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We evaluate the Hamiltonian particle methods (HPM) and the Nambu discretization applied to shallow-water equations on the sphere using the test suggested by Galewsky et al. (2004). Both simulations show excellent conservation of energy and are stable in long-term simulation. We repeat the test also using the ICOSWP scheme to compare with the two conservative spatial discretization schemes. The HPM simulation captures the main features of the reference solution, but wave 5 pattern is dominant in the simulations applied on the ICON grid with relatively low spatial resolutions. Nevertheless, agreement in statistics between the three schemes indicates their qualitatively similar behaviors in the long-term integration.
BACKGROUND: Addiction is supposedly characterized by a shift from goal-directed to habitual decision making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modeling goal-directed and habitual behavior as model-based and model-free control. In addicted patients, decision making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based versus model-free decision making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients. METHODS: Ninety detoxified, medication-free, alcohol-dependent patients and 96 age-and gender-matched control subjects underwent functional magnetic resonance imaging during the two-step task. Alcohol expectancies were measured with the Alcohol Expectancy Questionnaire. Over a follow-up period of 48 weeks, 37 patients remained abstinent and 53 patients relapsed as indicated by the Alcohol Timeline Followback method. RESULTS: Patients who relapsed displayed reduced medial prefrontal cortex activation during model-based decision making. Furthermore, high alcohol expectancies were associated with low model-based control in relapsers, while the opposite was observed in abstainers and healthy control subjects. However, reduced model-based control per se was not associated with subsequent relapse. CONCLUSIONS: These findings suggest that poor treatment outcome in alcohol dependence does not simply result from a shift from model-based to model-free control but is instead dependent on the interaction between high drug expectancies and low model-based decision making. Reduced model-based medial prefrontal cortex signatures in those who relapse point to a neural correlate of relapse risk. These observations suggest that therapeutic interventions should target subjective alcohol expectancies.
Crop model intercomparison studies have mostly focused on the assessment of predictive capabilities for crop development using weather and basic soil data from the same location. Still challenging is the model performance when considering complex interrelations between soil and crop dynamics under a changing climate. The objective of this study was to test the agronomic crop and environmental flux-related performance of a set of crop models. The aim was to predict weighing lysimeter-based crop (i.e., agronomic) and water-related flux or state data (i.e., environmental) obtained for the same soil monoliths that were taken from their original environment and translocated to regions with different climatic conditions, after model calibration at the original site. Eleven models were deployed in the study. The lysimeter data (2014-2018) were from the Dedelow (Dd), Bad Lauchstadt (BL), and Selhausen (Se) sites of the TERENO (TERrestrial ENvironmental Observatories) SOILCan network. Soil monoliths from Dd were transferred to the drier and warmer BL site and the wetter and warmer Se site, which allowed a comparison of similar soil and crop under varying climatic conditions. The model parameters were calibrated using an identical set of crop- and soil-related data from Dd. Environmental fluxes and crop growth of Dd soil were predicted for conditions at BL and Se sites using the calibrated models. The comparison of predicted and measured data of Dd lysimeters at BL and Se revealed differences among models. At site BL, the crop models predicted agronomic and environmental components similarly well. Model performance values indicate that the environmental components at site Se were better predicted than agronomic ones. The multi-model mean was for most observations the better predictor compared with those of individual models. For Se site conditions, crop models failed to predict site-specific crop development indicating that climatic conditions (i.e., heat stress) were outside the range of variation in the data sets considered for model calibration. For improving predictive ability of crop models (i.e., productivity and fluxes), more attention should be paid to soil-related data (i.e., water fluxes and system states) when simulating soil-crop-climate interrelations in changing climatic conditions.
Beyond good faith
(2021)
The ambitious climate targets set by industrialized nations worldwide cannot be met without decarbonizing the building stock. Using Germany as a case study, this paper takes stock of the extensive set of energy efficiency policies that are already in place and clarifies that they have been designed “in good faith” but lack in overall effectiveness as well as cost-efficiency in achieving these climate targets. We map out the market failures and behavioural considerations that are potential reasons for why realized energy savings fall below expectations and why the household adoption of energy-efficient and low-carbon technologies has remained low. We highlight the pressing need for data and modern empirical research to develop targeted and cost-effective policies seeking to correct these market failures. To this end, we identify some key research questions and identify gaps in the data required for evidence-based policy.
