The relationship between extreme weather and low crop yields

2020 ◽  
Author(s):  
Matias Heino ◽  
Weston Anderson ◽  
Michael Puma ◽  
Matti Kummu
Keyword(s):  
Soil Moisture ◽  
Crop Yield ◽  
Crop Yields ◽  
Explanatory Power ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Yield Variability ◽  
Weather Events

<p>It is well known that climate extremes and variability have strong implications for crop productivity. Previous research has estimated that annual weather conditions explain a third of global crop yield variability, with explanatory power above 50% in several important crop producing regions. Further, compared to average conditions, extreme events contribute a major fraction of weather induced crop yield variations. Here we aim to analyse how extreme weather events are related to the likelihood of very low crop yields at the global scale. We investigate not only the impacts of heat and drought on crop yields but also excess soil moisture and abnormally cool temperatures, as these extremes can be detrimental to crops as well. In this study, we combine reanalysis weather data with national and sub-national crop production statistics and assess relationships using statistical copulas methods, which are especially suitable for analysing extremes. Further, because irrigation can decrease crop yield variability, we assess how the observed signals differ in irrigated and rainfed cropping systems. We also analyse whether the strength of the observed statistical relationships could be explained by socio-economic factors, such as GDP, social stability, and poverty rates. Our preliminary results indicate that extreme heat and cold as well as soil moisture abundance and excess have a noticeable effect on crop yields in many areas around the globe, including several global bread baskets such as the United States and Australia. This study will increase understanding of extreme weather-related implications on global food production, which is relevant also in the context of climate change, as the frequency of extreme weather events is likely to increase in many regions worldwide.</p>

scholarly journals Impact of Extreme Weather on Healthcare Utilization by People with HIV in Metropolitan Miami

2021 ◽  
Vol 18 (5) ◽  
pp. 2442
Author(s):  
Daniel Samano ◽  
Shubhayu Saha ◽  
Taylor Corbin Kot ◽  
JoNell E. Potter ◽  
Lunthita M. Duthely
Keyword(s):  
Relative Risk ◽  
The United States ◽  
Extreme Weather ◽  
South Florida ◽  
Heat Index ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Steady Increase ◽  
Daily Weather Data ◽  
The Impact

Extreme weather events (EWE) are expected to increase as climate change intensifies, leaving coastal regions exposed to higher risks. South Florida has the highest HIV infection rate in the United States, and disruptions in clinic utilization due to extreme weather conditions could affect adherence to treatment and increase community transmission. The objective of this study was to identify the association between EWE and HIV-clinic attendance rates at a large academic medical system serving the Miami-Dade communities. The following methods were utilized: (1) Extreme heat index (EHI) and extreme precipitation (EP) were identified using daily observations from 1990–2019 that were collected at the Miami International Airport weather station located 3.6 miles from the studied HIV clinics. Data on hurricanes, coastal storms and flooding were collected from the National Oceanic and Atmospheric Administration Storms Database (NOAA) for Miami-Dade County. (2) An all-HIV clinic registry identified scheduled daily visits during the study period (hurricane seasons from 2017–2019). (3) Daily weather data were linked to the all-HIV clinic registry, where patients’ ‘no-show’ status was the variable of interest. (4) A time-stratified, case crossover model was used to estimate the relative risk of no-show on days with a high heat index, precipitation, and/or an extreme natural event. A total of 26,444 scheduled visits were analyzed during the 383-day study period. A steady increase in the relative risk of ‘no-show’ was observed in successive categories, with a 14% increase observed on days when the heat index was extreme compared to days with a relatively low EHI, 13% on days with EP compared to days with no EP, and 10% higher on days with a reported extreme weather event compared to days without such incident. This study represents a novel approach to improving local understanding of the impacts of EWE on the HIV-population’s utilization of healthcare, particularly when the frequency and intensity of EWE is expected to increase and disproportionately affect vulnerable populations. More studies are needed to understand the impact of EWE on routine outpatient settings.

scholarly journals Is there a Need to Integrate Human Thermal Models with Weather Forecasts to Predict Thermal Stress?

