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

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.

Hydrological Model Calibration with Streamflow and Remote Sensing Based Evapotranspiration Data in a Data Poor Basin

“…However, when the model performance is evaluated on two variables (streamflow and evapotranspiration) as in this study, the model result on the second variable can also be viewed as model validation. Similar examples can be found in several publications that utilized remote-sensing based ET data for hydrological model calibration, e.g., [40,44,46,50,54]. To find the 'good parameter set', we adopted a method described by Finger et al (2011) [51], where 100 simulations with the highest model performance were used.…”

“…However, the results also suggested that streamflow data is also required to identify model simulation errors in gauged or ungauged systems. Using a distributed hydrology-soil-vegetation model (DHSVM) in Jinhua basin in China, Pan et al (2018) [54] also mentioned that streamflow was simulated well in the single variable calibration, but not evapotranspiration, and that multivariables calibration showed more reasonable estimation of both streamflow and evapotranspiration simulated. Furthermore, in an application of GLEAM ET and ESA CCI (European Space Agency-Climate Change Initiative) surface soil moisture data for calibration of PCR-GLOBWB (PCRaster GLOBal Water Balance) in Oum er Rbia Basin in Morocco, López et al (2017) [42] showed that multivariable calibration may help to solve the problem of over-parameterization and equifinality.…”

A new fractal-theory-based criterion for hydrological model calibration

“…Ever since the first hydrological model was developed, appropriate methods of evaluating the performance of such models have been sought by the hydrological community, and a large variety of efficiency criteria have been proposed and used over the years. Most of these criteria are based on squared residuals or absolute errors (Pushpalatha et al, 2012). Krause et al (2005) compared nine efficiency criteria, including the correlation coefficient (r 2 ), the Nash-Sutcliffe efficiency (E), the index of agreement (d) and variants of these criteria, but none of them were found to be consider-ably better performance measures than the rest.…”

“…Hao and Singh (2013) proposed a method for constructing the bivariate distribution of drought duration and severity with different marginal distribution forms based on entropy theory. Pechlivanidis et al (2015) combined the conditioned entropy difference metric and the Kling-Gupta efficiency for the multi-objective calibration of hydrological models. Li et al (2010) used a Bayesian method to assess the uncertainty in hydrological model estimation.…”

“…Statistical parameters include the mean value, standard deviation, coefficient of skewness, coefficient of variance and quantile points. Most dimensionless coefficients (such as the Pearson correlation coefficient (r 2 ), the Nash-Sutcliffe efficiency coefficient (E) and the Kling-Gupta efficiency coefficient) are based on the squared residuals (Pushpalatha et al, 2012). According to the formula for calculating coefficients based on the squared residuals, closer simulated individual data and observed data make the coefficients better.…”