3.1. Un-Gauged Sub-Watersheds Baseflow
Figure 3 shows the monthly model-based and digital filter-based baseflow of the Upper Du Watershed during calibration and validation. The statistical performance was satisfactory according to the monthly
ENS,
R2, and
PBIAS (
Table 2). The statistical results showed good agreement by comparing the digital-based baseflow with the simulation, and the parameters calibrated for baseflow of the model could be used to simulate every sub-watershed. However, as shown in
Figure 3, there was a difference between the filtered-based value and the simulated value. The filter-based value accounted for 34.3% of the annual flow volume and the model-based baseflow volume accounted for 35.0% of the annual flow volume. Most summers, the simulated value was overestimated, whereas it tended to be too low in winter. The depletion of a portion of the shallow aquifer storage of the watershed during the simulation accounted for the slight difference. The simulation revealed that there was seasonal storage fluctuation and equilibrium was maintained for the deep aquifer. Rapid percolation of rainfall occurs during the summer, and the shallow aquifer, which is the important resource for baseflow, quickly receives recharge from the unsaturated soil profile percolation; in winter, the underground storage is released more slowly [
29].
Table 2.
Examination of the performance of SWAT in the Upper Du Watershed.
Table 2.
Examination of the performance of SWAT in the Upper Du Watershed.
Stations | Period | ENS a | PBIAS b | R2 | Rating |
---|
Zhushan | Calibration (1971–1980) | 0.83 | 3.9 | 0.85 | Very good c |
| Validation (1981–1990) | 0.80 | 4.5 | 0.81 | Very good |
| Overall (1971–1990) | 0.82 | 4.2 | 0.83 | Very good |
Xinzhou | Validation (1991–2000) | 0.77 | 1.5 | 0.87 | Very good |
| Overall (1991–2010) | 0.77 | 1.5 | 0.87 | Very good |
Figure 3.
Monthly digital filtered-based and model-based baseflow in the Upper Du Watershed for the calibration (from 1 January 1971 to 31 December 1980) at Zhushan station (A); validation (from 1 January 1981 to 31 December 1990) at Zhushan station (B); and validation (from 1 January 1991 to 31 December 2000) in Xinzhou station (C).
Figure 3.
Monthly digital filtered-based and model-based baseflow in the Upper Du Watershed for the calibration (from 1 January 1971 to 31 December 1980) at Zhushan station (A); validation (from 1 January 1981 to 31 December 1990) at Zhushan station (B); and validation (from 1 January 1991 to 31 December 2000) in Xinzhou station (C).
3.2. Temporal and Spatial Distribution of Baseflow
Figure 4 shows the changes in the timing of digital-based baseflow. The average annual baseflow over the entire watershed for four study periods (1970s, 1980s, 1990s, and 2000s) was 205.0, 220.1, 201.3, and 198.2 mm, respectively. The mean monthly baseflow showed a prominent increased in spring, which might have been affected by the rainfall increase (3 mm) from the 1970s to the 2000s in this season. The prominent late-autumn peaks were likely to diminish and a larger proportion of discharge shifted to early winter. The processes causing the temporal changes in baseflow also resulted in spatial changes.
Figure 5 shows the model-based baseflow changes between 1978 and 2007 at the sub-watershed scale and
Table 3 shows that the seasonal baseflow in the 107 sub-watersheds varied substantially according to CV. The spatial distribution of the baseflow changes of this watershed can be broadly divided into two parts: the area near the major stream channels mainly covered by farmland and urban areas, and the more distant area that is mainly covered by forest. The baseflow change during February, March, and April (spring) of 2007 was not substantially different from the conditions during 1978 for much of the Upper Du Watershed (
Figure 5A), as the increase was inconspicuous (less than 2 mm). The decrease in baseflow was less than 3.9 mm in the area along the major stream channels. Simulations indicated that the baseflow during May, June, and July (summer) of 2007 declined (0.1–1.9 mm) throughout the area, which was mainly covered by forest, relative to 1978. Larger decreases in baseflow, ranging from 2.0 to 18.9 mm, were simulated in the area mainly covered by farmland and urban land use near the main stream channel in the middle and northern parts of the watershed. The baseflow decreased during August, September, and October (autumn) in the middle and northern parts of the watershed in 2007. The baseflows in the northern, southwestern, and southeastern portions distant from the major stream network of the watershed showed a slight increase, whereas they showed a slight decrease in the summer months and were not markedly different volumetrically from the historic period. Patterns of baseflow changes in November, October, and January (winter) were similar to those in the autumn months; however, the baseflow recession expanded to areas distant from the major stream network.
