Topsoil porosity prediction across habitats at large scales using environmental variables.

Soil porosity and its reciprocal bulk density are important environmental state variables that enable modelers to represent hydraulic function and carbon storage. Biotic effects and their 'dynamic' influence on such state variables remain largely unknown for larger scales and may result in important, yet poorly quantified environmental feedbacks. Existing representation of hydraulic function is often invariant to environmental change and may be poor in some systems, particularly non-arable soils. Here we assess predictors of total porosity across two comprehensive national topsoil (0-15 cm) data sets, covering the full range of soil organic matter (SOM) and habitats (n = 1385 & n = 2570), using generalized additive mixed models and machine learning. Novel aspects of this work include the testing of metrics on aggregate size and livestock density alongside a range of different particle size distribution metrics. We demonstrate that porosity trends in Great Britain are dominated by biotic metrics, soil carbon and land use. Incorporating these variables into porosity prediction improves performance, paving the way for new dynamic calculation of porosity using surrogate measures with remote sensing, which may help improve prediction in data sparse regions of the world. Moreover, dynamic calculation of porosity could support representation of feedbacks in environmental and Earth System Models. Representing the hydrological feedbacks from changes in structural porosity also requires data and models at appropriate spatial scales to capture conditions leading to near-saturated soil conditions. Classification. Environmental Sciences.

Analytical modelling of soil porosity and bulk density across the soil organic matter and land-use continuum.

The thin layer of soil at the earth's surface supports life, storing water and nutrients for plant uptake. These processes occur in the soil pore space, often half the soil volume, but our understanding of how this volume responds to environmental change is poor. Convention, has been to predict soil porosity, or its reciprocal bulk density (BD), from soil texture using pedotransfer functions (PTFs). A texture based approach, invariant to environmental change, prevents feedback from land use or climate change to soil porosity. Moreover, PTFs are often limited to mineral soils with < 20% soil organic matter (SOM) content. Here, we develop an analytical model to predict soil porosity, or BD, as a function of SOM. We test it on two comprehensive, methodologically consistent, temperate national-scale topsoil data sets (0-15 cm) (Wales, n = 1385; Great Britain, n = 2570). The purpose of the approach is to generate an analytical function suitable for predicting soil porosity change with SOM content, while providing insight into the main grain-scale factors determining the porosity emergence. The newly developed function covering the entire SOM gradient allows for impacts of land use, management or climate change to feedback on soil porosity or bulk density through decadal dynamic changes in SOM.

Multiple soil map comparison highlights challenges for predicting topsoil organic carbon concentration at national scale.

Soil organic carbon (SOC) concentration is the fundamental indicator of soil health, underpinning food production and climate change mitigation. SOC storage is highly sensitive to several dynamic environmental drivers, with approximately one third of soils degraded and losing carbon worldwide. Digital soil mapping illuminates where hotspots of SOC storage occur and where losses to the atmosphere are most likely. Yet, attempts to map SOC often disagree. Here we compare national scale SOC concentration map products to reveal agreement of data in mineral soils, with progressively poorer agreement in organo-mineral and organic soils. Divergences in map predictions from each other and survey data widen in the high SOC content land types we stratified. Given the disparities are highest in carbon rich soils, efforts are required to reduce these uncertainties to increase confidence in mapping SOC storage and predicting where change may be important at national to global scales. Our map comparison results could be used to identify SOC risk where concentrations are high and should be conserved, and where uncertainty is high and further monitoring should be targeted. Reducing inter-map uncertainty will rely on addressing statistical limitations and including covariates that capture convergence of physical factors that produce high SOC contents.

Patterns and trends of topsoil carbon in the UK: Complex interactions of land use change, climate and pollution.

The UK Countryside Survey (CS) is a national long-term survey of soils and vegetation that spans three decades (1978-2007). Past studies using CS data have identified clear contrasting trends in topsoil organic carbon (tSOC) concentrations (0-15 cm) related to differences between habitat types. Here we firstly examine changes in tSOC resulting from land use change, and secondly construct mixed models to describe the impact of indirect drivers where land use has been constant. Where it occurs, land use change is a strong driver of SOC change, with largest changes in tSOC for transitions involving SOC-rich soils in upland and bog systems. Afforestation did not always increase tSOC, and the effect of transitions involving woodland was dependent on the other vegetation type. The overall national spatial pattern of tSOC concentration where land use has been constant is most strongly related to vegetation type and topsoil pH, with contributions from climate variables, deposition and geology. Comparisons of models for tSOC across time periods suggest that declining SO4 deposition has allowed recovery of topsoils from acidification, but that this has not resulted in the increased decomposition rates and loss of tSOC which might be expected. As a result, the relationship between pH and tSOC in UK topsoils has changed significantly between 1978 and 2007. The contributions of other indirect drivers in the models suggest negative relationships to seasonal temperature metrics and positive relationships to seasonal precipitation at the dry end of the scale. The results suggest that the CS approach of long-term collection of co-located vegetation and soil biophysical data provides essential tools both for identifying trends in tSOC at national and habitat levels, and for identifying areas of risk or areas with opportunities for managing topsoil SOC and vegetation change.

