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

Moctar Dembélé,Natalie Ceperley,Sander J Zwart,Elga Salvadore,Gregoire Mariethoz,Bettina Schaefli

doi:10.1016/j.advwatres.2020.103667

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.

Highlights

Assessing the spatiotemporal variability of hydrological processes is the crux of effective water resource management
The model performance for various hydrological processes in the Volta River Basin (VRB) reveals the potential of Satellite Remote Sensing (SRS) and reanalysis evaporation datasets to improve the model responses if the appropriate calibration strategy is used (Fig. 6)
The decrease in the model performance for Q in the multivariate calibration might be an artifact caused by equifinality that occurred with the Q-only calibration, which gives more degrees of freedom for constraining the model parameter space Dembélé et al (2020a)

Summary

Introduction

Assessing the spatiotemporal variability of hydrological processes is the crux of effective water resource management. Global warming is expected to intensify (i.e., accelerate) the hydrological cycle, increasing or decreasing evaporation depending on places (Donat et al, 2016; Famiglietti and Rodell, 2013; Huntington, 2006). Spatial location measure β Bias measure γ. ΦBA ΦE a ΦPW ΦQ ΦSB ΦSP DEEaE KG E p E ref E RMS E SP F DS I LA P

Methods

Results

Conclusion