scholarly journals Identifying meteorological drivers of extreme impacts: an application to simulated crop yields

2020 ◽  
Author(s):  
Johannes Vogel ◽  
Pauline Rivoire ◽  
Cristina Deidda ◽  
Leila Rahimi ◽  
Christoph Alexander Sauter ◽  
...  
Keyword(s):  
United States ◽  
Vapour Pressure ◽  
Crop Yields ◽  
Model Performance ◽  
Vapour Pressure Deficit ◽  
Growing Season ◽  
The United States ◽  
Lasso Regression ◽  
Crop Failure ◽  
Grid Points

Abstract. Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve understanding and forecasting. In this study we investigate whether key meteorological drivers of extreme impacts can be identified using Least Absolute Shrinkage and Selection Operator (Lasso) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the APSIM crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply the logistic Lasso regression to predict which weather conditions during the growing season lead to crop failure. We obtain good model performance in Central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields, that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects both between meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the Lasso regression model is a useful tool to automatically detect compound drivers of extreme impacts, and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.

scholarly journals Identifying meteorological drivers of extreme impacts: an application to simulated crop yields

2021 ◽  
Author(s):  
Jakob Zscheischler ◽  
Johannes Vogel ◽  
Pauline Rivoire ◽  
Cristina Deidda ◽  
Leila Rahimi ◽  
...  
Keyword(s):  
United States ◽  
Vapour Pressure ◽  
Crop Yields ◽  
Model Performance ◽  
Vapour Pressure Deficit ◽  
Growing Season ◽  
The United States ◽  
Climate Extreme ◽  
Crop Failure ◽  
Grid Points

<p>Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study we investigate whether key meteorological drivers of extreme impacts can be identified using Least Absolute Shrinkage and Selection Operator (Lasso) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the APSIM crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply Lasso logistic regression to determine which weather conditions during the growing season lead to crop failure.</p><p>We obtain good model performance in Central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields, that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance.</p><p>We conclude that the Lasso regression model is a useful tool to automatically detect compound drivers of extreme impacts, and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.</p>

scholarly journals Identifying meteorological drivers of extreme impacts: an application to simulated crop yields

2021 ◽  
Vol 12 (1) ◽  
pp. 151-172
Author(s):  
Johannes Vogel ◽  
Pauline Rivoire ◽  
Cristina Deidda ◽  
Leila Rahimi ◽  
Christoph A. Sauter ◽  
...  
Keyword(s):  
United States ◽  
Vapour Pressure ◽  
Crop Yields ◽  
Model Performance ◽  
Vapour Pressure Deficit ◽  
Growing Season ◽  
The United States ◽  
Climate Extreme ◽  
Crop Failure ◽  
Grid Points

Abstract. Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study, we investigate whether key meteorological drivers of extreme impacts can be identified using the least absolute shrinkage and selection operator (LASSO) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the Agricultural Production Systems sIMulator (APSIM) crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply LASSO logistic regression to determine which weather conditions during the growing season lead to crop failure. We obtain good model performance in central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields; that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points, the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the LASSO regression model is a useful tool to automatically detect compound drivers of extreme impacts and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.

Identifying compound meteorological drivers of extreme wheat yield loss using Lasso regression

2020 ◽  
Author(s):  
Christoph Sauter ◽  
Cristina Deidda ◽  
Leila Rahimi ◽  
Pauline Rivoire ◽  
Elisabeth Tschumi ◽  
...  
Keyword(s):  
Yield Loss ◽  
Wheat Yield ◽  
Model Performance ◽  
Weather Conditions ◽  
Growing Season ◽  
Lasso Regression ◽  
Yield Losses ◽  
Crop Failure ◽  
Several Variables ◽  
Model Environment

