Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020)
In soil erosion estimation models, the variables with the greatest impact are rainfall erosivity (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow>&l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/22/5432 |
| _version_ | 1827638813824909312 |
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| author | Leonardo Gutierrez Adrian Huerta Evelin Sabino Luc Bourrel Frédéric Frappart Waldo Lavado-Casimiro |
| author_facet | Leonardo Gutierrez Adrian Huerta Evelin Sabino Luc Bourrel Frédéric Frappart Waldo Lavado-Casimiro |
| author_sort | Leonardo Gutierrez |
| collection | DOAJ |
| description | In soil erosion estimation models, the variables with the greatest impact are rainfall erosivity (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula>), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>D</mi></mrow></semantics></math></inline-formula>), which relates <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula> to precipitation. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula> requires high temporal resolution records for its estimation. However, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula> estimation. This study evaluates the performance of a new gridded dataset of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>D</mi></mrow></semantics></math></inline-formula> in Peru (PISCO_reed) by merging data from the IMERG v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000–2020. By using this method, a correlation of 0.94 was found between PISCO_reed and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula> obtained by the observed data. An average annual <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula> for Peru of 7840 MJ · mm · ha<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>· h<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> was estimated with a general increase towards the lowland Amazon regions, and high values were found on the North Pacific Coast area of Peru. The spatial identification of the most at risk areas of erosion was evaluated through a relationship between the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>D</mi></mrow></semantics></math></inline-formula> and rainfall. Both erosivity datasets will allow us to expand our fundamental understanding and quantify soil erosion with greater precision. |
| first_indexed | 2024-03-09T16:28:07Z |
| format | Article |
| id | doaj.art-12fbf8c0ce0742de9b1a734a74b90c9b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T16:28:07Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-12fbf8c0ce0742de9b1a734a74b90c9b2023-11-24T15:04:52ZengMDPI AGRemote Sensing2072-42922023-11-011522543210.3390/rs15225432Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020)Leonardo Gutierrez0Adrian Huerta1Evelin Sabino2Luc Bourrel3Frédéric Frappart4Waldo Lavado-Casimiro5Servicio Nacional de Meteorología e Hidrología (SENAMHI), Lima 15072, PeruServicio Nacional de Meteorología e Hidrología (SENAMHI), Lima 15072, PeruServicio Nacional de Meteorología e Hidrología (SENAMHI), Lima 15072, PeruGéosciences Environnement Toulouse (GET), Université de Toulouse, CNRS, IRD, UPS, CNES, OMP, 31000 Toulouse, FranceISPA, Bordeaux Sciences Agro, INRAE, 33140 Villenave d’Ornon, FranceServicio Nacional de Meteorología e Hidrología (SENAMHI), Lima 15072, PeruIn soil erosion estimation models, the variables with the greatest impact are rainfall erosivity (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula>), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>D</mi></mrow></semantics></math></inline-formula>), which relates <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula> to precipitation. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula> requires high temporal resolution records for its estimation. However, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula> estimation. This study evaluates the performance of a new gridded dataset of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>D</mi></mrow></semantics></math></inline-formula> in Peru (PISCO_reed) by merging data from the IMERG v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000–2020. By using this method, a correlation of 0.94 was found between PISCO_reed and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula> obtained by the observed data. An average annual <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>E</mi></mrow></semantics></math></inline-formula> for Peru of 7840 MJ · mm · ha<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>· h<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> was estimated with a general increase towards the lowland Amazon regions, and high values were found on the North Pacific Coast area of Peru. The spatial identification of the most at risk areas of erosion was evaluated through a relationship between the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>E</mi><mi>D</mi></mrow></semantics></math></inline-formula> and rainfall. Both erosivity datasets will allow us to expand our fundamental understanding and quantify soil erosion with greater precision.https://www.mdpi.com/2072-4292/15/22/5432rainfall erosivityerosivity densitysatellite rainfall productIMERGhourly observed rainfallPeru |
| spellingShingle | Leonardo Gutierrez Adrian Huerta Evelin Sabino Luc Bourrel Frédéric Frappart Waldo Lavado-Casimiro Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020) Remote Sensing rainfall erosivity erosivity density satellite rainfall product IMERG hourly observed rainfall Peru |
| title | Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020) |
| title_full | Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020) |
| title_fullStr | Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020) |
| title_full_unstemmed | Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020) |
| title_short | Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020) |
| title_sort | rainfall erosivity in peru a new gridded dataset based on gpm imerg and comprehensive assessment 2000 2020 |
| topic | rainfall erosivity erosivity density satellite rainfall product IMERG hourly observed rainfall Peru |
| url | https://www.mdpi.com/2072-4292/15/22/5432 |
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