Simulation of runoff in karst influenced lianjiang watershed using the swat model

Scientific Journal of Earth Science June 2014, Volume 4, Issue 2, PP.85-92

Simulation of Runoff in Karst-influenced Lianjiang Watershed Using the SWAT Model Xizhi Wang, Zhaoxiong Liang, Jun Wang Department of Resource and Environment, Foshan University, Foshan, China

Abstract The SWAT (Soil and Water Assessment Tool) is a river basin, or watershed, scale model developed to predict the impact of land management practices on water, sediment, and agricultural chemical yields in large, complex watersheds with varying soils, land use, and management conditions over long periods of time. SWAT model was found to be appropriate in Karst watersheds due to its capability to represent almost all of the hydrological processes, its user-friendliness, and its ability to generate most of the parameters from available data. Based on the runoff daily observed data of Lianjiang watershed, which measured at Gaodao, Fenghuangshan and Huangjingtang gauging station from 2001 to 2005, the calibration and Sensitivity analysis of the model were carried out. The calibrated model was validated for three gauging stations for a period of 5 years (2006 - 2010).The simulated monthly stream flow has Nash Sutcliffe efficiency value of 0.97, 0.89 and 0.70 for the calibration period for the Gaodao, Fenghuangshan and Huangjingtang stations, respectively. The model was successful in simulating streamflow during validation period as indicated by Nash Sutcliffe efficiency value of 0.90, 0.69 and 0.69 and R2 value of 0.94, 0.93 and 0.92 for Gaodao, Fenghuangshan and Huangjingtang stations. The paper is also to provide the frontier information of Karst-influenced hydrological processes based on SWAT in order to obtain more attention of researchers in Karst hydrology field, and to make more application of the case study in the future.

Keywords: Runoff Simulation; SWAT Model; Karst region; Liangjiang Watershed

1 INTRODUCTION Understanding watershed hydrology is critical, as it is often a primary driving force for nutrient cycling and loading dynamics and subsequent downstream water quality impacts as a result of rapid urbanization and other land use changes (Amatya, et al, 2011). According to the Karst Water Institute (2009), approximately 25% of the world’s population either resides on or obtains water from aquifers in karst regions. Modeling hydrological processes on the watershed scale can provide a better indicating of the interactions between surface and ground water and how water quality and quantity are affected by human activities to protect water resources and public health, especially in karst-influenced watershed. A variety of karst models have been developed and applied to karst formation discharge (Nikolaidis, et al, 2013).

Semi-distributed watershed model such as SWAT has been used in the past to simulate the hydrologic response of karstic formations (Spruill et al, 2000). Although the SWAT model has been widely and successfully tested for various geographical regions, but not provided satisfactory estimates of runoff in karst watersheds (Ghanbarpour et al, 2010). Other efforts (Spruill et al, 2000; Coffey et al, 2004; Afinowicz et al, 2005; Benham et al, 2006) are reported by Gassman et al (2007) and show the difficulty of using that model to represent the baseflow of karst-fed streams. Recently, Baffaut and Benson (2009) modified the SWAT 2005 code to simulate faster aquifer recharge in a Missouri karstic watershed (SWAT-B&B) by modifying subroutines for deep groundwater recharge and maximizing the hydraulic conductivity for sink holes simulated as ponds and losing streams and tributaries. Yachtao (2009) further modified the work of SWAT-B&B in SWAT-Karst to represent karst environments at the hydrologic response unit (HRU) scale in the Opequon Creek watershed.

In practical studies, SWAT need to construct an appropriate model database depending on the scale of watershed and - 85 http://www.j-es.org

the level of accuracy required, especially soil data (Li R K et al, 2011), and also need to consider the principle of the model and the physical meanings of some key parameters to explain the simulation result (Ye, X C et al, 2009). The researches of Karst watershed based on SWAT should be enhanced to readjust the model databases of Karst features.

For example, in Karst regions, the soil physical attributes of new database need to be established by rocky desertification processes.

The objective of this research was to study the runoff in following ways. Firstly, three hydrological stations were simulated runoff by calibration and validation of values in Lianjiang River. Secondly, the different soil database was used to simulate runoff values of SWAT model. Thirdly, the frontier information of Karst-influenced hydrological processes is provided of using SWAT model in the karst watershed.

