Bhutan is highly prone to landslides, particularly during the monsoon season. Several landslides often occur along the Phuentsholing-Thimphu highway, a very important infrastructure for the country. Worldwide, empirical rainfall thresholds represent a widely used tool to predict the occurrence of rainfall-induced landslides. Nevertheless, no thresholds are currently designed and proposed for any region in Bhutan. In this work, we define empirical cumulated event rainfall-rainfall duration thresholds for the possible initiation of landslides using information on 269 landslides that occurred between 1998 and 2015 along the 90-km highway stretch between the towns of Phuentsholing and Chukha, in southwestern Bhutan, and daily rainfall measurements obtained from three rain gauges. For this purpose, we apply a consolidated frequentist method and use an automatic tool that identifies the rainfall conditions responsible for the failures and calculates thresholds at different exceedance probabilities and the uncertainties associated with them. Analyzing rainfall and landslide data, we exclude from the analysis all the landslides for which the rainfall trigger is not certain, so we reduce the number of landslides from 269 to 43. The calculated thresholds are useful to identify the triggering conditions of rainfall-induced landslides and to predict the occurrence of the failures in the study area, which is, to date, poorly studied. These rainfall thresholds might be implemented in an early warning system, in order to reduce the risk posed by these phenomena to the population living and traveling along the investigated road stretch.

Rapid assessment of spatial distribution of earthquake-induced landslides could provide valuable information in the emergency response phase. Previous studies proposed global analyses with the aim of predicting earthquake-induced landslide distributions in near real-time. However, in all those studies, mapping units are constituted by pixels, which do not reflect homogeneously distributed physical property for a given terrain unit and whose size do not match the resolution of existing thematic data at global scale. Moreover, none of the existing analyses considers sampling balance between different inventories or categorizing the inventories to construct a training set with higher statistical representativeness. We develop an improved global statistical method to address these drawbacks. We use slope units, which are terrain partitions attributed to similar hydrological and geomorphological conditions and to processes that shape natural landscapes. A set of 25 earthquake-induced landslide-events are selected and categorized based on the similarity between causal factors to determine the most relevant training set to make a prediction for a given landslide-event. As a result, we develop a specific model for each category. We sample an equal number of landslide points from each inventory to overcome the dominance of some inventories with large landslide population. We use seven independent thematic variables for both categorizing the inventories and modeling, based on logistic regression. The results show that categorizing landslide-events introduces a remarkable improvement in the modeling performance of many events. The categorization of existing inventories can be applied within any statistical, global approach to earthquake-induced landslide events. The proposed categorization approach and the classification performance can be further improved with the acquisition of new inventory maps.

This paper presents a new methodology to systematically quantify the shape of landslides by their ellipticity (eL) and length-to-width ratio (?L), along with variability in these measures over different geomorphic settings. Two large substantially complete triggered-event landslide inventories (source area and runout) are used_ (i) 11 111 earthquake-triggered landslides (1994 Northridge, USA); and (ii) 9594 rainfall-triggered landslides (1998, Guatemala). Three methods are trialled to abstract landslide shapes to ellipses. The best method fits a convex hull to each landslide shape, approximates an ellipse with the equivalent convex hull area and perimeter, and scales this ellipse to match the original landslide area. An ellipticity index (eL) is used based on the intersection of the original landslide shape and the elliptical approximation. We consider an ellipse a reasonable approximation of landslide shape if eL >= 0.5 (>80% of landslides in each of the two landslide inventories). Landslides with eL < 0.5 reflect processes such as coalescence. We calculate for each landslide an ellipse length-to-width ratio (?E), finding 1.2 <= ?E <= 15.1. The statistical distributions of ?E are examined for ten categories of landslide area (AL). An inverse-Gamma probability density function is found to be a good statistical model for landslide ?E, with model parameters dependent on landslide area category. As landslide area AL increases, ?E tends to decrease for the Northridge (earthquake-triggered) inventory and increase for Guatemala (rainfall-triggered). In three additional (rainfall-triggered) landslide inventories, ?E trends are similar to Guatemala. Our findings show that (i) an ellipse is a reasonable model for >80% of landslide shapes across different geomorphic settings, (ii) those landslides significantly deviating from an ellipse can be related to landscape processes, (iii) the length-to-width ratios of ellipses are non-normally distributed, with implications for modelling landslide hazard and risk. Supporting information includes code so that the new methodology may be applied more widely.

