Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils.

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

Large-scale integrated assessment of soil carbon and organic matter-related nitrogen fluxes in Saxony (Germany).

Changes in land-use, agricultural management and climate affect the turnover and storage of organic carbon in soils (SOC) as well as the nitrogen mobilization from soil organic matter (SOM), with potential side effects on nitrogen availability and leaching. When addressing the requests for increased carbon storage in soil as well as for the reduction of nitrogen losses, integrated approaches on regional scales are required that take into account the actual changes in agricultural management and climate. This study investigated the arable land (7345 km2) of Saxony (Germany) with regard to the following: (1) the trends of SOC storage and organic matter-related nitrogen fluxes, including their subregional and annual dynamics, (2) changes in the carbon input to arable soils and the turnover of organic matter, and (3) the contribution of different drivers (climate, crop production and fertilization, tillage system) to the simulated SOM changes for the period 1998-2014 on a 500 m grid. The model CANDY carbon balance (CCB) was specifically adapted for large-scale simulations of SOM turnover to link spatial data on soils and climate with regional statistics on agricultural management. This new 'regional mode' of CCB has been validated using data from 391 plots across different European locations. The initial SOC levels for Saxony assumed steady state conditions at the beginning of the simulation period and have been validated using data from 667 monitoring sites. The results showed an increase in the SOC stocks of the arable soils of Saxony of 785 × 103 t C (1.24‰ annually) during the simulation period. At the same time, the model simulated an average increase in organic nitrogen stored in SOM of approximately 7.5 kg N ha-1 a-1, with considerable differences between individual years and subregions. Both the increase in carbon inputs to soil (+8%) and the reduction of carbon turnover rates (-10%) had positive effects on SOC storage. While the increased use of conservation tillage was the most important driver for the overall increase in SOM storage in Saxony, climate variability and crop production and fertilization had the largest effect on its annual dynamics.

Biogas residue parameterization for soil organic matter modeling.

A variety of biogas residues (BGRs) have been used as organic fertilizer in agriculture. The use of these residues affects the storage of soil organic matter (SOM). In most cases, SOM changes can only be determined in long-term observations. Therefore, predictive modeling can be an efficient alternative, provided that the parameters required by the model are known for the considered BGRs. This study was conducted as a first approach to estimating the organic matter (OM) turnover parameters of BGRs for process modeling. We used carbon mineralization data from six BGRs from an incubation experiment, representing a range of substrate inputs, to calculate a turnover coefficient k controlling the velocity of fresh organic matter (FOM) decay and a synthesis coefficient η describing the SOM creation from FOM. An SOM turnover model was applied in inverse mode to identify both parameters. In a second step, we related the parameters k and η to chemical properties of the corresponding BGRs using a linear regression model and applied them to a long-term scenario simulation. According to the results of the incubation experiment, the k values ranged between 0.28 and 0.58 d-1 depending on the chemical composition of the FOM. The estimated η values ranged between 0.8 and 0.89. The best linear relationship of k was found to occur with pH (R2 = 0.863). Parameter η is related to the Ct/Norg ratio (R2 = 0.696). Long-term scenario simulations emphasized the necessity of specific k and η values related to the chemical properties for each BGR. However, further research is needed to validate and improve these preliminary results.

Spatially distributed lateral nitrate transport at the catchment scale.

In river catchments, N transformation and storage processes during lateral transport are important in controlling N loads of surface waters. There is a lack of approaches which capture lateral flows and associated N transformation in a spatially distributed way. The aim of this paper is to develop a new conceptual N transport and transformation model which simulates the lateral nitrate transport in subsurface flow from the source area to the receiving water body. The developed tool is based on the object modeling system (OMS) framework and consists of the analytical spatially distributed hydrological model J2000, the nitrate recharge model Meta Candy and a new groundwater N routing component. Nitrate degradation in groundwater is calculated stoichiometrically according to a predefined amount on oxidizable substrate. The new modeling approach was tested in a small agricultural lower mountain range catchment of Thuringia, Germany. The calibration of the N model using a 4-yr period showed reasonable results for nitrate load calculations with a Nash and Sutcliff coefficient of 0.78. The 3-yr validation period produced Nash-Sutcliff (NS) values of 0.75. There was a clear relationship of the goodness-of-fit between the hydrological simulations and the nitrate concentration calculations. Due to short residence times of the interflow nitrate degradation was restricted to slow base flow components. The new approach can be used to target N source areas within a catchment and assess the impact of these source areas on the N load of surface waters in a spatially distributed manner.

Impact of selected agricultural management options on the reduction of nitrogen loads in three representative meso scale catchments in Central Germany.

Nitrogen inputs into surface waters from diffuse sources are still unduly high and the assessment of mitigation measures is associated with large uncertainties. The objective of this paper is to investigate selected agricultural management scenarios on nitrogen loads and to assess the impact of differing catchment characteristics in central Germany. A new modelling approach, which simulates spatially distributed N-transport and transformation processes in soil and groundwater, was applied to three meso scale catchments with strongly deviating climate, soil and topography conditions. The approach uses the integrated modelling framework JAMS to link an agro-ecosystem, a rainfall-runoff and a groundwater nitrogen transport model. Different agricultural management measures with deviating levels of acceptance were analysed in the three study catchments. N-leaching rates in all three catchments varied with soil type, the lowest leaching rates being obtained for loess soil catchment (18.5 kg nitrate N ha(-1) yr(-1)) and the highest for the sandy soils catchment (41.2 kg nitrate N ha(-1) yr(-1)). The simulated baseflow nitrogen concentrations varied between the catchments from 1 to 6 mg N l(-1), reflecting the nitrogen reduction capacity of the subsurfaces. The management scenarios showed that the highest N leaching reduction could be achieved by good site-adapted agricultural management options. Nitrogen retention in the subsurface did not alter the ranking of the management scenarios calculated as losses from the soil zone. The reduction effect depended strongly on site specific conditions, especially climate, soil variety and the regional formation of the crop rotations.