2 SWAT MODEL DESCRIPTION SWAT (Soil and Water Assessment Tool, Arnold et al, 1998) is semi-distributed, process-oriented model for simulating water, nutrient and pesticide transport. It is used for multi-scale catchments, the basic units are called Hydrological Response Units (HRU) based on different land use, soil and slope. Without gonging into too much detail as it was generally introduced all-round in previous studies, it has been applied worldwide for most climate zones and many regions. More detailed information on the model is described in Amatya et al. (2011), Yachtao (2009), Baffaut and Benson (2009), Gassman et al. (2007) and Arnold et al. (1998) and the references therein.

3 MATERIALS AND METHODS 3.1 Study Area Liangjiang watershed is located in the North Guangdong province in China, lies between 112°10´- 113°18´E longitude and 24°09´- 25°07´N latitude (Fig. 1). It has a drainage area of 10061 km2 and a length of 275 km, the largest tributary of the Beijiang River in the Zhujiang drainage system (Zhou, 2005). The climate condition is primarily subtropical monsoon region with the average annual precipitation and temperature of 1700 mm and 19-20 ℃, respectively. It is a landscape dominated by Karst terrain, and surface and ground water resources are closely related in some sub-watershed. Karst regions appear about 40 percent of the watershed area. Three hydrologic stations exist in the watershed to characterize hydrological process: Gaodao (9016 km2) of Liangjiang River, Fenghuangshan (1548 km2) of Xingzi River and Huangjingtang (645 km2) of Tongguansgui River.

FIG. 1 LOCATION OF THE LIANJIANG WATERSHED

3.2 Data Description The ArcGIS 9.3.1 was used for the analyses satellite data, digitization of contours, construction of Digital Elevation - 86 http://www.j-es.org

Model (DEM), automatic extraction of watershed parameters and interpretation of results. The extracted watershed information was used to generate the input parameters of ArcSWAT 2009.93.6. Input for SWAT model includes topography, land use, soil, meteorological data and observed discharge over the watershed.

DEM data was extracted from ASTER Global Digital Elevation Model (ASTER GDEM) of METI/NASA, with a spatial resolution of 30 m.

Land use map was obtained from TM data (path no. 122-123 and row no. 43 2006a) and used the supervised method of classification to classify the land uses. The dominant land uses in Liangjiang watershed are forest (37.09%), shrub land (37.26%), grassland (7.93%), agricultural land (11.68%), construction land (5.14%) and water area (0.90%).

Soil map was taken from the national soil map of the scale 1:1000000 of Guangdong Institute of Geo-environment and Soil Sciences. In the research watershed, there are 17 types of soil including Terra rossa (HSSHT), Yellow earths (HGYHR), Red earths (SYYHRA), Hydromorphic paddy soils (ZYSDT), Lateritic red earths (CHHR), Phospho-calcic soils (GZSZT), Nutral purplish soils, Yellow red earths (ZXZST), Percogenic paddy soils (SYSDT), et al.

For weather data, based on distribution characteristics, duration and data quality of meteorological monitoring in Lianjiang watershed and its surrounding areas, four gages as Lianzhou, Shaoguan, Guangling and Fogang were used.

The data was obtained from China Meteorological Data Sharing Service System.

Discharge data was prepared at three monitoring stations named Gaodao, Fenghuangshan and Huangjingtang. The data was furnished by Institute of Geographic Sciences and Natural Resources Research, CAS.

(a)

(b)

(C) FIG. 2 THE STUDY AREA OF DEM MAP (A), LAND USE MAP (B) AND SOIL MAP (C) - 87 http://www.j-es.org

3.3 Model Performance Evaluation The SWAT model was evaluated using observed discharge data. The Nash- Sutcliffe efficiency (NSE) (Moriasi et al, 2007) is a common indicator used to quantify how daily simulated values fit the measured values. The NSE is computed as the ratio of residual variance to measured data variance. According to Moriasi et al (2007), model simulation is considered to be an acceptable value if NSE > 0.5, and a good value if NSE > 0.7. The coefficient of determination (R2) is calculated as between measured and simulated values that correspond to a same frequency of occurrence (Santhi et al, 2001), and value ranges from 0 – 1, an acceptable value if R2 > 0.5. It is utilized to evaluate how the range and distribution of the simulated values match the range and distribution of measured values.

4 RESULTS AND DISCUSSION The Lianjiang watershed was delineated into 112 sub-watersheds using as criterion the elemental catchment of 5000 ha and 843 hydrologic response units (HRUS).

