Quantifying model uncertainty to improve watershed-level ecosystem service quantification: a global sensitivity analysis of the RUSLE

Carmona, Natalia Estrada; Harper, Elizabeth B.; DeClerck, Fabrice; Fremier, Alexander K.

doi:10.1080/21513732.2016.1237383

Cited by 35 publications

(18 citation statements)

References 54 publications

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“…In favour of emphasising V-factor estimation is the remark of Garcia-Ruiz (2010) that “among the factors explaining the intensity of soil erosion, plant cover and land uses are considered the most important, exceeding the influence of rainfall intensity and slope gradient”. Moreover, Estrada-Carmona et al (2017) argue that C-factor produces the greatest degree of variation in RUSLE-based predictions and this factor can be improved with the application of better land management practices and agro-environmental friendly measures ( Borrelli et al, 2016 ). It is expected that broad availability of high resolution Sentinel-2 multispectral imagery by ESA, will soon allow replacement of the currently used low resolution FCover layers.…”

Section: Discussionmentioning

confidence: 99%

The G2 erosion model: An algorithm for month-time step assessments

Karydas¹,

Panagos

2018

Environmental Research

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A detailed description of the G2 erosion model is presented, in order to support potential users. G2 is a complete, quantitative algorithm for mapping soil loss and sediment yield rates on month-time intervals. G2 has been designed to run in a GIS environment, taking input from geodatabases available by European or other international institutions. G2 adopts fundamental equations from the Revised Universal Soil Loss Equation (RUSLE) and the Erosion Potential Method (EPM), especially for rainfall erosivity, soil erodibility, and sediment delivery ratio. However, it has developed its own equations and matrices for the vegetation cover and management factor and the effect of landscape alterations on erosion. Provision of month-time step assessments is expected to improve understanding of erosion processes, especially in relation to land uses and climate change. In parallel, G2 has full potential to decision-making support with standardised maps on a regular basis. Geospatial layers of rainfall erosivity, soil erodibility, and terrain influence, recently developed by the Joint Research Centre (JRC) on a European or global scale, will further facilitate applications of G2.

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Section: Discussionmentioning

confidence: 99%

The G2 erosion model: An algorithm for month-time step assessments

Karydas¹,

Panagos

2018

Environmental Research

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“…Based on nine nationwide soil loss data sets, including soil loss estimates for Europe (Panagos et al, 2015e), and the original USLE data set for the USA, Estrada-Carmona et al (2017) performed global sensi-tivity analysis to identify the dominant USLE input factors. For all nine countries Estrada-Carmona et al (2017) found that the C factor and the LS factor were the most influential USLE inputs. Bosco et al (2015) proposed a multi RUSLE model approach to account for the uncertainties in their soil loss estimates and therefore involve the impact of the different representations of the USLE inputs on soil loss estimation.…”

Section: Introductionmentioning

confidence: 94%

“…Such a large difference in the SDR does, however, not necessarily reflect the differences in soil erosion rates. Evans (1995) and Boardman (2006) point out that soil losses derived in plot-scale experiments do not reflect erosion taking place on the landscape scale. Evans (1995) found that plot-scale soil losses are larger than soil losses in the landscape by a factor of 2 to 10 under comparable conditions.…”

Section: Usle Input Realizations -Ranges Plausibility and Their Commentioning

confidence: 99%

“…A recent pan-European soil loss assessment started a broad discussion of the validity of the estimates when compared to in-field soil loss assessments in Great Britain (see the discussion in Panagos et al, 2015e;Evans and Boardman, 2016a;Panagos et al, 2016;Evans and Boardman, 2016b). Several authors question the applicability of the plot-scalebased USLE to the landscape scale (e.g., Boardman, 2006;Evans, 1995;Govers, 2011), particularly as in large domains other processes such as gully erosion, bank erosion, or sediment deposition can dominate the erosion response (Govers, 2011). Multiple approaches are available from the literature that account, for instance, for the deposition of eroded material by employing concepts such as the sediment delivery ratio (e.g., Rajbanshi and Bhattacharya, 2020;Ferro and Minacapilli, 1995;Graham, 1975).…”

Section: Introductionmentioning

confidence: 99%

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A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda

Schürz

Mehdi

Kiesel

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. The Universal Soil Loss Equation (USLE) is the most commonly used model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and steepness LS, soil cover C, and support measures P. A large variety of methods exist to derive these model inputs from readily available data. However, the estimated values of a respective model input can strongly differ when employing different methods and can eventually introduce large uncertainties in the estimated soil loss. The potential to evaluate soil loss estimates at a large scale is very limited due to scarce in-field observations and their comparability to long-term soil estimates. In this work we addressed (i) the uncertainties in the soil loss estimates that can potentially be introduced by different representations of the USLE input factors and (ii) challenges that can arise in the evaluation of uncertain soil loss estimates with observed data. In a systematic analysis we developed different representations of USLE inputs for the study domain of Kenya and Uganda. All combinations of the generated USLE inputs resulted in 972 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on the administrative level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation. The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude, particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large-scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively, while LS was relevant in small-scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.

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“…(b) Studies that have used backward or inverse uncertainty propagation in which modeled and observed values of SE are compared to evaluate model biases, check model's suitability for a basin, and estimate model parameters (Risse et al, 1993;Falk et al, 2010 andCarmona et al, 2017). The backward uncertainty analysis requires observed values of SE, and hence not applicable for ungauged basins.…”

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confidence: 99%