Improvement and Analysis for Accuracy of Baseflow Using SWAT-CUP Premium in the Yongjeon Stream, South Korea

doi:10.21203/rs.3.rs-1969896/v1

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Improvement and Analysis for Accuracy of Baseflow Using SWAT-CUP Premium in the Yongjeon Stream, South Korea

https://doi.org/10.21203/rs.3.rs-1969896/v1

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Here, SWAT simulation results are calibrated and validated using SWAT-CUP Premium, and R² (coefficient of determination) values of simulated and observed flows are compared. According to the SWAT analysis of Cheongsong Yongjeon-stream basin, a low accuracy of 0.40 was the R² (coefficient of determination) value for simulated and observed flows, and the base flow was calculated at an annual average of 46.659 m³/s. During the same period, after calibration and validation using SWAT-CUP Premium, the R² value of simulated and observed flows was improved to 0.71, and the evaluation indexes showed that NS was 0.51, PBIAS was 37.1, P-factor was 0.73, and R-factor was 0.33, indicating that the relationship between observed and simulated flows was improved. In addition, after calibration and validation, the base flow was 56.951 m³/s, which was a more accurate value compared to the one before calibration and validation.

SWAT-CUP Premium

Base flow

Coefficient of determination

Simulated flow

Recently, due to climate change and urbanization, rainfall patterns and infiltration rates have changed, which has caused a decrease in the groundwater levels and stream drying, causing problems in supply of water resources. In particular, a reduction in infiltration due to urbanization can lead to a reduction in groundwater levels and underlying runoff, and a decrease in baseflow rate can affect supply of stream flow during the dry season. For this reason, accurate analysis and verification of baseflow is essential; however, difficulty of integrated interpretation; considering surface water–groundwater, uncertainty about analysis of baseflow runoff, and technical limitations make the analyzation of baseflow challenging.

Since baseflow runoff is affected not only by rainfall and flow rate but also by groundwater flowing into and out of aquifers, it is closely related to groundwater recharge, and it is necessary to differentiate between direct runoff and baseflow runoff to understand the characteristics of baseflow runoff (Aizen et al. 2000; Ferket et al. 2010). Baseflow runoff separation has been widely studied, including master groundwater depletion curve, straight line, fixed base, and variable slope methods, and an interpretation using watershed hydrological modeling is currently dominated. In particular, the SWAT model is the most frequently used model for interpreting baseflow runoff among rainfall-runoff models (Rouholahnejad et al. 2014; Ligaray et al. 2015; Veettil and Mishra 2016).

Arnold et al. (2000) estimated groundwater recharge in the Mississippi River basin using SWAT model and evaluated baseflow runoff through it. A study was also conducted to simulate baseflow runoff using the SWAT model and compare the results (Abbaspour et al. 2007; Luo et al. 2012). In addition, a scenario-specific analysis of climate change, such as temperature and precipitation, and a baseflow runoff analysis according to changes in soil conditions and land use were conducted (Zhang and Schilling 2006; Duan et el. 2017). An analysis of stream runoff and baseflow runoff was also conducted for river basin management (Dechmi et al. 2012; Aboelnour et al. 2020).

In particular, conducted studies were the ones that analyzed change of baseflow and analyzed scenarios according to urbanization and changes in land use (Aboelnour et al. 2019) and those that calibrated and validated result values of stream flow using SWAT model and SWAT-CUP (Vilaysane et al. 2015).