4.1 Sensitivity Analysis Sensitivity analysis was performed to evaluate the effect of parameters on the performance of the SWAT model in simulating runoff. On the basis of sensitivity analysis performed, parameters with the highest relative sensitivity to simulated runoff were soil evaporation compensation factor (ESCO), curve number (CN), threshold depth of shallow aquifer water required for return flow (Gwqmn), and available water capacity of soil layer (SOL_AWC) et al, as shown in Table1. It is interesting to note that sensitive parameters are different for different catchments. These parameters were modified during the model calibration. After sensitivity analysis, both calibration and validation were done at both monthly and yearly time steps.

TABLE 1 LIST OF SENSITIVE PARAMETERS CALIBRATED Order 1 2 3 4 5 6 7 8 9 10

Gaodao Esco Gwqmn CN Sol_Z Sol_Awc Slope Sol_K Ch_K2 Canmx Gw_Revap

Fenghuangshan Esco Gwqmn Canmx CN Sol_Z Sol_Awc Sol_K Slope Blai Gw_Revap

Huangjingtang Esco Gwqmn CN Canmx Sol_Awc Sol_Z Sol_K Ch_K2 Slope Gw_Revap

4.2 Calibration and Validation Three separate models were prepared for Lianjiang watershed and two sub-watersheds. The model was manually calibrated for flow based on the 2001-2005 period and validated based on the years 2006-2010. The results of calibration and validation are discussed below.

In this study, calibration efforts focused on improving model performance at three gauging stations. Flow calibration was focused on monthly and yearly simulations. The capability of a hydrological model to adequately simulate stream flow process typically depends on the accurate calibration of parameters. A list of the parameters, their range of values and the final parameter values achieved after the calibration of the model are shown in Table 2.

TABLE 2 RANGES AND VALUES OF MAJOR PARAMETERS USED IN THIS STUDY Parameters

Ranges

Esco Gwqmn Canmx Sol_Awc Sol_Z Sol_K Gw_Revap

0-1 0-5000 0-100 0-1 0-3500 0-2000 0.02-0.2

Gaodao 0 0 0 0.11 100 20 0.02

Values used Fenghuangshan 0.5 1000 0 0.09 160 93 0.02

- 88 http://www.j-es.org

Huangjingtang 0.5 1000 0 0.25 60 119 0.02

Figure 3 compares graphically observed and simulated monthly runoff values at Gaodao, Fenghuangshan and Huangjingtang stations for the calibration and validation periods. It was observed a fairly good match between observed and simulated runoff at Gaodao, the better of Fenghuangshan and Huangjingtang.

(a)

(b)

(c) FIG. 3 SIMULATED MONTHLY RUNOFF AND OBSERVED VALUES FOR CALIBRATION (2001-2005) AND VALIDATION (2007-2010) OF GAODAO STATION (A), FENGHUANGSHAN STATION (B), HUANGJINGTANG STATION (C)

The results of acceptable tests performed agreement between observed and simulated the monthly and yearly runoff values are presented in Table 3. The Ens values of 0.97, 0.89 and 0.70, R2 values of 0.92, 0.92 and 0.91 of yearly runoff at Gaodao, Fnghuangshan and Huangjingtang stations of calibration period indicated fit observed and simulated values, and the model performed better at Gaodao station than at Fenghuangshan and Huangjingtang stations. Nash-Suctcliffe simulation efficiency of 0.90, 0.69 and 0.69 at three stations of validation period indicated - 89 http://www.j-es.org

that there was a better agreement between the observed and simulated yearly runoff. Great values (0.94, 0.93 and 0.92) of R2 indicated a good agreement of yearly runoff.

TABLE 3. MODEL PERFORMANCE FOR THE SIMULATION OF RUNOFF Period Calibration (2001-2005) Validation (2007-2010)

Gaodao Fenghuangshan Huangjingtang Ens R2 Ens R2 Ens R2 monthly yearly monthly yearly monthly yearly monthly yearly monthly yearly monthly yearly 0.94 0.97 0.94 0.92 0.83 0.89 0.83 0.92 0.76 0.70 0.79 0.91 0.93 0.90 0.94 0.94 0.75 0.69 0.79 0.93 0.77 0.69 0.78 0.92

Overall, simulation of monthly and yearly runoff by SWAT model during the calibration and validation period was found to be satisfactory of the whole Liangjiang watershed, and modified SWAT model for further analysis of small sub-watersheds.

