Watershed studies are essential for erosion research
because they embed real agricultural practices, heterogeneity along the flow
path, and realistic field sizes and layouts. An extensive literature review
covering publications from 1970 to 2018 identified a prominent lack of
studies, which (i) observed watersheds that are small enough to address
runoff and soil delivery of individual land uses, (ii) were considerably
smaller than erosive rain cells (<400ha), (iii) accounted for the
episodic nature of erosive rainfall and soil conditions by sufficiently long
monitoring time series, (iv) accounted for the topographic, pedological,
agricultural and meteorological variability by measuring at high spatial and
temporal resolution, (v) combined many watersheds to allow comparisons, and
(vi) were made available. Here we provide such a dataset comprising 8 years of comprehensive soil erosion monitoring (e.g. agricultural
management, rainfall, runoff, sediment delivery). The dataset covers 14
adjoining and partly nested watersheds (sizes 0.8 to 13.7 ha), which were
cultivated following integrated (four crops) and organic farming (seven
crops and grassland) practices. Drivers of soil loss and runoff in all
watersheds were determined with high spatial and temporal detail (e.g., soil
properties are available for 156 m2 blocks, rain data with
1 min resolution, agricultural practices and soil cover with daily
resolution). The long-term runoff and especially the sediment delivery data
underline the dynamic and episodic nature of associated processes,
controlled by highly dynamic spatial and temporal field conditions (soil
properties, management, vegetation cover). On average, the largest 10 % of
events lead to 85.4 % sediment delivery for all monitored watersheds. The
analysis of the Scheyern dataset clearly demonstrates the distinct need for
long-term monitoring in runoff and erosion studies.
Introduction
Soil erosion, due to arable land use, is a major environmental threat
(Montanarella et al., 2016; Pimentel, 2006) negatively affecting on-site
soil properties and leading to substantial off-site damage (Pimentel and
Burgess, 2013). Assessing soil erosion under natural rain can either be
carried out in plot or watershed scale studies (Fig. 1). Plot studies (Fang
et al., 2017; Nearing et al., 1999; Smets et al., 2009; Wischmeier, 1966)
prevail in number and usually comprise a large number of plots that are
simultaneously measured to account for comparability. On the other hand,
watershed studies usually focus only on one or very few watersheds.
The most
prominent plot set-up (the Wischmeier plots; 22.1 m long, 1.83 m wide; slope
9 %) were established while developing the still most used erosion model,
the Universal Soil Loss Equation (USLE; Wischmeier and Smith, 1960).
Nowadays, data of thousands of plot years of the Wischmeier plot types are
available for various regions of the world. The major advantages of plot
experiments are that plots are relatively easy to establish, represent a
more or less homogenous area, and can be compared in paired plots (Nearing
et al., 1999). The major disadvantage of plots is that they can only assess
runoff generation mainly driven by surface sealing, while other processes of
runoff generation like return flow are ignored. Similarly, sheet and partly
rill erosion can develop on plots while (ephemeral) gullying is neglected.
Furthermore, heterogeneities along the flow path, variations in slope,
watershed size and soil cover (that may cause highly relevant run-on
infiltration and sediment settling) are excluded in plot experiments.
Furthermore, plots typically examine a narrow range of dimensions (length,
width, length-to-width ratio) (Fiener et al., 2011) that differ considerably
from dimensions of fields to which the results are mostly supposed to be
applied (Auerswald et al., 2009, Fig. 1).
Watershed size and duration of continuous measurements of runoff and sediment delivery for watershed studies taken from literature since 1970 (black triangles) in comparison with the Scheyern dataset (red triangles). The 99.5 %-range of field sizes in Germany is shaded in green; the vertical line denotes the average (taken from Auerswald et al., 2018). The approximate range of plot studies with natural rain is shaded in grey; the vertical and horizontal lines denote the average plot size and the average study duration (taken from Cerdan et al., 2010). Watershed studies from literature were (Anderson and Potts, 1987; Baker and Johnson, 1979; Beasley, 1979; Beasley et al., 1986; Becht and Wetzel, 1989; Bingner et al., 1989; Bowie and Bolton, 1972; Brooks et al., 2010; Casali et al., 2008; Chow et al., 1999; Deasy et al., 2011; Dendy, 1981; Dickinson and Scott, 1975; Didone et al., 2017; Diyabalanage et al., 2017; Duvert et al., 2010; Edwards et al., 1993; Evrard et al., 2008; Foster et al., 1980; Garcia-Ruiz et al., 2008; Glendell and Brazier, 2014; Grangeon et al., 2017; Hamlett et al., 1983; Hasholt, 1992; Hasholt and Styczen, 1993; Inoubli et al., 2016; Khanbilvardi and Rogowski, 1984; Kimes and Baker, 1979; McDowell et al., 1984; Mielke, 1985; Mildner and Boyce, 1979; Minella et al., 2018; Monke et al., 1979; Murphree and Mutchler, 1981; Murphree et al., 1985; Mutchler and Bowie, 1979; Nunes et al., 2016; Onstad et al., 1976; Pieri et al., 2014; Porto et al., 2009; Ramos et al., 2015; Ribolzi et al., 2017; Schilling et al., 2011; Sheridan et al., 1982; Sherriff et al., 2015; Simanton and Osborn, 1983; Simanton et al., 1980; Simanton and Renard, 1982; Sith et al., 2017; Sran et al., 2012; Starks et al., 2014; Steegen et al., 2000; Stott et al., 1986; Valentin et al., 2008; Van Oost et al., 2005; Vongvixay et al., 2018; Walling et al., 2001; Zhang et al., 2015; Zuazo et al., 2012). Note that if watershed data appear in several studies, only one study was cited here. Data to calculate the decrease in mean rainfall intensity (blue line) with increasing watershed size were taken from Lochbihler et al. (2017), who analysed the rainfall intensity for the 1000 largest rainfall events of a 9 year period (at 13 to 20 ∘C; in the Netherlands); for this figure the centre of the rainfall cell is assumed to be located in the middle of the respective watershed.
To overcome these problems, a number of watershed scale monitoring studies
were carried out over the last decades (summarized in Fig. 1). They offer
the advantage of sufficiently large field sizes to represent: common
agricultural practices, the interaction between neighbouring sites, complex
morphologies and processes like return flow from shallow ground water or
subsurface flow. Thus, watershed studies offer large advantages and are an
indispensable supplement of plot studies. Despite the clear advantages of
watershed studies some drawbacks are inherent, which becomes clear from a
comparison of such studies performed since the 1970s (Fig. 1). These studies
can be distinguished into two size categories, (i) those that cover a size
range that allows for a quantification of field or hillslope processes
(sizes <50ha) and (ii) those including processes in river systems
(>10km2) to represent storage and release processes of
fluvial systems. However, process scale studies (i) are usually quite short
and rarely exceed five years of monitoring (Fig. 1). Taking into account the
large temporal and interannual variability of water erosion events (Fischer
et al., 2016), this is a serious constraint. Study durations longer than
five years can almost exclusively be found for watershed studies of larger
scale, although short durations prevail in this size range as well (Fig. 1).
