scholarly journals Assessing uncertainty and complexity in regional-scale crop model simulations

2017 ◽  
Vol 88 ◽  
pp. 84-95 ◽  
Author(s):  
Julian Ramirez-Villegas ◽  
Ann-Kristin Koehler ◽  
Andrew J. Challinor
Keyword(s):  
Regional Scale ◽  
Crop Model ◽  
Model Simulations

scholarly journals Implication of methodological uncertainties for mid-Holocene sea surface temperature reconstructions

2014 ◽  
Vol 10 (6) ◽  
pp. 2237-2252 ◽  
Author(s):  
I. Hessler ◽  
S. P. Harrison ◽  
M. Kucera ◽  
C. Waelbroeck ◽  
M.-T. Chen ◽  
...  
Keyword(s):  
Climate Model ◽  
Time Window ◽  
Planktonic Foraminifera ◽  
Regional Scale ◽  
Time Frame ◽  
Sea Surface ◽  
Model Simulations ◽  
Climate Model Simulations ◽  
Analytical Approaches ◽  
Baseline Climate

Abstract. We present and examine a multi-sensor global compilation of mid-Holocene (MH) sea surface temperatures (SST), based on Mg/Ca and alkenone palaeothermometry and reconstructions obtained using planktonic foraminifera and organic-walled dinoflagellate cyst census counts. We assess the uncertainties originating from using different methodologies and evaluate the potential of MH SST reconstructions as a benchmark for climate-model simulations. The comparison between different analytical approaches (time frame, baseline climate) shows the choice of time window for the MH has a negligible effect on the reconstructed SST pattern, but the choice of baseline climate affects both the magnitude and spatial pattern of the reconstructed SSTs. Comparison of the SST reconstructions made using different sensors shows significant discrepancies at a regional scale, with uncertainties often exceeding the reconstructed SST anomaly. Apparent patterns in SST may largely be a reflection of the use of different sensors in different regions. Overall, the uncertainties associated with the SST reconstructions are generally larger than the MH anomalies. Thus, the SST data currently available cannot serve as a target for benchmarking model simulations. Further evaluations of potential subsurface and/or seasonal artifacts that may contribute to obscure the MH SST reconstructions are urgently needed to provide reliable benchmarks for model evaluations.

Recent past and future patterns of the Etesian winds based on regional scale climate model simulations

2013 ◽  
Vol 42 (7-8) ◽  
pp. 1819-1836 ◽  
Author(s):  
Christina Anagnostopoulou ◽  
Prodromos Zanis ◽  
Eleni Katragkou ◽  
Ioannis Tegoulias ◽  
Konstantia Tolika
Keyword(s):  
Climate Model ◽  
Regional Scale ◽  
Recent Past ◽  
Model Simulations ◽  
Etesian Winds ◽  
Climate Model Simulations

scholarly journals Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs

2007 ◽  
Vol 34 ◽  
pp. 211-222 ◽  
Author(s):  
GA Baigorria ◽  
JW Jones ◽  
D Shin ◽  
A Mishra ◽  
JJ O’Brien
Keyword(s):  
Circulation Model ◽  
Crop Model ◽  
Model Simulations ◽  
Regional Circulation

scholarly journals Surface Mass Balance of the Antarctic Ice Sheet and its link with surface temperature change in model simulations and reconstructions

2019 ◽  
Author(s):  
Quentin Dalaiden ◽  
Hugues Goosse ◽  
François Klein ◽  
Jan T. M. Lenaerts ◽  
Max Holloway ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Mass Balance ◽  
Regional Scale ◽  
Ice Sheet ◽  
Surface Mass Balance ◽  
Antarctic Ice Sheet ◽  
Surface Mass ◽  
Model Simulations ◽  
Surface Temperatures ◽  
The Antarctic

