Impact of climate change on soil erosion

Catena 121 (2014) 99–109

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Impact of climate change on soil erosion — A high-resolution projection on catchment scale until 2100 in Saxony/Germany A. Routschek a,⁎, J. Schmidt a,1, F. Kreienkamp b,2 a b

Technical University Freiberg, Soil and Water Conservation Unit, Agricolastrasse 22, D-09599 Freiberg, Germany Climate & Environment Consulting Potsdam GmbH, David-Gilly-Strasse 1, D-14469 Germany

a r t i c l e

i n f o

Article history: Received 13 September 2013 Received in revised form 24 March 2014 Accepted 30 April 2014 Available online 27 May 2014 Keywords: Climate change Soil erosion Soil erosion model Regional climate modeling

a b s t r a c t This study investigates changes in erosion rates at high temporal and spatial resolution for three example catchments in West, North and East Saxony/Germany under climate change. The study is based on the A1B IPCCscenario and model outputs of four models: ECHAM5-OPYC3 (general circulation model), WETTREG2010 (statistical downscaling climate model), METVER (agricultural model for calculation of daily initial soil moisture) and EROSION 3D as a process-based soil erosion model. Simulations were run for measured and projected single rainstorm events at a temporal resolution of 5 min. Soil loss was simulated for two future periods from 2041 to 2050 and 2091 to 2100, respectively. Results were compared to simulated soil loss based on 10 years of measured climate data from 1989 to 2007. Expected changes in land use, soil management due to changed crop rotation and shifted harvest date are taken into account as scenario studies. Outputs of the regional climate model show that the total number of rainstorms with intensities ≥ 0.1 mm/min is decreasing in future while rainfall intensities are increasing. Periods of heavy rainstorms will mostly shift from summer to autumn. While the total amount of annual rainfall is decreasing and the duration of sunshine is strongly increasing, soils become drier. Dry periods will appear more often in late autumn. Results of the simulations with EROSION 3D quantify the impacts of climate change on erosion rates. Climate change will lead to a significant increase of soil loss by 2050 and a partial decrease by 2100. Not adapting soil management and land use will aggravate erosion rates. The impacts of land use, soil management and soil properties on soil erosion by water are higher than the effects of changed precipitation patterns. Current soil protection measures are suitable for soil conservation under conditions of a changed climate. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Increases in global air temperature cause a rise in the moistureholding capacity of the atmosphere at a rate about 7% per 1 K (Mullan et al., 2012). An increase of water vapor in the atmosphere influences regional weather circulation patterns. These altered circulation patterns will lead to a modified occurrence of heavy rainstorms with regard to frequency, intensity and their incidence during the year. Soil erosion is mostly the result of extreme but short rainfall events. Therefore changes of precipitation intensity and frequency should affect soil erosion processes. Furthermore climate change influences a variety of physical and chemical soil properties which affect infiltration and soil erosion

⁎ Corresponding author. Tel.: +49 3731 392220; fax: +49 3731 392502. E-mail addresses: [email protected] (A. Routschek), [email protected] (J. Schmidt), [email protected] (F. Kreienkamp). 1 Tel.: +49 3731 39281; fax: +49 3731 392502. 2 Tel.: +49 331 7452301.

http://dx.doi.org/10.1016/j.catena.2014.04.019 0341-8162/© 2014 Elsevier B.V. All rights reserved.

processes. The most climate-sensitive parameters are soil moisture regime, content of organic carbon and canopy cover. The reasons for changes of the soil surface canopy cover by plants and plant residues are manifold: A shifted phenology will cause a change of planting and harvest dates (Chmielewski et al., 2004; Estrella, 2007). Climate change forces land-use change. New crops, crop rotations and management practices will be implemented to accommodate the new climate regime. At the same time changes in plant biomass production are expected. Climate change forces land-use changes, sometimes also an alteration of land-use structure. To incorporate these processes, climate and soil erosion models have become indispensable tools for assessing the response of soil erosion to a future climate (Lal, 1998; Toy et al., 2002). The impact of the expected climate change of frequency and extent of soil erosion processes has been estimated by several scientists (e.g. Boardman et al., 1990; Favis-Mortlock and Boardman, 1995; Klik and Eitzinger, 2010; Michael et al., 2005; Mullan et al., 2012; Nearing et al., 2005; Nunes et al., 2013; Zhang, 2007; Zhang and Nearing, 2005; Zhang et al., 2004, 2009, 2010). A first approximation of the impacts of changed precipitation intensities on future soil loss was generated for

