Throughfall kinetic energy in young subtropical forests: Investigation on tree species richness effects and spatial variability

Goebes, Philipp; Seitz, Steffen; Kühn, Peter; Liu, Ying; Niklaus, Pascal A.; Oheimb, Goddert von; Scholten, Thomas

doi:10.1016/j.agrformet.2015.06.019

Cited by 44 publications

(35 citation statements)

References 55 publications

(91 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most studies that investigated the impact of afforestation in montane cultivated catchments have demonstrated a reduction of soil losses at the hillslope level 38 . This trend is attributed to rainfall interception by the canopy, and to a reduction of the throughfall kinetic energy and the resulting splash erosion as the soil is better protected by various strata of vegetation cover 39 , 40 . Furthermore, under these conditions, the soil surface hydrodynamic properties (i.e.…”

Section: Discussionmentioning

confidence: 99%

From shifting cultivation to teak plantation: effect on overland flow and sediment yield in a montane tropical catchment

Ribolzi

Evrard

Huon

et al. 2017

Sci Rep

View full text Add to dashboard Cite

Soil erosion supplies large quantities of sediments to rivers of Southeastern Asia. It reduces soil fertility of agro-ecosystems located on hillslopes, and it degrades, downstream, water resource quality and leads to the siltation of reservoirs. An increase in the surface area covered with commercial perennial monocultures such as teak plantations is currently observed at the expanse of traditional slash-and-burn cultivation systems in steep montane environments of these regions. The impacts of land-use change on the hydrological response and sediment yields have been investigated in a representative catchment of Laos monitored for 13 years. After the gradual conversion of rice-based shifting cultivation to teak plantation-based systems, overland flow contribution to stream flow increased from 16 to 31% and sediment yield raised from 98 to 609 Mg km−2. This result is explained by the higher kinetic energy of raindrops falling from the canopy, the virtual absence of understorey vegetation cover to dissipate drop energy and the formation of an impermeable surface crust accelerating the formation and concentration of overland flow. The 25-to-50% lower 137Cs activities measured in soils collected under mature teak plantations compared to soils under other land uses illustrate the severity of soil erosion processes occurring in teak plantations.

show abstract

Section: Discussionmentioning

confidence: 99%

From shifting cultivation to teak plantation: effect on overland flow and sediment yield in a montane tropical catchment

Ribolzi

Evrard

Huon

et al. 2017

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Further parameters reflecting vertical distribution in previous studies are the number of branches (Herwitz, ; Goebes et al, ) and the canopy thickness (Nanko et al, ). These parameters do not give any indication of potential falling heights.…”

Section: Methodsmentioning

confidence: 99%

A new concept for estimating the influence of vegetation on throughfall kinetic energy using aerial laser scanning

Senn

Fassnacht

Eichel

et al. 2020

Earth Surf Processes Landf

Self Cite

View full text Add to dashboard Cite

Soil loss caused by erosion has enormous economic and social impacts. Splash effects of rainfall are an important driver of erosion processes; however, effects of vegetation on splash erosion are still not fully understood. Splash erosion processes under vegetation are investigated by means of throughfall kinetic energy (TKE). Previous studies on TKE utilized a heterogeneous set of plant and canopy parameters to assess vegetation's influence on erosion by rain splash but remained on individual plant‐ or plot‐levels. In the present study we developed a method for the area‐wide estimation of the influence of vegetation on TKE using remote sensing methods. In a literature review we identified key vegetation variables influencing splash erosion and developed a conceptual model to describe the interaction of vegetation and raindrops. Our model considers both amplifying and protecting effect of vegetation layers according to their height above the ground and aggregates them into a new indicator: the Vegetation Splash Factor (VSF). It is based on the proportional contribution of drips per layer, which can be calculated via the vegetation cover profile from airborne LiDAR datasets. In a case study, we calculated the VSF using a LiDAR dataset for La Campana National Park in central Chile. The studied catchment comprises a heterogeneous mosaic of vegetation layer combinations and types and is hence well suited to test the approach. We calculated a VSF map showing the relation between vegetation structure and its expected influence on TKE. Mean VSF was 1.42, indicating amplifying overall effect of vegetation on TKE that was present in 81% of the area. Values below 1 indicating a protective effect were calculated for 19% of the area. For future work, we recommend refining the weighting factor by calibration to local conditions using field‐reference data and comparing the VSF with TKE field measurements. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

