Experimental study on head loss due to cluster of randomly distributed non-uniform roughness elements in supercritical flow

Accurate estimation of head loss introduced via randomly placed roughness elements found in natural or constructed streams (e.g., fish passages) is essential in order to estimate flow variables in mountain streams, understand formation of niches for aquatic life, and model flow structure. Owing to the complexity of the involved processes and the often missing detailed data regarding the roughness elements, the head loss in such streams is mostly approximated using empirical models. In our study, we utilize flume experiments to analyze the effects of the spatial distribution of roughness elements on water surface levels and head loss and, moreover, use the produced data to test three empirical models estimating head loss. The experiments were performed in a 15 m long, 0.9 m wide flume with a slope of 5% under large Froude numbers (2.5–2.8). Flow velocities and water levels were measured with different flow rates at 58 points within a 3.96 m test section of the flume. We could show that different randomly arranged patterns of roughness elements significantly affected head loss (differences up to 33.6%), whereas water jumps occurred when flow depths were in the same size range as the roughness elements. The roughness element position and its size influenced water surface profiles. None of the three tested empirical models were able to well reproduce the differences in head loss due to the different patterns of roughness elements, with overestimated head loss from 12 to 94.7%, R2 from 41 to 73%, NSE from −21.1 to 0.09, and RRMSE from 18.4 to 93%. This generally indicates that these empirical models are conditionally suitable to consider head loss effects of random patterns of roughness elements

Does soil thinning change soil erodibility? An exploration of long-term erosion feedback systems

Soil erosion rates on arable land frequently exceed the pace at which new soil is formed. This imbalance leads to soil thinning (i.e. truncation), whereby subsoil horizons and their underlying parent material become progressively closer to the land surface. As soil erosion is a selective process and subsurface horizons often have contrasting properties to the original topsoil, truncation-induced changes to soil properties might affect erosion rates and runoff formation through a soil erosion feedback system. However, the potential interactions between soil erosion and soil truncation are poorly understood due to a lack of empirical data and the neglection of long-term erodibility dynamics in erosion simulation models. Here, we present a novel model-based exploration of the soil erosion feedback system over a period of 500 years using measured soil properties from a diversified database of 265 agricultural soil profiles in the UK. For this, we adapted the Modified Morgan–Morgan–Finney model (MMMF) to perform a modelling experiment in which topography, climate, land cover, and crop management parameters were held constant throughout the simulation period. As selective soil erosion processes removed topsoil layers, the model gradually mixed subsurface soil horizons into a 0.2 m plough layer and updated soil properties using mass-balance mixing models. Further, we estimated the uncertainty in model simulations with a forward error assessment. We found that modelled erosion rates in 99 % of the soil profiles were sensitive to truncation-induced changes in soil properties. The soil losses in all except one of the truncation-sensitive profiles displayed a decelerating trend, which depicted an exponential decay in erosion rates over the simulation period. This was largely explained by decreasing silt contents in the soil surface due to selective removal of this more erodible particle size fraction and the presence of clayey or sandy substrata. Moreover, the soil profiles displayed an increased residual stone cover, which armoured the land surface and reduced soil detachment. Contrastingly, the soils with siltier subsurface horizons continuously replenished the plough layer with readily erodible material, which prevented the decline of soil loss rates over time. Although our results are limited by the edaphoclimatic conditions represented in our data, as by our modelling assumptions, we have demonstrated how modelled soil losses can be sensitive to erosion-induced changes in soil properties. These findings are likely to affect how we calculate soil lifespans and make long-term projections of land degradation

Non-inversion conservation tillage as an underestimated driver of tillage erosion

Tillage erosion is a widely underestimated process initiating soil degradation especially in case of large agricultural fields located in rolling topography. It is often assumed that, conservation, non-inversion tillage causes less tillage erosion than conventional inversion tillage. In this study, tillage erosion was determined on three paired plots comparing non-inversion chisel versus inversion mouldboard tillage. The experiments were performed at three sites in Northeast Germany with gentle, moderate, and steep slope, while tillage depth (0.25 m) and speed (≈ 6 km h−1) were kept constant during all experiments. The results indicate that non-inversion tillage produces significantly more soil movement compared to inversion tillage. The soil translocation distance was by a factor of 1.3–2.1 larger in case of chisel tillage. The largest difference in translocation distance and tillage transport coefficient (ktil) was found on the gentle slope exhibiting the lowest soil cohesion. Our results together with an evaluation of ktil values derived from literature and standardised for 0.25 m tillage depth contradict the general assumption that non-inversion tillage reduces tillage erosion. In tillage erosion dominated areas, non-inversion tillage applied with high tillage speed and depth potentially increases tillage erosion and fails its purpose to serve as soil conservation measure

A conceptual-model-based sediment connectivity assessment for patchy agricultural catchments

