arXiv subjects

Forrest M. Hoffman

Publications and source records attributed to Forrest M. Hoffman.

7 recordsLinked to original sources

arXiv2026

Intercomparison of Machine Learning Algorithms for Remote Sensing-based In-season Crop Mapping

In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops. Currently, the USDA Cropland Data Layer provides crop type labels at 30m resolution and is available the February after harvest, but no product exists that maps crop types before harvest with satisfactory accuracy that would allow emergency managers to respond to crop threats in near real time. Furthermore, the relative advantages of a wide range of algorithms have not been evaluated in a way that accounts for interannual variability, until this study. Here, Harmonized Landsat-Sentinel surface reflectance imagery time series and crop rotation history information are combined to map corn in Iowa and almonds in California at 30m resolution accurately by early June in unseen years, with robust quantification of uncertainty due to phenology and crop distribution. Thousands of model configurations across ten machine learning algorithms were compared using a year-wise cross-validation and a suite of metrics. Hyperparameter search revealed Support Vector Machines to be the most successful algorithm overall, with a mean F1 score of 0.74 (0.59) across five unseen validation years for almonds by early June in California (corn by early June in Iowa). Interannual variation was a large source of uncertainty, but patterns showed the potential to further improve performance with ensemble approaches or ancillary data. Future work may extend these methods to include multiclass maps of all crop types, CONUS-wide maps, and in-season crop yield forecasting.

cs.LG↗

arXiv2025

Land Cover Changes Cause Increased Losses during Photosynthetic Extremes

Human-induced carbon dioxide (CO2) emissions, primarily from fossil fuel combustion and changes in land use and land cover (LULCC), are a key contributor to climate change. As the climate warms, extreme events such as heatwaves, droughts, and wildfires have become more frequent and are projected to intensify throughout the 21st century. These escalating extremes are likely to further disrupt vegetation productivity, known as gross primary production (GPP), and reduce the ecosystem's capacity to absorb carbon. In this study, we employ a global Earth system model to assess how (a) CO2 emissions alone and (b) CO2 combined with LULCC influence the severity, frequency, and duration of GPP extremes. Our results show that negative GPP extremes periods of unexpectedly low carbon uptake are increasing more rapidly than positive extremes, especially under LULCC scenarios. The primary climate factor driving these extremes is soil moisture variability, which is influenced by fluctuations in both precipitation and temperature. The delayed responses of GPP to different climate drivers depend on the specific driver and geographical region. Overall, the highest incidence of GPP extremes arises from the combined influence of water stress, temperature anomalies, and fire-related disturbances.

physics.ao-ph↗

arXiv2025

Carbon Cycle Extremes Accelerate Weakening of the Land Carbon Sink in the Late 21st Century

Increasing surface temperature could lead to enhanced evaporation, reduced soil moisture availability, and more frequent droughts and heat waves. The spatiotemporal co-occurrence of such effects further drives extreme anomalies in vegetation productivity and net land carbon storage. However, the impacts of climate change on extremes in net biospheric production (NBP) over longer time periods are unknown. Using the percentile threshold on the probability distribution curve of NBP anomalies, we computed negative and positive extremes in NBP. Here we show that due to climate warming, about 88% of global regions will experience a larger magnitude of negative NBP extremes than positive NBP extremes toward the end of 2100, which accelerate the weakening of the land carbon sink. Our analysis indicates the frequency of negative extremes associated with declines in biospheric productivity was larger than positive extremes, especially in the tropics. While the overall impact of warming at high latitudes is expected to increase plant productivity and carbon uptake, high-temperature anomalies increasingly induce negative NBP extremes toward the end of the 21st century. Using regression analysis, we found soil moisture anomalies to be the most dominant individual driver of NBP extremes. The compound effect of hot, dry, and fire caused extremes at more than 50% of the total grid cells. The larger proportion of negative NBP extremes raises a concern about whether the Earth is capable of increasing vegetation production with growing human population and rising demand for plant material for food, fiber, fuel, and building materials. The increasing proportion of negative NBP extremes highlights the consequences of not only reduction in total carbon uptake capacity but also of conversion of land to a carbon source.