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A. Routschek et al. / Catena 121 (2014) 99–109

two single slopes with EROSION 2D using WETTREG-Model outputs (Michael et al., 2005). Mullan et al. (2012) compiled an overview, including authors, regions of application, the used models and scales. He emphasized three fundamental limitations of several previous studies: (1) the spatial scale at which climate changes are represented; (2) the temporal scale at which climate changes are represented; and (3) the representation of changes in land use and management. The present study aims to overcome these shortcomings. (1) The quality of global climate simulation and the accuracy of predicted climate are steadily increasing. Statistically or dynamically downscaling techniques are the tools at the current state of the art to overcome the gap between the coarse spatial resolution of the outputs of General Circulation Models (GCMs) and the much finer scale of soil erosion. In this study the statistical downscaling model WETTREG developed by Enke (Enke and Spekat, 1997; Enke et al., 2005; Kreienkamp et al., 2010) was applied. (2) A major limitation of simulated climate data is the temporal resolution of available outputs: predicted precipitation generally consists of monthly data. Weather generators incorporated in downscaling models are used to disaggregate monthly data into

finer resolved data. The highest temporal resolutions of precipitation data ever utilized up to now – daily and sub-daily data – were used by Klik and Eitzinger (2010) and Mullan et al. (2012) running the WEPP-Model (Water Erosion Prediction Project). However, peaks of rainfall intensity range mostly from several minutes to a few hours. Those peaks generate on- and off-site damages such as rill erosion and mud silting on fields, as well muddy floods off site. A daily or even hourly resolution of precipitation data cannot record the nature of heavy rainfall events adequately which are setting off soil erosion processes. In order to capture these peaks, precipitation data at a temporal resolution of five minutes was used in this study. The WETTREG-Model is able to generate time series of rainfall data with five-minute-resolution of rainfall intensity. In contrast to most other downscaling approaches, this method preserves the observed statistical structures (e.g. average and dispersion) of the predicted meteorological parameters. In addition, the scheme includes an algorithm for the generation of extremes that have not yet been observed at recent climate conditions. The WETTREG-Model allows the projection of future climate data at this high resolution for local climate stations, if long-term measurements at the same high temporal resolution are available for the past. These high resolved data (spatial and

Oschatz Rasslitz

Goerlitz Viebig

Chemnitz

Dittersdorf

Fig. 1. Location of the climate stations Chemnitz, Oschatz and Goerlitz and the example catchments in Germany/Saxony (background false colors DEM).

A. Routschek et al. / Catena 121 (2014) 99–109 Table 1 Catchment characteristics. Catchment

Dittersdorf Rasslitz Viebig

Size [km2]

Altitude [mNN]

Arable land [%]

1.01 2.32 2.69

465–628 134–213 185–365

56 88 74

Soil texture [%] Clay

Silt

Sand

8–b 17 12–b 25 12–b 25

50–b 65 65–b 88 65–b 88

18–b 42 0–b 23 0–b 23

temporal) are essential for the application of the event-based soil erosion model EROSION 3D (Schmidt, 1990; Von Werner, 1995). (3) Most of the previous modeling studies have been restricted to direct impact investigations, i.e. changes in rainfall and temperature, without consideration given to the potential for increased or even decreased erosion due to changes in land use and management (Mullan et al., 2012). In this study the scenario-based approach was applied to include aspects of future land-use and management changes. This approved concept was already used by Klik and Eitzinger (2010), Nunes et al. (2013) and Mullan et al. (2012). Weak spots in the process of soil erosion modeling are soil properties which are altered by climate change. Nunes et al. (2013) combined the regional climate model PROMES with the SWAT continuous hydrological and vegetation model. PROMES outputs were used to estimate changes of storm rainfall intensity. SWAT was applied using the PROMES results to estimate changes in soil moisture and saturation deficit, as well as vegetation cover at a daily time step. This principle was adopted in the current project: The outputs of the statistical downscaling model WETTREG served as inputs for the METVER-Model (Müller, 1987) to determine daily soil moisture for respective soils and crops. Calculated soil moisture was transferred to EROSION 3D as initial soil moisture. A broad study of recent literature provided data for most probable future crop varieties, crop rotations, future phenology, soil cover, time shifted management, land use and content of organic carbon. 2. Material and methods 2.1. Site description Three small catchments in the vicinity of the climate stations Chemnitz, Oschatz and Görlitz (Fig. 1) were chosen. Each catchment represents a natural region of Saxony: catchment “Dittersdorf” the lower and middle Erzgebirge mountain region in the Southwest, catchment “Rasslitz” the Saxon loess belt in the North and catchment “Viebig” the

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Lusatia loess belt in the East of Saxony. Table 1 shows the catchment characteristics. Figures 2, 3 and 4 illustrate the elevation and land-use distribution for each catchment. Table 2 shows typical crops and crop rotations for these sites, provided by the Saxon State Agency. 2.2. Simulation of soil erosion EROSION 3D is a field-tested and widely validated, process-based computer model that calculates rainfall induced soil erosion and deposition in watersheds. A broad overview about model physics, its validation and the state of the art on existing 3D soil erosion models is given among others by Klik et al., 1998; Schmidt et al., 1999; Michael, 2000; Arévalo and Schmidt, 2011; Schindewolf and Schmidt, 2012. The model EROSION 3D, as applied in this study, was developed with the intention to provide an easy-to-use tool for erosion prediction in soil and water conservation planning and assessment (Schmidt, 1990, 1996; Schmidt et al., 1999; Von Werner, 1995). With respect to the user-demand the model needed to comply with following requirements. It should: -

be easy to use with as few input parameters as possible; give valid results without calibration for each specific application; operate on event basis; and be compatible with existing Geo-Information-Systems.