Carbon (C) stored in soils represents the largest C pool of terrestrial ecosystems and consequently plays a crucial role in the global C cycle. So far, it is widely unclear to what extent different land uses and land use change influence soil C storage. The hummocky ground moraine landscape of northeastern Germany is characterized by distinct small-scale soil heterogeneity on the one hand, and intensive energy crop cultivation on the other. Both factors are assumed to significantly influence gaseous C exchange; as such, they likely drive soil organic carbon (SOC) stock dynamics in terrestrial agricultural ecosystems. To date, it is not clear to what extent N fertilization forms, which are associated with energy crop cultivation (e.g., application of biogas fermentation residues) and soil type relative to soil erosion state, influence soil C dynamics, nor is it clear whether one of these factors is more important than the other. To investigate the influence of soil erosion state and N fertilization form on soil C dynamics, we present dynamic and seasonal net ecosystem carbon balances (NECB) as a proxy for changes in soil organic carbon stocks. Measurements were conducted for maize (Zea mays L.) at five sites in the "CarboZALF-D" experimental field during the 2011 growing season. Measurement sites represent different soil erosion states (non-eroded Albic Luvisols, extremely eroded Calcaric Regosols and depositional Endogleyic Colluvic Regosols) and N fertilization forms (100% mineral fertilizer, 50% mineral and 50% organic fertilizer, and 100% organic fertilizer). Fertilization treatments were established on the Albic Luvisol. Net ecosystem CO2 exchange (NEE) and ecosystem respiration (R-eco) were measured every four weeks using a dynamic flow-through non-steady-state closed manual chamber system. Gap filling was performed based on empirical temperature and PAR dependency functions and was used to derive daily NEE values. In parallel, daily above-ground biomass production (NPFshoot) was estimated using a logistic growth equation, fitted on periodic biomass samples. Finally, C dynamics were calculated as the balance of daily NEE and NPPshoot based on the initial C input due to organic fertilization. Resulting NECB varied from pronounced soil C losses at the Endogleyic Colluvic Regosol (592 g C m(-2)) to soil C gains at the Calcaric Regosol (-124 g C m(-2)). Minor to modest C losses were observed for the Albic Luvisol. Compared to N fertilization forms, soil erosion states generally had a stronger impact on derived NECB. However, interannual variations in plant phonology and interactions between soil erosion states and fertilization forms might result in different NECB values over multiple years. Hence, long-term measurements of different fertilization treatments on characteristic soil landscape elements are needed.
To provide physically based wind modelling for wind erosion research at regional scale, a 3D computational fluid dynamics (CFD) wind model was developed. The model was programmed in C language based on the Navier-Stokes equations, and it is freely available as open source. Integrated with the spatial analysis and modelling tool (SAMT), the wind model has convenient input preparation and powerful output visualization. To validate the wind model, a series of experiments was conducted in a wind tunnel. A blocking inflow experiment was designed to test the performance of the model on simulation of basic fluid processes. A round obstacle experiment was designed to check if the model could simulate the influences of the obstacle on wind field. Results show that measured and simulated wind fields have high correlations, and the wind model can simulate both the basic processes of the wind and the influences of the obstacle on the wind field. These results show the high reliability of the wind model. A digital elevation model (DEM) of an area (3800 m long and 1700 m wide) in the Xilingele grassland in Inner Mongolia (autonomous region, China) was applied to the model, and a 3D wind field has been successfully generated. The clear implementation of the model and the adequate validation by wind tunnel experiments laid a solid foundation for the prediction and assessment of wind erosion at regional scale.
The aims of this study were to identify areas of wind erosion and dust deposition and to quantify the effects of different grazing intensities on soil redistribution rates in grasslands based on the Cs-137 technique. Because the method uses a reference inventory as threshold for erosion or deposition, the classification of any other site as source or sink for dust depends on the accurate selection of this reference site.
Measurements of Cs-137 inventories and depth distributions were carried out at pasture sites with predominant species of Stipa grandis and Leymus chinensis which are grazed with different intensities. Additional measurements were made at arable land, plant-covered sand dunes and alluvial plains. Wind-induced soil erosion and dust deposition rates were calculated from Cs-137 inventories by means of the "Profile-Distribution" and the "Mass Balance II" models.
The selection of the reference site was based on fluid dynamical and process-determining parameters. The chosen site should meet the following four conditions: (i) located at a summit position with obviously low deposition rates, (ii) sufficient vegetation cover to prevent wind erosion, (iii) plane to exclude water erosion and (iv) in the wind/dust shadow of a higher elevation. The measured reference inventory of Cs-137 was 1967(+/- 102) Bqm(-2) located at a summit position of moderately grazed Leymus chinensis steppe. The Cs-137 inventories at other sites ranged from 1330 Bqm(-2) at heavily grazed sites to 5119 Bqm(-2) at river deposits, representing annual average soil losses of up to 130 tkm(-2) and deposits of up to 540 tkm(-2), respectively. The calculated annual averages of dust depositions at ungrazed Leymus chinensis sites were related to the dust storm frequencies of the last 50 years resulting in a description of the temporal variability of annual dust depositions from about 154 tkm(-2) in the 1960s to 26 tkm(-2) at recent times. Based on this quantification already 80% of the total dust depositions can be related to the 20 years between the 1960s and the end of the 1970s and only 20% to the time between 1980 and 2001.