2019 ◽  
Vol 16 (22) ◽  
pp. 4586 ◽  
Author(s):  
Petersson ◽  
Kuklane ◽  
Gao
Keyword(s):  
Thermal Stress ◽  
Cold Stress ◽  
Extreme Weather ◽  
Data Availability ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Stress Index ◽  
Thermal Models ◽  
Weather Events ◽  
Heat And Cold Stress

More and more people will experience thermal stress in the future as the global temperature is increasing at an alarming rate and the risk for extreme weather events is growing. The increased exposure to extreme weather events poses a challenge for societies around the world. This literature review investigates the feasibility of making advanced human thermal models in connection with meteorological data publicly available for more versatile practices and a wider population. By providing society and individuals with personalized heat and cold stress warnings, coping advice and educational purposes, the risks of thermal stress can effectively be reduced. One interesting approach is to use weather station data as input for the wet bulb globe temperature heat stress index, human heat balance models, and wind chill index to assess heat and cold stress. This review explores the advantages and challenges of this approach for the ongoing EU project ClimApp where more advanced models may provide society with warnings on an individual basis for different thermal environments such as tropical heat or polar cold. The biggest challenges identified are properly assessing mean radiant temperature, microclimate weather data availability, integration and continuity of different thermal models, and further model validation for vulnerable groups.

scholarly journals Variability of extreme weather events and its impact on crop yield in Bundelkhand Agroclimatic zone of Madhya Pradesh

MAUSAM ◽  
2021 ◽  
Vol 71 (2) ◽  
pp. 275-284
Author(s):  
SRIVASTAVA A K ◽  
YOGRANJAN YOGRANJAN ◽  
BAL LALIT M
Keyword(s):  
Temporal Variation ◽  
Crop Yield ◽  
Extreme Weather ◽  
Madhya Pradesh ◽  
Extreme Weather Events ◽  
Soybean Yield ◽  
Agroclimatic Zone ◽  
Weather Events ◽  
Micro Level ◽  
Rainy Days

The Bundelkhand Agroclimatic Zone of Madhya Pradesh has witnessed many extreme weather events in recent decades like excessive hotness, dryness, coldness and number of consecutive drought years. Drought and water scarcity are the major resource limiting factors of this zone. There was sharp increase in numbers of hot days during last decade (2001-10) in Chhatarpur and Datia districts. The numbers of heavy rainfall days sharply decreased at Tikamgarh and Chhatarpur districts while frost days increased in Datia during last decade. The micro level variability of drought was much higher than the temporal scale variability. The occurrence of drought at micro level in the recent decade was much higher. The frequent occurrence of drought during recent past had increased the soybean and paddy yield variability in this zone. This paper attempts to present impact of variability of extreme weather events on paddy and soybean yield and also rural livelihood. The paddy and soybean yield were normally affected by number of heavy rainy days and number of rainy days. The number of heavy rainy days greater than equal to 6 days in Tikamgarh, 7 days in Chhatarpur per year were may be required for sustainable paddy production. It is observed that in those districts where the temporal variation in number of rainy days is decreasing, the decrease in number of rainy days below 5 days per year was crucial for sustainable yield. Whereas in the districts where little temporal variation in number of rainy days observed, a particular number of heavy rainy days is not necessary for adequate crop yield. In Datia and Chhatarpur district, the animal discomfort days increased over the decades.

Extreme Weather and Climate

2017 ◽  
Author(s):  
Friederike Otto
Keyword(s):  
Extreme Events ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Event ◽  
Weather Forecasts ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Weather Events ◽  
Global Mean

