Precipitation Variability for Protected Areas of Primary Forest and Pastureland in Southwestern Amazônia
Daily and monthly rainfall data provided by surface rain gauges in the Amazon Basin are sparse and defective, making it difficult to monitor rainfall patterns for certain portions of its territory, in this sense, estimations of precipitation from remote sensing calibrated with rain gauge data are ke...
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| Veröffentlicht in: | Climate (Basel) 2023-01, Vol.11 (2), p.27 |
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| creator | Moreira, Rodrigo Martins dos Santos, Bruno César Sanches, Rafael Grecco Bourscheidt, Vandoir de Sales, Fernando Sieber, Stefan de Souza, Paulo Henrique |
| description | Daily and monthly rainfall data provided by surface rain gauges in the Amazon Basin are sparse and defective, making it difficult to monitor rainfall patterns for certain portions of its territory, in this sense, estimations of precipitation from remote sensing calibrated with rain gauge data are key to overcome this problem. This paper presents a spatiotemporal analysis of the precipitation distribution for Rondônia State, in southwestern Amazonia. Data from Climate Hazards Group InfraRed Precipitation and Station (CHIRPS) were analyzed, using a pooled time analysis of a forty-year period (1981–2020). Data obtained from remote sensing were validated by rain gauges distributed over the study region. Pixel-by-pixel trend analyzes were developed by applying the Mann-Kendall test and Sen’s slope test to study the magnitude of the trend. The analysis revealed that CHIRPS presents a tendency to underestimate precipitation values in most cases. Among the metrics, mean values between very good ( 0.70) for about 64.7% of the stations. Sen’ slope spatialization results show a reduction of approximately −15 mm year−1, with decrease mainly in the Northern Region of Rondônia, which has extensive areas where the native forest has been replaced by pasture. |
| doi_str_mv | 10.3390/cli11020027 |
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This paper presents a spatiotemporal analysis of the precipitation distribution for Rondônia State, in southwestern Amazonia. Data from Climate Hazards Group InfraRed Precipitation and Station (CHIRPS) were analyzed, using a pooled time analysis of a forty-year period (1981–2020). Data obtained from remote sensing were validated by rain gauges distributed over the study region. Pixel-by-pixel trend analyzes were developed by applying the Mann-Kendall test and Sen’s slope test to study the magnitude of the trend. The analysis revealed that CHIRPS presents a tendency to underestimate precipitation values in most cases. Among the metrics, mean values between very good (<±15%) and good (±15–±35%) were observed using PBIAS; mean RMSE values range from 57.8 mm to 107.9 mm; an average agreement level of 0.9 and an average SES of 0.5; and good fit for the linear regression model (average R2 > 0.70) for about 64.7% of the stations. Sen’ slope spatialization results show a reduction of approximately −15 mm year−1, with decrease mainly in the Northern Region of Rondônia, which has extensive areas where the native forest has been replaced by pasture.</description><identifier>ISSN: 2225-1154</identifier><identifier>EISSN: 2225-1154</identifier><identifier>DOI: 10.3390/cli11020027</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Analysis ; Atmospheric precipitations ; Brazil ; Climate change ; Deforestation ; Ecosystems ; Environment ; Environmental aspects ; Environmental hazards ; Forests ; Gauges ; Germany ; Hydrologic data ; Hydrology ; Infrared analysis ; Measurement ; Missing data ; Monthly rainfall ; Monthly rainfall data ; Old growth forests ; Pasture ; Pastures ; Pixels ; Precipitation ; Precipitation distribution ; Precipitation variability ; Protected areas ; Rain ; Rain and rainfall ; Rain gauges ; Rainfall ; Rainfall patterns ; Regression models ; Remote sensing ; Roads & highways ; Seasons ; Slopes ; Time series ; Trends</subject><ispartof>Climate (Basel), 2023-01, Vol.11 (2), p.27</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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This paper presents a spatiotemporal analysis of the precipitation distribution for Rondônia State, in southwestern Amazonia. Data from Climate Hazards Group InfraRed Precipitation and Station (CHIRPS) were analyzed, using a pooled time analysis of a forty-year period (1981–2020). Data obtained from remote sensing were validated by rain gauges distributed over the study region. Pixel-by-pixel trend analyzes were developed by applying the Mann-Kendall test and Sen’s slope test to study the magnitude of the trend. The analysis revealed that CHIRPS presents a tendency to underestimate precipitation values in most cases. Among the metrics, mean values between very good (<±15%) and good (±15–±35%) were observed using PBIAS; mean RMSE values range from 57.8 mm to 107.9 mm; an average agreement level of 0.9 and an average SES of 0.5; and good fit for the linear regression model (average R2 > 0.70) for about 64.7% of the stations. Sen’ slope spatialization results show a reduction of approximately −15 mm year−1, with decrease mainly in the Northern Region of Rondônia, which has extensive areas where the native forest has been replaced by pasture.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/cli11020027</doi><orcidid>https://orcid.org/0000-0001-6794-6026</orcidid><orcidid>https://orcid.org/0000-0002-3420-9924</orcidid><orcidid>https://orcid.org/0000-0001-5419-323X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Analysis Atmospheric precipitations Brazil Climate change Deforestation Ecosystems Environment Environmental aspects Environmental hazards Forests Gauges Germany Hydrologic data Hydrology Infrared analysis Measurement Missing data Monthly rainfall Monthly rainfall data Old growth forests Pasture Pastures Pixels Precipitation Precipitation distribution Precipitation variability Protected areas Rain Rain and rainfall Rain gauges Rainfall Rainfall patterns Regression models Remote sensing Roads & highways Seasons Slopes Time series Trends |
| title | Precipitation Variability for Protected Areas of Primary Forest and Pastureland in Southwestern Amazônia |
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