The entropy budgets of UK peatlands – are some peatlands near equilibrium?

The energy budget of an ecosystem must obey the 2nd law of thermodynamics even if it is an open system. Several studies have sought to use a consideration of entropy budgets to understand ecosystem energy budgets and more specifically evaporation. It has been assumed that ecosystems are far-from-equilibrium systems and as such would always seek to maximise their entropy production. Although the approach has been used to consider the behaviour of environments there are no studies that have tested the approach or its implications: maximum entropy production (MEP) is a prediction of the far-from-equilibrium assumption that could be tested. The simplest way for an ecosystem to maximise entropy production is to maximise water loss through evaporation. To test whether a system is acting to maximise entropy production this study chose to consider how the energy budget of a peatland system responded to changes in incoming energy, specifically how a change in net radiation was transferred to changes in latent heat flux (E/Rn). An ecosystem maximising its entropy production would transfer the majority of change in net radiation to change in latent heat flux. Previously using this approach we have been able to show that for nine UK peatlands the average proportion of a change in net radiation that was transferred to change in latent heat flux varied from 24 to 63%. That is for some sites where the majority of change in input was transferred to latent heat while at another site where the majority was transferred to sensible heat flux. We now show that the sites significantly divided between two groups those with[U+F044][U+F06C]E/[U+F044]Rn > 0.4 and those with E/Rn < 0.3. To understand what this results means we have now considered the entropy budget of each site to test whether high values E/Rn are actually reflected in greater entropy production and how these approaches relate to the Bowen ratio

Carbon greenhouse gas fluxes from fenland soils under intensive agricultural use compared to seminatural and restoration management

Globally, peatlands represent a large carbon store in the global carbon cycle. In natural conditions they are typically a sink for carbon but constitute a methane source. Today, large fractions of the fens in the midlatitudes are being used for intensive agriculture due to their fertile organic soils. Conventional farming techniques require ongoing drainage leading to soil shrinkage and soil organic carbon loss via fluvial loss as well as decomposition to carbon dioxide. The carbon source-sink relationships - measured by eddy covariance and closed chambers - of three fens in the Fenlands in East Anglia, United Kingdom were compared over a time period of three years; one fen is being used for intensive horticultural production supporting up to two harvests per year, a second is in semi-natural condition and a third is under restoration management for more than 10 years. The fen under agricultural use showed a significant carbon loss year on year (mean ≈750gCm-2yr-1) and the semi-natural fen was a small carbon sink, but also a small methane source. The fen under restoration management including blocking of the drainage channels and re-seeding with wetland species was still a small and consistent carbon source to the atmosphere (mean ≈110gCm-2yr-1) even 15 years after the end of agricultural use. Methane flux measurements in the seminatural and regenerating fen indicate relevant methane fluxes from ditches and small temporary water bodies. These results highlight the susceptibility of organic soils to disturbance and carbon loss and the complexity of restoration strategies that may lead to renewed carbon storage after a period of intensive agricultural use

Comparing global models of terrestrial net primary productivity (NPP): Analysis of the seasonal behavior of NPP, LAI, and FPAR along climatic gradients across ecotones

Estimates of the seasonal absorbed fraction of photosynthetically active radiation (FPAR) and net primary productivity (NPP) are compared among four production efficiency models (PEMs) and seven terrestrial biosphere models simulating canopy development. In addition, the simulated FPARs of the models are compared to the FASIR-FPAR derived from NOAA-AVHRR satellite observations. All models reproduce observed summergreen phenology of temperate deciduous forests rather well, but perform less well for raingreen phenology of savannas. Some models estimate a much longer active canopy in savannas than indicated by satellite observations. As a result, these models estimate high negative monthly NPP during the dry season. For boreal and tropical evergreen ecosystems, several models overestimate LAI and FPAR. When the simulated canopy does respond to unfavourable periods, the seasonal NPP is largely determined by absorbed photosynthetically active radiation (APAR). When the simulated canopy does not respond to unfavourable periods, the light use efficiency (LUE) influences the seasonal NPP more. However, the relative importance of APAR and LUE can change seasonally

Comparing global models of terrestrial net primary productivity (NPP): Comparison of annual net primary productivity and climatic drivers

To analyse the broad-scale behaviour of 15 global models of the terrestrial biosphere, we evaluated the sensitivity of simulated net primary productivity (NPP) to spatial and seasonal variations in precipitation, temperature and solar radiation, and to the Normalized Difference Vegetation Index (NDVI). For annual NPP estimates, the models’ sensitivities to climate were the most similar in regions where NPP was not limited by precipitation. The largest differences in sensitivities occurred in regions where NPP was limited by both temperature and precipitation. Water use efficiencies within the models were relatively constant across latitudes so that higher correlations occurred between the latitudinal distribution of NPP and precipitation than with the other climate variables. The sensitivities of NPP estimates to solar radiation varied considerably with latitude. The largest differences in temperature sensitivity among NPP estimates occurred in the northern latitudes (50°N–70°N), i.e. the zone with the shortest active growing seasons. The sensitivity of NPP estimates to climate also varied seasonally. At the beginning and end of the active growing season in the boreal zone, monthly NPP estimates of all models were the most sensitive to temperature. In the tropics, sensitivities to climate varied widely among and within models. Seasonal changes in water balance and the structure of the vegetation canopy, as reflected by seasonal changes in NDVI, modified the sensitivity of NPP to climate in both boreal and tropical zones. Because these are both highly productive regions sensitive to climate change, continued investigations and better validation of models are necessary before we can fully understand and predict changes in ecosystem structure and function under various climatic conditions

