Andreas Klik

Rainfall erosivity in Europe

Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1541 precipitation stations in all European Union (EU) Member States and Switzerland, with temporal resolutions of 5 to 60 min. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 min using linear regression functions. Precipitation time series ranged from a minimum of 5 years to a maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression (GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha(-1) h(-1) yr(-1), with the highest values (>1000 MJ mm ha(-1) h(-1) yr(-1)) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha(-1) h(-1) yr(-1)) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also the highest in Mediterranean regions which implies high risk for erosive events and floods.

Global rainfall erosivity assessment based on high-temporal resolution rainfall records

The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha−1 h−1 yr−1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.

Impact of climate change on soil erosion and the efficiency of soil conservation practices in Austria

The goal of the present study was to assess the impact of selected soil protection measures on soil erosion and retention of rainwater in a 1·14 km 2 watershed used for agriculture in the north-east of Austria. Watershed conditions under conventional tillage (CT), no-till (NT) and under grassland use were simulated using the Water Erosion Prediction Project (WEPP) soil erosion model. The period 1961–90 was used as a reference and results were compared to future Intergovernmental Panel on Climate Change (IPCC) scenarios A1B and A2 (2040–60). The simulations for the NT and grassland options suggested runoff would decrease by 38 and 75%, respectively, under the current climatic conditions. The simulation results suggest that, under future climate scenarios, the effectiveness of the selected soil conservation measures with respect to runoff will be similar, or decreased by 16–53%. The actual average net soil losses in the watershed varied from 2·57 t/ha/yr for conventional soil management systems to 0.01 t/ha/yr for grassland. This corresponds to a maximum average annual loss of about 0·2 mm, which is considered to be the average annual soil formation rate and therefore an acceptable soil loss. The current soil/land use does not exceed this limit, with most of the erosion occurring during spring time. Under future climate scenarios, the simulations suggested that CT would either decrease soil erosion by up to 55% or increase it by up to 56%. Under these conditions, the acceptable limits will partly be exceeded. The simulations of NT suggested this would reduce annual soil loss rates (compared to CT) to 0·2 and 1·4 t/ha, i.e. about the same or slightly higher than for NT under actual conditions. The simulation of conversion to grassland suggested soil erosion was almost completely prevented. The selected soil conservation methods maintain their protective effect on soil resources, independent of the climate scenario. Therefore, with small adaptations, they can also be recommended as sustainable soil/land management systems under future climatic conditions. However, based on the available climate scenarios, climate-induced changes in the frequency and intensity of heavy rainstorms were only considered in a limited way in the present work. As the general future trend indicates a strong increase of rainstorms with high intensity during summer months, the results of the present study may be too optimistic.

Mapping monthly rainfall erosivity in Europe

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315 MJ mm ha− 1 h− 1) compared to winter (87 MJ mm ha− 1 h− 1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year.

Predicting daily streamflow in ungauged rural catchments : the case of Masinga catchment, Kenya

Abstract Access to daily streamflow data at the catchment scale, is a central component of many aspects of water resources and water quality management. However, the majority of river reaches in many catchments in Kenya are ungauged or poorly gauged, and in some cases existing measurement networks are declining. Long-term continuous monitoring is not being done due to high costs of equipment maintenance. Therefore, there is a need for an alternative tool such as a catchment-scale hydrological model that is capable of predicting the daily streamflow. An approach is presented of predicting daily streamflow using a physically-based catchment-scale model, the geospatial Stream Flow Model (SFM). The SFM was developed using the “C” programming language and the user interface was developed using the Avenue script of the ArcView software. The SFM simulates the dynamics of runoff processes by utilizing remotely sensed and widely available global or local data sets. The model was applied in the Masinga catchment, Kenya, and the results gave a model performance coefficient of 0.74 based on the Nash-Sutcliffe statistical criterion.

Soil detachment and transport processes from interrill and rill areas

Abstract The objective of this work is to investigate effects of rainfall intensity, slope steepness and soil surface condition on soil detachment and transport. All experiments were conducted in the laboratory using programmable rainfall simulation troughs equipped with oscillating nozzles. Interrill soil erosion experiments were conducted in Austria for six different soils. Sediment transport processes in a rill channel were studied for an Ava silt loam at the NSERL, USA. Experiments were conducted for different soil hydrologic conditions, rainfall intensities and slope steepness. Data showed the importance of raindrop impact as well as slope steepness, hydrologic conditions, and their complex interactions on erosion processes from interrill and rill areas.