4.3 Model by Modified Karst- influenced Soil Database Karstic regions are about 40% of the total area in the Liangiang watershed, and 30.91% of the Xingzi River, 37.53% of the Tongguanshui River. Changes in soil hydraulic properties due to rocky desertification processes can be expected to happen but have not been frequently studied in the past. Hence, a strategy is required to estimate changes in soil hydraulic properties from available information, such as soil layer properties et al. In non-karstic regions, the SWAT model is to be used generally condition.

In Karstic regions, the area of undiscoverable rocky desertification (covered karst) and rocky desertification were divided and superimposed on the soil map. Therefore, developing Karst-influenced soil database for Lianjiang watershed required the creation of a new soil map that covered the entire Karstic area. The Karstic thin soil layer, located at a depth no more than 50 cm, reduces the area soil’s permeability and increase the potential for surface and lateral flow. The average soil layer is the depth no more than 25 cm of rocky desertification regions, and a depth of 25 to 50 cm of undiscoverable rocky desertification regions. The new of soil types were set by suffixed -Q of the undiscoverable rocky desertification areas, and suffixed K of the rocky desertification areas. In the Karst-influenced soil database, there are 34 types of soil including HSSHTQ, SYYHRAQ, ZYSDTQ, SHYHRQ, HSSSTK, HSSLTQ, HGYHRQ, SYYCHRQ, et al.

Using the different soil database, the performance of observed and simulated monthly and yearly runoff values at Gaodao, Fenghuangshan and Huangjingtang stations for the before calibration and no calibration of validation are shown in table 4. With similar and no significant differences of the simulated results of the application of these two soil databases, it is necessary to propose a new database or parameters to assess influence of the soil hydrological process in Karst regions as the accuracy of database has to be upgraded.

TABLE 4. MODEL PERFORMANCE FOR THE SIMULATION OF RUNOFF FROM DIFFERENT SOIL DATABASES

Gaodao Fenghuangshan Huangjingtang

Gaodao Fenghuangshan Huangjingtang

General model performance Before Calibration Validation of no Calibration Ens R2 Ens R2 monthly yearly monthly yearly monthly yearly monthly yearly 0.94 0.96 0.94 0.98 0.93 0.90 0.94 0.94 0.84 0.24 0.93 0.98 0.89 0.29 0.92 0.80 0.88 0.74 0.90 0.75 0.89 0.40 0.91 0.89 Model performance of Karst-influenced soil database Before Calibration Validation of no Calibration Ens R2 Ens R2 monthly yearly monthly yearly monthly yearly monthly yearly 0.95 0.96 0.95 0.98 0.93 0.89 0.94 0.94 0.84 0.29 0.91 0.98 0.88 0.18 0.91 0.80 0.88 0.75 0.90 0.75 0.89 0.44 0.91 0.88

4.4 SWAT Application Based on Karst- influenced Feature The watershed has some karst features, with depressions, sinkhole, losing stream tributaries, springs, and caves.

- 90 http://www.j-es.org

Therefore, developing a Karst-influenced SWAT model for the Liangjiang basin required the creation of a new landuse and soil map, and new parameters of Karstic feature that covered the entire catchment area.

In the further study, the researches of small Karst watershed based on SWAT model should be enhanced from three aspects, (1) Different of land cover categories database were identified in the Karst watershed. In non-karst regions, there is a usual land cover categories database. In Karst regions, there is a Karst-influenced 4 land cover categories database such as undiscoverable rocky desertification, slight rocky desertification, moderate rocky desertification and severe rocky desertification. (2) In Karst regions, the soil physical attributes of new database is established by rocky desertification processes. (3) A new approach for spatial discretization of Karst hydrologic response units (KHRU) is proposed and applied to the Karst watersheds. Parameters used in SWAT should be modify and correct in Karst regions such as Sink, Delay, Alpha, Hydraulic conductivity, et al and try to setup new parameters of improved Karst-influenced SWAT model.

5 CONCLUSION SWAT, a widely used watershed-scale distributed hydrologic model, was applied to test its ability to predict runoff of a karst-influenced watershed at Lianjiang River of Guangdong province in China. Calibration and validation results of the model show the simulated monthly and yearly runoff were in reasonable agreement with observed values. It showed that the SWAT model could be used successfully to accurately simulated runoff in the Lianjiang watershed, the performed obviously better at Gaodao station than at Fenghuangshan and Huangjingtang stations. This indicates that the SWAT model was simulated at the large watershed better than at the location draining sub-watersheds.