The aim of this study is to improve the accuracy of baseflow and to analyze the uncertainty of baseflow by calculating baseflow and performing calibration and validation using the SWAT model. The SWAT model is a semi-distributed rainfall-runoff model, which can run on a daily time and simulate not only runoff and sediment but also pollution. However, further analysis of calibration, validation, and uncertainty are essential because uncertainty exists about results of simulation. SWAT-CUP is a program developed by the EAWAG Institute in Switzerland for calibration, validation, and uncertainty analysis of the SWAT model. SWAT-CUP Premium is an improved version of SWAT-CUP that can improve uncertainty about results of SWAT simulation. The study area is the Yongjeon-stream basin in Cheongsong County, which is a local liver with a watershed area of 381.0 km² and river length of 48.0 km. Yongjeon-stream consists of smaller streams, the third tributaries of the Nakdong River, such as Singi-stream, Sinheung-stream, Jubang-stream, Jusan-stream, Nobu-stream, Kwae-stream, and Gupyeong-stream. The left bank of Yongjeon-stream is a basin with a narrow watershed width and is widely developed in the right bank basin, located at 129°54′44″–129°13′50″ east longitude and 36°13′18″–36°30′ north latitude. The input data for simulation was analyzed using weather data (Cheongsong Weather Station, Juwang Mountain Weather Station) and measured data (Cheongsong Water Level Gauge Station) of the observatories located in the basin (Fig. 1).

The geology of the study area consists of plutonic rocks, sedimentary rocks, andesitic rocks, granitic rocks, acidic volcanic rocks, and alluvium (Fig. 2). Plutonic rocks contain many pink feldspars and have a coarse to medium granular texture. The main constituent minerals are quartz, orthoclase, microcline, plagioclase, and biotite. In addition, small amounts of hornblende and magnetite are distributed. Sedimentary rocks are most widely distributed in this study area and consist of the Iljik and Hupyeongdong, Jeomgok, Sagok, Chunsan, Donghwachi and Gasongdong, and Dogyedong Formations of the Cretaceous Hayang Group Andesitic rocks are the second most widely distributed next to sedimentary rocks in this study area, and it appears in various colors such as light gray, light pink or dark gray. The main constituent minerals are plagioclase, epidote, chlorite, sericite, and magnetite. Phenocryst is generally anhedral plagioclase, and sometimes a small amount of quartz is also present. The granitic rocks are produced as a small-scale stock in this study area. This rock formation was formed by magma mixing. The main constituent minerals are quartz, orthoclase, microcline, and biotite. Acidic volcanic rocks are distributed in the southern part of this study area. In general, it is characterized by having low-grade sedimentary rocks of the Hayang Group as xenoliths. The alluvium is particularly well distributed in the northwest direction, which appears similar to the water system in Figure 1. The alluvium covers all the rock layers in this study area as unconformity, and it is the Quaternary composed of sand and gravel.

SWAT analysis and accuracy improvement

Input data for SWAT model

The input data for the SWAT model requires weather data, digital elevation data (DEM), land use, soil map, and groundwater information. In the case of weather data, the input data for the model was constructed using data from weather stations located in the study area. Figure 3a shows DEM in the study area and smaller streams divided into three. Medium scales (1:25,000) of land use map were utilized; the largest distribution was of forest areas (82.5%), and distribution of farmland, including rice paddies and fields, was about 14.3% (Fig. 3b). A 1:25,000 scaled soil map was used. Figure 3c provides the simulated spatial distribution of the precision soil map of Yongjeon-stream like the prior land use map with the same spatial resolution as DEM. There are 50 types of soil series, and information on the area of each soil series can be shown in Tables 1 and 2.

Table 1 Land use items and area of Yongjeon-stream

Land use classification item	Description	Area (km²)	Ratio (%)
URMD	Residential-Medium Density	2.87	0.94
UIDU	Industrial	0.04	0.01
UCOM	Commercial	0.33	0.11
UTRN	Transportation	1.09	0.35
UINS	Institutional	0.16	0.05
RICE	Rice	8.35	2.72
AGRR	Agricultural Land-Row Crops	23.71	7.73
URML	Residential-Med/Low Density	0.05	0.02
ORCD	Orchard	9.26	3.02
AGRL	Agricultural Land-Generic	0.24	0.08
FRSD	Forest-Deciduous	75.30	24.55
FRSE	Forest-Evergreen	137.68	44.90
FRST	Forest-Mixed	39.93	13.02
PAST	Pasture	0.33	0.11
BROS	Smooth Bromegrass	0.61	0.20
WETF	Wetlands-Forested	1.18	0.38
AGRC	Agricultural Land-Close-grown	2.26	0.74
WATR	Water	3.26	1.07

