首页> 外文期刊>Land Degradation and Development >PHYSICALLY-BASED MODELLING OF THE POST-FIRE RUNOFF RESPONSE OF A FOREST CATCHMENT IN CENTRAL PORTUGAL: USING FIELD VERSUS REMOTE SENSING BASED ESTIMATES OF VEGETATION RECOVERY
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PHYSICALLY-BASED MODELLING OF THE POST-FIRE RUNOFF RESPONSE OF A FOREST CATCHMENT IN CENTRAL PORTUGAL: USING FIELD VERSUS REMOTE SENSING BASED ESTIMATES OF VEGETATION RECOVERY

机译:葡萄牙中部森林流域火灾后径流响应的基于物理的建模:使用基于场对遥感的植被恢复估算

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Forest fires are a recurrent phenomenon in Mediterranean forests, with impacts for human landscapes and communities, which must be understood before they can be managed. This study used the physically based Limburg Soil Erosion Model (LISEM) to simulate rainfall-runoff response, under soil water repellent (SWR) conditions and different stages of vegetation recovery. Five rainfall-runoff events were selected, representing wet and dry conditions, spread over two years after a wildfire which burned eucalypt and maritime pine plantations in the Colmeal experimental micro-catchment, central Portugal. Each event was simulated using three Leaf Area Index (LAI) estimates: indirect field-based measurements (TC-LAI), NDVI-based estimates derived from Landsat-5 TM and Landsat-7 ETM+ imagery (NDVI-LAI), and the LAI of a fully restored canopy to test model sensitivity to interception parameters. LISEM was able to simulate events in relative terms but underestimated peak runoff (r(2) = 0.36, mean error = -31%, and NSE = -0.15) and total runoff (r(2) = 0.52, mean error = -15% and NSE = 0.09), which could be related to the presence of SWR or saturated areas, according to pre-rainfall soil moisture conditions. The model performed better for individual hydrographs, especially under wet conditions. Modelling the full-cover scenario showed minor sensitivity of LISEM to the observed changes in LAI. NDVI-LAI data gave a close to equal model performance with TC-LAI and therefore can be considered a suitable substitute for ground-based measurements in post-fire runoff predictions. However, more attention should be given to representing pre-rainfall soil moisture conditions and especially the presence of SWR. Copyright (C) 2016 John Wiley & Sons, Ltd.
机译:森林大火是地中海森林中经常发生的现象,会对人类景观和社区造成影响,必须加以了解,然后才能进行管理。这项研究使用基于物理的林堡土壤侵蚀模型(LISEM)模拟在土壤防水(SWR)条件和植被恢复的不同阶段下的降雨-径流响应。在葡萄牙中部科尔梅勒试验性小流域烧毁桉树和海洋松树人工林的一场野火后,选择了五种降雨-径流事件,分别代表干湿条件,持续了两年。每个事件使用三个叶面积指数(LAI)估计值进行模拟:基于间接场的测量(TC-LAI),从Landsat-5 TM和Landsat-7 ETM +图像(NDVI-LAI)得出的基于NDVI的估计值完全恢复的顶篷以测试模型对拦截参数的敏感性。 LISEM能够以相对方式模拟事件,但低估了峰值径流量(r(2)= 0.36,平均误差= -31%,NSE = -0.15)和总径流量(r(2)= 0.52,平均误差= -15) %和NSE = 0.09),这可能与降雨之前的土壤湿度状况有关,SWR或饱和区域的存在。该模型对于单独的水文图表现更好,尤其是在潮湿条件下。对全覆盖情景进行建模显示,LISEM对观察到的LAI变化具有较小的敏感性。 NDVI-LAI数据提供了与TC-LAI几乎相同的模型性能,因此可以认为是火后径流预测中基于地面的测量的合适替代品。但是,应该更多地关注降雨前的土壤湿度状况,特别是存在SWR。版权所有(C)2016 John Wiley&Sons,Ltd.

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