forest cover change map (Hansen et al., 2013). Tree cover loss can be associated with a habitat disturbance and ecosystem service disruptions, which can make human landscapes more fragile to other impacts (Sodhi et al., 2010). And it can also undermine the livelihoods of poor people highly dependent on forest timber and other resources (Angelsen et al., 2014).

**3) Land degradation** is measured by the share of land area that experienced a significant biomass decline. This loss is calculated based on the inter‐annual mean trend of Normalized Difference Vegetation Index based on data from Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA) satellite between 1982 and 2006, which is corrected for climate effects to only measure human‐induced degradation (Vu et al., 2014b). Soil fertility, agricultural productivity and ultimately food security of smallholder farmers can be largely compromised on degraded lands (Von Braun et al., 2013).

**4) Slope** is measured by the area weighted average of slope categories. This variable is calculated based on data from the Harmonized World Soil Database version 1.2 with eight slope classes: 1 for least steep (elevation of 0‐0.5 percent) and 8 for most steep slope (elevation greater than 45 percent).<sup>3</sup> Steep slopes are much more prone to surface water runoff and soil erosion ‐ particularly in areas affected by heavy rainfalls and tree cover loss (Sidle et al., 2006; Vezina et al., 2006). At the same time tropical cyclones can cause fatal landslides, which already result in significant loss of life in mountainous areas of South and East Asia (Petley, 2012, Petley, 2010).

**5) Rainfall variability** is defined as the 1981‐2010 standard deviation of monthly rainfall levels. The variable is constructed from the global CRU TS3.21 dataset from the University of East Anglia, containing a long‐term time series of monthly rainfall levels at 0.5x0.5 grid resolution, which was produced using statistical interpolation based on data from 4,000 weather stations (Harris et al., 2014). Higher historic rainfall variability indicates higher inter‐annual and intra‐annual variation of precipitation and a higher incidence of extremely wet and dry conditions. In Vietnam excessive rainfall can have negative income effects for poor households, while wealthier households are more negatively affected by the lack of rainfall (Narloch, 2016).

**6) Temperature variability** is measured by the standard deviation of mean annual temperatures across the time period 1981‐2010.  The underlying data also comes from the global CRU TS3.21 dataset including a long‐term time series of monthly mean temperature values at 0.5x0.5 grid resolution. Temperature variability indicates varying temperature conditions between seasons and years and a higher incidence of heat waves and cold spells. In Vietnam hot conditions have generally an income‐reducing effect for rural households (Narloch, 2016).

**7) Flood hazards** are represented by the share of area at risk of a flood event (inundation depth > 0) with a 25 year return period under historical conditions.<sup>4</sup> The measures are based on the inundation depth estimated by state‐of‐the art flood models at a grid cell level of 3 arc‐seconds combining coastal surge hazard

> 3 http://www.fao.org/soils‐portal/soil‐survey/soil‐maps‐and‐databases/harmonized‐world‐soil‐database‐v12/en/

> 4 A 25 year return period corresponds to 0.04 annual probability of occurrence.

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