Selecting suitable climate models for examining future changes in soil erosion

2021 ◽  
Author(s):  
Neil Brannigan ◽  
Donal Mullan ◽  
Karel Vandaele ◽  
Conor Graham ◽  
Jennifer McKinley ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Model Selection ◽  
Climate Models ◽  
Climate Model ◽  
Study Region ◽  
Extreme Precipitation Events ◽  
Suitable Climate ◽  
Erosion Models ◽  
Soil Erosion By Water ◽  
The Impact

<p>Climate models consistently project large increases in the frequency and magnitude of extreme precipitation events in the 21st century, revealing the potential for widespread impacts on various aspects of society. While the impacts on flooding receive particular attention, there is also considerable damage and associated cost for other precipitation driven phenomena, including soil erosion and muddy flooding. Multiple studies have shown that climate change will worsen the impacts of soil erosion and muddy flooding in various regions. These studies typically drive erosion models with a single model or a few models with little justification. A blind approach to climate model selection increases the risk of simulating a narrower range of possible scenarios, limiting vital information for mitigation planning and adaptation. This study provides a comprehensive methodology to efficiently select suitable climate models for simulating soil erosion and muddy flooding. For a case study region in eastern Belgium using the WEPP soil erosion model, we compare the performance of our novel methodology against other model selection methods for a future period (2081 – 2100). The main findings reveal that our novel methodology is successful in generating the widest range of future scenarios from a small number of models, when compared with other ways of selecting climate models. This approach has not previously been achieved for modelling soil erosion by water. Other precipitation-driven impact sectors may also wish to consider applying this method to assess the impact of future climatic changes, so that the worst- and best-case scenarios can be adequately prepared for.</p>

Modelling Soil Erosion by Water: Some Conclusions

1998 ◽  
pp. 515-517 ◽  
Author(s):  
John Boardman ◽  
David Favis-Mortlock
Keyword(s):  
Soil Erosion ◽  
Soil Erosion By Water

REVISITING THE METHODOLOGICAL DEVELOPMENT IN SOIL EROSION RESEARCH

Ensemble ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 145-165
Author(s):  
Tanmoy Sarkar ◽  
◽  
Tapas Pal ◽  
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Soil Erosion ◽  
Geographic Information ◽  
Methodological Development ◽  
Advantages And Disadvantages ◽  
The Future ◽  
Soil Erosion By Water

Soil erosion (by water) is a major land degradation process that may threat the Sustainable Development Goals (SDG) by its negative impact on environment and human well-being. Soil erosion research demands scientific methods, tools and techniques to assess soil erosion with more accuracy and reliability. Soil erosion research has had experienced crude field-based techniques in early twentieth century to model-based approaches since the 1970s and very recent machine learning and artificial intelligence models to predict soil erosion susceptibility and risk. The paper aims to review the trend in methodological development in soil erosion by water through time. The brief background of different approaches, their relative advantages and disadvantages are reviewed. Depending on the time of establishment and wide application the approaches are classified and represented as erosion plot/runoff approach, erosion pin technique followed by environmental tracer method and model approach in combination with Remote Sensing (RS) and Geographic Information System (GIS). Recent advancement in artificial intelligence and application of statistical techniques have a great potential to contribute in soil erosion research by identifying various degrees of susceptibility in large scale and also to quantify the erosion rate with high accuracy. The Remote sensing (RS) and Geographic Information System (GIS) contribute to develop regional scale data base with exploration of real time data and spatial analysis. The combination of RS & GIS and process-based models must be more effective than the traditional soil erosion model in the context of prediction with greater reliability and validity. The future research on soil erosion is better to focus on the theoretical analysis and development of erosion prediction model with more quantitative refinement and to model the future.

Advances in techniques to assess soil erodibility

2021 ◽  
pp. 175-214
Author(s):  
R. J. Rickson ◽  
◽  
E. Dowdeswell Downey ◽  
G. Alegbeleye ◽  
S. E. Cooper ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Test Procedure ◽  
Aggregate Stability ◽  
Analytical Techniques ◽  
Soil Biology ◽  
Soil Erodibility ◽  
Erosion Rates ◽  
Erosion Processes ◽  
Assessment Techniques ◽  
Soil Erosion By Water

Soil erodibility is the susceptibility of soil to the erosive forces of rainsplash, runoff and wind. It is a significant factor in determining present and future soil erosion rates. Focusing on soil erosion by water, this chapter shows that erodibility is determined by static and dynamic soil properties that control a range of sub-processes affecting soil erosion, but there is no standardised test procedure, making comparison of erodibility assessment techniques and their results challenging. Most researchers agree that aggregate stability is the best indicator of soil erodibility. Selection of techniques to measure aggregate stability need to consider the type of disruptive forces and breakdown processes to which field aggregates are subjected. New indices must incorporate spatial and temporal variabilities in erodibility; the different erosion processes operating; the impact of climate change; and the role of soil biology. New analytical techniques such as computer aided tomography show promise in considering soil erodibility as a dynamic continuum operating over 3 dimensions.

