Impacts of land use change under observed weather conditions

52

in-stream bacteria module, which included bacteria resuspension, deposition, and hyporheic exchange.

Compared to the calibration conducted in our previous study (Kim et al. 2017), we excluded several parameters, including FILTERW, BIOMIX, BIO_INIT, BIO_MIN, FILTERW, LAI_INIT, and PHU_PLT, which were not directly related to E. coli concentration, and added parameters for in-land die-off to account for the E. coli concentration on the soil surface. Most simulations were within the 95% confidence band. Regarding the sensitivity analysis, the parameters associated with streambed resuspension and hyporheic exchange were sensitive (BSC⁴, BSC¹, Hyp_q, and CLAY). Among the original SWAT parameters, the in-stream die-off factor (WDPRCH), wash-off fraction (WOF_P), and fraction of manure that contained active colony forming units (BACT_SWF) were sensitive.

4.3.1.4 Comparison to previous calibration results

These calibration results were compared to the performance of a previously calibrated model (Kim et al. 2017) that used the same calibration procedure but used one single-year land use map instead (i.e., land use map of 2012) (Table 4.4). We found that the multi-year composite land use map improved the simulation in terms of discharge and suspended solids loads (the corresponding Nash-Sutcliffe efficiency (NSE) increased by 8% and 24%, respectively). The opposite trend was observed for E. coli simulation, as root mean square error (RMSE) increased by 8%. However, we cannot conclude that the multi-year composite land use map is not appropriate for E. coli simulation because the E. coli-related parameter set was different from that used in the previous study.

53

Table 4.5 Analysis of land use scenarios under observed and identical weather conditions with daily averaged values during the rainy season.

Land use scenarios

Under observed weather conditions

Under identical weather conditions (2011-2013) Land use

2011

Land use 2012

Land use 2013

Land use 2011

Land use 2012

Land use 2013

Precipitation (mm day^-1) 10.39 7.95 10.37 9.57 (1.15)^†

Curve number (day^-1) 85 77 79 80(4.4) 80(3.6) 79(4.0)

Leaf Area Index (day^-1) 1.52 1.25 1.50 1.59(0.04) 1.28(0.01) 1.54(0.02) Maximum canopy storage

(mm day^-1) 24.2 20.6 23.0 25.0(0.58) 20.8(0.20) 23.3(0.12)

Precipitation reaching

soil surface (mm day^-1) 7.59 4.93 7.06 6.33(1.41) 6.80(1.34) 6.28(1.39) Surface runoff (mm day^-1) 2.73 0.97 2.46 2.03(0.88) 2.10(0.85) 2.05(0.87)

Peak runoff rate

(10^-4 m³ s^-1 day^-1) 8.4 2.8 6.4 5.7(2.6) 5.9(2.8) 6.1(3.2) Suspended solids

in surface runoff (ton day^-1) 0.070 0.024 0.059 0.050(0.024) 0.049(0.026) 0.055(0.029) Initial

E. coli (MPN m^-2

day^-1)

On foliage 6,309 3,852 4,586 6,372(219) 3,973(81) 4,781(160)

In soil solution 90 140 125 88(4.4) 137(1.6) 121(3.2)

Attached to soil

particle 4,394 6,852 6,108 4,331(215) 6,733(78) 5,916(157)

Wash-off 2,642 968 1,726 2,230(455) 1,409(315) 1,709(374)

E. coli transported

(MPN m^-2 day^-1)

In soil solution 17.0 3.7 14.8 13.1(6.5) 10.3(4.8) 11.4(5.4) Attached to soil

particle 16.1 5.9 8.0 9.0(4.2) 9.0(4.4) 11.1(5.2)

In stream 33.1 9.6 22.8 22.1(9.9) 19.3(8.3) 22.5(9.7)

†: mean (standard deviation)

In this study, the initial number of E. coli indicated that the daily number of E. coli existed on the soil surface before undergoing bacterial fate and transport processes, including die-off, wash-off, percolation, and transfer by surface runoff. The initial number of E. coli was almost the same for each year, but relative numbers in each compartment (i.e., on foliage, in soil solution, and attached to soil particles) were different. This was because the number of E. coli on foliage was determined first by LAI, and the remaining numbers were allocated to the soil surface (Figure 4.2). As a result, approximately half of the total number of E. coli was allocated to foliage, and the other half was allocated to the soil surface. Most E. coli on the soil surface was attached to soil particles because the

54

calibrated bacteria partition coefficient (BACTKDDB) was only 0.02. However, the number of E. coli washed off was added to the number of E. coli in soil solution, and we found that 80% of daily E. coli on foliage could be added to the number of E. coli in soil solution (Figure 4.2). Therefore, the initial numbers of E. coli on foliage and in soil solution were consistent with the trend of LAI, while the number of E. coli attached to soil particles showed the opposite trend.

The number of E. coli transferred from the soil surface to the stream in overland flow and with sediment showed trends consistent with their drivers. The trend of E. coli in soil solution was consistent with the trend of surface runoff, while the trend of E. coli attached to soil particles was consistent with the trend of suspended solids. Changes in surface runoff can lead to variations in sediment and bacteria loads, as shown by several authors (Boithias et al. 2016; Rochelle-Newall et al. 2016; Strauch 2017), but few studies have physically simulated the chain of processes in a tropical watershed, which are listed in Table 4.5. This study found that the amount of precipitation was the most significant factor controlling FIB transport as also mentioned in previous studies (Goto and Yan 2011; Strauch et al. 2014).

Figure 4.4 summarizes the stages of simulated bacteria transport processes.

Figure 4.4 Summary of the impact of land use change on each stage of simulated bacteria transport processes during the rainy season (May to September) under observed weather

conditions. Blue numbers indicate a decrease and red numbers indicate an increase.

Our model was limited in estimating LAI. We used the current SWAT module to calculate plant growth, which is mainly based on temperature, with constraints of soil water and soil nutrient contents.

Previous studies demonstrated that rainfall and soil moisture were more critical in controlling plant growth than temperature in tropical regions (Alemayehu et al. 2017; Jolly and Running 2004; Strauch and Volk 2013; Zhang et al. 2005). Additionally, the value of BACTKDDB in this study was automatically calibrated; previous studies used different values for this parameter, ranging from 0.36 to 0.9 (Cho et al. 2012b; Kim et al. 2010; Parajuli et al. 2009). Chenu et al. (2001) and Guber et al. (2009) found that soil structure, soil texture, and water content played a key role in bacterial partitioning; hence, this can cause the differences in the parameter. However, we could not validate this value because of

55

the lack of measurements to compare it with. The calibrated value of 0.02 is lower than the values found in the literature, thereby implying higher rates of particle-attached E. coli in the water column resulting from the high turbidity levels in tropical rivers (Nguyen et al. 2016).