Managing agricultural vulnerability to phosphorus scarcity through bottom-up assessment of regional-scale opportunities

2020 ◽  
Vol 184 ◽  
pp. 102910
Author(s):  
Madhuri Nanda ◽  
Arun Kansal ◽  
Dana Cordell
Keyword(s):  
Regional Scale ◽  
Bottom Up ◽  
Agricultural Vulnerability

scholarly journals A bottom-up spatially explicit methodology to estimate the space heating demand of the building stock at regional scale

2020 ◽  
Vol 206 ◽  
pp. 109581 ◽  
Author(s):  
Valentina D'Alonzo ◽  
Antonio Novelli ◽  
Roberto Vaccaro ◽  
Daniele Vettorato ◽  
Rossano Albatici ◽  
...  
Keyword(s):  
Regional Scale ◽  
Space Heating ◽  
Spatially Explicit ◽  
Building Stock ◽  
Bottom Up

Top-down Support of Swiss non-CO2 Greenhouse Gas Emissions Reporting to UNFCCC

2020 ◽  
Author(s):  
Stephan Henne ◽  
Martin K. Vollmer ◽  
Martin Steinbacher ◽  
Markus Leuenberger ◽  
Frank Meinhardt ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Radiative Forcing ◽  
Dispersion Model ◽  
Regional Scale ◽  
Particle Dispersion ◽  
Mitigation Measures ◽  
Ratio Method ◽  
Top Down ◽  
Bottom Up ◽  
Halogenated Compound

<p>Globally, emissions of long-lived non-CO<sub>2</sub> greenhouse gases (GHG; methane, nitrous oxide and halogenated compounds) account for approximately 30 % of the radiative forcing of all anthropogenic GHG emissions. In industrialised countries, ‘bottom-up’ estimates come with relatively large uncertainties for anthropogenic non-CO<sub>2</sub> GHGs when compared with those of anthropogenic CO<sub>2</sub>. 'Top-down' methods on the country scale offer an independent support tool to reduce these uncertainties and detect biases in emissions reported to the UNFCCC. Based on atmospheric concentration observations these tools are also able to detect the effectiveness of emission mitigation measures on the long term.</p><p>Since 2012 the Swiss national inventory reporting (NIR) contains an appendix on 'top-down' studies for selected halogenated compound. Subsequently, this appendix was extended to include methane and nitrous oxide. Here, we present these updated (2020 submission) regional-scale (~300 x 200 km<sup>2</sup>) atmospheric inversion studies for non-CO<sub>2</sub> GHG emission estimates in Switzerland, making use of observations on the Swiss Plateau (Beromünster tall tower) as well as the neighbouring mountain-top sites Jungfraujoch and Schauinsland.</p><p>We report spatially and temporally resolved Swiss emissions for CH<sub>4</sub> (2013-2019), N<sub>2</sub>O (2017-2019) and total Swiss emissions for hydrofluorocarbons (HFCs) and SF<sub>6</sub> (2009-2019) based on a Bayesian inversion system and a tracer ratio method, respectively. Both approaches make use of transport simulations applying the high-resolution (7 x 7 km<sup>2</sup>) Lagrangian particle dispersion model (FLEXPART-COSMO). We compare these 'top-down' estimates to the 'bottom-up' results reported by Switzerland to the UNFCCC. Although we find good agreement between the two estimates for some species (CH<sub>4</sub>, N<sub>2</sub>O), emissions of other compounds (e.g., considerably lower 'top-down' estimates for HFC-134a) show larger discrepancies. Potential reasons for the disagreements are discussed. Currently, our 'top-down' information is only used for comparative purposes and does not feed back into the 'bottom-up' inventory.</p>

scholarly journals Intercomparison of NO<sub><i>x</i></sub> emission inventories over East Asia

2017 ◽  
Vol 17 (16) ◽  
pp. 10125-10141 ◽  
Author(s):  
Jieying Ding ◽  
Kazuyuki Miyazaki ◽  
Ronald Johannes van der A ◽  
Bas Mijling ◽  
Jun-ichi Kurokawa ◽  
...  
Keyword(s):  
Kalman Filter ◽  
East Asia ◽  
Rural Areas ◽  
Emission Inventory ◽  
Regional Scale ◽  
Policy Support ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Bottom Up ◽  
Temporal Behaviour

