**4.1 Water quality temporal and spatial characteristics**

The 24 water sampling points of this study were located across a range of land uses, geology types, and stream orders within the entire Wenyu River watershed (Figure 6). Thus, the Wenyu River watershed was firstly delineated into 42 sub-watersheds using DEM raster. According to the in-situ water quality measured data, water quality status of certain subwatershed can be obtained.

Fig. 6. There different spatial areas definition within the Wenyu River Watershed.

Considering their similarity of geographic location, topographic characteristic, land useland cover, and human activities, the delineated sub-watersheds were generally clustered into three types in which they located (see Figure 6): Upstream Mountain Area, Midstream Urban Area, and Downstream Plain Area. Table 5 summarizes the characteristic information of these three different spatial areas within Wenyu River watershed. And only those sub-watersheds containing in-situ water quality data were considered in this research.

The 24 water sampling points of this study were located across a range of land uses, geology types, and stream orders within the entire Wenyu River watershed (Figure 6). Thus, the Wenyu River watershed was firstly delineated into 42 sub-watersheds using DEM raster. According to the in-situ water quality measured data, water quality status of certain sub-

Fig. 6. There different spatial areas definition within the Wenyu River Watershed.

Considering their similarity of geographic location, topographic characteristic, land useland cover, and human activities, the delineated sub-watersheds were generally clustered into three types in which they located (see Figure 6): Upstream Mountain Area, Midstream Urban Area, and Downstream Plain Area. Table 5 summarizes the characteristic information of these three different spatial areas within Wenyu River watershed. And only those sub-watersheds containing in-situ water quality data were

**4. Results and discussion** 

watershed can be obtained.

considered in this research.

**4.1 Water quality temporal and spatial characteristics** 


Table 4. Three different spatial areas definition within Wenyu River Watershed.

Through the statistical computing process, water quality information in Upstream Mountain Area, Midstream Urban Area and Downstream Plain Area can be obtained based on the measured water quality data at total 24 water sampling sites. These water quality statistical information include the mean value (the sum of all observations divided by the number of observations) and the standard error of the mean (SEM, calculated by dividing the standard deviation by the square root of the sample size) of six water quality prameters's concentration, including TN, NO3- N, TP, PO4- P, COD and DO.

#### **4.2 Water quality comparison between different land-use types**

In order to conduct the further analysis of the relationship between land use and the water quality within Wenyu River watershed, in this section, the sub-watersheds are divided into

Assessment of the Impact of Land-Use Types

**0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5**

**4.3 Spearman's rank correlation analysis** 

Notes: \* \* indicates significance p < 0.01 while \* indicates p < 0.05;

Absolute coefficient value of 1.0 is a perfect fit.

Spearman's rank correlation coefficient.

types.

**Water quality Indicators**  **Nitrate Concentration (mg/l)**

on the Change of Water Quality in Wenyu River Watershed (Beijing, China) 287

**1.839 1.97 2.191**

**Forest Village-Farmland Village-Urban-**

Fig. 8. Nitrate (NO3-N) concentration (mean ± SEM) comparison between different land-use

The result from Spearman's rank correlation analysis between land use-land cover variables (%) and the water quality variables (mg/L) is shown in Table 5, which indicates that land use types are significantly correlated to many water quality variables within Wenyu River Watershed. For example, the water quality variables of total nitrogen, total phosphorous, phosphate and chemical oxygen demand have strong positive relationships with urban and village lands, while they are all present the negative correlation with the forest land use. Except for dissolved oxygen, forest is negatively correlated with the other five variables. In comparison, farmland, urban and village have the negative relationship with dissolved oxygen, while urban and village have the strong positive relationship with five variables.

**Land use types**

TN -0.401 0.181 0.456 0.462 0.187 0.632\* 13 NO3- N -0.055 -0.209 0.181 0.033 0.275 -0.242 13 TP -0.412 0.198 0.560\* 0.681\* 0.681\* 0.352 13 PO4- P -0.725\*\* 0.357 0.533 0.621\* 0.302 0.714\*\* 13 COD -0.297 -0.346 0.676\* 0.720\*\* 0.187 0.22 13 DO 0.082 -0.291 -0.28 -0.396 -0.385 -0.341 13

Table 5. Correlations analysis between land use types and water quality indicators based on

N Forest Farmland Urban Village Bare Water

**Different Land-use Structure**

**Farmland**

**2.911**

**Farmland**

different classes according to their different land-use structures. And the results of water quality comparison between different land-use structures tell us that land use types are significantly correlated to water quality variables in Wenyu River Watershed.