scholarly journals Environmental effectiveness of GAEC cross-compliance Standard 1.1a (temporary ditches) and 1.2g (permanent grass cover of set-aside) in reducing soil erosion and economic evaluation of the competitiveness gap for farmers

2016 ◽  
Vol 10 (1s) ◽  
Author(s):  
Paolo Bazzoffi ◽  
Rosa Francaviglia ◽  
Ulderico Neri ◽  
Rosario Napoli ◽  
Alessandro Marchetti ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Vegetation Cover ◽  
Bare Soil ◽  
Runoff Water ◽  
Rusle Model ◽  
Environmental Effectiveness ◽  
Additional Costs ◽  
Soil Erosion By Water ◽  
Cross Compliance ◽  
Made In

<p>This paper shows the results of the monitoring carried out in three hilly farms of the MONACO project in order to verify the effectiveness of the Standard 1.1 <sub>(commitment a)</sub> (temporary ditches) and Standard 1.2 <sub>(commitment g)</sub> (Vegetation cover throughout the year in set-aside land) in the reduction in soil erosion, contained in Rule 1: ‘minimum land management that meets specific conditions’ of the decree Mipaaf 2009 and following modifications, until the recent decree No. 180 of January 23, 2015. In addition, the assessment of the competitiveness gap was done. That is the evaluation of the additional costs borne by the beneficiary of the single payment determined from agronomic commitments. Monitoring has also compared the erosion actually observed in the field with that predicted by RUSLE model (Revised Universal Soil Loss Equation) (Renard et al., 1997) in the two situations: with and without the presence of temporary ditches, i.e. assuming Factual (compliance rules) and in that Counterfactual (infringement). This comparison was made in view of the fact that the RUSLE model was chosen by the 'European Evaluation Network for Rural Development (EEN, 2013) as a forecasting tool for the quantification of' Common Indicator ‘soil erosion by water’. The results of soil erosion survey carried out by using a new  UAV-GIS methodology  on two monitoring farms in two years of observations have shown that temporary ditches were effective in decreasing erosion, on average, by 42.5%, from 36. 59 t ha<sup>-1</sup> to 21.05 t ha<sup>-1</sup> during the monitoring period. It was also evaluated the effectiveness of grass strips (at variance with the commitment of temporary ditches). The results showed a strong, highly significant, reduction in erosion by about 35% times respect soil erosion observed in bare soil and also a significant reduction in the volume of runoff water.  With regard to Standard 1.2 <sub>(commitment g)</sub> the statistical analysis shows a strong and highly significant decrease in the erosion due to the vegetation cover of the soil compared to bare soil. The economic competitiveness gap of  Standard 1.1<sub>(commitment a)</sub> stood at € 4.07±1.42 € ha<sup>-1</sup> year<sup>-1</sup>, while CO<sub>2</sub> emissions due to execution of temporary ditches was 2.58 kg ha<sup>-1</sup>year<sup>-1</sup>. As for the Standard 1.2 <sub>(commitment g) </sub>the average differential competitiveness gap amounted to  50.22±13.7 € ha<sup>-1</sup> year<sup>-1</sup> and an output of CO<sub>2</sub> equal to 31.52  kg ha<sup>-1</sup> year.</p>

Assessing sediment connectivity and soil erosion by water in a representative catchment on the Loess Plateau, China

CATENA ◽  
2020 ◽  
Vol 185 ◽  
pp. 104284
Author(s):  
Guangju Zhao ◽  
Peng Gao ◽  
Peng Tian ◽  
Wenyi Sun ◽  
Jinfei Hu ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Loess Plateau ◽  
The Loess Plateau ◽  
Sediment Connectivity ◽  
Soil Erosion By Water

Evaluation of changes in soil erosion rates in Andalucia between 1990 and 2018

2020 ◽  
Author(s):  
Filippo Milazzo ◽  
Tom Vanwalleghem ◽  
Pilar Fernández, Rebollo ◽  
Jesus Fernández-Habas
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Management ◽  
Aerial Photographs ◽  
Frequency Distributions ◽  
Erosion Rates ◽  
Soil Erosion Rate ◽  
Soil Erosion Rates ◽  
Management Changes ◽  
Soil Erosion By Water

&lt;p&gt;Land use and land management changes impact significantly on soil erosion rates. The Mediterranean, and in particular Southern Spain, has been affected by important shifts in the last decades. This area is currently identified as a hotspot for soil erosion by water. In the effort to achieve the SDG Target 15, we aim to show the effect of land management change, assessing soil erosion rate based on historical data. We analyzed the evolution of land use from historical aerial photographs between 1990 and 2018. We then calculated soil erosion with RUSLE. For this, we first determined the distribution frequency of cover-management factors for each land use class, comparing current land use maps with the European Soil Erosion Map (Panagos et al., 2015). Past C factors where then assigned using a Monte Carlo approach, based on the obtained frequency distributions.&amp;#160;&lt;/p&gt;

Effect of cellulose on soil erosion by water

1976 ◽  
Vol 139 (4) ◽  
pp. 517-523 ◽  
Author(s):  
Subhash Chandra ◽  
S. K. De
Keyword(s):  
Soil Erosion ◽  
Soil Erosion By Water
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