of hillslope deformation. We combined both ascending and descending orbits to identify vertical and horizontal deformations. Our results showed that the crown is moving relatively fast in comparison to the nearby regions; 30 mm/year and 40 mm/year in downward and eastward directions, respectively. Also, step-like deformations observed over the crown reflect a deep-seated landslide. At the footslope, on the other hand, we captured relatively high deformations but in an upward direction; specifically 30 mm/year and 30 mm/year in upward and eastward directions, respectively. We have discussed the possible roles of meteorologic and anthropogenic factors causing hillslope deformation occurred during the six-year period under consideration. We observed a seasonal deformation patterns that might be mainly interpreted to be governed by the influence of snowmelt due to increasing temperatures during the start of spring. Overall, the same mechanism might be present in many other hillslopes across the whole Hindukush-Himalayan-Karakoram range, where seasonal snowmelt is an active agent. In this context, this research provides a case study shedding a light on the hillslope deformation mechanism at the western edge of the Himalayan range.</p

An integrated approach for mapping slow-moving hillslopes and characterizing their activity using InSAR, slope units and a novel 2-D deformation scheme

Strong earthquakes are not only able to change the earth's surface processes by triggering a large population of co-seismic landslides but also by influencing hillslope deformation rates in post-seismic periods. An increase in post-seismic hillslope deformation rates could also be linked to a change in post-seismic landslide hazard level and, thus, could be exploited to better assess post-seismic landslide risk in a given area. However, variations in hillslope deformations from pre- to post-seismic phases have rarely been examined for strong earthquakes. This paper examines pre- and post-seismic hillslope deformations, from 2014 to 2018, for an area (~ 2300 km2) affected by the 2016 Mw7.8 Kaikōura earthquake using time series Interferometric Synthetic Aperture Radar (InSAR) technique. To consistently analyse the entirety of the area from pre- to post-seismic phases, we aggregate InSAR-derived deformations for geomorphologically meaningful landscape partitions called Slope Units (SUs). We further examine the aggregated data through a 2-D hillslope deformation scheme, which we utilise as a method to systematically identify the variations in post-seismic hillslope deformation trends. In this context, we label newly activated, uninterruptedly deforming, and stabilized hillslopes in the post-seismic phase. We found 243 (4.76%) SUs out of 5104 SUs located in the study area to be active in the post-seismic phase. In addition to SUs, which may contain multiple landslides, we also analysed co-seismic landslides, in particular, showing active deformation in the post-seismic period. Results showed that 368 (4.69%) co-seismic landslides out of 7831 are actively deforming in the post-seismic phase. Overall, the areas affected by larger ground shaking show higher post-seismic deformations, which highlights the importance of the earthquake legacy effect as a factor controlling post-seismic hillslope deformations.</p

Prioritization of water erosion–prone sub-watersheds using three ensemble methods in Qareaghaj catchment, southern Iran

Water-induced erosion poses severe harm to the sustainable development of land and water resources that is essential for attaining agricultural sustainability in Qareaghaj catchment of Fars Province, Iran. This study evaluates the topo-hydrological, morphometric, climatic, and environmental characteristics of Qareaghaj catchment for prioritizing the sub-watersheds that are susceptible to erosion caused by water. We tested and compared a novel ensemble multi-criteria decision-making (MCDM) model, namely the weighted aggregated sum product assessment-analytical hierarchy process (WASPAS-AHP) with prevailing benchmark ensemble MCDM models including VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR)-AHP and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-AHP for ranking sub-watersheds and determining the most significant parameter that influences water erosion (WE) in Qareaghaj catchment. The outcome of weights using pairwise comparison matrix (PCM) of AHP reveals that normalized difference vegetation index (NDVI), mean annual rainfall (MAR), slope degree (SD), and slope length and steepness factor (LS) governs the WE in Qareaghaj catchment. The prioritization rankings of sub-watersheds obtained using the VIKOR-AHP, TOPSIS-AHP, and WASPAS-AHP models demonstrate that SW31, SW63, and SW94 had the highest priority rank with a score of 0.047, 0.69, and 0.477, respectively. The comparison of rankings from the models using Spearman’s correlation coefficient tests (SCCT) and Kendall’s tau correlation coefficient tests (KTCCT) revealed that WASPAS-AHP had a higher correlation with TOPSIS-AHP and VIKOR-AHP ensemble models. The outcome of MCDM models was validated based on the erosion potential method (EPM), which displayed that the VIKOR-AHP model was better for mapping the erosion susceptibility than TOPSIS-AHP and WASPAS-AHP models. Thus, the erosion susceptibility mapping based on the VIKOR-AHP ensemble model can be considered for developing new strategies and land use policies in order to control WE in Qareaghaj catchment

Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models

Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the—polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits

Monitoring and prediction of InSAR-derived post-seismic hillslope deformation rates

Strong earthquakes not only induce co-seismic mass wasting but also exacerbates the shear strength of hillslope materials and cause higher landslide susceptibility in the subsequent years following the earthquake. Previous studies have mainly investigated post-seismic landslide activity mainly by using landslide inventories. However, landslide inventories do not provide information on deformation given by ground shaking and limit our observations to only failed hillslopes. As a consequence, we lack comprehensive, quantitative analysis revealing how hillslopes behave in post- seismic periods. Satellite-based synthetic aperture radar interferometry (InSAR) could fill this gap and provide millimeter-scale measurements of ground surface displacements that can be used to monitor hillslope deformation. InSAR also provides a rich dataset to put shed light on spatiotemporal patterns of hillslope deformation, which are influenced by a combination of static and dynamic environmental characteristics specific to any landscape of interest. However, these influences are yet to be explored and exploited to train data-driven models and make predictions on the deformation one may expect in space or time. Here we use the Persistent Scatterer Interferometry technique to monitor pre- and post- seismic hillslope deformations for the area affected by the 2017 Mw 6.9 Nyingchi, China earthquake that occurred on the 2017 18th of November 2017 earthquake. We use Sentinel-1 satellite data acquired between 2016 and 2022 to examine post-seismic hillslope evolution. Using the same dataset, we also explore developing an interpretable multivariate model dedicated to InSAR-derived hillslope deformations Our results show that the average post-seismic hillslope deformation level in the study area is still higher than its pre-seismic counterpart approximately four and a half years after the earthquake. As for the multivariate model dedicated to InSAR-derived deformation data, the results we obtain are promising for we suitably retrieved the signal of environmental predictors, from which we then estimated the mean line of sight velocities for a number of hillslopes affected by seismic shaking