Abstract. Improving our knowledge of the temporal and spatial variability of the Antarctic Ice Sheet (AIS) Surface Mass Balance (SMB) is crucial to reduce the uncertainties of past, present and future Antarctic contributions to sea level rise. Here, we show that Global Climate Models (GCMs) can reproduce the present-day (1979–2005) AIS SMB and the temporal variations over the last two centuries. An examination of the surface temperature–SMB relationship in model simulations demonstrates a strong link between the two. Reconstructions based on ice cores display a weaker relationship, indicating a model-data discrepancy that may be due to model biases or to the non-climatic noise present in the records. We find that, on the regional scale, the modelled temperature-SMB relationship is stronger than the relationship between δ18O-temperature. This suggests that SMB data can be used to reconstruct past surface temperatures. Using this finding, we assimilate isotope-enabled model SMB and δ18O output with ice-core observations, to generate a new surface temperature reconstruction. Although an independent evaluation of the skill is difficult because of the short observational time series, this new reconstruction outperforms the previous reconstructions for the continental-mean temperature that were based on δ18O alone with a linear correlation coefficient with the observed surface temperatures (1958–2010 CE) of 0.73. The improvement is largest for the East Antarctic region, where the uncertainties are particularly large. Finally, we provide a spatial SMB reconstruction of the AIS over the last two centuries showing 1) large variability in SMB trends at regional scale; and 2) a large SMB increase (0.82 Gt year−2) in West Antarctica over 1957–2000 while at the same time, East Antarctica has experienced a large SMB decrease (−3.3 Gt year−2), which is consistent with a recent reconstruction.

Quantification of Yield, Water, and Carbon budget at intra-field scale using the AgriCarbon-EO tool

2021 ◽  
Author(s):  
Ahmad Al Bitar ◽  
Taeken Wijmer ◽  
Ludovic Arnaud ◽  
Remy Fieuzal ◽  
Gaetan Pique ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Water Budget ◽  
Carbon Budget ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Transfer Model ◽  
Sensing Data ◽  
The Impact

<p>Achieving the United Nations Sustainable Development Goal 2 that addresses food security and sustainable agriculture requires the promotion of readily transferable and scalable agronomical solutions. The combination of high-resolution remote sensing data, field information, and physical models is identified as a robust way of answering this requirement.  Here, we present the AgriCarbon-EO tool, a decision support system that provides the yield, biomass, water and carbon budget components of agricultural fields at a 10m resolution and at a regional scale. The tool assimilates high resolution optical remote sensing data from Copernicus Sentinel-2 satellites into a  radiative transfer model and a crop model. First, the application of a spatial Bayesian retrieval approach to the PROSAIL radiative transfer model provides Leaf Area Index (LAI) with its associated uncertainty. Second, LAI is assimilated into the SAFYE-CO2 crop model using a temporal Bayesian retrieval that enables the calculation of the yield, biomass, carbon and water budgets components with their associated uncertainties. In addition to remote sensing data, input datasets of crop types, weather and soil data are used to constrain the system. The concise weather data is provided from local weather stations or weather forecasts and is used to force the crop model (SAFYE-CO2) dynamics. The soil data are used in two folds. First to better parametrize the soil emissions in the radiative model retrievals and second to parametrise the water infiltration in the soil module of the crop model. The AgriCarbon-EO tool has been optimized to enable the computation of the yield, carbon, and water budget at high spatial resolution (10m) and large scale (100km2). The model is applied over the South-West of France covered by 3 Sentinel-2 tiles for major crops (wheat, maize,  sunflower). The outputs are validated over experimental plots for biomass, yield, soil moisture, and CO2 fluxes located all in the South-West of France. The experimental sites include the FR-AUR and FR-LAM ICOS sites and 22 cropland fields (biomass sampling). The validation exercise is done for the 2017-2018 and 2019-2020 cultural years. We show the added value of the use of high resolution in driving the crop model to take into account the impact of complex processes that are embedded in the LAI signal like vegetation water stress, disease, and agricultural practices. We show that the system is capable of providing the yield, carbon, and water budget of major crops accurately.  At the regional scale, we give global estimates of the carbon budget, water needs, and yields per crop type. We present the impact of intra-plot heterogeneity in the estimation of yield and the annual carbon and water budget showing the added value for high-resolution intra-plot modeling.</p>

scholarly journals Streamflow trends in Europe: evidence from a dataset of near-natural catchments

