ALBERT

Description: The Sacramento-San Joaquin Delta is at the heart of California's water supply system that provides water for irrigation and human consumption. It is also home to subsiding organic soils, decreasing native aquatic species populations, water quality degradation, vulnerable levees (levees are equivalent to dikes) and decreasing agricultural viability. There has been substantial progress in the interdisciplinary understanding and quantification of the nature and effects of subsidence and its mitigation. Because of the need for a drained rootzone, farming of crops such as vegetables, trees, vines, corn and alfalfa, results in an ongoing unsustainable cycle of continuing peat oxidation and deepening of drainage ditches to compensate for elevation loss. Despite substantial evidence for the increasing risks to the State's economy and water supply, the unsustainability of the status quo, and evidence for the benefits of alternatives, there has been limited progress in converting to land uses that can reduce, stop and reverse subsidence. Our overall approach has been to measure land-surface elevation changes; understand, quantify and model subsidence and greenhouse gas emissions from drained organic soil, and evaluate alternate land uses. Subsidence rates vary from less than 0.5 to over 2 cm yr−1, depending primarily on depth to groundwater and soil organic matter content. The primary cause of subsidence is the oxidation of organic matter, which has resulted in elevations of −3 to −9 m on about 100 000 ha. Using the results from micrometeorological measurements and modelling, we estimate that organic-matter oxidation causes an annual emission of over 2×106 t of CO2-equivalent which represents about 21 % of the State's plant-based agricultural emissions. Rewetting of the peat soils is emerging as a viable solution. Rice and wetlands stop and (in the case of wetlands) reverse the effects of subsidence and result in a net greenhouse-gas emission reduction benefit. Wetlands accrete about 3 cm of soil per year, can break the unsustainable subsidence/drainage cycle, reverse the trajectory of increasing hydraulic pressures on levees, reduce the probability of levee failure and seepage onto islands (islands are equivalent to polders), and may provide material for biofuels and animal feed. The recent implementation of a methodology for quantification of the GHG benefit is facilitating land use conversion and participation in the carbon market.

Description: Plant activity in semi-arid ecosystems is largely controlled by pulses of precipitation, making them particularly vulnerable to increased aridity that is expected with climate change. Simple bucket-model hydrology schemes in land surface models (LSMs) have had limited ability in accurately capturing semi-arid water stores and fluxes. Recent, more complex, LSM hydrology models have not been widely evaluated against semi-arid ecosystem in situ data. We hypothesize that the failure of older LSM versions to represent evapotranspiration, ET, in arid lands is because simple bucket models do not capture realistic fluctuations in upper-layer soil moisture. We therefore predict that including a discretized soil hydrology scheme based on a mechanistic description of moisture diffusion will result in an improvement in model ET when compared to data because the temporal variability of upper-layer soil moisture content better corresponds to that of precipitation inputs. To test this prediction, we compared ORCHIDEE LSM simulations from (1) a simple conceptual 2-layer bucket scheme with fixed hydraulic parameters and (2) an 11-layer discretized mechanistic scheme of moisture diffusion in unsaturated soil based on Richards equations, against daily and monthly soil moisture and ET observations, together with data-derived estimates of transpiration / evapotranspiration, T∕ET, ratios, from six semi-arid grass, shrub, and forest sites in the south-western USA. The 11-layer scheme also has modified calculations of surface runoff, water limitation, and resistance to bare soil evaporation, E, to be compatible with the more complex hydrology configuration. To diagnose remaining discrepancies in the 11-layer model, we tested two further configurations: (i) the addition of a term that captures bare soil evaporation resistance to dry soil; and (ii) reduced bare soil fractional vegetation cover. We found that the more mechanistic 11-layer model results in a better representation of the daily and monthly ET observations. We show that, as predicted, this is because of improved simulation of soil moisture in the upper layers of soil (top ∼ 10 cm). Some discrepancies between observed and modelled soil moisture and ET may allow us to prioritize future model development and the collection of additional data. Biases in winter and spring soil moisture at the forest sites could be explained by inaccurate soil moisture data during periods of soil freezing and/or underestimated snow forcing data. Although ET is generally well captured by the 11-layer model, modelled T∕ET ratios were generally lower than estimated values across all sites, particularly during the monsoon season. Adding a soil resistance term generally decreased simulated bare soil evaporation, E, and increased soil moisture content, thus increasing transpiration, T, and reducing the negative bias between modelled and estimated monsoon T∕ET ratios. This negative bias could also be accounted for at the low-elevation sites by decreasing the model bare soil fraction, thus increasing the amount of transpiring leaf area. However, adding the bare soil resistance term and decreasing the bare soil fraction both degraded the model fit to ET observations. Furthermore, remaining discrepancies in the timing of the transition from minimum T∕ET ratios during the hot, dry May–June period to high values at the start of the monsoon in July–August may also point towards incorrect modelling of leaf phenology and vegetation growth in response to monsoon rains. We conclude that a discretized soil hydrology scheme and associated developments improve estimates of ET by allowing the modelled upper-layer soil moisture to more closely match the pulse precipitation dynamics of these semi-arid ecosystems; however, the partitioning of T from E is not solved by this modification alone.

Description: American bison (Bison bison L.) have recovered from the brink of extinction over the past century. Bison reintroduction creates multiple environmental benefits, but impacts on greenhouse gas emissions are poorly understood. Bison are thought to have produced some 2 Tg yr−1 of the estimated 9–15 Tg yr−1 of pre-industrial enteric methane emissions, but few measurements have been made due to their mobile grazing habits and safety issues associated with measuring non-domesticated animals. Here, we measure methane and carbon dioxide fluxes from a bison herd on an enclosed pasture during daytime periods in winter using eddy covariance. Methane emissions from the study area were negligible in the absence of bison (mean ± standard deviation = −0.0009 ± 0.008 µmol m−2 s−1) and were significantly greater than zero, 0.048 ± 0.082 µmol m−2 s−1, with a positively skewed distribution, when bison were present. We coupled bison location estimates from automated camera images with two independent flux footprint models to calculate a mean per-animal methane efflux of 58.5 µmol s−1 per bison, similar to eddy covariance measurements of methane efflux from a cattle feedlot during winter. When we sum the observations over time with conservative uncertainty estimates we arrive at 81 g CH4 per bison d−1 with 95 % confidence intervals between 54 and 109 g CH4 per bison d−1. Uncertainty was dominated by bison location estimates (46 % of the total uncertainty), then the flux footprint model (33 %) and the eddy covariance measurements (21 %), suggesting that making higher-resolution animal location estimates is a logical starting point for decreasing total uncertainty. Annual measurements are ultimately necessary to determine the full greenhouse gas burden of bison grazing systems. Our observations highlight the need to compare greenhouse gas emissions from different ruminant grazing systems and demonstrate the potential for using eddy covariance to measure methane efflux from non-domesticated animals.