The theoretical concept of EROSION 3D covers the following erosional processes: -

generation of runoff; detachment of particles by raindrop impact and runoff; transport of detached particles by runoff; routing of runoff and sediment through the catchment; and sediment deposition.

The EROSION 3D model is predominantly based on physical principles. The model simulates the detachment of soil particles and the transport of detached particles by overland flow, including grain size distribution of the transported sediment and the sediment delivery into downstream water courses caused by single rainfall events. Erosion is limited either by the amount of sediment that can be detached from the soil surface or by the transport capacity of the overland flow. The basic assumption of the model is that the erosive impact of overland flow and droplets is proportional to the momentum fluxes exerted by the flow and the falling droplets, respectively. The resistance of the soil to erosion is expressed as the critical momentum flux. In this approach, the sum of all mobilizing forces (overland flow impact, impact

Fig. 2. DEM and land use — catchment Ditterdorf (climate station Chemnitz).

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Fig. 3. DEM and land use — catchment Rasslitz (climate station Oschatz).

Fig. 4. DEM and land use — catchment Viebig (climate station Goerlitz).

of the droplets) acting on soil particles is compared to the sum of those forces which prevent the particles from being detached and transported. Erosion will occur if the sum of mobilizing forces acting on particles is larger than that of the resisting forces. Otherwise, no particles are eroded from the soil surface. Erosion is limited either by the amount of sediment that can be detached from the soil surface or by the transport capacity of the flow. In order to transport detached particles a turbulent, vertical flow component must counteract the settling of the particles for deposition. The infiltration rate is estimated by an infiltration subroutine based on the modified approach of Green and Ampt (1911). As the Green and Ampt-approach is limited in simulating macropore flow and the

effects of surface crusting the infiltration model has been improved by an empirical correction procedure taking tillage practices, soil texture and time after tillage into account. EROSION 3D is a single-event-model: a soil file describing the actual soil conditions for every single storm has to be created. EROSION 3D is based on a regular grid. The grid size is variable, but must be consistent within the matrix. More than 5 ∗ 106 grid elements can be processed, while the number of grid elements is limited by hardware. A modified “lowest neighbor” routing algorithm (O'Callaghan and Mark, 1984) is used to calculate runoff and sediment as it flows from upland sources towards the catchment outlet. Due to the raster basis the model can be linked to various geographical information systems such as ArcInfo,

Table 2 Typical crops and 10-year crop rotations. Catchment


Main crop (N30% of the catchment area) and crop rotation/year 1998 2041 2091

1999 2042 2092

2000 2043 2093

2001 2044 2094

2002 2045 2095

2003 2046 2096

2004 2047 2097

2005 2048 2098

2006 2049 2099

2007 2050 2100

Summer barley Summer barley Summer barley

Winter wheat Winter rape Winter rape

Winter barley Winter wheat Winter wheat

Winter rape Winter barley Winter barley

Winter wheat Corn

Clover

Corn

Winter wheat Winter wheat

Winter barley Winter barley

Winter barley Winter rape Winter rape

Winter rape Winter wheat Winter wheat

Summer barley Sugar beets Corn

Corn

A. Routschek et al. / Catena 121 (2014) 99–109 Table 3 EROSION 3D — model inputs. Relief parameters

Soil parameters

Precipitation parameters

Digital elevation model (x-, y-, z-coordinates)

Texture [%] Bulk density [kg/m3] Initial soil moisture [vol.%] Carbon org. [%] Resistance to erosion [N/m2] Hydraulic roughness of the soil surface [Manning's n] [s/m1/3] Degree of soil cover [%]

Duration of rainfall [min]

Intensity of rainfall [mm/min]