Abstract. We compare nine emission inventories of nitrogen oxides including four satellite-derived NOx inventories and the following bottom-up inventories for East Asia: REAS (Regional Emission inventory in ASia), MEIC (Multi-resolution Emission Inventory for China), CAPSS (Clean Air Policy Support System) and EDGAR (Emissions Database for Global Atmospheric Research). Two of the satellite-derived inventories are estimated by using the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm, which is based on an extended Kalman filter applied to observations from OMI or from GOME-2. The other two are derived with the EnKF algorithm, which is based on an ensemble Kalman filter applied to observations of multiple species using either the chemical transport model CHASER and MIROC-chem. The temporal behaviour and spatial distribution of the inventories are compared on a national and regional scale. A distinction is also made between urban and rural areas. The intercomparison of all inventories shows good agreement in total NOx emissions over mainland China, especially for trends, with an average bias of about 20 % for yearly emissions. All the inventories show the typical emission reduction of 10 % during the Chinese New Year and a peak in December. Satellite-derived approaches using OMI show a summer peak due to strong emissions from soil and biomass burning in this season. Biases in NOx emissions and uncertainties in temporal variability increase quickly when the spatial scale decreases. The analyses of the differences show the importance of using observations from multiple instruments and a high spatial resolution model for the satellite-derived inventories, while for bottom-up inventories, accurate emission factors and activity information are required. The advantage of the satellite-derived approach is that the emissions are soon available after observation, while the strength of the bottom-up inventories is that they include detailed information of emissions for each source category.

scholarly journals On observational and modelling strategies targeted at regional carbon exchange over continents

2009 ◽  
Vol 6 (1) ◽  
pp. 1317-1343 ◽  
Author(s):  
C. Gerbig ◽  
A. J. Dolman ◽  
M. Heimann
Keyword(s):  
Spatial Scales ◽  
Regional Scale ◽  
Local Scale ◽  
Anthropogenic Sources ◽  
Carbon Exchange ◽  
Top Down ◽  
Bottom Up ◽  
Modelling Strategies ◽  
The Impact ◽  
Up Scaling

Abstract. Estimating carbon exchange at regional scales is paramount to understanding feedbacks between climate and the carbon cycle, but also to verifying climate change mitigation such as emission reductions and strategies compensating for emissions such as carbon sequestration. This paper discusses evidence for a number of important shortcomings of current generation modelling frameworks designed to provide regional scale budgets. Current top-down and bottom-up approaches targeted at deriving consistent regional scale carbon exchange estimates for biospheric and anthropogenic sources and sinks are hampered by a number of issues: We show that top-down constraints using point measurements made from tall towers, although sensitive to larger spatial scales, are however influenced by local areas much stronger than previously thought. On the other hand, classical bottom-up approaches using process information collected at the local scale, such as from eddy covariance data, need up-scaling and validation on larger scales. We therefore argue for a combination of both approaches, implicitly providing the important local scale information for the top-down constraint, and providing the atmospheric constraint for up-scaling of flux measurements. Combining these data streams necessitates quantifying their respective representation errors, which are discussed. The impact of these findings on future network design is highlighted, and some recommendations are given.

scholarly journals Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling

2016 ◽  
Vol 16 (6) ◽  
pp. 3683-3710 ◽  
Author(s):  
Stephan Henne ◽  
Dominik Brunner ◽  
Brian Oney ◽  
Markus Leuenberger ◽  
Werner Eugster ◽  
...  
Keyword(s):  
Natural Gas ◽  
Greenhouse Gas ◽  
Urban Areas ◽  
Large Scale ◽  
Transport Model ◽  
Regional Scale ◽  
Inverse Modelling ◽  
Gas Distribution ◽  
Bottom Up ◽  
Ch4 Emissions