<p>Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. The identification of the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. Here we investigate whether key meteorological drivers of extreme yield loss can be identified using Least Absolute Shrinkage and Selection Operator (Lasso) in a model environment. <br>We use yearly wheat yields as simulated by the APSIM crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth v2.3) under present-day conditions for the Northern Hemisphere. We define extreme yield loss as years with yield below the 5th percentile. We apply logistic Lasso regression to predict whether weather conditions during the growing season lead to crop failure. Lasso selects the most relevant variables from a large set of predictors that best explain the target variable via regularization. Our input variables include monthly averaged values of maximum temperature, vapour pressure deficit and precipitation as well as established extreme event indicators such as maximum and minimum temperature during the growing season, diurnal temperature range, total number of frost days, and maximum five-day precipitation sum.<br>We obtain good model performance in Central Europe and the American Corn Belt, while yield losses in Asian and African regions are less accurately predicted. Model performance and mean wheat yield strongly correlate, i.e. model performance is highest in regions with relatively large mean yield. Based on the selected predictors, we identify regions where crop loss is predominantly influenced by a single variable and regions where it is driven by the interplay of several variables, i.e. compound events. Especially in the Midwest and Eastern regions of the USA, several variables are required to correctly predict yield losses. This illustrates the importance of accounting for the interplay of various weather conditions over the course of the growing season to be able to determine crop yield losses more precisely.<br>We conclude that the Lasso regression is a useful tool to detect the compound drivers of extreme impacts, which can be applied for other impact variables such as fires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts. Furthermore, using the same model environment, the robustness of the identified relationships will be tested in a climate change context.</p>

scholarly journals Development of a semi-parametric PAR (Photosynthetically Active Radiation) partitioning model for the United States, version 1.0

2014 ◽  
Vol 7 (5) ◽  
pp. 2477-2484 ◽  
Author(s):  
J. C. Kathilankal ◽  
T. L. O'Halloran ◽  
A. Schmidt ◽  
C. V. Hanson ◽  
B. E. Law
Keyword(s):  
United States ◽  
Elevation Angle ◽  
Model Performance ◽  
Diffuse Radiation ◽  
The United States ◽  
Polynomial Model ◽  
Coefficient Of Determination ◽  
Data Set ◽  
Cubic Polynomial ◽  
Solar Elevation Angle

Abstract. A semi-parametric PAR diffuse radiation model was developed using commonly measured climatic variables from 108 site-years of data from 17 AmeriFlux sites. The model has a logistic form and improves upon previous efforts using a larger data set and physically viable climate variables as predictors, including relative humidity, clearness index, surface albedo and solar elevation angle. Model performance was evaluated by comparison with a simple cubic polynomial model developed for the PAR spectral range. The logistic model outperformed the polynomial model with an improved coefficient of determination and slope relative to measured data (logistic: R2 = 0.76; slope = 0.76; cubic: R2 = 0.73; slope = 0.72), making this the most robust PAR-partitioning model for the United States currently available.

Effects of ozone and climate on historical (1980-2015) crop yields in the United States: Implication for mid-21st century projection

2021 ◽  
Author(s):  
Yabin Da ◽  
Yangyang Xu ◽  
Bruce McCarl
Keyword(s):  
Climate Change ◽  
United States ◽  
Winter Wheat ◽  
Crop Yields ◽  
Time Window ◽  
Surface Ozone ◽  
The United States ◽  
Ozone Exposure ◽  
Future Climate Change ◽  
Ozone Pollution

<p>Surface ozone pollution has been proven to impose significant damages on crops. However, the quantification of the damages was extensively derived from chamber experiments, which is not representative of actual results in farm fields due to the limitations of spatial scale, time window, etc. In this work, we attempt to empirically fill this gap using county-level data in the United States from 1980 to 2015. We explore ozone impacts on corn, soybeans, spring wheat, winter wheat, barley, cotton, peanuts, rice, sorghum, and sunflower. We also incorporate a variety of climate variables to investigate potential ozone-climate interactions. More importantly, we shed light on future yield consequences of ozone and climate change individually and jointly under a moderate warming scenario. Our findings suggest significant negative impacts of ozone exposure for eight of the ten crops we examined, excepting barley and winter wheat, which contradicts experimental results. The average annual damages were estimated at $6.03 billion (in 2015 U.S. dollar) from 1980 to 2015. We also find rising temperatures tend to worsen ozone damages while water supply would mitigate that. Finally, elevated ozone driven by future climate change would cause much smaller damages than the direct effects of climate change itself.</p>

