Corrigendum to “Estimating irrigation consumptive use for the conterminous United States: coupling satellite-sourced estimates of actual evapotranspiration with a national hydrologic model” [J. Hydrol. 662 (2025) 133909] - Donald J. Martin; Richard G. Niswonger; R.Steve Regan; Justin L. Huntington; Thomas Ott; Charles Morton; Gabriel B. Senay; MacKenzie Friedrichs; Forrest S. Melton; Jonathan Haynes; Wesley Henson; Amy Read; et al. (Journal of Hydrology, 2026)

Estimating irrigation consumptive use for the conterminous United States: coupling satellite-sourced estimates of actual evapotranspiration with a national hydrologic model - Donald J. Martin; Richard G. Niswonger; R. Steve Regan; Justin L. Huntington; Thomas Ott; Charles Morton; Gabriel B. Senay; MacKenzie Friedrichs; Forrest S. Melton; Jonathan Haynes; Wesley Henson; Amy Read; et al. (Journal of Hydrology, 2025)

Intercomparison of the U.S. National Water Model with OpenET over the Bear River Basin, U.S. - Ayman Nassar; David Tarboton; Martha Anderson; Yun Yang; Joshua B. Fisher; Adam J. Purdy; Furqan Baig; Cenlin He; David Gochis; Forrest Melton; John Volk (Journal of Hydrology, 2025)

A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards - Kyle Knipper; Martha Anderson; Nicolas Bambach; Forrest Melton; Zac Ellis; Yun Yang; John Volk; Andrew J. McElrone; William Kustas; Matthew Roby; Will Carrara; Sebastian Castro; et al. (Agricultural and Forest Meteorology, 2024)

geeSEBAL-MODIS: Continental-scale evapotranspiration based on the surface energy balance for South America - Bruno Comini de Andrade; Leonardo Laipelt; Ayan Fleischmann; Justin Huntington; Charles Morton; Forrest Melton; Tyler Erickson; Debora R. Roberti; Vanessa de Arruda Souza; Marcelo Biudes; Nadja Gomes Machado; Carlos Antonio Costa dos Santos; et al. (ISPRS Journal of Photogrammetry and Remote Sensing, 2024)

Development of a Benchmark Eddy Flux Evapotranspiration Dataset for Evaluation of Satellite-Driven Evapotranspiration Models Over the CONUS - John M. Volk; Justin Huntington; Forrest S. Melton; Richard Allen; Martha C. Anderson; Joshua B. Fisher; Ayse Kilic; Gabriel Senay; Gregory Halverson; Kyle Knipper; Blake Minor; Christopher Pearson; et al. (Agricultural and Forest Meteorology, 2023)

Effects of meteorological and land surface modeling uncertainty on errors in winegrape ET calculated with SIMS - Conor T. Doherty; Lee F. Johnson; John Volk; Meagan S. Mauter; Nicolas Bambach; Andrew J. McElrone; Joseph G. Alfieri; Lawrence E. Hipps; John H. Prueger; Sebastian J. Castro; Maria Mar Alsina; William P. Kustas; et al. (Irrigation Science, 2022)
Characterization of model errors is important when applying satellite-driven evapotranspiration (ET) models to water resource management problems. This study examines how uncertainty in meteorological forcing data and land surface modeling propagate through to errors in final ET data calculated using the Satellite Irrigation Management Support (SIMS) model, a computationally efficient ET model driven with satellite surface reflectance values. The model is applied to three instrumented winegrape vineyards over the 2017–2020 time period and the spatial and temporal variation in errors are analyzed. We illustrate how meteorological data inputs can introduce biases that vary in space and at seasonal timescales, but that can persist from year to year. We also observe that errors in SIMS estimates of land surface conductance can have a particularly strong dependence on time of year. Overall, meteorological inputs introduced RMSE of 0.33–0.65 mm/day (7–27%) across sites, while SIMS introduced RMSE of 0.55–0.83 mm/day (19–24%). The relative error contribution from meteorological inputs versus SIMS varied across sites; errors from SIMS were larger at one site, errors from meteorological inputs were larger at a second site, and the error contributions were of equal magnitude at the third site. The similar magnitude of error contributions is significant given that many satellite-driven ET models differ in their approaches to estimating land surface conductance, but often rely on similar or identical meteorological forcing data. The finding is particularly notable given that SIMS makes assumptions about the land surface (no soil evaporation or plant water stress) that do not always hold in practice. The results of this study show that improving SIMS by eliminating these assumptions would result in meteorological inputs dominating the error budget of the model on the whole. This finding underscores the need for further work on characterizing spatial uncertainty in the meteorological forcing of ET.