Figure 4.
Watershed-scale average monthly baseflow for the 1970s, 1980s, 1990s, and 2000s.
Figure 4.
Watershed-scale average monthly baseflow for the 1970s, 1980s, 1990s, and 2000s.
Figure 5.
Changes in baseflow from 1970s to 2000s calculated for spring (A); summer (B); autumn (C); and winter (D) seasons.
Figure 5.
Changes in baseflow from 1970s to 2000s calculated for spring (A); summer (B); autumn (C); and winter (D) seasons.
Table 3.
Robust coefficient of variation (CV) for the average monthly baseflow of each sub-watershed in this study.
Table 3.
Robust coefficient of variation (CV) for the average monthly baseflow of each sub-watershed in this study.
Seasonal Baseflow | Land Use Scenarios | Robust Coefficient of Variation |
---|
Spring baseflow | 1978 | 216.8% |
2007 | 239.2% |
Summer baseflow | 1978 | 241.5% |
2007 | 260.4% |
Autumn baseflow | 1978 | 232.8% |
2007 | 234.5% |
Winter baseflow | 1978 | 219.3% |
2007 | 226.3% |
3.3. Influences of Forest and Other Land Use Changes on Baseflow
The land use changes during the four periods are given in
Table 4. Comparing the land cover maps for 1978 and 1999, the area corresponding to forest decreased from 6365.5 km
2 to 6232.1 km
2 with an annual reduction of 6.1 km
2, whereas the area corresponding to farmland increased by 15.2 km
2 per year. However, after 1999, rapid forest expansion and urban development occurred in this region. The most remarkable land use variations are the increase in forest of 76.1 km
2 per year and the decline in farmland by 86.9 km
2 per year. These dynamics were associated with government policy. In 1978, “Household Responsibility System” was initiated by the central government, and most areas of China entered into the period of cultivation [
33]. In 1999, Grain for Green (GFG) was implemented and directly engaged millions of farmers in protecting certain areas, thus, China entered into the period of ecological restoration [
34].
Table 4.
Percent of land use areas and changes in the Upper Du Watershed (1978–2007).
Table 4.
Percent of land use areas and changes in the Upper Du Watershed (1978–2007).
Land Use (%) | 1978 | 1987 | 1999 | 2007 | 1978–1987 | 1987–1999 | 1999–2007 | 1978–2007 |
---|
Forest | 70.9 | 70.4 | 69.3 | 76.2 | −0.5 | −1.1 | +6.9 | +5.3 |
Farmland | 9.8 | 10.2 | 13.6 | 5.8 | +0.4 | +3.4 | −7.8 | −4.0 |
Urban | 0.8 | 0.9 | 1.1 | 1.4 | +0.1 | +0.2 | +0.4 | +0.6 |
Grassland | 7.6 | 7.3 | 5.9 | 6.1 | −0.3 | −1.4 | +0.2 | −1.5 |
Shrubland | 10.2 | 10.4 | 9.4 | 9.5 | −0.2 | +1.0 | −0.1 | −0.7 |
Barren | 0.3 | 0.4 | 0.4 | 0.7 | +0.1 | 0 | +0.3 | +0.4 |
Water | 0.4 | 0.4 | 0.3 | 0.3 | 0 | −0.1 | 0 | −0.1 |
Table 5 shows statistics of individual land use changes at the sub-watershed scale. The CV values indicated that the land use types varied substantially in the 107 sub-watersheds, except the water. This phenomenon was caused by the non-uniform distribution of forests and changes in farmland. First, before 1999, diminishing forests are linked with the efficiency of deforestation, which was affected by the physical accessibility of the forest stand, as exemplified through such metrics as the linear distances to highways, roads, and navigable rivers [
35]. Second, expanded farmland associated with cultivation mainly occurred in land near stream channels over the entire watershed. Finally, after 1999, the land use distribution at the sub-watersheds scale became more irregular because only specific farmland (normally with slopes >25°) was transformed into forests [
36].
