Spatial Variation Pattern Analysis of Hydrologic Processes and Water Quality in Three Gorges Reservoir Area

Abstract

The Three Gorges Project (TGP) has greatly enhanced the heterogeneity of the underlying surface in the Three Gorges Reservoir Area (TGRA), thereby affecting the hydrologic processes and water quality. However, the influence of the differences of underlying surfaces on the hydrologic processes and water quality in the TGRA has not been studied thoroughly. In this research, the influence of the heterogeneity of landscape pattern and geographical characteristics on the spatial distribution difference of hydrologic processes and water quality in the different tributary basins of the TGRA was identified. The TGRA was divided into 23 tributary basins with 1840 sub-basins. The spatial differentiation of the hydrologic processes and water quality of the 23 tributary basins was examined by the Soil and Water Assessment Tool (SWAT). The observed data between 1 January 2010 and 31 December 2013 were used to calibrate and validate the model, after which the SWAT model was applied to further predict the runoff and water quality in the TGRA. There are 25 main model parameters, including CN2, CH_K2 and SOL_AWC, which were calibrated and validated with SWAT-Calibration and Uncertainty Procedures (SWAT-CUP). The landscape patterns and geomorphologic characteristics in 23 tributary basins were investigated and spatially visualized to correlate with surface runoff and nutrient losses. Due to geographical difference, the average total runoff depth (2010-2013) in the left bank area (538.6 mm) was 1.4 times higher than that in the right bank area (384.5 mm), total nitrogen (TN) loads in the left bank area (6.23 kg/ha) were 1.9 times higher than in the right bank area (3.27 kg/ha), and total phosphorus (TP) loads in the left bank area (1.27 kg/ha) were 2.2 times higher than in the right bank area (0.58 kg/ha). The total runoff depth decreased from the head region (553.3 mm) to the tail region (383.2 mm), while the loads of TN and TP were the highest in the middle region (5.51 kg/ha for TN, 1.15 kg/ha for TP), followed by the tail region (5.15 kg/ha for TN, 1.12 kg/ha for TP) and head region (3.92 kg/ha for TN, 0.56 kg/ha for TP). Owing to the different spatial distributions of land use, soil and geographical features in the TGRA, correlations between elevation, slope gradient, slope length and total runoff depth, TN and TP, were not clear and no consistency was observed in each tributary basin. Therefore, the management and control schemes of the water security of the TGRA should be adapted to local conditions.

Impact of Land Use Changes on the Surface Runoff and Nutrient Load in the Three Gorges Reservoir Area, China

“…The TGRA is partitioned into 26 sub-basins, as described in our previous study [53]. In order to better recognize the critical area, the whole research area was divided into left bank and right, head, middle and tail regions (as shown in Figure 1); details were given in [53].…”

“…Shen et al [52] and Shen et al [39] explored nutrient loads and causal factors from different LULC and soil types. Chen et al [53] identified the influence of the landscape pattern on the spatial distribution difference of hydrologic processes and water quality in the TGRA. Nevertheless, these studies only focused on single temporal scales.…”

Response of Variation of Water and Sediment to Landscape Pattern in the Dapoling Watershed

“…The SWAT-VRR model divides the study area into 33 sub-basins by DEM data, each sub-basin is divided into four slope grade zones (Figure 3) consisting of 0-5 The runoff and sediment yield for 2006-2008 at the outlet of the Dapoling watershed was used to calibrate the SWAT and SWAT-VRR models, the observed data for 2009-2011 was used for model validation [62,63]. In this study, SUIF-2 algorithm in SWAT-CUP (SWAT-Calibration Uncertainty Programs) was used to perform parameter sensitivity analysis, calibration, and validation on the SWAT model and SWAT-VRR model.…”

“…Table 1 demonstrates the results of model calibration and validation by using observed data, the NSE and R 2 values for SWAT and SWAT-VRR in monthly and daily simulation are all above 0.6, which show a marginally good or good performance in simulating runoff and sediment yield. During model calibration, the NSE of monthly runoff sim- The runoff and sediment yield for 2006-2008 at the outlet of the Dapoling watershed was used to calibrate the SWAT and SWAT-VRR models, the observed data for 2009-2011 was used for model validation [62,63]. In this study, SUIF-2 algorithm in SWAT-CUP (SWAT-Calibration Uncertainty Programs) was used to perform parameter sensitivity analysis, calibration, and validation on the SWAT model and SWAT-VRR model.…”

“…The time series of runoff and sediment simulation is 2005-2011, that of meteorological data is 2005-2015. To ensure accuracy and stability in simulation, a one-year warm-up period (2005) is set, and the calibration and validation periods are 2006-2008 and 2009-2011, respectively.The runoff and sediment yield for 2006-2008 at the outlet of the Dapoling watershed was used to calibrate the SWAT and SWAT-VRR models, the observed data for 2009-2011 was used for model validation[62,63]. In this study, SUIF-2 algorithm in SWAT-CUP (SWAT-Calibration Uncertainty Programs) was used to perform parameter sensitivity analysis, calibration, and validation on the SWAT model and SWAT-VRR model.Table1demonstrates the results of model calibration and validation by using observed data, the NSE and R 2 values for SWAT and SWAT-VRR in monthly and daily simulation are all above 0.6, which show a marginally good or good performance in simulating runoff and sediment yield.…”