Recent and Future Changes in Rainfall Erosivity and Implications for the Soil Erosion Risk in Brandenburg, NE Germany

Abstract

The universal soil loss equation (USLE) is widely used to identify areas of erosion risk at regional scales. In Brandenburg, USLE R factors are usually estimated from summer rainfall, based on a relationship from the 1990s. We compared estimated and calculated factors of 22 stations with 10-minutes rainfall data. To obtain more realistic estimations, we regressed the latter to three rainfall indices (total and heavy-rainfall sums). These models were applied to estimate future R factors of 188 climate stations. To assess uncertainties, we derived eight scenarios from 15 climate models and two representative concentration pathways (RCP), and compared the effects of index choice to the choices of climate model, RCP, and bias correction. The existing regression model underestimated the calculated R factors by 40%. Moreover, using heavy-rainfall sums instead of total sums explained the variability of current R factors better, increased their future changes, and reduced the model uncertainty. The impact of index choice on future R factors was similar to the other choices. Despite all uncertainties, the results indicate that average R factors will remain above past values. Instead, the extent of arable land experiencing excessive soil loss might double until the mid-century with RCP 8.5 and unchanged land management.

Soil Hydrology for a Sustainable Land Management: Theory and Practice

“…This Special Issue collects 12 original contributions focused on soil hydrology and aimed to address the challenging topic of sustainable land management. From a methodological point of view, the contributions involve both field [8][9][10][11]13,19] and laboratory [14,15] experiments, and modelling [6,[16][17][18] studies. The Special Issue includes studies carried out at different spatial scales, from the field-to regional-scales.…”

“…The Special Issue includes studies carried out at different spatial scales, from the field-to regional-scales. A wide range of geographic regions are also covered, including Brazil [11,13], Mexico [16], Mediterranean basin [6,8,10,14,18], and Central [15,17,19] and Western [9] Europe. Specifically, contributions focus on five main topics including (i) land-use change [8,12,18,19], (ii) water use efficiency [14], (iii) erosion risk [6,17], (iv) solute transport [15], and (v) new methods and devices for improved characterization of soil physical and hydraulic properties [9][10][11]16].…”

“…Contributions of this SI were focused on: BEST-procedure [7] application, for a simple and expeditious method for estimating hydraulic properties of the soil [8][9][10][11]; Steady version of the Simplified method based on a Beerkan Infiltration run (SSBI method) [12] for field saturated hydraulic conductivity (K s ) estimation [13]; Hydrus-1D and SWAP codes for modelling soil water dynamics [11,14,15]; a new low-cost device for the automation of K s measurements in the laboratory [16]. Also, investigations on the topic of rainfall erosivity [6,17], or the impact of land-vegetation cover interaction on soil water content [18] and soil water repellency [19], increased our knowledge on the sustainable management of specific agro-environments. A synthesis of main results and/or innovative methods to assess those changes from a soil hydrology point of view were reported in the following section.…”

Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests

“…Several issues may arise due to accelerated soil losses on achieving of the Sustainable Development Goals of the United Nations [8], as these goals are dependent on a healthy biophysical environment in which the soil is the base [9]. In order to predict these soil erosion future changes it is necessary precipitation [37,38], monthly [39,40] and daily rainfall indices [41,42]. A different approach estimated projected R changes, using a weather generator with spatial and temporal downscaled precipitation values coming from various GCMs [43].…”

“…A number of papers in Europe examined the potential increase of rainfall erosivity using temporal trends of high resolution precipitation data Water 2020, 12, 687 3 of 20 in Western Germany [34], Belgium [35] and in the Czech Republic [36]. Other studies in various parts of the world used GCMs in conjunction with empirical equations that predict R using annual precipitation [37,38], monthly [39,40] and daily rainfall indices [41,42]. A different approach estimated projected R changes, using a weather generator with spatial and temporal downscaled precipitation values coming from various GCMs [43].Random Forests [44] is a data-driven algorithm in the area of supervised learning which tries to fit a model using a set of paired input variables and their associated output response and can be used in classification and regression problems.…”