An important and unavoidable trade-off associated with large watershed sizes
is that internal dynamics within the river system modify the terrestrial
erosion signal (Auerswald and Geist, 2018; Walling and Amos, 1999).
Moreover, surface runoff and sediment delivery is sensitive to the watershed
size. Particularly for the upscaling of processes from plot to landscape
scale, the mechanistic understanding on field and small watershed scale is
essential. However, small watershed studies are rare relative to meso-scale
investigations. Furthermore, recent studies have shown that cells of high
intensity rainfall only have a radius of about 2 km based on rain radar
measurement (Fischer et al., 2018; Lochbihler et al., 2017). Hence,
watersheds exceeding the size of 1 km2 are usually only
partly covered by high-intensity rains, while larger watersheds may respond
strongly to medium intensity rains of large spatial extent. Due to the
increasing complexity of spatial patterns in rainfall and internal sediment
redistribution and corresponding long-term storage, we restricted our review
of watershed studies in Fig. 1 to watersheds <1000km2.
A further characteristic of watershed studies in
comparison to plot studies is that usually only few watersheds are compared.
Numerous monitoring studies have been carried out in single watersheds (see
all watershed sizes in Fig. 1 with unique study duration). Furthermore, the
majority of studies do not compare more than three watersheds. This small
number limits a direct comparison and usually does not allow for an analysis
of the influence of spatial variability in watershed properties. Thus, it
does not surprise that all watershed studies found in literature report a
rather superficial description of topographic, pedologic and agronomic
properties of the watersheds and of the meteorological conditions during the
study period. This becomes evident when compared to plot studies that at
least describe in detail plot morphology, soil properties and agricultural
treatment. The lack in a detailed description of boundary conditions also
impedes the combination of data from different studies, although this would
greatly increase the value of such studies. Unfortunately, a combination and
comparison of different watershed studies is impossible because sufficient
data are usually not reported.
Here we report about the Scheyern dataset that overcomes some of the
limitations in watershed studies. (i) The dataset allows for the comparison
of a large number of adjacent and partly cascading watersheds (14) that are
amended by many plot data under simulated rainfall. (ii) It covers a
relatively long study period (8 years). (iii) The dataset is available and can
be used for comparisons within this dataset, against other datasets or
modelling results (for data overview see Table 1). (iv) All watershed sizes
are within the range of fields and hillslopes and thus exclude interference
of processes along the aquatic flow path. (v) Finally and importantly the
data of soil loss and runoff during erosion events are complemented by a
very detailed set of soil properties (e.g., spatial resolution of 12.5m×12.5m), weather data (e.g., tipping bucket rainfall is for some
years available up to a spatial resolution of 11 km-2), agronomic
data (all agricultural operations were recorded), soil cover data and
topographic conditions. Based on this comprehensive dataset, we illustrate
the importance of long-term monitoring and of internal temporal dynamics for
interpreting watershed deliveries (e.g. the gradual and asynchronous
vegetation cover development on individual fields within a watershed that
additionally experience abrupt changes due to agricultural management and/or
may receive different amounts of erosive rain due to small scale variability
in rainfall depths).
Materials and methodsTest site
The Scheyern Experimental Farm was located about 40 km north of Munich,
Germany. The test site covered an area of approximately 150 ha (Fig. 2) and
is part of the Tertiary hills, an important agricultural landscape in
Central Europe. The Tertiary sediments are mainly sandy to gravelly,
quarzitic, fluviatile materials of poor fertility. Especially hilltops are
often covered by shallow clayey sediments (either calciferous or not) of
former oxbow lakes in the fluviatile Tertiary landscape. Hills were
developed during the Pleistocene within these horizontally deposited
Tertiary sediments. These hills are steep on the warm south and west facing
slopes due to erosion facilitated by the lack of permafrost. The cold east
and north facing slopes had permafrost and solifluction that left gentle
slopes. Furthermore the gentle east facing slopes received some loess (0
to 2 m), which made them suitable for cropland, which in turn lead to
colluvial soils in toe slope positions (Sinowski and Auerswald, 1999). As a
result of these formation conditions, the research area exhibits a wide
range of soils, from shallow to deep, from gravelly to sandy to silty to
clayey and a wide range of slope gradients. Well-sorted textures dominate in
sediments at greater depths (>30cm) while surface soils are
poorly sorted and loamy textures dominate (Auerswald et al., 2001).
Following the IUSS Working Group WRB (WRB, 2015), soils at the research farm
are classified Haplic Luvisols, Endogleyic or Haplic or Leptic Cambisols,
Gleyic or Haplic Fluvisols, Mollic Gleysols.
Structure of the Scheyern data base. The zip-files (bold) combine
all data and meta-data within one topic, with an individual DOI. Each
zip-file contains several csv files with data, shape files (which are
zipped) for geographic information and corresponding pdf files describing
the meta-data.
