Dynamics of hydrological-model parameters: mechanisms, problems and solutions
- 1School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
- 2Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Guangzhou, 510275, China
- 3School of Civil Engineering, Sun Yat-sen University, Guangzhou, 510275, China
- 4Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, 0316 Oslo, Norway
Correspondence: Kairong Lin (email@example.com)
It has been demonstrated that the application of time-varying hydrological-model parameters based on dynamic catchment behavior significantly improves the accuracy and robustness of conventional models. However, the fundamental problems for calibrating dynamic parameters still need to be addressed. In this study, five calibration schemes for dynamic parameters in hydrological models were designed to investigate the underlying causes of poor model performance. The five schemes were assessed with respect to the model performance in different flow phases, the transferability of the dynamic parameters to different time periods, the state variables and fluxes time series, and the response of the dynamic parameter set to the dynamic catchment characteristics. Furthermore, the potential reasons for the poor response of the dynamic parameter set to the catchment dynamics were investigated. The results showed that the underlying causes of poor model performance included time-invariant parameters, “compensation” among parameters, high dimensionality and abrupt shifts in the parameters. The recommended calibration scheme exhibited good performance and overcame these problems by characterizing the dynamic behavior of the catchments. The main reason for the poor response of the dynamic parameter set to the catchment dynamics may be the poor convergence performance of the parameters. In addition, the assessment results of the state variables and fluxes and the convergence performance of the parameters provided robust indications of the dominant response modes of the hydrological models in different sub-periods or catchments with distinguishing catchment characteristics.
Hydrological modeling is an essential tool for understanding the hydrological processes of a catchment and forecasting streamflow (Liu et al., 2015, 2018; Turner et al., 2017; Delorit et al., 2017; Fenicia et al., 2014, 2018; Hublart et al., 2016; Höge et al., 2018; Sarrazin et al., 2016; Wi et al., 2015; Herman et al., 2013; Wagener et al., 2001, 2003; Madsen, 2000; Osuch et al., 2019; Zhang et al., 2019). Unfortunately, the paucity of progress in model development is partly due to structural inadequacy. For example, dynamic components in hydrological models are oversimplified due to a poor understanding of their physical mechanisms (Xiong et al., 2019; Deng et al., 2016, 2018; Dakhlaoui et al., 2017; Sarhadi et al., 2016; Pathiraja et al., 2016; Ouyang et al., 2016). Previous studies have demonstrated that the assumption of time-invariant parameters is usually inappropriate. The reasons are that a unique parameter set optimized by hydrological models only represents the average hydrological processes, which do not accurately represent the dynamic response modes of the catchments processes (Pathiraja et al., 2018; Fowler et al., 2018; Zhao et al., 2017; Kim and Han, 2017; Golmohammadi et al., 2017; Delorit et al., 2017; Chen et al., 2017). To investigate the problems caused by time-invariant parameters, a control scheme, i.e., scheme 1, is designed and assessed in this study. In this regard, the dynamics of the hydrological-model parameters may be a type of compensation for models that are missing key processes such as climate- and land-surface-related changes (Xiong et al., 2019; Deng et al., 2016, 2018; Wang et al., 2017b; Dakhlaoui et al., 2017; Sarhadi et al., 2016; Pathiraja et al., 2016; Ouyang et al., 2016; Todorovic and Plavsic, 2015).
However, a critical but often overlooked issue related to dynamic parameters is that there are linear or nonlinear correlations among hydrological-model parameters, also called the “compensation” between parameters (Wagener and Kollat, 2007). The compensation between parameters could even result in the dynamics of the individual parameters may not represent the time-varying properties of river catchments (Höge et al., 2018; Cibin et al., 2010; Bárdossy and Singh, 2008; Bárdossy, 2007; Huang, 2005; Wagener and Kollat, 2007). Hence, it has been conclusively demonstrated that the optimal parameters in hydrological models should not be considered as individual parameters but instead as parameter vector “teams” (Wagener and Kollat, 2007). In this research, the effects of the compensation between the parameters on the dynamics of hydrological-model parameters are investigated using a control scheme i.e., scheme 2.
In view of the compensation between parameters, the most common approach for assessing the dynamics of the hydrological-model parameters is that the calibration period is partitioned into different sub-periods based on the temporal dynamic catchment characteristics (Sarhadi et al., 2016; Merz et al., 2011; Lan et al., 2018; Xiong et al., 2019; Motavita et al., 2019; Deng et al., 2016, 2018; Dakhlaoui et al., 2017; Choi and Beven, 2007; Brigode et al., 2013; Kim et al., 2015; Kim and Han, 2017; Zhao et al., 2017; Pfannerstill et al., 2015; Me et al., 2015; Coron et al., 2014; Vormoor et al., 2018; Luo et al., 2012; Guse et al., 2016; Zhang et al., 2011, 2015; Ouyang et al., 2016). The parameter set in each sub-period is optimized to obtain the dynamic parameter team. Previous studies have demonstrated that sub-period calibration based on the dynamic catchment behavior accurately captures the temporal variations of the catchment characteristics, thereby compensating for structural inadequacy (Lan et al., 2018; Zhao et al., 2017; Kim and Han, 2017; Zhang et al., 2011; de Vos et al., 2010; Gupta et al., 2009; Choi and Beven, 2007; van Griensven et al., 2006; Freer et al., 2003). In the study of Choi and Beven (2007), the sub-periods were identified based on different hydrological characteristics using a clustering technique. The results showed that the model that considered the dynamic catchment characteristics exhibited good performance at the global level (i.e., overall calibration and validation periods). Merz et al. (2011) demonstrated that the parameters of the catchment model related to snow and soil moisture showed clear time trends for the climate indicators. Zhang et al. (2011) proposed a general multi-period calibration approach for improving the performance of hydrological models based on the fuzzy c-means clustering technique under time-varying climatic conditions. The results indicated that model simulations using parameters obtained from the multi-period calibration approach exhibited considerable improvements over those from the conventional single-period model. Brigode et al. (2013) demonstrated the dependence of the optimal parameter set on the climate characteristics of the calibration period. Lan et al. (2018) focused on the sub-period clustering or partition based on the climate–land-surface variations and relevant studies, such as the choice and pre-process of clustering indices in the light of various catchment characteristics and the clustering operation based on different clustering index systems. The results showed that the sub-annual calibration with the clustering preprocessing (CPP) framework exhibited significant improvements in overall performance.
Even though the sub-period calibration performed well for describing the dynamics of the hydrological-model parameters, some fundamental problems still need to be addressed because the analysis involves the hydrological model structure, global optimization, physical mechanisms of dynamic catchment characteristics, as well as complex relationships between the parameters, state variables and fluxes. For example, multiple parameter sets are optimized simultaneously in different sub-periods. Questions like which possible disaster would be brought by parameter optimization in a high-dimensional parameter space remain to be answered. This study aims to investigate the underlying causes of poor model performance in hydrological models with dynamic parameters via designing five calibration schemes and exploring the potential reasons for the poor response of the dynamic parameter set to the catchment dynamics are explored. In addition to schemes 1 and 2 described above, this study designed and assessed a control scheme, i.e., scheme 3, to investigate the problem of high dimensionality. Also, abrupt changes in the parameter set between two sub-periods may result in anomalous or incorrect values in the fluxes and state variables of the time series. Hence, control scheme 4 is designed to investigate potential problems caused by abrupt changes in the parameters. These control schemes are assessed as follows: (1) the model performance is assessed at very low, low, medium, high and very high phases of flow, and the transferability of the dynamic parameter set to different time periods is determined; (2) the state variable and flux time series and their changes between two consecutive sub-periods are evaluated; and (3) the response of the dynamic parameter set to the dynamic catchment characteristics is evaluated. The underlying causes for poor model performance when sub-period calibration is used are investigated, and an effective calibration scheme for dynamic hydrological-model parameters, scheme 5, is recommended as a solution. Furthermore, the underlying mechanism of the lack of a response of the dynamic parameter set to the dynamic catchment characteristics is investigated.
The paper is structured as follows. Section 2 presents the study cases and data, the partition methods, and the results of the sub-periods based on the dynamic catchment characteristics. Section 3 elaborates on the five calibration schemes for the dynamic parameters of the hydrological model and the assessment approaches. Section 4 presents the assessment results of the different schemes, the potential problems and the recommendation of an effective calibration scheme. Section 5 summarizes the underlying causes of poor model performance, followed by a discussion of the poor response of the dynamic parameters to the catchment dynamics. Section 6 summarizes the key conclusions of the study and outlines directions for future research.
