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| Author |
Sanna, M.; Acutis, M.; Bellocchi, G. |
| Title |
Interrelationship between evaluation metrics to assess agro-ecological models |
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Report |
| Year |
2014 |
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FACCE MACSUR Reports |
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3 |
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Sp3-5 |
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| Abstract |
When evaluating the performances of simulation models, the perception of the quality of the outputs may depend on the statistics used to compare simulated and observed data. In order to have a comprehensive understanding of model performance, the use of a variety of metrics is generally advocated. However, since they may be correlated, the use of two or more metrics may convey the same information, leading to redundancy. This study intends to investigate the interrelationship between evaluation metrics, with the aim of identifying the most useful set of indicators, for assessing simulation performance. Our focus is on agro-ecological modelling. Twenty-three performance indicators were selected to compare simulated and observed data of four agronomic and meteorological variables: above-ground biomass, leaf area index, hourly air relative humidity and daily solar radiation. Indicators were calculated on large data sets, collected to effectively apply correlation analysis techniques. For each variable, the interrelationship between each pair of indicators was evaluated, by computing the Spearman’s rank correlation coefficient. A definition of “stable correlation” was proposed, based on the test of heterogeneity, allowing to assess whether two or more correlation coefficients are equal. An optimal subset of indicators was identified, striking a balance between number of indicators, amount of provided information and information redundancy. They are: Index of Agreement, Squared Bias, Root Mean Squared Relative Error, Pattern Index, Persistence Model Efficiency and Spearman’s Correlation Coefficient. The present study was carried out in the context of CropM-LiveM cross-cutting activities of MACSUR knowledge hub. No Label |
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MA @ admin @ |
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2222 |
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| Author |
Bellocchi, G.; Sándor, R. |
| Title |
Model intercomparison |
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Report |
| Year |
2015 |
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FACCE MACSUR Reports |
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6 |
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D-L2.4 |
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This deliverable focuses on some illustrative results obtained with different grassland- specific, grassland adapted crop and dynamic vegetation models selected out of the first list of models compiled in D-L2.1.1 to simulate biomass and flux data from grassland sites in Europe and peri-Mediterranean regions (D-L2.1.1 and D-L2.1.2). Results from uncalibrated simulations were documented in the D-L2.3 report as a blind exercise. Some model improvements are emphasized in this report due to the higher information level of the model calibrations. The complete set of results will include simulations from uncalibrated and calibrated models. No Label |
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MA @ admin @ |
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2108 |
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Cammarano, D.; Rivington, M.; Matthews, K.; B,; Bellocchi, G. |
| Title |
Estimates of crop responses to climate change with quantified ranges of uncertainty |
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Report |
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2015 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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6 |
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Pages |
D-C4.1.3 |
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In estimating responses of crops to future climate realisations, it is necessary to understand and differentiate between the sources of uncertainty in climate models and how these lead to errors in estimating the past climate and biases in future projections, and how these affect crop model estimates. This paper investigates the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies. This is illustrated by simulating spring barley with three crop models run using site-specific observed, original (50•50 km) and bias corrected downscaled (site-specific) hindcast (1960-1990) weather data from the HadRM3 Regional Climate Model (RCM). Original and bias corrected downscaled weather data were evaluated against the observed data. The comparisons made between the crop models were in the light of lessons learned from this data evaluation. Though the bias correction downscaling method improved the match between observed and hindcast data, this did not always translate into better matching of crop models estimates. At four sites the original HadRM3 data produced near identical mean simulated yield values as from the observed weather data, despite differences in the weather data, giving a situation of ‘right results for the wrong reasons’. This was likely due to compensating errors in the input weather data and non-linearity in crop models processes, making interpretation of results problematic. Overall, bias correction downscaling improved the quality of simulated outputs. Understanding how biases in climate data manifest themselves in crop models gives greater confidence in the utility of the estimates produced using downscaled future climate projections. The results indicate implications on how future projections of climate change impacts are interpreted. Fundamentally, considerable care is required in determining the impact weather data sources have in climate change impact and adaptation studies, whether from individual models or ensembles. No Label |
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MA @ admin @ |
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2098 |
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Eza, U.; Shtiliyanova, A.; Borras, D.; Bellocchi, G.; Carrère, P.; Martin, R. |
| Title |
An open platform to assess vulnerabilities to climate change: An application to agricultural systems |
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Journal Article |
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2015 |
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Ecological Informatics |
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Ecological Informatics |
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30 |
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389-396 |
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climate change; grasslands; modeling platform; vulnerability assessment; pasture simulation-model; software component; solar-radiation; crop production; change impacts; adaptation; indicator; makers |
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Numerous climate futures are now available from global climate models. Translation of climate data such as precipitation and temperatures into ecologically meaningful outputs for managers and planners is the next frontier. We describe a model-based open platform to assess vulnerabilities of agricultural systems to climate change on pixel-wise data. The platform includes a simulation modeling engine and is suited to work with NetCDF format of input and output files. In a case study covering a region (Auvergne) in the Massif Central of France, the platform is configured to characterize climate (occurrence of arid conditions in historical and projected climate records), soils and human management, and is then used to assess the vulnerability to climate change of grassland productivity (downscaled to a fine scale). We demonstrate how using climate time series, and process-based simulations vulnerabilities can be defined at fine spatial scales relevant to farmers and land managers, and can be incorporated into management frameworks. (C) 2015 Elsevier B.V. All rights reserved. |
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1574-9541 |
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CropM LiveM, ft_macsur |
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no |
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MA @ admin @ |
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4708 |
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Ben Touhami, H.; Bellocchi, G. |
| Title |
Bayesian calibration of the Pasture Simulation model (PaSim) to simulate European grasslands under water stress |
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Journal Article |
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2015 |
Publication |
Ecological Informatics |
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Ecological Informatics |
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30 |
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356-364 |
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Bayesian calibration framework; Grasslands; Pasture Simulation model; (PaSim); integrated assessment models; chain monte-carlo; climate-change; computation; impacts; vulnerability; likelihoods; france |
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As modeling becomes a more widespread practice in the agro-environmental sciences, scientists need reliable tools to calibrate models against ever more complex and detailed data. We present a generic Bayesian computation framework for grassland simulation, which enables parameter estimation in the Bayesian formalism by using Monte Carlo approaches. We outline the underlying rationale, discuss the computational issues, and provide results from an application of the Pasture Simulation model (PaSim) to three European grasslands. The framework was suited to investigate the challenging problem of calibrating complex biophysical models to data from altered scenarios generated by precipitation reduction (water stress conditions). It was used to infer the parameters of manipulated grassland systems and to assess the gain in uncertainty reduction by updating parameter distributions using measurements of the output variables. |
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1574-9541 |
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CropM, LiveM, ft_macsur |
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MA @ admin @ |
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4697 |
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