Structure of data base Files1.Soil data1_SoilData.zip10.13140/RG.2.2.14231.83365 (Auerswald et al., 2019b)1_SoilData.pdf1.1. Soil profile data: The data set contains 15 properties of entire soil profiles determined at 606 locations.11_SoilProfilData.csv 11_SoilProfilData.pdf1.2. Soil horizon data: The data set contains a total of 46 soil properties determined in 2827 horizons from 504 soil profiles.12_SoilHorizonData.csv12_SoilHorizonData.pdf1.3. Soil block data: The data set contains a total of 30 soil property averages of 9309 contiguous 12.5×12.5m2 blocks.13_SoilBlockData.csv13_SoilBlockData.pdf1.4. Soil physical data: The data set contains 29 physical soil properties of 97 soil horizons for 19 benchmark soils.14_SoilPhysData.csv14_SoilPhysData.pdf1.5. Adsorbed cation composition and clay mineral composition: The data set contains 7 location variables and 18 chemical and mineralogical soil properties that were determined in 108 horizons from 19 benchmark soils.15_SoilCatMin.csv15_SoilCatMin.pdf2.Topographic data:2_TopoData.zip10.13140/RG.2.2.32044.51845 (Wilken et al., 2019a)2_TopoData.pdf2.1. Topographic and surface point data in a regular 5m×5m grid. Data comprise elevation, slope, aspect, field and watershed information.21_Topo5m.csv21_Topo5m.pdf2.2. Topographic and surface point data in a regular 12.5m×12.5m. Data comprise elevation, slope, aspect, field and watershed information.22_Topo12_5m.csv 22_Topo12_5m.pdf3.Meteorological data: 10.13140/RG.2.2.34561.10088 (Wilken et al. 2019b)3_MeteoData.zip 3_MeteoData.pdf3.1. Meteostation locations: The data set contains the coordinates and elevation of all 13 meteorological and precipitation stations, respectively.31_MeteoStationsLocation.csv 31_MeteoStationsLocation.pdf3.2. Meteorological station data: The data set contains two files (32_MeteostatM01.csv and 32_MeteostatM02.csv) with hourly data for 13 parameters measured at the two main meteorological stations on the research farm between 1994 and 2001.32_MeteoStationM01M02.pdf 32_MeteoStationM01.csv 32_MeteoStationM02.csv3.3. Triggered precipitation data: Tipping bucket precipitation on minute resolution of 13 precipitation stations for the years 1994–1997 and of two precipitation stations for the years 1998–2002.33_TrigPcpData.csv 33_TrigPcpData.pdf3.4. Continuous and corrected minute-by-minute precipitation data of 13 precipitation stations for the years 1994–1997 and of two precipitation stations for the years 1998-2002. Data are derived from 33_TrigPcpData.csv.34_ContStatPcpData.csv 34_ContStatPcpData.pdf3.5. Watershed precipitation data: continuous mean minute-by-minute precipitation data calculated for all 14 individual watersheds.35_ContWtshPcpData.csv 35_ContWtshPcpData.pfd3.6. Data sets 3.4 and 3.5 sub-divided into annual packages to reduce individual file size.36_AnnualPcpData.zip4.Land use data: 10.13140/RG.2.2.26172.49285 (Auerswald et al., 2019d)4_LandUseData.zip 4_LandUseData.pdf4.1. Land use data. The data set contains two zipped files with the spatial land use information of 1993 (before restructuring the farm) and 1996 (after restructuring the farm) for use within GIS.41_LandUseData1993_2001.pdf 41_LandUseData1993.zip 41_LandUseData1994_2001.zip4.2. Land management data. The data set contains 17 variables of 1734 individual land management activities that occurred on 21 arable fields.42_CropManagData.csv 42_CropManagData.pdf4.3. Cover and plant height data. Data on daily soil cover by residues and plants and measurements of plant heights on ten organically managed fields and on six conventionally managed fields during the years 1993 to 1997.43_CovData.csv 43_CovData.pdf4.4. Standardized cover and plant height: Data on the mean daily soil cover by residues and plants and mean plant heights for an entire year are given for 20 different crops (conventionally or organically grown). The data allow estimation of cover and height from the crop type also in years in which no measurements were made.44_CoverStandard.csv 44_CoverStandard.pdf4.5. Main crops: The file compiles the main crops and the catch crops grown on each field between 1993 and 2002. The number of the most appropriate standardized cover and plant height is given.45_AnnualCrops.csv 45_AnnualCrops.pdf4.6. Tillage direction data. The data set contains the raster based tillage direction of all fields during the monitoring period 1994–2002 (148 430 5×5m2 blocks).44_TildirData.csv 44_TildirData.pdf5.Runoff and sediment data from 14 watersheds 10.13140/RG.2.2.30786.22729 (Fiener et al., 2019).5_RunSediData.zip 5_RunSediData.pdf5.1. Watershed data: The data set contains watershed characteristics (51_WatershedData.csv) and vector data for the location of the 14 watersheds (51_WatershedData.zip).51_WatershedData.zip 51_WatershedData.csv 51_WatershedData.pdf5.2. Runoff data: The data set contains continuous event runoff of 14 watersheds from 1994 to 2001.52_RunData.csv 52_RunData.pdf5.3. Sediment data: The data set contains measured event sediment concentration of 14 watersheds from 1994 to 2001.53_SediData.csv 53_SediData.pdf5.4. Runoff event precipitation data: The data set contains the watershed-specific event precipitation for each of the watersheds.54_EventPrecData.csv 54_EventPrecData.pdf5.5. Pond data: The data set contains information characterizing the retention ponds located at the down slope end of 6 of the 14 watersheds and gives sediment trapping efficiencies.55_PondData.csv 55_PondData.pdf
Continued.
Structure of data base Files6.Runoff and sediment delivery data of 114 rainfall simulation experiments on 57 plots situated in 14 small adjacent watersheds 10.13140/RG.2.2.27430.78401 (Auerswald et al., 2019c).6_RainSimData.zip 6_RainSimData.pdf6.1. Plot property data: The data set contains 38 properties of 57 rainfall simulation plots.61_PlotData.csv 61_PlotData.pdf6.2. Simulation conditions: The data set contains a total of 15 properties determined for 114 rainfall simulation runs (57 dry runs and 57 very wet runs).62_RunData.csv 62_RunData.pdf6.3. Runoff and sediment data: The data set contains a total of 4461 runoff and sediment concentration measurements that were made during 114 rainfall simulation runs.63_RoffSedData.csv 63_RoffSedData.pdf
Land use and monitored watersheds at the research farm (without area for cropping experiments). Numbers ≤6 indicate integrated management, numbers ≥7 indicate organic management.
The elevation ranged from 448 to 497 m above sea level with a mean slope of
10.1 % (±6.1 %). Slopes facing south and east were gentle
(approx. 10 %) while in contrast the slopes facing north and west are
partly much steeper (up to 30 %). An intense tachymetric survey was
conducted to determine slope angles and watershed boundaries, whereas
precise elevation was recorded at approximately 4500 positions (30
measurements per ha); for details see Warren et al. (2004). Moreover, a 5m×5m LiDAR digital elevation model (DEM) is available. The
watershed borders were determined from tachymetric survey and in-situ runoff
tracking during long-lasting runoff events (snowmelt). This was necessary as
the LiDAR DEM did not properly resolve watershed borders due to small scale
structures like tillage induced roughness and grassed ditches along field
borders.
The climate was temperate humid with a mean annual air temperature of
8.4 ∘C during the monitoring phase from 1994 to 2001. The average
precipitation was 804 mmyr-1 (1994–2001) with the highest
precipitation occurring from May to July (average maximum 116 mm per
month in July) and the lowest occurring in the winter months (average
minimum 33 mm per month in January). The mean annual erosivity was 97 Nh-1yr-1 (Auerswald et al., 2019a).
At the research farm, two types of farming systems (conventional and organic
farming) were established after harvest in 1992. The border between both
farming systems followed the main watershed boundary in order to have only
one system within a certain watershed. One system followed the principles of
conventional integrated farming (total size: 46 ha) (not to be confused with
the European agriculture organic standard of integrated farming) and the
other followed certified organic farming according to the rules of the
German Association for Ecological farming (AGOL; total size: 68 ha). In
general, the organic farming was located in areas with higher soil
variability, partly situated at steeper slopes (mainly grazed) and on less
productive soils compared to the fields of integrated farming. The higher
soil variability and the steeper slopes required smaller field sizes.
Methodologically this was advantageous, because it allowed for the
cultivation of two fields with the same crop every year despite the more
complex crop rotation. Thus, in both farm types each crop was replicated in
each year. The remaining area of the farm was used for cropping studies, where
treatments were applied that would have been in conflict with the initially
defined and continuously applied land use principles of the two farming
systems.