In this study, three sub-basins with different spatial scales in the Han Jiang basin, i.e., Hanzhong basin, Muma He basin and Xun He basin, were selected to demonstrate the proposed approach (Fig. 1a). Climatically, the Han Jiang basin is located in the monsoon region of the eastern Asia subtropical zone. The area is cold and dry in winter and warm and humid in summer (Lin et al., 2010), and there are seasonal changes in vegetation density and types (Fang et al., 2002). Subtropical vegetation affects temporal moisture conditions. Significant intra-annual changes in the climate and land surface conditions allow for exploring the seasonal dynamics of the hydrological processes. Therefore, the three basins are ideal locations for investigating the dynamics of hydrological-model parameters. Daily streamflow and climatic data from 1980 to 1990 were used. Nearly 73 % of the data samples (1980–1987) were used for calibration, and the remainder (1988–1990) was utilized to verify the model.
The flowchart of the reasonable sub-period partition based on the dynamic catchment characteristics is shown in Fig. 1b. The data mining techniques were integrated to develop a CPP framework for sub-period partition to simulate dynamic behavior. The hydrological model was calibrated in each sub-period to achieve the dynamics of the parameter set, as illustrated in Fig. 1b. In the CPP, a set of climate–land-surface indices was provided and pre-processed using the maximal information coefficient (MIC) and principal components analysis (PCA). The climatic indices included total precipitation, maximum 1 d precipitation, maximum 5 d precipitation, moderate precipitation days, heavy precipitation days, total pan evaporation, maximum 1 d pan evaporation and minimum 1 d pan evaporation. The land surface indices included antecedent streamflow and runoff coefficient. Two clustering operations were performed based on the pre-processed climatic index system and land surface index system, respectively. The clustering results are shown in Fig. 1c. The results showed that the performance of the model with a CPP framework was significantly improved at high, middle and low streamflow. The transferability of the dynamic parameter set from the calibration to the validation period was also greatly improved.
It is worth emphasizing that the dynamic parameters during the validation period are set to the same as in the calibration period. The values are dependent on the calendar days. This is because our previous research (Lan et al., 2018) showed that the clustering results of the validation period are almost the same as the results of the calibration period. In that study, the hydrological model was calibrated in each sub-period to achieve the dynamics of the parameter set. The calendar year is clustered into four sub-annual periods based on hydrological similarities, and the clustering results were further verified by the hydrological data in the validation period. The reason is that the study area, i.e., the Han Jiang basin, is located in the monsoon region of the eastern Asia subtropical zone, and the seasonal variations of both climate conditions and vegetation density and types are more important than inter-annual variations (Fang et al., 2002).
3.1 Calibration schemes
Five calibration schemes are designed and compared (see Fig. 2). The potential problems when dynamics of hydrological-model parameters are used include time-invariant parameters, compensation among parameters, the high dimensionality of the parameters and abrupt changes in the parameters; these factors are investigated, and a solution is recommended. For illustration purposes, the HYMOD (HYdrological MODel) model (Moore, 1985; Wagener et al., 2001; Vrugt et al., 2002; Yadav et al., 2007; de Vos et al., 2010; Pathiraja et al., 2018), which is a commonly used lumped rainfall–runoff model with five parameters, is utilized. It consists of a simple rainfall excess model based on the probability-distributed moisture store which characterizes the catchment storage as a Pareto distribution of buckets of varying depths as the soil moisture accounting component. It routes through three parallel tanks for quick flow and a tank for slow flow and required five adjustable parameters: HUZ, B, α, Kq and Ks. XHUZ and XCUZ are state variables characterizing the upper soil moisture content; AE is actual evapotranspiration, which is calculated by linear correlations between the soil moisture state and the potential evapotranspiration; effP is effective precipitation; OV is excess precipitation to the routing module generated from the overflow of the soil moisture accounting component; see Moore (1985) for a detailed description of the soil moisture accounting model; , , and Xs are the state variables of the individual tanks of the routing module; and Qq and Qs are the flow values generated from the quick- and slow-flow tanks, respectively. The definitions of the model parameters, state variable and fluxes are presented in Table 1. All schemes with the same set of shuffled complex evolution method were developed at the University of Arizona (SCE-UA). The SCE-UA algorithm is a subset of a global evolution algorithm (Duan et al., 1993; Hanne, 2000; Michalewicz and Schoenauer, 1996; Omran and Mahdavi, 2008; Storn and Price, 1997; Yiu-Wing and Yuping, 2001) which was used as an example of a global optimization algorithm in this study. More information is presented in Sect. S1 of the Supplement. The simulations have a warm-up period of 1 year in the calibration period and 3 months in the validation period. The objective function is defined as the combination of the Nash–Sutcliffe efficiency index (NSE) and the logarithmic transformation (LNSE) (Nash and Sutcliffe, 1970). The NSE is sensitive to the discharge dynamics, and the LNSE emphasizes the low flows because the log of the discharge is used (Nash and Sutcliffe, 1970; Guntner et al., 1999; Kiptala et al., 2014; Nijzink et al., 2016). It is expressed as
where OF(0, ∞) is the objective function value. The closer the value of OF is to zero, the better the model performance is.
Scheme 1. This scheme investigates the problem of time-invariant parameters. The parameters do not change during the entire calibration and validation periods.
Scheme 2. This scheme investigates the compensation among the parameters. In the calibration period, a specific dynamic parameter and the other fixed parameters in different sub-periods are optimized simultaneously. For example, eight parameters, namely one specific parameter in the four sub-periods and another four fixed (i.e., temporally invariant) parameters, are optimized simultaneously during one run in HYMOD. The transition of the state variables and fluxes between two consecutive sub-periods is achieved by considering the last values of the former period as the initial values of the next period. In the validation period, the model is run using the inputs with the specific dynamic parameter and other fixed parameters. The transitions of parameters, state variables and fluxes between two consecutive sub-periods are handled the same as in the calibration period.
The specific dynamic parameter is usually identified by whether it responds to the dynamic catchment characteristics. However, due to the complex correlations among the parameters and imperfect model structures (missing processes or oversimplified parameterizations in the model), the individual parameters may not represent their defined physical characteristics, such as temporal changes in soil, land cover and climate conditions. Hence, the parameter with the highest sensitivity was chosen as the dynamic parameter (Merz et al., 2011; Pfannerstill et al., 2014; Zhang et al., 2015; Deng et al., 2016, 2018; Guse et al., 2016; Ouyang et al., 2016; Xiong et al., 2019). In this study, the dynamic parameter Kq with the highest identifiability and the other fixed parameters are optimized. The chosen parameter is marked in Table 1.
Scheme 3. This scheme investigates the high dimensionality of the parameters. In the calibration period, the parameter sets in different sub-periods are optimized simultaneously. For example, 20 parameters, namely 5 parameters of the hydrological model in 4 sub-periods, are optimized simultaneously in one run. The transition of the state variables and fluxes between two consecutive sub-periods is achieved by considering the last values of the former period as the initial values of the next period. In the validation period, the model is run using the dynamic parameter set. The transitions of the parameters, state variables and fluxes between two consecutive sub-periods are handled the same as in the calibration period.
Scheme 4. This scheme investigates the abrupt changes in the parameters. In the calibration period, only the data from the individual sub-periods are used for minimizing the objective function, while the model is run for the whole period. For example, five parameters of the hydrological model in four sub-periods are optimized in four runs. The calibrated flow data from each sub-period are then combined and compared with the observed flow. In the validation period, the transitions of parameters, state variables and fluxes between two consecutive sub-periods are handled the same as in the validation period of scheme 3. In the validation period, the effects of the correlations and high dimensions of the parameters are excluded, and the influences caused by the abrupt changes in the parameters are investigated.
Scheme 5. A solution is recommended to overcome the above problems that are caused by the time-invariant parameters, compensation among parameters, high dimensionality and abrupt shifts in the parameters. In the calibration period, the model run is the same as that of the calibration period of scheme 4. In the validation period, the simulated flow data from each sub-period are combined and compared with the observed flow.
