Flooding represents one of the most severe natural disasters threatening the
development of human society. A model that is capable of predicting the
hydrological responses in watershed with management practices during flood
period would be a crucial tool for pre-assessment of flood reduction
measures. The Soil and Water Assessment Tool (SWAT) is a semi-distributed
hydrological model that is well capable of runoff and water quality modeling
under changed scenarios. The original SWAT model is a long-term yield model.
However, a daily simulation time step and a continuous time marching limit
the application of the SWAT model for detailed, event-based flood simulation.
In addition, SWAT uses a basin level parameter that is fixed for the whole
catchment to parameterize the unit hydrograph (UH), thereby ignoring the
spatial heterogeneity among the sub-basins when adjusting the shape of the
UHs. This paper developed a method to perform event-based flood simulation on
a sub-daily timescale based on SWAT2005 and simultaneously improved the UH
method used in the original SWAT model. First, model programs for surface
runoff and water routing were modified to a sub-daily timescale.
Subsequently, the entire loop structure was broken into discrete flood events
in order to obtain a SWAT-EVENT model in which antecedent soil moisture and
antecedent reach storage could be obtained from daily simulations of the
original SWAT model. Finally, the original lumped UH parameter was refined
into a set of distributed ones to reflect the spatial variability of the
studied area. The modified SWAT-EVENT model was used in the Wangjiaba
catchment located in the upper reaches of the Huaihe River in China. Daily
calibration and validation procedures were first performed for the SWAT model
with long-term flow data from 1990 to 2010, after which sub-daily
(
A flood represents one of the most severe natural disasters in the world. It has been reported that nearly 40 % of losses originating from natural catastrophes are caused by floods (Adams III and Pagano, 2016). Floods have caused enormous losses to economies, societies, and ecological environments around the world (Doocy et al., 2013; Werritty et al., 2007; Guan et al., 2015). China is a flood-prone country, which suffers from severe flooding almost every year (Zhang et al., 2002). In this situation, protection against flooding has always been the government's primary task that brooks no delay. A series of structural and non-structural flood mitigation measures have been conducted to control and manage the floods (Guo et al., 2018). However, accurate flood simulations would be particularly important for such design- or management-related issues.
Numerous hydrological models have been developed since their first appearance. According to the spatial discretization method, these existing hydrological models can be divided into two categories: lumped models and distributed (semi-distributed) models (Maidment, 1994). Although lumped models are generally accepted for flood forecast and simulation due to the structural simplicity, computational efficiency and lower data requirements, they are not applicable to complex catchments since they do not account for the heterogeneity of the catchments (Yao et al., 1998; Hapuarachchi et al., 2011). Meanwhile, distributed (semi-distributed) models subdivide the entire catchment into a number of smaller heterogeneous sub-units with dissimilar attributes. It is the advantage for distributed (semi-distributed) models to incorporate the spatial characteristics of catchment such as land cover, soil properties, topography and meteorology (Yang et al., 2001, 2004). A large number of distributed or semi-distributed hydrological models have been applied in flood simulation. Beven et al. (1984) firstly tested the applicability of the TOPMODEL in flood simulation for three UK catchments and suggested that the model could be a useful approach for ungauged catchments. The Variable Infiltration Capacity (VIC) model is also playing an increasing role in flood simulation (Wu et al., 2014; Yigzaw and Hossain, 2012). The applications of the HBV model for flood simulation could be found in many studies (Haggstrom et al., 1990; Grillakis et al., 2010; Kobold and Brilly, 2006). The HEC-HMS model was able to provide reasonable flood simulation results in the San Antonio River basin (Ramly and Tahir, 2016). Among many distributed (semi-distributed) models, the one that is capable of predicting the hydrological responses in watersheds with management practices would provide scientific reference for preventing flood and mitigating its adverse effects.
The Soil and Water Assessment Tool (SWAT) model (Arnold et al., 1998) is a typical semi-distributed hydrological model that delineates a catchment into a number of sub-basins, which were subsequently divided into hydrologic response units (HRUs) representing the unique combination of land cover, soil type, and slope class within a sub-basin. The SWAT model integrates well with the Geographic Information System (GIS), having great potential in dealing with spatial flood control measures. In addition, the SWAT model is widely applied for runoff and water quality modeling under changed scenarios (Glavan et al., 2015; Yu et al., 2018; Qiu et al., 2017; Baker and Miller, 2013; Yan et al., 2013).