In recent decades, the Entella River basin, in the Liguria Apennines, northern Italy, was hit by numerous intense rainfall events that triggered shallow landslides and earth flows, causing casualties and extensive damage. We analyzed landslide information obtained from different sources and rainfall data recorded in the period 2002-2016 by rain gauges scattered throughout the catchment, to identify the event rainfall duration, D (in h), and rainfall intensity, I (in mmh(-1)), that presumably caused the landslide events. Rainfall-induced landslides affected the whole catchment area, but were most frequent and abundant in the central part, where the three most severe events hit on 23-24 November 2002, 21-22 October 2013 and 10-11 November 2014. Examining the timing and location of the slope failures, we found that the rainfall-induced landslides occurred primarily at the same time or within 6 h from the maximum peak rainfall intensity, and at or near the geographical location where the rainfall intensity was largest. Failures involved mainly forested and natural surfaces, and secondarily cultivated and terraced slopes, with different levels of maintenance. Man-made structures frequently characterize the landslide source areas. Adopting a frequentist approach, we define the event rainfall intensity-event duration (ID) threshold for the possible initiation of shallow landslides and hyper-concentrated flows in the Entella River basin. The threshold is lower than most of the curves proposed in the literature for similar mountain catchments, local areas and single regions in Italy. The result suggests a high susceptibility to rainfall-induced shallow landslides of the Entella catchment due to its high-relief topography, geological and geomorphological settings, meteorological and rainfall conditions, and human interference. Analysis of the antecedent rainfall conditions for different periods, from 3 to 15 days, revealed that the antecedent rainfall did not play a significant role in the initiation of landslides in the Entella catchment. We expect that our findings will be useful in regional to local landslides early warning systems, and for land planning aimed at reducing landslide risk in the study area.

Subsidence over the last 0.78 myr of a basin in central Italy bounded by a normal fault caused the deactivation and uplift of an Early-Middle Pleistocene alluvial fan at the fault footwall. Uplift of the fan occurred with a basin-bounding fault slip-rate of the order of 0.2 mm a-1. Subsidence resulted in the reorganization of the river network due to a fall in base level, which triggered headward erosion, stream piracy effects and drainage inversion. The mapped river inversions and catchment piracy were related to the distribution of a quantile regression of 134 alluvial fans v. basin areas. Despite the fact that the two parameters werewell fitted by a power law relationship, all the fans corresponding to the captured rivers lay above the regression line (in the fan area field), whereas those corresponding to the capturing rivers were below the regression line (in the basin area field). We propose a general model of alluvial fan growth in active extensional settings that helps to interpret this scatter of fan v. basin area distribution and to identify the most active fault segments. Such an approach can better constrain fault activity in a time window that bridges long-term deformation and the present day deformation inferred from geodesy and/or seismology, increasing our understanding of the steadiness/unsteadiness behaviour of faults.

To promote the advancement of novel observation techniques that may lead to new sources of information to help better understand the hydrological cycle, the International Association of Hydrological Sciences (IAHS) established the Measurements and Observations in the XXI century (MOXXI) Working Group in July 2013. The group comprises a growing community of tech-enthusiastic hydrologists that design and develop their own sensing systems, adopt a multi-disciplinary perspective in tackling complex observations, often use low-cost equipment intended for other applications to build innovative sensors, or perform opportunistic measurements. This paper states the objectives of the group and reviews major advances carried out by MOXXI members toward the advancement of hydrological sciences. Challenges and opportunities are outlined to provide strategic guidance for advancement of measurement, and thus discovery.

Indirect measurements of field-scale (hectometer grid-size) spatial-average near-surface soil moisture are becoming increasingly available by exploiting new-generation ground-based and satellite sensors. Nonetheless, modeling applications for water resources management require knowledge of plot-scale (1-5 m grid-size) soil moisture by using measurements through spatially-distributed sensor network systems. Since efforts to fulfill such requirements are not always possible due to time and budget constraints, alternative approaches are desirable. In this study, we explore the feasibility of determining spatial-average soil moisture and soil moisture patterns given the knowledge of long-term records of climate forcing data and topographic attributes. A downscaling approach is proposed that couples two different models_ the Eco-Hydrological Bucket and Equilibrium Moisture from Topography. This approach helps identify the relative importance of two compound topographic indexes in explaining the spatial variation of soil moisture patterns, indicating valley- and hillslope-dependence controlled by lateral flow and radiative processes, respectively. The integrated model also detects temporal instability if the dominant type of topographic dependence changes with spatial-average soil moisture. Model application was carried out at three sites in different parts of Italy, each characterized by different environmental conditions. Prior calibration was performed by using sparse and sporadic soil moisture values measured by portable time domain reflectometry devices. Cross-site comparisons offer different interpretations in the explained spatial variation of soil moisture patterns, with time-invariant valley-dependence (site in northern Italy) and hillslope-dependence (site in southern Italy). The sources of soil moisture spatial variation at the site in central Italy are time-variant within the year and the seasonal change of topographic dependence can be conveniently correlated to a climate indicator such as the aridity index. (C) 2017 Elsevier B.V. All rights reserved.

