Modeling climate change and biophysical impacts of crop production in the Austrian Marchfeld Region

Climate change affects major biophysical processes in agricultural crop production (e.g. evaporation of plants and soils, nutrient cycles, and growth of plants). This analysis aims to assess some of these effects by simulating regional climate projections that are integrated in the biophysical process model EPIC (Environmental Policy Integrated Climate). Statistical climate models have been developed for six weather parameters based on daily weather records of a weather station in the Austrian Marchfeld region from 1975 to 2006. These models have been used to estimate daily weather parameters for the period 2007–2038. The resulting projections have been compared to climate scenarios provided from the TYNDALL Centre for Climate Change Research, which are based on General Circulation Models (GCMs). The comparison indicates some differences, namely a smaller temperature increase and a higher precipitation amount in the TYNDALL data. Both climate datasets have been used to simulate impacts of climate change on crop yields, topsoil organic carbon content, and nitrate leaching with EPIC and thus to perform a sensitivity analysis of EPIC. Yield impacts have been assessed for four simulated crops, i.e. 6.2?t/ha for winter wheat for statistical climate projections compared to 5.7?t/ha for TYNDALL scenarios, 10.6?t/ha for corn compared to 10.5?t/ha, 3.9?t/ha for sunflower compared to 3.7?t/ha, and 4.5?t/ha for spring barley compared to 4.3?t/ha—all values as an average over the period 2007–2038. Smaller differences have been simulated for topsoil organic carbon content i.e. 55.1?t/ha for the statistical climate projections compared to 55.3?t/ha for the TYNDALL scenarios and nitrate leaching i.e. 7.1?kg/ha compared to 11.1?kg/ha. All crop yields as well as topsoil organic carbon content and nitrate leaching show highest sensitivity to temperature and solar radiation.     

Validation of official erosion modelling based on high‐resolution radar rain data by aerial photo erosion classification

The universal soil loss equation (USLE) is the most frequently applied erosion prediction model and it is also implemented as an official decision‐making instrument for agricultural regulations. The USLE itself has been already validated using different approaches. Additional errors, however, arise from input data and interpolation procedures that become necessary for field‐specific predictions on a national scale for administrative purposes. In this study, predicted event soil loss using the official prediction system in Bavaria (Germany) was validated by comparison with aerial photo erosion classifications of 8100 fields. Values for the USLE factors were mainly taken from the official Bavarian high‐resolution (5 × 5 m²) erosion cadastre. As series of erosion events were examined, the cover and management factor was replaced by the soil loss ratio. The event erosivity factor was calculated from high‐resolution (1 × 1 km², 5 min), rain gauge‐adjusted radar rain data (RADOLAN). Aerial photo erosion interpretation worked sufficiently well and average erosion predictions and visual classifications correlated closely. This was also true for data broken down to individual factors and different crops. There was no reason to assume a general invalidity of the USLE and the official parametrization procedures. Event predictions mainly suffered from errors in the assumed crop stage period and tillage practices, which do not reflect interannual and farm‐specific variation. In addition, the resolution of radar data (1 km²) did not seem to be sufficient to predict short‐term erosion on individual fields given the strong spatial gradients within individual rains. The quality of the input data clearly determined prediction quality. Differences between USLE predictions and observations are most likely caused by parametrization weaknesses but not by a failure of the model itself. Copyright © 2017 John Wiley & Sons, Ltd.     

On the interaction of pure and impure supercritical CO2 with rock forming minerals in saline aquifers: An experimental geochemical approach

The aim of this experimental study was to evaluate and compare the geochemical impact of pure and impure CO₂ on rock forming minerals of possible CO₂ storage reservoirs. This geochemical approach takes into account the incomplete purification of industrial captured CO₂ and the related effects during injection, and provides relevant data for long-term storage simulations of this specific greenhouse gas. Batch experiments were conducted to investigate the interactions of supercritical CO₂, brine and rock-forming mineral concentrates (albite, microcline, kaolinite, biotite, muscovite, calcite, dolomite and anhydrite) using a newly developed experimental setup. After up to 42 day (1000 h) experiments using pure and impure supercritical CO₂ the dissolution and solution characteristics were examined by XRD, XRF, SEM and EDS for the solid, and ICP–MS and IC for the fluid reactants, respectively. Experiments with mixtures of supercritical CO₂ (99.5 vol.%) and SO₂ or NO₂ impurities (0.5 vol.%) suggest the formation of H₂SO₄ and HNO₃, reflected in pH values between 1 and 4 for experiments with silicates and anhydrite and between 5 and 6 for experiments with carbonates. These acids should be responsible for the general larger amount of cations dissolved from the mineral phases compared to experiments using pure CO₂. For pure CO₂ a pH of around 4 was obtained using silicates and anhydrite, and 7–8 for carbonates. Dissolution of carbonates was observed after both pure and impure CO₂ experiments. Anhydrite was corroded by approximately 50 wt.% and gypsum precipitated during experiments with supercritical CO₂ + NO₂. Silicates do not exhibit visible alterations during all experiments but released an increasing amount of cations in the reaction fluid during experiments with impure CO₂. Nonetheless, precipitated secondary carbonates could not be identified.