通用生态水文景观分类法,用于评估澳大利亚水系变化造成的生态系统风险

A. Herr, L. Merrin, P. Mitchell, A. O'Grady, Kate L. Holland, Richard Mount, D. Post, C. Pavey, A. Sparrow
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引用次数: 0

摘要

摘要为进行环境影响和风险评估而对景观进行描述和分类是一项非同小可的挑战,因为这需要针对特定区域的影响进行特定的景观分类。要评估大区域水资源开采对生态系统的影响,就需要在景观特征和水资源需求之间建立因果联系。我们介绍了针对煤矿和煤层气开发可能对水产生影响的地区进行生态水文分类的原理和实施方法。我们的分类方法为模拟未来煤炭资源开发对景观层面水文变化的潜在影响提供了基本框架。我们开发了一个基于属性的系统,该系统提供了生态水文实体及其与地貌特征之间联系的表征,并将现有的广义分类方案应用到基于属性的系统中。在已经存在为其他目的而开发的空间景观或生态系统分类方案的情况下,我们纳入了一个具有优先级的规则集,该规则集是风险建模的基础,并使该方案具有资源效率。一致的规则集以及概念化的景观过程和功能允许将不同的数据与现有的分类方案相结合。这使得分类具有透明度、可重复性和可调整性,以应对新数据的出现。我们在三个地理位置不同、信息来源大相径庭的地区应用该方法进行分类,并提供了详细的应用实例。我们建议,该方法可广泛应用于世界各地,将生态水文学与环境影响联系起来。
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A generalised ecohydrological landscape classification for assessing ecosystem risk in Australia due to an altering water regime
Abstract. Describing and classifying a landscape for environmental impact and risk assessment purposes is a non-trivial challenge because this requires region-specific landscape classifications that cater for region-specific impacts. Assessing impacts on ecosystems from the extraction of water resources across large regions requires a causal link between landscape features and their water requirements. We present the rationale and implementation of an ecohydrological classification for regions where coal mine and coal seam gas developments may impact on water. Our classification provides the essential framework for modelling the potential impact of hydrological changes from future coal resource developments at the landscape level. We develop an attribute-based system that provides representations of the ecohydrological entities and their connection to landscape features and make use of existing broad-level classification schemes into an attribute-based system. We incorporate a rule set with prioritisation, which underpins risk modelling and makes the scheme resource efficient, where spatial landscape or ecosystem classification schemes, developed for other purposes, already exist. A consistent rule set and conceptualised landscape processes and functions allow for the combination of diverse data with existing classification schemes. This makes the classification transparent, repeatable and adjustable, should new data become available. We apply the approach in three geographically different regions, with widely disparate information sources, for the classification, and provide a detailed example of its application. We propose that it is widely applicable around the world for linking ecohydrology to environmental impacts.
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