Engineering topics

Pastorello, Gilberto

Publications and source records attributed to Pastorello, Gilberto.

8 records

DOE OSTI2024

Hysteresis area at the canopy level during and after a drought event in the Central Amazon

Understanding forest water limitation during droughts within a warming climate is essential for accurate predictions of forest-climate interactions. In hyperdiverse ecosystems like the Amazon forest, the mechanisms shaping hysteresis patterns in transpiration relative to environmental factors are not well understood. From this perspective, we investigated these dynamics by conducting in situ leaf-level measurements throughout and after the 2015 El Niño-Southern Oscillation (ENSO) drought. Our findings indicate a substantial increase in the hysteresis area (H area ) among transpiration (E), vapor pressure deficit (VPD), and stomatal conductance (g s ) at canopy level during the ENSO peak, attributed to both temporal lag and differences in magnitude between g s and VPD peaks. Specifically, the canopy species Pouteria anomala exhibited an increased H area , due to earlier maximum g s rates leading to a greater temporal lag with VPD compared to the post-drought period. Additionally, leaf water potential (ψ L ) and canopy temperature (T canopy ) showed larger H area during the ENSO peak compared to post-drought conditions across all studied species, suggesting that stomatal closure, particularly during the afternoon, acts to minimize water loss and may explain the counterclockwise hysteresis observed between ψ L and T canopy . Here, the pronounced H area during the drought points to a potential imbalance between water supply and demand, underlining the role of stomatal behavior of isohydric species in response to drought.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Harmonized wood density data for Central Amazon species in the BIONTE experimental area in Manaus, Brazil

BIONTE (BIOmass and NuTrient Experiment) is a selective logging experiment established at the Experimental Station of Tropical Forestry (EEST, aka “ZF2”) field research station in the mid 1980s in the central Amazon (Higuchi et al. 1997, Amaral et al. 2019). The main data related to BIONTE are available as a separate dataset (Lima et al. 2022). Only species identified within the BIONTE plots were included in this dataset. Wood density, expressed in g cm-3, was collated from multiple sources that were integrated to compose the wood density for BIONTE. The starting point used was wood density data available from Chave et al. (2006), which was then adapted by Marra et al. (2016) and Marra et al. (2018). We also included data collected and synthesized by Ramírez-Méndez (2018) and Gimenez et al.(2021), who combined local estimates of wood density at the site with other sources collected in the Central Amazon. Data are included in .csv files, while BIONTE_WD_headers.txt provides descriptions of data file headers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Growth, mortality, wood density, biomass data from BIONTE inventories in Manaus, Brazil

BIONTE (BIOmass and NuTrient Experiment) is a selective logging experiment established at the Experimental Station of Tropical Forestry (EEST, aka “ZF2”) field research station in the mid 1980s in the central Amazon (Higuchi et al. 1997, Amaral et al. 2019). Led by the National Institute for Amazon Research (INPA) in Brazil, the project aimed at assessing the effects of logging intensity on forest dynamics and enabling the creation of a model of forest management for the Central Amazon. The experiment included three levels of increasing selective logging intensity and controls, with 1 hectare sample plots (12 total) located at the center of 4 hectare treatment plots. The site’s Köppen classification is tropical rainforest (Af), characterized by high temperatures and humidity, with mean annual temperatures around 27 ℃ and mean annual precipitation around 2200 mm of rain. The vegetation has a high floristic diversity, the soils of the region are poor in nutrients, and the topography is characterized by plateaus (where BIONTE is located), and also valley bottoms and slopes. The inventory (growth and mortality) and biomass data included here covers the 1990 to 2019 period, with wood density being averaged from existing datasets. This dataset includes a data file in .csv file format and a .txt file, BIONTE_mortality-rates_headers.txt, that provides descriptions for the data file headers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Forcing data (CESM2/CMIP6) for projection of drought impacts (2015-2100) at the K34 site in Manaus, Brazil