Modelling inorganic nitrogen in runoff: Seasonal dynamics at four European catchments as simulated by the MAGIC model.

Nitrogen (N) deposition is globally considered as a major threat to ecosystem functioning with important consequences for biodiversity, carbon sequestration and N retention. Lowered N retention as manifested by elevated concentrations of inorganic N in surface waters indicates ecosystem N saturation. Nitrate (NO3) concentrations in runoff from semi-natural catchments typically show an annual cycle, with low concentrations during the summer and high concentrations during the winter. Process-oriented catchment-scale biogeochemical models provide tools for simulation and testing changes in surface water and soil chemistry in response to changes in sulphur (S) and N deposition and climate. Here we examine the ability of MAGIC to simulate the observed monthly as well as the long-term trends over 10-35 years of inorganic N concentrations in streamwaters from four monitored headwater catchments in Europe: Certovo Lake in the Czech Republic, Afon Gwy at Plynlimon, UK, Storgama, Norway and G2 NITREX at Gårdsjön, Sweden. The balance between N inputs (mineralization+deposition) and microbial immobilization and plant uptake defined the seasonal pattern of NO3 leaching. N mineralization and N uptake were assumed to be governed by temperature, described by Q10 functions. Seasonality in NO3 concentration and fluxes were satisfactorily reproduced at three sites (R2 of predicted vs. modelled concentrations varied between 0.32 and 0.47 and for fluxes between 0.36 and 0.88). The model was less successful in reproducing the observed NO3 concentrations and fluxes at the experimental N addition site G2 NITREX (R2=0.01 and R2=0.19, respectively). In contrast to the three monitored sites, Gårdsjön is in a state of change from a N-limited to N-rich ecosystem due to 20 years of experimental N addition. At Gårdsjön the measured NO3 seasonal pattern did not follow typical annual cycle for reasons which are not well understood, and thus not simulated by the model. CAPSULE: The MAGIC model is able to simulate NO3 leaching on a monthly as well as an annual basis, and thus to reproduce the seasonal and short-term variations in N dynamics.

Modelling soil nitrogen: the MAGIC model with nitrogen retention linked to carbon turnover using decomposer dynamics.

We present a new formulation of the acidification model MAGIC that uses decomposer dynamics to link nitrogen (N) cycling to carbon (C) turnover in soils. The new model is evaluated by application to 15-30 years of water chemistry data at three coniferous-forested sites in the Czech Republic where deposition of sulphur (S) and N have decreased by >80% and 40%, respectively. Sulphate concentrations in waters have declined commensurately with S deposition, but nitrate concentrations have shown much larger decreases relative to N deposition. This behaviour is inconsistent with most conceptual models of N saturation, and with earlier versions of MAGIC which assume N retention to be a first-order function of N deposition and/or controlled by the soil C/N ratio. In comparison with earlier versions, the new formulation more correctly simulates observed short-term changes in nitrate leaching, as well as long-term retention of N in soils. The model suggests that, despite recent deposition reductions and recovery, progressive N saturation will lead to increased future nitrate leaching, ecosystem eutrophication and re-acidification.

Regionalization of soil base cation weathering for evaluating stream water acidification in the Appalachian Mountains, USA.

Estimation of base cation supply from mineral weathering (BC(w)) is useful for watershed research and management. Existing regional approaches for estimating BC(w) require generalized assumptions and availability of stream chemistry data. We developed an approach for estimating BC(w) using regionally specific empirical relationships. The dynamic model MAGIC was used to calibrate BC(w) in 92 watersheds distributed across three ecoregions. Empirical relationships between MAGIC-simulated BC(w) and watershed characteristics were developed to provide the basis for regionalization of BC(w) throughout the entire study region. BC(w) estimates extracted from MAGIC calibrations compared reasonably well with BC(w) estimated by regression based on landscape characteristics. Approximately one-third of the study region was predicted to exhibit BC(w) rates less than 100 meq/m(2)/yr. Estimates were especially low for some locations within national park and wilderness areas. The regional BC(w) results are discussed in the context of critical loads (CLs) of acidic deposition for aquatic ecosystem protection.