However, despite the karst-influenced soil database, SWAT was only slightly accurately capturing the monthly and yearly runoff at Gaodao, Fenghuangshan and Huangjingtang stations. As for the future trend of runoff in the Lianjiang watershed, especially small sub-watershed of the influence by karst features, it is being constructed Karst-influenced SWAT databases, and the related results will be given in the forthcoming paper under preparation.

ACKNOWLEDGMENT The research was supported by National Natural Science Foundation of China (NO. 31070426)

REFERENCES [1]

Afinowicz J D, Munster C L and Wilcox B P. 2005. Modeling effects of brush management on the rangeland water budget: Edwards Plateau Texas. J. American Water Resources Assoc. 41(1): 181-193

[2]

Amatya D M, Jha M, Edwards A E, Williams T M, Hitchcock D R. 2011. SWAT-based streamflow and Embayment modeling of Karst-affected Chapel branch watershed, South Carolina. Transactions of the ASABE, 54(4): 1311-1323

[3]

Arnold J G, Srinivasan R, Muttiah R S, and Williams J R. 1998. Large area hydrologic modeling and assessment part I: Model development. Journal of American Water Resources Association, 34(1): 73-89

[4]

Baffaut C and Benson V W. 2009. Modeling the flow and pollutant transport in a Karst watershed with SWAT. Transactions of the ASABE, 52(2): 469-479

[5]

Benham B L, Baffaut R W, Zeckoski R W, Mankin K R, Pachepsky Y A, Sadeghi A M, Brannan K M, Soupir M L and Habersack M J. 2006. Modeling bacteria fate and transport in watershed to support TMDLs. Transactions of the ASABE, 49(4): 987-1002

[6]

Gassman P W, Reyes M R, Green C H, Arnold J G. 2007. the soil and water assessment tool: historical development, applications and future research directions. Transactions of the ASABE, 50(4): 1211-1250

[7]

Coffey M E, Workman S R, Taraba J L and Fogle W A. 2004. Statistical produces for evaluations daily and monthly model predictions. Trans. ASAE, 47(1): 59-68

[8]

Ghanbarpour M R, Abbaspour K C, Jalalvand G and Moghaddam G A. 2010. Stochastic modeling of surface stream flow at different time scales: Sangsoorakh karst basin, Iran. J. Cave and Karst Studies, 72(1): 1-10

[9]

Li R K, Zhu A X, Li B L, Pei T and Qin C Z. 2011. Response of simulated stream flow to soil data spatial detail across different routing areas. Progress in GeoGraphy, 30(1): 80-86 (in Chinese)

[10] Moriasi D N, Arnold J G, Van Liew M W, Bingner R L, Harmel R D and Veith T L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3): 885-900

- 91 http://www.j-es.org

[11] Nikolaidis N P, Bouraoui F and Bidoglio G. 2013. Hydrologic and geochemical modeling of a karstic Mediterranean watershed.

Hydrological Processes, 477: 129-138 [12] Santhi C, Arnold J G, Williams J R, Dugas W A, Srinivasan R and Hauck L M. 2001. Validation of the SWAT model on a large river basin with point and nonpoint sources. J. American Water Resources Assco. 37(5): 1169-1188 [13] Spruill C A, Workman S R and Taraba J L. 2000. Simulation of daily and monthly stream discharge from small watersheds using the SWAT model. Trans. ASAE, 43(6): 1431-1439 [14] Xu Z X, Pang J P, Liu C M and Li J Y. 2009. Assessment of runoff and sediment yield in the Miyun Reservoir catchment by using SWAT model. Hydrological Processes, 23: 3619-3630 [15] Yachtao G A. 2009. Modification of the SWAT model to simulate hydrologic processes in a Karst watershed. MS thesis.

Blacksburg, VA: Virginia Tech, Department of Biosystems Engineering [16] Ye X C, Zhang Q, Liu J, Li L J and Zuo H J. 2009. Effects of spatial of soil data on hydrological processes modeling. Progress in GeoGraphy, 28(4): 575-583 (in Chinese) [17] Zhou M. 2005. Analysis of hydrologic feature of Lianjiang watershed. GUANGDONG WATER RESOURCES AND HYDROPOWER, 6: 74-75

AUTHORS Wang xizhi (PhD, Professor) was born on October 04, 1971, in Lanzhou, China.

Email: wangxizhi71@163.com

- 92 http://www.j-es.org