SWAT is a continuous (i.e., long-term) model with a limited applicability toward simulating instantaneous hydrologic responses. Therefore, Jeong et al. (2010) extended the capability of SWAT to simulate operational sub-daily or even sub-hourly hydrological processes, the modifications of which primarily focused on the model algorithms to enable the SWAT model to operate at a finer timescale with a continuous modeling loop. Constrained by data availability in China (MWR, 2008), rainfall and discharge observations at a sub-daily timescale are usually collected during flood periods, while daily data are measured otherwise. In this respect, hydrological models are usually applied at different timescales (i.e., a daily timescale for continuous simulations and a sub-daily timescale for event-based flood simulation) according to the availability of observed rainfall and discharge data (Yao et al., 2014a). Hence, a major constraint for the application of the SWAT model as modified by Jeong et al. (2010) is the conflict between a continuous simulation loop and the discontinuous observed sub-daily data in China.
To capture the sophisticated characteristics of flood events at a sub-daily timescale, a refinement of the spatial representation within the SWAT model is necessary. A dimensionless unit hydrograph (UH), which was distributed as a triangular shape and embedded within an sub-daily overland flow routing process in the SWAT model, was applied to relate hydrologic responses to specific catchment characteristics, such as the dimensions of the main stream and basin area, through applications of GIS or remote sensing (RS) software (Jena and Tiwari, 2006). Due to the spatial discretization in the SWAT model, the model parameters are grouped into three levels: (1) basin level parameters are fixed for the whole catchment; (2) sub-basin level parameters are varied with sub-basins; (3) HRU level parameters are distributed in different HRUs. By default, the UH-specific parameter in the SWAT model is programmed on the basin level, which means that spatial variation within a catchment is disregarded when adjusting the shape of the UH in each sub-basin. Given the spatial heterogeneity of the catchment, the application of this basin level adjustment parameter seems to be rather unconvincing. Moreover, because a great deal of research has primarily focused on daily, monthly or yearly simulations using the SWAT model, little effort has actually been provided toward demonstrating the usage of the UH method in the SWAT model.
SWAT model input data and sources for the Wangjiaba (WJB) catchment.
This study developed a method to perform event-based flood simulation on a sub-daily timescale based on the SWAT model and simultaneously improved the UH method used in the original SWAT model in the upper reaches of the Huaihe River in China. SWAT is an open-source code model, which makes it possible to produce such a modification. The source code of SWAT2005 has an internal auto-calibration module and such integrated design of model simulation and auto-calibration is easily manageable and modified since there is no need to couple external optimization algorithms. The accessible SWAT2009 (rev. 528) and SWAT2012 (rev. 664) have removed auto-calibration routines, however, an independent program SWAT-CUP (Abbaspour et al., 2007) is provided instead. Admittedly, many improvements have been made from the SWAT2005 to the latest SWAT2012. According to the SWAT model updates in Seo et al. (2014), the major enhancements focused on the water quality modeling components, whereas the runoff modeling components in new SWAT versions were not so far different from those in SWAT2005. This study was specific to the model modifications in runoff simulation; thus, SWAT2005 was considered to be appropriate. There are some other model modification studies (Dechmi et al., 2012; Jeong et al., 2010) based on the SWAT2005 version.
The Wangjiaba (WJB) catchment.
The Huaihe River basin (30
To construct and execute the SWAT model, a digital elevation model (DEM), together with land use and soil type data, is required. Climate data, including that of rainfall, temperature, wind speed, etc., are also used. Table 1 lists the model data used in this study.
The DEM data in this study were downloaded from the website of the US Geological Survey (USGS) with a spatial resolution of 90 m. The study catchment was divided into 136 sub-basins according to the catchment delineation, as shown in Fig. 1.
A land use map was produced from the Global Land Cover 2000 (GLC2000) data product with a grid size of 1 km (Bartholomé and Belward, 2005). Six categories of land use were identified for this catchment: agricultural land (80.51 %), forest-deciduous (6.76 %), forest-evergreen (2.26 %), range-brush (1.09 %), range-grasses (8.09 %), and water (1.29 %).
