Search

Soil moisture is important in many hydrological and ecological processes. However, data sets which are currently available have issues with accuracy and resolution. To translate remotely-sensed data to an absolute measure of soil moisture requires mapped estimates of soil hydrological properties and estimates of vegetation properties, and this introduces considerable uncertainty. We present an alternative methodology for producing daily maps of soil moisture over the UK at 2-km resolution ("SMUK"). The method is based on a simple empirical model, calibrated with five years of daily data from cosmic-ray neutron sensors at ~40 sites across the country. The model is driven by precipitation, humidity, a remotely-sensed "soil water index" satellite product, and soil porosity. The model explains around 70 % of the variance in the daily observations. The spatial variation in the parameter describing the soil water retention (and thereby the response to precipitation) was estimated using daily water balance data from ~1200 catchments with good coverage across the country. The model parameters were estimated by Bayesian calibration using a Markov chain Monte Carlo method, so as to characterise the posterior uncertainty in the parameters and predictions. We found that the simple model could emulate the behaviour of a more complex process-based model. Given the high resolution of the inputs in time and space, the model can predict the very detailed variation in soil moisture which arises from the sporadic nature of precipitation events, including the small-scale and short-term variations associated with orographic and convective rainfall. Predictions over the period 2016 to 2023 demonstrated realistic patterns following the passage of weather fronts and prolonged droughts. The model has negligible computation time, and inputs and predictions are updated daily, lagging approximately one week behind real time.

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901–2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

In Southeast Asia, oil palm (OP) plantations have largely replaced tropical forests. The impact of this shift in land use on greenhouse gas (GHG) fluxes remains highly uncertain, mainly due to a relatively small pool of available data. The aim of this study is to quantify differences of nitrous oxide (N2O) and methane (CH4) fluxes as well as soil carbon dioxide (CO2) respiration rates from logged forests, oil palm plantations of different ages, and an adjacent small riparian area. Nitrous oxide fluxes are the focus of this study, as these emissions are expected to increase significantly due to the nitrogen (N) fertilizer application in the plantations. This study was conducted in the SAFE (Stability of Altered Forest Ecosystems) landscape in Malaysian Borneo (Sabah) with measurements every 2 months over a 2-year period. GHG fluxes were measured by static chambers together with key soil physicochemical parameters and microbial biodiversity. At all sites, N2O fluxes were spatially and temporally highly variable. On average the largest fluxes (incl. 95 % CI) were measured from OP plantations (45.1 (24.0–78.5) µg m−2 h−1 N2O-N), slightly smaller fluxes from the riparian area (29.4 (2.8–84.7) µg m−2 h−1 N2O-N), and the smallest fluxes from logged forests (16.0 (4.0–36.3) µg m−2 h−1 N2O-N). Methane fluxes were generally small (mean ± SD): −2.6 ± 17.2 µg CH4-C m−2 h−1 for OP and 1.3 ± 12.6 µg CH4-C m−2 h−1 for riparian, with the range of measured CH4 fluxes being largest in logged forests (2.2 ± 48.3 µg CH4-C m−2 h−1). Soil respiration rates were larger from riparian areas (157.7 ± 106 mg m−2 h−1 CO2-C) and logged forests (137.4 ± 95 mg m−2 h−1 CO2-C) than OP plantations (93.3 ± 70 mg m−2 h−1 CO2-C) as a result of larger amounts of decomposing leaf litter. Microbial communities were distinctly different between the different land-use types and sites. Bacterial communities were linked to soil pH, and fungal and eukaryotic communities were linked to land use. Despite measuri

The future status of peatlands as carbon stores/sinks is uncertain given current and predicted environmental change. Several factors can affect the magnitude of the peatland carbon sink including disturbances such as wildfire. There is at present little evidence of how wildfire affects the emission of carbon dioxide (CO2) and methane (CH4) via perturbation to aerobic and anaerobic respiration. The greatest effects, which are likely to vary according to wildfire severity, would be expected in the immediate post-fire stages when little recovery has taken place. Here, we investigate five UK peatland wildfires (2011–2012) in the immediate post-wildfire period measuring CO2 and CH4 fluxes using static chambers. Fire severity was described using a modified form of the composite burn index. A hierarchical partitioning approach indicated time since fire was the most strongly associated variable that fluxes of both CO2, and CH4 followed by soil temperature for CO2 and fire severity for CH4. Using a liner mixed modelling approach to account for repeated measures; fire severity was a significant term for CH4 and borderline significant for CO2. Mean fluxes of CH4 were consistently lower on burnt sites. In contrast, data from a fire in the north of Scotland appeared to show the opposite relationship for CH4 with higher fluxes on the burnt sites. These results suggest that wildfire can affect gaseous carbon fluxes, but the responses can be variable in both space and time and that disruption to anaerobic processes may be site and/or fire dependent.