In general, integrated farming and organic farming allow a wide range of
management options. The management of both farming systems at the research
farm aimed to improve in parallel the economic returns and soil protection
(i.e., minimizing erosion and soil compaction), water protection (i.e.,
minimizing leaching of agrochemicals), and of biodiversity enhancement
(Auerswald et al., 2000). This multiple-goal approach required a set of
sophisticated and rather unusual management options like the use of
ultra-wide tires on light tractors or avoiding temporal gaps in soil cover
by consequent application of cover crops, catch crops and residues
management. Hence, the management in both systems differed considerably from
what can be found on typical farms that also apply integrated or organic
farming.
The 4-year crop rotation in the integrated farming system was potato (Solanum tuberosum L.), winter
wheat (Triticum aestivum L.), maize (Zea mays L.), and winter wheat. The organic farming system had a 7-year crop
rotation starting with a grass-clover mixture (typically containing
perennial ryegrass Lolium perenne L., Italian rye-grass Lolium multiflorum Lam., meadow fescue Festuca pratensis Huds., red
clover Trifolium pratense L., and white clover Trifolium repens L.) and followed by potato, winter wheat,
winter rye (Secale cereale L.), white lupine (Lupinus albus L.), and sunflowers (Helianthus annuus L.) (Auerswald et
al., 2000). To meet the rules of nutrient use of the AGOL, the organic farm
ran a herd of 30 suckler cows with a bull. The cattle were grazing the
pastures during summer (for details see Auerswald et al., 2010; Schnyder et
al., 2010), whereas manure from the winter stall period was used for
fertilizing the organic fields. In the integrated system, maize was produced
that was externally used to feed 49 steers. The slurry from this herd was
applied as manure at the integrated farming system.
In order to reduce surface runoff and sediment-bound matter fluxes, land use
and soil management were adapted (see below) and a number of near-field
buffer features were installed. The latter mainly comprised small retention
ponds with sub-surface outflows at the downslope end of the watersheds W01,
W02, W05, W06 and W14 (Fig. 2). The retention ponds were designed to retain
water for a maximum of three days with extreme events (for details see
Fiener et al., 2005). A grassed waterway to prevent ephemeral gullying and
reducing surface runoff was established in 1993 in the watersheds W05 and
W06 (for details see Fiener and Auerswald, 2003).
The main cropping principle in both farming systems was to keep the soil
cover high as long as possible, preferably by growing plants or plant
residues where this was not possible. This intended to lower nitrate
leaching and erosion but also to increase the input of organic matter into
the soil food chain. To this end, cover crops were sown and mulch tillage
(Kainz, 1989) was applied in the integrated system while catch crops were
used in the organic system. Also unconventional methods were applied, e.g. sowing mustard (Sinapsis alba L.) into potato fields, when the potato leaf cover at the
end of the growing season decreased due to Phythophtora infestans infection (Kainz et al., 1997).
To prevent soil compaction and allow reduced tillage, it was necessary using
the lightest machinery for a given task and using ultra-wide tires on all
farming machinery. Mouldboard ploughs were used that allowed to run with
both wheel tracks on the unploughed land, while with the usual mouldboard
plough one wheel runs on the subsoil of the furrow and compacts the subsoil;
non-inverting shallow-depth tillage and stabilization of the soil structure
by increasing biological activity further assisted this concept (Auerswald
et al., 2000).
DataSoil management and soil cover
Any soil and crop management performed at one of the 23 arable fields was
documented by the farm manager. The available data comprise e.g. sowing
date, sowing density, crop type and sowing machinery. Any application of
fertilizer and agro-chemicals was documented including date, machinery used,
type of fertilizer and/or agro-chemical, amounts etc.
During the 8 year monitoring period, plant and residue cover was measured for
3 1/2 years (January 1993 to April 1997) in all fields. During the
vegetation period, measurements were carried out bi-weekly; during autumn to
spring cover was measured monthly and additionally before and after each
soil management operation. Measurements were repeated at a minimum of three
geodetically defined locations within each field. Residue cover and cover of
plants near the surface were measured manually using a meter stick. Plant
height was also determined with a meter stick. Plant cover of higher plants
were derived from photographs taken around noon from a height up to 4 m (in
the case of full-grown maize) using image analysis (Kaemmerer, 2000).
Soil
A combination of geostatistics and pedotransfer-functions were used to
determine the spatial distribution of important soil properties in three
dimensions and at high resolution (Scheinost et al., 1997). Therefore, soil
sampling in a rectangular 50m×50m grid (471 grid nodes) using a
machine-auger down to a depth of 1.2 m with a soil core diameter of 0.1 m
was carried out. In total 2448 soil horizons were sampled and analysed for
texture, plant available P and K according to Schüller (1969), pH in
0.01 M CaCl, total and carbonate C by dry combustion, and total N. Soil
texture was determined for 3 stone fractions and 15 fine earth fractions
(Auerswald and Schimmack, 2000). Additionally 19 benchmark soils between the
grid nodes were sampled and analysed in more detail. In areas of steep
gradients between grid node soils, additional hand augering was applied for
soil categorization using field methods (for more details regarding soil
sampling and analysis see Auerswald et al., 2001; Scheinost et al., 1997;
Sinowski et al., 1997).
All soil data were combined in an extensive geostatistical analysis to
interpolate soil properties, e.g. C content and texture, for 12.5m×12.5m
grid blocks. For details of the procedure see Scheinost et al. (1997). The
geostatistical interpolation scheme was also applied to derive a high
resolution K factor map, which is used in this study to illustrate the
richness of the data set and also to underline the importance to account for
spatial variability within watersheds to understand differences in
hydrological properties. The K factor was determined at 544 locations (471
grid nodes and 73 points in-between the grid nodes) according to the K
factor nomograph (Wischmeier et al., 1971). Bulk soil fractions (in %) of
silt (fSi), very fine sand (fvfSa), clay (fCl) and organic
matter (fOM) in the fine earth fraction and the fraction of rock
fragments (frf) were measured; aggregate size class (a) was obtained by
visual classification; permeability class (p) was estimated from saturated
conductivity calculated by using a pedotransfer function that had been
developed from measured saturated conductivities of 737 soil cores taken
from various soils and horizons at the research farm. The range of soils
exceeded the validity range of the K factor equation given by Wischmeier and
Smith (1978). In order to avoid manual reading of the K factor nomograph for
544 soils, we used the K factor equation by Auerswald et al. (2014) that
includes all peculiarities of the nomograph, which are not included in the
simpler equation by Wischmeier and Smith (1978). It is a combination of 4
equations; note that there were typing errors in the original publication by
Auerswald et al. (2014); we used the correct equations:
1K1=2.77×10-5×(fSi+vfSa×(100-fCl))1.14 for fSi+vfSa≤70%K1=1.75×10-5×(fSi+vfSa×(100-fCl)1.14+0.0024×fSa-vfSa+0.161 for fSi+vfSa>70%2K2=K1×(12-fOM)/10 for fOM≤4%K2=K1×0.8 for fOM>4%3K3=K2+0.043×(a-2)+0.033×(p-3) for K2>0.2K3=0.091-0.34×K2+1.79×K22+0.24×K2×a+0.033×(p-3) for K2≤0.24K=K3 for frf≤1.5%K=K3×(1.1×exp(-0.024×frf)-0.06) for frf>1.5%
These equations use the unit [thha-1N-1] for K and the interim
values K1 to K3. The unit can be converted to the unit [tMJ-1hmm-1], commonly used in the USA, by dividing by 10. Subsequently, the K
factor was geostatistically interpolated for 12.5m×12.5m blocks
using the gstat package (version 1.1-6; Gräler et al., 2016; version 3.5.0; R-Core-Team, 2018).
