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In this paper we present new and review already existing landslide and earthquake data for a large part of the Tien Shan, Central Asia. For the same area, only partial databases for sub-regions have been presented previously. They were compiled and new data were added to fill the gaps between the databases. Major new inputs are products of the Central Asia Seismic Risk Initiative (CASRI): a tentative digital map of active faults (even with indication of characteristic or possible maximum magnitude) and the earthquake catalogue of Central Asia until 2009 that was now updated with USGS data (to May 2014). The new compiled landslide inventory contains existing records of 1600 previously mapped mass movements and more than 1800 new landslide data. Considering presently available seismo-tectonic and landslide data, a target region of 1200 km (E–W) by 600 km (N–S) was defined for the production of more or less continuous geohazards information. This target region includes the entire Kyrgyz Tien Shan, the South-Western Tien Shan in Tajikistan, the Fergana Basin (Kyrgyzstan, Tajikistan and Uzbekistan) as well as the Western part in Uzbekistan, the North-Easternmost part in Kazakhstan and a small part of the Eastern Chinese Tien Shan (for the zones outside Kyrgyzstan and Tajikistan, only limited information was available and compiled). On the basis of the new landslide inventory and the updated earthquake catalogue, the link between landslide and earthquake activity is analysed. First, size–frequency relationships are studied for both types of geohazards, in terms of Gutenberg–Richter Law for the earthquakes and in terms of probability density function for the landslides. For several regions and major earthquake events, case histories are presented to outline further the close connection between earthquake and landslide hazards in the Tien Shan. From this study, we concluded first that a major hazard component is still now insufficiently known for both types of geohazards – and even totally unknown for most giant landslides: the time information. Thus, dating rockslides and any large mass movement is a crucial factor in order to better constrain related hazards. Second, we highlight the role of coupled hazards in the Tien Shan – inside the mountain range the largest disasters were caused by earthquake-triggered landslides and massive earth flows. Therefore, it is of prime importance for these mountain regions to complement single hazard assessment by coupled hazard scenarios. On the basis of these scenarios, risk evaluations may then be completed considering direct impacts on artificial dams, hydropower schemes, mining and road infrastructure as well as remote effects from possible dam breaches and mobilisation of mining and nuclear waste storages. Basic hazard and risk components are partly analysed in this paper and partly in a companion paper ‘Tien Shan geohazards database: Landslide susceptibility and impacts’. [ABSTRACT FROM AUTHOR]

Debris flow is a major geohazard in mountainous regions and poses a significant threat to life and property. The damage caused by debris flows have increased with the expansion of human settlements and activity into the mountainous regions of China. In regards to risks from debris flows, previously unrecognized low-frequency debris flow catchments constitute an especially significant threat. According to our investigation, only about 500 catchments have debris flow records in >2000 catchments of Bailong River basin. The main purpose of this paper is to introduce a new methodology using Artificial Intelligence (AI) that can simultaneously input parameters related to geomorphological conditions and material conditions to better distinguish low-frequency debris flow catchments (LFD s) from medium-high frequency debris flow catchments (MHFD s). A total of 449 prototypical debris flow catchments, 15 parameters, and 9 commonly used learning machines were used to build identification models. Debris flow catchments are divided into 4 cases (LO1-LO4) based on different sample ratios of LFD s and MHFD s, which are input into each classifier one by one. Based on model evaluation, the CHAID model in the case LO2 performs best, which only uses five parameters (formation lithology index, land use index, vegetation coverage index, drainage density and landslide density index) to predict LFD s. The results indicate that LFD s are mainly distributed in areas with less landslide distribution and better vegetation coverage compared with MHFD s. However, the distribution of LFD s is concentrated on FLI (formation lithology index) =4, which is the weak lithology area. The tree classifier seems to be better at classifying fluvial processes. The model developed in this paper can help us quickly find LFD s in similar areas, and help to assess the risk of debris flows. • A simple decision tree model was built to identify low-frequency debris flows. • Geomorphological and material parameters were used simultaneously for modeling. • Materials seem to be the main influencing factor of low-frequency debris flows. • The tree classifier seems to be better at classifying fluvial processes. [ABSTRACT FROM AUTHOR]

This paper is the second part of a new geohazards analysis applied to a large part of the Tien Shan, Central Asia, focused on landslide susceptibility computations that are based on recently compiled geographic, geological and geomorphological data. The core data are a digital elevation model, an updated earthquake catalogue, an active fault map as well as a new landslide inventory. The most recently added digital data are a new simplified geological map, an annual precipitation map, as well as river and road network maps that were produced for the Kyrgyz and Tajik parts of the Tien Shan. On the basis of these records we determine landslide densities with respect to morphological (M), geological (G), river distance (R), precipitation (P), earthquake (E) and fault (F) distance factors. Correlations were also established between scarp locations and the slope angle, distance to rivers, curvature. These correlations show that scarps tend to be located on steeper slopes, farther from rivers and on more convex terrain than the entire landslides. On the basis of the landslide density values computed for each class of the aforementioned factors, two landslide susceptibility maps are created according to the Landslide Factor analysis: the first one considers correlations between the landslide occurrences and the first four factors (MGRP); the second one is based on the first map (MGRP) combined with the seismo-tectonic influence (+ E + F) on landslide distributions. From the comparison of these two maps with actual landslide distributions we infer that the distances to rivers as well as to faults and past earthquakes most strongly constrain the susceptibility of slopes to landslides. We highlight several zones where the landslide susceptibilities computed for the MGRP + E + F factors fit better the observed concentration of landslides than those computed for the MGRP factors alone. For a few zones, both maps produce high landslide susceptibilities that do not well reflect the observed low sub-regional landslide activity; for some cases, we consider that some influencing factors must not have been well taken into consideration, for others we show that we simply had missed landslide detections. At the scale of the mountain range, the computed landslide susceptibility maps fit the observed landslide distributions relatively well, but these maps only represent the spatial component of landslide hazards. Temporal aspects are not considered by this analysis. [ABSTRACT FROM AUTHOR]