arXiv subjects

Robert B. Lund

Publications and source records attributed to Robert B. Lund.

5 recordsLinked to original sources

arXiv2026

Quantifying Impacts of Wildfire Smoke on Crop Yields in California

Climate change and increasingly severe wildfires pose significant threats to both people and ecosystems, yet little research has focused on the impacts of wildfires on crops. In addition to direct effects such as health risks to agricultural workers and crop damage, there is evidence that smoke may affect crop productivity and quality. This study aims to analyze the effects of wildfire smoke on crop production in Northern California. We quantified the relationship of particulate matter on grape yields using statistical regression models with a wide range of different climate and air quality predictor variables. Our analysis identifies an association between air quality deterioration due to wildfire smoke emissions and reduction in wine grape yields. For two of the three study sites, particulate matter emerged as a significant predictor variable in combination with temperature. Final models for Sonoma and Napa had R2 values of 0.79 and 0.66 respectively, suggesting a shift from climate only-controlled yields to wildfire and climate-controlled yields. This work sets the stage for the next generation of agronomic models that incorporate the growing threat of wildfire on agriculture production systems.

stat.AP↗

arXiv2025

Hierarchical Count Echo State Network Models with Application to Graduate Student Enrollments

Poisson autoregressive count models have evolved into a time series staple for correlated count data. This paper proposes an alternative to Poisson autoregressions: count echo state networks. Echo state networks can be statistically analyzed in frequentist manners via optimizing penalized likelihoods, or in Bayesian manners via MCMC sampling. This paper develops Poisson echo state techniques for count data and applies them to a massive count data set containing the number of graduate students from 1,758 United States universities during the years 1972-2021 inclusive. Negative binomial models are also implemented to better handle overdispersion in the counts. Performance of the proposed models are compared via their forecasting performance as judged by several methods. In the end, a hierarchical negative binomial based echo state network is judged as the superior model.

stat.ME↗

arXiv2024

Spatial Deep Convolutional Neural Networks

Spatial prediction problems often use Gaussian process models, which can be computationally burdensome in high dimensions. Specification of an appropriate covariance function for the model can be challenging when complex non-stationarities exist. Recent work has shown that pre-computed spatial basis functions and a feed-forward neural network can capture complex spatial dependence structures while remaining computationally efficient. This paper builds on this literature by tailoring spatial basis functions for use in convolutional neural networks. Through both simulated and real data, we demonstrate that this approach yields more accurate spatial predictions than existing methods. Uncertainty quantification is also considered.

stat.ME↗

arXiv2022

Changepoint Methods in Climatology

Changepoint methods have multiple uses in climatology, including stationary checks and record homogenization. There are still many open problems in the area, especially in the multiple changepoint setting, and statisticians are needed to help develop the methods and analyze the data.

stat.ME↗

arXiv2022

Good Practices and Common Pitfalls in Climate Time Series Changepoint Techniques: A Review

Climate changepoint (homogenization) methods abound today, with a myriad of techniques existing in both the climate and statistics literature. Unfortunately, the appropriate changepoint technique to use remains unclear to many. Further complicating issues, changepoint conclusions are not robust to small perturbations in assumptions; for example, allowing for a trend or correlation in the series can drastically change conclusions. This paper is a review of the changepoint topic, with an emphasis on illuminating the models and techniques that allow the scientist to make reliable conclusions. Pitfalls to avoid are demonstrated via actual applications. The discourse begins by narrating the salient statistical features of most climate time series. Thereafter, single and multiple changepoint problems are considered. Several pitfalls are discussed en route and good practices are recommended. While the majority of our applications involve temperature series, other settings are mentioned.

stat.AP↗