Weather
Hourly climate variables were measured at two meteorological stations
located at the research farm from 1 April 1994 to the 31 December 2001 (for location see Fig. 2). Data from a nearby meteorological
station of the German Weather Service Voglried (approx. 3 km north of the
research farm) were included to complete the 8 year monitoring data set for
the time span 1 January to 31 March 1994 and to fill gaps in
the data from the research farm for the time span 13 August 1999 to
7 July 2000. The meteorological stations provided the following
standard variables: air temperature and relative humidity measured at 0.5
and 2.0 m above ground; global radiation, wind speed and wind direction at
2.0 m above ground; soil temperature and moisture under grass at depths of
0.05 and 0.5 m; precipitation in 1.0 m above ground. Precipitation at both
stations was recorded with tipping buckets (resolution 0.2 mm; collecting
area 0.04 m2; measuring height 1.0 m) from the 1 April 1994 onwards. Precipitation was additionally measured at 11 stations
(resolution 0.1–0.2 mm; collecting area 0.02 m2; measuring
height 1.0 m), which were located more or less equally distributed over the
research farm, to capture the spatial variability of (erosive) rainfall
events between April 1994 and March 1998. Eight of the overall 13 rain
gauges at the research site were heated, to measure precipitation
continuously also in case of snowfall during the winter months. The
tipping-bucket rainfall data of all stations were recorded in minute
temporal resolution (more details regarding this dataset and the spatial
distribution of rainfall is given in Fiener and Auerswald, 2009).
Watershed land use and mean topsoil properties based on a 50×50m inventory in 1992 geostatistically interpolated to a raster of 12.5×12.5m (Scheinost et al., 1997).
Land use [%] Topsoil properties No.SizeMean slopeManagementArableGrasslandLong-termLinear structuresFieldClayaSiltaSandaStonesb>2mmSOCa content[ha][%]systemlandfallow(hedges etc.)roads[kgkg-1][kgkg-1][kgkg-1][kgkg-1][gkg-1]W011.607.40Integrated53.10.0030.713.82.380.170.380.450.1316.5W023.576.91Integrated94.90.000.003.421.660.220.470.310.0512.6W034.237.31Integrated92.90.000.006.570.570.210.580.210.0215.4W040.827.55Integrated90.30.000.005.574.140.190.560.250.0113.6W0513.678.95Integrated82.10.0012.73.861.370.190.470.340.0613.1W067.969.32Integrated80.70.0013.94.431.000.190.440.370.0713.3W073.119.18Organic90.40.005.374.230.000.150.400.450.1112.4W103.2612.35Organic85.36.183.824.340.370.160.410.430.1317.3W111.6612.91Organic0.001000.000.000.000.200.490.310.0427.0W122.6014.86Organic10.379.11.967.830.870.140.310.550.2117.2W1311.4412.21Organic55.518.919.44.072.060.150.360.490.1415.3W141.568.18Organic57.633.20.006.023.180.190.430.380.0815.2W152.8411.27Organic31.762.50.004.061.750.170.400.430.0528.2W162.027.41Organic87.90.003.496.252.340.180.440.380.0415.5
a proportion of fine earth <2mm; clay <2µm, silt 2 to 63 µm; b proportion of total soil including stones
Surface runoff and sediment delivery
Surface runoff and sediment delivery was continuously monitored for all
events at the outlet of 14 watersheds (Fig. 2; Table 2) from 1994 to 2001.
All watershed outlets collected surface runoff by small dams that
transmitted runoff via an underground-tile outlet (diameter of pipes 15.6
and 29 cm) to the measuring device. In case of W01, W02, W05, W06 and W014
the peak surface runoff rates were dampened by 4 cm effective opening widths
of the underground-tile outlets, thus the small dams acted as small
retention ponds (volumes: W01=420m3, W02=490m3, W05=340m3, W06=220m3, W14=43m3). For this study, only
sediment delivery data at the outlet of the watersheds are analysed; it is
important to note, especially in case of comparing watersheds with and
without ponds, that the ponding resulted in substantial sediment trapping,
which was determined after the first monitoring year. The average trapping
efficiency of the main ponds (W01/02/05/06) was 56 % (Fiener et al.,
2005).
Coshocton-type wheel surface runoff sampling device and collecting tank used to monitor surface runoff and sediment delivery at all watershed outlets.
From the underground-tile outlet pipes, the surface runoff was channelled to
Coshocton-type wheel surface runoff samplers. The setup is similar to that
used by Carter and Parsons (1967), collecting an aliquot of 0.5 % from the
outlet surface runoff (Fig. 3). The aliquot precision of the Coshocton wheel
setup was tested in a laboratory flume. The measured aliquot showed reliable
precision in the range of ±10 % of the intended aliquot (for more
details regarding the precision of the measuring set-up see Fiener and
Auerswald, 2003).
The aliquot volumes were collected in 1.0 to 3.5 m3 tanks and measured
after or during (large) surface runoff events. During water and sediment
sampling, the tank content was vigorously mixed using a submersible pump to
homogenize sediment concertation before water samples were taken.
Subsequently, the water samples were dried at 105 ∘C to determine
sediment concentrations. In 1995 some of the collecting tanks (at W01, W02,
and W06) were replaced by tipping buckets (volume = approximately 85 mL)
at the outlets of the aliquot wheels. The tipping buckets were connected to
Model 3700 portable samplers (Isco, Lincoln, NE) that counted the number of
tips and automatically collected a surface runoff sample after a defined
runoff volume (Fiener and Auerswald, 2003). This modification (used for
those watersheds that produced most surface runoff) resulted in more data
per event, which provides more information on intra event dynamics. We
limited the data set used in this study to total event runoff volumes and
sediment delivery as inter event data is not available for all watersheds
and measurements. However, the corresponding data publication (Fiener et
al., 2019) covers the sub-event information.