The above description reveals that the model run in the calibration period is the same in scheme 4 and scheme 5. However, the model run in the validation period is actually different. In the validation period of scheme 4, the model runs one time using the dynamic parameter set. The parameter set between two consecutive sub-periods is switched. As a result, the transition of the state variables and fluxes between two consecutive sub-periods is abrupt and achieved by considering the last values of the former period as the initial values of the next period. In the validation period of scheme 5, the model runs N times (N is the number of the divided sub-periods), combining the simulated flow data in the sub-periods. The comparison between scheme 4 and scheme 5 is to investigate the effect of the abrupt shifts in the parameters on the model run with dynamic parameters.
3.2.1 Assessment of model performance
The performance assessments of the calibration schemes include (1) an assessment of the performance in different phases of the streamflow and (2) an assessment of the transferability of the dynamic parameters to different time periods. Seven performance metrics are used to assess the performance for different parts of the hydrograph in the calibration and validation periods. The metrics are listed and defined in Table 2. The differences in these metrics between the calibration period and the validation period are used to assess the transferability of the optimized parameters. The transferability of the parameters to different time periods is considered a requirement for the successful validation of the model (Gharari et al., 2013; Klemeš, 1986).
Note that the flow duration curve (FDC) is usually split into different segments to describe different flow characteristics of a catchment (Cheng et al., 2012; Coopersmith et al., 2012; Kim and Kaluarachchi, 2014; Pugliese et al., 2014; Pfannerstill et al., 2014). The RMSE with quadratic character is usually used to evaluate poor model performance due to the strong sensitivity to extreme positive and negative error values.
3.2.2 Assessment of the state variables and fluxes
The internal processes of the hydrological model run include the state variable and flux time series. The abrupt changes in the parameters between two consecutive sub-periods may result in changes in the state variables and fluxes, thereby affecting the simulation results. Hence, all the state variables and fluxes obtained from the different schemes are investigated, and the underlying physical mechanisms are discussed (Kim and Han, 2017).
3.2.3 Assessment of the dynamic parameter set
The response of the dynamic parameter sets to the dynamic catchment characteristics in all schemes is investigated for the two response modes of HYMOD, i.e., the soil moisture mode and routing mode. Furthermore, the underlying physical mechanisms based on dynamic catchment characteristics are analyzed.
4.1 Model performance
For a concise model evaluation, the model performance is analyzed with multi-metric frameworks with appropriate performance metrics, including five-segment evaluation (5 FDC; flow duration curve with the root mean square error) (Pfannerstill et al., 2014), the Nash–Sutcliffe efficiency index (NSE) (Nash and Sutcliffe, 1970) and the logarithmic transformation. For the robustness of model evaluation, the transferability of the optimized parameters between the calibration period and the validation period is considered. The results of the assessment are shown in Fig. 3.
The performance of scheme 2 is only slightly better than that of scheme 1, which indicates only a slight increase in the model performance. Scheme 3 has the worst model performance at the global level; i.e., all metrics are much higher than one in the calibration period and validation period. Scheme 4 has the highest overall model performance in the calibration period. For example, the NSE and LNSE are 45.3 % and 13.8 %, respectively; these values are considerably higher than the metrics of scheme 1. The other metrics also indicate that scheme 4 performs best in all flow phases in the calibration period. However, the model performance of scheme 4 in the validation period is only slightly better than that of scheme 1. Scheme 5 has the same model performance as scheme 4 in the calibration period. Nevertheless, the overall model performance of scheme 5 is significantly higher than that of the other schemes in the validation period. The transferability of the optimized parameters is analyzed in all schemes. Scheme 5 has the smallest differences, and scheme 4 has the largest differences in the metrics between the calibration period and validation period.
In summary, scheme 5 does not only have the highest overall performance under different flow conditions in the calibration period and validation period but also exhibits good transferability of the model parameters. Scheme 4 exhibits good performance in the calibration period but does not perform well in the validation period. Scheme 3 has extremely poor model performance at the global level. Scheme 2 does not have better performance than scheme 1. The evaluation results of the five schemes in the Muma He basin and Xun He basin are listed in Sect. S3. The results are similar to those of the Hanzhong basin and will be discussed in Sect. 5.1.
4.2 State variables and fluxes
The assessment results of the state variables and fluxes are shown in Figs. 4 and 5. The variables of scheme 2 are similar to those of scheme 1. The model performance of scheme 2 is only slightly better than that of scheme 1. In scheme 3, there are some unexpected values of the state variables in the time series. In scheme 4, invalid values of the fluxes and state variables are found at the junction of sub-periods, where the parameter set exhibits abrupt changes. In scheme 5, (1) Qs, XHUZ and XCUZ are lower in the dry period and higher in rainfall period II than those in scheme 1. The results indicate that the performance of the model run is better in the dry period and rainfall period II because the runoff is usually overestimated in the dry period (Pool et al., 2017; Wang et al., 2017a; Tongal and Booij, 2018; Xiong et al., 2018) and underestimated in the wettest period (Guo et al., 2018; Höge et al., 2018; Pande and Moayeri, 2018; Wang et al., 2018). It is observed that the state variable Xs and the flux Qs have larger effects on simulating runoff than the quick-flow (Qq and Xq) mode in rainfall period II. The reason is that most of the excess streamflow is diverted to the slow-flow routing, hence the fluxes and state variables present more representativeness of the slow-flow tank mode. (2) A comparison of the observations and simulations of the runoff in scheme 1 and scheme 5 indicates that both peak flows in rainfall period II are more accurately simulated by scheme 5. (3) Scheme 5 also exhibits superior performance in the two normal periods because the state variables provide a good representation of the physical mechanism. The state variables XHUZ and XCUZ are lower in rainfall period I and higher in rainfall period III than in scheme 1. The reason is that the antecedent soil moisture content in rainfall period III is higher than in rainfall period I (Lan et al., 2018). Consequently, the results are consistent with the results in Sect. 4.1. The dynamic parameters in scheme 5 provided a good representation of the dynamic catchment characteristics.
4.3 Dynamic parameter set
The dynamic parameter values optimized by the four sub-period calibration schemes in the Hanzhong basin are shown in Fig. 6. In scheme 2, the dynamic parameter Kq with the highest identifiability and the other fixed parameters are optimized. The result shows that the responses of the dynamic parameter Kq to the dynamic catchment characteristics are not clear. In scheme 3, the parameters HUZ and B in the soil moisture mode of HYMOD (Moore, 1985; Vrugt et al., 2002) show no regular patterns in any of the schemes, and this is similar for α, Kq and Ks in the slow- and quick-flow routing mode. In short, the dynamic parameters do not show clear responses to the dynamic catchment characteristics in scheme 2 or scheme 3. In scheme 5, which is the same as scheme 4 in the calibration period, Ks accurately describes the model responses in the sub-periods for the different catchment characteristics. The value of Ks is lowest in the dry period and highest in the wettest period. However, the parameter Kq exhibits no significantly regular changes. The main reason is that most of the excess streamflow in the three rainfall periods is diverted to the slow-flow tank because the α values are close to zero. This means that the quick-flow tanks do not have an effect on the simulations. The parameter sets optimized by scheme 1 and scheme 5 in the Muma He basin and Xun He basin are listed in Sect. S3. The results are similar to those of the Hanzhong basin.
In summary, scheme 5 performs best for identifying the dominant parameters and their responses to the dynamic catchment characteristics. The dynamic features of the parameters also demonstrate the necessity for sub-period calibration. Furthermore, it is interesting that the state variables and fluxes describe the dynamic catchment behavior more robustly than the dynamic parameters. In light of this, the underlying causes for the poor response of the dynamic parameter set to the catchment dynamics are investigated.
5.1 Underlying causes of poor model performance
The evaluation results of the five schemes are summarized to explore the possible reasons for poor model performance:
Time-invariant parameters. Scheme 1, with the time-invariant parameter set, averages the hydrological responses. As a result, scheme 1 resulted in poor simulation accuracy or weak transferability of the optimized parameters in different flow conditions. The results were consistent with Delorit et al. (2017), Fowler et al. (2018), and Xiong et al. (2019).
Compensation among parameters. In scheme 2, the individual parameters with high identifiability did show clear responses to the dynamic catchment characteristics. Bárdossy (2007) demonstrated that changes in one parameter may be compensated for by changes in other parameters due to their interdependence (Westra et al., 2014; Klotz et al., 2017; Wang et al., 2017b, 2018). Therefore, although a specific parameter is dynamic, the other parameters may counteract those changes, resulting in no overall change in the hydrological processes. Hence, the model performance in scheme 2 was relatively low.