Comparison among model estimates of critical loads of acidic deposition using different sources and scales of input data.

The critical load (CL) of acidic atmospheric deposition represents the load of acidity deposited from the atmosphere to the earth's surface at which harmful acidification effects on sensitive biological receptors are thought to occur. In this study, the CL for forest soils was estimated for 27 watersheds throughout the United States using a steady-state mass balance approach based on both national and site-specific data and using different approaches for estimating base cation weathering. Results suggested that the scale and source of input data can have large effects on the calculated CL and that the most important parameter in the steady-state model used to estimate CL is base cation weathering. These results suggest that the data and approach used to estimate weathering must be robust if the calculated CL is to be useful for its intended purpose.

Streamwater acid-base chemistry and critical loads of atmospheric sulfur deposition in Shenandoah National Park, Virginia.

A modeling study was conducted to evaluate the acid-base chemistry of streams within Shenandoah National Park, Virginia and to project future responses to sulfur (S) and nitrogen (N) atmospheric emissions controls. Many of the major stream systems in the park have acid neutralizing capacity (ANC) less than 20 microeq/L, levels at which chronic and/or episodic adverse impacts on native brook trout are possible. Model hindcasts suggested that none of these streams had ANC less than 50 microeq/L in 1900. Model projections, based on atmospheric emissions controls representative of laws already enacted as of 2003, suggested that the ANC of those streams simulated to have experienced the largest historical decreases in ANC will increase in the future. The levels of S deposition that were simulated to cause streamwater ANC to increase or decrease to three specified critical levels (0, 20, and 50 microeq/L) ranged from less than zero (ANC level not attainable) to several hundred kg/ha/year, depending on the selected site and its inherent acid-sensitivity, selected ANC endpoint criterion, and evaluation year for which the critical load was calculated. Several of the modeled streams situated on siliciclastic geology exhibited critical loads <0 kg/ha/year to achieve ANC >50 microeq/L in the year 2040, probably due at least in part to base cation losses from watershed soil. The median modeled siliciclastic stream had a calculated critical load to achieve ANC >50 microeq/L in 2100 that was about 3 kg/ha/year, or 77% lower than deposition in 1990, representing the time of model calibration.

Modelling the effect of climate change on recovery of acidified freshwaters: relative sensitivity of individual processes in the MAGIC model.

The MAGIC model was used to evaluate the relative sensitivity of several possible climate-induced effects on the recovery of soil and surface water from acidification. A common protocol was used at 14 intensively studied sites in Europe and eastern North America. The results show that several of the factors are of only minor importance (increase in pCO(2) in soil air and runoff, for example), several are important at only a few sites (seasalts at near-coastal sites, for example) and several are important at nearly all sites (increased concentrations of organic acids in soil solution and runoff, for example). In addition changes in forest growth and decomposition of soil organic matter are important at forested sites and sites at risk of nitrogen saturation. The trials suggest that in future modelling of recovery from acidification should take into account possible concurrent climate changes and focus specially on the climate-induced changes in organic acids and nitrogen retention.

Climate variability and forecasting surface water recovery from acidification: modelling drought-induced sulphate release from wetlands.

Climate-induced drought events have been shown to have a significant influence on sulphate (SO(4)(2-)) export from forested catchments in central Ontario, subsequently delaying recovery of surface waters from acidification. Field and modelling studies have demonstrated that water table drawdown during drought periods promotes oxidation of previously stored (reduced) sulphur (S) compounds in wetlands, with subsequent efflux of SO(4)(2-) upon re-wetting. Although climate-induced changes in processes are generally not integrated into soil-acidification models, MAGIC (Model of Acidification of Groundwater in Catchments) includes a wetland compartment that incorporates redox processes driven by drought events. The potential confounding influence of climate-induced drought events on acidification recovery at Plastic Lake, south-central Ontario (under proposed future S emission reductions) was investigated using MAGIC and two climate scenarios: monthly precipitation and runoff based on long-term means (average-climate scenario), and variable precipitation and runoff based on the past 20 years of observed monthly data (variable-climate scenario). The variable-climate scenario included several periods of summer drought owing to lower than average rainfall and higher then average temperature. Nonetheless, long-term regional trends in precipitation and temperature suggest that the variable-climate scenario may be a conservative estimate of future climate. The average-climate scenario indicated good recovery potential with acid neutralising capacity (ANC) reaching approximately 40 micromol(c)L(-1) by 2020 and 50 micromol(c)L(-1) by 2080. In contrast, the forecasted recovery potential under the variable-climate scenario was very much reduced. By 2080, ANC was forecasted to increase to 2.6 micromol(c)L(-1) from -10.0 micromol(c)L(-1) in 2000. Elevated SO(4)(2-) efflux following drought events (introduced under the variable-climate scenario) has a dramatic impact on simulated future surface water chemistry. The results clearly demonstrate that prediction of future water quality, using models such as MAGIC, should take into account changes or variability in climate as well as acid deposition.