The accelerated sediment supply from agricultural soils to riverine and lacustrine environments leads to negative off-site consequences. In particular, the sediment connectivity from agricultural land to surface waters is strongly affected by landscape patchiness and the linear structures that separate field parcels (e.g. roads, tracks, hedges, and grass buffer strips). Understanding the interactions between these structures and sediment transfer is therefore crucial for minimising off-site erosion impacts. Although soil erosion models can be used to understand lateral sediment transport patterns, model-based connectivity assessments are hindered by the uncertainty in model structures and input data. Specifically, the representation of linear landscape features in numerical soil redistribution models is often compromised by the spatial resolution of the input data and the quality of the process descriptions. Here we adapted the Water and Tillage Erosion Model and Sediment Delivery Model (WaTEM/SE-DEM) using high-resolution spatial data (2 m x 2 m) to analyse the sediment connectivity in a very patchy mesoscale catchment (73 km(2)) of the Swiss Plateau. We used a global sensitivity analysis to explore model structural assumptions about how linear landscape features (dis)connect the sediment cascade, which allowed us to investigate the uncertainty in the model structure. Furthermore, we compared model simulations of hillslope sediment yields from five sub-catchments to tributary sediment loads, which were calculated with long-term water discharge and suspended sediment measurements. The sensitivity analysis revealed that the assumptions about how the road network (dis)connects the sediment transfer from field blocks to water courses had a much higher impact on modelled sediment yields than the uncertainty in model parameters. Moreover, model simulations showed a higher agreement with tributary sediment loads when the road network was assumed to directly connect sediments from hillslopes to water courses. Our results ultimately illustrate how a high-density road network combined with an effective drainage system increases sediment connectivity from hillslopes to surface waters in agricultural landscapes. This further highlights the importance of considering linear landscape features and model structural uncertainty in soil erosion and sediment connectivity research

Comment on "Rainfall erosivity in Europe" by Panagos et al. (Sci. Total Environ., 511, 801–814, 2015)

Recently a rainfall erosivity map has been published. We show that the values of this map contain considerable bias because (i) the temporal resolution of the rain data was insufficient, which likely underestimates rain erosivity by about 20%, (ii) no attempt had been included to account for the different time periods that were used for different countries, which can modify rain erosivity by more than 50%, (iii) and likely precipitation data had been used instead of rain data and thus rain erosivity is overestimated in areas with significant snowfall. Furthermore, the seasonal distribution of rain erosivity is not provided, which does not allow using the erosivity map for erosion prediction in many cases. Although a rain erosivity map for Europe would be highly desirable, we recommend using the national erosivity maps until these problems have been solved. Such maps are available for many European countries. (C) 2015 Elsevier B.V. All rights reserved

Pristine levels of suspended sediment in large German river channels during the Anthropocene?

Suspended sediment is an integral part of riverine transport and functioning that has been strongly altered during the Anthropocene due to the overwhelming human pressure on soils, sediments, and the water cycle. Understanding the controls of changing suspended sediment in rivers is therefore vital for effective management strategies. Here we present results from a trend analysis of suspended sediments covering 62 monitoring stations along the German waterways (catchment sizes range between 2000 and 160 000 km2) with more than 440 000 water samples taken between 1990 and 2010. Based on daily monitoring of suspended sediment concentration (SSC), we found significant declines in mean annual SSC and annual suspended sediment loads (SSLs) at 49 of 62 monitoring stations totaling −0.92 mg L−1 yr−1. At some stations decreases during the 20 years represent up to 50 % of the long-term average SSC. Significant decreases in SSC are associated with declining SSL despite an increase in sheet and rill erosion by almost 150 % derived from modeling due to changes in land use and management as well as rainfall erosivity. The contemporary suspended sediment loads of the Rhine at the German–Dutch border are approaching the natural base level of ∼1 Mt yr−1, which was reached by the Rhine during the mid-Holocene when the suspended sediment load was adjusted to the Holocene climatic conditions and before the onset of increased loads due to human-induced land use changes in the Rhine catchment. At this point we can only speculate regarding potential reasons for a decline in sediment transport in larger rivers despite erosion increase. We argue that increased sediment retention in upstream headwaters is presumably the major reason for declining SSC in the large river channels studied.</p

Feedbacks between water erosion and soil thinning

Abstract onlySoil erosion rates frequently exceed the pace at which new soil is formed. This imbalance can lead to soil thinning (i.e., truncation) whereby subsoil horizons, and the underlying parent material, emerge progressively closer to the land surface. These subsurface horizons may have contrasting physical, chemical, and biological properties from those of the original topsoil. Hence, soil thinning can induce changes in topsoil erodibility – a fact that has been largely overlooked in erosion modelling research and could affect long-term projections of soil erosion rates. Here we present a model-based exploration of the potential feedbacks between water erosion and soil thinning, using measured data from 265 agricultural soil profiles in the United Kingdom. We simulated annual erosion rates on these soil profiles with the Modified Morgan-Morgan-Finey model, assuming time-constant land cover, topographic, and rainfall parameters. As the original topsoil was successively removed, our model gradually mixed the subsurface horizons into a 20 cm ploughing layer. We applied this modelling framework on a yearly time-step over a 500-year period, or until the ploughing layer reached the bottom of the lowermost soil horizon. Soil texture, stone cover, and soil organic carbon content for the ploughing layer were recalculated for each time-step through a mass-balance model. Soil bulk density and soil moisture content at field capacity were estimated for each time-step by pedo-transfer functions developed from our own dataset. In addition, we employed a Monte Carlo simulation with 100 iterations per year to provide a forward error assessment of the modelled soil losses. We found that simulated erosion rates on 42 % of the soil profiles were sensitive to truncation-induced changes in soil properties during the analysed period. Among the profiles sensitive to soil thinning, 68 % displayed a negative trend in modelled erosion rates. This was largely explained by decreasing silt contents on the surface soil due to selective removal of this more erodible particle size fraction and the presence of clayey or sandy substrata. Moreover, an increased residual stone cover shielded the surface soils from detachment by raindrop impact and surface runoff. The soil profiles with a positive trend in erosion rates were characterised by the presence of siltier subsoil horizons, which increased topsoil erodibility as they were mixed into the ploughing layer. Overall, our results demonstrated how modelled erosion rates could be sensitive to truncation-induced changes in soil properties, which in turn may accelerate or slow down soil thinning. These feedbacks are likely to affect how we calculate soil lifespans and make long-term projections of land degradation