Global ecosystems and fire: Multi‐model assessment of fire‐induced tree‐cover and carbon storage reduction - Gitta Lasslop; Stijn Hantson; Sandy P. Harrison; Dominique Bachelet; Chantelle Burton; Matthias Forkel; Matthew Forrest; Fang Li; Joe R. Melton; Chao Yue; Sally Archibald; Simon Scheiter; et al. (Global Change Biology, 2020)
In this study, we use simulations from seven global vegetation models to provide the first multi‐model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001–2012. Fire globally reduces the tree covered area and vegetation carbon storage by 10%. Regionally, the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to 0. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire‐induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state‐of‐the‐art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems.

High‐resolution mapping of daily climate variables by aggregating multiple spatial data sets with the random forest algorithm over the conterminous United States - Hirofumi Hashimoto; Weile Wang; Forrest S. Melton; Adam L. Moreno; Sangram Ganguly; Andrew R. Michaelis; Ramakrishna R. Nemani (International Journal of Climatology, 2019)
High‐resolution gridded climate data products are crucial to research and practical applications in climatology, hydrology, ecology, agriculture, and public health. Previous works to produce multiple data sets were limited by the availability of input data as well as computational techniques. With advances in machine learning and the availability of several daily satellite data sets providing unprecedented information at 1 km or higher spatial resolutions, it is now possible to improve upon earlier data sets in terms of representing spatial variability. We developed the NEX (NASA Earth Exchange) Gridded Daily Meteorology (NEX‐GDM) model, which can estimate the spatial pattern of regional surface climate variables by aggregating several dozen two‐dimensional data sets and ground weather station data. NEX‐GDM does not require physical assumptions and can easily extend spatially and temporally. NEX‐GDM employs the random forest algorithm for estimation, which allows us to find the best estimate from the spatially continuous data sets. We used the NEX‐GDM model to produce historical 1‐km daily spatial data for the conterminous United States from 1979 to 2017, including precipitation, minimum temperature, maximum temperature, dew point temperature, wind speed, and solar radiation. In this study, NEX‐GDM ingested a total of 30 spatial variables from 13 different data sets, including satellite, reanalysis, radar, and topography data. Generally, the spatial patterns of precipitation and temperature produced were similar to previous data sets with the exception of mountain regions in the western United States. The analyses for each spatially continuous data set show that satellite and reanalysis led to better estimates and that the incorporation of satellite data allowed NEX‐GDM to capture the spatial patterns associated with urban heat island effects. The NEX‐GDM data is available to the community through the NEX data portal.