Natural disasters and extreme weather events have been of great societal importance throughout history and often brought everyday life to a catastrophic halt, in a way sometimes comparable to wars and epidemics, only without the lead time. Extreme weather events with large impacts serve as an anchor point of the collective memory of the population in the affected area. Every northern German of the right age remembers the storm surge of 1962 and where they were at the time and has friends or family effected by the event. The “dust bowl” of the 1930s with extensive droughts and heat waves shaped the life of a generation in the United States, and the Sahel droughts in the 1960s and 1970s led to famine and dislocation of population on a massive scale the region arguably never quite recovered from. Hurricane Hyian in 2013 is said to have directly influenced the outcome of the annual Conference of the Parties (COP) United Nation Framework Convention for Climate Change Negotiations in Warsaw, leading to the inclusion of a mechanism to deal with loss and damage from climate-related disasters. Though earthquakes are still fairly unpredictable on short timescales, this is not the case for weather events. Weather forecasts today are so good that we normally know the time and location of the landfall of a hurricane within a 100-mile radius days in advance. Improvements in the prediction of slow-onset events such as droughts (which depend on the rainfall over a large region and whole season) are less striking but have still improved dramatically in the late 20th and early 21st centuries. One of the major reasons for the large increase in the accuracy of weather forecasts is the exponential increase in computing power, which allows scientists to predict and study extreme weather events using complex computer models, simulating possible weather events under certain conditions to understand the statistics of and physical mechanisms behind extreme events. Extreme events are by definition rare and thus impossible to understand from historical records of weather observation alone. Despite the progress on our understanding of and ability to predict extreme weather events, substantial uncertainties remain. Two aspects are of particular importance. Firstly, we know that the climate is changing, having observed almost a one-degree increase in global mean temperature. However, global mean temperature doesn’t kill anyone, extreme weather events do. Their frequency and intensity is changing and will continue to change, but the extent of these changes depends on a host of both global and local factors. Secondly, whether or not a rare weather event leads to extreme impacts depends largely on the vulnerability and exposure of the affected societies. If these are high, even a perfectly forecasted weather event leads to disaster.

scholarly journals Income-based Inequality in Post-disaster Migration is Lower in High Resilience Areas: Evidence from U.S. Internal Migration

2021 ◽  
Author(s):  
Ted Hsuan Yun Chen ◽  
Boyoon Lee
Keyword(s):  
Climate Change ◽  
Internal Migration ◽  
Local Level ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Disaster Resilience ◽  
Migration Patterns ◽  
Weather Events ◽  
Post Disaster

Residential relocation following extreme weather events is among the costliest individual-level measures of climate change adaptation. Consequently, they are fraught with inequalities, with disadvantaged groups most adversely impacted. As climate change continues to exacerbate extreme weather events, it is imperative that we better understand how existing socioeconomic inequalities affect climate migration and how they may be offset. In this study we use network regression models to look at how internal migration patterns in the United States vary by disaster-related property damage, household income, and local-level disaster resilience. Our results show that post-disaster migration patterns vary considerably by the income level of sending and receiving counties, which suggests that income-based inequality impacts both access to relocation for individuals and the ability to rebuild for disaster-afflicted areas. We further find evidence that these inequalities are attenuated in areas with higher disaster resilience. However, because existing resilience incentivizes in situ incremental adaptation which can be a long term drain on individual wellbeing and climate adaptation resources, they should be balanced with policies that encourage relocation where appropriate.

Extreme weather events will hit insurance industry

2015 ◽  
Keyword(s):  
Climate Change ◽  
United States ◽  
Insurance Industry ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Causal Connection ◽  
Economic Implications ◽  
Content Type ◽  
Weather Events

Subject The political and economic implications of greater scientific understanding of extreme weather events. Significance Preparatory talks for the UN climate summit in Paris have seen representatives from developing countries ask the United States and EU for greater compensation for damages caused by extreme weather. The link between climate change and more extreme weather events is clear -- energy from higher temperature levels can be translated into kinetic energy and disrupts usual weather patterns -- but distinguishing the extent of a causal connection, especially for specific events, has until recently been difficult. Impacts Extreme weather events will affect the insurance industry, agriculture, tourism, and food and beverage sectors. In the United States, the South-east will see the highest risks of coastal property losses due to climate change impacts. Hurricanes and other coastal storms combined with rising sea levels are likely to cause growing annual storm losses in the Caribbean. Infrastructure will grow in cost as it must be proofed against new extremes in weather stress.

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