Table 2 Area by soil in Yongjeon-stream

Soil classification	Area (km²)	Ratio (%)	Soil classification	Area (km²)	Ratio (%)
ANRYONG	2.26	0.74	HWADONG	0.33	0.11
ASAN	0.95	0.31	HWANGRYONG	4.39	1.43
BANHO	2.59	0.85	ISAN	0.97	0.32
BIGOG	0.58	0.19	JANGWEON	2.20	0.72
BONGGOG	0.14	0.04	JIGOG	3.28	1.07
BUYEO	0.31	0.1	JISAN	1.80	0.59
CHILGOG	1.19	0.39	JUGGOG	0.08	0.03
CHOGYE	0.07	0.02	JUNGDONG	0.19	0.06
DAEGU	27.30	8.9	MASAN	6.25	2.04
DEOGCHEON	1.84	0.6	MUDEUNG	53.39	17.41
DEOGPYEONG	0.15	0.05	NAMGYE	0.44	0.14
DEOGSAN	2.97	0.97	PANGOG	0.20	0.07
GAMGOG	0.67	0.22	SAMGAG	3.63	1.18
GANGSEO	0.33	0.11	SANGJU	0.60	0.19
GEUMGOG	0.65	0.21	SEOGTO	12.11	3.95
GOCHEON	2.99	0.98	SINHEUNG	0.75	0.25
GOSAN	65.74	21.44	SINJEONG	1.31	0.43
GUISAN	0.90	0.29	SONGSAN	66.13	21.57
GYEONGSAN	0.13	0.04	TAEHWA	2.73	0.89
HABIN	4.75	1.55	UGOG	0.06	0.02
HAENGGOG	1.12	0.37	UPYEONG	0.34	0.11
HAENGSAN	5.16	1.68	YEONGOG	6.85	2.23
HAMPYEONG	1.26	0.41	YONGGYE	2.01	0.66
HEUNGPYEONG	8.29	2.7	YONGJI	1.89	0.62
HOGYE	2.04	0.66	YUGA	0.36	0.12

SWAT analysis

The simulation period of the study area was selected considering the obtained weather data. Analysis was conducted for a total of 9 years (January 1^st, 2011 to December 31^st, 2019), including the initial warm-up period of 2 years, and the simulation results were compared for 4 years (January 1^st, 2013 to December 31^st, 2016) with the measured data. Figures 4 and 5 show the comparison graph of the SWAT analysis results and observed flow and coefficient of determination (R²) for each year, and Figure 6 shows the comparison results for the entire period (4 years). Results of the model analysis showed that patterns were similar to observations according to precipitation, but the difference in flow rates was significant. The comparison of R² values for simulated and observatory values showed that they were highest in 2014 (0.52). In 2015, they were low (0.17) and the R² value for the entire period was also 0.40 with very low accuracy.

Calibration and validation using SWAT-CUP Premium

Studies have been conducted consistently to increase the prediction and accuracy of the SWAT model and improve the uncertainty of simulation results. Common techniques for calibration of parameters are MCMC (Vrugt et al. 2008), GLUE (Generalized Likelihood Uncertainty Estimation, Beven & Binley 1992), PSO (Particle swarm optimization, Abbaspour 2011), ParaSol (Parameter Solution, van Griensven and Meixner 2007), and SUFI-2 (Sequential Uncertainty Fitting, Abbaspour et al. 2004), and they can be easily applied in conjunction with the SWAT model using SWAT-CUP. SWAT-CUP Premium is a program that calibrates, validates, and improves SWAT analysis results through algorithms, such as SPE (Swat Parameter Estimator), which can be used to perform validation and sensitivity analysis (Fig. 7).

