Evaluating the Potential of Different Evapotranspiration Datasets for Distributed Hydrological Model Calibration

Xiao Guo,Hai He,Zhiyong Wu,Zhengguang Xu

doi:10.3390/rs14030629

Abstract

Evapotranspiration (ET), a key component of the hydrological cycle, has a direct impact on runoff and water balance. Various global satellite-based and numerical datasets provide continuous and high spatiotemporal resolution data, which makes it possible to calibrate hydrological parameters against ET. However, the accuracy of ET datasets varies with region and algorithm, introducing uncertainties in hydrological parameter calibration. This study focused on evaluating the potential of different ET datasets in the calibration of distributed hydrological model parameters. Five different ET datasets (PML, SEBAL, EB-ET, GLASS, REA-ET) were evaluated using the water balance method to explore the effect of intrinsic dataset accuracy on applications. The benchmark calibration scheme calibrated parameters by using observed streamflow data from the outlet. Two calibration schemes were proposed to take advantage of the temporal dynamics and spatial patterns of the raw ET datasets. The results show that the model parameters calibrated by all selected ET datasets produced satisfactory results in streamflow simulations. These results were dependent on the calibration schemes and accuracy of ET datasets. Overall, the scheme calibrated by using temporal dynamics of ET at the grid scale provided better streamflow simulations at the basin outlet than the scheme calibrated by using spatial patterns of ET at the basin scale. Three metrics (bias, root mean square error [RMSE], and correlation coefficient [R]) showed that there is a high potential for selected ET datasets to improve soil moisture simulations, as compared to the benchmark scheme. Parameters calibrated by EB-ET and PML datasets provided the best performance in the simulation of streamflow at the outlet and the sub-basin scale. The calibration case with the SEBAL dataset showed the highest potential to improve soil moisture simulation. The annual average ET estimates of these three datasets were closest to the water balance-based ET values.

Highlights

Distributed hydrological models, which provide spatial variations in catchment characteristics, are important tools for hydrological forecasting and water resource management [1,2,3]
Compared with the water balance-based ET, three datasets (EB-ET, PML, and SEBAL) performed well on annual basin-average ET estimates, while two datasets (GLASS and reliability ensemble averaging (REA)-ET) overestimated the annual basin-average ET with an relative error (RE) more than
The results show that the distribution of RE from different calibration cases is consistent in the Ganjiang River Basin (GRB), with the simulations tending to underestimate streamflow in the west and overestimate streamflow in the large sub-basins

Summary

Introduction

Distributed hydrological models, which provide spatial variations in catchment characteristics, are important tools for hydrological forecasting and water resource management [1,2,3]. Easy access and high-quality observed streamflow datasets from basin outlets have been used to constrain parameters for hydrological model calibration [5,6]. This method may be reliable in small watersheds with available datasets, but not for large and heterogeneous basins, especially in ungauged basins [6,7,8,9]. In the past 10 years, satellite remote-sensing observations and reanalysis datasets have been frequently applied in model calibrations [10,11,12] They can provide valuable information about hydrology-relevant variables at increasingly finer spatial and temporal resolutions, including precipitation, total water storage, evapotranspiration (ET), snow

Objectives

Results

Discussion

Conclusion