The Importance of Hail Events for Landslides and Bedload Transport in Austrian Alpine Torrents

Landslides and bedload transport can be a threat to people, infrastructure, and vegetation. Many detailed hydrometeorological trigger mechanisms of such natural hazards are still poorly understood. This is in particular valid concerning hail as a trigger of these processes. Therefore, this study aims to determine the influence of hail on landslides and bedload transport in alpine torrents. Based on a generated table from an event register of mountain processes maintained by the Avalanche and Torrent Control Unit (WLV) and weather data provided by the Centre for Meteorology and Geodynamics (ZAMG), 1,573 observed events between 1980 and 2019 in 79 Austrian alpine sites are analysed. Thiessen polygons are used to regionalise local weather data to adjacent regions. The spatial extend of these regions are merged with the registered torrential events. As a result of a stepwise filtering of the used data, the final inventory was created.The results show that 95.1% of the investigated torrential processes triggered by hailstorms are debris flows or debris flow-like transports. Within the study period, a peak of hail-triggered landslides and bedload transport can be recognised in the first 10 days of August in all 39 years. Furthermore, the results suggest that hail is rather a direct than an indirect trigger for landslides and bedload transport.Overall, we conclude that the influence of hail on landslides and bedload transport is significant. Respective hydrometeorological triggering conditions should be included in any regions. Further research for this topic is required to explore the process dynamics in greater detail.

Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products.

Estimating the Precipitation Amount at Regional Scale Using a New Tool, Climate Analyzer

Different methods are known for interpolating spatial data. Introduced a few years ago, the initial version of the Most Probable Precipitation Method (MPPM) proved to be a valuable competitor against the Thiessen Polygons Method, Inverse Distance Weighting and kriging for estimating the regional trend of precipitation series. Climate Analyzer, introduced here, is a user-friendly toolkit written in Matlab, which implements the initial and modified version of MPPM and new selection criteria of the series that participate in estimating the regional precipitation series. The software provides the graphical output of the estimated regional series, the modeling errors and the comparisons of the results for different segmentations of the time interval used in modeling. This article contains the description of Climate Analyzer, accompanied by a case study to exemplify its capabilities.

The Response of the HydroGeoSphere Model to Alternative Spatial Precipitation Simulation Methods

This paper presents the simulation results obtained from a physically based surface-subsurface hydrological model in a 5730 km2 watershed and the runoff response of the physically based hydrological models for three methods used to generate the spatial precipitation distribution: Thiessen polygons (TP), Co-Kriging (CK) interpolation and simulated annealing (SA). The HydroGeoSphere model is employed to simulate the rainfall-runoff process in two watersheds. For a large precipitation event, the simulated patterns using SA appear to be more realistic than those using the TP and CK method. In a large-scale watershed, the results demonstrate that when HydroGeoSphere is forced by TP precipitation data, it fails to reproduce the timing, intensity, or peak streamflow values. On the other hand, when HydroGeoSphere is forced by CK and SA data, the results are consistent with the measured streamflows. In a medium-scale watershed, the HydroGeoSphere results show a similar response compared to the measured streamflow values when driven by all three methods used to estimate the precipitation, although the SA case is slightly better than the other cases. The analytical results could provide a valuable counterpart to existing climate-based drought indices by comparing multiple interpolation methods in simulating land surface runoff.

Small Catchment Runoff Sensitivity to Station Density and Spatial Interpolation: Hydrological Modeling of Heavy Rainfall Using a Dense Rain Gauge Network

Precipitation is the most important input to hydrological models, and its spatial variability can strongly influence modeled runoff. The highly dense station network WegenerNet (0.5 stations per km2) in southeastern Austria offers the opportunity to study the sensitivity of modeled runoff to precipitation input. We performed a large set of runoff simulations (WaSiM model) using 16 subnetworks with varying station densities and two interpolation schemes (inverse distance weighting, Thiessen polygons). Six representative heavy precipitation events were analyzed, placing a focus on small subcatchments (10–30 km2) and different event durations. We found that the modeling performance generally improved when the station density was increased up to a certain resolution: a mean nearest neighbor distance of around 6 km for long-duration events and about 2.5 km for short-duration events. However, this is not always true for small subcatchments. The sufficient station density is clearly dependent on the catchment area, event type, and station distribution. When the network is very dense (mean distance < 1.7 km), any reasonable interpolation choice is suitable. Overall, the station density is much more important than the interpolation scheme. Our findings highlight the need to study extreme precipitation characteristics in combination with runoff modeling to decompose precipitation uncertainties more comprehensively.

