Simulating surface flow and baseflow in Poko catchment, Kon Tum province, Vietnam

Variables

Description

R²

Coefficient of determination

NSI

Nash–Sutcliffe index

PBIAS

Percent bias

Q_sf

The direct surface runoff (mmH₂0)

Q_lt

The lateral flow from unsaturated soil profile (mmH₂O)

Q_tl

The drainage (mmH₂O)

w_seep

The total amount of water exiting the bottom of the soil profile (mmH₂O)

w_rchrg,sh

The recharge in the shallow aquifer storage (mmH₂O)

S_sh

The water storage in the shallow aquifer storage (mmH₂O)

Q_{b, sh}

The baseflow in the shallow aquifer storage (mmH₂O)

w_seep,dp

The total amount of water exiting the bottom of shallow aquifer storage (mmH₂O)

w_rchrg,dp

The recharge in the deep aquifer storage (mmH₂O)

S_dp

The water storage in the deep aquifer storage (mmH₂O)

Q_{b, dp}

The baseflow in the deep aquifer storage (mmH₂O)

Q_b

The total baseflow (mmH₂O)

Q_s

The total flow (mmH₂O)

DEM

Digital elevation model

r

The existing parameter value is multiplied by (1+ a given value)

v

The existing parameter value is to be replaced by the given value)

r_HRU_SLP

Average slope steepness of hydrological response unit

v_GWQMN

Threshold depth of water in the shallow aquifer required for return flow to occur (mm)

r_ESCO

Soil evaporation compensation factor

r_SOL_AWC

Available water capacity of the soil layer

r_GW_DELAY

Groundwater delay (days)

r_REVAPMN

Threshold depth of water in the shallow aquifer for ‘revap’ to occur (mm)

v_PLAPS

Precipitation lapse rate

r_GW_REVAP

Groundwater ‘revap’ coefficient

r_CN2

Soil Conservation Service (SCS) runoff curve number

r_SLSUBBSN

Average slope length of subbasin

v_ALPHA_BF

Baseflow alpha factor (days)

Poko catchment is one of three main basins located in Kon Tum province, Vietnam. It covers approximately 3,530 km², is 121 km in length and plays an important role in developing socio-economic as well as environmental aspects of the province. According to Cuong (2012), the water resource in Poko catchment is used for four sectors, including agriculture, industry, domestic, and environmental flow with the corresponding ratio, in turn being 81.24%, 1.98%, 3.74%, and 13.04%. Thanks to the large annual average rainfall, surface water contributes mainly to water discharge in the basin. However, under the pressure of socio-economic development and climate change, it is necessary to undertake a comprehensive assessment of the distribution of surface water as well as groundwater from a spatio-temporal perspective.

Water is essential to support humanity, and the understanding of water storage is indispensable to future use of water resources. As regards the hydrology aspect, many different approaches have been used in water resources management. Among them, mathematic modeling is a common method, such as HEC-HMS model (Kar et al. 2015), MIKE BASIN and ASM models (Ireson et al. 2006), SWAT model (Schuol et al. 2008; Liem 2011). Moreover, to separate surface flow and baseflow from water discharge, several filtering algorithms have been developed, such as exponential smoothing method (Tularam & Ilahee 2008), Lyne and Hollick filter (Ladson et al. 2013), automatic baseflow filtering (Luo et al. 2012). So far, no research has been found that focuses on applying the SWAT model and filtering algorithm to separate water discharge into surface flow and baseflow in Poko catchment.

This aims of the paper are the following objectives: (1) calibrating and validating the SWAT model for the simulation of water discharge from 1990 to 2013 in Poko catchment; (2) separating surface flow and baseflow from water discharge in the study area; (3) assessing the spatio-temporal distribution of surface flow and baseflow in the catchment.

Poko catchment, a tributary of the Sesan river basin, is located in the west of Kon Tum province with an approximately 3,210 km² area and 152 km length, as shown in Figure 1. The catchment flows through three districts including Dak Glei, Dak To, and Ngoc Hoi of Kon Tum province. Three major topography types in the catchment are mountain (average slope from 20 to 25 degrees), hills (average slope from 15 to 20 degrees), and valleys (average slope from 10 to 15 degrees). The highest elevation reaches about 2,000 meters in the upstream area and descends to the confluence between the Poko river and Dakbla river before flowing into Yaly lake. Average annual rainfall in the basin is approximately 2,500 mm, resulting in high-density river (1 km of river length per 1 km² area) and large flow module (approximately 40 liters/s/km²). Total water volume reaches about 3.7 billion cubic meters per year. The quantity of surface water stored in Poko catchment is 3.8 times more than the total amount of groundwater (Cuong 2012).