An individual event number and corresponding time span was assigned if at
least one watershed recorded surface runoff. If more than one watershed
produced runoff, the time span between the first recorded runoff in one of
the watersheds and the last recorded runoff in one of the watersheds was
associated to the event number. This simple definition can lead to prolonged
runoff events that consist of a series of precipitation events as runoff
events of different watersheds may overlap. Especially during winter events,
a clear definition of events was partly difficult as some watersheds
produced prolonged surface runoff resulting from return flow. Within the
dataset, detected errors are flagged, e.g. in case of large events, the
runoff tanks needed to be emptied during the events that led to a slight
underestimation of runoff and sediment delivery volumes.
Rainfall simulation data
The natural rainfall data were complemented by rainfall simulation data that
were obtained before the monitoring period under natural rain started. At 57
plots within the studied watersheds, a simulation was performed on dry soils
(dry runs) lasting 60 min at a mean intensity of 64 mmh-1 using a
Veejet 80100 rainfall simulator (the so-called Kainz-and-Eicher simulator;
Kainz et al., 1992). The rainfall simulator applies rainfall kinetic energy
of 20 Jm-2mm-1. Following the standard protocol of
Auerswald et al. (1992), at all 57 plots an additional very wet run under
pre-sealed soil conditions was applied. This very wet run started 30 min
after the end of the initial dry run and applied 30 min rainfall. The
rainfall simulations were carried out immediately after harvest. For plot
preparation, above-ground crop residues were carefully removed, the soil was
tilled, and seedbed was prepared using a rotary harrow. The plot
installation followed the standard of Auerswald et al. (1992) with the
exception that plot width covered half the working width (1.5 m) of the
rotary harrow (wheel track included). With regards to similar aging
conditions of aggregate stability for all plots (Auerswald, 1993), seedbed
preparation was carried out less than three hours before the dry runs
started. Soil moisture was determined before the start of the dry run. Soil
cover by stones and residues was determined before the dry run and after the
very wet run. Surface roughness, following Morgan et al. (1998), was
determined before the dry run started. Soil properties were measured for
each individual plot. Slope steepness was determined with a water level on
each plot (Warren et al., 2004). Time to ponding was determined according to
the first occurrence of a soil surface water film that did not disappear
between two subsequent sprays of the nozzles. Time to runoff was defined as
the first continuous runoff leaving the gutter at the lower end of the plot.
The plot coordinates denote the centrum of the plots and were geodetically
determined (accuracy <2cm). The runoff data have been already
analysed by Fiener et al. (2013).
Statistics and data availability
Apart from the geostatistical analysis described above, the statistical
analysis was performed with CoStat 6.451 (CoHort Software, US). Mean values
are often given with standard deviation (SD) (mean ± SD).
In some cases other basic statistical measures of variability were
calculated as well (e.g., intervals of confidence; range, minimum and
maximum, skewness) that all followed standard methods (Sachs, 1984).
Although the data were in most cases highly skewed (skewness between 4 and
9) and should be transformed prior to statistical analysis, we analysed the
untransformed data because they are easier to report and we only intend to
give a general description of the dataset without hypothesis testing (the
untransformed data carry the usual units; they have symmetric confidence
bands; they do not require different transformations for different
watersheds). However, this makes comparison troublesome; a transformation is
hardly possible when all events are included, even if they did not produce
runoff and sediment delivery in a specific watershed, because a log
transformation is then not possible anymore and often bimodal distributions
resulted.
Results and discussion
A total of 287 events produced runoff in at least one of the watersheds. In
most cases, not all watersheds produced runoff during an event and hence the
number of events per watershed was lower and differed considerably between
watersheds (69 to 275 in total or 9 to 36 events per year, Table 3). The
mean runoff per event differed between 0.12 and 2.49 mm (mean 1.17 mm).
The surface runoff ratio (cumulative surface runoff / cumulative
precipitation) during the 8 years monitoring in the different watersheds ranged
between 0.2 % and 7.8 % (mean 3.0%±2.3%). In comparison,
those watersheds of only few runoff events did not necessarily produce the
lowest runoff per event. This indicated substantial variation among the
events within a watershed. The coefficient of variation for event runoff
varied between 200 % and 700 % (mean 365 %) for the individual
watersheds. For a 1-year measuring period, the mean event runoff could only be
predicted with a 95 % interval of confidence of ±183 % around the
mean. In other words, it is hardly possible to derive a reasonable mean of
erosion from a 1-year study period. This is also true for a 3-year study period,
which is a commonly found monitoring period in soil erosion studies. The
mean 95 % interval of confidence for a 3-year period would be ±99 %
(ranging up to ±183 % for individual watersheds). The uncertainty
was still large at the full 8-year study period with a mean 95 % interval of
confidence of ±60 % (ranging up to ±111 % for individual
watersheds). Statistical uncertainty was even higher for sediment delivery.
In this case, the mean coefficient of variation was 477 % (compared to
365 % for runoff), which means that also the confidence bands around the
mean would be about 1.5 times higher than those reported for runoff.
Remarkably, the width of the confidence band correlated only weakly with
site or land use conditions, e.g. the variation in watersheds dominated by
grass was not smaller than the variation in watersheds dominated by arable
use (variation expressed in percent of mean). Hence, in ecosystems of
episodically occurring erosive rainfall, short monitoring periods may
enhance the mechanistic understanding of soil erosion processes but do not
support predictions on long-term soil erosion rates.
Cumulative event surface runoff (a) and sediment delivery (b) for all watersheds versus the number of observed events in each individual watershed between 1994 and 2001 (except for watershed W11: 1998–2001; and watershed W04 due to an error in most extreme event). All cumulative events are sorted in ascending order. Cumulative precipitation and erosivity is calculated for all erosive events; erosivity was determined following Schwertmann et al. (1987).
Skewness was considerably higher for sediment delivery compared to runoff
while highest skewness was found between the different watersheds (range 4
to 13). This large skewness resulted from the fact that among all watersheds
at least 50 % of surface runoff did occur in only 10 % of the events
(mean 75.8%±14.7 %; Fig. 4a). At least 67 % of all sediment
was delivered by the largest 10 % of events while the mean of all
watersheds is substantially higher (mean 85.4%±11.5%; Fig. 4b). Large events were also much more important for sediment delivery than
for rainfall erosivity (largest 10 % of erosive rainfall events represent
53 % of cumulative erosivity, Fig. 4b). This is because the variability of
sediment delivery depends on the variability of rain events but also on the
variability of soil cover. Extreme soil erosion was limited to heavy
rainfall events that hit seldom and short periods of low soil cover. The
general behaviour that especially soil erosion and sediment delivery is
governed by extreme events was also found in plot experiments (Nearing et
al., 1999), and is also demonstrated in the analysis of single extreme
events on plot (Martinez-Casasnovas et al., 2002) and watershed scale
(Coppus and Imeson, 2002).