ArcGis, IDRISI or GRASS. The optimum spatial resolution depends on catchment size and on the aim of simulations. Usually it alters between 20 × 20m (for catchments N 100 km2) and 5 × 5 to 10 × 10m (for catchments b100 km2). The temporal resolution of the model depends on the rainfall data available and can range from 1 to 15 min. EROSION 3D is able to provide long-term simulations by connecting soil-data-files with precipitationdata-files in a batch routine. All these features underline the applicability of EROSION 3D within this project. The input parameters (Table 3) can be assigned to three main groups: relief parameters (DEM), landuse and soil parameters, and precipitation parameters. Table 4 shows the model outputs. Erosion, deposition, surface runoff and infiltration are displayed for every raster-cell. Total soil loss or sediment, total deposition, runoff and infiltration are given by the lowest raster-cell of the catchment (=watershed outlet). All spatially distributed data are imported from a GIS. Simulations were performed on the basis of an official data base provided by the state agencies of Saxony in order to ensure the transferability of the method: For the relief the DEM25 was utilized, for the land-use distribution the ATKIS_DLM25 and for soil information the soil reference map 1: 200,000 was used. Spatial resolution of input data was adjusted to a 10 × 10m-grid (ASCII-format). When the project started in 2010, a higher resolved DEM (2 × 2m) was in progress, but it was neither verified nor available area wide. To facilitate the applicability of the model a detailed parameter catalogue was published including a compilation of experimentally obtained model parameters including their seasonal variation for different soils, crops and different land use such as deciduous forest, coniferous forest and pasture (Michael, 2000; Siebert et al., 2011). Work on the parameter database is still in progress. On the basis of the catalogue and newer data derived from ongoing rainfall experiments a database-tool was developed by Schindewolf and Schmidt (2012), which was used within this study. Up to now the compiled data cover the most frequent soils and crops in Germany and 100% of the soils and crops in the areas of investigation. 2.3. Climate data Continuous precipitation data from 1995 to 2007 with a resolution of five minute-sums were provided by the German Weather Service for the three climate stations Chemnitz, Oschatz and Goerlitz. The Table 4 EROSION 3D — model outputs. Parameter related to an area

Parameter related to a cross section of flow

Erosion, deposition and netto erosion for the watershed (chosen grid cell as watershed outlet, lowest point within the watershed) [mass/unit area] Erosion, deposition, runoff and infiltration for a chosen grid cell [mass/unit area]

Runoff [volume/unit width] Sediment delivery [mass/unit width] Sediment concentration [mass/unit flow volume] Particle-size distribution of the transported sediment [% clay, % silt by mass]

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decade 1998–2007 was picked as the reference period. Climate data for the two future periods from 2041 to 2050 and 2091 to 2100 were generated by the statistical downscaling model WETTREG2010 by Climate and Environment Consulting Potsdam GmbH (Kreienkamp et al., 2010). The simulations are based upon the results of the coupled global ocean atmosphere model ECHAM5-MPI/OM developed at the MaxPlanck-Institute Hamburg (Jungclaus et al., 2006; Kreienkamp et al., 2008, 2011; Roeckner et al., 2003). Specifically, the output of the A1B scenario as specified by the Intergovernmental Panel on Climate Change (IPCC, 2007) was used. WETTREG (weather situation-based regression method) is an empirical statistical downscaling method that links large-scale features of the atmosphere to locally measured climate data. In a nutshell, WETTREG assumes that general circulation models (GCMs) merit to be used as vantage ground for downscaling, since, owing to their ability to produce climate change patterns in a consistent way, they constitute a meaningful tool for obtaining local information from global patterns. Translated into terms of statistical climatology this means that changes of a parameter over time measured at the earth surface should coincide with changes of the occurred frequency of large-scale atmospheric patterns. This is the key to WETTREG where the regionalization is addressed at three stages: (1) Patterns are defined using the environment-to-circulation strategy (Yarnal, 1993). Intervals of a measured climate parameter (e.g., temperature) are specified and subsequently composites of the atmospheric situations on those days belonging to a temperature interval are built. This set of patterns, developed as a linkage between local time series and reanalysis data, are the building blocks for a subsequent objective re-identification procedure in the data of a climate model. Details on these aspects can be found in Spekat et al. (2007, 2010) and Enke et al. (2005). Goodess (2009) and Timbal et al. (2008) refer to the importance of choosing optimal predictors — this is taken into account within WETTREG by using relative topography fields for the temperature-related patterns and vorticity fields for the precipitation-related patterns. What emerges from this stage is a frequency distribution of the patterns that shift over time. (2) A stochastic weather generator is employed. It randomly rearranges episodes from the current climate, of which the frequency of the patterns is known, into new, synthesized time series. The signature of the changing climate is grafted onto these synthesized series whereby the (shifted) frequency of the patterns for a specific future time frame is a feature of the synthesized time series. Consequently some episodes appear more frequently than others. (3) As a further refinement, the large-scale models are consulted again, and changes over time in the modeled physical properties of the atmosphere are introduced to the synthesized time series by way of regression. In contrast to many other downscaling approaches, the method is capable of preserving the observed statistical structures (e.g. average and dispersion) of the predicted meteorological parameters. In addition, the scheme includes an algorithm for generating extremes that have not yet been observed within recent climate. The WETTREG method as it was described in Enke et al. (2005) and Spekat et al. (2010) has been subject to a few modifications, sketched in Kreienkamp et al. (2010), leading to the version WETTREG2010. First off all, there was a transition in the property upon which the environmentto-circulation approach builds its classification — in WETTREG2010 it is the deviation from the annual cycle instead of the value itself. Secondly, additional weather situations, so-called Trans Weather Patterns, were introduced that emerge in the future and cannot be inferred from statistics of the current climate. The net effect is a greater proximity of the climate signals, in particular with respect to temperature. WETTREG is conditioned to reproduce the climate of 1971–2000 with an accuracy

