DOI: 10.5194/hess-24-4189-2020
论文题名: Data-driven estimates of evapotranspiration and its controls in the Congo Basin
作者: Burnett M.W. ; Quetin G.R. ; Konings A.G.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期: 8 起始页码: 4189
结束页码: 4211
语种: 英语
Scopus关键词: Evapotranspiration
; Forestry
; Geodetic satellites
; Neural networks
; Remote sensing
; Soil moisture
; Tropics
; Water supply
; Gravity recovery and climate experiment satellites
; Interannual variability
; Photosynthetically active radiation
; Precipitation estimation from remotely sensed information
; Seasonal and interannual variability
; Soil water availability
; Vapor pressure deficit
; Water-use efficiency
; Digital storage
; annual variation
; climate cycle
; data assimilation
; estimation method
; evapotranspiration
; global climate
; GRACE
; in situ measurement
; remote sensing
; river discharge
; satellite altimetry
; tropical forest
; water budget
; water storage
; Congo Basin
; Satellites
英文摘要: Evapotranspiration (ET) from tropical forests serves as a critical moisture source for regional and global climate cycles. However, the magnitude, seasonality, and interannual variability of ET in the Congo Basin remain poorly constrained due to a scarcity of direct observations, despite the Congo being the second-largest river basin in the world and containing a vast region of tropical forest. In this study, we applied a water balance model to an array of remotely sensed and in situ datasets to produce monthly, basin-wide ET estimates spanning April 2002 to November 2016. Data sources include water storage changes estimated from the Gravity Recovery and Climate Experiment (GRACE) satellites, in situ measurements of river discharge, and precipitation from several remotely sensed and gauge-based sources. An optimal precipitation dataset was determined as a weighted average of interpolated data by Nicholson et al. (2018), Climate Hazards InfraRed Precipitation with Station data version 2 (CHIRPS2) , and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record product (PERSIANN-CDR), with the relative weights based on the error magnitudes of each dataset as determined by triple collocation. The resulting water-balance-derived ET (ETwb) features a long-term average that is consistent with previous studies (117:23:5 cm yr-1) but displays greater seasonal and interannual variability than seven global ET products. The seasonal cycle of ETwb generally tracks that of precipitation over the basin, with the exception that ETwb is greater in March-April-May (MAM) than in the relatively wetter September-October-November (SON) periods. This pattern appears to be driven by seasonal variations in the diffuse photosynthetically active radiation (PAR) fraction, net radiation (Rn), and soil water availability. From 2002 to 2016, Rn, PAR, and vapor-pressure deficit (VPD) all increased significantly within the Congo Basin; however, no corresponding trend occurred in ETwb.We hypothesize that the stability of ETwb over the study period despite sunnier and less humid conditions may be due to increasing atmospheric CO2 concentrations that offset the impacts of rising VPD and irradiance on stomatal water use efficiency (WUE). © 2020 EDP Sciences. All rights reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162611
Appears in Collections: 气候变化与战略
There are no files associated with this item.
作者单位: Burnett, M.W., School of Earth, Energyg and Environmental Sciences, Stanford University, Stanford, CA, United States; Quetin, G.R., Department of Earth System Science, Stanford University, Stanford, CA, United States; Konings, A.G., Department of Earth System Science, Stanford University, Stanford, CA, United States
Recommended Citation:
Burnett M.W.,Quetin G.R.,Konings A.G.. Data-driven estimates of evapotranspiration and its controls in the Congo Basin[J]. Hydrology and Earth System Sciences,2020-01-01,24(8)