Abstract. Atmospheric inverse modelling has the potential to provide observation-based estimates of greenhouse gas emissions at the country scale, thereby allowing for an independent validation of national emission inventories. Here, we present a regional-scale inverse modelling study to quantify the emissions of methane (CH4) from Switzerland, making use of the newly established CarboCount-CH measurement network and a high-resolution Lagrangian transport model. In our reference inversion, prior emissions were taken from the "bottom-up" Swiss Greenhouse Gas Inventory (SGHGI) as published by the Swiss Federal Office for the Environment in 2014 for the year 2012. Overall we estimate national CH4 emissions to be 196 ± 18 Gg yr−1 for the year 2013 (1σ uncertainty). This result is in close agreement with the recently revised SGHGI estimate of 206 ± 33 Gg yr−1 as reported in 2015 for the year 2012. Results from sensitivity inversions using alternative prior emissions, uncertainty covariance settings, large-scale background mole fractions, two different inverse algorithms (Bayesian and extended Kalman filter), and two different transport models confirm the robustness and independent character of our estimate. According to the latest SGHGI estimate the main CH4 source categories in Switzerland are agriculture (78 %), waste handling (15 %) and natural gas distribution and combustion (6 %). The spatial distribution and seasonal variability of our posterior emissions suggest an overestimation of agricultural CH4 emissions by 10 to 20 % in the most recent SGHGI, which is likely due to an overestimation of emissions from manure handling. Urban areas do not appear as emission hotspots in our posterior results, suggesting that leakages from natural gas distribution are only a minor source of CH4 in Switzerland. This is consistent with rather low emissions of 8.4 Gg yr−1 reported by the SGHGI but inconsistent with the much higher value of 32 Gg yr−1 implied by the EDGARv4.2 inventory for this sector. Increased CH4 emissions (up to 30 % compared to the prior) were deduced for the north-eastern parts of Switzerland. This feature was common to most sensitivity inversions, which is a strong indicator that it is a real feature and not an artefact of the transport model and the inversion system. However, it was not possible to assign an unambiguous source process to the region. The observations of the CarboCount-CH network provided invaluable and independent information for the validation of the national bottom-up inventory. Similar systems need to be sustained to provide independent monitoring of future climate agreements.

scholarly journals Statistical mechanics of neocortical interactions: EEG eigenfunctions of short-term memory

2000 ◽  
Vol 23 (3) ◽  
pp. 403-405 ◽  
Author(s):  
Lester Ingber
Keyword(s):  
Statistical Mechanics ◽  
Short Term Memory ◽  
Probability Distributions ◽  
Regional Scale ◽  
Top Down ◽  
Short Term ◽  
Bottom Up ◽  
Term Memory ◽  
Future Studies ◽  
Scalp Eeg

This commentary focuses on how bottom-up neocortical models can be developed into eigenfunction expansions of probability distributions appropriate to describe short-term memory in the context of scalp EEG. The mathematics of eigenfunctions are similar to the top-down eigenfunctions developed by Nunez, despite different physical manifestations. The bottom-up eigenfunctions are at the local mesocolumnar scale, whereas the top-down eigenfunctions are at the global regional scale. Our respective approaches have regions of substantial overlap, and future studies may expand top-down eigenfunctions into the bottom-up eigenfunctions, yielding a model of scalp EEG expressed in terms of columnar states of neocortical processing of attention and short-term memory.

scholarly journals On observational and modelling strategies targeted at regional carbon exchange over continents

2009 ◽  
Vol 6 (10) ◽  
pp. 1949-1959 ◽  
Author(s):  
C. Gerbig ◽  
A. J. Dolman ◽  
M. Heimann
Keyword(s):  
Spatial Scales ◽  
Regional Scale ◽  
Local Scale ◽  
Anthropogenic Sources ◽  
Carbon Exchange ◽  
Top Down ◽  
Bottom Up ◽  
Modelling Strategies ◽  
The Impact ◽  
Up Scaling

Abstract. Estimating carbon exchange at regional scales is paramount to understanding feedbacks between climate and the carbon cycle, but also to verifying climate change mitigation such as emission reductions and strategies compensating for emissions such as carbon sequestration. This paper discusses evidence for a number of important shortcomings of current generation modelling frameworks designed to provide regional scale budgets from atmospheric observations. Current top-down and bottom-up approaches targeted at deriving consistent regional scale carbon exchange estimates for biospheric and anthropogenic sources and sinks are hampered by a number of issues: we show that top-down constraints using point measurements made from tall towers, although sensitive to larger spatial scales, are however influenced by local areas much more strongly than previously thought. On the other hand, classical bottom-up approaches using process information collected at the local scale, such as from eddy covariance data, need up-scaling and validation on larger scales. We therefore argue for a combination of both approaches, implicitly providing the important local scale information for the top-down constraint, and providing the atmospheric constraint for up-scaling of flux measurements. Combining these data streams necessitates quantifying their respective representation errors, which are discussed. The impact of these findings on future network design is highlighted, and some recommendations are given.