Soil data were obtained from the Harmonized World Soil Database (HWSD) with a spatial resolution of 30 arc-seconds. The HWSD also provides an attributed database that contains the physico-chemical characteristics of soil data worldwide (FAO et al., 2012). Since the built-in soil database within the SWAT model does not cover the study area, additional soil parameters were calculated using the method proposed by Jiang et al. (2014). Soil reclassification in the study area was in accordance with the FAO-90 soil system. Consequently, Eutric Planosols and Cumulic Anthrosols are the two main soil types, with area percentages of 24.71 % and 19.95 %, respectively.
The SWAT model has developed a weather generator (WXGEN) to fill the missing climate data by the use of monthly statistics. Relative humidity, wind speed, solar radiation and the minimum and maximum air temperatures were obtained from the Climate Forecast System Reanalysis (CFSR), which was designed based on the forecast system of the National Centers for Atmospheric Prediction (NCEP) to provide estimation for a set of climate variability from 1979 to the present day. There were 30 weather stations included in the study catchment.
A dense rain gauge network consisting of 138 gauges is distributed throughout
the study area as illustrated in Fig. 1. By default, SWAT structure allows
only one rainfall input for each delineated sub-basin. Thus, sub-basins
without available rainfall gauge would be automatically assigned the nearest
one. For sub-basins with multiple rainfall gauges, Thiessen polygon method
(Thiessen, 1911) was utilized to derive the rainfall input. Rainfall is the
main driving force for hydrological models, and therefore accurate
representation of spatially distributed rainfall is essential in hydrological
modeling. Cho et al. (2009) compared three different methods to incorporate
spatially variable rainfall into the SWAT model and recommended the Thiessen
polygon approach in catchments with high spatial variability of rainfall due
to its robustness to catchment delineation. Daily observed rainfall data were
retrieved from 1991 to 2010 with coverage during the entire year, while
sub-daily (
The original SWAT model was designed for continuous simulations using a daily time step. The SWAT model operates most effectively during the prediction of long-term hydrological responses to land cover changes or soil management practices with daily time step (Jeong et al., 2011). When faced with flood simulation issues, a finer timescale is required to realistically capture the instantaneous changes representative of flood processes.
SWAT-EVENT model for the simulation of event-based flood data based on the initial conditions extracted from daily simulation results produced by the original SWAT model.
Therefore, the original daily simulation-based SWAT model first needs to be modified in order to perform sub-daily simulations. In a previous study, the sub-daily and even the sub-hourly modeling capacities of the SWAT model have been developed to allow flow simulations with any time step less than a day (Jeong et al., 2010). In the original SWAT model, the surface runoff lag was estimated by a first-order lag equation, which was represented by a function of the concentration time and the lag parameter. However, this lag equation was implicitly fixed with a daily time interval. Jeong et al. (2010) then introduced the simulation time interval into the lag equation to lag a fraction of the surface runoff at the end of each time step. In addition, channel and impoundment routings were also estimated at operational time interval while other processes such as base flow and evapotranspiration were calculated by equally dividing the daily results over the time steps. In this study, the modifications from daily modeling to sub-daily modeling followed the methods proposed by Jeong et al. (2010). Second, the modified sub-daily SWAT model must be applied in such a manner to achieve the simulation of individual flooding events rather than to simulate in a continuous way, as performed in the original SWAT model. Event-based sub-daily flood modeling is necessary for these reasons: (1) to enable the modelers to acknowledge the detailed information of upcoming floods and (2) to potentially conduct flood simulation within a watershed without possessing continuously recorded hydrologic data at a short time step. To enable the SWAT model to simulate individual flood events, the original source codes were modified and compiled into a new version known as SWAT-EVENT. In the source code of SWAT2005, the “simulate” subroutine contains the loops governing the hydrological processes following the temporal marching during the entire simulation period. Here, the continuous yearly loop was set into several flood events, meanwhile, the continuous daily loop was broken into flood events according to the specific starting and ending dates.
However, the event-based modeling requires a separate method to derive the antecedent conditions of model states. The combination of daily continuous modeling and sub-daily event-based modeling was used in this study (Fig. 2). A continuous daily rainfall sequence was imported into the original SWAT model to independently perform long-term daily simulations. In the SWAT model, there are another two subroutines “varinit” and “rchinit” initializing the daily simulation variables for the land phase of the hydrologic cycle and the channel routing, respectively. In the SWAT-EVENT model, condition judgments were added into those two initialization subroutines. That is, when the simulation process is at the beginning of a given flood event, antecedent soil moisture and antecedent reach storage are set equal to the respective values extracted from the long-term daily simulations of the original SWAT model; otherwise, they should be updated by the SWAT-EVENT model simulation states of the previous day.