High dimensionality of parameters. In scheme 3, it has a sound logic by continuously running the model with the dynamic parameter set like the real system. However, the results showed that all fluxes and state variables in the time series were anomalous, and the model exhibited extremely poor performance. It was demonstrated that parameter optimization in high-dimensional parameter space with correlated parameters resulted in the failure of the modeling run (Beven and Binley, 1992; Sivakumar, 2004; Bárdossy and Singh, 2008; Laloy and Vrugt, 2012).
Abrupt shifts in parameters. In scheme 4, the abrupt shifts in the parameter set between the sub-periods resulted in anomalous values in the fluxes and state variables in the time series, which results in the failure of the model in the validation period. Kim and Han (2017) also emphasized the negative effects of abrupt shifts in the parameter set on the model performance.
In summary, scheme 5 is recommended for dynamic hydrological-model parameters because it can capture the temporal variations of the dynamic catchment characteristics and overcome the underlying problems responsible for poor model performance. Although scheme 5 has a higher computational cost, this does not represent a large problem with current computing devices.
5.2 Underlying causes of the poor response of the dynamic parameters to the catchment dynamics
The dynamic parameter set was estimated using global optimization algorithms. However, if the convergence fails, the global optimum cannot be determined, and the optimal parameter values may be anomalous. In this case, the optimal results do not represent the hydrological processes in a catchment (Gomez, 2019; Weise, 2009). In order to investigate the underlying causes of the poor response of the dynamic parameters to the catchment dynamics, we assessed the convergence performance of the dynamic parameters and determined the ability of the parameters to respond to the catchment dynamics (Zecchin et al., 2012; Zheng et al., 2017; Azad and Optimization, 2019).
5.2.1 A tool for the convergence evaluation of the dynamic parameters
In order to overcome the limitation of traditional tools for evaluating the convergence behavior of global optimization algorithms for hydrological models, including the visualization of the high-dimensional parameter response surface, rough response surfaces with discontinuous derivatives, poor or inconsistent sensitivities of the response surface, non-convex mesh surfaces, and the dynamic convergence process in high-dimensional parameter spaces (Duan et al., 1992, 1994; Sorooshian et al., 1993; Cooper et al., 1997; Gupta et al., 1998; Vrugt et al., 2005; Weise, 2009; Zhang et al., 2009; Sun et al., 2012; Arora and Singh, 2013; Derrac et al., 2014; Piotrowski et al., 2017; Gomez, 2019), a simple and powerful approach is proposed, namely, Evaluate the Convergence Performance using Violin Plots (ECP-VP). This tool represents the potential features of the fitness landscapes (see Fig. 7) and provides a visualization of the convergence behavior in multi-parameter space. The strategy is as follows.
The end of each evolution loop in the optimization process is regarded as a cut-off point. The parameter set with the best objective function value in each evolution loop is recorded in the “convergence process”.
Violin plots, which are an excellent tool to visualize the kernel density distribution of the data points (Hintze and Nelson, 1998; Piel et al., 2010), are used to configure the convergence process in the individual parameter spaces. The probability distributions of the violin plots are used to represent the possible properties of the fitness landscapes. The anatomy of the violin plot and the associated information can be found in Sect. S2. With an adequate parameter space and sufficient density of coverage, the four types of distributions of violin plots are matched to the property sketches of the fitness landscapes (Weinberger, 1990; Forrest, 1995; Harik et al., 1999; Gibbs et al., 2004; Arsenault et al., 2014; Maier et al., 2014).
A decrease in the performance of the convergence and the candidate mechanisms are interpreted as (I) an unimodal distribution: an ideal global convergence process is used to estimate the best solution. The unimodal distribution matches two types of fitness landscape sketches including the best case and low variation. These can also be interpreted as (II) a bimodal distribution: there are two main local optima and the distance to the two local convergence regions is far. It becomes more complicated for the optimization process to find the global optimum, and the premature convergence to a local optimum may occur (Duan et al., 1992, 1993, 1994; Weise, 2009; Sun et al., 2012; Derrac et al., 2014; Gomez, 2019). The bimodal distribution symbolizes the two types of fitness landscape sketches including the multimodal and deceptive types. These can also be interpreted as (III) a multimodal distribution: the response surface may be multimodal plus steep ascends and descends. This means that multiple local optima exist. With the maze of minor local optima, the calibration algorithm may fail to reach the global optimum. Because the minor optima may be found quite far from the global optimum, the search may terminate prematurely without finding an approximate solution (Dakhlaoui et al., 2017; Duan et al., 1992, 1993, 1994). The multimodal distribution matches the three types of fitness landscape sketches including the multimodal, rugged and deceptive types. These can also be interpreted as (IV) a flat distribution: this is similar to the multimodal distribution, and its surface may be noisy. The very poor sensitivity of the objective function to the parameter fluctuation causes weak convergence of the parameter (Duan et al., 1992, 1993, 1994; Dakhlaoui et al., 2017; Rahnamay Naeini et al., 2018; Vrugt and Beven, 2018). The flat distribution matches the three types of fitness landscape sketches, including the neutral, needle-in-a-haystack and nightmare types.
5.2.2 Convergence assessment
The convergence assessment results of scheme 1 and scheme 5 in the Hanzhong basin are shown in Fig. 8. In scheme 1, (1) the parameter B represents the bimodal distribution in the parameter space, indicating that the fitness landscape of B is unsteady or fluctuating (see Fig. 8a). It is inferred that the convergence processes of the parameter B may be affected by a prominent local optimum. The outcomes of the search operations may be arbitrary, which leads to a divergence away from the global optima. As a result, the convergence performance of B is poor. (2) Although HUZ, α, Kq and Ks rapidly converge, and the range is small, the magnification (Fig. 8b) shows bimodal or multimodal distributions. The global optima cannot be determined in the HUZ, α, Kq and Ks parameter space. As a consequence, the convergence of the parameters in scheme 1 is poor and the response of the parameters to the catchment behavior with a low level of confidence.
In scheme 5, the four sub-periods are evaluated separately. (1) In the dry period, except for Ks, the distributions of the other parameters are oscillating in the entire feasible parameter space. Indeed, the magnification of parameter Ks (see Fig. 8b) shows a multimodal distribution. The result indicates that the convergence performance of the parameter set in the dry period is poor. Due to the weak relationship between precipitation and runoff in the dry period (Moore, 1985; Yadav et al., 2007; de Vos et al., 2010), most modules of the model in the dry period do not accurately characterize the behavior of the catchment. (2) In rainfall periods I–III, the parameters α and Ks with a unimodal distribution have the best convergence performance. The α values in the three rainfall periods are close to the minimum; hence the slow-flow tank controls the cascade routing component of the model. The α and Ks parameters, with high identicality and the best convergence performance, also demonstrate that the chosen model is most suitable for the streamflow simulation in the three rainfall periods. The main reason is that the HYMOD model is well suited for catchments dominated by “saturation excess overland flow” processes. Intense rainfall events contribute to saturation excess overland flow in rainfall periods (Herman et al., 2013; Sarrazin et al., 2016; Wang et al., 2017a; 2018). Moreover, the results also illustrate that the optimal α and Kq (or Ks) in the cascade routing component have higher reliability than the optimal HUZ and B in the soil moisture component. In summary, the dynamic parameters α and Kq (or Ks) in scheme 5 have good convergence performance and accurately describe the response to the dynamic catchment characteristics. However, the parameters HUZ and B, with poor convergence performance, exhibit a poor ability to describe the response to the dynamic catchment characteristics. Interestingly, the convergence performance results of the parameters for the dominant response modes in HYMOD are consistent with the results of the performance of the state variables and fluxes and the dynamic parameter set. The evaluation results of ECP-VP for scheme 1 and scheme 5 in the Muma He basin and Xun He basin are shown in Sect. S4. The results are similar to those of the Hanzhong basin.
