Dealing with Uncertainty in Erosion Model Predictions

Beven, Keith; Brazier, Richard E.

doi:10.1002/9781444328455.ch4

Cited by 13 publications

(13 citation statements)

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“…At present, most erosion models can be calibrated to give ''good'' predictions at the hillslope or catchment outlet, however, evaluation of the quality of model predictions internally (spatially) is often poor , particularly at the subgrid scale, which is invariably modeled as a homogeneous cell . We believe that the integration of geostatistical descriptions of surface characteristics into erosion models, will make the provision of good erosion predictions for the correct spatial reasons a realistic goal [Beven and Brazier, 2011] and furthermore will hasten the advent of physically based soil erosion models that are formulated on a ''real'' representation of SSR. 4.6.2.…”

Section: Importance Of Length Scalementioning

confidence: 99%

“…However, ''estimation'' of this parameter is subjective and therefore will not represent a quantitative assessment of the spatial variability of surface roughness on a hillslope or within a catchment [Young et al, 1987]. Fisher et al [1997] note that the spatial data required to parameterize erosion models are rarely available, so in most model applications some form of calibration of these spatial parameters is necessary, effectively rendering the physical structure of the model to be meaningless, certainly in a spatial sense [Beven and Brazier, 2011].…”

Section: Importance Of Length Scalementioning

confidence: 99%

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Modeling fine‐scale soil surface structure using geostatistics

Croft

Anderson

Brazier

et al. 2013

Water Resources Research

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[1] There is widespread recognition that spatially distributed information on soil surface roughness (SSR) is required for hydrological and geomorphological applications. Such information is necessary to describe variability in soil structure, which is highly heterogeneous in time and space, to parameterize hydrology and erosion models and to understand the temporal evolution of the soil surface in response to rainfall. This paper demonstrates how results from semivariogram analysis can quantify key elements of SSR for such applications. Three soil types (silt, silt loam, and silty clay) were used to show how different types of structural variance in SSR evolve during simulated rainfall events. All three soil types were progressively degraded using artificial rainfall to produce a series of roughness states. A calibrated laser profiling instrument was used to measure SSR over a 10 cm Â 10 cm spatial extent, at a 2 mm resolution. These data were geostatistically analyzed in the context of aggregate breakdown and soil crusting. The results show that such processes are represented by a quantifiable decrease in sill variance, from 7.81 (control) to 0.94 (after 60 min of rainfall). Soil surface features such as soil cracks, tillage lines and erosional areas were quantified by local maxima in semivariance at a given length scale. This research demonstrates that semivariogram analysis can retrieve spatiotemporal variations in soil surface condition; in order to provide information on hydrological pathways. Consequently, geostatistically derived SSR shows strong potential for inclusion as spatial information in hydrology and erosion models to represent complex surface processes at different soil structural scales.Citation: Croft, H., K. Anderson, R. E. Brazier, and N. J. Kuhn (2013), Modeling fine-scale soil surface structure using geostatistic,

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Section: Importance Of Length Scalementioning

confidence: 99%

Section: Importance Of Length Scalementioning

confidence: 99%

Modeling fine‐scale soil surface structure using geostatistics

Croft

Anderson

Brazier

et al. 2013

Water Resources Research

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“…Although this study provides new data effectively challenging conventional views of landscape lowering at the site of the Battle of Hastings, certain limitations are present in modeling at this scale and these warrant discussion (Beven & Brazier, ).…”

Section: Discussion and Limitationsmentioning

confidence: 99%

The historical topography of the Battle of Hastings: A geographical reconstruction

Hewitt

2019

Transactions in GIS

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“…Indeed, the problem of spatial equifinality, where multiple expressions of a catchment response to rainfall might result in the same catchment outlet hydrograph, is a problem that is widespread throughout the hydro-geomorphological literature (Lamb, 1996;Beven and Brazier, 2011;Beven, 2006). Thus, spatially explicit datasets that may help to constrain model predictive uncertainty, by eliminating inappropriate representations of contributing source areas or ground water storage, are needed (Blöschl and Sivapalan, 1995).…”

Section: Introductionmentioning

confidence: 98%

How does drainage alter the hydrology of shallow degraded peatlands across multiple spatial scales?

Luscombe

Anderson

Grand-Clément

et al. 2016

Journal of Hydrology

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a b s t r a c tShallow, degraded peatlands differ in both their structure and function from deeper, peatland ecosystems. Previous work has shown that shallow, drained peatlands demonstrate rapid storm runoff that is only minimally controlled by antecedent hydrological conditions. However, such peatlands are also known to exhibit significant variation in ecohydrological organisation and structure across different spatial scales. In addition, predictions of hydrological response using spatially distributed numerical models of rainfall-runoff may be flawed unless they are evaluated with datasets describing the spatial variability of hydrological responses. This paper evaluates to what extent, flow generation and water storage within shallow, degraded peatland catchments may be controlled by the spatial attributes of the contributing area of the peatland, the drainage ditch size, morphology and geometry.Results from an experiment conducted over multiple spatial scales and multi-annual timescales highlights that subtle variations in the local slope and topography account for the long-term spatial patterns of water table depth. Neither the local scale of the drainage feature or the topographic contributing area is shown to be a definitive predictor of runoff in the studied catchments. Results also highlight the importance of using spatially distributed observations to ensure that estimates of water storage and runoff are representative of the fine scale spatial variability that occurs in such damaged and shallow peatlands.

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Dealing with Uncertainty in Erosion Model Predictions

Cited by 13 publications

References 85 publications

Modeling fine‐scale soil surface structure using geostatistics

Modeling fine‐scale soil surface structure using geostatistics

The historical topography of the Battle of Hastings: A geographical reconstruction

How does drainage alter the hydrology of shallow degraded peatlands across multiple spatial scales?

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