scholarly journals Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance

2015 ◽  
Vol 19 (1) ◽  
pp. 209-223 ◽  
Author(s):  
A. J. Newman ◽  
M. P. Clark ◽  
K. Sampson ◽  
A. Wood ◽  
L. E. Hay ◽  
...  
Keyword(s):  
United States ◽  
Seasonal Cycle ◽  
Model Performance ◽  
The United States ◽  
Hydrologic Model ◽  
Low Flow ◽  
Reference Level ◽  
Hydrologic Response ◽  
Data Set ◽  
Wide Range

Abstract. We present a community data set of daily forcing and hydrologic response data for 671 small- to medium-sized basins across the contiguous United States (median basin size of 336 km2) that spans a very wide range of hydroclimatic conditions. Area-averaged forcing data for the period 1980–2010 was generated for three basin spatial configurations – basin mean, hydrologic response units (HRUs) and elevation bands – by mapping daily, gridded meteorological data sets to the subbasin (Daymet) and basin polygons (Daymet, Maurer and NLDAS). Daily streamflow data was compiled from the United States Geological Survey National Water Information System. The focus of this paper is to (1) present the data set for community use and (2) provide a model performance benchmark using the coupled Snow-17 snow model and the Sacramento Soil Moisture Accounting Model, calibrated using the shuffled complex evolution global optimization routine. After optimization minimizing daily root mean squared error, 90% of the basins have Nash–Sutcliffe efficiency scores ≥0.55 for the calibration period and 34% ≥ 0.8. This benchmark provides a reference level of hydrologic model performance for a commonly used model and calibration system, and highlights some regional variations in model performance. For example, basins with a more pronounced seasonal cycle generally have a negative low flow bias, while basins with a smaller seasonal cycle have a positive low flow bias. Finally, we find that data points with extreme error (defined as individual days with a high fraction of total error) are more common in arid basins with limited snow and, for a given aridity, fewer extreme error days are present as the basin snow water equivalent increases.

scholarly journals Proposed virulence-associated genes of Streptococcus suis isolates from the United States serve as predictors of pathogenicity

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
April A. Estrada ◽  
Marcelo Gottschalk ◽  
Aaron Rendahl ◽  
Stephanie Rossow ◽  
Lacey Marshall-Lund ◽  
...  
Keyword(s):  
United States ◽  
Streptococcus Suis ◽  
The United States ◽  
Limited Information ◽  
Lasso Regression ◽  
Chi Square ◽  
Pathogenic Potential ◽  
Multiple Production ◽  
Pan Genome ◽  
Protein Factor

Abstract Background There is limited information on the distribution of virulence-associated genes (VAGs) in U.S. Streptococcus suis isolates, resulting in little understanding of the pathogenic potential of these isolates. This lack also reduces our understanding of the epidemiology associated with S. suis in the United States and thus affects the efficiency of control and prevention strategies. In this study we applied whole genome sequencing (WGS)-based approaches for the characterization of S. suis and identification of VAGs. Results Of 208 S. suis isolates classified as pathogenic, possibly opportunistic, and commensal pathotypes, the genotype based on the classical VAGs (epf, mrp, and sly encoding the extracellular protein factor, muramidase-release protein, and suilysin, respectively) was identified in 9% (epf+/mrp+/sly+) of the pathogenic pathotype. Using the chi-square test and LASSO regression model, the VAGs ofs (encoding the serum opacity factor) and srtF (encoding sortase F) were selected out of 71 published VAGs as having a significant association with pathotype, and both genes were found in 95% of the pathogenic pathotype. The ofs+/srtF+ genotype was also present in 74% of ‘pathogenic’ isolates from a separate validation set of isolates. Pan-genome clustering resulted in the differentiation of a group of isolates from five swine production companies into clusters corresponding to clonal complex (CC) and virulence-associated (VA) genotypes. The same CC-VA genotype patterns were identified in multiple production companies, suggesting a lack of association between production company, CC, or VA genotype. Conclusions The proposed ofs and srtF genes were stronger predictors for differentiating pathogenic and commensal S. suis isolates compared to the classical VAGs in two sets of U.S. isolates. Pan-genome analysis in combination with metadata (serotype, ST/CC, VA genotype) was illustrated to be a valuable subtyping tool to describe the genetic diversity of S. suis.