stat.AP↗

arXiv2025

Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes

Rising atmospheric carbon dioxide due to human activities through fossil fuel emissions and land use changes have increased climate extremes such as heat waves and droughts that have led to and are expected to increase the occurrence of carbon cycle extremes. Carbon cycle extremes represent large anomalies in the carbon cycle that are associated with gains or losses in carbon uptake. Carbon cycle extremes could be continuous in space and time and cross political boundaries. Here, we present a methodology to identify large spatiotemporal extremes (STEs) in the terrestrial carbon cycle using image processing tools for feature detection. We characterized the STE events based on neighborhood structures that are three-dimensional adjacency matrices for the detection of spatiotemporal manifolds of carbon cycle extremes. We found that the area affected and carbon loss during negative carbon cycle extremes were consistent with continuous neighborhood structures. In the gross primary production data we used, 100 carbon cycle STEs accounted for more than 75\% of all the negative carbon cycle extremes. This paper presents a comparative analysis of the magnitude of carbon cycle STEs and attribution of those STEs to climate drivers as a function of neighborhood structures for two observational datasets and an Earth system model simulation.

stat.AP↗

arXiv2024

Automated, Reliable, and Efficient Continental-Scale Replication of 7.3 Petabytes of Climate Simulation Data: A Case Study

We report on our experiences replicating 7.3 petabytes (PB) of Earth System Grid Federation (ESGF) climate simulation data from Lawrence Livermore National Laboratory (LLNL) in California to Argonne National Laboratory (ANL) in Illinois and Oak Ridge National Laboratory (ORNL) in Tennessee. This movement of some 29 million files, twice, undertaken in order to establish new ESGF nodes at ANL and ORNL, was performed largely automatically by a simple replication tool, a script that invoked Globus to transfer large bundles of files while tracking progress in a database. Under the covers, Globus organized transfers to make efficient use of the high-speed Energy Sciences network (ESnet) and the data transfer nodes deployed at participating sites, and also addressed security, integrity checking, and recovery from a variety of transient failures. This success demonstrates the considerable benefits that can accrue from the adoption of performant data replication infrastructure.

cs.DC↗

arXiv2022

Evaluation of CMIP models with IOMB: Rates of contemporary ocean carbon uptake linked with vertical temperature gradients and transport to the ocean interior

The International Ocean Model Benchmarking (IOMB) software package is a new community resource used here to evaluate surface and upper ocean variables from CMIP5 and CMIP6 Earth System Models (ESMs) Our analysis reveals general improvement in the multi-model mean of CMIP6 compared to CMIP5 for most of the variables we examined including surface nutrients, temperature, and salinity. We find that both CMIP5 and CMIP6 ocean models underestimate anthropogenic carbon dioxide uptake after the 1970s. For the period of 1994 to 2007, the multi-model mean from CMIP6 yields a mean cumulative carbon uptake of 27.2 +-2.2 Pg C, which is about 15% lower than the 32.0+-5.7 Pg C estimate derived from two sets of observations. Negative biases in the change in anthropogenic carbon inventory exist in the northern North Atlantic and at mid-latitudes in the southern hemisphere (30-60°S). For the few models that provided simulations of chlorofluorocarbon (CFC), we demonstrate that regions with negative anthropogenic DIC biases coincide with regions that have a negative bias in CFC concentrations. This relationship suggests that underestimates of anthropogenic carbon storage in some models originates, in part, from weak transport between the surface and interior ocean. To examine the robustness of this attribution across the full suite of CMIP5 and CMIP6 models, we examined the vertical temperature gradient between 200 and 1000m as a metric for stratification and exchange between the surface and deeper waters. On a global scale across different models and different MIPs we find a linear relationship between the bias of vertical temperature gradients and the bias in anthropogenic carbon uptake, consistent with the hypothesis that model biases in the ocean carbon sink are related to biases in surface-to-interior transport.

physics.ao-ph↗

arXiv2016

Transit times and mean ages for nonautonomous and autonomous compartmental systems

We develop a theory for transit times and mean ages for nonautonomous compartmental systems. Using the McKendrick-von Förster equation, we show that the mean ages of mass in a compartmental system satisfy a linear nonautonomous ordinary differential equation that is exponentially stable. We then define a nonautonomous version of transit time as the mean age of mass leaving the compartmental system at a particular time and show that our nonautonomous theory generalises the autonomous case. We apply these results to study a nine-dimensional nonautonomous compartmental system modeling the terrestrial carbon cycle, which is a modification of the Carnegie-Ames-Stanford approach (CASA) model, and we demonstrate that the nonautonomous versions of transit time and mean age differ significantly from the autonomous quantities when calculated for that model.

math.DS↗