Grasslands are an important component of the global carbon (C) cycle, with a strong potential for C sequestration. However, an improved capacity to quantify grassland C stocks and monitor their variation in space and time, particularly in response to management, is needed in order to conserve and enhance grassland C reservoirs. To meet this challenge we outline and test here an approach to combine C cycle modelling with observational data. We implemented an intermediate complexity model, DALEC-Grass, within a probabilistic model-data fusion (MDF) framework, CARDAMOM, at two managed grassland sites (Easter Bush and Crichton) in the UK. We used 3 years (Easter Bush, 2002–2004) of management data and observations of leaf area index (LAI) and Net Ecosystem Exchange (NEE) from eddy covariance to calibrate the distributions of model parameters. Using these refined distributions, we then assimilated the remaining 7 years (Easter Bush, 2005–2010 and Crichton, 2015) of LAI observations and evaluated the simulated NEE, above and below-ground biomass and other C fluxes against independent data from the two grasslands. Our results show that fusing model predictions with LAI observations allowed the CARDAMOM MDF system to diagnose the effects of grazing and cutting realistically. The overlap of MDF-predicted and measured NEE (both sites) and ecosystem respiration (Easter Bush) was 92% and 83% respectively while the correlation coefficient (r) was 0.79 for both variables. This study lays the foundation for using MDF with satellite data on LAI to produce the spatially and temporally-resolved estimates of C cycling needed in shaping and monitoring the implementation of relevant policies and farm-management decisions.

Current understanding of greenhouse gas (GHG) fluxes associated with land-use change from forest to oil palm on mineral soil is not sufficient to provide reliable estimates of emission rates or advice on GHG mitigation strategies. Monocultures of oil palm have expanded in Southeast Asia, mostly replacing tropical forests. The limited data available have indicated that the land-use conversion is associated with a potentially aggravated GHG burden, including nitrous oxide (N2O) and nitric oxide (NO) emissions, with unclear underlying biological mechanisms. In this study, we investigated N2O and NO emission potentials of tropical soils with different land-uses from Sabah, Malaysian Borneo, under laboratory incubation. Under similar controlled conditions, logged forest and oil palm soils showed high and similar potentials of N2O and NO emissions following increase in soil moisture, while the emissions were negligible in a riparian reserve soil irrespective of moisture conditions. Soil N2O and NO emission rates from logged forest soils and oil palm (OP) plantations were of similar magnitude, with average fluxes over the 35 and 22 day incubation periods, respectively, of 11.5 and 1.6 ng N g−1 h−1 (OP) and 15.6 and 6.0 ng N g−1 h−1 (logged forest). Contrarily, the riparian reserve soil did not respond to rewetting and nitrogen application and fluxes were negligible. Furthermore, N2O fluxes were on average about 10 times higher than NO fluxes. The fact that forest soils also have the potential to emit large amounts of N2O and NO, has important implications for land-use change scenarios in the tropics, especially as some scenarios suggest atmospheric N deposition is likely to drastically increase in tropical regions due to biomass burning, increased N-fertilizer use and fossil fuel consumption. Quantification of related gene transcripts implied that Proteobacterial nirS and AniA-nirK (betaproteobacterial clade of Neisseria) containing denitrifiers might continuously contribu

Intensively managed grazed grasslands in temperate climates are globally important environments for the exchange of the greenhouse gases (GHGs) carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4). We assessed the N and C budget of a mostly grazed and occasionally cut and fertilised grassland in SE Scotland by measuring or modelling all relevant imports and exports to the field as well as changes in soil C and N stocks over time. The N budget was dominated by import from inorganic and organic fertilisers (21.9 g N m−2 a−1) and losses from leaching (5.3 g N m−2 a−1), N2 emissions (2.9 g N m−2 a−1), and NOx and NH3 volatilisation (3.9 g N m−2 a−1), while N2O emission was only 0.6 g N m−2 a−1. The efficiency of N use by animal products (meat and wool) averaged 9.9 % of total N input over only-grazed years (2004–2010). On average over 9 years (2002–2010), the balance of N fluxes suggested that 6.0 ± 5.9 g N m−2 a−1 (mean ± confidence interval at p > 0.95) were stored in the soil. The largest component of the C budget was the net ecosystem exchange of CO2 (NEE), at an average uptake rate of 218 ± 155 g C m−2 a−1 over the 9 years. This sink strength was offset by carbon export from the field mainly as grass offtake for silage (48.9 g C m−2 a−1) and leaching (16.4 g C m−2 a−1). The other export terms, CH4 emissions from the soil, manure applications and enteric fermentation, were negligible and only contributed to 0.02–4.2 % of the total C losses. Only a small fraction of C was incorporated into the body of the grazing animals. Inclusion of these C losses in the budget resulted in a C sink strength of 163 ± 140 g C m−2 a−1. By contrast, soil stock measurements taken in May 2004 and May 2011 indicated that the grassland sequestered N in the 0–60 cm soil layer at 4.51 ± 2.64 g N m−2 a−1 and lost C at a rate of 29.08 ± 38.19 g C m−2 a−1. Potential reasons for the discrepancy between these estimates are probably an underestimation of C losses, especially from leaching f

