FSE Publications

Precipitation extremes have increased in many regions of the United States, suggesting that climate change may be exacerbating the cost of flooding. However, the impact of historical precipitation change on the cost of US flood damages remains poorly quantified. Applying empirical analysis to historical precipitation and flood damages, we estimate that approximately one-third (36%) of the cost of flood damages over 1988 to 2017 is a result of historical precipitation changes.

Recent dramatic and deadly increases in global wildfire activity have increased attention on the causes of wildfires, their consequences, and how risk from wildfire might be mitigated. Here we bring together data on the changing risk and societal burden of wildfire in the United States. We estimate that nearly 50 million homes are currently in the wildland–urban interface in the United States, a number increasing by 1 million houses every 3 y.

As climate change leads to increased frequency and severity of drought in many agricultural regions, a prominent adaptation goal is to reduce the drought sensitivity of crop yields. Yet many of the sources of average yield gains are more effective in good weather, leading to heightened drought sensitivity. Here we consider two empirical strategies for detecting changes in drought sensitivity and apply them to maize in the United States, a crop that has experienced myriad management changes including recent adoption of drought-tolerant varieties.

Cloud computing and freely available, high-resolution satellite data have enabled recent progress in crop yield mapping at fine scales. However, extensive validation data at a matching resolution remain uncommon or infeasible due to data availability. This has limited the ability to evaluate different yield estimation models and improve understanding of key features useful for yield estimation in both data-rich and data-poor contexts.

High resolution satellite imagery and modern machine learning methods hold the potential to fill existing data gaps in where crops are grown around the world at a sub-field level. However, high resolution crop type maps have remained challenging to create in developing regions due to a lack of ground truth labels for model development. In this work, we explore the use of crowdsourced data, Sentinel-2 and DigitalGlobe imagery, and convolutional neural networks (CNNs) for crop type mapping in India.

Sugar is the second largest agro-based industry in India and has a major influence on the country's water, food, and energy security. In this paper, we use a nexus approach to assess India's interconnected water-food-energy challenges, with a specific focus on the political economy of the sugar industry in Maharashtra, one of the country's largest sugar producing states. Our work underscores three points. First, the governmental support of the sugar industry is likely to persist because policymakers are intricately tied to that industry.

This paper presents one of the rst randomized evaluations of collective pay-for-performance payments for ecosystem services. We test whether community-level scal incentives can curtail the use of land-clearing re, a major source of emissions and negative health externalities.

We study a program that funded 39,000 Jewish households in New York City to leave enclave neighborhoods circa 1910. Compared to their neighbors with the same occupation and income score at baseline, program participants earned 4 percent more ten years after removal, and these gains persisted to the next generation. Men who left enclaves also married spouses with less Jewish names, but they did not choose less Jewish names for their children. Gains were largest for men who spent more years outside of an enclave.

Recent dramatic and deadly increases in global wildfire activity have increased attention on the causes of wildfires, their consequences, and how risk from fire might be mitigated. Here we bring together data on the changing risk and societal burden of wildfire in the US. We estimate that nearly 50 million homes are currently in the wildland-urban interface in the US, a number increasing by 1 million houses every 3 years.

Estimation of pollution impacts on health is critical for guiding policy to improve health outcomes. Estimation is challenging, however, because economic activity can worsen pollution but also independently improve health outcomes, confounding pollution–health estimates. We leverage variation in exposure to local particulate matter of diameter <2.5 μm (PM_2.5) across Sub-Saharan Africa driven by distant dust export from the Sahara, a source uncorrelated with local economic activity.

In response to the important benefits forests provide, there is a growing effort to reforest the world. Past policies and current commitments indicate that many of these forests will be plantations. Since plantations often replace more carbon-rich or biodiverse land covers, this approach to forest expansion may undermine objectives of increased carbon storage and biodiversity. We use an econometric land use change model to simulate the carbon and biodiversity impacts of subsidy driven plantation expansion in Chile between 1986 and 2011.

Research has consistently identified firearm availability as a risk factor for suicide. However, existing studies are relatively small in scale, estimates vary widely, and no study appears to have tracked risks from commencement of firearm ownership.

Deforestation and landscape fragmentation have been identified as processes enabling direct transmission of zoonotic infections. Certain human behaviors provide opportunities for direct contact between humans and wild nonhuman primates (NHPs), but are often missing from studies linking landscape level factors and observed infectious diseases.

Marshall Burke and fellow researchers study productivity in smallholder farms to understand variation across the adbundant but understudied firms. They use a novel framework, satellite data, and machine learning to understand such variation, and they find that output measurement error contributes significantly to this discrepancy in productivity.

California has experienced devastating autumn wildfires in recent years. These autumn wildfires have coincided with extreme fire weather conditions during periods of strong offshore winds coincident with unusually dry vegetation enabled by anomalously warm conditions and late onset of autumn precipitation. In this study, we quantify observed changes in the occurrence and magnitude of meteorological factors that enable extreme autumn wildfires in California, and use climate model simulations to ascertain whether these changes are attributable to human-caused climate change.

Independent verification of anthropogenic influence on specific extreme climate events remains elusive. This study presents a framework for such verification. This framework reveals that previously published results based on a 1961–2005 attribution period frequently underestimate the influence of global warming on the probability of unprecedented extremes during the 2006–2017 period. This underestimation is particularly pronounced for hot and wet events, with greater uncertainty for dry events.

Floods and other climate hazards pose a widespread and growing threat to housing and infrastructure around the world. By incorporating climate risk into asset prices, markets can discourage excessive development in hazardous areas. However, the extent to which markets actually price these risks remains poorly understood. Here we measure the effect of information about flood risk on residential property values in the United States.

Researchers including David Lobell analyze how human-caused climate change has impacted a water defecit in Southern Africa and might contribute to a rising food security crisis in the region.

Accurate automated segmentation of remote sensing data could benefit applications from land cover mapping and agricultural monitoring to urban development surveyal and disaster damage assessment. While convolutional neural networks (CNNs) achieve state-of-the-art accuracy when segmenting natural images with huge labeled datasets, their successful translation to remote sensing tasks has been limited by low quantities of ground truth labels, especially fully segmented ones, in the remote sensing domain.

The recent shift in the United States from coal to natural gas as a primary feedstock for the production of electric power has reduced the intensity of sectoral carbon dioxide emissions, but—due to gaps in monitoring—its downstream pollution-related effects have been less well understood. Here, I analyse old units that have been taken offline and new units that have come online to empirically link technology switches to observed aerosol and ozone changes and subsequent impacts on human health, crop yields and regional climate.

The advent of multiple satellite systems capable of resolving smallholder agricultural plots raises possibilities for significant advances in measuring and understanding agricultural productivity in smallholder systems. However, since only imperfect yield data are typically available for model training and validation, assessing the accuracy of satellite-based estimates remains a central challenge.

Many mountainous and high‐latitude regions have experienced more precipitation as rain rather than snow due to warmer winter temperatures. Further decreases in the annual snow fraction are projected under continued global warming, with potential impacts on flood risk. Here, we quantify the size of streamflow peaks in response to both seasonal and event‐specific rain‐fraction using stream gage observations from watersheds across the western United States.

Machine learning and satellite data of crops shows that farms that till the soil less can increase yields of corn and soybeans and improve the health of the soil. Farmers have resisted a switch to reduced tilling because it was believed to reduce yields. Instead, it may increase yields while lowering production costs and reducing soil erosion.