4. Assessing urban sprawl impact on catchment hydrology
4.6. Results
4.6.2. Multiple regression modelling
Weighted least squares regression analyses were performed between each of the three flow component changes (the dependent variables) and the 16 independent catchment characteristics and landscape metrics (Tableau 4-3). The standard deviations of the estimated flow changes were used to weight the regression analysis.
Final regression equations are shown in Tableau 4-4. For each stream flow, the independent variables with significant p-value (less than 0.05) were kept for each of the three regression equations. Results show that the change of urban areal fraction (d.Urban) is always selected as a significant explanatory variable, whatever the flow components analyzed. The coefficients for this variable are always positive meaning that an increase of urban areas is positively correlated with the increase of high, low and mean flow. This is in agreement with the general idea for Qa and Q95, which indicates that urbanization could significantly increase stream flow (Brandes et al., 2005; Konrad et Booth, 2005). However this impact could be more complicated for the Q05. The fragmentation Index of undeveloped area was selected for Qa and Q95 with positive coefficient indicating that greater fragmentation of undeveloped area is associated with increased Qa and Q95. The fragmentation of undeveloped areas, generally caused by the development of impervious areas like roads, tends to emphasize the increase of Qa and Q95 in urban catchments. The variation of edge density, which represents the fragmentation of urban area, demonstrated a negative relationship with Qa and Q05.
Fragmentation of urban area might favor the infiltration of rainfall or surface runoff and thus mitigate the effect of increased imperviousness in the catchment.
104 4. Assessing urban sprawl impact on catchment hydrology
Tableau 4-4. Multiple regression model for streamflow characteristices.
Change
As presented in the, the explained variance of the linear regression are generally not very high, meaning that we failed to understand most of the variability of estimated changes in urbanized catchments. This is particularly the case of the changes of low flows, which remains largely unexplained (adjusted R² of 0.14). This might be due either to relatively poor model efficiency to simulated low flow or to the lack of catchment characteristics related to low flow such as effluent discharge. The simulation change results for Qa and Q95 are exhibited in Figure 4-8showing relatively good agreement between simulated and observed change for Qa and Q95.
4.6. Results 105
Figure 4-8. Simulated and observed relative change for Qa (above) and Q95 (below).
To shed more lights on the failures of the regression equations, we established two distinct groups of catchments. The lower-error (error < 10%) group includes 140 catchments for Qa with error less than 0.10, and the higher error group include the remaining 17 catchments.
Then, we compared the distribution of several variables for these two groups to determine the reasons of the failure of the regression equations (Figure 4-9). Results show that the values of urban fraction in the pre-urbanization period are more variable among the catchments of the
“higher error group” than among the “lower error” one, which means that some catchments with more error were already partly urbanized in 1940. The median value of NSE of “higher error” catchments group is inferior to the “lower error” group median NSE value, which means that the predictive ability of the hydrological model is lower for the group with the
−0.2 0.0 0.2 0.4 0.6 0.8 1.0
−0.20.00.10.2
observed change
simulated change
−0.2 0.0 0.2 0.4 0.6 0.8 1.0
−0.150.000.100.20
observed change
simulated change
106 4. Assessing urban sprawl impact on catchment hydrology higher error. Therefore the methodology relies significantly upon the ability of the model to simulate streamflow for the period before urbanization.
Figure 4-9. Boxplot of lower error (blue) and higher error (red) groups of simulated and observed Qa catchments for NSE obtained for the pre urbanization (calibration) period (left) and fraction of urban area for the pre urbanization (calibration) period of catchment (right).
4.7. Conclusion
Literature review reports various results on the impacts of urbanization on stream flow characteristics. It remains unclear how the catchment characteristics and land use interact to influence flow changes. This paper investigates flow change estimations with a residual model for urban catchments. Then, multiple regressions were applied on 16 independent catchment characteristics, landscape metrics for three flow components change detection (Qa, Q95 and Q05). 157 urban catchments in the USA were selected for this study. Land use data were collected from NLCD and reconstituted for the 1940-2000 period using historical unit housing database.
4.7. Conclusion 107 We demonstrated that the impervious pattern provide further possible explanatory variables of urban impacts on flow change besides the cumulative impervious area. The results of multi regressions were more significant for Qa and Q95 and indicated that increasing the fragmentation of undeveloped area is associated with increased of Qa and Q95. However, the fragmentations of urban area decrease the Qa. This suggests that the effects of urbanization on catchment stream flow appear to be mitigated when developed land patches are spread across the basin (and intermixed with other land cover types) rather than agglomerated into large patches, which corroborates previous statements (McMahon et al., 2003).
For the Q05 and base flow the results are more complicated to generalize. The result of this study declined the theory that urban area only reduces baseflow value. The increasing trends in base flow found in our study could be due to the increase in leakage from water supply infrastructure, irrigation (lawn watering), decrease of evapotranspiration due to the reduce of vegetation cover (Meyer et Wilson, 2002; Brandes et al., 2005; Poff et al., 2006) or slope, drainage density and catchment shape variables (Vogel et Kroll, 1992; Woods et al., 1997;
Cherkauer et Ansari, 2005). The finding of increasing base flow or decreasing peak flow in some of the urbanizing catchments studied may appear counterintuitive, and further examination of this result is needed on these specific catchments.
Our analysis showed that the methodology is strongly dependent on the urban fraction in the period considered as “non-urban” period. If the catchment had a large fraction of urban areas in the first years of the data time-series, the parameters of model calibration could not simulate non-urban condition as well as possible. It is likely one of the main difficulties to estimate flow change detection with the proposed framework.
The multiple regressions that we propose is an effective method for estimating flow change due to the urbanization in urban catchments, but it has only been tested on catchments in the United States. Further research will focus on enlarging the sample of urban catchments including catchments from Europe and other countries, with more contrasted urban patterns and different histories of urban evolution. This method provides an increased understanding about impervious area patterns and their impacts on stream flow.
Acknowledgements
The funding support for this study came from the Campus France. This study utilizes data from several sources. Daily streamflow were collected from the USGS website (available at
108 4. Assessing urban sprawl impact on catchment hydrology
http://waterdata.usgs.gov/). The newer 1/16 degree daily rainfall and temperature gridded data beginning in 1915 (see (Livneh et al., 2013)) are available from ftp://ftp.hydro.washington.edu/pub/blivneh/CONUS/. Geospatial data and classifications for stream gages maintained by the U.S. Geological Survey (USGS) termed Gages II are available from http://water.usgs.gov/lookup/getspatial?gagesII_Sept2011.
National Land Cover Database (NLCD) data were obtained from the Multi-Resolution Land Characteristics (MRLC) Consortium website (available at http://www.mrlc.gov/about.php).
House Density data were collected in 2014 from the SILVIS Lab, Spatial Analysis for
Conservation and Sustainability (available at http://silvis.forest.wisc.edu/old/Library/HousingDataDownload.php?state=United%20States&
abrev=US). We would also like to thank David Theobald and Thomas Over for their help about House Density data.
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