Search

The emergence of decreasing trends in snowfall frequency and snow depth highlights the challenges arising from shifts in snow regimes. In particular, snow‐dependent southern Europe regions may be negatively impacted by such changes. Snow regimes strongly influence the water availability in reservoirs and groundwater. This study presents the world's longest series of monthly snowfall (fresh snow depth, snow days per year, days with snow on the ground) for the Parma Observatory, northern Italy (44°48′N and 10°19′E) between 1777 and 2018. These datasets were collected over the centuries using early rain gauges and recently, SIAP tipping bucket models. Sources of inhomogeneity were identified and analysed through the study of metadata and of nonparametric tests on datasets. Homogenized time series were obtained after correcting the observed snowfall data for the instruments response and localization characteristics, for the observing practices with the help of the standard normal homogeneity tests. The Buishand and the Mann–Kendall tests were further applied to check the correction and detect possible monotonic trends. Over the study period, days per year of snow decreased after the change point detected in 1897, in association with a rise in surface air temperature (including a distinct urban warming trend). In this study high numbers of snowy days per year (hereafter referred as snowfall frequency) and snow depth values during the latest phase of the LIA at Parma are consistent with a cycle of minimum solar activity, suggesting that enhanced, solar‐induced blocking activity dominated, with the arrival into the Mediterranean of large cold air masses developing over Siberia and northern Europe. Over the last century, snow appears to have resonance‐like characteristics as similar trends were observed over the Northern Hemisphere, where the extent of the snow cover has been reported to markedly decline in the transition spring and autumn seasons. [ABSTRACT FROM AUTHOR]

A potential strategy for mitigating nitrous oxide (N2O) emissions from permanent grasslands is the partial substitution of fertilizer nitrogen (Nfert) with symbiotically fixed nitrogen (Nsymb) from legumes. The input of Nsymb reduces the energy costs of producing fertilizer and provides a supply of nitrogen (N) for plants that is more synchronous to plant demand than occasional fertilizer applications. Legumes have been promoted as a potential N2O mitigation strategy for grasslands, but evidence to support their efficacy is limited, partly due to the difficulty in conducting experiments across the large range of potential combinations of legume proportions and fertilizer N inputs. These experimental constraints can be overcome by biogeochemical models that can vary legume‐fertilizer combinations and subsequently aid the design of targeted experiments. Using two variants each of two biogeochemical models (APSIM and DayCent), we tested the N2O mitigation potential and productivity of full factorial combinations of legume proportions and fertilizer rates for five temperate grassland sites across the globe. Both models showed that replacing fertilizer with legumes reduced N2O emissions without reducing productivity across a broad range of legume‐fertilizer combinations. Although the models were consistent with the relative changes of N2O emissions compared to the baseline scenario (200 kg N ha−1 yr−1; no legumes), they predicted different levels of absolute N2O emissions and thus also of absolute N2O emission reductions; both were greater in DayCent than in APSIM. We recommend confirming these results with experimental studies assessing the effect of clover proportions in the range 30–50% and ≤150 kg N ha−1 yr−1 input as these were identified as best‐bet climate smart agricultural practices. Key Points: A partial substitution of fertilizer nitrogen with symbiotically fixed nitrogen could mitigate nitrous oxide (N2O) emissions in grasslands by around 130 Gg yr−1Experimentally testing this mitigation option is challenging so modeling offers means to identify the optimum legume/fertilizer combinationThe models showed that net benefits to N2O mitigation and yield can be achieved across a wide range of legume/fertilizer combinations [ABSTRACT FROM AUTHOR]