Shape of the dimensionless triangular UH.
The dimensionless UH method employed in the SWAT model exhibits a triangular
shape (SCS, 1972), as shown in Fig. 3, wherein the time
Geographic features of sub-basins for the Wangjiaba (WJB) catchment.
The time of concentration
Parameters and parameter ranges used in sensitivity analysis and the final ranks of sensitivity analysis results.
Effect of a basin level UH parameter
According to catchment discretization, Table 2 appears obvious spatial
differences of the geographical attributes among sub-basins. For instance,
the values of sub-basin area
Sensitivity analysis is a process employed to identify parameters that
significantly influence model performance (Holvoet et al., 2005). Generally,
sensitivity analysis takes priority over the calibration process to reduce
the complexity of the latter (Sudheer et al., 2011). Here, a combined Latin
hypercube and one-factor-at-a-time (LH-OAT) sampling method embedded within
the SWAT model (Griensven et al., 2006) was used to conduct a sensitivity
analysis. LH-OAT method firstly subdivides each parameter into
Comparisons between the observed and simulated daily discharges for
calibration
Comparisons between the observed and simulated sub-daily flood events for the calibration period at WJB.
Comparisons between the observed and simulated sub-daily flood events for the validation period at WJB.
It is highly recommended to identify the model parameters that can represent
the hydrological characteristics of specific catchment before blindly
applying sensitivity analysis. Based on the reviews of the SWAT model
applications (Griensven et al., 2006; Cibin et al., 2010; Roth and Lemann,
2016) and the analysis of the SWAT model parameters, a total of 16 parameters
related to the streamflow simulation in study area were involved in
sensitivity analysis (see Table 3) for daily simulation with the SWAT model.
When it came to the event-based sub-daily flood simulation with SWAT-EVENT
model, additional distributed UH parameter
Before effectively applying a hydrological model, a calibration process aims to estimate the model parameters that minimize the errors between the observed and simulated results is usually necessary. The Shuffled Complex Evolution (SCE-UA) algorithm (Duan et al., 1992) is a global optimization technique that is incorporated as a module into the SWAT model. The SCE-UA algorithm has been applied to multiple physically based hydrological models (Sorooshian et al., 1993; Luce and Cundy, 1994; Gan and Biftu, 1996) and has exhibited good performance similar to other global search procedures (Cooper et al., 1997; Thyer et al., 1999; Kuczera, 1997; Jeon et al., 2014).
Daily simulations were performed within the time span, from 1990 to 2010, using daily observed data at the outlet of WJB. During this phase, the SWAT model was also conducted in two ways, calibrating for long-term period and calibrating for flood period. For long-term period case, one year (1990) was selected as the model warm-up period, the period from 1991 to 2000 was used for the model calibration, and the remaining data from 2001 to 2010 were employed for validation. For flood period calibrating, what was different was that the objective function only covered several flood events, which were consistent with the SWAT-EVENT application.
Calibrated parameter values for the SWAT model and the SWAT-EVENT model.
SWAT model performance statistics for long-term period calibrating and flood period calibrating.
Multiple statistical values, including the Nash–Sutcliffe efficiency
coefficient (
Performance evaluations for the daily SWAT model calibrating only for flood periods, and the sub-daily SWAT-EVENT model performances with sub-basin level UH parameters and basin level UH parameters.
In this study, the SWAT-EVENT model employed the same built-in automatic calibration subroutine as the SWAT model did. Sub-daily simulations with the SWAT-EVENT model were conducted within the same time span as the daily simulation, with a primary focus on the flood season with a series consisting of 24 flood events, two-thirds of which were utilized for the calibration while the rest were used for validation. Preferential implementation was applied to daily calibration from which the antecedent conditions were extracted.
Comparisons of the daily simulations conducted using the SWAT model and the aggregated sub-daily simulations conducted using the SWAT-EVENT model.
