Can we trust remote sensing evapotranspiration products over Africa?

Weerasinghe, Imeshi; Bastiaanssen, W.G.M.; Mul, Marloes; Li, Jia; Griensven, Ann van

doi:10.5194/hess-24-1565-2020

Cited by 90 publications

(53 citation statements)

References 48 publications

(72 reference statements)

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“…Indeed, careful examination of long-term precipitation plots reveals marked declines in rainfall during the 1990s and early 2000s that did not continue significantly into our 2002-2016 study period (Diem et al, 2014;Dezfuli, 2017;Hua et al, 2019). The lack of an interannual ETwb trend is consistent with the recent findings of Weerasinghe et al (2020).…”

Section: Effects Of Long-term Climatic Shifts On Etsupporting

confidence: 86%

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

Burnett¹,

Quetin²,

Konings³

2020

Preprint

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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).

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Section: Effects Of Long-term Climatic Shifts On Etsupporting

confidence: 86%

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

Burnett¹,

Quetin²,

Konings³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Since different ET products have different spatial patterns, temporal trends and long-term magnitudes, it is vital to check ET products used in the HMBC against long-term precipitation (P long ) subtracted by long-term discharge (Q long ), i.e. P long − Q long (Weerasinghe et al 2020).…”

Section: Data Quality and Model Performancementioning

confidence: 99%

Mass balance calibration and reservoir representations for large-scale hydrological impact studies using SWAT+

Chawanda

Arnold²,

Thiery

et al. 2020

Climatic Change

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Climate change (CC) has a high impact on hydrological processes which calls for reliable projections of CC hydrological impacts at large scales. However, there are several challenges in hydrological modelling at large scales. Large-scale models are often not adapted and evaluated at regional scale due to high computation time requirements or lack of information on human interactions, such as dam operations and irrigation practices at local scale. In this study, we present a regionalised methodology that uses a hydrological mass balance calibration (HMBC) and global datasets to represent reservoir and irrigation practices and apply these to a SWAT+ model for Southern Africa. We evaluate the influence of HMBC and the representation on irrigation and reservoirs on model performance and climate projections. We propose a generalised implementation of reservoirs using global datasets and decision tables to represent irrigation and reservoir management. Results show that inclusion of irrigation, reservoirs and HMBC leads to improved simulation of discharge and evapotranspiration with fewer iterations than a full parameter calibration. There is a substantial difference between projections made by the regionalised model and default model when looking at local impacts. We conclude that large-scale hydrological studies that involve local analysis and spatial mapping of results benefit from HMBC and representation of management practices. The proposed methodology can be scaled up and improve overall projections made by global models.

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“…Studies of tropical trees have found that new leaves can take 1-2 months to reach peak photosynthetic capacity and WUE and that both traits tend to decline as leaves reach 5-6 months old (Sobrado, 1994;Shirke, 2001). Leaf age has been linked to photosynthetic seasonality within the Amazon rainforest (Wu et al, 2016), although this effect has not been investigated in the Congo Basin. Connecting the phenology of Congolese forests to basin-wide photosynthesis and transpiration must account for multiple broad ecoregions that span the Equator and therefore face inverted seasonalities (Figs.…”

Section: Leaf-age-related Wue Variationsmentioning

confidence: 99%

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

Burnett

Quetin

Konings

2020

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

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 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 (117.2±3.5 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).

show abstract

Can we trust remote sensing evapotranspiration products over Africa?

Cited by 90 publications

References 48 publications

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

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

Mass balance calibration and reservoir representations for large-scale hydrological impact studies using SWAT+

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

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