Stephanie Eisner

Research Scientist

(+47) 904 13 678
stephanie.eisner@nibio.no

Place
Ås H8

Visiting address
Høgskoleveien 8, 1433 Ås

Abstract

Forests, especially in the northern latitudes, are vulnerable ecosystems to climate change, and tree-ring data offer insights into growth-climate relationships as an important effect. Using the National Forest Inventory plot network, we analysed these correlations for the two dominant conifer species in Norway – Norway spruce and Scots pine – for the 1960–2020 period. For both species, the June climate was an important driver of radial growth during this period. Countrywide, the climate-growth correlations divided the Norwegian forests into spatial clusters following a broad shift from temperature- to water-sensitivity of growth with latitude and altitude. The clusters were delineated by a mean 1960–2020 June temperature of ca. 12°C for Norway spruce and Scots pine. The annual mean growing season and July temperatures – but not June temperature – has increased by 1.0 °C between the 1960–1990 and 1990–2020 periods, with a slight increase in precipitation. Despite this warming and wetting trend, the long-term growth-climate relationship has remained relatively stable between 1960 and 1990 and 1990–2020 for both species. The threshold between temperature and water-sensitive growth has not changed in the last two 31-year periods, following the stability of the June temperature compared with other months during the growing season. These findings highlight geographically coherent regions in Norway, segregating between temperature- and water-sensitive radial growth for the two major conifer species, temporally stable in the long-term for the 1960–2020 period studied.

To document

Abstract

Decision Support Indicators (DSIs) are metrics designed to inform local and regional stakeholders about the characteristics of a predicted (or ongoing) event to facilitate decision-making. In this paper, the DSI concept was developed to clarify the different aims of different kinds of indicators by naming them, and a framework was developed to describe and support the usage of such DSIs. The framework includes three kinds of DSI: hydroclimatic DSIs which are easy to calculate but hard to understand by non-experts; impact-based DSIs which are often difficult to calculate but easy to understand by non-experts; and event-based DSIs, which compare a current or projected state to a locally well-known historical event, where hydroclimatic and impact-based DSIs are currently mainly used. Tables and figures were developed to support the DSI development in collaboration with stakeholders. To develop and test the framework, seven case studies, representing different hydrological pressures on three continents (South America, Asia, and Europe), were carried out. The case studies span several temporal and spatial scales (hours-decades; 70–6,000 km2) as well as hydrological pressures (pluvial and riverine floods, drought, and water scarcity), representing different climate zones. Based on stakeholder workshops, DSIs were developed for these cases, which are used as examples of the conceptual framework. The adaptability of the DSI framework to this wide range of cases shows that the framework and related concepts are useful in many contexts.