The chemical composition of river waters gives a measure of the atmospheric CO2 fixed by chemical weathering processes. Since the dominating factors controlling these processes are lithology and runoff, as well as uplift and erosion, we introduce a new simplified geo-lithological map of the Alps (Alpine-Geo-LiM) that adopted a lithological classification compliant with the methods most used in literature for estimating the consumption of atmospheric CO2 by chemical weathering. The map was used together with published alkalinity data of the 33 main Alpine rivers (1) to investigate the relationship between bicarbonate concentration in the sampled waters and the lithologies of the corresponding drained basins, and (2) to quantify the atmospheric CO2 consumed by chemical weathering. The analyses confirm (as known by the literature) that carbonates are lithologies highly prone to consuming atmospheric CO2. Moreover, the analyses show that sandstone (which could have a nonnegligible carbonate component) plays an important role in consuming atmospheric CO2. Another result is that in multilithological basins containing lithologies more prone to consuming atmospheric CO2, the contribution of igneous rocks to the atmospheric CO2 consumption is negligible. Alpine-Geo-LiM has several novel features when compared with published global lithological maps. One novel feature is due to the attention paid in discriminating metamorphic rocks, which were classified according to the chemistry of protoliths. The second novel feature is that the procedure used for the definition of the map was made available on the Web to allow the replicability and reproducibility of the product.

The global availability of satellite rainfall products (SRPs) at an increasingly high temporal/spatial resolution has made possible their exploitation in hydrological applications, especially over in-situ data scarce regions. In this context, understand how uncertainties transfer from SRPs into flood simulation, through the hydrological model, is a main research question. SRPs accuracy is normally characterized by comparing them with ground observations via the calculation of categorical (e.g., threat score, false alarm ratio, probability of detection) and/or continuous (e.g., bias, root mean square error, Nash-Sutcliffe index, Kling-Gupta efficiency index, correlation coefficient) metrics. However, whether these metrics are informative about the associated performance in flood simulations (when the SRP is used as input to an hydrological model) is an underdiscussed research topic. This study aims to relate the accuracy of different SRPs both in terms of rainfall and in terms of flood simulation. That is, the following research question are addressed_ is (are) there appropriate performance metric (s) to drive the choice of the best performing rainfall product for flood simulation? To answer this question three SRPs, namely the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA; the Climate Prediction Center Morphing algorithm, CMORPH, and the SM2RAIN algorithm applied to the ASCAT (Advanced SCATterometer) soil moisture product, SM2RAIN-ASCAT, have been used as input into a lumped hydrologic model (MISDc, "Modello Idrologico Semi-Distribuito in continuo") on 1318 basins over Europe with different physiographic characteristics. Results have suggested that, among the continuous metrics, correlation coefficient and Kling-Gupta efficiency index are not reliable scores to select rainfall product performing best for hydrological modelling whereas bias and root mean square error seem more appropriate. In particular, by constraining the relative bias to values lower than 0.2 and the relative root mean square error to values lower than 2, good hydrological performances (Kling-Gupta efficiency index on discharge greater than 0.5) are ensured for almost 75 % of the basins fulfilling these criteria. Conversely, the categorical scores have not provided suitable information to address the SRPs selection for hydrological modelling.

The increasing availability of free-access satellite data represents a relevant opportunity for the analysis and assessment of natural hazards. The systematic acquisition of spaceborne imagery allows for monitoring areas prone to geohydrological disasters, providing relevant information for risk evaluation and management. In cases of major landslide events, for example, spaceborne radar data can provide an effective solution for the detection of slope failures, even in cases with persistent cloud cover. The information about the extension and location of the landslide-affected areas may support decision-making processes during emergency responses. In this paper, we present an automatic procedure based on Sentinel-1 Synthetic Aperture Radar (SAR) images, aimed at facilitating the detection of landslides over wide areas. Specifically, the procedure evaluates changes of radar backscattered signals associated with land cover modifications that may be also caused by mass movements. After a one-time calibration of some parameters, the processing chain is able to automatically execute the download and preprocessing of images, the detection of SAR amplitude changes, and the identification of areas potentially affected by landslides, which are then displayed in a georeferenced map. This map should help decision makers and emergency managers to organize field investigations. The process of automatization is implemented with specific scripts running on a GNU/Linux operating system and exploiting modules of open-source software. We tested the processing chain, in back analysis, on an area of about 3000 km2 in central Papua New Guinea that was struck by a severe seismic sequence in February-March 2018. In the area, we simulated a periodic survey of about 7 months, from 12 November 2017 to 6 June 2018, downloading 36 Sentinel-1 images and performing 17 change detection analyses automatically. The procedure resulted in statistical and graphical evidence of widespread land cover changes that occurred just after the most severe seismic events. Most of the detected changes can be interpreted as mass movements triggered by the seismic shaking.

Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties, and the environment in many areas. Investigators have for long attempted to estimate landslide hazard in an effort to determine where, when (or how frequently), and how large (or how destructive) landslides are expected to be in an area. This information may prove useful to design landslide mitigation strategies, and to reduce landslide risk and societal and economic losses. In the geomorphology literature, most of the attempts at predicting the occurrence of populations of landslides by adopting statistical approaches are based on the empirical observation that landslides occur as a result of multiple, interacting, conditioning and triggering factors. Based on this observation, and under the assumption that at the spatial and temporal scales of our investigation individual landslides are discrete "point" events in the landscape, we propose a Bayesian modelling framework for the prediction of the spatio-temporal occurrence of landslides of the slide type caused by weather triggers. We build our modelling effort on a Log-Gaussian Cox Process (LGCP) by assuming that individual landslides in an area are the result of a point process described by an unknown intensity function. The modelling framework has two stochastic components_ (i) a Poisson component, which models the observed (random) landslide count in each terrain subdivision for a given landslide "intensity", i.e., the expected number of landslides per terrain subdivision (which may be transformed into a corresponding landslide "susceptibility"); and (ii) a Gaussian component, used to account for the spatial distribution of the local environmental conditions that influence landslide occurrence, and for the spatio-temporal distribution of "unobserved" latent environmental controls on landslide occurrence. We tested our prediction framework in the Collazzone area, Umbria, Central Italy, for which a detailed multi-temporal landslide inventory covering the period from before 1941 to 2014 is available together with lithological and bedding data. We subdivided the 79 km2 area into 889 slope units (SUs). In each SU, we computed the mean and standard deviation of 16 morphometric covariates derived from a 10 m × 10 m digital elevation model. For 13 lithological and bedding attitude covariates obtained from a 1_10,000 scale geological map, we computed the proportion of each thematic class intersecting the given SU. We further counted how many of the 3,379 landslides in the multi-temporal inventory affect each SU and grouped them into six periods. We used this complex space-time information to prepare five models of increasing complexity. Our "baseline" model (Mod1) carries the spatial information only through the covariates mentioned above. It does not include any additional information about the spatial and temporal structure of the data, and it is therefore equivalent to the predominantly used landslide susceptibility model in the literature. The second model (Mod2) is analogous, but it allows for time-interval-specific regression constants. Our next two models are more complex. In particular, our third model (Mod3) also accounts for latent spatial dependencies among neighboring SUs. These are inferred for each of the six time intervals, to explain variations in the landslide intensity and susceptibility not explained by the thematic covariates. By contrast, our fourth model (Mod4) accounts for the latent temporal dependence, separately for each SU, disregarding neighboring influences. Ultimately, our most complex model (Mod5) contextually features all these relations. It contains the information carried by morphometric and thematic covariates, six time-interval-specific regression constants, and it also accounts for the latent temporal effects between consecutive slope instabilities at specific SUs as well as the latent spatial effects between adjacent SUs. We also show that the intensity is strongly related to the aggregated landslide area per SU. Because of this, our most complex model largely fulfills the definition of landslide hazard commonly accepted in the literature, at least for this study area. We quantified the spatial predictive performance of each of the five models using a 10-fold cross-validation procedure, and the temporal predictive performance using a leave-one-out cross-validation procedure. We found that Mod5 performed better than the others. We then used it to test a novel strategy to classify the model results in terms of both landslide intensity and susceptibility, which provides more information than traditional susceptibility zonations for land planning and management--hereafter we use the term "traditional" simply to refer to the majority of modelling procedures in the literature. We discuss the advantages and limitations of the new modelling framework, and its potential application in other areas, making specific and general hazard and geomorphological considerations. We also give a perspective on possible developments in landslide prediction modelling and zoning. We expect our novel approach to the spatio-temporal prediction of landslides to enhance the currently limited ability to evaluate landslide hazard and its temporal and spatial variations. We further expect it to lead to better projections of future landslides, and to improve our collective understanding of the evolution of complex landscapes dominated by mass-wasting processes under multiple geophysical and weather triggers.