2010 ◽  
Vol 14 (12) ◽  
pp. 2367-2382 ◽  
Author(s):  
K. Stahl ◽  
H. Hisdal ◽  
J. Hannaford ◽  
L. M. Tallaksen ◽  
H. A. J. van Lanen ◽  
...  
Keyword(s):  
Large Scale ◽  
Regional Scale ◽  
Low Flow ◽  
Scale Model ◽  
Model Simulations ◽  
Continental Scale ◽  
Low Flows ◽  
Spatial Coverage ◽  
Monthly Streamflow ◽  
Streamflow Trends

Abstract. Streamflow observations from near-natural catchments are of paramount importance for detection and attribution studies, evaluation of large-scale model simulations, and assessment of water management, adaptation and policy options. This study investigates streamflow trends in a newly-assembled, consolidated dataset of near-natural streamflow records from 441 small catchments in 15 countries across Europe. The period 1962–2004 provided the best spatial coverage, but analyses were also carried out for longer time periods (with fewer stations), starting in 1932, 1942 and 1952. Trends were calculated by the slopes of the Kendall-Theil robust line for standardized annual and monthly streamflow, as well as for summer low flow magnitude and timing. A regionally coherent picture of annual streamflow trends emerged, with negative trends in southern and eastern regions, and generally positive trends elsewhere. Trends in monthly streamflow for 1962–2004 elucidated potential causes for these changes, as well as for changes in hydrological regimes across Europe. Positive trends were found in the winter months in most catchments. A marked shift towards negative trends was observed in April, gradually spreading across Europe to reach a maximum extent in August. Low flows have decreased in most regions where the lowest mean monthly flow occurs in summer, but vary for catchments which have flow minima in winter and secondary low flows in summer. The study largely confirms findings from national and regional scale trend analyses, but clearly adds to these by confirming that these tendencies are part of coherent patterns of change, which cover a much larger region. The broad, continental-scale patterns of change are mostly congruent with the hydrological responses expected from future climatic changes, as projected by climate models. The patterns observed could hence provide a valuable benchmark for a number of different studies and model simulations.

Marker-based crop model-assisted ideotype design to improve avoidance of abiotic stress in bread wheat

2020 ◽  
Author(s):  
Matthieu Bogard ◽  
Delphine Hourcade ◽  
Benoit Piquemal ◽  
David Gouache ◽  
Jean-Charles Deswartes ◽  
...  
Keyword(s):  
Climate Change ◽  
Heat Stress ◽  
Stem Elongation ◽  
Heading Date ◽  
Crop Model ◽  
Weather Data ◽  
Prediction Errors ◽  
Model Simulations ◽  
Stress Avoidance ◽  
Climate Analysis

Abstract Wheat phenology allows escape from seasonal abiotic stresses including frosts and high temperatures, the latter being forecast to increase with climate change. The use of marker-based crop models to identify ideotypes has been proposed to select genotypes adapted to specific weather and management conditions and anticipate climate change. In this study, a marker-based crop model for wheat phenology was calibrated and tested. Climate analysis of 30 years of historical weather data in 72 locations representing the main wheat production areas in France was performed. We carried out marker-based crop model simulations for 1019 wheat cultivars and three sowing dates, which allowed calculation of genotypic stress avoidance frequencies of frost and heat stress and identification of ideotypes. The phenology marker-based crop model allowed prediction of large genotypic variations for the beginning of stem elongation (GS30) and heading date (GS55). Prediction accuracy was assessed using untested genotypes and environments, and showed median genotype prediction errors of 8.5 and 4.2 days for GS30 and GS55, respectively. Climate analysis allowed the definition of a low risk period for each location based on the distribution of the last frost and first heat days. Clustering of locations showed three groups with contrasting levels of frost and heat risks. Marker-based crop model simulations showed the need to optimize the genotype depending on sowing date, particularly in high risk environments. An empirical validation of the approach showed that it holds good promises to improve frost and heat stress avoidance.

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