Here the total nitrogen (TN) is an example of water quality parameters to be monitored from May to August in 2008. Figure 7 illustrates that, between the four different land-use structures, the TN concentration of class Ⅲ has the largest value, while the TN concentration of classⅠis the smallest. And the total nitrogen counts produced from class Ⅲ is about three times greater than that from class I. The sub-watersheds belonging to the class Ⅲ have three mixed dominant land use types, village, urban and farmland, and all of these subwatersheds are located in the midstream urban area of Wenyu River watershed, where have the high density of population and the human activities must give rise to the considerable influence on the water quality. The sub-watersheds of w2 and w4 belonging to the class I, they locate in the upstream mountain area with the single significant land use of forest and sparse human population. The result indicates that contribution from forest is the smallest to the total nitrogen loading compared with those from farmland, urban and village.

The water quality parameters of NO3-N concentration was also monitored in the months of May, July and August. Figure 8 shows that, between the four different land-use structures, NO3-N concentration of class Ⅳ has the largest value, while the value of classⅠ is the smallest. Both class Ⅰand class Ⅳ are the land-use structures with single dominant land use; the dominant land use of the former is forest while the latter is farmland cover. It is clear that the contribution from the farmland is larger than the forest to the nitrate loading in the surface water within Wenyu River watershed.

Fig. 7. TN concentration (mean ± SEM) comparison between different land-use structures.

different classes according to their different land-use structures. And the results of water quality comparison between different land-use structures tell us that land use types are

Here the total nitrogen (TN) is an example of water quality parameters to be monitored from May to August in 2008. Figure 7 illustrates that, between the four different land-use structures, the TN concentration of class Ⅲ has the largest value, while the TN concentration of classⅠis the smallest. And the total nitrogen counts produced from class Ⅲ is about three times greater than that from class I. The sub-watersheds belonging to the class Ⅲ have three mixed dominant land use types, village, urban and farmland, and all of these subwatersheds are located in the midstream urban area of Wenyu River watershed, where have the high density of population and the human activities must give rise to the considerable influence on the water quality. The sub-watersheds of w2 and w4 belonging to the class I, they locate in the upstream mountain area with the single significant land use of forest and sparse human population. The result indicates that contribution from forest is the smallest

significantly correlated to water quality variables in Wenyu River Watershed.

to the total nitrogen loading compared with those from farmland, urban and village.

**3.413**

Forest Village-Farmland Village-Urban-

**Different Land-use Structure**

Fig. 7. TN concentration (mean ± SEM) comparison between different land-use structures.

**4.923**

Farmland

**3.861**

Farmland

in the surface water within Wenyu River watershed.

**1.295**

0

1

2

3

4

**Total Nitrogen Concentration (mg/l)**

5

6

7

The water quality parameters of NO3-N concentration was also monitored in the months of May, July and August. Figure 8 shows that, between the four different land-use structures, NO3-N concentration of class Ⅳ has the largest value, while the value of classⅠ is the smallest. Both class Ⅰand class Ⅳ are the land-use structures with single dominant land use; the dominant land use of the former is forest while the latter is farmland cover. It is clear that the contribution from the farmland is larger than the forest to the nitrate loading

Fig. 8. Nitrate (NO3-N) concentration (mean ± SEM) comparison between different land-use types.

#### **4.3 Spearman's rank correlation analysis**

The result from Spearman's rank correlation analysis between land use-land cover variables (%) and the water quality variables (mg/L) is shown in Table 5, which indicates that land use types are significantly correlated to many water quality variables within Wenyu River Watershed. For example, the water quality variables of total nitrogen, total phosphorous, phosphate and chemical oxygen demand have strong positive relationships with urban and village lands, while they are all present the negative correlation with the forest land use. Except for dissolved oxygen, forest is negatively correlated with the other five variables. In comparison, farmland, urban and village have the negative relationship with dissolved oxygen, while urban and village have the strong positive relationship with five variables.


Notes: \* \* indicates significance p < 0.01 while \* indicates p < 0.05; Absolute coefficient value of 1.0 is a perfect fit.

Table 5. Correlations analysis between land use types and water quality indicators based on Spearman's rank correlation coefficient.

Assessment of the Impact of Land-Use Types

*Predictors:* 

*Equation:* 

*Predictors:* 

*Equation:* 

because their normality assumptions were not met

**4.5 Water quality changes with land-use types** 

Forest, Urban, Village and Water

*Ln (TN)* = -0.086Forest-0.057Village +0.301Urban+0.7Water+0.954

Forest, Urban, Village and Bare

*NO3- N* = -0.083Forest-0.240Village +0.794Urban+1.209Bare+2.819

**Water Quality Parameters** 

Total Nitrogen (TN)

Nitrate (NO3- N)

deterioration.

on the Change of Water Quality in Wenyu River Watershed (Beijing, China) 289

**Value** 

*R*  **Square** 

0.729 0.531 0.639

0.828 0.685 0.857

**Std. Error of the Estimate** 

**Regression Equations** *<sup>R</sup>*

Note: The independent variables of %Forest, %Urban and %Water were natural log-transformed

Fig. 9. Goodness-of-fit of statistical models for six water quality variables prediction.