Background: In the UK, government legislation allows the use of prescribed fire in peatlands for land management purposes. The use of fire, however, remains controversial, partly because of a distinct lack of data on the response of key peatland species to fire. Sphagnum species are key components of peatland ecosystems, yet a fundamental knowledge gap in the debate is the response of Sphagnum species to fire. Aims: To determine if a widespread species (Sphagnum capillifolium) has the ability to recover from exposure to high temperatures, analogous to those recorded in managed peatland fires. Methods: Samples of S. capillifolium were exposed to a range of temperature treatments. Recovery was monitored using chlorophyll fluorescence, CO2 exchange and physical damage and new growth assessed. Results: We found that the degree of recovery of S. capillifolium was related to the temperature treatment, post-treatment environmental conditions and pre-treatment stem moisture content. The slowest recovery was found when samples were heated to 400°C for 30 s. Conclusions: Our results demonstrate that S. capillifolium has the ability to recover following exposure to the temperatures experienced in prescribed fire, provided that at least some living material remains. Our results suggest that prescribed burning in the spring may allow for a quicker recovery than autumnal fires.

Subarctic and boreal emissions of CH4 are important contributors to the atmospheric greenhouse gas (GHG) balance and subsequently the global radiative forcing. Whilst N2O emissions may be lower, the much greater radiative forcing they produce justifies their inclusion in GHG studies. In addition to the quantification of flux magnitude, it is essential that we understand the drivers of emissions to be able to accurately predict climate-driven changes and potential feedback mechanisms. Hence this study aims to increase our understanding of what drives fluxes of CH4 and N2O in a subarctic forest/wetland landscape during peak summer conditions and into the shoulder season, exploring both spatial and temporal variability, and uses satellite-derived spectral data to extrapolate from chamber-scale fluxes to a 2 km × 2 km landscape area. From static chamber measurements made during summer and autumn campaigns in 2012 in the Sodankylä region of northern Finland, we concluded that wetlands represent a significant source of CH4 (3.35 ± 0.44 mg C m−2 h−1 during the summer campaign and 0.62 ± 0.09 mg C m−2 h−1 during the autumn campaign), whilst the surrounding forests represent a small sink (−0.06 ± < 0.01 mg C m−2 h−1 during the summer campaign and −0.03 ± < 0.01 mg C m−2 h−1 during the autumn campaign). N2O fluxes were near-zero across both ecosystems. We found a weak negative relationship between CH4 emissions and water table depth in the wetland, with emissions decreasing as the water table approached and flooded the soil surface and a positive relationship between CH4 emissions and the presence of Sphagnum mosses. Temperature was also an important driver of CH4 with emissions increasing to a peak at approximately 12 °C. Little could be determined about the drivers of N2O emissions given the small magnitude of the fluxes. A multiple regression modelling approach was used to describe CH4 emissions based on spectral data from PLEIADES PA1 satellite imagery across a 2 km ×

Nitrogen deposition was experimentally increased on a Scottish peatbog over a period of 13 years (2002–2015). Nitrogen was applied in three forms, NH3 gas, NH4Cl solution, and NaNO3 solution, at rates ranging from 8 (ambient) to 64 kg N ha−1 yr−1, and higher near the NH3 fumigation source. An automated system was used to apply the nitrogen, such that the deposition was realistic in terms of rates and high frequency of deposition events. We measured the response of nitrous oxide (N2O) flux to the increased nitrogen input. Prior expectations, based on the IPCC default emission factor, were that 1 % of the added nitrogen would be emitted as N2O. In the plots treated with NH4+ and NO3− solution, no response was seen, and there was a tendency for N2O fluxes to be reduced by additional nitrogen, though this was not significant. Areas subjected to high NH3 emitted more N2O than expected, up to 8.5 % of the added nitrogen. Differences in the response are related to the impact of the nitrogen treatments on the vegetation. In the NH4+ and NO3− treatments, all the additional nitrogen is effectively immobilised in the vegetation and top 10 cm of peat. In the NH3 treatment, much of the vegetation was killed off by high doses of NH3, and the nitrogen was presumably more available to denitrifying bacteria. The design of the wet and dry experimental treatments meant that they differed in statistical power, and we are less likely to detect an effect of the NH4+ and NO3− treatments, though they avoid issues of pseudo-replication.

