What is wrong with post-fire soil erosion modelling?

Author(s):  
Antonio Girona-García ◽  
Ana Rita Lopes ◽  
Sofia Corticeiro ◽  
Ricardo Martins ◽  
Jacob Keizer ◽  
...  

<p>Wildfire patterns are shifting all over the world as a consequence, among others, of changes in land use and climate [1], which may entail remarkable social, environmental, and economic implications. The occurrence of wildfires is often linked to increased post-fire hydrological and erosive responses, which are hard to predict due to the complexity of factors involved [2]. Against this background, soil erosion models arise as a resourceful tool in the decision-making process for environments that are or could be affected by wildfires: from prevention to mitigation and from emergency actions to long-term planning. Nevertheless, the current soil erosion models were not originally developed for post-fire conditions, so they are not adapted to include fire-related changes into their predictions [3]. This work aimed to review the scientific advances in the last twenty years in post-fire soil erosion modelling research from a meta-analysis approach.</p><p>To this end, the Scopus database was searched using different combinations of the terms “model”, “modelling”, “fire”, “wildfire” “hydrology”, “erosion”, “runoff”, “burn”, “burnt”, “erosion”, “soil erosion”, “sediment” and “rill”. Afterwards, the following publications were excluded: a) reviews; b) journals without peer-review process; c) books or book chapters; d) reports; e) editorials; f) conference proceedings; g) works in which the modelling was conducted on individual processes; h) studies modelling debris flows and landslides; i) works that did not conduct post-fire and/or erosion modelling; j) works that are not in English. Then, it was identified whether authors included in the models important factors related to soil erosion in fire-affected environments such as changes in water infiltration, burn severity, and/or the application of post-fire mitigation treatments. The main modelling approaches used, the calibration and validation of predicted data, and the use of efficiency indexes were also evaluated. </p><p>The screening resulted in 33 works (43 cases based on the model used) that were not homogeneously distributed worldwide, neither according to the model type used, nor by regions most affected by wildfires. For the calibration process, in 70% of the cases models were adapted to burned conditions but only in 25% of them, individual input parameters were improved to accommodate processes that were not previously represented. Additionally, burn severity and changes in infiltration were considered in 77 and 65% of the cases, respectively, whereas only 26% of the cases corresponded to studies where post-fire mitigation treatments were applied. It is noteworthy that only in 19% of the cases, the predicted data were validated with independent field datasets and uncertainty was assessed in 5% of the studies.</p><p>It is highlighted that further efforts are required on the adaptation of erosion models to burned conditions, evaluating the model performance in both calibration and validation stages for a wider variety of environments and scenarios, in order to accurately predict the hydrological and erosive response after fires.</p><p>[1] Andela et al. (2017). Science 356: 1356-1362. DOI: 10.1126/science.aal4108</p><p>[2] Larsen & MacDonald (2007). Water Resour. Res. 43: W11412. DOI: 10.1029/2006WR005560</p><p>[3] Vieira et al. (2018). Environ. Res. 165: 365-378. DOI: 10.1016/j.envres.2018.04.029</p>

2021 ◽  
Author(s):  
Lea Epple ◽  
Andreas Kaiser ◽  
Marcus Schindewolf ◽  
Anette Eltner

Abstract. Climate change, accompanied by intensified extreme weather events, results in changes in intensity, frequency and magnitude of soil erosion. These unclear future developments make adaption and improvement of soil erosion modelling approaches all the more important. Hypothesizing that models cannot keep up with the data, this review gives an overview of 44 process based soil erosion models, their strengths and weaknesses and discusses their potential for further development with respect to new and improved soil and soil erosion assessment techniques. We found valuable tools in areas, as remote sensing, tracing or machine learning, to gain temporal and spatial distributed high resolution parameterization and process descriptions which could lead to a more holistic modelling approach. Most process based models are so far not capable to implement cross-scale erosional processes or profit from the available resolution on a temporal and spatial scale. We conclude that models need further development regarding their process understanding, adaptability in respect to scale as well as their parameterization and calibration. The challenge is the development of models which are able to simulate soil erosion processes as close to reality as possible, as user-friendly as possible and as complex as it needs to be. 


Author(s):  
Pasquale Borrelli ◽  
Christine Alewell ◽  
Pablo Alvarez ◽  
Jamil Alexandre Ayach Anache ◽  
Jantiene Baartman ◽  
...  

2020 ◽  
Author(s):  
Nejc Bezak ◽  

<p>Systematic bibliometric investigations are useful to evaluate and compare the scientific impact of journal papers, book chapters and conference proceedings. Such studies allow the detection of emerging research topics, the analyses of cooperation networks, and the collection of in-depth insights into a specific research topic. In the presented work, we carried out a bibliometric study in order to obtain an in-depth knowledge on soil erosion modelling applications worldwide.</p><p>As a starting point, we used the soil erosion modelling meta-analysis data collection generated by the authors of this abstract in a joint community effort. This database contains meta-information of more than 3,000 documents published between 1994 and 2018 that are indexed in the SCOPUS database. The documents were reviewed and database entries verified. The database contains various types of meta-information about the modelling studies (e.g., model used, study area, input data, calibration, etc.). The bibliometric information was also included in the database (e.g., number of citations, type of publication, Scopus category, etc.). We investigated differences among publication types and differences between papers published in journals that are part of various Scopus categories. Moreover, relationships between publication CiteScore, number of authors, and number of citations were analyzed. A boosted regression tree model was used to detect the relative impact of the selected meta-information such as erosion model used, spatial modelling scale, study period, field activity on the total number of citations. Detailed investigation of the most cited papers was also conducted. The VOSviewer software was used to analyze citations, co-citations, bibliographic coupling, and co-authorship networks of the database entries.  </p><p>Our bibliometric investigations demonstrated that journal publications, on average, receive more citations than book series or conference proceedings. There were differences among the erosion models used, and some specific models such as the WaTEM/SEDEM model, on average, receive more citations than other models (e.g., USLE). It should also be noted that self-citation rates in case of most frequently used models were similar. Global studies, on average, receive more citations than studies dealing with plot, regional, or national scales. According to the boosted regression tree model, model calibration, validation, or field activity do not have significant impact on the obtained publication citations. Co-citation investigation revealed some interesting patterns. Our results also indicate that papers about soil erosion modeling also attract citations from different fields and better international cooperation is needed to advance this field of research with regard to its visibility and impact on human societies.    </p>


2021 ◽  
Author(s):  
Neil Brannigan ◽  
Donal Mullan ◽  
Karel Vandaele ◽  
Conor Graham ◽  
Jennifer McKinley ◽  
...  

<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>


2021 ◽  
Author(s):  
Manash Jyoti Bora ◽  
Sanandam Bordoloi ◽  
Sreeja Pekkat ◽  
Ankit Garg ◽  
Sreedeep Sekharan ◽  
...  

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