show abstract

“…Soil texture ranges from silt loam to silty clay loam [47]. The climate is typically subtropical with monsoons in summer, a mean annual temperature of about 17 °C and long-term average annual rainfall of about 1800 mm [48] but with a drier period from 2009 to 2012 [49].…”

Section: Methodsmentioning

confidence: 99%

Comparison of catchment scale 3D and 2.5D modelling of soil organic carbon stocks in Jiangxi Province, PR China

et al. 2019

Self Cite

View full text Add to dashboard Cite

As limited resources, soils are the largest terrestrial sinks of organic carbon. In this respect, 3D modelling of soil organic carbon (SOC) offers substantial improvements in the understanding and assessment of the spatial distribution of SOC stocks. Previous three-dimensional SOC modelling approaches usually averaged each depth increment for multi-layer two-dimensional predictions. Therefore, these models are limited in their vertical resolution and thus in the interpretability of the soil as a volume as well as in the accuracy of the SOC stock predictions. So far, only few approaches used spatially modelled depth functions for SOC predictions. This study implemented and evaluated an approach that compared polynomial, logarithmic and exponential depth functions using non-linear machine learning techniques, i.e. multivariate adaptive regression splines, random forests and support vector machines to quantify SOC stocks spatially and depth-related in the context of biodiversity and ecosystem functioning research. The legacy datasets used for modelling include profile data for SOC and bulk density (BD), sampled at five depth increments (0-5, 5-10, 10-20, 20-30, 30-50 cm). The samples were taken in an experimental forest in the Chinese subtropics as part of the biodiversity and ecosystem functioning (BEF) China experiment. Here we compared the depth functions by means of the results of the different machine learning approaches obtained based on multi-layer 2D models as well as 3D models. The main findings were (i) that 3 rd degree polynomials provided the best results for SOC and BD (R 2 = 0.99 and R 2 = 0.98; RMSE = 0.36% and 0.07 g cm -3 ). However, they did not adequately describe the general asymptotic trend of SOC and BD. In this respect the exponential (SOC: R 2 = 0.94; RMSE = 0.56%) and logarithmic (BD: R 2 = 84; RMSE = 0.21 g cm -3 ) functions provided more reliable estimates. (ii) random forests with the exponential function for SOC correlated better with the corresponding 2.5D predictions (R 2 : 0.96 to 0.75), compared to the 3 rd degree polynomials (R 2 : 0.89 to 0.15) which support vector machines fitted best. We recommend not to use polynomial functions with sparsely sampled profiles, as they have many turning points and tend to overfit the data on a given profile. This may limit the spatial prediction capacities. Instead, less adaptive functions with a higher degree of generalisation such as exponential and logarithmic functions should be used to spatially map sparse vertical soil profile datasets. We conclude that spatial prediction of SOC using exponential depth functions, in conjunction with random forests is well suited for 3D SOC stock modelling, and provides much finer vertical resolutions compared to 2.5D approaches.

show abstract

Throughfall kinetic energy in young subtropical forests: Investigation on tree species richness effects and spatial variability

Cited by 44 publications

References 55 publications

From shifting cultivation to teak plantation: effect on overland flow and sediment yield in a montane tropical catchment

From shifting cultivation to teak plantation: effect on overland flow and sediment yield in a montane tropical catchment

A new concept for estimating the influence of vegetation on throughfall kinetic energy using aerial laser scanning

Comparison of catchment scale 3D and 2.5D modelling of soil organic carbon stocks in Jiangxi Province, PR China

Contact Info

Product

Resources

About