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of − 0.1 K (mean temperature 2 m above ground) and 5% (precipitation) for each season. The output of WETTREG2010 is a set of continuous dated time series from 2041 to 2050 and 2091 to 2100 at the for the three climate stations — Chemnitz, Oschatz and Goerlitz located in the federal state of Saxony (Fig. 1). The generated data bear the signature of a shifting climate. Furthermore, ten simulations were produced which are equally probable and constitute variants of the projected climate. This approach ensures that future climate variability is assessed to the greatest possible extent and adds to the variation of the predicted variables. Out of these ten equally probable simulations the runs with the highest (MAX) and the lowest medium precipitation intensity (MIN) were chosen for this study. All single rainfall events with intensity ≥ 0.1 mm/min (+/−60 min) were selected out of the MAX and the MIN runs. This intensity matches the lowest infiltration rates measured during rainfall experiments in Saxony (Michael, 2000; Schindewolf and Schmidt, 2012). Precipitations during days with medium temperature below 0 °C were neglected. 2.4. Simulation of soil moisture METVER is a meteorological evaporation model, developed by Müller, 1987. Applications, validations and further developments of the model are performed by the National German Weather Service. METVER is a one-layer-model based on the approach of Turc (1961) and its modification by Wendling et al. (1991). Potential and real evapotranspiration are calculated for the root zone and the whole zone of exhaustion. The availability of water in both zones depends on soil properties, weather variables and plant development throughout the year. As the soil moisture is one of the most important factors for determination of evaporation, it is explicitly calculated as an intermediate step in the program METVER and can be displayed. The model describes a soil profile with a depth of 2 m, which can be divided into 20 decimeter layers. Three types of input data were required to run METVER: climate, land-use and soil-hydrological data. The following climate data were required at a daily resolution: medium temperature at 2 m height above ground, precipitation at 1 m height with internal correction algorithm by Richter (1995) and medium solar radiation. These data were provided by the German Weather Service for the reference period from 1998 to 2007 for the climate stations Chemnitz, Oschatz and Goerlitz. Climate data for the prospected periods from 2041 to 2050 and 2091 to 2100

were calculated by WETTREG (Kreienkamp et al., 2010). Again, out of ten equally probable WETTREG-simulations the runs with the highest (MAX) and the lowest medium precipitation intensity (MIN) were chosen for the calculation of the soil moisture. Land use is described by phenology, interception, rooting depth and a plant specific parameter (Müller, 1987) for the most common crops, also for pasture and forest. Soil-hydrological conditions are specified by soil texture, field capacity and permanent wilting point. Simulations were performed by Pilz (2011). Daily water contents [vol.%] in the uppermost layer of the topsoil (0–10 cm) were simulated for the prevailing soils and crops within the three catchments for the time periods from 1998 to 2007, 2041 to 2050 (MIN and MAX) and 2091 to 2100 (MIN and MAX). 2.5 . Scenarios Table 5 gives an overview of computed scenarios. Crop distribution on fields within the catchments was allocated randomly with the years 1998, 2041 and 2091. Crop rotation on each field was specified according to the scheme in Table 2. Typical crops and percentage of area for individual crops in the crop rotations within the catchments were provided by the state agency for environment, agriculture and geology of Saxony. The main field-crop (N 30% of the total area of the catchment) within the crop distribution is given in Table 2. Soil parameters for the date of the rainfall event (measured and simulated) were derived from the EROSION 3D parameter catalogue (Michael, 2000) according to soil texture (from map), soil cover, tillage practice and season. 3. Results and discussion 3.1. Impact of climate change on occurrence of heavy rainstorms Measured and simulated heavy rainstorms ≥ 0.1 mm/min have been analyzed with regard to frequency and intensity by Michael (2013). Simulated rainstorms for both future periods (2041–2050 and 2091–2100, runs MIN and MAX) were compared to observed rainstorms during the reference period (1998–2007). This comparison was performed for the three climate stations. 3.1.1. Number of rainfall events with intensities ≥ 0.1 mm/min The total amount of precipitation caused by heavy rainstorms will slightly increase in future. The number of rainfall events with intensities

Table 5 Scenarios. Scenario name

Decade

Input data

Land use

0

Reference scenario

1998–2007

1

Future scenario “conventional tillage”

2041–2050 2091–2100

Conventional tillage Stubbles after harvest conventional tillage stubbles after harvest

2

Future scenario “conservation tillage”

2041–2050 2091–2100

3

Future scenario “no tillage”

2041–2050 2091–2100

4

Future scenario “decreasing TOC”

2041–2050 2091–2100

5

Future scenario “changed phenology”

Month

6

Future scenario “monoculture corn”