Rainfall Erosivity in Northeastern Austria

Abstract. Rainfall erosivity is the main driver for soil detachment and sediment transport, and represents the climatic factor most influencing the risk of soil erosion at a given site. Our objectives were to explore 15 min rainfall breakpoint data from 51 rain gauges in the northeastern part of Austria to (1) calculate rainfall erosivity values for this region, and (2) analyze statistically whether there exists a temporal evolution in rainfall erosivity. Rainfall data (May to October) covered between nine and 53 years. Rain gauges were located at elevations between 150 and 970 m a.s.l., and mean rainfall depths were 514 (±169) mm and 652 (±156) mm for Lower Austria (LA) and Upper Austria (UA), respectively. The calculated rainfall erosivity factor (R factor) ranged from 273 to 1599 with an average of 884 MJ mm ha -1 h -1 in LA and from 637 and 1697 with an average of 982 MJ mm ha -1 h -1 in UA. Rainfall depths and erosivities showed greater variability in LA than in UA, which can be attributed to the different ranges of topographic regions in LA. For both states, relationships could be established between average rainfall from May to October and the R factor, with correlation coefficients between 0.77 (LA) and 0.85 (UA). Overall, for the same rainfall amounts, rainfall erosivity was lower in UA than in LA. In LA, time series analyses showed a significant temporal increasing trend in rainfall intensity, rainfall erosivity, and storm number and intensity. These positive trends at 90% of the stations with data sets of >25 years indicate possible future R factor increases and increasing erosion risk. In UA, only 25% of the rain gauges showed a significant positive trend in rainfall erosivity, and 40% showed a significant increase in rainfall intensity. Changes in rainfall patterns were more distinct in eastern Austria than in northern Austria, thus impacting runoff, infiltration, and erosion processes in this area in the future.

Radioisotopic Measurements (137Cs and 210Pb) to Assess Erosion and Sedimentation Processes: Case Study in Austria

Twelve to seventeen percentage of the European soil is threatened by water erosion and around 13% of Austrian territory is affected. Only scarce information based on conventional assessment and measurements are available on erosion and sedimentation rates in Austria. The magnitude of sedimentation processes was evaluated in a small agricultural Austrian watershed using both nuclear techniques (137Cs and 210Pb) and conventional non-isotopic measurements in runoff erosion plots during the 1994–2006 periods. Using the erosion data provided by the plots (29.4 t ha–1 year–1 for the conventional tilled plot, 4.2 t ha–1 year–1 for the plot receiving conservation tillage and 2.7 t ha–1 year–1 for the plot receiving direct seeding treatment) and the 137Cs soil profiles content and the conversion model mass balance 2 (MBM 2), a sedimentation rate of 13.2 t–1 ha–1 year–1 (value determined down slope of the runoff plot under direct seeding treatment) to 50.5 t–1 ha–1 year–1 (value determined in the lowest sedimentation area of the watershed under conventional tillage) was estimated. Under the experimental condition the conservation tillage and direct seeding system were effective in reducing the sedimentation magnitude by 65%. However, due to a high variability of the initial fallout inventory and a high γ-spectrometry measurement error, information provided by the 210Pb method was not usable in the study area. The combined use of conventional erosion measurements and nuclear techniques appears to be a promising and complementary approach to evaluate sedimentation processes.

Spatial and temporal distribution of rainfall erosivity in New Zealand

Rainfall and its kinetic energy, expressed by rainfall erosivity, drives soil erosion processes by water. One of the most commonly used erosivity parameters is the rainfall-runoff erosivity factor R of the Revised Universal Soil Loss Equation. The goal of this study was to investigate for the first time the spatial distribution of annual rainfall erosivity in New Zealand. High-resolution data from 35 weather stations were used to calculate the R-factors. Based on these results, region-specific equations were developed and were applied by using long-term precipitation records from 597 stations. The values were interpolated with a geographic information system to generate a map showing spatial variations of rainfall erosivity. Annual R-values vary across both islands by a factor of 30, from 16 000 MJ mm ha–1 h–1 in the Southern Alps. These large differences are related to climatic and topographic features. Nevertheless, the data show a high correlation to the precipitation. In most parts of New Zealand, highest erosivity values occurred in December and January, whereas the lowest values were observed in August.

Assessment of rill erosion development during erosive storms at Angereb watershed, Lake Tana sub-basin in Ethiopia

Application of simple and locally based erosion assessment methods that fit to the local condition is necessary to improve the performance and efficiency of soil conservation practices. In this study, rill erosion formation and development was investigated on the topo-sequence of three catchments (300–500 m slope length); and on agricultural fields (6 m and 14 m slope lengths) with different crop-tillage surfaces during erosive storms. Rill density and rill erosion rates were measured using rill cross section survey and close range digital photogrammetry. Rill formation and development was commonly observed on conditions where there is wider terrace spacing, concave slope shapes and unstable stone terraces on steep slopes. At field plot level, rill development was controlled by the distribution and abrupt change in the soil surface roughness and extent of slope length. At catchment scale, however, rill formation and development was controlled by landscape structures, and concavity and convexity of the slope. Greater rill cross sections and many small local rills were associated to the rougher soil surfaces. For instance, relative comparison of crop tillage practices have showed that faba-bean tillage management was more susceptible to seasonal rill erosion followed by Teff and wheat tillage surfaces under no cover condition. Surface roughness and landscape structures played a net decreasing effect on the parallel rill network development. This implies that spatial and temporal variability of the rill prone areas was strongly associated with the nature and initial size of surface micro-topography or tillage roughness. Thus, it is necessary to account land management practices, detail micro-topographic surfaces and landscape structures for improved prediction of rill prone areas under complex topographic conditions. Application of both direct rill cross section survey and close range digital photogrammetric techniques could enhance field erosion assessment for practical soil conservation improvement.