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Preprints
https://doi.org/10.5194/hess-2020-186
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-186
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 04 May 2020

Submitted as: research article | 04 May 2020

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This preprint is currently under review for the journal HESS.

Data-driven estimates of evapotranspiration and its drivers in the Congo Basin

Michael W. Burnett1, Gregory R. Quetin2, and Alexandra G. Konings2 Michael W. Burnett et al.
  • 1Earth Systems Program, Stanford University, Stanford, CA, USA
  • 2Department of Earth System Science, Stanford University, Stanford, CA, USA

Abstract. 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 it being the second-largest river basin 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 Recover and Climate Experiment (GRACE) satellites, in-situ measurements of river discharge, and precipitation from several remotely sensed 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 Version 2 (CHIRPS2) data, 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 in 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 (117.2 ± 3.5 cm/year), but displays greater seasonal and interannual variability than six 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 diffuse photosynthetically-active radiation (PAR) fraction, net radiation (Rn), and soil water availability. From 2002–2016, Rn, PAR, and vapor-pressure deficit (VPD) all increase 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 is likely due to increasing atmospheric CO2 concentrations that offset the impacts of rising VPD and irradiance on stomatal water use efficiency (WUE).

Michael W. Burnett et al.

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Congo Basin Evapotranspiration (ET) M. W. Burnett, G. R. Quetin, and A. G. Konings https://doi.org/10.17605/OSF.IO/JPVMB

Michael W. Burnett et al.

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Latest update: 30 May 2020
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Short summary
Water that evaporates from Africa's tropical forests provides rainfall throughout the continent. However, there are few sources of meteorological data in central Africa, so we use observations from satellites to estimate evaporation from the Congo River Basin at different times of the year. We find that existing evaporation estimates in tropical Africa do not accurately capture seasonal variations in evaporation, and that fluctuations in soil moisture and solar radiation drive evaporation rates.
Water that evaporates from Africa's tropical forests provides rainfall throughout the continent....
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