The assessment of precipitation over land is extremely important for a number of scientific purposes related to the mitigation of natural hazards, climate modelling and prediction, famine and disease monitoring, to cite a few. Due to the difficulties and the cost to maintain ground monitoring networks, i.e., raingauges and meteorological radars, remote sensing is receiving more and more attention in the recent decade(s). However, the accuracy of satellite observations of rainfall should be assessed with ground information as it is affected by a number of factors (topography, vegetation density, land-sea interface). Calabria is a peninsular region in southern Italy characterized by complex topography, dense vegetation and a narrow North-South elongated shape, thus being a very challenging place for rainfall retrieval from remote sensing. In this study, we built a high-quality rainfall datasets from raingauges and meteorological radars for testing three remotely sensed rainfall products_ 1) the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement product (IMERG), 2) the SM2RASC product obtained from the application of SM2RAIN (Soil Moisture TO RAIN) algorithm to the Advanced SCATterometer (ASCAT) derived satellite soil moisture data, and 3) a product derived from a simple combination of IMERG and SM2RASC. The assessment of the products is carried out at different rainfall time accumulation (e.g., from 0.5 to 24 h) for a ~2-year period from 10th March 2015, to 31st December 2016. Results show that IMERG has good performance at time resolutions higher than 6 h. At daily time scale, IMERG and SM2RASC show similar results with median correlations, R, ~0.60, and root mean square error, RMSE, ~7.6 mm/day (BIAS is -0.85 and +0.51 mm/day, respectively). The combined product outperforms the parent products (median R > 0.70, RMSE<6.5 mm/day, BIAS -0.07 mm/day). Among the different factors affecting products quality, topographic complexity seems to play the more relevant role, particularly for SM2RASC but also for IMERG. Overall, this study shows that the investigated satellite-based products agree reasonably well with observations notwithstanding the challenging features of the region, and the combination of IMERG and SM2RASC provides a way to overcome their limitations and to produce a higher quality satellite rainfall product.

Accurate and long-term rainfall estimates are the main inputs for several applications, from crop modeling to climate analysis. In this study, we present a new rainfall data set (SM2RAIN-CCI) obtained from the inversion of the satellite soil moisture (SM) observations derived from the ESA Climate Change Initiative (CCI) via SM2RAIN (Brocca et al., 2014). Daily rainfall estimates are generated for an 18-year long period (1998-2015), with a spatial sampling of 0.25° on a global scale, and are based on the integration of the ACTIVE and the PASSIVE ESA CCI SM data sets.The quality of the SM2RAIN-CCI rainfall data set is evaluated by comparing it with two state-of-the-art rainfall satellite products, i.e. the Tropical Measurement Mission Multi-satellite Precipitation Analysis 3B42 real-time product (TMPA 3B42RT) and the Climate Prediction Center Morphing Technique (CMORPH), and one modeled data set (ERA-Interim). A quality check is carried out on a global scale at 1° of spatial sampling and 5 days of temporal sampling by comparing these products with the gauge-based Global Precipitation Climatology Centre Full Data Daily (GPCC-FDD) product. SM2RAIN-CCI shows relatively good results in terms of correlation coefficient (median value > 0.56), root mean square difference (RMSD, median value < 10.34mm over 5 days) and bias (median value < 14.44%) during the evaluation period. The validation has been carried out at original resolution (0.25°) over Europe, Australia and five other areas worldwide to test the capabilities of the data set to correctly identify rainfall events under different climate and precipitation regimes.The SM2RAIN-CCI rainfall data set is freely available at https://doi.org/10.5281/zenodo.846259.

Landslide susceptibility modelling--a crucial step towards the assessment of landslide hazard and risk--has hitherto not included the local, transient effects of previous landslides on susceptibility. In this contribution, we implement such transient effects, which we term Blandslide path dependency^, for the first time. Two landslide path dependency variables are used to characterise transient effects_ a variable reflecting how likely it is that an earlier landslide will have a follow-up landslide and a variable reflecting the decay of transient effects over time. These two landslide path dependency variables are considered in addition to a large set of conditioning attributes conventionally used in landslide susceptibility. Three logistic regression models were trained and tested fitted to landslide occurrence data from a multi-temporal landslide inventory_ (1) a model with only conventional variables, (2) a model with conventional plus landslide path dependency variables, and (3) a model with only landslide path dependency variables. We compare the model performances, differences in the number, coefficient and significance of the selected variables, and the differences in the resulting susceptibility maps. Although the landslide path dependency variables are highly significant and have impacts on the importance of other variables, the performance of the models and the susceptibility maps do not substantially differ between conventional and conventional plus path dependent models. The path dependent landslide susceptibility model, with only two explanatory variables, has lower model performance, and differently patterned susceptibility map than the two other models. A simple landslide susceptibility model using only DEM-derived variables and landslide path dependency variables performs better than the path dependent landslide susceptibility model, and almost as well as the model with conventional plus landslide path dependency variables--while avoiding the need for hard-to-measure variables such as land use or lithology. Although the predictive power of landslide path dependency variables is lower than those of the most important conventional variables, our findings provide a clear incentive to further explore landslide path dependency effects and their potential role in landslide susceptibility modelling.