Comparisons between sub-basin level and basin level UH parameter
cases for relative peak discharge error
Sensitivity results for daily simulation with the SWAT model are listed in Table 3. The sensitivity rank for a single parameter shows tiny differences between the two types of analysis period for SWAT simulation, with the changes in all parameter ranks less than.three According to a previous study (Cibin et al., 2010), the sensitivity of SWAT parameters was proved to vary in low, medium and high streamflow regimes. The long-term period analysis in Table 3 consists of different flow regimes, but presents almost the same sensitivity ranks as the flood period case, indicating that the high streamflow would dominate the sensitivity results in the long-term period analysis. Unexpectedly, compared to the long-term analysis, the initial SCS runoff curve number (CN2) shows less effect on streamflow output during flood period, whereas the groundwater parameter ALPHA_BF becomes more sensitive to high streamflow regime. As declared by Bondelid et al. (2010), the effects of CN2 variation on surface runoff yield decreased as the rainfall increased, especially for the larger storm events. Bondelid et al. (2010) further explained that the proportion of the rainfall that went into initial abstraction and infiltration decreased along with the increasing of rainfall, so the proportional change in surface runoff associated with a unit change in CN2 would decrease. Furthermore, from a previous sensitivity study with the SWAT model (Cibin et al., 2010), the parameter CN2 in wet year simulation was found to be less important than that in entire simulation, and the greatest sensitivity index of CN2 was found in low flow. Thus, there is reason to believe, the sensitivity ranking of CN2 would be reduced when it comes to flood period analysis in Table 3. Instead, in this process, the model output changes resulting from the perturbation of parameter ALPHA_BF would be more prominent, as there is more water recharging the shallow aquifer, and meanwhile the parameter ALPHA_BF strongly influences groundwater response to changes in recharge (Sangrey, 1984). Considering that the shallow aquifer in the Huaihe River basin has good drainage condition (Zuo et al., 2006), a relatively high value of ALPHA_BF would be expected in this study. Generally, the identified seven sensitive parameters of the daily SWAT model cover multiple main hydrological processes, i.e., channel routing (CH_N2 and CH_K2), runoff (SURLAG and CN2), groundwater (ALPHA_BF), evaporation (ESCO), and soil water (SOL_AWC), not only for the long-term period, but also for the flood period. According to Table 3, it is clear that both the year-round streamflow and the high streamflow are most sensitive to CH_N2 due to its top sensitivity rank.
Table 3 also presents the sensitivity results for event-based flood
simulation with the SWAT-EVENT model at a sub-daily timescale. Sensitivity of
some parameters differs widely from its performance in flood period analysis
with the SWAT model at a daily timescale. The sensitivity ranks of BLAI,
CH_K2, ESCO, SOL_K, and SURLAG have changed more than five, which could be
caused by the differences in hydrological simulation between the SWAT model
and the SWAT-EVENT model. It is noteworthy that the UH parameter
The final calibrated parameters for daily simulation with the SWAT model are
presented in Table 4. The model performances for daily streamflow simulations
at outlet WJB are summarized in Table 5. For long-term calibration, the
When focusing on event period calibration and validation, all statistical criteria in Table 5 indicate high accuracy of the daily SWAT model for flood period simulation.
Table 4 shows the optimum values of parameters used in the SWAT-EVENT model
simulation. The sub-daily simulation results for 24 flood events, as shown in
Table 6, exhibit reliable performances of the SWAT-EVENT model, with
Table 6 also displays the model performances of the daily simulation results
using the SWAT model specific for flood period. All daily
All statistical indicators suggest that the SWAT-EVENT model can accurately reproduce the dynamics of observed flood events based upon antecedent conditions extracted from SWAT daily simulations.
To analyze the effects of the level of UH parameters on SWAT-EVENT model
simulations, the default lumped UH parameter
The SWAT-EVENT simulation results using the basin level UH parameter are also
presented in Table 4. Compared with the sub-basin level case, the basin level
case induces significant decrease in the qualified ratio of
Box plots of ENS values for the SWAT-EVENT model results for sub-basin level UH parameters and basin level UH parameters.