In this study, SWAT-CUP Premium was used to perform calibration and validation, and the number of 1,000 simulations was performed during the calibration in consideration of the efficiency. In addition, the simulated flow was optimized with the SPE algorithm of SWAT-CUP Premium for the observed flow; the optimized parameter results are shown in Table 3.

Table 3

Maximum and minimum ranges and optimal parameter values for SWAT input parameters
Input	Parameters	Initial range	Fitted value
Gw	ALPHA_BF	−0.048 to 0.952	0.316
Hru	CANMX	0–100	58.950
Rte	CH_K2	−0.01 to 500	294.746
Sub	CH_N1	−0.004 to 29.986	10.058
Rte	CH_N2	−0.024 to 0.286	0.065
Mgt	CN2	−0.25 to 0.25	−0.115
Hru	EPCO	−1 to 0	−0.350
Hru	ESCO	−0.95 to 0.05	0.008
Gw	GW_DELAY	0–500	382.250
Gw	GW_REVAP	0.02–0.2	0.074
Gw	GWQMN	0–5000	1327.500
Gw	RCHRG_DP	−0.05 to 0.95	0.844
Gw	REVAPMN	0–500	209.250
Bsn	SFTMP	−6 to 0	−1.377
Hru	SLSUBBSN	−0.25 to 0.25	−0.043
Bsn	SMFMN	−4.5 to 5.5	−4.355
Bsn	SMFMX	−4.5 to 5.5	0.445
Bsn	SMTMP	−0.25 to 0.25	0.122
Sol	SOL_AWC	−0.25 to 0.25	−0.004
Sol	SOL_K	−0.25 to 0.25	0.248
Sol	SOL_Z	−0.25 to 0.25	−0.227
Bsn	SURLAG	−3.95 to 20	3.175
Bsn	TIMP	−1 to 0	−0.650

R² (Coefficient of determination), NS (Nash-Sutcliffe Efficiency), MNS (Modified NS), and PBIAS (Percent Bias) were used to evaluate the simulation results with calibrated parameters. Where MNS, R², and NS were well matched as they were closer to 1. The closer PBIAS was to zero, the better the observed and simulated values were matched (Gupta et al. 1999). Eq. 1 shows the calculation of PBIAS, and in the case of NS values, if it is 0.50 or more, it can be determined that the improvement effect on uncertainty is high, and can be expressed as shown in Eq. 2 (Nash and Sutcliffe 1970).

$$PBIAS =\frac{[{\sum }_{i=1}^{n}\left({O}_{i}-{S}_{i}\right)*100]}{[\sqrt{{\sum }_{i=1}^{n}\left({O}_{i}\right)]}}$$

Where: ${O}_{i}$ is observed values

${S}_{i}$ is simulated values

$$NS=1- \frac{\sum {({O}_{o}-{O}_{s})}^{2}}{\sum {({O}_{o}-{\stackrel{-}{O}}_{o})}^{2}}$$

Where: ${O}_{o}$ is observed values

${O}_{s}$ is simulated values

${\stackrel{-}{O}}_{o}$ is average observed values

In SPE algorithm, uncertainty of the simulation results is quantified by 95 PPU (95% prediction uncertainty band). The 95 PPU is calculated with parameters, including a measured 95% prediction interval, based on this, P- and R-factors are measured. The P-factor is an indicator of the percentage at which the observed values are included in 95 PPU, and when the P-factor is 1, it means that the observed value is 100% included in the 95% prediction interval. On the contrary, the R-factor represents an average band width of 95 PPU, the closer the value is to zero, the better the calibration results coincide with the observed values. In general, for the simulation of runoff, if the P-factor is between 0.70 and 0.75, it is considered suitable.