Geomorphological field mapping is a conventional method to prepare landslide inventories. The approach is typically hampered by the accessibility and visibility, during field campaigns for landslide mapping, of the different portions of the study area. Statistical significance of landlside susceptibility maps can be significantly reduced if the classification algorithm is trained in unsurveyed regions of the study area, for which landslide absence is tipically assumed, while ingnorance about landslide presence should actually be aknowledged. We compare different landslide susceptibility zonations obtained by training the classification model either in the entire study area or in the only portion of the area that was actually surveyed, which we name effective surveyed area. The latter was delineated by an automatic procedure specifically devised for the purpose, which uses information gathered during surveys, along with landslide locations. The method was tested in Gipuzkoa Province (Basque Country), North of the Iberian Peninsula, where digital thematic maps were available and a landslide survey was performed. We prepared the landslide susceptibility maps and the associated uncertainty within a logistic regression model, using both slope units and regular grid cells as reference mapping unit. Results indicate that the use of effective surveyed area for landslide susceptibility zonation is a valid approach to minimize the limitations stemming from unsurveyed regions at landslide mapping time. Use of slope units as mapping units, instead of grid cells, mitigates the uncertainties introduced by training the automatic classifier within the entire study area. Our method pertains to data preparation and, as such, the relevance of our conclusions is not limited to the logistic regression but are valid for virtually all the existing multivariate landslide susceptibility models.

We describe a semi-automatic procedure for the classification of satellite imagery into landslide or no landslide categories, aimed at preparing event landslide inventory maps. The two-steps procedure requires knowledge of the occurrence of a landslide event, availability of a pre- and post- event pseudo-stereo pair and a digital elevation model. The first step consists in the evaluation of a discriminant function, applied to a combination of well-known change detection indices tuned on landslide spectral response. The second step is devoted to discriminant function classification, aimed at distinguishing the only landslide class, through an improvement of the usual 'thresholding' method. We devised a multi-threshold classification, in which thresholding is applied separately in small subsets of the scene. We show that using slope units as topographic-aware subsets produces best classification performance when compared to the ground truth of a landslide inventory prepared by visual interpretation. The method proved to be superior to the use of a single threshold and to any multi-threshold procedure based on topography-blind subdivisions of the scene, especially in the validation stage. We argue that the improved classification performance and limited training requirements represent a step forward towards an automatic, real-time landslide mapping from satellite imagery.

Satellite rainfall products have been available for many years (since '90) with an increasing spatial/temporal resolution and accuracy. Their global scale coverage and near real-time products perfectly fit the need of an early warning landslide system. Notwithstanding these characteristics, the number of studies employing satellite rainfall estimates for predicting landslide events is quite limited. In this study, we propose a procedure that allows us to evaluate the capability of different rainfall products to forecast the spatial-temporal occurrence of rainfall-induced landslides using rainfall thresholds. Specifically, the assessment is carried out in terms of skill scores, and receiver operating characteristic (ROC) analysis. The procedure is applied to ground observations and four different satellite rainfall estimates_ 1) the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA, real time product (3B42-RT), 2) the SM2RASC product obtained from the application of SM2RAIN algorithm to the Advanced SCATterometer (ASCAT) derived satellite soil moisture (SM) data, 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), and 4) the Climate Prediction Center (CPC) Morphing Technique (CMORPH). As case study, we consider the Italian territory for which a catalogue listing 1414 rainfallinduced landslides in the period 2008-2014 is available. Results show that satellite products underestimate rainfall with respect to ground observations. However, by adjusting the rainfall thresholds, satellite products are able to identify landslide occurrence, even though with less accuracy than ground-based rainfall observations. Among the four satellite rainfall products, CMORPH and SM2RASC are performing the best, even though differences are small. This result is to be attributed to the high spatial/temporal resolution of CMORPH, and the good accuracy of SM2RSC. Overall, we believe that satellite rainfall estimates might be an important additional data source for developing continental or global landslide warning systems.