Relation between the upper 10 % (a, d), 5 % (b, e) and the largest (c, f) surface runoff and sediment delivery events and mean surface runoff and sediment delivery in each watershed without the upper 10 %, 5 % or the largest events. Except for watershed W04 due to a measurement failure for the most extreme event. Insignificant regressions were omitted.
In the Scheyern dataset, the proportion of large surface runoff events in
total runoff correlates negatively with the total runoff without these large
events. This indicates that watersheds with small surface runoff sums were
more dominated by extreme events (Fig. 5a–c). Hence, longer monitoring
periods are required for watersheds of low runoff potential, either because
of site conditions (no severe rains; permeable soils) or because of land-use
conditions. A similar behaviour was not evident in case of sediment
delivery. Neither the largest 5 % of all sediment delivery events nor the
largest individual events showed a significant correlation to the cumulative
sediment delivery of a watershed (Fig. 5d–f). This is because low sediment
deliveries were always associated with a continuously large soil cover.
Hence, there was less variation in such watersheds than in watersheds that
produce high soil loss due to periods of little soil cover.
Especially for sediment delivery, the majority of cumulative 8-year sediment
delivery was caused by large events. To assess the drivers of extreme
events, we will focus in the following on the importance of monitoring the
internal dynamics of watersheds. From the fact that the total number of
rainfall events was considerably larger than the number of runoff events
already follows that in some cases a watershed must have produced runoff
while others did not. Such events can only be understood if land use,
spatial rainfall distribution and site conditions are known in detail. This
dataset study comprises such data in unprecedented detail, which is
illustrated by event #229 in watershed W03 that produced the largest
sediment delivery per hectare for all watersheds during the entire
monitoring period. The event rainfall erosivity was only 9.7 Nh-1,
which is one tenth of the mean annual erosivity. This event did not result
in substantial erosion in the other watersheds. This extreme event was able
to take place because the field in W03 was at seedbed conditions for winter
wheat after potato had been harvested four weeks earlier. Therefore, the
field had no soil cover at all (see arrow Fig. 6) and the soil structure was
substantially damaged by potato harvest. Furthermore, a smaller event one
week before the extreme event (#228) had already produced a rill network,
which increased the sediment connectivity during the largest event. Both
events together comprised 61.4 % of all sediment delivery measured during
the 8 years in watershed W03. Watershed W03 was under integrated arable
management, which in general, produced the largest events, while arable land
and grassland under organic management showed substantially lower
event-based sediment delivery (Table 4). Under organic management, all
extremes (except for W15) occurred in late winter to early spring and were
associated with snowmelt and/or prolonged rainfall with minor event rainfall
erosivity (Table 4). In contrast, extremes (except for W06 which produced
anyway very small sediment delivery rates (Tables 3, 4)) under integrated
farming were associated with large erosivities and times of low soil cover
similar to event #229 in W03.
Characteristics of measured surface runoff and sediment delivery events (W01 … W07, W10, W12 … W16: 1994–2001; W11: 1998–2001) in the different watersheds; C.V. is coefficient of variation. “Sum” is the total of eight years while all other columns are event based. In total 287 events were recorded that produced runoff in at least one of the watersheds.
Conditions during the largest sediment delivery events measured in each watershed between 1994 and 2001.
WatershedEventEvent endRain durationaSedDbProportioncRainfall M01 | M02dΔe M01 | M02RfSoil temperaturgMain crophCommentNo.[h][kgha-1][%][mm][%][Nh-1][∘C]W019914 Apr 19946614111125 | 923120.0no datafrozen mustardminimum temperature -9∘C at night before eventW029914 Apr 199466100234125 | 923120.0no datawheatminimum temperature -9∘C at night before eventW059914 Apr 19946660840125 | 923120.0no datawheat/frozen mustard on potato ridgesminimum temperature -9∘C at night before eventW159914 Apr 199466802446 | 391720.0no datamainly grasslandminimum temperature -9∘C at night before eventW0716219 Feb 199660142131 | 3122.0-0.1wheatsoil frost since mid of January; precipitation might be partly snowW1216220 Feb 1996144363946 | 39172.5-0.2grasslandsoil frost since mid of January; precipitation might be partly snowW1416219 Feb 199660551731 | 3122.0-0.1clover-grass-mixturesoil frost since mid of January; precipitation might be partly snowW1016721 Mar 1996snowmelt20653 | 2400.0-0.1clover-grass-mixture & oat/ryebeginning of snow melt after several weeks of frostW1316721 Mar 1996snowmelt295683 | 2400.0-0.1wheatbeginning of snow melt after several weeks of frostW032286 Nov 199815617154893 | 107149.711.3wheat1 week after sowing following potato harvest end of SeptemberW1632317 Jan 20011451281258 | no data1.52.2wheatno soil frost during event, but in the weeks before event; precipitation partly snow as temperatures sometimes below 0 ∘CW0633128 Mar 2001145302168 | 7032.42.0frozen mustard / wheatlast soil frost beginning of MarchW1133128 Mar 2001145672268 | 7032.42.0grasslandlast soil frost beginning of March
a Start of rainfall till last rainfall during surface runoffb Sediment deliveryc Contribution of event to 8-year sediment deliveryd Event rainfall at meteorological stations M01 and M02e Relative difference in event rainfall between stations M01 and M02
calculated as [|M01-M02|/Mean(M01&M02)]fR factor (the unit Nh-1 can be converted to MJ mmha-1h-1
by multiplication with 10)g At the beginning of the eventh In case of two fields in the watershed the two crops are separated by a
slash; “frozen mustard” means a frozen-down catch crop; wheat and rye were
sown in fall while oat was sown in spring
Averaged soil cover derived from measurements within the field drained by watersheds W03 and W04; between 1994 and 1996 the cover was measured; from 1997 to 2001 the soil cover was derived from the average cover measurements (1994–1996), taking into account the crop and the year-specific times of field operations within the test field occurring from 1997 to 2001 (W: wheat, C: maize, P: potato, M: mustard used as catch crop) (modified after Fiener et al., 2008). The arrow indicates the timing of the combined largest two soil delivery events in this watershed.
Without such detailed watershed data, it is hardly possible to understand
the processes driving such a series of large events. A lack of such detailed
data becomes especially critical if runoff and sediment delivery data are
used for model development and testing. Large events play an essential role
in model development, calibration, and testing to ensure a robust prediction
of extremes that are mostly of highest relevance.
K factor map of the research farm; K factor was determined according to Wischmeier et al. (1971) at the sampling locations from measured soil properties and then geostatistically interpolated for 12.5m×12.5m blocks. The krige standard deviation was about 0.02 thha-1N-1. The small panel displays the experimental semivariogram calculated from 544 sampling locations and a spherical semivariogram model.