2041–2050

7

Future scenario “changed land use”

Month

Measured precipitation at climate stations Chemnitz, Oschatz, Goerlitz Simulated soil moisture (METVER) Simulated precipitation (WETTREG2010) Runs MIN and MAX for Chemnitz, Oschatz, Goerlitz Simulated soil moisture (METVER) Simulated data (WETTREG2010) runs MIN and MAX for Chemnitz, Oschatz, Goerlitz Simulated soil moisture (METVER) simulated data (WETTREG2010) Runs MIN and MAX for Chemnitz, Oschatz, Goerlitz simulated soil moisture (METVER) Simulated data (WETTREG2010) Runs MIN and MAX for Chemnitz, Oschatz, Goerlitz Simulated soil moisture (METVER) Simulated data (WETTREG20120) Simulated soil moisture (METVER) Simulated data (WETTREG20120) Run MAX for Oschatz Simulated soil moisture (METVER) Simulated data (WETTREG20120) Simulated soil moisture (METVER)

Conservation tillage Stubbles after harvest 20% plant residues Direct seeding Stubbles after harvest 90% plant residues Conventional tillage TOC until 2050: −0.1% TOC until 2100: −0.2% Conventional tillage Earlier harvest Conventional tillage Winter wheat Corn Pasture Forest


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Fig. 5. Number of rainstorms with intensities ≥ 0.1 mm/min.

≥ 0.1 mm/min will decrease. Differentiation from West to East (higher number of rainstorms in West, lower in East) is persisting. The decline of the number of extremes is highest in Chemnitz in West Saxony (−17% by 2050, −18% by 2100), followed by Goerlitz (−7% by 2050, − 19% by 2100). In the region Oschatz (North of Saxony) the number of heavy rainstorms first declines (− 9% by 2050) and rises again to the present level until the end of the century (Fig. 5). However, the temporal distribution of heavy rainstorms during the year will change: The number of extremes will decrease from May to September and increase from October to April. This trend will intensify until the end of the century (as shown in Fig. 6 for climate station Goerlitz).

3.1.2 . Intensities of rainfall events ≥ 0.1 mm/min The number of rainstorms with low intensities (N0.1 and ≤0.2 mm/ min) is declining for Chemnitz and Goerlitz as compared to the present. In contrast to this trend, the amount of rainstorms with high and very high intensities (N 1.0 mm/min) is increasing — particularly in the region Chemnitz (West of Saxony). The number of events with medium intensities (N0.2 and ≤ 0.5 mm/min) is decreasing from West to East Saxony currently as well as in the future. Heavy rainfall events with

top-level intensities N 2.0 mm/min will occur more often in Goerlitz (East Saxony) than in Chemnitz (West Saxony) and Oschatz (North Saxony) during the second future period from 2091 to 2100. Differentiation within the year is not unitary: The probability for the occurrence of heavy rainstorms with high and very high intensities is increasing in all simulations for the climate station Chemnitz with exception of February and May. Such extremes are projected for Oschatz (North Saxony) mainly for the months March, April, August and September. In February, May and October, however, the probability is decreasing. In the East of Saxony the probability of extremes with medium and high intensities (N0.5 mm/min, b1.0 mm/min) will rise from October to April (except January) and decrease from May to September.

3.2. Impacts of climate change on soil moisture With a decrease in total precipitation and a strong increase in duration of sunshine, soils are likely to become drier. For ease of visualization of soil humidity, soil moisture as calculated with METVER was assigned to the following categories according to

Fig. 6. Deviation of the number of heavy rainstorms in relation to the reference period (zero line) — climate station Görlitz.

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Table 6 Soil loss [t/ha]: comparison reference scenario versus future scenarios. Catchment

Reference scenario 1998–2007 soil loss [t/ha]

Future scenario 2041–2050 soil loss [t/ha]

Future scenario 2091–2100 soil loss [t/ha]


22 111 160

MIN 58 128 197

MIN 16 152 13

MAX 71 123 54

MAX 22 104 70

Bundesanstalt fuer Geowissenschaften und Rohstoffe (2005): “dry” (N 104 hPa), “low moist” (104 –N102.7 hPa), “moist” (102.7–N 102.1 hPa) and “very moist” (≤ 102.1 hPa). Afterwards a frequency distribution over all categories was created for each year. At all climate stations – as well as for the MIN- and for the MAX-run – the frequency of the “very moist” states is decreasing and the frequency of “low moist” states is increasing. The location Oschatz shows an extreme change, as the values even reach the “dry” category. However, the overall changes are highest in Görlitz. The frequency of days when field capacity is reached is decreasing. For Chemnitz and Görlitz they drop from 62 to 35 days and in Oschatz from 38 to 25. Dry periods will appear more often in late autumn. 3.3. Impacts of climate change on soil erosion 3.3.1. Reference scenario First, total soil loss at the catchment outlet was calculated for every single event during the reference period from 1998 to 2007. Conventional tillage and crop rotation as shown in Tables 2 and 4 were assumed. Soil moisture was calculated with METVER for all soil textures and crops within the crop rotation based on measured climate data. A total soil loss of 22 t/ha (as a sum of all erosion events) during the reference period was calculated for the catchment Dittersdorf (Chemnitz), 111 t/ha for Rasslitz (Oschatz) and 160 t/ha for Viebig (Goerlitz). The differences result mainly from different soil textures (sandy-loam in Dittersdorf, loess in Rasslitz and Viebig). 3.3.2. Future scenario “conventional tillage” Secondly, total soil loss for the two future periods was calculated for the MAX and MIN-run (WETTREG). In comparison to the reference scenario, only the initial soil moisture was changed in the soil parameter sets. Results present the direct impact of climate change – inclusive climate induced changes of initial soil moisture – on soil loss (Table 6).