Rockfall frequency size distributions are used in Austria for the definition of a design block for the planning of technical rockfall protection. Rockfall size datasets are often incomplete. Here, we study fifteen catalogues of rockfall size in Austria, Italy, and the USA to analyse the impact of the data collection and mapping methods on the representativeness of the catalogues and on the estimates of frequency-size statistics. To describe and compare the catalogues of rockfall size, we first use Empirical Cumulative Distribution Functions (ECDFs), followed by parametric distribution estimates in the form of Probability Density Functions (PDFs), and Cumulative Distribution Functions (CDFs). We discuss the output of Kolmogorov-Smirnov tests, the position of the frequency-size distribution rollover, and the p-value and the standard errors associated to the distribution parameters estimates to determine the reliability of our model results. In addition, we analyse the variations in the modelled CDFs for different percentiles of the frequency-size distributions to describe and discuss the representativeness of the rockfall catalogues. Our results show that different mapping strategies may affect the estimates of frequency-size distribution of rock fall volume, a relevant information when evaluating the possible impacts of rockfall processes. We conclude offering recommendations for rockfall mapping, and the use and of a non-parametric statistical method being capable to deal with small datasets, which is very typical when dealing with rockfall data. Such recommendations help for a correct dimensioning of designing rockfall mitigation measures.

Gully head erosion is a significant process in semi-arid regions contributing to land degradation and hillslope dynamics. Predicting when and where gully heads will expand under different seasonal scenarios is an essential step to take the appropriate mitigation measures. The LANDPLANER model was applied in this study area in order to analyze and investigate specific issues related to the triggering of gully head processes. The model is mainly designed to describe the dynamic response of slopes (or basins) under changing scenarios, including meteorological factors, vegetation or land use, and slope morphology. This model enabled us to predict the occurrence of gully heads under different seasonal conditions defined by Curve Number and rainfall scenarios. The LANDPLANER erosion modeling schema is twofold and integrates two different approaches. The first is empirical and exploits an erosion topographic threshold equation, while the second is aimed to define an erosion index calculated starting from the results of the hydrological model. These two indices predict where erosion processes can occur. To analyze the impact of land use changes on slope response to rainfall events, we simulated seasonal land use changes scenarios, modifying the Curve Number values associated to each specific land use classes. The twofold strategy was also adopted to validate the model, namely testing the model with synthetic dataset, and applying the model to real cases. The results indicated that the density of gully heads is more frequent in the South facing slopes with low elevation (around 200-300 m), steeper slope (within 20-40°), and higher average accumulation value (>100 m). Higher values of topographic threshold and erosion index (region 1_ 13.1, region 2_ 8.9) are obtained for the cold season in winter, where rainfall is intense and vegetation cover is lowest in the rangelands. Given to both observing receiver operating characteristic curve, the best and worst performances of gully head occurrence belong to the third Curve Number scenario (the lowest vegetation cover in terms of rangelands in winter) and the second Curve Number scenario (the lowest vegetation covers in terms of agricultural lands in summer) in turn. Further, the LANDPLANNER model effectively characterizes the status and trend of gully head formation in the study area.

Finding a digital elevation model (DEM) of suitable spatial resolution is vital to investigate piping erosion using aerial remote-sensing platforms like unmanned aerial vehicles (UAV). Previous studies have implied that the best spatial resolution is a DEM with the most detail. This study evaluates piping-affected areas with five DEMs (1, 5, 10, 20, and 30 m resolutions) with three trained machine-learning methods_ support vector machine (SVM), maximum entropy (ME), and boosted regression tree (BRT). This method enables the identification of the specific impacts caused by changing pixel resolution to guide the selection of the most effective DEM. This study employs piping morphometry data to predict the locations of completely collapsed pipes. The performance of the methods for mapping of pipes was assessed against a piping inventory map. The results demonstrate that the finest resolution DEM is not always the most useful. Though 1 m-resolution DEMs show the most detail, the best performance was the 5 m-resolution DEM when tested for all three mapping models. The 5 m-resolution DEM-SVM combination was the best predictor of known piping sites (AUC = 81.0%). The 5-m DEM-ME was second most effective model (AUC = 75.8%). And 5-m DEM-BRT was third (AUC = 72.9%). Applying more DEM derivatives may increase confidence in the selection of the most appropriate resolution.