The relation between climate change and its potential effects on the stability of slopes remains an open issue. For rainfall induced landslides, the point consists in determining the effects of the projected changes in the duration and amounts of rainfall that can initiate slope failures. We investigated the relationship between fine-scale climate projections obtained by downscaling and the expected modifications in landslide occurrence in Central Italy. We used rainfall measurements taken by 56 rain gauges in the 9-year period 2003 - 2011, and the RainFARM technique to generate downscaled synthetic rainfall fields from global climate model projections for the 14-year calibration period 2002-2015, and for the 40-year projection period 2010-2049. Using a specific algorithm, we extracted a number of rainfall events, i.e. rainfall periods separated by dry periods of no or negligible amount of rain, from the measured and the synthetic rainfall series. Then, we used the selected rainfall events to force TRIGRS v. 2.1, the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model. We analyzed the results in terms of variations (or lack of variations) in the pluviometric thresholds for the possible initiation of landslides, in the probability distribution of landslide size (area), and in landslide hazard. Results showed that the downscaled rainfall fields obtained by RainFARM can be used to single out rainfall events, and to force the slope stability model. Results further showed that while the rainfall thresholds for landslide occurrence are expected to change in future scenarios, the probability distribution of landslide areas are not. We infer that landslide hazard in the study area is expected to change in response to the projected variations in the rainfall conditions. We expect that our results will contribute to regional investigations of the expected impact of projected climate variations on slope stability conditions and on landslide hazards.

Abstract: Background_ Damaging Hydrogeological Events (DHEs) are severe weather periods during which floods, landslides, lightning, windstorms, hail or storm surges can harm people. Climate change is expected to increase the frequency/intensity of DHEs and, consequently, the potential harm to people. Method_ We investigated the impacts of DHEs on people in Calabria (Italy) over 37 years (1980-2016). Data on 7288 people physically affected by DHEs were gathered from the systematic analysis of regional newspapers and collected in the database named PEOPLE. The damage was codified in three severity levels as follows_ fatalities (people who were killed), injured (people who suffered physical harm) and involved (people who were present at the place where an accident occurred but survived and were not harmed). During the study period, we recorded 68 fatalities, 566 injured and 6654 people involved in the events. Results_ Males were more frequently killed, injured and involved than females, and females who suffered fatalities were older than males who suffered fatalities, perhaps indicating that younger females tended to be more cautious than same-aged males, while older females showed an intrinsic greater vulnerability. Involved people were younger than injured people and fatalities, suggesting that younger people show greater promptness in reacting to dangerous situations. Floods caused the majority of the fatalities, injured and involved people, followed by landslides. Lightning was the most dangerous phenomenon, and it affected a relatively low number of people, killing 11.63% of them and causing injuries to 37.2%. Fatalities and injuries mainly occurred outdoors, largely along roads. In contrast, people indoors, essentially in public or private buildings, were more frequently involved without suffering harm. Being "dragged by water/mud" and "surrounded by water/mud", respectively, represented the two extremes of dynamic dangerousness. The dragging effect of rapid-flowing water totally or partially obstructed the attempts of people to save their lives. In contrast, people surrounded by steady water/mud encountered difficulties but ultimately could survive. Conclusions_ The study outcomes can be used in informational campaigns to increase risk awareness among both administrators and citizens and to improve community resilience, particularly in promoting self-protective behaviors and avoiding the underestimation of hazardous situations.

We present an updated version of the European landslide susceptibility map ELSUS 1000 that was released through the European Soil Data Centre in 2013. The ELSUS V2 map shows the landslide susceptibility zonation for individual climate-physiographic zones across Europe. ELSUS V2 covers a larger area of Europe than ELSUS 1000 as it includes Iceland, Cyprus, the Faroes, and the Shetlands, at a higher spatial resolution (200 x 200 m). The updated map was prepared using the same semi-quantitative method as for ELSUS 1000, combining landslide frequency ratios information with a spatial multi-criteria evaluation model of three thematic predictors_ slope angle, shallow subsurface lithology and land cover. However, the new map was prepared using also_ i) an extended landslide inventory, containing 30% of additional locations for model calibration, map validation and classification, and ii) a new lithological data set, based on harmonized shallow subsurface information derived from the International Hydrogeological Map of Europe (IHME). The new version of the map increases the overall predictive performance of ELSUS by 8%.