A Rainfall Erosivity database for Brazil from MSWEP rainfall dataset

&lt;p&gt;In erosion studies the behavior of rainfall is primordial, since rain is responsible for the first stage of the erosion process: the detachment of soil particles. The erosive potential of rainfall, erosivity, is considered in the universal soil loss equations (R)USLE family through the parameter R, or R factor. This factor is calculated from the rainfall erosivity index, which is the product of kinetic energy of the rain by the maximum intensity of the rain of 30 minutes of duration. As sub-hour rainfall data is not always available, there are in the literature a series of equations obtained from regression, which use monthly and annual rainfall and present a good estimate of erosivity for your study site. In Brazil, in addition to limitations regarding the temporal resolution of rainfall data, there are also spatial limitations. Monitoring stations are concentrated mostly in urbanized areas, usually near the coast. The other regions, such as agricultural and forest areas, are poorly monitored, and these areas are of great interest for monitoring erosion, not only because they are periodically exposed soil areas, but also because of the high rainfall rates that humid forests like Amazon have. MSWEP is a rainfall database that combines observed, satellite and reanalysis data. It has global coverage, temporal resolution of 3 hours, spatial 0.1&amp;#186; and data from 1979 to 2016. Databases like this have great potential to be used in areas such as Brazil, due to its spatial and temporal resolution. In this context, considering the relevance that the soil loss equations still present today, this work developed a rainfall erosivity database entitled REDB-BR (Rainfall Erosivity Database for Brazil). It provides the R factor in a 0.1&amp;#186; resolution grid, developed with 37 years of rainfall data from the MSWEP dataset. The R factor was calculated trough 73 erosivity index regression equations, which mostly uses the Modified Fournier Index (MFI), a relation between monthly precipitation and annual precipitation. Thiessen polygons were used in order to spatialize and define the areas of each equation. Over the Brazilian territory, the R factor ranges from 1.200 to 20.000 MJ mm ha-1 h-1 year-1, with the higher values in the North region, and the lowest values in the Northeast. The spatial patterns of erosivity are very similar to the climatic zones of Brazil. The R factor map takes advantage of MSWEP dataset and presents a spatial resolution very detailed to a country with continental scale such as Brazil. The database includes the equations shapefile and table, Thiessen Polygons shapefile and the R factor map in raster format, which allows more possibilities of application. The database can be accessed at &lt;https://zenodo.org/record/4428308#.X_hxsOhKiUk&gt;. We identified sudden changes in behavior between the delimited areas, which suggests a need for more regression equations in order to better represent the behavior of the erosivity in the Brazilian territory.&lt;/p&gt;

Trend Analysis of Lakes and Sinkholes in Konya Closed Basin, Turkey

Determining changes in the water level of lakes is essential in terms of flood control, water resource management, economic development, water-supply planning sustainability, and the sustainability of the ecosystem. Trend analysis is one of the most commonly used tools for detecting changes in the hydrological time series such as lake levels, precipitation and temperature. Trend analyses of meteorological variables and groundwater levels (baseflow components) are crucial toward the assessment of long-term changes in lake levels. This study aims to investigate the trend of long-term change in lakes (Lake Tuz and Lake Beyşehir) and sinkholes (Timraş and Kızören) in the Konya Closed Basin in Turkey. Changes in these lakes and sinkholes were examined along with changes in precipitation and groundwater trends representing the climate in the region. With the assistance of Thiessen polygons, precipitation stations, which affect the lakes and sinkholes, were determined. Several statistical tests exist that help determine the significance of hydrological trends over time. These tests are divided into two categories: parametric and nonparametric. In this study, the non parametric Innovative Sen trend test, the Modified Mann–Kendall trend test, and the parametric Linear Trend test were used. As a result of the trend analysis, it was observed that the water levels of Kızören and Timraş sinkholes decreased over time, and the water levels of Tuz Gölü and Beyşehir lakes increased over time. These results are supported by the trends of precipitation data and groundwater level data of the stations determined by the Thiessen polygons and sub-basin boundaries.