3.3.2.1 . Catchment Dittersdorf (climate station Chemnitz). Soil loss triples by 2050. The months June to September are at risk with a peak in July and August. Until the end of the century, the projected soil loss for the catchment decreases again to the current level. 3.3.2.2 . Catchment Rasslitz (climate station Oschatz). Soil loss increases for about 13% by 2050. The projection for the second future period is vague. A moderate increase (+ 37%) was calculated for the MIN-run, for the MAX-run a slight decrease (− 6%) was projected. Low initial soil moisture, calculated with METVER particularly for the MAX-run, can explain the decrease. The soil erosion risk remains at a very high level. 3.3.2.3 . Catchment Viebig (climate station Goerlitz). For the MIN-run a higher (+ 23%), and for the MAX-run a lower soil loss (− 66%) were simulated as compared to the reference period. The future soil erosion risk remains – likewise with Rasslitz – at a high level until the mid of the century, but it does not increase clearly. Until the end of the century, the soil erosion risk drops markedly (−56 to – 92%) — due to the much lower initial soil moisture. Nevertheless, 84% (58 t/ha) of the soil loss of the whole decade (MAX-run) was generated by a short sequence of four extreme rainfall events, dated in March 2098. Such events would imply a total damage of the human infrastructure in the catchment. The probability of soil erosion events declines during summer and increases during spring and autumn. This applies to all three catchments. Values for the MIN-run are often higher than for the MAX-run. The reason for that is the lower initial soil moisture (due to the higher decrease in total precipitation and the stronger increase in duration of sunshine), simulated for the MAX-alternative. 3.3.3. Future scenarios “conservation tillage” and “no tillage” The changeover from conventional tillage to profitable conservation tillage by cultivator or no-tillage can last 10 to 20 years, depending on starting conditions. The cover of plants and plant residues is responsible for the protection of the soil surface and contributes to an enrichment of organic carbon in the upper centimeters of the soil. Aggregate stability increases and the reproduction of macro fauna species, especially earth worms, leads to a stable system of macro-pores. Conservation tillage by cultivator has been carried out in Saxony since the last decade of the 20th century. Today about 33% of the agricultural land are under conservation tillage (Schmidt, 2011). Soil cover by plant residues of total field area varies from 15 to 50% in dependence of the amount of plant residues, organic carbon enriches in the top-layer of the soil. In most cases, as documented during field experiments by Schindewolf and Schmidt (2012), not more than 20% of the soil surface

Fig. 7. Influence of soil management: catchment Dittersdorf, August 2034 (MAX).

A. Routschek et al. / Catena 121 (2014) 99–109 Table 7 Changed average dates of harvesting (Estrella, 2007; Pöhler et al., 2007). Crop

Harvest 1998–2007

Harvest 2041–2050

+/− [days]

Harvest 2091–2100

+/− [days]

Winter wheat Winter barley Winter rape Summer barley Corn Sugarbeets

09.08 15.07 28.07 10.08 20.09 10.10

31.07 10.07 10.08 31.07 07.09 08.10

−9 −5 +13 −10 −13 −2

20.07 04.07 27.08 20.07 21.08 05.10

−20 −11 +30 −21 −30 −5

was covered. To account for this, 20% soil cover by plant residues was assumed for the scenario “conservation tillage”. Yet, this percentage is considered as too low for an enrichment of organic carbon. For the scenario “no tillage” 70% of coverage by plant residues and an enrichment of 1% of organic carbon for both periods were applied. The scenario “conservation tillage” resulted in a reduction of soil loss by average 75% in comparison with the future scenario “conventional tillage”. A reduction of even 91% was calculated for the scenario “no-tillage”. Fig. 7 illustrates the impact of a changed tillage exemplarily for the catchment Dittersdorf for August 2034 (MAX-run). 3.3.4. Future scenarios “decreasing total organic carbon” Review of recent literature implies a moderate decrease of organic carbon in soils due to faster residue decomposition by enhanced microbial activity. The degradation velocity depends on many physical, chemical and management factors. Kolbe (2009) listed the expected development of the contents of organic carbon in top-soils of Saxony. Based on these data, an average decrease of 0.1% by 2050 and 0.2% by 2100 is assumed for the future scenario “decreasing TOC”. The expected decrease of TOC induces an additional increase of soil loss of about 2–4% in the model runs by 2050 and of about 5–14% by 2100. 3.3.5. Future scenarios “changed phenology” Chmielewski et al. (2004), Estrella (2007) and Pöhler et al. (2007) investigated the trends of the future phenology of crops for Germany. While the shift of sowing time will be only marginal, the harvesting date of many crops will be shifted notably. Table 7 assembles the