In this paper, we do a critical review of statistical methods for landslide susceptibility assessment and associated terrain zonations. Landslide susceptibility is the likelihood of a landslide occurring in an area depending on local terrain conditions, estimating "where" landslides are likely to occur. Since the first attempts to assess landslide susceptibility in the mid-1970s, hundreds of papers have been published using a variety of approaches and methods in different geological and climatic settings. Here, we critically review the statistically-based landslide susceptibility assessment literature by systematically searching for and then compiling an extensive database of 565 peer-review articles from 1983 to 2016. For each article in the literature database, we noted 31 categories/sub-categories of information including study region/extent, landslide type/number, inventory type and period covered, statistical model used, including variable types, model fit/prediction performance evaluation method, and strategy used to assess the model uncertainty. We present graphical visualisations and discussions of commonalities and differences found as a function of region and time, revealing a significant heterogeneity of thematic data types and scales, modelling approaches, and model evaluation criteria. We found that the range of thematic data types used for susceptibility assessment has not changed significantly with time, and that for a number of studies the geomorphological significance of the thematic data used is poorly justified. We also found that the most common statistical methods for landslide susceptibility assessment include logistic regression, neural network analysis, data-overlay, index-based and weight of evidence analyses, with an increasing preference towards machine learning methods in the recent years. Although an increasing number of studies in recent years have assessed the model performance, in terms of model fit and prediction performance, only a handful of studies have evaluated the model uncertainty. Adopting a Susceptibility Quality Level index, we found that the quality of published models has improved over the years, but top-quality assessments remain rare. We identified a clear geographical bias in susceptibility study locations, with many studies in China, India, Italy, South Korea and Turkey, and only a few in Africa, South America and Oceania. Based on previous literature reviews, the analysis of the information collected in the literature database, and our own experience on the subject, we provide recommendations for the preparation, evaluation, and use of landslide susceptibility models and associated terrain zonations.

We perform landslide susceptibility zonation with slope units using three digital elevation models (DEMs) of varying spatial resolution of the Ubaye Valley (South French Alps). In so doing, we applied a recently developed algorithm automating slope unit delineation, given a number of parameters, in order to optimize simultaneously the partitioning of the terrain and the performance of a logistic regression susceptibility model. The method allowed us to obtain optimal slope units for each available DEM spatial resolution. For each resolution, we studied the susceptibility model performance by analyzing in detail the relevance of the conditioning variables. The analysis is based on landslide morphology data, considering either the whole landslide or only the source area outline as inputs. The procedure allowed us to select the most useful information, in terms of DEM spatial resolution, thematic variables and landslide inventory, in order to obtain the most reliable slope unit-based landslide susceptibility assessment.

We test the hypothesis that configurations of a proton with a large-x parton, xp?0.1, have a smaller than average transverse size. The application of the QCD Q2 evolution equations shows that these small configurations also have a significantly smaller interaction strength, which has observable consequences in proton -- nucleus collisions. We perform a global analysis of jet production data in proton-- and deuteron--nucleus collisions at RHIC and the LHC. Using a model which takes a distribution of interaction strengths into account, we quantitatively extract the xp-dependence of the average interaction strength, ?(xp), over a wide kinematic range. By comparing the RHIC and LHC results, our analysis finds that the interaction strength for small configurations, while suppressed, grows faster with collision energy than does that for average configurations. We check that this energy dependence is consistent with the results of a method which, given ?(xp) at one energy, can be used to quantitatively predict that at another. This finding further suggests that at even lower energies, nucleons with a large-xp parton should interact much more weakly than those in an average configuration, a phenomenon in line with explanations of the EMC effect for large-xp quarks in nuclei based on color screening.

Physically based approaches for the regional assessment of slope stability using DEM topography usually consist of one-dimensional descriptions and include many simplifying assumptions with respect to more realistic, three-dimensional analyses. We investigated a new application of the well-known, publicly available software TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis) in combination with Scoops3D, to analyze three-dimensional slope stability throughout a digital landscape in a time-dependent fashion, typically non implemented in three-dimensional models. TRIGRS was used to simulate the dynamic hydraulic conditions within the slopes induced by a rainstorm. Scoops3D then used the resulting pore water pressure for three-dimensional stability assessment. We applied this approach to the July 2011 landslide event in Mt. Umyeon, South Korea, and results were compared with the landslide initiation locations reported for this rainfall event. Soil depth in the study area was described by three different simple models. Stability maps, obtained by the one-dimensional (TRIGRS only) and three-dimensional (TRIGRS and Scoops3D), time-dependent approaches, were compared to observations to assess the timing and locations of unstable sites by means of a synthetic index, previously specifically developed for dealing with point landslide locations. We highlight the performance of the three-dimensional approach with respect to the one-dimensional method represented by TRIGRS alone, and the consistency of the time-dependence of the results obtained using the combined approach with observations.