The overall distributions of
Floods are always triggered by intense rainfall events with short duration. In order to adequately capture and analyze the rapid response of flood events, simulation time step at sub-daily resolution is preferred. Normally, an appropriate simulation time step is chosen depend on the catchment response time to a rainfall event. According to the catchment delineation and geographical features of sub-basins in Table 2, the general average concentration time of sub-basins is found to be less than 24 h. Moreover, considering the time interval of observed data acquisition (i.e., 2 to 6 h), the 2 h simulation step chosen in this study was more than sufficient for flood simulation. The remarkable performances of the sub-daily SWAT-EVENT model for peak flow simulations (as shown in Table 6 and Fig. 8) adequately confirmed the superiority of using sub-daily time step in simulating flood hydrographs. In this study, daily surface runoff was calculated using the SCS curve number method in the SWAT model, whereas sub-daily surface runoff was calculated using the Green & Ampt infiltration method in the SWAT-EVENT model. In terms of the comparison of these two methods, (King et al., 1999) argued that the advantage of Green & Ampt method was the considerations of sub-daily rainfall intensity and duration, meanwhile, a rainstorm might not be fully represented by total daily rainfall used in SCS method due to its high variation in temporal distribution. Beyond that, as stated by Jeong et al. (2010), the physically based hydrological processes simulating at a short timescale would contribute to the reinforcement of model simulation accuracy.
Pathiraja et al. (2012) may argue that the continuous simulation for design flood estimation was becoming increasingly important. Nevertheless, in operational flood simulation and prediction perspectives, many endusers and practitioners are still in favor of the event-based models (Coustau et al., 2012; Berthet et al., 2009). The emphasis on event-based modeling in this study was due to the unavailability of the long continuous hydrological data at a sub-daily timescale. Such a data scarcity issue has also promoted the applications of the event-based models in some developing countries (Hughes, 2011; Tramblay et al., 2012). More broadly, the preferred event-based approach is highlighted when the hydrological model is used for investigating the effect of heavy rainfall on environmental problems such as soil erosion and contaminant transport (Maneta et al., 2007).
Several studies have declared that the catchment's antecedent moisture conditions prior to a flood event can have a strong influence on flood responses, including the flood volume, flood peak flow and its duration (Rodrã-Guez-Blanco et al., 2012; Tramblay et al., 2012; Coustau et al., 2012). However, the major drawback of event-based models lies in its initialization: external information is needed to set the antecedent conditions of a catchment (Berthet et al., 2009; Tramblay et al., 2012). To address the initialization issue, some efforts have been made to set up the initial conditions of event-based models, such as in situ soil moisture measurements, retrieved soil moisture from the remote sensing products, and continuous soil moisture modeling. Among these methods, continuous soil moisture modeling using the daily data series to estimate sub-daily initial conditions would be a traditional solution, as suggested by Nalbantis (1995) (Tramblay et al., 2012) also tested different estimations of the antecedent moisture conditions of the catchment for an event-based hydrological model and concluded that the continuous daily soil moisture accounting method performed the best. However, there might be some deficiencies in the continuous simulation of the SWAT model in this study. On the one hand, the continuous soil moisture modeling required long data series and took a long time to implement. On the other hand, the continuous SWAT model was calibrated using the sum of squares of the residuals as the objective function, which was more sensitive to high flows than low flows. As a consequence, the SWAT model ensured the simulation accuracy at the expense of the low flow performances, which would certainly bring errors to the estimations of antecedent moisture conditions. As Coustau et al. (2012) declared, event-based models were very convenient for operational purposes, if the initial wetness state of the catchment would be known with good accuracy. Although the continuous modeling approach used in this study was not the perfect solution for the determination of the catchment antecedent conditions, it was still an effective method as the preliminary preparation for the simulation of the SWAT-EVENT model due to the good goodness-of-fit in Figs. 6 and 7. Since the goal of this research was to ascertain the applicability of the newly developed SWAT-EVENT model on event-based flood simulation, it was accepted to have a lower performance in calculating the antecedent conditions. Active microwave remote sensing has proved the feasibility and rationality of obtaining temporal and spatial soil moisture data. It means that there is a potential interest of using the remote sensing data to estimate the initial conditions (Tramblay et al., 2012).
The UH method is used to spread the net rainfall over time and space, representing the most widely practiced technique for determining flood hydrographs. The main difference between the two applications of the UH parameter is, in essence, the method for surface runoff routing within the sub-basins. The application of the sub-basin level UH parameters allowed distributed parameter value for each sub-basin, while the basin level UH parameter application consistently applied a lumped value for all sub-basins. All but the derived UH shape of the distributed UH case were identical to these of the lumped UH case. Therefore, the difference in the simulations of the two UH parameter cases resulted from the surface runoff routing method.