Process‐based crop and grassland models estimating carbon (C) and nitrogen (N) dynamics are widely used to investigate best management practices in agriculture. They integrate several processes in a complex structure, but studies where modules corresponding to specific processes extracted from the whole model structure are assessed independently are uncommon. With the support of documented aerobic incubation trials in manure‐amended soils, a sensitivity analysis was performed on the C–N cycling processes of four modules (MOD1–4), corresponding to the models APSIM, EPIC, FASSET and STICS. The results showed that the parameter 'substrate use efficiency' had the most effect on the predicted values of net CO2 emissions and net N mineralization, together with the C/N ratio of the soil microbial biomass. They explained 74–75% on average of both output variances, whereas parameters determining manure C and N partitioning and first‐order decomposition constants of manure pools explained, on average, an additional 17–19%. Efforts should be focused on calibrating these parameters for more accurate simulations. The greater sensitivity of both outputs to parameters related to manure pools in more complex modules (MOD2–4) facilitates their adaptation to specific contexts, whereas MOD1 probably requires that parameters related to soil pools are also adapted to specific applications. Parameter interactions were limited, becoming noticeable only in situations of N‐limited soil organic matter decomposition. Models MOD1 and MOD3 allowed the C/N ratio of the soil microbial biomass to vary temporarily; therefore, they were less sensitive to mineral N availability and more easily adapted to a wide range of situations. This study provides essential information to support the development of state‐of‐the‐art biogeochemical models. Highlights: We compared four C–N modules embedded in process‐based biogeochemical models.We used sensitivity analysis to assess the simulation of manure decomposition in soil.We identified a few parameters that influenced CO2 emissions and N mineralization.We found that substrate use efficiency explained most of the output variance for all models. [ABSTRACT FROM AUTHOR]

The brevity of the instrumental record limits our understanding of snowfall variability and its directional patterns in the Mediterranean region. Here, we develop a 1,208‐year‐long (800–2017 CE) reconstruction of central Mediterranean snowfall variability based on documentary evidence from Italy. The record suggests that the recent reduction in Italian snowfall intensity is not unprecedented over the past millennium, since comparable patterns of low snowfall intensity also occurred during the Medieval Climate Anomaly. Increased snowfall during the Little Ice Age, however, was most likely associated with a shift of the Atlantic multi‐decadal variability towards negative values, and this overall cold phase further coincided with increased volcanic activity. Our findings on natural snowfall variability over the central Mediterranean in the past millennium provide a unique winter proxy for validating output from climate model simulations. Snow extent and accumulation are critical for water availability in many world areas. Climate variability and change could have important impacts on natural and human systems in snow‐dependent Mediterranean regions. Our 1,208‐year‐long (800–2017 CE) high‐resolution reconstruction of the snowfall events based on documentary evidence from across Italy (like the extended snowfall over Vesuvius occurred in 1776, http://tinyurl.com/lwe2wnj) shows that recent widespread increases in the occurrence of low‐snowfall years are likely not unprecedented in the long‐term record. [ABSTRACT FROM AUTHOR]

Abstract: Soils are vital for supporting food security and other ecosystem services. Climate change can affect soil functions both directly and indirectly. Direct effects include temperature, precipitation, and moisture regime changes. Indirect effects include those that are induced by adaptations such as irrigation, crop rotation changes, and tillage practices. Although extensive knowledge is available on the direct effects, an understanding of the indirect effects of agricultural adaptation options is less complete. A review of 20 agricultural adaptation case‐studies across Europe was conducted to assess implications to soil threats and soil functions and the link to the Sustainable Development Goals (SDGs). The major findings are as follows: (a) adaptation options reflect local conditions; (b) reduced soil erosion threats and increased soil organic carbon are expected, although compaction may increase in some areas; (c) most adaptation options are anticipated to improve the soil functions of food and biomass production, soil organic carbon storage, and storing, filtering, transforming, and recycling capacities, whereas possible implications for soil biodiversity are largely unknown; and (d) the linkage between soil functions and the SDGs implies improvements to SDG 2 (achieving food security and promoting sustainable agriculture) and SDG 13 (taking action on climate change), whereas the relationship to SDG 15 (using terrestrial ecosystems sustainably) is largely unknown. The conclusion is drawn that agricultural adaptation options, even when focused on increasing yields, have the potential to outweigh the negative direct effects of climate change on soil degradation in many European regions. [ABSTRACT FROM AUTHOR]

Abstract: 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 (N2O) 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 N2O 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 N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O 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 N2O emissions. Yield‐scaled N2O emissions (N2O 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 N2O emissions at field scale is discussed. [ABSTRACT FROM AUTHOR]

