干旱区生态环境知识资源中心(Arid): Improvement of seasonal runoff and soil loss predictions by the MMF (Morgan-Morgan-Finney) model after wildfire and soil treatment in Mediterranean forest ecosystems

Arid

DOI	10.1016/j.catena.2019.104415
	Improvement of seasonal runoff and soil loss predictions by the MMF (Morgan-Morgan-Finney) model after wildfire and soil treatment in Mediterranean forest ecosystems
	Zema, Demetrio Antonio 1; Nunes, Joao Pedro 2; Lucas-Borja, Manuel Esteban 3
通讯作者	Zema, Demetrio Antonio
来源期刊	CATENA
ISSN	0341-8162
EISSN	1872-6887
出版年	2020
卷号	188
英文摘要	The negative hydrological effects of wildfire are very difficult to predict in Mediterranean forest ecosystems, due the intrinsic climate and soil characteristics of these areas. Among the hydrological models simulating surface runoff and soil erosion in these environmental contexts, the semi-empirical Morgan-Morgan-Finney (MMF) model can ensure the representation of the main physical processes, while offering ease of use and limiting the number of input parameters. However, literature reports very few modelling studies using MMF in burned areas of the Mediterranean environment with or without post-fire rehabilitation measures. To fill this gap, the capacity of the MMF model to predict the seasonal surface runoff and soil loss in a Mediterranean forest was verified and improved for unburned plots and areas affected by a wildfire, with and without post-fire straw mulch treatment. The application of MMF with default input parameters (set up according to the original guidelines of the model's developers) led to poor performance. Conversely, after introducing some changes in input data for both the hydrological and erosive components (seasonal values of evapotranspiration, reduction of the soil hydrological depth, including soil water repellency effects in burned soils, and modelling erosive precipitation only), MMF was able to predict seasonal runoff volumes and soil loss with good reliability in all the experimented conditions. This modelling experiment has shown the capacity of the MMF model to simulate the seasonal hydrological and erosion response of the experimental unburned and burned soils of Mediterranean semi-arid forests. Although more research is needed to validate the model's prediction capacity in these conditions, the use of MMF as a management tool may be suggested to predict the hydrogeological risk in these delicate ecosystems threatened by wildfire, as well as to evaluate the potential efficiency of soil treatments after fire.
英文关键词	Erosion Hydrological model Effective hydrological layer Soil water repellency Straw mulching
类型	Article
语种	英语
国家	Italy ; Portugal ; Spain
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000518488500014
WOS关键词	REDUCING POSTFIRE RUNOFF ; WATER REPELLENCY ; EROSION MODELS ; REHABILITATION TREATMENTS ; HYDROLOGIC-SIMULATION ; PINE FORESTS ; FIRE ; IMPACT ; SCALE ; RUSLE
WOS类目	Geosciences, Multidisciplinary ; Soil Science ; Water Resources
WOS研究方向	Geology ; Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314235
作者单位	1.Univ Mediterranea Reggio Calabria, Dept Agr, I-89122 Reggio Di Calabria, Italy; 2.Univ Lisbon, Fac Ciencias, CE3C, P-1749016 Lisbon, Portugal; 3.Univ Castilla La Mancha, Dept Ciencia & Tecnol Agroforestal & Genet, Campus Univ S-N, Albacete 02071, Spain
推荐引用方式 GB/T 7714	Zema, Demetrio Antonio,Nunes, Joao Pedro,Lucas-Borja, Manuel Esteban. Improvement of seasonal runoff and soil loss predictions by the MMF (Morgan-Morgan-Finney) model after wildfire and soil treatment in Mediterranean forest ecosystems[J],2020,188.
APA	Zema, Demetrio Antonio,Nunes, Joao Pedro,&Lucas-Borja, Manuel Esteban.(2020).Improvement of seasonal runoff and soil loss predictions by the MMF (Morgan-Morgan-Finney) model after wildfire and soil treatment in Mediterranean forest ecosystems.CATENA,188.
MLA	Zema, Demetrio Antonio,et al."Improvement of seasonal runoff and soil loss predictions by the MMF (Morgan-Morgan-Finney) model after wildfire and soil treatment in Mediterranean forest ecosystems".CATENA 188(2020).