Cs-137 technique is a promising method to assess the effect of grazing intensity and land use types on the spatial variability of wind-induced soil and dust redistribution processes in semi-arid grasslands. However, considerable efforts are needed to identify a reliable reference site, because erosion and deposition induced by wind may occur at the same places. The combination of the dust deposition rates derived from Cs-137 profile data with the dust storm frequencies is helpful for a better reconstruction of the temporal variability of dust deposition and wind erosion in this region. The calculated recent deposition rates of about 20 tkm(-2) are in good agreement with data of other authors.
Intensive grazing leads to land degradation and desertification of grassland ecosystems followed by serious environmental and social problems. The Xilingol steppe grassland in Inner Mongolia, China, which has been a sink area for dust for centuries, is strongly affected by the negative effects of overgrazing and wind erosion. The aim of this study is the provision of a wind erosion risk map with a spatial high resolution of 25 m to identify actual source and sink areas. In an integrative approach, field measurements of vegetation features and surface roughness length z0 were combined with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image data for a land use classification. To determine the characteristics of the different land use classes, a field observation (ground truth) was performed in April 2009. The correlation of vegetation height and z0 (R2 = 0.8, n = 55) provided the basis for a separation of three main classes, grassland, non-vegetation and other. The integration of the soil-adjusted vegetation index (SAVI) and the spectral information from the atmospheric corrected ASTER bands 1, 2 and 3 (visible to near-infrared) led to a classification of the overall accuracy (OA) of 0.79 with a kappa () statistic of 0.74, respectively. Additionally, a digital elevation model (DEM) was used to identify topographical effects in relation to the main wind direction, which enabled a qualitative estimation of potential dust deposition areas. The generated maps result in a significantly higher description of the spatial variability in the Xilingol steppe grassland reflecting the different land use intensities on the current state of the grassland less, moderately and highly degraded. The wind erosion risk map enables the identification of characteristic mineral dust sources, sinks and transition zones.
In order to identify the areas in the Xilingele grassland which are sensitive to wind erosion, a computational fluid dynamics model (CFD-WEM) was used to simulate the wind fields over a region of 37 km(2) which contains different topography and land use types. Previous studies revealed the important influences of topography and land use on wind erosion in the Xilingele grassland. Topography influences wind fields at large scale, and land use influences wind fields near the ground. Two steps were designed to implement the CFD wind simulation, and they were respectively to simulate the influence of topography and surface roughness on the wind. Digital elevation model (DEM) and surface roughness length were the key inputs for the CFD simulation. The wind simulation by CFD-WEM was validated by a wind data set which was measured simultaneously at six positions in the field. Three scenarios with different wind velocities were designed based on observed dust storm events, and wind fields were simulated according to these scenarios to predict the sensitive areas to wind erosion. General assumptions that cropland is the most sensitive area to wind erosion and heavily and moderately grazed grasslands are both sensitive etc. can be refined by the modelling of CFD-WEM. Aided by the results of this study, the land use planning and protection measures against wind erosion can be more efficient. Based on the case study in the Xilingele grassland, a method of regional wind erosion assessment aided by CFD wind simulation is summarized. The essence of this method is a combination of CFD wind simulation and determination of threshold wind velocity for wind erosion. Because of the physically-based simulation and the flexibility of the method, it can be generalised to other regions.
The open source computational fluid dynamics (CFD) wind model (CFD-WEM) for wind erosion research in the Xilingele grassland in Inner Mongolia (autonomous region, China) is compared with two open source CFD models Gerris and OpenFOAM. The evaluation of these models was made according to software technology, implemented methods, handling, accuracy and calculation speed. All models were applied to the same wind tunnel data set. Results show that the simplest CFD-WEM has the highest calculation speed with acceptable accuracy, and the most powerful OpenFOAM produces the simulation with highest accuracy and the lowest calculation speed. Gerris is between CFD-WEM and OpenFOAM. It calculates faster than OpenFOAM, and it is capable to solve different CFD problems. CFD-WEM is the optimal model to be further developed for wind erosion research in Inner Mongolia grassland considering its efficiency and the uncertainties of other input data. However, for other applications using CFD technology, Gerris and OpenFOAM can be good choices. This paper shows the powerful capability of open source CFD software in wind erosion study, and advocates more involvement of open source technology in wind erosion and related ecological researches.