Estimating the effects of water-induced shallow landslides on soil erosion

Bosco, Claudio; Sander, G. C.

doi:10.1101/011965

Cited by 4 publications

(5 citation statements)

References 32 publications

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“…The applied modelling architecture has its basis in the geospatial semantic array programming paradigm (GeoSemAP) [39,40] with efforts focused towards computational reproducibility and semantic modularization of the many data-transformation components. A data-transformation module (D-TM) may be considered as a handy portable formulation for applying a mathematical function to a set of input data and parameters.…”

Section: Methodsmentioning

confidence: 99%

“…The BGS [ 34 , 35 ], generalized linear models [ 36 ] (simple or with mixed effect) and machine learning (ANN in this case) [ 37 , 38 ] techniques based on geolocated surveys and gridded spatial covariate layers were applied and tested to construct high-resolution gender-disaggregated maps of stunting, literacy and use of modern contraceptive methods. The applied modelling architecture has its basis in the geospatial semantic array programming paradigm (GeoSemAP) [ 39 , 40 ] with efforts focused towards computational reproducibility and semantic modularization of the many data-transformation components. A data-transformation module (D-TM) may be considered as a handy portable formulation for applying a mathematical function to a set of input data and parameters.…”

Section: Methodsmentioning

confidence: 99%

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Exploring the high-resolution mapping of gender-disaggregated development indicators

Bosco

Alegana

Bird

et al. 2017

J. R. Soc. Interface.

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Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74–75% for female literacy in Nigeria and Kenya, and in the 50–70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2–30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Exploring the high-resolution mapping of gender-disaggregated development indicators

Bosco

Alegana

Bird

et al. 2017

J. R. Soc. Interface.

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“…Soil erosion is also part of a system of multiple interacting processes operating in a complex hierarchy. Mass movements may play an important role within soil erosion processes due to their capacity to remove and expose large parts of slopes in a relatively short time (Van Beek, 2002;Bosco and Sander, 2014). At the same time, high soil erosion rates can also lead to an increase of landslide susceptibility (Pradhan et al, 2012) especially because of the reduced capacity of the soil to support a good vegetation cover.…”

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confidence: 99%

Modelling soil erosion at European scale: towards harmonization and reproducibility

Bosco

Rigo

Dewitte

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. Soil erosion by water is one of the most widespread forms of soil degradation. The loss of soil as a result of erosion can lead to decline in organic matter and nutrient contents, breakdown of soil structure and reduction of the water-holding capacity. Measuring soil loss across the whole landscape is impractical and thus research is needed to improve methods of estimating soil erosion with computational modelling, upon which integrated assessment and mitigation strategies may be based. Despite the efforts, the prediction value of existing models is still limited, especially at regional and continental scale, because a systematic knowledge of local climatological and soil parameters is often unavailable. A new approach for modelling soil erosion at regional scale is here proposed. It is based on the joint use of low-data-demanding models and innovative techniques for better estimating model inputs. The proposed modelling architecture has at its basis the semantic array programming paradigm and a strong effort towards computational reproducibility. An extended version of the Revised Universal Soil Loss Equation (RUSLE) has been implemented merging different empirical rainfall-erosivity equations within a climatic ensemble model and adding a new factor for a better consideration of soil stoniness within the model. Pan-European soil erosion rates by water have been estimated through the use of publicly available data sets and locally reliable empirical relationships. The accuracy of the results is corroborated by a visual plausibility check (63 % of a random sample of grid cells are accurate, 83 % at least moderately accurate, bootstrap p ≤ 0.05). A comparison with country-level statistics of pre-existing European soil erosion maps is also provided.

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“…The landslide erosion accounted for 49.0%–88.5% of the erosion amount in six small watersheds under extreme rainfall in the Yanhe watershed of Shaanxi Province on the Loess Plateau from July to September 2013 (Jiao et al., 2017). The landslide activity resulted in a mean increase in total soil erosion rate between 22% and 26% above the pre–failure estimate in southern Italy (Bosco & Sander, 2015).…”

Section: Discussionmentioning

confidence: 93%

Revegetation Changes Main Erosion Type on the Gully–Slope on the Chinese Loess Plateau Under Extreme Rainfall: Reducing Gully Erosion and Promoting Shallow Landslides

Xu,

Luo,

Guo

et al. 2024

Water Resources Research

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Extreme rainfall events pose a severe challenge to soil and water conservation, even in areas with high vegetation cover on the Loess Plateau. In this study, the artificial extreme rainfalls with cumulative rainfall of 270 mm and intensity of 60 mm · hr−1 were conducted on in‐situ experimental plots (20 × 2.5 m) on a loess gully–slope with gradients of 35°–40° that were treated with different grass coverage: (0%, 30%–40%, 70%–80%, >90%). The ephemeral gully/rill and shallow landslide occurred in plots were analyzed. Revegetation changed the erosion type on gully–slope, reducing gully erosion but promoting shallow landslide due to the change from infiltration–excess runoff to saturation–excess runoff. Under grass coverage of >90%, over 95% of rainfall seeped into the soil, and subsurface flow was generated due to the lower saturated hydraulic conductivity of underlying soil, which increased the possibility of landslides. The average erosion rate (0.36–3.29 g · m−2 min−1; no obvious erosion) in plots with 70%–80% coverage was 95.5% lower than that in bare land plots (27.8–47.5 g · m−2 min−1; ephemeral gully erosion), while due to landslides the average erosion rate in plots with >90% coverage (135.1–184.3 g · m−2 min−1) was 86.5 times higher than that in plots with 70%–80%. For grass, a coverage of 70%–80% was most effective in preventing soil erosion, controlling gully erosion and preventing landslides under extreme rainfall. These results deepen the understanding of the complex relationship between vegetation, gully erosion, and landslides in ecologically sensitive areas.

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Estimating the effects of water-induced shallow landslides on soil erosion

Cited by 4 publications

References 32 publications

Exploring the high-resolution mapping of gender-disaggregated development indicators

Exploring the high-resolution mapping of gender-disaggregated development indicators

Modelling soil erosion at European scale: towards harmonization and reproducibility

Revegetation Changes Main Erosion Type on the Gully–Slope on the Chinese Loess Plateau Under Extreme Rainfall: Reducing Gully Erosion and Promoting Shallow Landslides

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