The island of Gran Canaria (Canary Islands, Spain) is characterized by a large variability of volcanic rocks, reflecting its volcanic evolution resulting from the built-up process of an intraplate oceanic island. The geological map provided by Geological Survey of Spain at 1_25.000 scale shows more than 109 different lithologies and it is too complex for environmental and engineering purposes. This work presents a simplified geotechnical map with a small number of classes grouping up units with similar geotechnical behaviours. The original lithologies were grouped using about 350 representative rock samples, collected in the seven major islands of the Archipelago. The samples were characterized by laboratory tests and in situ analysis. The geotechnical map was used to model and evaluate rockfall hazard in the entire island of Gran Canaria, where rockfalls are an important threat with a high social, economic impact. The rockfall map was validated with 128 rockfall events, occurred during the period 2010-2016, along the GC-200 road (34 Km), located in the NW sector of Gran Canaria. About 96% of the events occurred along sections of the road where the number of expected trajectories is high or moderate.

The Cosmic-Ray Neutron Sensor (CRNS) technique for estimating landscape average soil water content (SWC) is now a decade old and includes many practical methods for implementing measurements, such as identification of detection area and depth and determining crop biomass water equivalent. However, in order to maximize the societal relevance of CRNS SWC data, practical value-added products need to be developed that can estimate both water flux (i.e., rainfall, deep percolation, evapotranspiration) and root zone SWC changes. In particular, simple methods that can be used to estimate daily values at landscape average scales are needed by decision makers and stakeholders interested in utilizing this technique. Moreover, landscape average values are necessary for better comparisons with remote sensing products. In this work we utilize three well-established algorithms to enhance the usability of the CRNS data. The algorithms aim to_ (1) temporally smooth the neutron intensity and SWC time series, (2) estimate a daily rainfall product using the Soil Moisture 2 Rain (SM2RAIN) algorithm, and (3) estimate daily root zone SWC using an exponential filter algorithm. The algorithms are tested on the CRNS site at the Hydrological Open Air Laboratory experiment in Petzenkirchen, Austria over a 3 years period. Independent observations of rainfall and point SWC data are used to calibrate the algorithms. With respect to the neutron filter, we found the Savitzky-Golay (SG) had the best results in preserving the amplitude and timing of the SWC response to rainfall as compared to the Moving Average (MA), which shifted the SWC peak by 2-4 h. With respect to daily rainfall using the SM2RAIN algorithm, we found the MA and SG filters had similar results for a range of temporal windows (3-13 h) with cumulative errors of <9% against the observations. With respect to daily root zone SWC, we found all filters behaved well (Kling-Gupta-Efficiency criteria > 0.9). A methodological framework is presented that summarizes the different processes, required data, algorithms, and products.

Collected rainfall records by gauges lead to key forcings in most hydrological studies. Depending on sensor type and recording systems, such data are characterized by different time-resolutions (or temporal aggregations), t. We present an historical analysis of the time-evolution of t based on a large database of rain gauge networks operative in many study areas. Globally, t data were collected for 25,423 rain gauge stations across 32 geographic areas, with larger contributions from Australia, USA, Italy and Spain. For very old networks early recordings were manual with coarse time-resolution, typically daily or sometimes monthly. With a few exceptions, mechanical recordings on paper rolls began in the first half of the 20th century, typically with t of 1 h or 30 min. Digital registrations started only during the last three decades of the 20th century. This short period limits investigations that require long time-series of sub-daily rainfall data, e.g, analyses of the effects of climate change on short-duration (sub-hourly) heavy rainfall. In addition, in the areas with rainfall data characterized for many years by coarse time-resolutions, annual maximum rainfall depths of short duration can be potentially underestimated and their use would produce errors in the results of successive applications. Currently, only 50% of the stations provide useful data at any time-resolution, that practically means t = 1 min. However, a significant reduction of these issues can be obtained through the information content of the present database. Finally, we suggest an integration of the database by including additional rain gauge networks to enhance its usefulness particularly in a comparative analysis of the effects of climate change on extreme rainfalls of short duration available in different locations.

