Isolating climatic, tectonic, and lithologic controls on mountain landscape evolution.

Establishing that climate exerts an important general influence on topography in tectonically active settings has proven an elusive goal. Here, we show that climates ranging from arid to humid consistently influence fluvial erosional efficiency and thus topography, and this effect is captured by a simple metric that combines channel steepness and mean annual rainfall, ksnQ. Accounting for spatial rainfall variability additionally increases the sensitivity of channel steepness to lithologic and tectonic controls on topography, enhancing predictions of erosion and rock uplift rates, and supports the common assumption of a reference concavity near 0.5. In contrast, the standard channel steepness metric, ksn, intrinsically assumes that climate is uniform. Consequently, its use where rainfall varies spatially undermines efforts to distinguish climate from tectonic and lithologic effects, can bias reference concavity estimates, and may ultimately lead to false impressions about rock uplift patterns and other environmental influences. Capturing climate is therefore a precondition to understanding mountain landscape evolution.

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

Altered energy partitioning across terrestrial ecosystems in the European drought year 2018.

Drought and heat events, such as the 2018 European drought, interact with the exchange of energy between the land surface and the atmosphere, potentially affecting albedo, sensible and latent heat fluxes, as well as CO2 exchange. Each of these quantities may aggravate or mitigate the drought, heat, their side effects on productivity, water scarcity and global warming. We used measurements of 56 eddy covariance sites across Europe to examine the response of fluxes to extreme drought prevailing most of the year 2018 and how the response differed across various ecosystem types (forests, grasslands, croplands and peatlands). Each component of the surface radiation and energy balance observed in 2018 was compared to available data per site during a reference period 2004-2017. Based on anomalies in precipitation and reference evapotranspiration, we classified 46 sites as drought affected. These received on average 9% more solar radiation and released 32% more sensible heat to the atmosphere compared to the mean of the reference period. In general, drought decreased net CO2 uptake by 17.8%, but did not significantly change net evapotranspiration. The response of these fluxes differed characteristically between ecosystems; in particular, the general increase in the evaporative index was strongest in peatlands and weakest in croplands. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.

Peaks of in situ N2 O emissions are influenced by N2 O-producing and reducing microbial communities across arable soils.

Agriculture is the main source of terrestrial N2 O emissions, a potent greenhouse gas and the main cause of ozone depletion. The reduction of N2 O into N2 by microorganisms carrying the nitrous oxide reductase gene (nosZ) is the only known biological process eliminating this greenhouse gas. Recent studies showed that a previously unknown clade of N2 O-reducers (nosZII) was related to the potential capacity of the soil to act as a N2 O sink. However, little is known about how this group responds to different agricultural practices. Here, we investigated how N2 O-producers and N2 O-reducers were affected by agricultural practices across a range of cropping systems in order to evaluate the consequences for N2 O emissions. The abundance of both ammonia-oxidizers and denitrifiers was quantified by real-time qPCR, and the diversity of nosZ clades was determined by 454 pyrosequencing. Denitrification and nitrification potential activities as well as in situ N2 O emissions were also assessed. Overall, greatest differences in microbial activity, diversity, and abundance were observed between sites rather than between agricultural practices at each site. To better understand the contribution of abiotic and biotic factors to the in situ N2 O emissions, we subdivided more than 59,000 field measurements into fractions from low to high rates. We found that the low N2 O emission rates were mainly explained by variation in soil properties (up to 59%), while the high rates were explained by variation in abundance and diversity of microbial communities (up to 68%). Notably, the diversity of the nosZII clade but not of the nosZI clade was important to explain the variation of in situ N2 O emissions. Altogether, these results lay the foundation for a better understanding of the response of N2 O-reducing bacteria to agricultural practices and how it may ultimately affect N2 O emissions.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions.

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2 O emissions. Yield-scaled N2 O emissions (N2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2 O emissions at field scale is discussed.

Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes.

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

The diversity of the N2O reducers matters for the N2O:N2 denitrification end-product ratio across an annual and a perennial cropping system.

Agriculture is the main source of terrestrial emissions of N2O, a potent greenhouse gas and the main cause of ozone layer depletion. The reduction of N2O into N2 by microorganisms carrying the nitrous oxide reductase gene (nosZ) is the only biological process known to eliminate this greenhouse gas. Recent studies showed that a previously unknown clade of N2O-reducers was related to the capacity of the soil to act as an N2O sink, opening the way for new strategies to mitigate emissions. Here, we investigated whether the agricultural practices could differently influence the two N2O reducer clades with consequences for denitrification end-products. The abundance of N2O-reducers and producers was quantified by real-time PCR, and the diversity of both nosZ clades was determined by 454 pyrosequencing. Potential N2O production and potential denitrification activity were used to calculate the denitrification gaseous end-product ratio. Overall, the results showed limited differences between management practices but there were significant differences between cropping systems in both the abundance and structure of the nosZII community, as well as in the [rN2O/r(N2O+N2)] ratio. More limited differences were observed in the nosZI community, suggesting that the newly identified nosZII clade is more sensitive than nosZI to environmental changes. Potential denitrification activity and potential N2O production were explained mainly by the soil properties while the diversity of the nosZII clade on its own explained 26% of the denitrification end-product ratio, which highlights the importance of understanding the ecology of this newly identified clade of N2O reducers for mitigation strategies.