The following results were observed: (1) the proposed ECP-VP tool accurately described the convergence behavior of the models in the individual parameter spaces, demonstrating the reliability of the optimized dynamic parameter values in responding dynamic catchment characteristics. The tool can be used to determine the reason for the potentially poor convergence performance. (2) The convergence performance can be used to identify the operation modes of hydrological models and provides valuable guidance for the improvement of hydrological models with different catchment characteristics. (3) The convergence performance of the parameters in one sub-period might be superior or inferior to those of other sub-periods. For example, the convergence performance of all parameters was worse in the dry period than in the three rainfall periods. Indeed, due to the complex correlations between the parameters in a parameter set, the convergence performance of an individual parameter may be significantly affected by the other parameters. For this reason, it is not recommended to use the convergence performance of individual parameters but rather the convergence performance of the parameter set. However, the application of this solution requires a significant amount of experiments, validation, analysis and discussion, and these points will be investigated in future studies.
We designed five calibration schemes for the dynamics of hydrological-model parameters to investigate the underlying causes of poor model performance. An assessment system was proposed to determine an appropriate calibration scheme. The potential reasons for the poor response of the dynamic parameter set to the catchment dynamics were discussed. The following conclusions were drawn:
The five schemes were systematically evaluated with respect to the model performance in different flow phases, the transferability of the dynamic parameters to different time periods, the state variable and flux time series, and the response of the dynamic parameter set to the dynamic catchment characteristics. The possible reasons for the poor model performance included (1) time-invariant parameters, (2) compensation among parameters, (3) high dimensionality of the parameters and (4) abrupt shifts of the parameters. Interestingly, the results also proved that changes in the state variables and fluxes time series provided a more robust description of the dynamic catchment characteristics than the dynamic parameters.
The proposed calibration (1) compensated for the deficiencies in the model structure, (2) provided high forecast accuracy for different flow phases, (3) exhibited good transferability of the model parameters between the calibration and validation periods, (4) improved the ability to identify the dominant parameters and their responses to the catchment processes, and (5) accurately characterized the dynamic behavior of the catchments.
The reasons for the poor response of the dynamic parameter to the catchment dynamics were determined by assessing the convergence performance of the dynamic parameters. The results indicated that the dynamic parameters with good convergence performance accurately described the response to the dynamic catchment characteristics, whereas parameters with a poor convergence performance had poor ability to describe the response to the dynamic catchment characteristics.
The assessment results of the state variables and fluxes and the convergence performance of the parameters provided robust indications of the dominant response modes of the hydrological models in different sub-periods or catchments with distinguishing catchment characteristics.
The digital elevation model (DEM) of the study area is derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) with a cell size of 30×30 m, which can be obtained from https://asterweb.jpl.nasa.gov/gdem.asp (NASA, 2019). The climatic datasets consist of daily rainfall datasets and pan evaporation datasets provided by the China Climatic Data Sharing Service System, which can be obtained from https://data.cma.cn/en/?r=data/online&t=6 (CMDC, 2019). Daily streamflow used to support this paper can be made available for interested readers by contacting the corresponding author at firstname.lastname@example.org.
The supplement related to this article is available online at: https://doi.org/10.5194/hess-24-1347-2020-supplement.
TT, LK, XCY, CX and PB each contributed to the development of the calibration schemes and assessment, analyzed the results, and discussed the poor model performance.
The authors declare that they have no conflict of interest.
This study is financially supported by the Excellent Young Scientists Fund of the NSFC (grant no. 51822908), the National Natural Science Foundation of China (grant no. 51779279), the National Key R&D Program of China (grant no. 2017YFC0405900), the BaiQianWan Young Talents plan of special support in Guangdong province (grant no. 42150001) and the Research Council of Norway (FRINATEK project no. 274310).
This research has been supported by the Excellent Young Scientists Fund of the NSFC (grant no. 51822908), the National Natural Science Foundation of China (grant no. 51779279), the National Key R&D Program of China (grant no. 2017YFC0405900), the BaiQianWan Young Talents plan of special support program in Guangdong province (grant no. 42150001) and the Research Council of Norway (FRINATEK project no. 274310).
This paper was edited by Fabrizio Fenicia and reviewed by two anonymous referees.
Aldrich, J.: R. A. Fisher and the making of maximum likelihood 1912–1922, Statist. Sci., 12, 162–176, https://doi.org/10.1214/ss/1030037906, 1997.
Arora, S. and Singh, S.: The firefly optimization algorithm: convergence analysis and parameter selection, Int. J. Comput. Appl., 69, 48–52, https://doi.org/10.5120/11826-7528, 2013.
Arsenault, R., Poulin, A., Côté, P., and Brissette, F.: Comparison of Stochastic Optimization Algorithms in Hydrological Model Calibration, J. Hydrol. Eng., 19, 1374–1384, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000938, 2014.
Azad, S. K. J. S. and Optimization, M.: Monitored convergence curve: a new framework for metaheuristic structural optimization algorithms, Struct. Multidiscip. O., 60, 481–499, https://doi.org/10.1007/s00158-019-02219-5, 2019.
Bárdossy, A.: Calibration of hydrological model parameters for ungauged catchments, Hydrol. Earth Syst. Sci., 11, 703–710, https://doi.org/10.5194/hess-11-703-2007, 2007.
Bárdossy, A. and Singh, S. K.: Robust estimation of hydrological model parameters, Hydrol. Earth Syst. Sci., 12, 1273–1283, https://doi.org/10.5194/hess-12-1273-2008, 2008.
Beven, K. and Binley, A.: The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992.
Brigode, P., Oudin, L., and Perrin, C.: Hydrological model parameter instability: A source of additional uncertainty in estimating the hydrological impacts of climate change?, J. Hydrol., 476, 410–425, https://doi.org/10.1016/j.jhydrol.2012.11.012, 2013.
Chen, Y., Chen, X. W., Xu, C. Y., Zhang, M. F., Liu, M. B., and Gao, L.: Toward improved calibration of SWAT using season-based multi-objective optimization: A case study in the Jinjiang basin in southeastern China, Water Resour. Manage,, 32, 1193–1207, https://doi.org/10.1007/s11269-017-1862-8, 2017.
Cheng, L., Yaeger, M., Viglione, A., Coopersmith, E., Ye, S., and Sivapalan, M.: Exploring the physical controls of regional patterns of flow duration curves – Part 1: Insights from statistical analyses, Hydrol. Earth Syst. Sci., 16, 4435–4446, https://doi.org/10.5194/hess-16-4435-2012, 2012.
Choi, H. T. and Beven, K.: Multi-period and multi-criteria model conditioning to reduce prediction uncertainty in an application of TOPMODEL within the GLUE framework, J. Hydrol., 332, 316–336, https://doi.org/10.1016/j.jhydrol.2006.07.012, 2007.
Cibin, R., Sudheer, K. P., and Chaubey, I.: Sensitivity and identifiability of stream flow generation parameters of the SWAT model, Hydrol. Process., 24, 1133–1148, https://doi.org/10.1002/hyp.7568, 2010.
CMDC: One Hour of Precipotation and Hour Temperature datasets during 1980–1990 on China Meteorological Data Service Center (CMDC), available at: https://data.cma.cn/en/?r=data/online&t=6 (last access: 20 March 2020), 2019.
Cooper, V. A., Nguyen, V. T. V., and Nicell, J. A.: Evaluation of global optimization methods for conceptual rainfall-runoff model calibration, Water Sci. Technol., 36, 53–60, https://doi.org/10.1016/S0273-1223(97)00461-7, 1997.
Coopersmith, E., Yaeger, M. A., Ye, S., Cheng, L., and Sivapalan, M.: Exploring the physical controls of regional patterns of flow duration curves – Part 3: A catchment classification system based on regime curve indicators, Hydrol. Earth Syst. Sci., 16, 4467–4482, https://doi.org/10.5194/hess-16-4467-2012, 2012.
Coron, L., Andreassian, V., Perrin, C., Bourqui, M., and Hendrickx, F.: On the lack of robustness of hydrologic models regarding water balance simulation: a diagnostic approach applied to three models of increasing complexity on 20 mountainous catchments, Hydrol. Earth Syst. Sci., 18, 727–746, https://doi.org/10.5194/hess-18-727-2014, 2014.
Dakhlaoui, H., Ruelland, D., Tramblay, Y., and Bargaoui, Z.: Evaluating the robustness of conceptual rainfall-runoff models under climate variability in northern Tunisia, J. Hydrol., 550, 201–217, https://doi.org/10.1016/j.jhydrol.2017.04.032, 2017.