Equally important as the temporal dynamics of farming activities that affect
soil cover and other properties are detailed data regarding spatial and
spatio-temporal variability of natural drivers. Within short distances
almost the entire range of soil erodibilities can be found in the study area
(Fig. 7). The K factor at the grid nodes ranged from 0.03 to 0.65 thha-1N-1, while it ranged from 0.09 to 0.47 thha-1N-1
for the 12.5m×12.5m blocks (Fig. 7) derived from the grid
nodes. Only 3.5 % of all 20 000 soils covering Germany, that were analysed
by Auerswald et al. (2014), had a K factor outside this range that can
already be found within the 150 ha of the research farm. This fact points to
a large and short-distance variability in hilly terrain, were gravely, sandy
and clayey Tertiary material is partly covered by Pleistocene loess. The
pronounced short-range variability was even more evident from the
semivariogram (Fig. 7, small panel), which indicated a strong pattern with a
range of only 98 m. In other words, the entire K factor variation can be
found within a distance of only 100 m.
The differences in soils between most watersheds under integrated vs. organic farming, as evident also from the K factor (compare Figs. 2 and
7), was potentially one of the reasons why watersheds under integrated
farming produced larger events mostly during summer, while watersheds under
organic farming produced generally smaller events occurring mostly in
winter. This association between soils and farming practices was
intentionally created in the design of the study as it reflects agricultural
practice. Thus, organic farming can be predominantly found on less fertile
soils compared to conventional farming (Auerswald et al., 2003).
Nevertheless, due to a large number of adjoining watersheds, both land-use
systems can be compared under similar soil conditions.
Geostatistically interpolated rain depth (mm) of an erosive event with a substantial rainfall gradient (event 116, 26 August 1996); average rain depth calculated from the geostatistical interpolation in 10m×10m blocks was 23.6 mm and average gradient in rain depth was 15.7 mmkm-1. Figure adapted from Fiener and Auerswald (2009).
More generally, the dataset indicated that a comparison of watersheds with
different land use or management can only be reasonably done if the
variability in soil properties is taken into account. This is even more
important for variables with pronounced spatio-temporal dynamics like
field-specific soil cover (Fig. 6) or spatial rainfall gradients of large
events (Fig. 8). The latter were studied at the test site for four years
using 12 rain gauges. These data indicated that 50 % of all erosive events
had substantial spatial rainfall gradients. Variation in rain erosivity was
up to 255 % and thus much more pronounced than the variation in total rain
depth (for details see Fiener and Auerswald, 2009). Even for the rainfall
event with the largest erosivity (approximately half of the long-term mean
annual erosivity) in the data set, erosivity was zero within a distance of
about 500 m (Fischer et al., 2018). By analysing a much larger data set of
about 40 000 erosive events in Germany, Fischer et al. (2018) showed that
this extreme behaviour of including zero within such a short distance was
true for about half of all events but that strong gradients existed also for
most of the other events. This emphasized that also for small watersheds,
spatial variability in rainfall has to be taken into account.
Conclusions
Watershed studies are indispensable to understand soil erosion as they
integrate (i) real agricultural practices, (ii) natural heterogeneity along
the flow path, and (iii) realistic field sizes and layouts. However, there
is a prominent lack of watershed studies, (1) which observed watersheds
small enough to associate runoff and soil delivery with individual land
uses, (2) which are considerably smaller than erosive rain cells (<400ha), (3) which cover many years to account for the variability of rain
regarding erosivity and timing, (4) which combine many watersheds to allow
comparisons, (5) which obtained topographic, pedological, agricultural and
meteorological variation in high spatial and temporal resolution, and (6) which were made available. Here we provide such a dataset.
An 8 year monitoring in 14 watersheds yielded unprecedented high resolution
data in time and space. The data may be used for in-depth analyses or in
modelling studies to disentangle the complex interactions that result from
the simultaneous variation in space and in time, which is most pronounced
for crop development but which involves all other parameters as well.
The data were gathered under conditions where field layout and field
managements were optimized to reduce soil loss. Under such conditions, the
importance of rare events increases and requires long measuring intervals.
This was illustrated by the still large uncertainties of mean surface runoff
and sediment delivery (mean 95 %-confidence interval of ±75 % and
±95 % in case of surface runoff and sediment delivery,
respectively). To gather sufficient events under a variety of conditions, 14
watersheds were monitored over 8 year. Six watersheds were subject to the same
field management of integrated farming but differed in the position within
the 4-year rotation. Eight watersheds were subject to the same field
management of organic farming but again differed in the position within the
7-year rotation and covered grassland.
Overall, the presented data set underlined the importance of long-term
monitoring to determine the huge temporal variability of surface runoff and
sediment delivery from small watersheds. However, to use the full potential
of labour intensive long-term monitoring, it is essential that not only
runoff and sediment delivery is monitored. We strongly suggest putting more
efforts in monitoring of agroecosystem variables (e.g. soil management, soil
properties, soil cover, meteorology etc.) that spatially and temporally vary
within watersheds.
Data availability
All data created in this study are freely available. The soil data
(Auerswald et al., 2019b) can be obtained from
10.13140/RG.2.2.14231.83365. The topographic data (Wilken
et al., 2019a) can be obtained from 10.13140/RG.2.2.32044.51845. The meteorological data
(Wilken et al., 2019b) can be obtained from 10.13140/RG.2.2.34561.10088. The land use and land
management data (Auerswald et al., 2019d) can be obtained from 10.13140/RG.2.2.26172.49285. The runoff and soil loss data
during natural rain events (Fiener et al., 2019) can be obtained from
10.13140/RG.2.2.30786.22729. The runoff and
soil loss data from small plots under simulated rainfall (Auerswald et al.,
2019c) can be obtained from 10.13140/RG.2.2.27430.78401.
Author contributions
This paper represents a result of collegial teamwork. PF and KA designed the data analysis and prepared the manuscript. All
authors conducted the literature research. All authors read and approved the
final manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Innovative monitoring techniques and modelling approaches for analysing hydrological processes in small basins”. It is a result of the 17th Biennial Conference ERB 2018, Darmstadt, Germany, 11–14 September 2018.
Acknowledgements
The scientific activities of the research network Forschungsverbund
Agrarökosysteme München (FAM) were financially supported by the
German Federal Ministry of Education and Research (BMBF 0339370). Overhead
costs of the research station of Scheyern were funded by the Bavarian State
Ministry for Science, Research and Arts. This research, which is a summary
of nearly a decade of intensive field monitoring, would have not be possible
without the unresting efforts of so many colleagues, technicians, and
students. Outstanding contributions were made by Max Kainz, Georg Gerl and
Stephan Weigand. The efforts of all collaborators are gratefully
acknowledged. This study was also supported by the German Research
Foundation (DFG) and the Technical University of Munich (TUM) in the
framework of the Open Access Publishing Program.
Financial support
This research has been supported by the German Federal Ministry of Education and Research (grant no. BMBF 0339370).This work was supported by the German Research Foundation (DFG) and the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program.
Review statement
This paper was edited by Britta Schmalz and reviewed by Thomas Hoffmann and one anonymous referee.
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