| Home | << 1 2 3 4 5 6 7 8 9 10 >> [11–20] |
|
Cammarano, D., Rivington, M., Matthews, K., B,, & Bellocchi, G. (2015). Estimates of crop responses to climate change with quantified ranges of uncertainty (Vol. 6).
Abstract: 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
|
|
Ewert, F., & al, E. (2015). Uncertainties in Scaling-Up Crop Models for Large-Area Climate Change Impact Assessments (Vol. 6).
Abstract: Problems related to food security and sustainable development are complex (Ericksenet al., 2009) and require consideration of biophysical, economic, political, and social factors, as well as their interactions, at the level of farms, regions, nations, and globally. While the solution to such societal problems may be largely political, there is a growing recognition of the need for science to provide sound information to decision-makers (Meinke et al., 2009). Achieving this, particularly in light of largely uncertain future climate and socio-economic changes, will necessitate integrated assessment approaches and appropriate integrated assessment modeling (IAM) tools to perform them. Recent (Ewertet al., 2009; van Ittersumet al., 2008) and ongoing (Rosenzweiget al., 2013) studies have tried to advance the integrated use of biophysical and economic models to represent better the complex interactions in agricultural systems that largely determine food supply and sustainable resource use. Nonetheless, the challenges for model integration across disciplines are substantial and range from methodological and technical details to an often still-weak conceptual basis on which to ground model integration (Ewertet al., 2009; Janssenet al., 2011). New generations of integrated assessment models based on well-understood, general relationships that are applicable to different agricultural systems across the world are still to be developed. Initial efforts are underway towards this advancement (Nelsonet al., 2014; Rosenzweiget al., 2013). Together with economic and climate models, crop models constitute an essential model group in IAM for large-area cropping systems climate change impact assessments. However, in addition to challenges associated with model integration, inadequate representation of many crops and crop management systems, as well as a lack of data for model initialization and calibration, limit the integration of crop models with climate and economic models (Ewertet al., 2014). A particular obstacle is the mismatch between the temporal and spatial scale of input/output variables required and delivered by the various models in the IAM model chain. Crop models are typically developed, tested, and calibrated for field-scale application (Booteet al., 2013; see also Part 1, Chapter 4 in this volume) and short time-series limited to one or few seasons. Although crop models are increasingly used for larger areas and longer time-periods (Bondeauet al., 2007; Deryng et al., 2011; Elliottet al., 2014) rigorous evaluation of such applications is pending. Among the different sources of uncertainty related to climate and soil data, model parameters, and structure, the uncertainty from methods used to scale-up crop models has received little attention, though recent evaluations indicate that upscaling of crop models for climate change impact assessment and the resulting errors and uncertainties deserve attention in order to advance crop modeling for climate change assessment (Ewertet al., 2014; R¨ otteret al., 2011). This reality is now reflected in the scientific agendas of new international research projects and programs such as the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweiget al., 2013) and MACSUR (MACSUR, 2014). In this chapter, progress in evaluation of scaling methods with their related uncertainties is reviewed. Specific emphasis is on examining the results of systematic studies recently established in AgMIP and MACSUR. Main features of the respective simulation studies are presented together with preliminary results. Insights from these studies are summarized and conclusions for further work are drawn. No Label
|
|
Janssen, S. (2015). Inventory of data and data sharing mechanism for model linking and scaling exercises (Vol. 6).
Abstract: This deliverable lays out the work as done as part of MACSUR CropM on ‘Inventory of data and data sharing mechanism for model linking and scaling exercises’. In summary not much work was done, as it was found that there was not real demand for the activity in this task. The task in itself was servicing the other work as part of MACSUR, and as the service was not in demand, it was decided to take a low profile and wait for specific requests by partners for data in relation to model linking and upscaling. No Label
|
|
Hoffmann, H., & Ewert, F. (2015). Review on scaling methods for crop models (Vol. 6).
Abstract: Agricultural systems cover a range of organisational levels and spatial and temporal scales. To capture multi-scale problems of sustainable management in agricultural systems, Integrated assessment modelling (IAM) including crop models is often applied which require methods of scale changes (scaling methods). Scaling methods, however, are often not well understood and are therefore sources of uncertainty in models. The present report summarizes scaling methods as developed and applied in recent years (e.g. in SEAMLESS-IF and MACSUR) in a classification scheme based on Ewert et al. (2011, 2006). Scale changes refer to different spatial, temporal and functional scales with changes in extent, resolution, and coverage rate. Accordingly, there are a number of different scaling methods that can include data extrapolation, aggregation and disaggregation, sampling and nested simulation. Comparative quantitative analysis of alternative scaling methods are currently under way and covered by other reports in MACSUR and several publications (e.g. Ewert et al., 2014; Hoffmann et al., 2015; Zhao et al., 2015). The following classification of scaling methods assists to structure such analysis. Improved integration of scaling methods in IAM may help to overcome modelling limitations that are related to high data demand, complexity of models and scaling methods considered. No Label
|
|
Janssen, S., Hansen, J. G., Jorgensen, J., & Jørgensen, M. S. (2015). Operational database for storing and extracting data (Vol. 6).
Abstract: This deliverable lays out the work as done as part of MACSUR CropM on data, with the focus on improving data management and have shared data curation for future use. The issue was tackled with help from the MACSUR central hub coordination in the form of Jason Jargenson from University of Reading. The data management as proposed and implemented in this deliverable is very much a bottom up process, in which partners in a meeting in Spring 2013 in Aarhus investigated the best way forward for data management across activities in CropM.As a follow up to this, the work was mainly divided in three parts: 1. The Open Data Journal for Agricultural Research, mainly focused on long term data archival and citation of data sets, as input and outputs to the modelling work, as part of MACSUR, lead by Wageningen UR 2. The Geonetwork data catalog hosted at Aarhus Universitet, that allows for operational access and storage of data sets as part of the ongoing work, also for restricted access of the consortium, and as a first step to visualization, lead by Aarhus Universitet. 3. The work on rating data sets, that provides a tool for improving data set access in an early phase for connecting them to models, lead by Reading University. At the end of the deliverable some next steps are giving for data activities in the context of AgMIP and beyond. No Label
|