The results can provide insight into the linkage between land-use types and stream water quality. The regression models have used in several ways by environmental planners and others interested in watershed management. The models can help examine the relative sensitivity of water quality variables to alterations in land-use types within a watershed. If the pattern of land-use changed, the levels of contaminants should be changed accordingly. Only with a better land-use planning, it is able to reduce the water quality

In the study, we also examined the changes of water quality in relation to the changes of land-use types in the Wenyu River watershed. It is very clear that most of water quality variables were degraded from 2000 to 2008. For example, both TN and TP increased

Table 7. Regression equations developed for TN and NO3-N in Wenyu River Watershed.



Table 6. The order of water quality variables for different land-use structures.

Three water quality variables including total nitrogen, total phosphorous and phosphate, have strong positive relationships with urban and village lands, while are negatively related to the forest land. This means that the observed concentration values of the three variables would increase if the persentage area of urban or village land cover increases, whereas the concentration values would decrease if the percentage area of forest land increases. Therefore, the same order exists of the three variables for different land-use structures: Village- urban-Farmland > Village-Farmland > Forest. In comparison, dissolved oxygen has the negative relationships with urban, village and farmland, so the order represents as Village- urban-Farmland < Farmland < Village-Farmland < Forest.

#### **4.4 The linkage model**

Based on the exponential model, separate multiple regression models are developed to estimate the contributions of different land types on six stream water quality variables, including TN, NO3- N, TP, PO4- P, COD and DO, in Wenyu River watershed. The resulted models are identified to well explain the water quality variables using land use types. And the goodness-of-fit of these models are reasonably satisfactory. Table 7 presents the examples of regression models developed for TN and NO3-N in this case study, in which each model is selected with the highest *R* and *R2*, which indicates the significant level of using land use types to explain the water quality of the watershed.

For this land regression analyses, the concentration data of total nitrogen and nitrate are respectively natural log-transformed. The use of predictive equations allows city planners to model various scenarios of landscape alterations and observe the effects on water quality. From the table, it is determined that the regression models have a reasonably high degree of "goodness of fit", i.e., the *R2* values > 0.65, but the result of total nitrogen is less than 0.65. The observed and predicted data for total nitrogen and nitrate are compared using scatter plots in Figure 9. In the figure, most data distribute around the 45 degree lines, indicating a strong linear relationship between the two concentrations. The further investigation will be performed with more water samples of *in situ* measurements in the near future.

The above results can provides insight into the linkage between land use types and stream water quality, which is just in line with the comparison results (as Table 6 listed) of water

(TN) Village- urban-Farmland > Farmland > Village-Farmland > Forest

(NO3- N) Farmland > Village- urban-Farmland > Village-Farmland > Forest

(TP) Village- urban-Farmland > Village-Farmland > Farmland > Forest

(PO4- P) Village- urban-Farmland > Farmland > Village-Farmland > Forest

(DO) Village- urban-Farmland < Farmland < Village-Farmland < Forest

Demand (COD) Village-Farmland > Village- urban-Farmland > Forest > Farmland

Three water quality variables including total nitrogen, total phosphorous and phosphate, have strong positive relationships with urban and village lands, while are negatively related to the forest land. This means that the observed concentration values of the three variables would increase if the persentage area of urban or village land cover increases, whereas the concentration values would decrease if the percentage area of forest land increases. Therefore, the same order exists of the three variables for different land-use structures: Village- urban-Farmland > Village-Farmland > Forest. In comparison, dissolved oxygen has the negative relationships with urban, village and farmland, so the order represents as

Based on the exponential model, separate multiple regression models are developed to estimate the contributions of different land types on six stream water quality variables, including TN, NO3- N, TP, PO4- P, COD and DO, in Wenyu River watershed. The resulted models are identified to well explain the water quality variables using land use types. And the goodness-of-fit of these models are reasonably satisfactory. Table 7 presents the examples of regression models developed for TN and NO3-N in this case study, in which each model is selected with the highest *R* and *R2*, which indicates the significant level of

For this land regression analyses, the concentration data of total nitrogen and nitrate are respectively natural log-transformed. The use of predictive equations allows city planners to model various scenarios of landscape alterations and observe the effects on water quality. From the table, it is determined that the regression models have a reasonably high degree of "goodness of fit", i.e., the *R2* values > 0.65, but the result of total nitrogen is less than 0.65. The observed and predicted data for total nitrogen and nitrate are compared using scatter plots in Figure 9. In the figure, most data distribute around the 45 degree lines, indicating a strong linear relationship between the two concentrations. The further investigation will be

performed with more water samples of *in situ* measurements in the near future.