Land use transformation maps were produced based on the intersecting of the 1978 and 2007 land use maps (
Figure 6). Since the land use maps have six land use types, the land use transformations can have a maximum of 36 classes. However, many transformations were not evident in the maps. In this study, only forest/farmland/urban transformations were considered. The farmland and urban expansion mainly development in the lower stretches and middle of the northern area of the watershed, largely matching the decreases in baseflow. Baseflow change during the spring of 2007 was only slightly different from the conditions during 1978 throughout most of the watershed with a change of less than 2 mm over most of the watershed away from the main stream network, which is mainly covered by forest. This is because spring is the initial growing season for trees and ET is much lower than in other growing seasons [
37]. In summer, the majority of the farmland converted to forest and there was high seasonal evapotranspiration in the sub-watersheds and this spatially corresponded with the decrease in baseflow in the northern, southwestern and southeastern portions of the watershed.
Table 5.
Robust Coefficient of Variation for the land use types of each sub-watershed in Upper Du Watershed.
Table 5.
Robust Coefficient of Variation for the land use types of each sub-watershed in Upper Du Watershed.
Land Use Maps | Land Use Types | Robust Coefficient of Variation |
---|
1978 | Forest | 159.6% |
| Farmland | 350.0% |
| Urban | 304.0% |
| Grassland | 159.8% |
| Shrubland | 223.1% |
| Barren | 197.9% |
| Water | 279.8% |
2007 | Forest | 193.7% |
| Farmland | 394.8% |
| Urban | 352.9% |
| Grassland | 396.7% |
| Shrubland | 203.5% |
| Barren | 514.1% |
| Water | 213.3% |
Figure 6.
Land use transformation maps of the Upper Du Watershed from 1978 to 2007.
Figure 6.
Land use transformation maps of the Upper Du Watershed from 1978 to 2007.
In autumn and winter, simulations indicated a general increase in the baseflow of 0.1–2.0 mm in 2007 compared to 1978 over much of northern, southwestern and southeastern portions of the watershed. This change in baseflow was due to the large effects of forests in this area far from the stream network as well as the low ET during later growing and non-growing seasons. A larger proportion of baseflow recharge occurred in these seasons. Evapotranspiration diminished the baseflow recharge pulse from May to January, and the comparison of variations of baseflow and changes in land use types suggests a strong negative relationship between baseflow and the forest and farmland in these three seasons (average R2 is 0.83 and 0.79, respectively).
3.4. Contribution of Land Use Changes to Baseflow
Table 6 provided the summaries of the PLS model constructed for the four seasons. For the spring, autumn, and winter models, the first component explains 74.7%, 74.4%, and 69.1% of the variation in baseflow, respectively. The addition of the second component explained, respectively, 79.1%, 78.8%, and 71.3% of the variation and generated a minimum RMSECV. The addition of components to the PLS led to higher RMSECV values (
Table 6). For these models, two predictor variables, namely, forest and urban land, had VIP scores greater than 1, followed by farmland, shrubland, grassland, and barren land with VIP scores less than 1 (ranges from 0.991 to 0.433). Forest also had larger negative regression coefficients (−0.708, −0.854, and −1.108) (
Figure 7).
Figure 7.
Regression coefficients (lines) and the Variable Influence on Projection (VIP) (bars) of each land use type.
Figure 7.
Regression coefficients (lines) and the Variable Influence on Projection (VIP) (bars) of each land use type.
Table 6.
Summary of partial least-squares (PLS) regression models of baseflow for all sub-watersheds.
Table 6.
Summary of partial least-squares (PLS) regression models of baseflow for all sub-watersheds.
