32 X. Wang Table 3. Independent t-test on the water monitoring data in LMR watershed Para- High human Low human t p a meter impact area impact area Mean N Mean N IBI 33Ð27 77 44Ð50 68 8Ð625 5Ð55E-15 b ICI 34Ð94 36 43Ð77 35 3Ð288 0Ð0008 b QHEI 62Ð45 73 73Ð77 61 5Ð869 1Ð67E-08 b a Significance level or the less-than and equal-to probability of the t value. b Significant at the 0Ð05 level. agricultural-land percentages varied between 12% and 95% (Figure 2). The IBI and ICI have similar relationships to habitat quality (QHEI) and land uses although the levels of significance vary (Table 4). These biological indicators are negatively related to the percentages of urban land use and positively related to agricultural land use. They also are positively related to habitat quality. The correlation analysis showed that at 0Ð05 significant level the IBI scores were significantly correlated with percentage of urban land use ( 0Ð59) and agricultural land use (0Ð53). IBI was also positively correlated with QHEI (0Ð67). The correlations between ICI and QHEI and land uses were not statistically significant. The results suggest that IBI may be a more sensitive to land-use composition and riparian-habitat quality. Table 4. Pearson product movement correlation coefficients Land use and habitat IBI ICI Urban Pearson correlation 0Ð59 a 0Ð22 Significance (2-tailed) 0Ð00 0Ð40 Sample size 22 16 Agriculture Pearson correlation 0Ð53 a 0Ð30 Significance (2-tailed) 0Ð01 0Ð26 Sample size 22 16 Wooded Pearson correlation 0Ð27 a 0Ð28 Significance (2-tailed) 0Ð23 0Ð30 Sample size 22 16 QHEI Pearson correlation 0Ð67 a 0Ð41 Significance (2-tailed) 0Ð00 0Ð11 Sample size 22 16 a Correlation is significant at the 0Ð05 level (2-tailed). In a previous study Dyer et al. (1998a) applied a multivariate forward stepwise regression model to determine the relative importance of water chemistry and habitat on biological indicators in the Little Miami River watershed. Their study concluded that the habitat quality was primarily responsible for the biological integrity of receiving waters in the watershed. A similar regression analysis was conducted in this study on the 22 selected catchments. Percentages of urban and wooded land uses by catchment were included in the multiple regression analysis, in addition to the habitat and water chemistry indicators used in Dyer et al. (1998a). The percentage of agricultural land was not included because it was highly correlated with the percentage of urban land. Other independent variables were the six water chemistry variables—dissolved oxygen, pH, total suspended solids, nitrogen-total ammonia, total organic carbon, and hardness, and QHEI. The dependent variables were IBI and ICI, respectively. Figure 4 shows the scatter plots of the predicted values against measured values for IBI and ICI, respectively. The results shown in Table 5 indicate that the land-use components within the catchments could be major predictors for biotic integrity. The percentage of urban land was the second strongest predictor for both IBI and ICI. The negative signs of those coefficients indicate that as the intensity of human activities increase there is a tendency that the biological integrity of the rivers decreases. The percentage of wooded land was the third strongest predictor for IBI. The positive sign of the coefficient shows that higher river biological quality may be expected in areas of less intensity of human impact. When the results for the two dependent variables, it appears that the independent variables can explain IBI better than ICI. Water quality consideration in land-use planning This study exhibits the complexity of water quality indicators and their spatial distribution. Such complexity implies that different indicators often reflect different aspects of a water body and the status of water quality may be affected by many factors in different ways. Although water chemistry in the Little
Water-quality and land-use planning 33 Regression adjusted (press) predicted value Regression adjusted (press) predicted value 60 50 40 30 20 10 50 40 30 20 10 0 (a) (b) 10 20 30 40 Measured value 20 30 40 Measured value Figure 4. Comparison of predicted and measured biological indicator values. (a) Dependent variable: IBI; (b) dependent variable: ICI. Miami River was at good condition (several of the water chemistry variables were at or below detection limit, which might have contributed to the fewer data available for the analysis), biotic indicators have picked up some effects of human activities on the receiving water. The t-test showed that urban land and point sources (WWTPs, IFDs, and TRIs) together might explain the lower biotic quality throughout the watershed. This finding confirms that one of the greatest causes 50 50 60 60 of water-quality problem derives from urban land use as a result of the increasing intensity of human activities. Pollution has resulted in loss of species diversity within rivers (Haycock and Muscutt, 1995). The hydrological relationship between water systems and the land requires coordination between the water management and land management fields. Once the land–water relationship is identified, it leads to the need of protecting water quality through proper land-use planning by identifying cost-effective pollution prevention and pollution correction approaches that can address all the sources of pollution in a comprehensive way. To take such challenge, it is necessary to look into water-quality management and land-useplanning practices and draw the connection between the two. By tradition, waterquality management and land-use planning are implemented by different agencies with different objectives. The purpose of water-quality management is to maintain and improve ambient water quality, which requires designation of water usage, establishment of criteria to protect designated uses, and development of waterquality management plans accordingly. The objective of land-use planning is to maximize the uses of land by humans while minimizing the negative impact to humans’ health and welfare. Land-use planning, after systematically analyzing different alternatives and the need for land use changes, determines future land uses, improves physical conditions for the planned land uses, and manages activities associated with the planned land uses (van Lier, 1998). In practice, land-use planning is often fragmented temporally and spatially since most land-use plan is often produced for area within a political boundary and Table 5. Results of forward stepwise multiple regression analysis Dependent variable IBI ICI Adjusted R 2 0Ð934 0Ð773 Coefficient Constant 16Ð823 2Ð345 Predictor 1 QHEI 0Ð761 Hardness 0Ð058 Predictor 2 % of Urban land 45Ð078 % of Urban land 81Ð472 Predictor 3 % of Wooded land 35Ð194 Dissolved oxygen 3Ð793 Use probability of F less than or equal to 0Ð1 for inclusion of the independent variables (predictors) and the coefficients are different from zero at 0Ð5 significance level.