Water Quality Variables Order for different land-use structure

Table 6. The order of water quality variables for different land-use structures.

Village- urban-Farmland < Farmland < Village-Farmland < Forest.

using land use types to explain the water quality of the watershed.

quality variables between different land-use structures.

Total Nitrogen

Nitrate

Total Phosphorous

Phosphate

Chemical Oxygen

Dissolved Oxygen

**4.4 The linkage model** 


Note: The independent variables of %Forest, %Urban and %Water were natural log-transformed because their normality assumptions were not met

Table 7. Regression equations developed for TN and NO3-N in Wenyu River Watershed.

Fig. 9. Goodness-of-fit of statistical models for six water quality variables prediction.

The results can provide insight into the linkage between land-use types and stream water quality. The regression models have used in several ways by environmental planners and others interested in watershed management. The models can help examine the relative sensitivity of water quality variables to alterations in land-use types within a watershed. If the pattern of land-use changed, the levels of contaminants should be changed accordingly. Only with a better land-use planning, it is able to reduce the water quality deterioration.

#### **4.5 Water quality changes with land-use types**

In the study, we also examined the changes of water quality in relation to the changes of land-use types in the Wenyu River watershed. It is very clear that most of water quality variables were degraded from 2000 to 2008. For example, both TN and TP increased

Assessment of the Impact of Land-Use Types

quality, one of the most precious resources on earth.

water quality.

subject of the future study.

**6. Acknowledgments** 

Ecology (SKLURE2010-2-3).

**7. References** 

247.

Publication.

on the Change of Water Quality in Wenyu River Watershed (Beijing, China) 291

and other physical or biological variables. Nevertheless, the models in this study fail to reflect these variations for the sake of discussion. Hence, in the near future, other characteristics as the research background information will be helpful in the identification of the problems and developing a more rigorous linkage models between land-use types and

Several previous studies argued that the significant influences from land-use on water quality only exist within a shorter distance of the receiving water body. Hence, estimating the relationship between the buffer landscape and stream water quality will be another

Estimating the links between land-use types and water quality over an extended period is crucially important task in the future works. The further study can help understand the response of water quality change to the change of land-use types, and give the environmental planners more information for the decision-making in land management. Furthermore, persistent water quality monitoring is useful to assist in identifying how landuse planning brings help in the control of water quality change in the watershed scale. This study also demonstrates an example of the issue of how LULC change is linked to water

The authors are extremely grateful to Prof. Liding Chen and Dr. Ranhao Sun for their support of the field work and data collection at the Research Center for Eco-Environmental Sciences (RCEES) under the Chinese Academy of Sciences (CAS), and Jinrong Hu and Xiaofei Chen from the Chinese University of Hong Kong (CUHK) for their help in data processing and discussion. The authors would like to thank the two anonymous reviewers who gave helpful and critical comments to improve the original manuscript. The research is jointly supported by the CUHK Direct Grants, GRF (CUHK454909 and CUHK459210), National Outstanding Youth Science Foundation of China (40925003) and the Open Fund of State Key Laboratory of Urban and Regional

Ahearn, D.S., Sheibley, R.W., Dahlgren, R.A., Anderson, M., Johnson, J. and Tate, K.W.

Allan, J.D., Erickson, D.L. and Fay, J. (1997). The influence of catchment land use on stream integrity across multiple spatial scales. *Freshwater Biology*, 37, 149-161. Anderson, B. W. and Ohmart, R. D. (1985). Riparian Revegetation as a Mitigating

(2005). Land use and land cover influence on water quality in the last free-flowing river draining the western Sierra Nevada, California. *Journal of Hydrology*, 313, 234-

Process in Stream and River Restoration. In *The Restoration of Rivers and Streams: Theories and Experience* (pp. 41-80), Gore, J. (Editor). Boston: Butterworth

relatively high in farmland, urban and village areas, but very little change in forest areas. Since urban areas are dramatically increasing from 2000 to 2008, their impacts on TN and TP are quite obvious. These results not only provide the linkages between land-use types and stream water quality, but also show the high correlation of land-use types and water quality variables. The results indicate that water quality improvement and ecological restoration have great effects on the regional sustainable development. Thus, if the sustainable development is pursued, land management should consider the potential impacts of landuse on water quality changes in the watershed scale.