As seen from the aforementioned model performance assessment in Table 6 and Fig. 9, the capability of the SWAT-EVENT model with basin level UH parameter for event-based flood simulation was downgraded relative to the sub-basin level case. It is known that Sherman (1932) first proposed the UH concept in 1932. However, because the UH proposed by Sherman is based on observed rainfall–runoff data at gauging sites for hydrograph derivations, it is only applicable for gauged basins (Jena and Tiwari, 2006). A prominent lack of observed data promoted the appearance of the Synthetic Unit Hydrograph (SUH), which extended the application of the UH technique to ungauged catchments. The triangular dimensionless UH used in this study denotes the traditional derivation of SUHs, which relates hydrologic responses to the catchment geographic characteristics according to Eqs. (2)–(6). Therefore, it can be inferred that the shape feature of the UH should be region-dependent. A lumped UH parameter used for the whole catchment would lead to either sharpening the peak flows in large sub-basins, or flattening the peak flows in small sub-basins. On the whole, hydrological behaviors among sub-basins would tend to be homogenized. As indicated in Table 6 and Figs. 9 and 10, there was a positive effect from the application of the distributed UH parameters on flood simulation.
In addition to the triangular dimensionless UH used in this study, there are many other available methods for derivation of the SUH (Bhunya et al., 2007) compared four probability distribution functions (pdfs) in developing SUH and concluded that such statistical distributions method performed better than the traditional synthetic methods. Furthermore, the instantaneous unit hydrograph (IUH) is more capable of mathematically expressing the effective rainfall hyetograph and direct runoff hydrograph relationship in a catchment (Jeng and Coon, 2003). And Yao et al. (2014b) improved the flood prediction performance of the Xinanjiang model by the coupling of the geomorphologic instantaneous unit hydrograph (GIUH) (Khaleghi et al., 2011) compared the accuracy and reliability of different UH methods and confirmed the high efficiency of the GIUH for flood simulation. There might be room for further improving the current UH method used in the SWAT-EVENT model.
The original SWAT model was not competent for flood simulation due to its initial design of long-term simulations with daily time steps. This paper mainly focused on the modification of the structure of the original SWAT model to perform event-based flood simulation, which was applicable for the area without continuous long-term observations. The newly developed SWAT-EVENT model was applied in the upper reaches of the Huaihe River. Model calibration and validation were made by the using of historical flood events, showing good simulation accuracy. To improve the spatial representation of the SWAT-EVENT, the lumped UH parameters were then adjusted to the distributed ones. Calibration and validation results revealed the improvement of event-based simulation performances, especially for the flood peak simulation. This study expands the application of the original SWAT model in event-based flood simulation. Event-based runoff quantity and quality modeling has become a challenge task since the impact of hydrological extremes on the water quality is particularly important. The improvement of the SWAT model for event-based flood simulation in this study will lay the foundation for dealing with the event-based water quality issues.
The optimal parameters of the SWAT-EVENT model were obtained by the automatic parameter calibration module that integrated the SCE-UA algorithm in this study. However, several factors such as interactions among model parameters, complexities of spatio-temporal scales, and statistical features of model residuals may lead to the parameter non-uniqueness, which is the source of the uncertainty in the estimated parameters. Uncertainty of model parameters will be finally passed to the model results, hence leading to certain risks in flood simulation. In the future, emphasis will be placed on the quantification of the parameter uncertainty to provide better support for flood operations.
The DEM data were downloaded from the website
XD and PX contributed to the conception of this study. XD and DY contributed significantly to analysis and manuscript preparation. DY performed the data analyses and wrote the manuscript. XH, JL, YL, TP, and HM helped perform the analysis with constructive discussions. All authors read and approved the manuscript. KW and SX assisted in data acquisition and collation.
The authors declare that they have no conflict of interest.
This research has been supported by the Non-profit Industry Financial Program of Ministry of Water Resources of China (no. 201301066), the National key research and development program (2016YFC0402700), the National Natural Science Foundation of China (nos. 91547205, 51579181, 51409152, 41101511, and 40701024), and the Hubei Provincial Collaborative Innovation Center for Water Security.Edited by: Markus Weiler Reviewed by: two anonymous referees