Delineating boundaries of urban areas is no easy task, due to the inherent complexity of the problem, heterogeneity of relevant data and little consensus on how to properly measure the results. Any such delineation must eventually be cast onto administrative boundaries, an essential requirement for real-world applications. In the effort of relating administrative and alternative boundaries, we investigated in Italy the validity of general scaling laws, such as the area-population relation, and proposed a practical application. Relying on open data for population, settlements and road networks, we showed the extent to which scaling relations hold for different boundaries for urban areas, and how they compare to each other. We considered, beside Italian municipalities, urban areas based on the idea of "natural cities", obtained using head/tail breaks of areas related to human mobility as an explicit indicator of existence of a city. Area-population data for administrative boundaries can be reconciled with scaling relations valid for both the world's cities data and with those obtained from natural cities, provided an effective area is adopted in place of polygon planimetric area of municipalities. We eventually proposed an aggregation of administrative units using the empirical scaling relation as an objective function for accepting or rejecting pairwise fusion of boundaries. We suggest considering such a method, along with expert considerations, as an additional tool for real-world urban planning as seen from the very general perspective of seemingly abstract scaling laws.

This paper presents a community effort to develop good practice guidelines for the validation of global coarse-scale satellite soil moisture products. We provide theoretical background, a review of state-of-the-art methodologies for estimating errors in soil moisture data sets, practical recommendations on data pre-processing and presentation of statistical results, and a recommended validation protocol that is supplemented with an example validation exercise focused on microwave-based surface soil moisture products. We conclude by identifying research gaps that should be addressed in the near future.

Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km(2). The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications, as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative is satellite-based rainfall estimates, yet comparisons with in situ data suggest they are often not optimal.

Reconstruction of last millennia Sea Surface Temperature (SST) evolution is challenging due to the difficulty retrieving good resolution marine records and to the several uncertainties in the available proxy tools. In this regard, the Roman Period (1 CE to 500 CE) was particularly relevant in the sociocultural development of the Mediterranean region while its climatic characteristics remain uncertain. Here we present a new SST reconstruction from the Sicily Channel based in Mg/Ca ratios measured on the planktonic foraminifer Globigerinoides ruber. This new record is framed in the context of other previously published Mediterranean SST records from the Alboran Sea, Minorca Basin and Aegean Sea and also compared to a north Hemisphere temperature reconstruction. The most solid image that emerges of this trans-Mediterranean comparison is the persistent regional occurrence of a distinct warm phase during the Roman Period. This record comparison consistently shows the Roman as the warmest period of the last 2 kyr, about 2 °C warmer than average values for the late centuries for the Sicily and Western Mediterranean regions. After the Roman Period a general cooling trend developed in the region with several minor oscillations. We hypothesis the potential link between this Roman Climatic Optimum and the expansion and subsequent decline of the Roman Empire.

A marine sediment core from the western Mediterranean provides a newhigh-resolution 4,500-year record of palaeomagnetic secular variation and relativepalaeointensity. In 2013, the 7.1-metre C5 core was recovered from the TyrrhenianSea as part of the NextData climate data project. The coring site, 15 km offshorefrom the Volturno river mouth, is well-located to record combined marine andterrestrial palaeoclimatic influences, and the fine-grained, rapidly deposited sedimentsare effective palaeomagnetic recorders. We investigate the palaeomagnetic field direction and strength recorded in the core, which provide a valuable high-resolution recordof Holocene geomagnetic variation in the area. Using rock magnetic techniques,we constrain the magnetic mineralogy of the studied sediments and confirm theirsuitability for palaeomagnetic analysis. Palaeomagnetic declination and inclinationrecords were determined by stepwise alternating-field demagnetization, and relativepalaeointensity estimates were obtained based on normalization to anhysterestic andisothermal remanent magnetization and to magnetic susceptibility. The age of the coreis well-constrained with a tephro- and biostratigraphic age model, and its magneticrecords are compared with relevant core and model data for the region, demonstratingthat our record is compatible with previous results from the area. An automated curvematching approach is applied to assess the compatibility of our data with the existingsecular variation path for the Mediterranean area.