Figures 8–10 show graphs comparing the simulated results and observed flow using optimal parameters obtained by the SPE algorithm. The graphs showed that the tendency of daily and seasonal variability was well reproduced, and the flow level corresponding to each day was similar to others. Table 4 shows a comparison of R² values before and after calibration and validation. The R² value for SWAT analysis before calibration and validation is 0.40, but after calibration and validation using SWAT-CUP Premium, the simulation result was improved to 0.71. Table 5 shows the model performance evaluation, with NS 0.51, PBIAS 37.1, P-factor 0.73, and R-factor 0.33, indicating that the relationship between the observed and simulated flows has improved, which is considered to be well matched.

Table 4

Comparison of R² values by year before and after calibration and validation
Year	2013	2014	2015	2016	2013–2016
Before calibration and validation (SWAT)	0.21	0.52	0.17	0.49	0.40
After calibration and validation (SWAT-CUP Premium)	0.55	0.80	0.56	0.74	0.71

Table 5

Simulation results using SWAT-CUP Premium after calibration and validation
Evaluation factor	R²	NS	PBIAS	MNS	P-factor	R-factor
Simulation results	0.71	0.51	37.1	0.52	0.73	0.33

Baseflow analysis and comparison

To compare baseflow before and after calibration and validation, SWAT Output Data was used to extract surface water runoff (SURQ_mm), intermediate runoff (LATQ_mm), and groundwater runoff (GWQ_mm) for the basin. The extracted data per unit area was calculated by runoff through unit conversion, and the baseflow was used for an intermediate runoff and groundwater runoff. Table 6 and Figure 11 show the results of the baseflow analysis for each year. The average baseflow before calibration and validation was 46.659 m³/s and it was increased to 56.951 m³/s after calibration and validation. Although there is a difference in baseflow depending on precipitation for each year, it was shown to increase after calibration and validation in all the years, and in 2015, the overall baseflow is low due to the effects of precipitation. Figure 12 shows a comparison of precipitation in the study area with the baseflow runoff before and after calibration and validation. The change of baseflow was shown depending on season and precipitation. It was confirmed that the difference in baseflow occurred according to the improvement of calibration, validation, and accuracy.

Table 6 Baseflow comparison before and after calibration and validation

Baseflow	2013	2014	2005	2016	Average
Before calibration and validation (m³/s)	42.553	60.631	23.404	60.048	46.659
After calibration and validation (m³/s)	52.533	73.446	28.764	73.060	56.951

The SWAT model, most commonly used in hydrological analysis, was used in this study to simulate runoff and baseflow and compare the results after improving calibration, validation, and uncertainty using SWAT-CUP Premium. According to the SWAT analysis of Cheongsong Yongjeon-stream basin, the R² value of simulated and observed flows was 0.40 with low accuracy, and the baseflow was calculated at an annual average of 46.659 m³/s. During the same period, after calibration and validation using SWAT-CUP Premium, the R² values of simulated and observed flows was improved to 0.71, and the evaluation indexes showed that NS was 0.51, PBIAS was 37.1, P-factor was 0.73, and R-factor was 0.33, indicating that the relationship between observed and simulated flows was improved. In addition, after calibration and validation, the baseflow was calculated as 56.951 m³/s, which is a more accurate value than the value calculated before calibration and validation. Baseflow requires an integrated interpretation considering surface water and groundwater. Although it is difficult to analyze due to uncertainty about baseflow analysis, SWAT-CUP Premium can be used to improve calibration, validation, and uncertainty. It is concluded that a more accurate baseflow can be calculated, and this can be used as basic data for future analysis of the connections between surface water and groundwater in study areas.

Acknowledgments This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Advanced Water Management Research Program, funded by Korea Ministry of Environment (MOE)(21AWMP-B121100-07)

Data Availability Statement Data can be provided upon request.

Funding Statement This research was supported by Korea Ministry of Environment (MOE).

Conflict of Interest All authors declare that they have no confict of interest.

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No competing interests reported.

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Improvement and Analysis for Accuracy of Baseflow Using SWAT-CUP Premium in the Yongjeon Stream, South Korea

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