A Protocol for Collecting Burned Area Time Series Cross-Check Data
Data on wildfire growth are useful for multiple research purposes but are frequently unavailable and often have data quality problems. For these reasons, we developed a protocol for collecting daily burned area time series from the InciWeb website, Incident Management Situation Reports (IMSRs), and...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Fire |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-6255/5/5/153 |
| _version_ | 1797441161444458496 |
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| author | Harry R. Podschwit Brian Potter Narasimhan K. Larkin |
| author_facet | Harry R. Podschwit Brian Potter Narasimhan K. Larkin |
| author_sort | Harry R. Podschwit |
| collection | DOAJ |
| description | Data on wildfire growth are useful for multiple research purposes but are frequently unavailable and often have data quality problems. For these reasons, we developed a protocol for collecting daily burned area time series from the InciWeb website, Incident Management Situation Reports (IMSRs), and other sources. We apply this protocol to create the Warehouse of Multiple Burned Area Time Series (WoMBATS) data, which are a collection of burned area time series with cross-check data for 514 wildfires in the United States for the years 2018–2020. We compare WoMBATS-derived distributions of wildfire occurrence and size to those derived from MTBS data to identify potential biases. We also use WoMBATS data to cross tabulate the frequency of missing data in InciWeb and IMSRs and calculate differences in size estimates. We identify multiple instances where WoMBATS data fails to reproduce wildfire occurrence and size statistics derived from MTBS data. We show that WoMBATS data are typically much more complete than either of the two constituent data sources, and that the data collection protocol allows for the identification of otherwise undetectable errors. We find that although disagreements between InciWeb and IMSRs are common, the magnitude of these differences are usually small. We illustrate how WoMBATS data can be used in practice by validating two simple wildfire growth forecasting models. |
| first_indexed | 2024-03-09T12:18:59Z |
| format | Article |
| id | doaj.art-8f193bdbb37c4eabbb0ce07112e40d4f |
| institution | Directory Open Access Journal |
| issn | 2571-6255 |
| language | English |
| last_indexed | 2024-03-09T12:18:59Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fire |
| spelling | doaj.art-8f193bdbb37c4eabbb0ce07112e40d4f2023-11-30T22:43:00ZengMDPI AGFire2571-62552022-09-015515310.3390/fire5050153A Protocol for Collecting Burned Area Time Series Cross-Check DataHarry R. Podschwit0Brian Potter1Narasimhan K. Larkin2College of the Environment Special Programs, Quantitative Ecology & Resource Management (QERM), University of Washington, Seattle, WA 98195, USAPacific Wildland Fire Sciences Laboratory, U.S. Forest Service, 400 N. 34th Street #201, Seattle, WA 98103, USAPacific Wildland Fire Sciences Laboratory, U.S. Forest Service, 400 N. 34th Street #201, Seattle, WA 98103, USAData on wildfire growth are useful for multiple research purposes but are frequently unavailable and often have data quality problems. For these reasons, we developed a protocol for collecting daily burned area time series from the InciWeb website, Incident Management Situation Reports (IMSRs), and other sources. We apply this protocol to create the Warehouse of Multiple Burned Area Time Series (WoMBATS) data, which are a collection of burned area time series with cross-check data for 514 wildfires in the United States for the years 2018–2020. We compare WoMBATS-derived distributions of wildfire occurrence and size to those derived from MTBS data to identify potential biases. We also use WoMBATS data to cross tabulate the frequency of missing data in InciWeb and IMSRs and calculate differences in size estimates. We identify multiple instances where WoMBATS data fails to reproduce wildfire occurrence and size statistics derived from MTBS data. We show that WoMBATS data are typically much more complete than either of the two constituent data sources, and that the data collection protocol allows for the identification of otherwise undetectable errors. We find that although disagreements between InciWeb and IMSRs are common, the magnitude of these differences are usually small. We illustrate how WoMBATS data can be used in practice by validating two simple wildfire growth forecasting models.https://www.mdpi.com/2571-6255/5/5/153data cleaningdata collectionInciWebuncertaintymissing datawildfire growth |
| spellingShingle | Harry R. Podschwit Brian Potter Narasimhan K. Larkin A Protocol for Collecting Burned Area Time Series Cross-Check Data Fire data cleaning data collection InciWeb uncertainty missing data wildfire growth |
| title | A Protocol for Collecting Burned Area Time Series Cross-Check Data |
| title_full | A Protocol for Collecting Burned Area Time Series Cross-Check Data |
| title_fullStr | A Protocol for Collecting Burned Area Time Series Cross-Check Data |
| title_full_unstemmed | A Protocol for Collecting Burned Area Time Series Cross-Check Data |
| title_short | A Protocol for Collecting Burned Area Time Series Cross-Check Data |
| title_sort | protocol for collecting burned area time series cross check data |
| topic | data cleaning data collection InciWeb uncertainty missing data wildfire growth |
| url | https://www.mdpi.com/2571-6255/5/5/153 |
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