Improving our understanding of abiotic and biotic thresholds that are transgressed during ecosystem degradation is vital for effective landscape‐level restoration. Establishing logical and consistent typologies of degradation is an important first‐step in this process. Areas of degraded alpine meadow in western China are referred to as Heitutan (or ‘black soil beach’) and cover vast areas. Previously, four classes of grassland degradation have been differentiated in this large area: non‐degraded alpine meadow and moderate, severe and extreme Heitutan. We evaluated existing field‐based classifications of Heitutan in the Sanjiangyuan region, and examined the criteria on which these classifications are made, by using multivariate statistical approaches. First, we asked whether existing qualitative classifications, based on vegetation cover and the abundance of impalatable plant species, adequately describe the nature of degradation at different locations. We then used a suite of vegetation and soil measures collected across 175 sites spanning a range of degradation conditions to develop a new quantitative assessment of degradation across the alpine meadows of the Qinghai‐Tibetan Plateau. Hierarchical classification highlights two broad classes of degradation: non‐degraded alpine meadow and degraded Heitutan. The non‐degraded category effectively combines the non‐degraded grassland and moderate classes, whereas the degraded Heitutan combines the severe and extreme Heitutan classes. Ordination analyses suggest that the four previously recognized classes of degradation intergrade. Of the 14 biophysical variables used to classify subsites on the basis of their degradation, bare ground area and vegetation cover are the two most useful predictors of grassland condition and associated ecological threshold conditions. Copyright © 2012 John Wiley & Sons, Ltd.

The vulnerability to flood disaster is addressed by a number of studies. It is of great importance to analyze the vulnerability of different regions and various periods to enable the government to make policies for distributing relief funds and help the regions to improve their capabilities against disasters, yet a recognized paradigm for such studies seems missing. Vulnerability is defined and evaluated through either physical or economic–ecological perspectives depending on the field of the researcher concerned. The vulnerability, however, is the core of both systems as it entails systematic descriptions of flood severities or disaster management units. The research mentioned often has a development perspective, and in this article we decompose the overall flood system into several factors: disaster driver, disaster environment, disaster bearer, and disaster intensity, and take the interaction mechanism among all factors as an indispensable function. The conditions of flood disaster components are demonstrated with disaster driver risk level, disaster environment stability level and disaster bearer sensitivity, respectively. The flood system vulnerability is expressed as vulnerability = f(risk, stability, sensitivity). Based on the theory, data envelopment analysis method (DEA) is used to detail the relative vulnerability's spatiotemporal variation of a flood disaster system and its components in the Dongting Lake region.

The study finds that although a flood disaster system's relative vulnerability is closely associated with its components' conditions, the flood system and its components have a different vulnerability level. The overall vulnerability is not the aggregation of its components' vulnerability. On a spatial scale, zones central and adjacent to Dongting Lake and/or river zones are characterized with very high vulnerability. Zones with low and very low vulnerability are mainly distributed in the periphery of the Dongting Lake region. On a temporal scale, the occurrence of a vibrating flood vulnerability trend is observed. A different picture is displayed with the disaster driver risk level, disaster environment stability level and disaster bearer sensitivity level.

The flood relative vulnerability estimation method based on DEA is characteristic of good comparability, which takes the relative efficiency of disaster system input–output into account, and portrays a very diverse but consistent picture with varying time steps. Therefore, among different spatial and time domains, we could compare the disaster situations with what was reflected by the same disaster. Additionally, the method overcomes the subjectivity of a comprehensive flood index caused by using an a priori weighting system, which exists in disaster vulnerability estimation of current disasters