The large heterogeneity in soil surface conditions makes it impracticable to obtain reliable estimates of soil hydraulic parameters for areas larger than few squared kilometers. However, identifying these parameters on a global scale is essential for many hydrological and climatic applications. In this study, a new approach named Drainage from Drydown (DfD) is proposed to estimate the coefficients of drainage using soil moisture observations. DfD firstly selects multiple drydown events when surface runoff and evapotranspiration rates are negligible compared to the drainage rate. Secondly, by inverting the soil water balance equation, the drainage coefficients are obtained. Synthetic experiments are carried out in order to tune the overall procedure. DfD is then tested with in situ observations at 8 different sites worldwide characterized by different climates and soil types. The reliability of the DfD is evaluated by using the DfD drainage coefficients in a physically based soil water balance model (SWB) for simulating soil moisture and a rainfall estimation model (SM2RAIN). The results indicate that the climate and the soil conditions exert an important role in the occurrence and magnitude of drainage rate. DfD is found capable of correctly identify periods in which drainage rate is the dominant process. Drainage coefficients obtained from DfD are consistent with the expected soil hydraulic properties based on the soil texture and land cover at each site. By using DfD drainage coefficients to estimate rainfall and soil moisture via SM2RAIN and SWB, promising results are obtained with median correlation of 0.83 and 0.91 between estimated and in situ data. However, in sites characterized by high rate of evapotranspiration (>700 mm/year) and low permeable soil (e.g., clay) the DfD performance is reduced. Overall, DfD demonstrates the ability to decouple drainage and evapotranspiration processes and to estimate the drainage coefficients from in situ observations.

Soil moisture is a key environmental variable, important to e.g., farmers, meteorologists, and disaster management units. We fuse surface soil moisture (SSM) estimates from spatio-temporally complementary radar sensors through temporal filtering of their joint signal and obtain a kilometre-scale, daily soil water content product named SCATSAR-SWI. With 25 km Metop ASCAT SSM and 1 km Sentinel-1 SSM serving as input, the SCATSAR-SWI is globally applicable and achieves daily full coverage over operated areas. We employ a near-real-time-capable SCATSAR-SWI algorithm on a fused 3 year ASCAT-Sentinel-1-SSM data cube over Italy, obtaining a consistent set of model parameters, unperturbed by coverage discontinuities. An evaluation of a therefrom generated SCATSAR-SWI dataset, involving a 1 km Soil Water Balance Model (SWBM) over Umbria, yields comprehensively high agreement with the reference data (median R = 0.61 vs. in situ; 0.71 vs. model; 0.83 vs. ASCAT SSM). While the Sentinel-1 signal is attenuated to some extent, the ASCAT's signal dynamics are fully transferred to the SCATSAR-SWI and benefit from the Sentinel-1 parametrisation. Using the SM2RAIN approach, the SCATSAR-SWI shows excellent capability to reproduce 5 day-accumulated rainfall over Italy, with R = 0.89 against observed rainfall. The SCATSAR-SWI is currently in preparation towards operational product dissemination in the Copernicus Global Land Service (CGLS).

The topic of rainfall thresholds for landslide occurrence was thoroughly investigated, producing abundance of case studies at different scales of analysis and several technical and scientific advances. We reviewed the most recent papers published in scientific journals, highlighting significant advances and critical issues. We collected and grouped all the information on rainfall thresholds into four categories_ publication details, geographical distribution and uses, dataset features, thresholds definition. In each category, we selected descriptive information to characterize each one of the 115 rainfall threshold published in the last 9 years. The main improvements that stood out from the review are the definition of standard procedures for the identification of rainfall events and for the objective definition of the thresholds. Numerous advances were achieved in the cataloguing of landslides too, which can be defined as one of the most important variables, together with rainfall data, for drawing reliable thresholds. Another focal point of the reviewed articles was the increased definition of thresholds with different exceedance probabilities to be employed for the definition of warning levels in landslide early warning systems. Nevertheless, drawbacks and criticisms can be identified in most part of the recent literature on rainfall thresholds. The main issues concern the validation process, which is seldom carried out, and the very frequent lack of explanations for the rain gauge selection procedure. The paper may be used as a guide to find adequate literature on the most used or the most advanced approaches followed in every step of the procedure for defining reliable rainfall thresholds. Therefore, it constitutes a guideline for future studies and applications, in particular in early warning systems. The paper also aims at addressing the gaps that need to be filled to further enhance the quality of the research products in this field. The contribution of this manuscript could be seen not only as a review of the state of the art, but also an effective method to disseminate the best practices among scientists and stakeholders involved in landslide hazard management.

Satellite-based rainfall products (SRPs) are nowadays available at ever increasing accuracy and higher spatial and temporal resolution with respect to the past. Despite this, they are scarcely used in hydrological modeling. The main reasons may be related to_ 1) the large bias characterizing satellite precipitation estimates, which is dependent on rainfall intensity and the season, 2) the relatively large spatial/temporal resolution with respect to the applications, 3) the timeliness, which is often insufficient for operational purposes, and 4) a general (often not justified) skepticism of the hydrological community in the use of satellite products for land applications.