Delorit, J., Ortuya, E. C. G., and Block, P.: Evaluation of model-based seasonal streamflow and water allocation forecasts for the Elqui Valley, Chile, Hydrol. Earth Syst. Sci., 21, 4711–4725, https://doi.org/10.5194/hess-21-4711-2017, 2017.
Deng, C., Liu, P., Guo, S. L., Li, Z. J., and Wang, D. B.: Identification of hydrological model parameter variation using ensemble Kalman filter, Hydrol. Earth Syst. Sci., 20, 4949–4961, https://doi.org/10.5194/hess-20-4949-2016, 2016.
Deng, C., Liu, P., Wang, D. B., and Wang, W. G.: Temporal variation and scaling of parameters for a monthly hydrologic model, J. Hydrol., 558, 290–300, https://doi.org/10.1016/j.jhydrol.2018.01.049, 2018.
Derrac, J., García, S., Hui, S., Suganthan, P. N., and Herrera, F.: Analyzing convergence performance of evolutionary algorithms: A statistical approach, Inform. Sci., 289, 41–58, https://doi.org/10.1016/j.ins.2014.06.009, 2014.
de Vos, N. J., Rientjes, T. H. M., and Gupta, H. V.: Diagnostic evaluation of conceptual rainfall-runoff models using temporal clustering, Hydrol. Process., 24, 2840–2850, https://doi.org/10.1002/hyp.7698, 2010.
Duan, Q., Sorooshian, S., and Gupta, V.: Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28, 1015–1031, https://doi.org/10.1029/91WR02985, 1992.
Duan, Q., Sorooshian, S., and Gupta, V. K.: Optimal use of the SCE-UA global optimization method for calibrating watershed models, J. Hydrol., 158, 265–284, 1994.
Duan, Q. Y., Gupta, V. K., and Sorooshian, S.: Shuffled Complex Evolution Approach for Effective and Efficient Global Minimization, J. Optimiz. Theor. Appl., 76, 501–521, https://doi.org/10.1007/Bf00939380, 1993.
Fang, J. Y., Song, Y. C., Liu, H. Y., and Piao, S. L.: Vegetation-climate relationship and its application in the division of vegetation zone in China, Acta Bot. Sin., 44, 1105–1122, 2002.
Fenicia, F., Kavetski, D., Savenije, H. H. G., Clark, M. P., Schoups, G., Pfister, L., and Freer, J.: Catchment properties, function, and conceptual model representation: is there a correspondence?, Hydrol. Process., 28, 2451–2467, https://doi.org/10.1002/hyp.9726, 2014.
Fenicia, F., Kavetski, D., Reichert, P., and Albert, C.: Signature-Domain Calibration of Hydrological Models Using Approximate Bayesian Computation: Empirical Analysis of Fundamental Properties, Water Resour. Res., 54, 3958–3987, https://doi.org/10.1002/2017wr021616, 2018.
Forrest, T. J. A. S.: Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms, in: Proceedings of the Sixth International Conference on Genetic Algorithms, 15–19 July 1995, University of Pittsburgh, Pittsburgh, PA 15260, United States, 184–192, 1995.
Fowler, K., Coxon, G., Freer, J., Peel, M., Wagener, T., Western, A., Woods, R., and Zhang, L.: Simulating Runoff Under Changing Climatic Conditions: A Framework for Model Improvement, Water Resour. Res., 54, 9812–9832, https://doi.org/10.1029/2018wr023989, 2018.
Freer, J., Beven, K., and Peters, N.: Multivariate seasonal period model rejection within the generalised likelihood uncertainty estimation procedure, in: Calibration of watershed models, 69–87, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/WS006p0069 (last access: 11 March 2020), 2003.
Gharari, S., Hrachowitz, M., Fenicia, F., and Savenije, H. H. G.: An approach to identify time consistent model parameters: sub-period calibration, Hydrol. Earth Syst. Sci., 17, 149–161, https://doi.org/10.5194/hess-17-149-2013, 2013.
Gibbs, M. S., Maier, H. R., and Dandy, G. C.: Applying fitness landscape measures to water distribution optimization problems, in: Hydroinformatics, World Scientific Publishing Company, Singapore, 795–802, 2004.
Golmohammadi, G., Rudra, R., Dickinson, T., Goel, P., and Veliz, M.: Predicting the temporal variation of flow contributing areas using SWAT, J. Hydrol., 547, 375–386, https://doi.org/10.1016/j.jhydrol.2017.02.008, 2017.
Gomez, J.: Stochastic global optimization algorithms: A systematic formal approach, Inform. Sci., 472, 53–76, https://doi.org/10.1016/j.ins.2018.09.021, 2019.
Guntner, A., Uhlenbrook, S., Seibert, J., and Leibundgut, C.: Multi-criterial validation of TOPMODEL in a mountainous catchment, Hydrol. Process., 13, 1603–1620, https://doi.org/10.1002/(sici)1099-1085(19990815)13:11<1603::aid-hyp830>3.3.co;2-b, 1999.
Guo, D., Johnson, F., and Marshall, L.: Assessing the Potential Robustness of Conceptual Rainfall-Runoff Models Under a Changing Climate, Water Resour. Res., 54, 5030–5049, https://doi.org/10.1029/2018WR022636, 2018.
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34, 751–763, https://doi.org/10.1029/97WR03495, 1998.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Guse, B., Pfannerstill, M., Strauch, M., Reusser, D. E., Ludtke, S., Volk, M., Gupta, H., and Fohrer, N.: On characterizing the temporal dominance patterns of model parameters and processes, Hydrol. Process., 30, 2255–2270, https://doi.org/10.1002/hyp.10764, 2016.
Hanne, T. J. J. O. H.: Global Multiobjective Optimization Using Evolutionary Algorithms, J. Heuristics, 6, 347–360, https://doi.org/10.1023/a:1009630531634, 2000.
Harik, G., Cantú-Paz, E., Goldberg, D. E., and Miller, B. L.: The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations, 7, 231-253, 10.1162/evco.19184.108.40.206, 1999.
Herman, J. D., Reed, P. M., and Wagener, T.: Time-varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior, Water Resour. Res., 49, 1400–1414, https://doi.org/10.1002/wrcr.20124, 2013.
Hintze, J. L. and Nelson, R. D.: Violin plots: a box plot-density trace synergism, Am. Statist., 52, 181–184, https://doi.org/10.2307/2685478, 1998.
Höge, M., Wöhling, T., and Nowak, W.: A primer for model selection: The decisive role of model complexity, Water Resour. Res., 54, 1688–1715, 2018.
Huang, G. H.: Model identifiability, Wiley StatsRef: Statistics Reference Online, available at: https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118445112.stat06411.pub2 (last access: 2016), 2005.
Hublart, P., Ruelland, D., De Cortázar-Atauri, L. G., Gascoin, S., Lhermitte, S., and Ibacache, A.: Reliability of lumped hydrological modeling in a semi-arid mountainous catchment facing water-use changes, Hydrol. Earth Syst. Sci., 20, 3691–3717, https://doi.org/10.5194/hess-20-3691-2016, 2016.
Kim, D. and Kaluarachchi, J.: Predicting streamflows in snowmelt-driven watersheds using the flow duration curve method, Hydrol. Earth Syst. Sci., 18, 1679–1693, https://doi.org/10.5194/hess-18-1679-2014, 2014.
Kim, K. B. and Han, D.: Exploration of sub-annual calibration schemes of hydrological models, Hydrol. Res., 48, 1014–1031, https://doi.org/10.2166/nh.2016.296, 2017.
Kim, K. B., Kwon, H.-H., and Han, D.: Hydrological modelling under climate change considering nonstationarity and seasonal effects, Hydrol. Res., 47, nh2015103, https://doi.org/10.2166/nh.2015.103, 2015.
Kiptala, J. K., Mul, M. L., Mohamed, Y. A., and van der Zaag, P.: Modelling stream flow and quantifying blue water using a modified STREAM model for a heterogeneous, highly utilized and data-scarce river basin in Africa, Hydrol. Earth Syst. Sci., 18, 2287–2303, https://doi.org/10.5194/hess-18-2287-2014, 2014.
Klemeš, V.: Operational testing of hydrological simulation models, Hydrolog. Sci. J., 31, 13–24, 1986.