scholarly journals Intercomparison of NO<sub><i>x</i></sub> emission inventories over East Asia

2017 ◽  
Author(s):  
Jieying Ding ◽  
Kazuyuki Miyazaki ◽  
Ronald Johannes van der A ◽  
Bas Mijling ◽  
Jun-ichi Kurokawa ◽  
...  
Keyword(s):  
Kalman Filter ◽  
East Asia ◽  
Rural Areas ◽  
Emission Inventory ◽  
Regional Scale ◽  
Policy Support ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Bottom Up ◽  
Temporal Behaviour

Abstract. We compare 9 emission inventories of nitrogen oxides including four satellite-derived NOx inventories and the following bottom-up inventories for East Asia: REAS (Regional Emission inventory in ASia), MEIC (Multi-resolution Emission Inventory for China), CAPSS (Clean Air Policy Support System) and EDGAR (Emissions Database for Global Atmospheric Research). Two of the satellite-derived inventories are estimated by using the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm, which is based on an extended Kalman filter applied to observations from OMI or from GOME-2. The other two are derived with the EnKF algorithm, which is based on an ensemble Kalman Filter applied to observations of multiple species using either the chemical transport model CHASER and MIROC-chem. The temporal behaviour and spatial distribution of the inventories are compared on a national and regional scale. A distinction is also made between urban and rural areas. The intercomparison of all inventories shows good agreement in total NOx emissions over Mainland China, especially for trends, with an average bias of about 20 % for yearly emissions. All the inventories show the typical emission reduction of 10 % during the Chinese New Year and a peak in December. Satellite-derived approaches using OMI show a summer peak due to strong emissions from soil and biomass burning in this season. Biases in NOx emissions and uncertainties in temporal variability increase quickly when the spatial scale decreases. The analyses of the differences show: the importance of using observations from multiple instruments and a high spatial resolution model for the satellite-derived inventories, while for bottom-up inventories, accurate emission factors and activity information are required. The advantage of the satellite derived approach is that the emissions are soon available after observation, while the strength of the bottom-up inventories is that they include detailed information of emissions for each source category.

scholarly journals Trends and inter-annual variability of methane emissions derived from 1979-1993 global CTM simulations

2002 ◽  
Vol 2 (2) ◽  
pp. 249-287 ◽  
Author(s):  
F. Dentener ◽  
M. van Weele ◽  
M. Krol ◽  
S. Houweling ◽  
P. van Velthoven
Keyword(s):  
Transport Model ◽  
Meteorological Data ◽  
Stratospheric Ozone ◽  
Regional Scale ◽  
Methane Emissions ◽  
Anthropogenic Emission ◽  
Top Down ◽  
Bottom Up ◽  
Annual Variability ◽  
The Difference

Abstract. The trend and interannual variability of methane sources are derived from multi-annual simulations of tropospheric photochemistry using a 3D global chemistry-transport model. Our semi-inverse analysis uses the fifteen years (1979 -1993) re-analysis of ECMWF meteorological data and annually varying including photo-chemistry, in conjunction with observed CH4 concentration distributions and trends derived from the NOAA-CMDL surface stations. Dividing the world in four zonal regions, (45-90 N, 0-45 N, 0-45 S; 45-90 S) we find good agreement in each region between (top-down) calculated emission trends from model simulations and (bottom-up) estimated anthropogenic emission trends based on the EDGAR global anthropogenic emission database, which amounts for the period 1979 -1993 2.7 Tg CH4 yr -1. Also the top-down determined total global methane emission compares well with the total of the bottom-up estimates. We use the difference between the bottom-up and top-down determined emission trends to calculate residual emissions. These residual emissions represent the inter-annual variability of the methane emissions. Simulations have been performed in which the year-to-year meteorology, the emissions of ozone precursor gases, and the stratospheric ozone column distribution are either varied, or kept constant. The analyses reveals that the variability of the emissions is of the order of 8 Tg CH4 yr -1, and most likely related to mid- and low-latitude wetland emissions and/or biomass burning. Indeed, a weak correlation is found between the residual emissions and regional scale temperatures.

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