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Biogeosciences 12 (2015): 653-679, doi:10.5194/bg-12-653-2015., The land and ocean absorb on average just over half of the anthropogenic emissions of carbon dioxide (CO2) every year. These CO2 "sinks" are modulated by climate change and variability. Here we use a suite of nine dynamic global vegetation models (DGVMs) and four ocean biogeochemical general circulation models (OBGCMs) to estimate trends driven by global and regional climate and atmospheric CO2 in land and oceanic CO2 exchanges with the atmosphere over the period 1990–2009, to attribute these trends to underlying processes in the models, and to quantify the uncertainty and level of inter-model agreement. The models were forced with reconstructed climate fields and observed global atmospheric CO2; land use and land cover changes are not included for the DGVMs. Over the period 1990–2009, the DGVMs simulate a mean global land carbon sink of −2.4 ± 0.7 Pg C yr−1 with a small significant trend of −0.06 ± 0.03 Pg C yr−2 (increasing sink). Over the more limited period 1990–2004, the ocean models simulate a mean ocean sink of −2.2 ± 0.2 Pg C yr−1 with a trend in the net C uptake that is indistinguishable from zero (−0.01 ± 0.02 Pg C yr−2). The two ocean models that extended the simulations until 2009 suggest a slightly stronger, but still small, trend of −0.02 ± 0.01 Pg C yr−2. Trends from land and ocean models compare favourably to the land greenness trends from remote sensing, atmospheric inversion results, and the residual land sink required to close the global carbon budget. Trends in the land sink are driven by increasing net primary production (NPP), whose statistically significant trend of 0.22 ± 0.08 Pg C yr−2 exceeds a significant trend in heterotrophic respiration of 0.16 ± 0.05 Pg C yr−2 – primarily as a consequence of widespread CO2 fertilisation of plant production. Most of the land-based trend in simulated net carbon uptake originates from natural ecosystems in the tropics (−0.04 ± 0.01 Pg C yr−2), with almost no trend over the northern land region, where rece, S. Sitch acknowledges financial support by RCUK through NERC (grant no. NE/J010154/). N. Gruber and C. Heinze acknowledge financial support by the European Commission through the EU FP7 projects CARBOCHANGE (grant no. 264879) and GEOCARBON (grant no. 283080). N. Gruber was additionally supported through ETH Zurich. S. C. Doney acknowledges support from the US National Science Foundation (NSF AGS-1048827). P. Friedlingstein, A. Arneth, and S. Zaehle acknowledge support by the European Commission through the EU FP7 project EMBRACE (grant no. 282672). A. Arneth and S. Sitch acknowledge the support of the European Commission-funded project LUC4C (grant no. 603542). The research leading to these results received funding from the European Community’s Seventh Framework Programme (FP7 2007–2013) under grant agreement no. 238366. A. Ahlström and B. Smith acknowledge funding through the Mistra Swedish Research Programme on Climate, Impacts and Adaptation (SWECIA). C. Heinze acknowledges support from NOTUR/NorStore projects NN2980K and NS2980K.

In this study, we assessed the performance of discharge simulations by coupling the runoff from seven Dynamic Global Vegetation Models (DGVMs; LPJ, ORCHIDEE, Sheffield-DGVM, TRIFFID, LPJ-GUESS, CLM4CN, and OCN) to one river routing model for 16 large river basins. The results show that the seasonal cycle of river discharge is generally modeled well in the low and middle latitudes but not in the high latitudes, where the peak discharge (due to snow and ice melting) is underestimated. For the annual mean discharge, the DGVMs chained with the routing model show an underestimation. Furthermore, the 30 year trend of discharge is also underestimated. For the interannual variability of discharge, a skill score based on overlapping of probability density functions (PDFs) suggests that most models correctly reproduce the observed variability (correlation coefficient higher than 0.5; i.e., models account for 50% of observed interannual variability) except for the Lena, Yenisei, Yukon, and the Congo river basins. In addition, we compared the simulated runoff from different simulations where models were forced with either fixed or varying land use. This suggests that both seasonal and annual mean runoff has been little affected by land use change but that the trend itself of runoff is sensitive to land use change. None of the models when considered individually show significantly better performances than any other and in all basins. This suggests that based on current modeling capability, a regional-weighted average of multimodel ensemble projections might be appropriate to reduce the bias in future projection of global river discharge.