Klotz, D., Herrnegger, M., and Schulz, K.: Symbolic Regression for the Estimation of Transfer Functions of Hydrological Models, Water Resour. Res., 53, 9402–9423, https://doi.org/10.1002/2017wr021253, 2017.
Laloy, E. and Vrugt, J. A.: High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing, Water Resour. Res., 48, W01526, https://doi.org/10.1029/2011wr010608, 2012.
Lan, T., Lin, K. R., Liu, Z. Y., He, Y. H., Xu, C. Y., Zhang, H. B., and Chen, X. H.: A Clustering Preprocessing Framework for the Subannual Calibration of a Hydrological Model Considering Climate-Land Surface Variations, Water Resour. Res., 54, 10034–10052, https://doi.org/10.1029/2018wr023160, 2018.
Lin, K., Zhang, Q., and Chen, X.: An evaluation of impacts of DEM resolution and parameter correlation on TOPMODEL modeling uncertainty, J. Hydrol., 394, 370–383, https://doi.org/10.1016/j.jhydrol.2010.09.012, 2010.
Liu, Z. Y., Zhou, P., Chen, X. Z., and Guan, Y. H.: A multivariate conditional model for streamflow prediction and spatial precipitation refinement, J. Geophys. Res.-Atmos., 120, 10116–110129, https://doi.org/10.1002/2015JD02378, 2015.
Liu, Z. Y., Cheng, L. Y., Hao, Z. C., Li, J. J., Thorstensen, A., and Gao, H. K.: A framework for exploring joint effects of conditional factors on compound floods, Water Resour. Res., 54, 2681–2696, https://doi.org/10.1002/2017WR021662, 2018.
Luo, J. M., Wang, E. L., Shen, S. H., Zheng, H. X., and Zhang, Y. Q.: Effects of conditional parameterization on performance of rainfall-runoff model regarding hydrologic non-stationarity, Hydrol. Process., 26, 3953–3961, https://doi.org/10.1002/hyp.8420, 2012.
Madsen, H.: Automatic calibration of a conceptual rainfall–runoff model using multiple objectives, J. Hydrol., 235, 276–288, https://doi.org/10.1016/S0022-1694(00)00279-1, 2000.
Maier, H. R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L. S., Cunha, M. C., Dandy, G. C., Gibbs, M. S., Keedwell, E., Marchi, A., Ostfeld, A., Savic, D., Solomatine, D. P., Vrugt, J. A., Zecchin, A. C., Minsker, B. S., Barbour, E. J., Kuczera, G., Pasha, F., Castelletti, A., Giuliani, M., and Reed, P. M.: Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions, Environ. Modell. Softw., 62, 271–299, 2014.
Me, W., Abell, J. M., and Hamilton, D. P.: Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand, Hydrol. Earth Syst. Sci., 19, 4127–4147, https://doi.org/10.5194/hess-19-4127-2015, 2015.
Merz, R., Parajka, J., and Bloschl, G.: Time stability of catchment model parameters: Implications for climate impact analyses, Water Resour. Res., 47, W02531, https://doi.org/10.1029/2010wr009505, 2011.
Michalewicz, Z. and Schoenauer, M.: Evolutionary Algorithms for Constrained Parameter Optimization Problems, Evol. Comput., 4, 1–32, https://doi.org/10.1162/evco.19220.127.116.11, 1996.
Moore, R. J.: The probability-distributed principle and runoff production at point and basin scales, Hydrol. Sci. J., 30, 273–297, https://doi.org/10.1080/02626668509490989, 1985.
Motavita, D. F., Chow, R., Guthke, A., and Nowak, W.: The comprehensive differential split-sample test: A stress-test for hydrological model robustness under climate variability, J. Hydrol., 573, 501–515, https://doi.org/10.1016/j.jhydrol.2019.03.054, 2019.
NASA: Global digital elevation model (GDEM) with a cell size of 30×30 m on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), available at: https://asterweb.jpl.nasa.gov/gdem.asp (last access: 20 March 2020), 2019.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nijzink, R. C., Samaniego, L., Mai, J., Kumar, R., Thober, S., Zink, M., Schäfer, D., Savenije, H. H. G., and Hrachowitz, M.: The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models, Hydrol. Earth Syst. Sci., 20, 1151–1176, https://doi.org/10.5194/hess-20-1151-2016, 2016.
Omran, M. G. H. and Mahdavi, M.: Global-best harmony search, Appl. Math. Comput., 198, 643–656, https://doi.org/10.1016/j.amc.2007.09.004, 2008.
Osuch, M., Wawrzyniak, T., and Nawrot, A.: Diagnosis of the hydrology of a small Arctic permafrost catchment using HBV conceptual rainfall-runoff model, Hydrol. Res., 50, 459–478, 2019.
Ouyang, Y., Xu, D., Leininger, T. D., and Zhang, N.: A system dynamic model to estimate hydrological processes and water use in a eucalypt plantation, Ecol. Eng., 86, 290–299, https://doi.org/10.1016/j.ecoleng.2015.11.008, 2016.
Pande, S. and Moayeri, M.: Hydrological Interpretation of a Statistical Measure of Basin Complexity, Water Resour. Res., 54, 7403–7416, https://doi.org/10.1029/2018WR022675, 2018.
Pathiraja, S., Marshall, L., Sharma, A., and Moradkhani, H.: Hydrologic modeling in dynamic catchments: A data assimilation approach, Water Resour. Res., 52, 3350–3372, https://doi.org/10.1002/2015wr017192, 2016.
Pathiraja, S., Anghileri, D., Burlando, P., Sharma, A., Marshall, L., and Moradkhani, H.: Time-varying parameter models for catchments with land use change: the importance of model structure, Hydrol. Earth Syst. Sci., 22, 2903–2919, https://doi.org/10.5194/hess-22-2903-2018, 2018.
Pfannerstill, M., Guse, B., and Fohrer, N.: Smart low flow signature metrics for an improved overall performance evaluation of hydrological models, J. Hydrol., 510, 447–458, https://doi.org/10.1016/j.jhydrol.2013.12.044, 2014.
Pfannerstill, M., Guse, B., Reusser, D., and Fohrer, N.: Process verification of a hydrological model using a temporal parameter sensitivity analysis, Hydrol. Earth Syst. Sci., 19, 4365–4376, https://doi.org/10.5194/hess-19-4365-2015, 2015.
Piel, F. B., Patil, A. P., Howes, R. E., Nyangiri, O. A., Gething, P. W., Williams, T. N., Weatherall, D. J., and Hay, S. I.: Global distribution of the sickle cell gene and geographical confirmation of the malaria hypothesis, Nat. Commun., 1, 104, https://doi.org/10.1038/ncomms1104, 2010.
Piotrowski, A. P., Napiorkowski, M. J., Napiorkowski, J. J., and Rowinski, P. M.: Swarm Intelligence and Evolutionary Algorithms: Performance versus speed, Inform. Sci., 384, 34–85, https://doi.org/10.1016/j.ins.2016.12.028, 2017.
Pool, S., Viviroli, D., and Seibert, J.: Prediction of hydrographs and flow-duration curves in almost ungauged catchments: Which runoff measurements are most informative for model calibration?, J. Hydrol., 554, 613–622, https://doi.org/10.1016/j.jhydrol.2017.09.037, 2017.
Pugliese, A., Castellarin, A., and Brath, A.: Geostatistical prediction of flow–duration curves in an index-flow framework, Hydrol. Earth Syst. Sci., 18, 3801–3816, https://doi.org/10.5194/hess-18-3801-2014, 2014.
Rahnamay Naeini, M., Yang, T., Sadegh, M., AghaKouchak, A., Hsu, K.-L., Sorooshian, S., Duan, Q., and Lei, X.: Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework, Environ. Model. Softw., 104, 215–235, https://doi.org/10.1016/j.envsoft.2018.03.019, 2018.
Sarhadi, A., Burn, D. H., Concepción Ausín, M., and Wiper, M. P.: Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula, Water Resour. Res., 52, 2327–2349, https://doi.org/10.1002/2015wr018525, 2016.
Sarrazin, F., Pianosi, F., and Wagener, T.: Global Sensitivity Analysis of environmental models: Convergence and validation, Environ. Model. Softw., 79, 135–152, https://doi.org/10.1016/j.envsoft.2016.02.005, 2016.