Hydro-economic analysis of groundwater pumping for irrigated agriculture in California’s Central Valley, USA - Josué Medellín-Azuara; Duncan MacEwan; Richard E. Howitt; George Koruakos; Emin C. Dogrul; Charles F. Brush; Tariq N. Kadir; Thomas Harter; Forrest Melton; Jay R. Lund (Hydrogeology Journal, 2015)

Field and climate‐based model for predicting the density of host‐seeking nymphal Ixodes scapularis , an important vector of tick‐borne disease agents in the eastern United States - Maria A. Diuk‐Wasser; Gwenaël Vourc'h; Paul Cislo; Anne Gatewood Hoen; Forrest Melton; Sarah A. Hamer; Michelle Rowland; Roberto Cortinas; Graham J. Hickling; Jean I. Tsao; Alan G. Barbour; Uriel Kitron; et al. (Global Ecology and Biogeography, 2010)
Aim Ixodes scapularis is the most important vector of human tick‐borne pathogens in the United States, which include the agents of Lyme disease, human babesiosis and human anaplasmosis, among others. The density of host‐seeking I. scapularis nymphs is an important component of human risk for acquiring Borrelia burgdorferi , the aetiological agent of Lyme disease. In this study we used climate and field sampling data to generate a predictive map of the density of host‐seeking I. scapularis nymphs that can be used by the public, physicians and public health agencies to assist with the diagnosis and reporting of disease, and to better target disease prevention and control efforts. Location Eastern United States of America. Methods We sampled host‐seeking I. scapularis nymphs in 304 locations uniformly distributed east of the 100th meridian between 2004 and 2006. Between May and September, 1000 m 2 were drag sampled three to six times per site. We developed a zero‐inflated negative binomial model to predict the density of host‐seeking I. scapularis nymphs based on altitude, interpolated weather station and remotely sensed data. Results Variables that had the strongest relationship with nymphal density were altitude, monthly mean vapour pressure deficit and spatial autocorrelation. Forest fragmentation and soil texture were not predictive. The best‐fit model identified two main foci – the north‐east and upper Midwest – and predicted the presence and absence of I. scapularis nymphs with 82% accuracy, with 89% sensitivity and 82% specificity. Areas of concordance and discordance with previous studies were discussed. Areas with high predicted but low observed densities of host‐seeking nymphs were identified as potential expansion fronts. Main conclusions This model is unique in its extensive and unbiased field sampling effort, allowing for an accurate delineation of the density of host‐seeking I. scapularis nymphs, an important component of human risk of infection for B. burgdorferi and other I. scapularis ‐borne pathogens.

Evaluating the impacts of climate and elevated carbon dioxide on tropical rainforests of the western Amazon basin using ecosystem models and satellite data - HIROFUMI HASHIMOTO; FORREST MELTON; KAZUHITO ICHII; CRISTINA MILESI; WEILE WANG; RAMAKRISHNA R. NEMANI (Global Change Biology, 2010)
Forest inventories from the intact rainforests of the Amazon indicate increasing rates of carbon gain over the past three decades. However, such estimates have been questioned because of the poor spatial representation of the sampling plots and the incomplete understanding of purported mechanisms behind the increases in biomass. Ecosystem models, when used in conjunction with satellite data, are useful in examining the carbon budgets in regions where the observations of carbon flows are sparse. The purpose of this study is to explain observed trends in normalized difference vegetation index (NDVI) using climate observations and ecosystem models of varying complexity in the western Amazon basin for the period of 1984–2002. We first investigated trends in NDVI and found a positive trend during the study period, but the positive trend in NDVI was observed only in the months from August to December. Then, trends in various climate parameters were calculated, and of the climate variables considered, only shortwave radiation was found to have a corresponding significant positive trend. To compare the impact of each climate component, as well as increasing carbon dioxide (CO 2 ) concentrations, on evergreen forests in the Amazon, we ran three ecosystem models (CASA, Biome‐BGC, and LPJ), and calculated monthly net primary production by changing a climate component selected from the available climate datasets. As expected, CO 2 fertilization effects showed positive trends throughout the year and cannot explain the positive trend in NDVI, which was observed only for the months of August to December. Through these simulations, we demonstrated that the positive trend in shortwave radiation can explain the positive trend in NDVI observed for the period from August to December. We conclude that the positive trend in shortwave radiation is the most likely driver of the increasing trend in NDVI and the corresponding observed increases in forest biomass.