Informing grassland ecosystem modeling with in-situ and remote sensing observations
We simulated historical grassland aboveground plant productivity (ANPP) across the midwestern and western contiguous United States using the DayCent-UV ecosystem model. For this study we developed new methods for informing DayCent-UV of growing season length and validating its plant productivity est...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Environmental Research Letters |
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| Online Access: | https://doi.org/10.1088/1748-9326/adb04f |
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| author | Johny Arteaga Melannie D Hartman William J Parton Maosi Chen Wei Gao |
| author_facet | Johny Arteaga Melannie D Hartman William J Parton Maosi Chen Wei Gao |
| author_sort | Johny Arteaga |
| collection | DOAJ |
| description | We simulated historical grassland aboveground plant productivity (ANPP) across the midwestern and western contiguous United States using the DayCent-UV ecosystem model. For this study we developed new methods for informing DayCent-UV of growing season length and validating its plant productivity estimates for grasslands by utilizing a wide range of data sources at multiple scales, from field observations to remotely sensed satellite data. The model’s phenology was informed by the MODIS MCD12Q2 product, which showed good agreement with in-situ observations of growing season commencement and duration across different grassland ecosystems, and with observed historical trends. Model results from each simulated grid cell were compared to a remote-sensing estimate of grassland plant productivity offered by the Rangeland Analysis Platform (RAP). We determined that a modified RAP ANPP calculation that incorporated total annual precipitation instead of mean annual temperature to estimate the fraction of total productivity allocated to roots improved temporal correlations between RAP and field measurements and between RAP and DayCent-UV, We found that RAP provides a valuable data set for evaluating grassland ANPP predictions from ecosystem and other types of models because it provides estimates of grassland plant productivity over large spatial regions and a long historical period and captures temporal variablilty in plant production. This work provides the foundation for using the DayCent-UV model to predict climate change impacts on grassland cecosystem dynamics in the contiguous US. |
| format | Article |
| id | doaj-art-03c5c4f664834de7b3a79e241255bbc2 |
| institution | Kabale University |
| issn | 1748-9326 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj-art-03c5c4f664834de7b3a79e241255bbc22025-02-11T07:12:37ZengIOP PublishingEnvironmental Research Letters1748-93262025-01-0120303400410.1088/1748-9326/adb04fInforming grassland ecosystem modeling with in-situ and remote sensing observationsJohny Arteaga0https://orcid.org/0009-0003-4028-5063Melannie D Hartman1William J Parton2Maosi Chen3Wei Gao4United States Department of Agriculture UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University , Fort Collins, CO 80521, United States of AmericaUnited States Department of Agriculture UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University , Fort Collins, CO 80521, United States of America; Natural Resource Ecology Laboratory, Colorado State University , Fort Collins, CO 80523, United States of AmericaUnited States Department of Agriculture UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University , Fort Collins, CO 80521, United States of America; Natural Resource Ecology Laboratory, Colorado State University , Fort Collins, CO 80523, United States of AmericaUnited States Department of Agriculture UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University , Fort Collins, CO 80521, United States of AmericaUnited States Department of Agriculture UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State University , Fort Collins, CO 80521, United States of America; Department of Ecosystem Science and Sustainability, Colorado State University , Fort Collins, CO 80523, United States of AmericaWe simulated historical grassland aboveground plant productivity (ANPP) across the midwestern and western contiguous United States using the DayCent-UV ecosystem model. For this study we developed new methods for informing DayCent-UV of growing season length and validating its plant productivity estimates for grasslands by utilizing a wide range of data sources at multiple scales, from field observations to remotely sensed satellite data. The model’s phenology was informed by the MODIS MCD12Q2 product, which showed good agreement with in-situ observations of growing season commencement and duration across different grassland ecosystems, and with observed historical trends. Model results from each simulated grid cell were compared to a remote-sensing estimate of grassland plant productivity offered by the Rangeland Analysis Platform (RAP). We determined that a modified RAP ANPP calculation that incorporated total annual precipitation instead of mean annual temperature to estimate the fraction of total productivity allocated to roots improved temporal correlations between RAP and field measurements and between RAP and DayCent-UV, We found that RAP provides a valuable data set for evaluating grassland ANPP predictions from ecosystem and other types of models because it provides estimates of grassland plant productivity over large spatial regions and a long historical period and captures temporal variablilty in plant production. This work provides the foundation for using the DayCent-UV model to predict climate change impacts on grassland cecosystem dynamics in the contiguous US.https://doi.org/10.1088/1748-9326/adb04fRangeland Analysis Platformlong-term ANPP comparisonsDayCent ecosystem modeling |
| spellingShingle | Johny Arteaga Melannie D Hartman William J Parton Maosi Chen Wei Gao Informing grassland ecosystem modeling with in-situ and remote sensing observations Environmental Research Letters Rangeland Analysis Platform long-term ANPP comparisons DayCent ecosystem modeling |
| title | Informing grassland ecosystem modeling with in-situ and remote sensing observations |
| title_full | Informing grassland ecosystem modeling with in-situ and remote sensing observations |
| title_fullStr | Informing grassland ecosystem modeling with in-situ and remote sensing observations |
| title_full_unstemmed | Informing grassland ecosystem modeling with in-situ and remote sensing observations |
| title_short | Informing grassland ecosystem modeling with in-situ and remote sensing observations |
| title_sort | informing grassland ecosystem modeling with in situ and remote sensing observations |
| topic | Rangeland Analysis Platform long-term ANPP comparisons DayCent ecosystem modeling |
| url | https://doi.org/10.1088/1748-9326/adb04f |
| work_keys_str_mv | AT johnyarteaga informinggrasslandecosystemmodelingwithinsituandremotesensingobservations AT melanniedhartman informinggrasslandecosystemmodelingwithinsituandremotesensingobservations AT williamjparton informinggrasslandecosystemmodelingwithinsituandremotesensingobservations AT maosichen informinggrasslandecosystemmodelingwithinsituandremotesensingobservations AT weigao informinggrasslandecosystemmodelingwithinsituandremotesensingobservations |