scholarly journals Divergence in Photoperiod Responses of a Classical Biological Control Agent, Galerucella calmariensis (Coleoptera: Chrysomelidae), Across a Climatic and Latitudinal Gradient

2020 ◽  
Author(s):  
Tyson Wepprich ◽  
Fritzi S Grevstad
Keyword(s):  
United States ◽  
Biological Control ◽  
Biological Control Agent ◽  
Growing Season ◽  
Classical Biological Control ◽  
The United States ◽  
Day Length ◽  
Season Length ◽  
Galerucella Calmariensis ◽  
Growing Season Length

Abstract A key knowledge gap in classical biological control is to what extent insect agents evolve to novel environments. The introduction of biological control agents to new photoperiod regimes and climates may disrupt the coordination of diapause timing that evolved to the growing season length in the native range. We tested whether populations of Galerucella calmariensis L. have evolved in response to the potential mismatch of their diapause timing since their intentional introduction to the United States from Germany in the 1990s. Populations collected from 39.4° to 48.8° latitude in the western United States were reared in growth chambers to isolate the effects of photoperiod on diapause induction and development time. For all populations, shorter day lengths increased the proportion of beetles that entered diapause instead of reproducing. The critical photoperiods, or the day length at which half of a population diapauses, differed significantly among the sampled populations, generally decreasing at lower latitudes. The latitudinal trend reflects changes in growing season length, which determines the number of generations possible, and in local day lengths, at the time when beetles are sensitive to this cue. Development times were similar across populations, with one exception, and did not vary with photoperiod. These results show that there was sufficient genetic variation from the two German source populations to evolve different photoperiod responses across a range of environmental conditions. This study adds to the examples of rapid evolution of seasonal adaptations in introduced insects.

scholarly journals Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance

2014 ◽  
Vol 11 (5) ◽  
pp. 5599-5631
Author(s):  
A. J. Newman ◽  
M. P. Clark ◽  
K. Sampson ◽  
A. Wood ◽  
L. E. Hay ◽  
...  
Keyword(s):  
United States ◽  
Seasonal Cycle ◽  
Model Performance ◽  
The United States ◽  
Hydrologic Model ◽  
Low Flow ◽  
Reference Level ◽  
Hydrologic Response ◽  
Daily Streamflow ◽  
Wide Range

Abstract. We present a community dataset of daily forcing and hydrologic response data for 671 small- to medium-sized basins across the contiguous United States (median basin size of 336 km2) that spans a very wide range of hydroclimatic conditions. Areally averaged forcing data for the period 1980–2010 was generated for three basin delineations – basin mean, Hydrologic Response Units (HRUs) and elevation bands – by mapping the daily, 1 km gridded Daymet meteorological dataset to the sub-basin and basin polygons. Daily streamflow data was compiled from the United States Geological Survey National Water Information System. The focus of this paper is to (1) present the dataset for community use; and (2) provide a model performance benchmark using the coupled Snow-17 snow model and the Sacramento Soil Moisture Accounting conceptual hydrologic model, calibrated using the Shuffled Complex Evolution global optimization routine. After optimization minimizing daily root mean squared error, 90% of the basins have Nash–Sutcliffe Efficiency scores > 0.55 for the calibration period. This benchmark provides a reference level of hydrologic model performance for a commonly used model and calibration system, and highlights some regional variations in model performance. For example, basins with a more pronounced seasonal cycle generally have a negative low flow bias, while basins with a smaller seasonal cycle have a positive low flow bias. Finally, we find that data points with extreme error (defined as individual days with a high fraction of total error) are more common in arid basins with limited snow, and, for a given aridity, fewer extreme error days are present as basin snow water equivalent increases.

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