Historical and projected output data variables extracted and derived from Community Earth System Model 2 (CESM2) runs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) archive. CESM2 is a fully coupled Earth system model used in simulations of Earth's past, present, and future climates (Danabasoglu et al., 2020). Variables in this dataset are in six-hourly resolution, and include air temperature (both in K and ℃), specific humidity (kg kg-1), air pressure (Pa), relative humidity (%), and vapor pressure deficit (kPa). CESM2 was the only CMIP6 model that provided VPD at the high temporal resolution required for this analysis. Data are included in .csv files, and the text file CESM2-CMIP6_forcing_K34-Manaus_headers.txt provides descriptions of data file headers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Drought index using micrometeorological data from Embrapa weather station at Adolpho Ducke Reserve in Manaus, Brazil

This dataset includes daily resolution time series data including precipitation, minimum and maximum daily air temperature, and air relative humidity downloaded from the Embrapa InfoClima portal (https://www.cnpaf.embrapa.br/infoclima/), with data for the Adolpho Ducke Reserve climatological station in Manaus, Brazil, for the period of January 1, 1980 to December 31, 2016. Using this precipitation record, a Standardized Precipitation Index (SPI) was calculated and added to the dataset, using daily resolution for 180 day intervals and with 20 years for calibration (1980-1999) and adopting a gamma distribution. These data were applied as a proxy for analyzes of precipitation and SPI for the Manaus ZF2 Research station, located approximately 50 Km North of the Adolpho Ducke Reserve. Data are included in a .csv file, and the text file Drought-Indices-Embrapa-Ducke_met_spi_headers.txt provides descriptions of the data file headers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Selected micrometeorological and soil data from the Manaus ZF2 K34 eddy covariance tower for the 2015/16 El Niño event

The K34 tower site (2.609 S, 60.209 W, 130 masl), located within the Tropical Silviculture Experimental Station (EEST, also known as ZF2) research station, in an area of undisturbed tropical forest (Araújo et al. 2002). It features long term measurements using micrometeorological, eddy covariance, and soil instrumentation, among others. This dataset covers January 2012 to December 2017, and includes air temperature (TA, ℃) and air relative humidity (RH, %), measured with thermohygrometers (HC2S3, Campbell Scientific, Logan, UT, USA) deployed above the canopy at the top of the tower (at 51.1 m height), and net radiation (NETRAD, W m-2) using an NR-LITE sensor (Kipp & Zonen, Delft, Netherlands), also at the top of the tower (at 35.0 m above the canopy). The soil moisture data included here is from a soil profile located approximately 12 m from the tower, and covers the period between February 2015 and September 2016. Volumetric soil water content (SWC, m3 m-3) measurements used Water Content Reflectometers (CS655 Campbell Scientific, Logan, UT, USA) at six depths: 10 cm, 20 cm, 30 cm, 40 cm, 60 cm, and 100 cm. Data are included in .csv files, and the text file ZF2-K34_Tower_headers.txt provides descriptions of data file headers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

AmeriFlux BASE Flux/Met Data QA/QC and Processing (AMF-BASE-QAQC) v1.0.0

The AmeriFlux BASE Flux/Met Data QA/QC and Processing (AMF-BASE-QAQC) code provides tools to review and prepare continuous flux/met data submitted to the AmeriFlux Management Project for publication as the AmeriFlux BASE data product. The code provides 3 core functionalities: Format QA/QC assesses submitted data files for compliance with the required submission format; Data QA/QC assesses the data quality; BASE Publish prepares the data for publication.

Christianson, Danielle↗

DOE OSTI2022

Long-term missing value imputation for time series data using deep neural networks

We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron, for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuous gap (e.g., multiple months of missing daily observations) rather than on individual randomly missing observations. Our proposed gap filling algorithm uses an automated method for determining the optimal MLP model architecture, thus allowing for optimal prediction performance for the given time series. We tested our approach by filling gaps of various lengths (three months to three years) in three environmental datasets with different time series characteristics, namely daily groundwater levels, daily soil moisture, and hourly Net Ecosystem Exchange. We compared the accuracy of the gap-filled values obtained with our approach to the widely used R-based time series gap filling methods ImputeTS and mtsdi. The results indicate that using an MLP for filling a large gap leads to better results, especially when the data behave nonlinearly. Thus, our approach enables the use of datasets that have a large gap in one variable, which is common in many long-term environmental monitoring observations.

97 MATHEMATICS AND COMPUTING↗