In the SWAT model, water routed through a channel system to the outlet of the basin contains four components: direct surface runoff (Q_sf), lateral flow from unsaturated soil profile (Q_lt), drainage (Q_tl), and baseflow from underground storage (Q_b) including baseflow in the shallow aquifer storage (Q_b,sh) and baseflow in the deep aquifer storage (Q_b,dp) (Luo et al. 2012). All water components are described in Figure 2.

The SWAT model requires daily meteorological data (precipitation, maximum air temperature, minimum air temperature, relative humidity, wind speed, and solar radiation) and spatial datasets (digital elevation model (DEM), land use/land cover, and soil maps). Water discharge data are used for calibration and validation of water discharge simulation. The descriptions of input data are shown in Figure 3 and Table 1.

The implementation process of the study was divided into two parts (see Figure 4): (1) calibration and validation of water discharge simulated in the SWAT model; (2) assessing the accuracy of the baseflow filtering algorithm in separating water discharge into surface flow and baseflow.

The performance of the SWAT model and baseflow filtering algorithm was evaluated by using three statistics: quantitative indices including Nash–Sutcliffe index (NSI), percent bias (PBIAS), and coefficient of determination (R²). The performance ratings for these indices are shown in Table 2.

The results of comparing simulated water discharge in the SWAT model with observed water discharge data in monthly timestep from 1996 to 2013 at Dak Mot stream gauge (Dak To District, Kon Tum Province) (see Figure 5) showed that simulated values were underestimated in both non-flood season and flood season with R² = 0.61, NSI = 0.38, and PBIAS = −43.74. Most simulated flood peaks were lower than observed ones, except for flood peaks in 2009 and 2010 which were overestimated. In brief, the accuracy of simulated water discharge in the SWAT model was unsatisfactory, resulting in the need for calibration and validation.

The SUFI-2 algorithm in the SWAT-CUP software was used for SWAT model calibration, validation, and sensitivity analysis. A total of 220 interactions were done sequentially for 11 parameters related to surface flow, baseflow, flood peak, and flow tendency to capture most of the observed data within the 95% prediction uncertainty (95PPU) of the SWAT model as shown in Figure 6. The optimal range and sensitivity ranking of these parameters are described in Table 3. t-stat provides a measure of sensitivity (larger absolute values are more sensitive), while p-values determined the significance of the sensitivity (a value close to zero has more significance).

The calibration result of water discharge at Dak Mot stream gauge from 1996 to 2004 was good with R² = 0.81, NSI = 0.75, and PBIAS = 12.3 (see Figure 7). Similar to the calibration period, during the validation period (2005–2013), water discharge was simulated well with R² = 0.85, NSI = 0.83, and PBIAS = 11.6 (see Figure 8). In general, the simulated values in the non-flood season were estimated better than those in the flood season. To explain the above finding, besides the parameter uncertainty of the SWAT model, the impact of Plei Krong hydropower operation which began in August 2008 should be considered.

The result of separating water discharge into surface flow and baseflow at Dak Mot stream gauge from 1996 to 2013 is shown in Figure 9. In order to assess the accuracy of surface flow and baseflow, three statistical quantitative indices including R², NSI, and PBIAS were calculated for each year, then the statistical parameters (minimum, maximum, median, standard deviation) of these indices were defined for the whole period (see Table 4). It can be seen that the simulated values were almost lower than the observed values for both surface flow and baseflow. The correlation of surface flow (R² = 0.64) was lower than the correlation of baseflow (R² = 0.90). However, considering NSI and PBIAS, surface flow was more accurately simulated than baseflow.

Based on Figure 10, it can be seen that the average monthly water discharge in Poko catchment during the period 1996–2013 ranged from 40 cm to 310 cm. Flow distribution shows a clear contrast between flood season and non-flood season. Total water discharge in the flood season accounted for over 60% of the total annual water discharge. Considering the flow components, surface flow prevailed at a contribution rate ranging from 51% to 58%.

The monthly water discharge in Poko catchment simulated by using the SWAT model gave good results at Dak Mot stream gauge in both the calibration period 1996–2004 (NSI = 0.75, PBIAS = 12.3) and validation period 2005–2013 (NSI = 0.83, PBIAS = 11.6). Based on baseflow filtering algorithm with filter parameter β = 0.925, surface flow and baseflow were separated from water discharge. The result showed that the simulated values were almost lower than the observed values for both surface flow and baseflow. Considering the contribution rate to flow, surface flow prevailed. These findings were expected to provide useful information for management, protection, exploitation, and use of water resources in Poko catchment. Further research should be undertaken to assess water resources availability of both surface water and groundwater in the catchment for four water use sectors including agriculture, industry, domestic, and environmental flow.