A GIS-based multi-criteria decision support approach to stormwater Best Management Practices (BMPS): Identifying potential BMP vulnerable sites for effective water conservation and water reuse in Bernalillo County, New Mexico.

摘要

Stormwater Best Management Practices (BMPs) have emerged in order to address soil and water concerns that plague many local governments around the world. However, the importance of BMPs is much less realized in the desert Southwest. This lack of BMP representation is often attributed to the scant rainfall and the unique precipitation characteristics of this region. Contrary to this belief, there is a greater need for BMP implementation. Unless efforts are taken to efficiently manage stormwater resources, soil degradation and water scarcity will continue to be a major concern for the arid Southwest. Using a semi-quantitative analytic hierarchy process (AHP) and a fuzzy inference system (FIS) approach, this paper assessed the BMP vulnerability risk in the Bernalillo County New Mexico, an area that is characterized by intermittent precipitation events and limited water availability. The model was designed using a three-step multi-criteria decision support (MCDS) methodology implemented in Geographic Information System (GIS): (i) in the first step, thematic layers for the model were defined and prepared; (ii) the second step involved the extraction of AHP priority weights and the construction of fuzzy membership functions and rule aggregations; (iii) finally, a BMP vulnerability map was produced by means of a weighted overlay analysis that combined infiltration and soil erosion maps derived from the respective AHP and FIS methods.;The development of the BMP model relied on the assessment of several environmental factors such as slope gradient, precipitation, soil erodibility, soil texture, soil permeability, Normalized Difference Vegetation Index (NDVI), and drainage density. ArcGISRTM processes and modeling tools were used to develop the final BMP risk map. The model results were categorized into five suitability classes: not vulnerable (N), slightly vulnerable (SV), moderately vulnerable (MV), highly vulnerable (HV), and extremely vulnerable (EV). Based on the analysis of the results, it was determined that about an average of 9% of the study area was susceptible to high risk, whereas about less than 1% of the total area fell within the extremely vulnerable class. Shrub landuse classes were identified to experience the heaviest BMP vulnerability. In general, most eastern portions of Bernalillo County showed high to extreme BMP vulnerability. In terms of areal extent, the model outputs correlated moderately (r2 = 0.52-0.72) with the results predicted by Revised Universal Soil Loss Equation (RUSLE) model. However, further field investigations and analysis would be required in order to establish the extent of BMP risk predicted in this study. The results obtained from this study can provide useful information to guide local governments and decision makers in selection suitable stormwater solutions.