Complexity-reduction modelling can be useful for increasing the understanding of how the climate affects basin soil moisture response upon historical times not covered by detailed hydrological data. For this purpose, here is presented and assessed an empirical regression-based model, the European Soil Moisture Empirical Downscaling (ESMED), in which different climatic variables, easily available on the web, are addressed for simplifying the inherent complexity in the long-time studies. To accommodate this simplification, the Palmer Drought Severity Index, the precipitation, the elevation and the geographical location were used as input data in the ESMED model for predicting annual soil moisture budget. The test area was a large region including central Europe and Mediterranean countries, and the spatial resolution was initially set at 50 km. ESMED model calibration was made according to the soil moisture values retrieved from the Terrestrial Water Budget Data archive by selecting randomly 285 grid points (out of 2606). Once parameterized, ESMED model was performed at validation stage both spatially and temporally. The spatial validation was made for the grid points not selected in the calibration stage while the comparison with the soil moisture outputs of the Global Land Data Assimilation System-NOAH10 simulations upon the period 1950-2010 was carried out for the temporal validation. Moreover, ESMED results were found to be in good agreement with a root-zone soil moisture product obtained from active and passive microwave sensors from various satellite missions. ESMED model was thus found to be reliable for both the temporal and spatial validations and, hence, it might represent a useful tool to characterize the long-term dynamics of soil moisture-weather interaction. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Periods characterized by heavy rain and storms can trigger Multiple Damaging Hydrological Events (MDHE), in turn developing flooding, erosion, downpours and other phenomena affecting human habitats and ecosystems. However, approaches and applications to model MDHE and weather warning under specific environmental conditions are still poor. Based on the interpretation of the rainstorm-MDHE relationship, it was reconsidered and extended to a model-based rainstorms hazard index (RHI) developed in a previous paper, where rainstorm-pulsing force and resistance state are combined. In this way, landscape response was achieved by individual formative rainfall events, while the specific sequence of events that can affect landscape was only assessed in a qualitative way. A retrospective evaluation of rainstorm hazard modelling, control runs, are also given for different precipitation durations (from 1 to 240 h), to be compared with MDHE at two test-sites of Southern Italy (Benevento, Campania region; Scilla, Calabria region). For these sites, the complete series of historical MDHE which occurred during a 10 year period (1997-2008) was gathered and used to validate the results of the rainfall model. The tool is relatively simple and potentially attractive to rapidly prediction of adverse consequences of rainstorms. Perspectives for real-time applications in emergency planning are ultimately given for an agenda in future research. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

We study the interdecadal-scale variability over the Himalayas/Tibetan Plateau (H/TP) to deepen the understanding of Earth's important ecosystem after the recent controversy about glaciers melting in the Himalayan Mountains. We present a new statistical reconstruction of annual temperature variability back to 1901 for the Pyramid Automated Weather Station of the Ev-K2-CNR Committee, located at the base of the Mount Everest. Our reconstruction using recorded weather station data compared with the Climate Research Unit temperature pattern data indicate that in recent decades the warming trend over the H/TP has been faster than during any equivalent period in the last century. Positive temperature anomalies (mostly observed after 1970) were found to be correlated with the Atlantic Multidecadal Oscillation and the Pacifical Decadal Oscillation indices, but unevenly correlated with the Northern Hemisphere (NH) land-surface temperature. Such decoupled response between H/TP and NH suggests the mechanisms of global warming are only marginally influencing the H/TP climate. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This work presents methods where monthly based climate data are used to estimate reference evapotranspiration ( ET0). The objective was to evaluate two monthly ET0 models (Hargreaves-Samani, HS; Droogers-Allen HS, DAHS) and compare the results with an improved model (reference evapotranspiration model for complex terrains, REMCT). HS and DAHS are both based on the monthly temperature range (Δ T), while REMCT replaces Δ T with a monthly adjusted function (reference minimum air temperature). The test area was peninsular-insular Italy, where 13 stations with sufficient data to calculate FAO-56 Penman-Monteith ET0 were available. The three models were evaluated against FAO-56 over a validation data set of six stations, using a multiple-statistics indicator: 0 (best) ≤ IET ≤ 1 (worst). The REMCT estimates generally compared well with the FAO-56 estimates (mean IET = 0.029 against 0.187 and 0.255 with HS and DAHS, respectively). The three models performed similarly at low-altitude sites. REMCT was superior at sites higher than 500 m above sea level, where the ET0 − Δ T relationship was distorted (e.g., by an asymmetric lapse rate between maximum and minimum air temperatures). [ABSTRACT FROM AUTHOR]