Response Y | R2 a | Q2 b | Component | % of Explained Variability in Y | Cumulative Explained in Y (%) | RMSECV c | Q2cum d |
---|
Spring baseflow | 0.79 | 0.67 | 1 | 74.7 | 74.7 | 0.88 | 0.634 |
| | | 2 | 4.4 | 79.1 | 0.80 | 0.666 |
| | | 3 | 0.4 | 79.5 | 0.81 | 0.656 |
Summer baseflow | 0.82 | 0.72 | 1 | 71.8 | 71.8 | 6.09 | 0.658 |
| | | 2 | 9.8 | 81.6 | 5.12 | 0.718 |
| | | 3 | 1.2 | 82.8 | 5.70 | 0.709 |
Autumn baseflow | 0.79 | 0.68 | 1 | 74.4 | 74.4 | 0.88 | 0.644 |
| | | 2 | 4.4 | 78.8 | 0.80 | 0.682 |
| | | 3 | 0.4 | 79.2 | 0.81 | 0.667 |
Winter baseflow | 0.71 | 0.55 | 1 | 69.1 | 69.1 | 1.29 | 0.597 |
| | | 2 | 2.2 | 71.3 | 1.26 | 0.547 |
| | | 3 | 0.1 | 71.4 | 1.27 | 0.494 |
In the summer model, the first component was dominated by forest and farmland and explained 71.8% of the variance in the dataset regarding changes in baseflow (
Table 6). The second component was dominated by farmland and urban areas and addition of this component explained 81.6% of the total variance. Adding more components to the PLS models failed to substantially improve the explained variance (
Table 6). The lower importance of some variance predictors in a particular component was indicated by the distance of the PLS weights from the original variables. Also, a more convenient and comprehensive expression of the relative importance of predictors can be derived from exploring their VIP values [
38]. As shown in
Figure 7, two predictor variables, namely forest and farmland, had VIP scores greater than 1 (1.834 and 1.149, respectively) and regression coefficients of −1.135 and −0.350, respectively, followed by the percentage of urban (VIP = 0.889; coefficient = −0.231), grassland (VIP = 0.498; coefficient = 0.020), and barren land (VIP = 0.398; coefficient = 0.091). Hence, these variables are used in the prediction model to obtain projected predictands. The negative regression coefficient of forestland was due to the greater interception of the canopy and ET rates (trees transfer subsurface water to leaves and then to the atmosphere) [
39]. According to Price [
3], the baseflow response to farmland may be positivity or negativity associated with the crop irrigation practices, natural losses via ET, and variable infiltration. Our results suggest that the negative relationships between farmland and baseflow in the Upper Du Watershed may be correlated with crops, which are irrigated from surface water storage associated with the stream network, and the great ET loss of crops, which is agreed with the conclusions of many researchers [
9,
40]. Following urbanization, throughfall decreases in building zones where rainfall interception occurs; additionally, infiltration is reduced by soil compaction and impervious surface additions, and water flushes more quickly through the watershed as a result of decreases in the hydraulic resistance of land surfaces and channels [
3,
41,
42].
The effects of forested areas are greater than that of agricultural areas. This phenomenon can be explained as follows. First, baseflow regression associated with ET in a watershed with perennial vegetation (e.g., trees) is generally higher than crops (
i.e., farmland) [
37,
39]. Second, the transpiration of perennial forest vegetation influences the baseflow regression throughout the whole growing season, whereas the transpiration of seasonal crops influences the baseflow regression only during the mid-growing and late-growing seasons [
9]. Overall, the strong baseflow regression of forests is sustained for a longer time. Thus, we can suggest that the baseflow will increase with the replacement of forest by farmland. These results are consistent with Sun [
43] pertaining to a study on the water yield response to forest management. The effects of forested areas are greater than that of urban land due to the increase of non-contributing impervious surfaces in urban areas, which mitigate the negative influences of urbanization on baseflow [
44]. The impacts of urbanization are characterized by the total area of impervious surfaces in a watershed [
45]. The total impervious area can be divided into the effective impervious area and the non-contributing impervious area (such as pervious areas and leaky water infrastructure) [
45,
46]. However, considering only the effective impervious area of a watershed is not sufficient [
44]. Non-contributing impervious areas, which are formally addressed through effective impervious areas [
47], increase with increases in urban land. Thus, baseflow maybe increase when forestland changes to urban. Similar conclusions were reported by Boggs and Sun [
48].