Seasonal and annual throughfall and stemflow in Andean temperate rainforests

The partitioning of gross rainfall into throughfall, stemflow, and interception loss and their relationships with forest structure was studied for a period of four years (October 2002–September 2006) and two years (October 2005–September 2007) in seven experimental catchments of temperate rainforest ecosystems located in the Andes of south‐central Chile (39°37′S, 600–925 m a.s.l.). The amount of throughfall, stemflow, and interception loss was correlated with forest structure characteristics such as basal area, canopy cover, mean quadratic diameter (MQD), and tree species characteristics in evergreen and deciduous forests. Annual rainfall ranged from 4061 to 5308 mm at 815 m a.s.l. and from 3453 to 4660 mm at 714 m a.s.l. Throughfall ranged from 64 to 89% of gross rainfall. Stemflow contributed 0·3–3·4% of net precipitation. Interception losses ranged from 11 to 36% of gross rainfall and depended on the amount of rainfall and characteristics as well as on forest structure, particularly the MQD. For evergreen forests, strong correlations were found between stemflow per tree and tree characteristics such as diameter at breast height (R² = 0·92, P < 0·01) and crown projection area (R² = 0·65, P < 0·01). Stemflow per tree was also significantly correlated with epiphyte cover of trunks in the old‐growth evergreen forests (R² = 0·29, P < 0·05). The difference in the proportion of throughfall and interception loss among stands was significant only during winter. The reported relationships between rainfall partitioning and forest structure and composition provide valuable information for management practices, which aimed at producing other ecosystem services in addition to timber in native rainforests of southern Chile. Copyright © 2010 John Wiley & Sons, Ltd.     

What explains public support for climate policies? A review of empirical and experimental studies

The lack of broad public support prevents the implementation of effective climate policies. This article aims to examine why citizens support or reject climate policies. For this purpose, we provide a cross-disciplinary overview of empirical and experimental research on public attitudes and preferences that has emerged in the last few years. The various factors influencing policy support are divided into three general categories: (1) social-psychological factors and climate change perception, such as the positive influences of left-wing political orientation, egalitarian worldviews, environmental and self-transcendent values, climate change knowledge, risk perception, or emotions like interest and hope; (2) the perception of climate policy and its design, which includes, among others, the preference of pull over push measures, the positive role of perceived policy effectiveness, the level of policy costs, as well as the positive effect of perceived policy fairness and the recycling of potential policy revenues; (3) contextual factors, such as the positive influence of social trust, norms and participation, wider economic, political and geographical aspects, or the different effects of specific media events and communications. Finally, we discuss the findings and provide suggestions for future research.

Policy relevance

Public opinion is a significant determinant of policy change in democratic countries. Policy makers may be reluctant to implement climate policies if they expect public opposition. This article seeks to provide a better understanding of the various factors influencing public responses to climate policy proposals. Most of the studied factors include perceptions about climate change, policy and its attributes, all of which are amenable to intervention. The acquired insights can thus assist in improving policy design and communication with the overarching objective to garner more public support for effective climate policy.     

Changes in future flood risk due to climate and development in a Dutch polder area

Damages from weather related disasters are projected to increase, due to a combination of increasing exposure of people and assets, and expected changes in the global climate. Only few studies have assessed in detail the potential range of losses in the future and the factors contributing to the projected increase. Here we estimate future potential damage from river flooding, and analyse the relative role of land-use, asset value increase and climate change on these losses, for a case study area in The Netherlands. Projections of future socioeconomic change (land-use change and increase in the value of assets) are used in combination with flood scenarios, projections of flooding probabilities, and a simple damage model. It is found that due to socioeconomic change, annual expected losses may increase by between 35 and 172% by the year 2040, compared to the baseline situation in the year 2000. If no additional measures are taken to reduce flood probabilities or consequences, climate change may lead to an increase in expected losses of between 46 and 201%. A combination of climate and socioeconomic change may increase expected losses by between 96 and 719%. Asset value increase has a large role, as it may lead to a doubling of losses. The use of single loss estimates may lead to underestimation of the impact of extremely high losses. We therefore also present loss–probability curves for future risks, in order to assess the increase of the most extreme potential loss events. Our approach thus allows a more detailed and comprehensive assessment than previous studies that could also be applied in other study areas to generate flood risk projections. Adaptation through flood prevention measures according to currently planned strategies would counterbalance the increase in expected annual losses due to climate change under all scenarios.     

How to improve attribution of changes in drought and flood impacts

For the development of sustainable, efficient risk management strategies for the hydrological extremes of droughts and floods, it is essential to understand the temporal changes of impacts, and their respective causes and interactions. In particular, little is known about changes in vulnerability and their influence on drought and flood impacts. We present a fictitious dialogue between two experts, one in droughts and the other in floods, showing that the main obstacles to scientific advancement in this area are both a lack of data and a lack of commonly accepted approaches. The drought and flood experts “discuss” available data and methods and we suggest a complementary approach. This approach consists of collecting a large number of single or multiple paired-event case studies from catchments around the world, undertaking detailed analyses of changes in impacts and drivers, and carrying out a comparative analysis. The advantages of this approach are that it allows detailed context- and location-specific assessments based on the paired-event analyses, and reveals general, transferable conclusions based on the comparative analysis of various case studies. Additionally, it is quite flexible in terms of data and can accommodate differences between floods and droughts.     

Estimates of spatial variation in evaporation using satellite‐derived surface temperature and a water balance model

Evaporation dominates the water balance in arid and semi‐arid areas. The estimation of evaporation by land‐cover type is important for proper management of scarce water resources. Here, we present a method to assess spatial and temporal patterns of actual evaporation by relating water balance evaporation estimates to satellite‐derived radiometric surface temperature. The method is applied to a heterogeneous landscape in the Krishna River basin in south India using 10‐day composites of NOAA advanced very high‐resolution radiometer satellite imagery. The surface temperature predicts the difference between reference evaporation and modelled actual evaporation well in the four catchments (r² = 0·85 to r² = 0·88). Spatial and temporal variations in evaporation are linked to vegetation type and irrigation. During the monsoon season (June–September), evaporation occurs quite uniformly over the case‐study area (1·7–2·1 mm day^?1), since precipitation is in excess of soil moisture holding capacity, but it is higher in irrigated areas (2·2–2·7 mm day^?1). In the post‐monsoon season (December–March) evaporation is highest in irrigated areas (2·4 mm day^?1). A seemingly reasonable estimate of temporal and spatial patterns of evaporation can be made without the use of more complex and data‐intensive methods; the method also constrains satellite estimates of evaporation by the annual water balance, thereby assuring accuracy at the seasonal and annual time‐scales. Copyright © 2007 John Wiley & Sons, Ltd.     

A framework for dealing with uncertainty due to model structure error

Although uncertainty about structures of environmental models (conceptual uncertainty) is often acknowledged to be the main source of uncertainty in model predictions, it is rarely considered in environmental modelling. Rather, formal uncertainty analyses have traditionally focused on model parameters and input data as the principal source of uncertainty in model predictions. The traditional approach to model uncertainty analysis, which considers only a single conceptual model, may fail to adequately sample the relevant space of plausible conceptual models. As such, it is prone to modelling bias and underestimation of predictive uncertainty.