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average dates of harvesting for several crops for Germany for the reference and the two future periods. During the selected decades, only four heavy rainstorms felt into the timeslot between former and future harvest dates. In these rare cases the shift of phenology causes a significant increase in soil loss. Certainly, the amount of soil loss depends on the soil conditions after harvest. If stubbles and plant residues remain on the soil surface, the increase will be moderate (+ 25 to + 75%). If a plain and cleared up soil surface remains, heavy rainstorms can cause extreme high soil losses (+ 211 to + 600% – as shown in Fig. 8 for the catchment Rasslitz/September 2043 – MIN-run). 3.3.6. Future scenarios “monoculture corn” The future scenario “monoculture corn” was modeled for the decade 2041–2050 (Max-run) for the catchment Rasslitz (climate station Oschatz). First, it was assumed that stubbles after harvesting and 20% plant residues remain: An increase of 47% was calculated as compared to the mixed crop rotation. The thinned out soil cover induces fast silting. High soil losses were calculated for spring (March and April) and late autumn (October, November and December). Secondly, the case of fallow after harvest was studied: Soil loss decreases in comparison with a mixed crop rotation (−32%), due to the high roughness of the fallow. The soil erosion risk increases in early spring because of the decreasing soil roughness after imposed load of snow layers and snow melt. Fig. 9 shows erosion and deposition for December 2049 for the scenario “monoculture corn” under these two managements. It clearly shows that the development of soil surface is linked to the amount of plant residues. 3.3.7. Future scenarios “changed land use” Land-use change from agricultural land into pasture or forest with the aim of soil protection against erosion is not a realistic scenario. The market demands for food and energy crops are rising continuously. Nevertheless, the effect of land-use change was tested for several examples with equal results: Land-use change is a suitable tool against soil erosion. The total conversion into pasture leads to a reduction of soil loss by 97%. The total conversion into forest (after minimum 15 years growth) inhibits erosion almost completely.

Fig. 8. Climate change and shifted harvest with different managements in September 2043 (MIN-run) – catchment Rasslitz – in comparison with present harvest dates.

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Fig. 9. Erosion and deposition for December 2049 (catchment Rasslitz), scenario “monoculture corn”. - left picture: 20% plant residues, crusted — 31.5 t/ha soil loss. - right picture: 90% plant residues, not crusted — 16.6 t/ha soil loss.

4. Conclusion and implications

References

The impact of the predicted changes of the regional weather circulation patterns in Saxony/Germany leads to increasing or constant high soil erosion rates by 2050. Until the end of the century, soil erosion rates are generally projected to decrease. Precipitation intensities of extreme rainfall events are increasing, although the total number of events is decreasing. Additionally, the distribution of rainstorms during the year is changing: The number of rainstorms will decrease during summer and increase during the winter half-year. This trend is projected to intensify until the end of the century. The main reason for the decrease of soil erosion rates in the second half of the century is lower initial soil moisture due to higher temperatures, radiation and a decline of total precipitation amount. Climate change induces land-use change. This study confirms the results of Mullan et al. (2012): Soil management controls soil erosion rates much more than the expected climate change. The outcomes of the study confirm: Permanent conservation tillage and no-tillage are most suitable to protect soils under changing climate conditions. A stable soil structure due to minimal soil disturbance and a high soil cover all year round are of prime importance. Current soil protection measures are suitable for soil conservation at conditions of climate change. The application of the statistical downscaling model WETTREG proved to be a robust state of the art method. One major advantage over all previous approaches is the reproduction of rainstorms at a very fine scale (resolution of 5 min). This fine scale is necessary to drive event-based soil erosion models in order to predict soil erosion as precisely as possible. Further analysis of the impacts of climate change on soil loss must include the variation of social and economic boundary conditions and possible adaptation strategies. This implies land-use changes and changes of land-use structure, soil management and related to the soil structure, cover, roughness, porosity, and content of organic carbon. Of course, the use of other global circulation models and different IPCC-scenarios would define the frames of the future soil erosion rates more precisely. But this would have to go beyond the scope of the financial budget of this project. This study served as a first approximation of the impact of climate and land-use changes on soil loss at fine temporary scale in catchments. Improvements in climate research and model concepts of regionalization will support detailed, fast and robust projections.

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