Crop simulation models, which are mainly being utilized as tools to assess the consequences of a changing climate and different management strategies on crop production at the field scale, are increasingly being used in a distributed model at the regional scale. Spatial data analysis and modelling in combination with geographic information systems (GIS) integrates information from soil, climate, and topography data into a larger area, providing a basis for spatial and temporal analysis. In the current study, the crop growth model Decision Support System for Agrotechnology Transfer (DSSAT) was used to evaluate five gridded precipitation input data at three locations in Austria. The precipitation data sets consist of the INtegrated Calibration and Application Tool (INCA) from the Meteorological Service Austria, two satellite precipitation data sourcesMultisatellite Precipitation Analysis (TMPA) and Climate Prediction Center MORPHing (CMORPH)and two rainfall estimates based on satellite soil moisture data. The latter were obtained through the application of the SM2RAIN algorithm (SM2R(ASC)) and a regression analysis (RA(ASC)) applied to the Metop-A/B Advanced SCATtermonter (ASCAT) soil moisture product during a 9-year period from 2007-2015. For the evaluation, the effect on winter wheat and spring barley yield, caused by different precipitation inputs, at a spatial resolution of around 25 km was used. The highest variance was obtained for the driest area with light-textured soils; TMPA and two soil moisture-based products show very good results in the more humid areas. The poorest performances at all three locations and for both crops were found with the CMORPH input data.

Flood modelling in data-sparse regions have been always limited to empirical, statistical or geomorphic approaches that are suitable to produce regional hazard maps. Such coarse resolution maps are not adapted for basin-scale applications, small to medium sized basins (< 1000 km(2)), especially when detailed estimates of flows and water levels of a particular event is required and hence cannot replace the hydrological/hydraulic modelling. The latter is a challenging task in data-sparse regions characterized by floods of typical duration times of a few hours which offer little opportunity for real-time recording by traditional rain-gauge networks, remote sensing or satellite imaging. Such data sparseness is not always compatible with the resolution, in both space and time, of the hydrological and hydraulic models. We propose a framework for flood modelling using sparse data from a coupled hydrological-hydraulic model constrained by past storm events and post-event measurements in space. The approach is applied to the Awali river basin (301 km(2)), in Lebanon, particularly to simulate the investigated early January 2013 extreme flood event, which is considered one of the largest events in the last three decades. The hydrological model was calibrated and evaluated with 12 past storm events aiming at defining narrow parameter ranges and uncertainty was performed with Monte Carlo simulations for these parameter ranges. The hydraulic model, based on a fine resolution DEM, was simulated using hydrological outflows and validated with 27 post-event measurements in space of high water marks. The resulting outflow values were satisfactory, and uncertainty was reduced when compared with arbitrarily wide parameter ranges. The hydrological model performance was highly variable but for the hydraulic model, 93% of the observed water levels fall within the simulated uncertainty bounds with an RMSE error of 0.26 m. The proposed framework allows mapping the possible inundation and can be compared to other approaches dealing with model complexity and associated performances.

Knowledge of irrigation is essential for ensuring food and water security, and to cope with the scarcity of water resources, which is expected to exacerbate under the pressure of climate change and population increase. Even though irrigation is likely the most important direct human intervention in the hydrological cycle, we have only partial knowledge on the areas of our planet in which irrigation takes place, and almost no information on the amount of water that is applied for irrigation.

Despite abundant information on landslides, and on landslide hazard and risk, in Italy little is known on the direct impact of event landslides on road networks, and on the related economic costs. We investigated the physical and economic damage caused by two rainfall-induced landslide events in Central and Southern Italy, to obtain road restoration cost statistics. Using a GIS-based method, we exploited road maps and landslide event inventory maps to compute different metrics that quantify the impact of the landslide events on the natural landscape, and on the road networks, by road type. The maps were used with cost data obtained from multiple sources, including local authorities, and specific legislation, to evaluate statistically the unit cost per metre of damaged road, and the unit cost per square metre of damaging landslide, separately for main and secondary roads. The obtained unit costs showed large variations which we attribute to the different road types in the two study areas and to the different abundance of landslides. Our work confirms the long-standing conundrum of obtaining accurate landslide damage data, and outlines the need for reliable, standardized methods to evaluate landslide damage and associated restoration costs that regional and local administrations can use rapidly in the aftermath of a landslide event. We conclude recommending that common standardized procedures to collect landslide cost data following each landslide event are established, in Italy and elsewhere. This will allow for more accurate and reliable evaluations of the economic costs of landslide events.