The Wheal Jane wetlands model for bioremediation of acid mine drainage.

Acid mine drainage (AMD) is a widespread environmental problem associated with both working and abandoned mining operations. As part of an overall strategy to determine a long-term treatment option for AMD, a pilot passive treatment plant was constructed in 1994 at Wheal Jane Mine in Cornwall, UK. The plant consists of three separate systems, each containing aerobic reed beds, anaerobic cell and rock filters, and represents the largest European experimental facility of its kind. The systems only differ by the type of pretreatment utilised to increase the pH of the influent minewater (pH <4): lime dosed (LD), anoxic limestone drain (ALD) and lime free (LF), which receives no form of pretreatment. Historical data (1994-1997) indicate median Fe reduction between 55% and 92%, sulphate removal in the range of 3-38% and removal of target metals (cadmium, copper and zinc) below detection limits, depending on pretreatment and flow rates through the system. A new model to simulate the processes and dynamics of the wetlands systems is described, as well as the application of the model to experimental data collected at the pilot plant. The model is process based, and utilises reaction kinetic approaches based on experimental microbial techniques rather than an equilibrium approach to metal precipitation. The model is dynamic and utilises numerical integration routines to solve a set of differential equations that describe the behaviour of 20 variables over the 17 pilot plant cells on a daily basis. The model outputs at each cell boundary are evaluated and compared with the measured data, and the model is demonstrated to provide a good representation of the complex behaviour of the wetland system for a wide range of variables.

Joint application of an empirical and mechanistic model for regional lake acidification.

The empirical direct distribution model for lake acidification is calibrated for use in an integrated assessment model which predicts the regional impact of an acid deposition control strategy. The calibration is based on the mechanistic Model of Acidification of Groundwater in Catchments (MAGIC). The models are applied jointly to a set of 33 statistically-selected lakes in the Adirondack region of New York. Calibration of the direct distribution model is based on a step-function application of acid deposition to MAGIC. Comparative evaluations of the resulting model predictions are made using historic deposition estimates and two alternative future deposition scenarios. The predictions of the direct distribution model match well the shapes and patterns of change of the regional distributions of ANC and pH predicted by MAGIC, the short- and medium-term dynamics of these changes, and the effect of including organic acids. However, small, long-term decreases in the fraction of incoming acid deposition neutralized by lakes and their watersheds predicted by MAGIC are not reproduced.

Use of historical assessment for evaluation of process-based model projections of future environmental change: Lake acidification in the Adirondack mountains, New York, USA.

Because of the considerable uncertainties associated with modeling complex ecosystem processes, it is essential that every effort be made to test model performance prior to relying on model projections for assessment of future surface water chemical response to environmental perturbation. Unfortunately, long-term chemical data with which to validate model performance are seldom available. The authors present here an evaluation of historical acidification of lake waters in the northeastern United States, and compare historical changes in a set of lakes to hindcasts from the same watershed model (MAGIC) used to estimate future changes in response to acidic deposition. The historical analyses and comparisons with MAGIC model hindcasts and forecasts of acid-base response demonstrate that the acidic and low-ANC lakes in this region are responsive to strong acid inputs. However, the model estimates suggest lakewater chemistry is more responsive to atmospheric inputs of sulfur than do the estimates based on paleolimnological historical analyses. A 'weight-of-evidence approach' that incorporates all available sources of information regarding acid-base response provides a more reasonable estimate of future change than an approach based on model projections alone. The results of these analyses have important implications for predicting future surface water chemical change in response to acidic deposition, establishing critical loads of atmospheric pollutants, and other environmental assessment activities where natural variation often exceeds the trends under investigation (high noise-to-signal ratio). Under these conditions, it is particularly important to evaluate future model projections in light of historical trends data.