Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. 1The following paper is the final version prior to publication on 22 September 2015. are proposed, the way in which indicators could contribute to classification is discussed. All of the methods described in Table 1 consider a hierarchy of spatial units, but the degree to which they develop the other aspects of the conceptual approach proposed by Frissell et al.(1986) varies widely.2. Many of the frameworks focus entirely on hydromorphological processes and forms that are either directly measured or inferred. This is because interactions between processes and forms control the dynamic morphology or behaviour of rivers and their mosaics of habitats.Hydromorphological processes drive longitudinal and lateral connectivity within river networks and corridors, the assemblage and turnover of physical habitats, and the sedimentary and vegetation structures associated with those habitats.3. Some frameworks are conceptual, providing a way of thinking about or structuring analyses of river systems, and interpreting their processes, morphology and function (e.g. Frissell et al., 1986;Habersack, 2000;Fausch et al., 2002;Thorp et al., 2006;Beechie et al., 2010;McCluney et al., 2014). Some frameworks are more quantitative, generating one or more indices or classifications of spatial units that support assessment of river systems (e.g. Rosgen, 1994;González del Tánago and García de Jalón, 2004;Merovich et al., 2013;Rinaldi et al., 2013Rinaldi et al., , 2015a MacDonald, 2002;Brierley and Fryirs, 2005;Beechie et al., 2010; Rinaldi et al., 2013a Rinaldi et al., , 2015.In some cases, theoretical or historical analyses or consideration of specific future scenarios are used to develop space-time understanding that can support management decisionmaking (e.g. Buffington, 1997, 1998;Montgomery and MacDonald, 2002;Benda et al., 2004;Brierley and Fryirs, 2005;McCluney et al., 2014 , 1997, 1998Montgomery and MacDonald, 2002;Benda et al., 2004;Brierley and Fryirs, 2005;Merovich et al., 2013;Rinaldi et al., 2013Rinaldi et al., , 2015a. Furthermore, some of the frameworks include indicators of human pressures and their impacts (e.g. Merovich et al., 2013;McCluney et al., 2014;Rinaldi et al., 2013Rinaldi et al., , 2015a.6. Finally, although most frameworks could be described as incorporating processes to some degree, some methods are particularly process-based, even when processes are inferred from forms and associations rather than being quantified by direct measurements.Frameworks that consider temporal dynamics and trajectories of historical change (see point 4, above) are particularly effective in developing understanding of processes and the impacts of changed processes cascading through time and across spatial scales.Although the list of frameworks presented in Table 1 is far from comprehensive, ...

Abstract. Globally, peat lands are considered to be a sink of CO 2 , but a source when drained. Additionally, wet peat lands are thought to emit considerable amounts of CH 4 and N 2 O. Hitherto, reliable and integrated estimates of emissions and emission factors for this type of land cover have been lacking and the effects of wetland restoration on methane emissions have been poorly quantified. In this paper we estimate the full greenhouse gas (GHG) balance of a restored natural peat land by determining the fluxes of CO 2 , CH 4 and N 2 O through atmosphere and water, while accounting for the different Global Warming Potentials (GWP's).The site is an abandoned agricultural peat meadow, which has been converted into a wetland nature reserve ten years ago, after which the water level was raised. GHG fluxes were measured continuously with an eddy covariance system (CO 2 ) and flux chamber measurements (CH 4 land and water. CO 2 emission has decreased significantly as result of the raised water table, while CH 4 fluxes have increased. In GWP's the area was a small net GHG sink given as CO 2 -equiv. of −86 g m −2 yr −1 (over a 100-year period).

Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate-carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO 2 exchange with the atmosphere across biomes and continents are lacking. Here we present data describing the relationships between net ecosystem exchange of carbon (NEE) and climate factors as measured using the eddy covariance method at 125 unique sites in various ecosystems over six continents with a total of 559 site-years. We find that NEE observed at eddy covariance sites is (1) a strong function of mean annual temperature at mid-and high-latitudes, (2) a strong function of dryness at mid-and low-latitudes, and (3) a function of both temperature and dryness around the mid-latitudinal belt (45 • N). The sensitivity of NEE to mean annual temperature breaks down at ∼16 • C (a threshold value of mean annual temperature), above which no further increase of CO 2 uptake with temperature was observed and dryness influence overrules temperature influence.

Vertical mixing in lakes is a key driver of transport of ecologically important dissolved constituents, such as oxygen and nutrients. In this study we focus our attention on biomixing, which refers to the contribution of living organisms towards the turbulence and mixing of oceans and lakes. While several studies of biomixing in the ocean have been conducted, no in situ studies exist that assess the turbulence induced by freshwater zooplanktonic organisms under real environmental conditions. Here, turbulence is sampled during three different sampling days during the sunset diel vertical migration of Daphnia spp. in a small man-made lake. This common genus may create hydrodynamic disturbances in the lake interior where the thermal stratification usually suppresses the vertical diffusion. Concurrent biological sampling assessed the zooplankton vertical concentration profile. An acoustic-Doppler current profiler was also used to track zooplankton concentration and migration via the backscatter strength. Our datasets do not show biologically-enhanced dissipation rates of temperature variance and turbulent kinetic energy in the lake interior, despite Daphnia concentrations as high as 60 org. L −1 . No large and significant turbulent patches were created within the migrating layer to generate irreversible mixing. This suggests that Daphnia do not affect the mixing in the lake at the organism concentrations observed here.

Riparian vegetation actively interacts with fluvial systems affecting river hydrodynamics, morphodynamics and groundwater. These interactions can be coupled because both vegetation and hydromorphology (i.e. the combined scientific study of hydrology and fluvial geomorphology) involve dynamic processes with similar temporal and spatial scales. To predict and assess the consequences of restoration measures, maintenance operations or human pressures in rivers, managers and planners may wish to model these interactions considering the different and interdisciplinary implications in the fields of ecology, geomorphology and hydrology. In this paper, we review models that are currently available and that incorporate the processes that relate riparian vegetation to hydromorphology. The models that are considered include those emphasizing hydraulic-geomorphological processes (such as flow resistance, sediment transport and bank dynamics) as well as those emphasizing ecological processes (seed dispersal, plant survival, growth, succession and mortality). Models interpreting the coupled evolution between riparian vegetation and river morphology and groundwater are also presented. The aim is to provide an overview of current modelling capabilities and limitations and to identify future modelling challenges.