Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2020
Sammendrag
Soil respiration is an important ecosystem process that releases carbon dioxide into the atmosphere. While soil respiration can be measured continuously at high temporal resolutions, gaps in the dataset are inevitable, leading to uncertainties in carbon budget estimations. Therefore, robust methods used to fill the gaps are needed. The process-based non-linear least squares (NLS) regression is the most widely used gap-filling method, which utilizes the established relationship between the soil respiration and temperature. In addition to NLS, we also implemented three other methods based on: 1) artificial neural networks (ANN), driven by temperature and moisture measurements, 2) singular spectrum analysis (SSA), relying only on the time series itself, and 3) the expectation-maximization (EM) approach, referencing to parallel flux measurements in the spatial vicinity. Six soil respiration datasets (2017–2019) from two boreal forests were used for benchmarking. Artificial gaps were randomly introduced into the datasets and then filled using the four methods. The time-series-based methods, SSA and EM, showed higher accuracies than NLS and ANN in small gaps (<1 day). In larger gaps (15 days), the performance was similar among NLS, SSA and EM; however, ANN showed large errors in gaps that coincided with precipitation events. Compared to the observations, gap-filled data by SSA showed similar degree of variances and those filled by EM were associated with similar first-order autocorrelation coefficients. In contrast, data filled by both NLS and ANN exhibited lower variance and higher autocorrelation than the observations. For estimations of the annual soil respiration budget, NLS, SSA and EM resulted in errors between −3.7% and 5.8% given the budgets ranged from 463 to 1152 g C m−2 year−1, while ANN exhibited larger errors from −11.3 to 16.0%. Our study highlights the two time-series-based methods which showed great potential in gap-filling carbon flux data, especially when environmental variables are unavailable.
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Forfattere
Gunnhild Søgaard Gry Alfredsen Clara Antón Fernández Rasmus Astrup Helmer Belbo Nicholas Clarke Rune Eriksen Aksel Granhus Kjersti Holt Hanssen Ari Hietala Frans Kockum Christian Wilhelm Mohr Jørn-Frode Nordbakken Jogeir N. Stokland Jennie Sverker Tonje ØklandSammendrag
Det årlige netto opptaket i skogen i Norge økte frem til 2009 (over 35 mill. tonn), og har etter det vist en avtakende trend. I 2018 var det et netto opptak på i underkant av 28 millioner tonn CO2- ekvivalenter. Størrelsen på opptaket påvirkes av forvaltningen av skogarealene, både gjennom endringer i totalarealet (avskoging og påskoging), og forvaltningen av de eksisterende skogarealene. I en første rapport til Klimakur 2030 – skrevet på oppdrag fra Miljødirektorat og Landbruksdirektoratet - ble det presentert en første vurdering av syv klimatiltak som ikke tidligere var utredet, samt en kunnskapsoppdatering for noen tidligere utredede klimatiltak. I denne rapporten presenteres ytterligere vurderinger av fire av disse tiltakene; ungskogpleie, grøfterensk, stubbebehandling mot råte og gjødsling med treaske. Rapporten er skrevet på bestilling fra Landbruks- og matdepartementet (LMD) og Klima- og miljødepartementet (KLD), og det er departementene som har gjort utvalget av tiltak som skulle vurderes videre...
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In a climate model, surface energy and water fluxes of the vegetated ecosystem largely depend on important structural attributes like leaf area index and canopy height. For forests, management can greatly alter these attributes with resulting consequences for the surface albedo, surface roughness, and evapotranspiration. The sensitivity of surface energy and water budgets to alterations in forest structure is relatively unknown in boreal regions, particularly in Nordic Fennoscandia (Norway, Sweden, and Finland), where the forest management footprint is large. Here we perform offline simulations to quantify the sensitivity of surface heat and moisture fluxes to changes in forest composition and structure across daily, seasonal, and annual time scales. For the region on average, it is found that broadleaved deciduous forests cool the surface by 0.16 K annually and 0.3 K in the growing season owed to higher year‐round albedo and lower Bowen ratio, yet in some locations the local cooling can be as much as 2.4 K and 3.0 K, respectively. Moreover, fully developed forests cool the surface by 0.04 K annually in our domain owed to higher evapotranspiration, reaching up to 0.4 K locally in some locations, whereas undeveloped forests warm annually by 0.14 K owed to much lower evapotranspiration reaching up to 0.8 K for some locations. If regional forests are ever to be managed for the local climate regulation services that they provide, our results are an important first step illuminating the potential adverse impacts or benefits across space and time.
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