Spatial-temporal variation and prediction of rainfall in northeastern Nigeria

In Northeastern Nigeria seasonal rainfall is critical for the availability of water for domestic use through surface and sub-surface recharge and agricultural production, which is mostly rain fed. Variability in rainfall over the last 60 years is the main cause for crop failure and water scarcity in the region, particularly, due to late onset of rainfall, short dry spells and multi-annual droughts. In this study, we analyze 27 years (1980–2006) of gridded daily rainfall data obtained from a merged dataset by the National Centre for Environmental Prediction and Climate Research Unit reanalysis data (NCEP-CRU) for spatial-temporal variability of monthly amounts and frequency in rainfall and rainfall trends. Temporal variability was assessed using the percentage coefficient of variation and temporal trends in rainfall were assessed using maps of linear regression slopes for the months of May through October. These six months cover the period of the onset and cessation of the wet season throughout the region. Monthly rainfall amount and frequency were then predicted over a 24-month period using the Auto Regressive Integrated Moving Average (ARIMA) Model. The predictions were evaluated using NCEP-CRU data for the same period. Kolmogorov Smirnov test results suggest that despite there are some months during the wet season (May–October) when there is no significant agreement (p < 0.05) between the monthly distribution of the values of the model and the corresponding 24-month NCEP-CRU data, the model did better than simply replicating the long term mean of the data used for the prediction. Overall, the model does well in areas and months with lower temporal rainfall variability. Maps of the coefficient of variation and regression slopes are presented to indicate areas of high rainfall variability and water deficit over the period under study. The implications of these results for future policies on Agriculture and Water Management in the region are highlighted

Mapping terrestrial oil spill impact using machine learning random forest and Landsat 8 OLI imagery: a case site within the Niger Delta region of Nigeria.

Terrestrial oil pollution is one of the major causes of ecological damage within the Niger Delta region of Nigeria and has caused a considerable loss of mangroves and arable croplands since the discovery of crude oil in 1956. The exact extent of landcover loss due to oil pollution remains uncertain due to the variability in factors such as volume and size of the oil spills, the age of oil, and its effects on the different vegetation types. Here, the feasibility of identifying oil-impacted land in the Niger Delta region of Nigeria with a machine learning random forest classifier using Landsat 8 (OLI spectral bands) and Vegetation Health Indices is explored. Oil spill incident data for the years 2015 and 2016 were obtained from published records of the National Oil Spill Detection and Response Agency and Shell Petroleum Development Corporation. Various health indices and spectral wavelengths from visible, near-infrared, and shortwave infrared bands were fused and classified using the machine learning random forest classifier to distinguish between oil-free and oil spill-impacted landcover. This provided the basis for the identification of the best variables for discriminating oil polluted from unpolluted land. Results showed that better results for discriminating oil-free and oil polluted landcovers were obtained when individual landcover types were classified separately as opposed to when the full study area image including all landcover types was classified at once. Similarly, the results also showed that biomass density plays a significant role in the characterization and classification of oil contaminated and oil-free pixels as tree cover areas showed higher classification accuracy compared to cropland and grassland

Comparing global models of terrestrial net primary productivity (NPP): Global pattern and differentiation by major biomes

Annual and seasonal net primary productivity estimates (NPP) of 15 global models across latitudinal zones and biomes are compared. The models simulated NPP for contemporary climate using common, spatially explicit data sets for climate, soil texture, and normalized difference vegetation index (NDVI). Differences among NPP estimates varied over space and time. The largest differences occur during the summer months in boreal forests (50° to 60°N) and during the dry seasons of tropical evergreen forests. Differences in NPP estimates are related to model assumptions about vegetation structure, model parameterizations, and input data sets

Land Degradation Assessment using Residual Trend Analysis of GIMMS NDVI3g, Soil Moisture and Rainfall in Sub-Saharan West Africa from 1982-2012

Areas affected by land degradation in Sub-Saharan West Africa between 1982 and 2012 are identified using time-series analysis of vegetation index data derived from satellites. The residual trend (RESTREND) of a Normalized Difference Vegetation Index (NDVI) time-series is defined as the fraction of the difference between the observed NDVI and the NDVI predicted from climate data. It has been widely used to study desertification and other forms of land degradation in drylands. The method works on the assumption that a negative trend of vegetation photosynthetic capacity is an indication of land degradation if it is independent from climate variability. In the past, many scientists depended on rainfall data as the major climatic factor controlling vegetation productivity in drylands when applying the RESTREND method. However, the water that is directly available to vegetation is stored as soil moisture, which is a function of cumulative rainfall, surface runoff, infiltration and evapotranspiration. In this study, the new NDVI third generation (NDVI3g), which was generated by the National Aeronautics and Space Administration-Goddard Space Flight Center Global Inventory Modeling and Mapping Studies (NASA-GSFC GIMMS) group, was used as a satellite-derived proxy of vegetation productivity, together with the soil moisture index product from the Climate Prediction Center (CPC) and rainfall data from the Climate Research Unit (CRU). The results show that the soil moisture/NDVI pixel-wise residual trend indicates land degraded areas more clearly than rainfall/NDVI. The spatial and temporal trends of the RESTREND in the region follow the patterns of drought episodes, reaffirming the difficulties in separating the impacts of drought and land degradation on vegetation photosynthetic capacity. Therefore, future studies of land degradation and desertification in drylands should go beyond using rainfall as a sole predictor of vegetation condition, and include soil moisture index datasets in the analysis