A simplified Excel tool for implementation of RUSLE2 in vineyards for stakeholders with limited dataset
A simplified Excel tool for implementation of RUSLE2 in vineyards for stakeholders with limited dataset
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一个简化的 Excel 工具，用于在葡萄园中为数据集有限的利益相关者实施 RUSLE2
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
E. Cavallo
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作者：
José Alfonso Gómez Calero;M. Biddoccu;G. Guzmán;E. Cavallo
在许多情况下，用模拟模型分析是评估土壤管理变化对土壤侵蚀风险的影响的唯一途径，并修订了通用土壤流失方程RUSLE(Renard等人。1997年，Dabney等人。2012)仍然是使用最广泛的。即使与其他更多基于过程的侵蚀模型相比，它们相对简单，但在建模社区之外的给定情况下进行正确的RUSLE校准可能是具有挑战性的，特别是在美国广泛覆盖的情况之外的情况下。这是戈麦斯等人采取的一种方法。(2003)为了克服校准RUSLE的这一问题，特别是封面管理因子C，使用RUSLE手册(Renard等人)定义的方程建立了一个汇总模型。1997年)，但考虑到校准子因素所需的基本信息，如土壤表面粗糙度和地面覆盖、土壤湿度。。。在其他地方计算(或从现有来源获得)，并添加到汇总模型中，而不是由RUSLE软件计算。这一策略简化了校准过程，并简化了专家用户对RUSLE参数和模型行为的理解和解释，以便在广泛的管理条件下应用于橄榄园。戈麦斯等人。(2003)在Excel中建立了这一总结模型，并展示了针对广泛的管理条件校准RUSLE的能力。后来的几项研究(Vanwallegem等人，2011年，Marin，2013年)展示了这个总结模型如何成功地预测了接近实验确定的山坡尺度的土壤流失。
Analysis with simulation models is in many situations the only way to evaluate the impact of changes in soil management on soil erosion risk, and the Revised Universal Soil Loss Equation RUSLE (Renard et al. 1997, Dabney et al. 2012) remains as the most widely used. Even with their relative simplicity compared to other, more process based, erosion models proper RUSLE calibration for a given situation outside the modelling community can be challenging, especially in situations outside of those widely covered in the USA. An approach pursued by Gómez et al. (2003) to overcome this problems for calibrating RUSLE, specially the cover-management factor, C, was to build a summary model using the equations defined by the RUSLE manual (Renard et al. 1997) but considering that the basic information required to calibrate the subfactors, such as soil surface roughness and ground cover, soil moisture, . . . were calculated (or taken from available sources) elsewhere and added to the summary model instead of calculated by the RUSLE software. This strategy simplified the calibration process as well as the understanding and interpretation of the RUSLE parameters and model behavior by on-expert users for its application in olive orchards under a broad range of management conditions. Gómez et al. (2003) build this summary model in Excel and demonstrated the ability to calibrate RUSLE for a broad range of management conditions. Later on several studies (Vanwalleghem et al., 2011, Marin, 2013) demonstrated how this summary model successfully predicted soil losses at hillslope scale close to those determined experimentally.