Potential of satellite and reanalysis evaporation datasets for hydrological modelling under various model calibration strategies

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License: CC BY 4.0
Serval ID
serval:BIB_320A56663B51
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Potential of satellite and reanalysis evaporation datasets for hydrological modelling under various model calibration strategies
Journal
Advances in Water Resources
Author(s)
Dembélé Moctar, Ceperley Natalie, Zwart Sander J., Salvadore Elga, Mariethoz Gregoire, Schaefli Bettina
ISSN
0309-1708
Publication state
Published
Issued date
09/2020
Peer-reviewed
Oui
Volume
143
Pages
103667
Language
english
Abstract
Twelve actual evaporation datasets are evaluated for their ability to improve the performance of the fully distributed mesoscale Hydrologic Model (mHM). The datasets consist of satellite-based diagnostic models (MOD16A2, SSEBop, ALEXI, CMRSET, SEBS), satellite-based prognostic models (GLEAM v3.2a, GLEAM v3.3a, GLEAM v3.2b, GLEAM v3.3b), and reanalysis (ERA5, MERRA-2, JRA-55). Four distinct multivariate calibration strategies (basin-average, pixel-wise, spatial bias-accounting and spatial bias-insensitive) using actual evaporation and streamflow are implemented, resulting in 48 scenarios whose results are compared with a benchmark model calibrated solely with streamflow data. A process-diagnostic approach is adopted to evaluate the model responses with in-situ data of streamflow and independent remotely sensed data of soil moisture from ESA-CCI and terrestrial water storage from GRACE. The method is implemented in the Volta River basin, which is a data scarce region in West Africa, for the period from 2003 to 2012.
Results show that the evaporation datasets have a good potential for improving model calibration, but this is dependent on the calibration strategy. All the multivariate calibration strategies outperform the streamflow-only calibration. The highest improvement in the overall model performance is obtained with the spatial bias-accounting strategy (+29%), followed by the spatial bias-insensitive strategy (+26%) and the pixel-wise strategy (+24%), while the basin-average strategy (+20%) gives the lowest improvement. On average, using evaporation data in addition to streamflow for model calibration decreases the model performance for streamflow (-7%), which is counterbalance by the increase in the performance of the terrestrial water storage (+11%), temporal dynamics of soil moisture (+6%) and spatial patterns of soil moisture (+89%). In general, the top three best performing evaporation datasets are MERRA-2, GLEAM v3.3a and SSEBop, while the bottom three datasets are MOD16A2, SEBS and ERA5. However, performances of the evaporation products diverge according to model responses and across climatic zones. These findings open up avenues for improving process representation of hydrological models and advancing the spatiotemporal prediction of floods and droughts under climate and land use changes.
Keywords
Actual evaporation, Satellite remote sensing, Reanalysis, Model parametrization, Hydrological processes, Spatial patterns, Multi-variable calibration, Multi-objective function
Web of science
Open Access
Yes
Create date
17/10/2020 16:10
Last modification date
03/12/2022 6:48
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