Integration of Remote Sensing Evapotranspiration into Multi-Objective Calibration of Distributed Hydrology–Soil–Vegetation Model (DHSVM) in a Humid Region of China

Suli Pan,Zhixu Bai,Yue-Ping Xu,Li Liu

doi:10.3390/w10121841

Abstract

This study presents an approach that integrates remote sensing evapotranspiration into multi-objective calibration (i.e., runoff and evapotranspiration) of a fully distributed hydrological model, namely a distributed hydrology–soil–vegetation model (DHSVM). Because of the lack of a calibration module in the DHSVM, a multi-objective calibration module using ε-dominance non-dominated sorted genetic algorithm II (ε-NSGAII) and based on parallel computing of a Linux cluster for the DHSVM (εP-DHSVM) is developed. The module with DHSVM is applied to a humid river basin located in the mid-west of Zhejiang Province, east China. The results show that runoff is simulated well in single objective calibration, whereas evapotranspiration is not. By considering more variables in multi-objective calibration, DHSVM provides more reasonable simulation for both runoff (NS: 0.74% and PBIAS: 10.5%) and evapotranspiration (NS: 0.76% and PBIAS: 8.6%) and great reduction of equifinality, which illustrates the effect of remote sensing evapotranspiration integration in the calibration of hydrological models.

Highlights

Parameter values of hydrological models must be evaluated through calibration to match the model response to historical observed data [1,2,3,4]
Owing to the lack of actual evapotranspiration observation in the study area, potential evapotranspiration (PET) calculated by the FAO Penman-Monteith method is used to examine the accuracy of moderate resolution imaging spectrometer (MODIS)-ET [39,41,67]
MODIS-ET is lower than PET, which is rational, owing to the fact that PET is the quantification of evapotranspiration ability with sufficient moisture, i.e., the maximum of actual evapotranspiration

Summary

Introduction

Parameter values of hydrological models must be evaluated through calibration to match the model response to historical observed data [1,2,3,4]. Considering the multiple processes and numerous parameters in distributed hydrological models, this approach is not very effective and often leads to parameter equifinality problems [5]. Calibration with single model objective (that is to say, runoff) is unlikely to optimize other model outputs (such as evapotranspiration and soil moisture) owing to the underlying principle in distributed hydrological models. The application of multi-objective calibration in distributed hydrological models can optimize multiple model outputs simultaneously and reduce possible equifinality problems [6]. Remote sensing data have been widely used for precipitation estimation, land use classification, evapotranspiration inversion, vegetation indices and soil moisture prediction [10,11,12,13,14,15,16,17].

Objectives

Results

Discussion

Conclusion