AMOSTRAS VIRTUAIS DE ATRIBUTOS DO SOLO COMO SUBSÍDIO AO PLANEJAMENTO PARA ANÁLISE GEOESTATÍSTICA

AMOSTRAS VIRTUAIS DE ATRIBUTOS DO SOLO COMO SUBSÍDIO AO PLANEJAMENTO PARA ANÁLISE GEOESTATÍSTICA DIEGO AUGUSTO DE CAMPOS MORAES1, ANDERSON ANTÔNIO DA CONCEIÇÃO SARTORI2 1 Professor Doutor, Departamento de Análise e Desenvolvimento de Sistemas, Faculdade Eduvale de Avaré, Av. Prefeito Misael Eufrásio Leal, 347 - Centro, Avaré - SP, 18705-050, [email protected]. 2 Professor Doutor, Grupo de Estudos e Pesquisas Agrárias Georreferenciadas, Faculdade de Ciências Agronômicas de Botucatu – FCA/UNESP, Avenida Universitária, 3780, Altos do Paraíso, Botucatu – SP, 18610-034, [email protected]. RESUMO: O objetivo deste artigo foi propor uma metodologia de amostragem virtual para atributos do solo em área agrícola, a qual pode subsidiar o planejamento para análise geoestatística. Foram selecionadas, aleatoriamente, 23 amostras de solo (profundidades de 0-20 cm e 20-40 cm) do conjunto de dados original, com o objetivo de realizar a validação externa. Foi aplicado o procedimento de polígonos de Thiessen com base nas demais amostras originais do solo (47 amostras) e, em seguida, foram inseridas, aleatoriamente, amostras virtuais (53 amostras). A análise do variograma, validação cruzada, krigagem ordinária e validação externa foram executadas com a finalidade de verificar a robustez da metodologia. A inserção de amostras virtuais mostrou-se promissora, uma vez que o GDE (Grau de Dependência Espacial) e a validação cruzada dos atributos do solo foram aprimorados, situação que não foi observada nos dados originalmente amostrados. A validação externa obteve bons resultados, indicando que a amostragem virtual pode ser utilizada unicamente no planejamento para análise geoestatística. Palavras-chaves: variograma, validação cruzada, solos. VIRTUAL SAMPLES OF SOIL ATTRIBUTES AS A SUBSIDY FOR GEOSTATISTICAL ANALYSIS PLANNING ABSTRACT: The aim of this article was to propose a virtual sampling methodology for soil attributes in an agricultural area, which can support planning for geostatistical analysis. Twenty-three soil samples (depths of 0-20 cm and 20-40 cm) from the original data set were selected randomly, for an external validation process. The Thiessen polygons procedure was applied based on the remaining original soil samples (47 samples), and then, virtual samples (53 samples) were randomly inserted. The analysis of the variogram, cross-validation, ordinary kriging and external validation were performed in order to verify the robustness of the methodology. The insertion of virtual samples was promising, since the GDE (Degree of Spatial Dependence) and the cross-validation of soil attributes were improved, which was not observed in the data originally sampled. The external validation obtained good results, indicating that the virtual sampling can be used only in the planning for geostatistical analysis. Keywords: variogram, cross-validation, soil.

Extended TOBIA model for the assessment of deep-seated landslides

&lt;p&gt;In order to assess landslide susceptibility, the selection of the controlling factors (i.e., the predictor variables) is crucial. The most important factors for deep-seated landslides are geological settings such as the bedding conditions of rock formations and the distance to faults. We developed a GIS-based semi-automatic method to extract information on the orientation of bedding planes. This method uses information captured by the interpretation of high-resolution digital terrain models (DTMs). In order to calculate dip and dip direction of the bedding planes we have developed the Morpho-Line concept, which uses geometrical information captured by a detailed interpretation of DTMs. To increase the number of data points, additional field measurements were added to the morpho-line data. We have implemented the &quot;accumulated cost&quot; tool, which is similar to thiessen polygons, to interpolate between the data points. This method takes valleys and faults as break lines into account when interpolating bedding orientation values. Dip and dip direction data has been used, in combination with the slope and aspect, to calculate an extended TOBIA model. TOBIA classifies slopes into anaclinal, cataclinal and orthoclinal classes. To obtain a more accurate picture of orthoclinal bedding conditions and their connection to landslides in these areas, we have subdivided the orthoclinal classes. The angle difference between topography and bedding dip has been calculated and divided into classes. According to that model, the highest abundance of landslides is found in slopes classified as cataclinal and orthoclinal. This means that landslides preferably occur where the geological layers are inclined with the slope (cataclinal) or the dip direction is perpendicular to the slope direction (orthoclinal).&lt;/p&gt;