Sivakumar, B.: Dominant processes concept in hydrology: moving forward, Hydrol. Process., 18, 2349–2353, https://doi.org/10.1002/hyp.5606, 2004.
Sorooshian, S., Duan, Q., and Gupta, V. K.: Calibration of rainfall-runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model, Water Resour. Res., 29, 1185–1194, https://doi.org/10.1029/92wr02617, 1993.
Storn, R. and Price, K.: Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces, J. Global Optimiz., 11, 341–359, https://doi.org/10.1023/a:1008202821328, 1997.
Sun, J., Wu, X., Palade, V., Fang, W., Lai, C.-H., and Xu, W.: Convergence analysis and improvements of quantum-behaved particle swarm optimization, Inform. Sci., 193, 81–103, https://doi.org/10.1016/j.ins.2012.01.005, 2012.
Todorovic, A. and Plavsic, J.: The role of conceptual hydrologic model calibration in climate change impact on water resources assessment, J. Water Clim. Change, 7, 16–28, https://doi.org/10.2166/wcc.2015.086, 2015.
Tongal, H. and Booij, M. J.: Simulation and forecasting of streamflows using machine learning models coupled with base flow separation, J. Hydrol., 564, 266–282, https://doi.org/10.1016/j.jhydrol.2018.07.004, 2018.
Turner, S. W. D., Bennett, J. C., Robertson, D. E., and Galelli, S.: Complex relationship between seasonal streamflow forecast skill and value in reservoir operations, Hydrol. Earth Syst. Sci., 21, 4841–4859, https://doi.org/10.5194/hess-21-4841-2017, 2017.
van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., and Srinivasan, R.: A global sensitivity analysis tool for the parameters of multi-variable catchment models, J. Hydrol., 324, 10–23, https://doi.org/10.1016/j.jhydrol.2005.09.008, 2006.
Vormoor, K., Heistermann, M., Bronstert, A., and Lawrence, D.: Hydrological model parameter (in)stability – “crash testing” the HBV model under contrasting flood seasonality conditions, Hydrolog. Sci. J., 63, 991–1007, https://doi.org/10.1080/02626667.2018.1466056, 2018.
Vrugt, J. A. and Beven, K. J.: Embracing equifinality with efficiency: Limits of Acceptability sampling using the DREAM (LOA) algorithm, J. Hydrol., 559, 954–971, https://doi.org/10.1016/j.jhydrol.2018.02.026, 2018.
Vrugt, J. A., Bouten, W., Gupta, H. V., and Sorooshian, S.: Toward improved identifiability of hydrologic model parameters: The information content of experimental data, Water Resour. Res., 38, 48-41–48-13, https://doi.org/10.1029/2001WR001118, 2002.
Vrugt, J. A., Diks, C. G. H., Gupta, H. V., Bouten, W., and Verstraten, J. M.: Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation, Water Resour. Res., 41, W01017, https://doi.org/10.1029/2004wr003059, 2005.
Wagener, T. and Kollat, J.: Numerical and visual evaluation of hydrological and environmental models using the Monte Carlo analysis toolbox, Environ. Model. Softw., 22, 1021–1033, https://doi.org/10.1016/j.envsoft.2006.06.017, 2007.
Wagener, T., Boyle, D. P., Lees, M. J., Wheater, H. S., Gupta, H. V., and Sorooshian, S.: A framework for development and application of hydrological models, Hydrol. Earth Syst. Sci., 5, 13–26, https://doi.org/10.5194/hess-5-13-2001, 2001.
Wagener, T., McIntyre, N., Lees, M. J., Wheater, H. S., and Gupta, H. V.: Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis, Hydrol. Process., 17, 455–476, https://doi.org/10.1002/hyp.1135, 2003.
Wang, S., Huang, G. H., Baetz, B. W., and Ancell, B. C.: Towards robust quantification and reduction of uncertainty in hydrologic predictions: Integration of particle Markov chain Monte Carlo and factorial polynomial chaos expansion, J. Hydrol., 548, 484–497, https://doi.org/10.1016/j.jhydrol.2017.03.027, 2017a.
Wang, S., Huang, G. H., Baetz, B. W., Cai, X. M., Ancell, B. C., and Fan, Y. R.: Examining dynamic interactions among experimental factors influencing hydrologic data assimilation with the ensemble Kalman filter, J. Hydrol., 554, 743–757, https://doi.org/10.1016/j.jhydrol.2017.09.052, 2017b.
Wang, S., Ancell, B., Huang, G., and Baetz, B.: Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre-and Post-Processing in Sequential Data Assimilation, Water Resour. Res., 54, 2129–2151, 2018.
Weinberger, E. J. B. C.: Correlated and uncorrelated fitness landscapes and how to tell the difference, Biol. Cyber., 63, 325–336, https://doi.org/10.1007/bf00202749, 1990.
Weise, T.: Global optimization algorithms-theory and application, Self-Published, second edition, available at: http://www.it-weise.de/projects/book.pdf (last access: 20 March 2020), 2009.
Westra, S., Thyer, M., Leonard, M., Kavetski, D., and Lambert, M.: A strategy for diagnosing and interpreting hydrological model nonstationarity, Water Resour. Res., 50, 5090–5113, 2014.
Wi, S., Yang, Y. C. E., Steinschneider, S., Khalil, A., and Brown, C. M.: Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change, Hydrol. Earth Syst. Sci., 19, 857–876, https://doi.org/10.5194/hess-19-857-2015, 2015.
Xiong, B., Xiong, L., Chen, J., Xu, C.-Y., and Li, L.: Multiple causes of nonstationarity in the Weihe annual low-flow series, Hydrol. Earth Syst. Sci., 22, 1525–1542, https://doi.org/10.5194/hess-22-1525-2018, 2018.
Xiong, M., Liu, P., Cheng, L., Deng, C., Gui, Z., Zhang, X., and Liu, Y.: Identifying time-varying hydrological model parameters to improve simulation efficiency by the ensemble Kalman filter: A joint assimilation of streamflow and actual evapotranspiration, J. Hydrol., 568, 758–768, https://doi.org/10.1016/j.jhydrol.2018.11.038, 2019.
Yadav, M., Wagener, T., and Gupta, H.: Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins, Adv. Water Resour., 30, 1756–1774, https://doi.org/10.1016/j.advwatres.2007.01.005, 2007.
Yiu-Wing, L. and Yuping, W.: An orthogonal genetic algorithm with quantization for global numerical optimization, IEEE T. Evol. Comput., 5, 41–53, https://doi.org/10.1109/4235.910464, 2001.
Zecchin, A. C., Simpson, A. R., Maier, H. R., Marchi, A., and Nixon, J. B.: Improved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problem, Water Resour. Res., 48, W09505, https://doi.org/10.1029/2011wr011652, 2012.
Zhang, D. J., Chen, X. W., Yao, H. X., and Lin, B. Q.: Improved calibration scheme of SWAT by separating wet and dry seasons, Ecol. Model., 301, 54–61, https://doi.org/10.1016/j.ecolmodel.2015.01.018, 2015.
Zhang, H., Huang, G. H., Wang, D. L., and Zhang, X. D.: Multi-period calibration of a semi-distributed hydrological model based on hydroclimatic clustering, Adv. Water Resour., 34, 1292–1303, https://doi.org/10.1016/j.advwatres.2011.06.005, 2011.
Zhang, X., Srinivasan, R., Zhao, K., and Liew, M. V.: Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model, Hydrol. Process., 23, 430–441, https://doi.org/10.1002/hyp.7152, 2009.
Zhang, Y., Hao, Z., Xu, C.-Y., and Lai, X.: Response of melt water and rainfall runoff to climate change and their roles in controlling streamflow changes of the two upstream basins over the Tibetan Plateau, Hydrol. Res., nh2019075, https://doi.org/10.2166/nh.2019.075, 2019.
Zhao, B., Dai, H., Han, D., and Rong, G.: The sub-annual calibration of hydrological models considering climatic intra-annual variations, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-396, 2017.
Zheng, F., Zecchin, A. C., Newman, J. P., Maier, H. R., and Dandy, G. C.: An Adaptive Convergence-Trajectory Controlled Ant Colony Optimization Algorithm With Application to Water Distribution System Design Problems, IEEE T. Evol. Comput., 21, 773–791, https://doi.org/10.1109/TEVC.2017.2682899, 2017.