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.

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 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.

Multimodel Estimates of Atmospheric Response to Modes of SST Variability and Implications for Droughts

2010 ◽  
Vol 23 (16) ◽  
pp. 4327-4341 ◽  
Author(s):  
Philip J. Pegion ◽  
Arun Kumar
Keyword(s):  
United States ◽  
Climate Variability ◽  
General Circulation ◽  
Dominant Role ◽  
Circulation Model ◽  
The United States ◽  
Precipitation Variability ◽  
Climate Extreme ◽  
Central Pacific ◽  
Sst Variability

Abstract A set of idealized global model experiments was performed by several modeling centers as part of the Drought Working Group of the U.S. Climate Variability and Predictability component of the World Climate Research Programme (CLIVAR). The purpose of the experiments was to assess the role of the leading modes of sea surface temperature (SST) variability on the climate over the continents, with particular emphasis on the influence of SSTs on surface climate variability and droughts over the United States. An analysis based on several models gives more creditability to the results since it relies on the assessment of impacts that are robust across different models. Coordinated atmospheric general circulation model (AGCM) simulations forced with three modes of SST variability were analyzed. The results show that the SST-forced precipitation variability over the central United States is dominated by the SST mode with maximum loading in the central Pacific Ocean. The SST mode with loading in the Atlantic Ocean, and a mode that is dominated by trends in SSTs, lead to a smaller response. Based on the response to the idealized SSTs, the precipitation response for the twentieth century was also reconstructed. A comparison with the Atmospheric Model Intercomparison Project (AMIP) simulations forced with the observed SSTs illustrates that the reconstructed precipitation variability was similar to the one in the AMIP simulations, further supporting the conclusion that the SST modes identified in the present analysis play a dominant role in the precipitation variability over the United States. One notable exception is the Dust Bowl of the 1930s, and further analysis regarding this major climate extreme is discussed.

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.

scholarly journals COVID-19 scenarios for the United States

2020 ◽  
Author(s):  
◽  
Simon I Hay
Keyword(s):  
United States ◽  
Model Performance ◽  
State Level ◽  
The United States ◽  
Economic Damage ◽  
Reference Scenario ◽  
Level Model ◽  
Per Capita ◽  
Novel Coronavirus ◽  
The Impact

The United States (US) has not been spared in the ongoing pandemic of novel coronavirus disease. COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to cause death and disease in all 50 states, as well as significant economic damage wrought by the non-pharmaceutical interventions (NPI) adopted in attempts to control transmission. We use a deterministic, Susceptible, Exposed, Infectious, Recovered (SEIR) compartmental framework to model possible trajectories of SARS-CoV-2 infections and the impact of NPI at the state level. Model performance was tested against reported deaths from 01 February to 04 July 2020. Using this SEIR model and projections of critical driving covariates (pneumonia seasonality, mobility, testing rates, and mask use per capita), we assessed some possible futures of the COVID-19 pandemic from 05 July through 31 December 2020. We explored future scenarios that included feasible assumptions about NPIs including social distancing mandates (SDMs) and levels of mask use. The range of infection, death, and hospital demand outcomes revealed by these scenarios show that action taken during the summer of 2020 will have profound public health impacts through to the year end. Encouragingly, we find that an emphasis on universal mask use may be sufficient to ameliorate the worst effects of epidemic resurgences in many states. Masks may save as many as 102,795 (55,898-183,374) lives, when compared to a plausible reference scenario in December. In addition, widespread mask use may markedly reduce the need for more socially and economically deleterious SDMs.

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