Wednesday, September 14, 2011

Final Project: Urban Runoff and Hydrology Analysis for South Bay, LA



Introduction:
                  Storm water runoff pollution is considered a major public health issue for just about any coastal urban area. The south end of the Santa Monica Bay of Los Angeles experiences quantifiable fluctuations of oceanic pollutants. These urban runoff toxins include fecal bacteria, trash, and chemical pollutants that are harmful to the local ecology as well as Los Angeles’ beach goers.   During periods of high runoff, the poor water quality of the bay can cause skin infection, respiratory irritation, and even hospitalizing sickness. Southern California is recognized worldwide for its amazing weather and beautiful beach. It is truly a shame to witness such deteriorating anthropogenic impacts within our coastal zones. Environmentally minded communities, such as the City of Santa Monica, began capturing and treating urban runoff to combat this issue. This effort has improved water quality directly off Santa Monica Pier, but the southern reaching regions of the bay are still experiencing a surplus of harmful runoff. I propose a project to better understand “runoff hotspots” within the South Bay of Los Angeles. Through GIS-based, terrain analysis and a customized reclassification I can quantify the best suited locations for storm water capture. By systematically approaching this issue, coastal communities can implement minimum infrastructural investments while still diverting maximum urban runoff. It is important to note that I am not exploring the best locations to install a storm water treatment center. My project is centered on step 1 of the mitigation of coastal pollution: street level water capture.
Methods:
                  First and foremost, I need to define my study area of interest. The South Bay of Los Angeles is comprised of 6 coastal cities. These include El Segundo, Manhattan Beach, Hermosa Beach, Redondo Beach, Torrance, and Palos Verdes. I will be exploring and manipulating a digital elevation model (DEM) that includes all 6 of these defined regions. This will be acquired for no cost though the USGS Seamless Viewer website. With this DEM, I can efficiently run my complete terrain analysis. It is important to define all of the factors that will be utilized within this specific suitability analysis. A combination of steep slope, western facing aspect (orientation), and large volume of street runoff define the perfect scenario for storm water capture. For the sake of mitigating ocean pollution, I will also define a coastal buffer of 1 mile to insure the catch basin technology will be fully diverting runoff from entering our beaches. Each of these 4 factors is then reclassified accordingly. The slope data frame is broken down into five categories ranging 1 (worst) to 5 (best). The value 1 represents minimal slope while 5 represents the steepest terrain. The watershed data frame is broken into ten categories ranging 1 (worst) to 10 (best). Again a value of 1 is equivalent to very low runoff regions while a 10 indicates high flow rates. The top watershed value is exactly double the top slope value. This is my way of weighting the watershed factor. Street runoff is the most important factor within my analysis. These two data frames will be summed giving a total range of 2 (worst) to 15 (best). My other two factors, aspect and buffer, are categorized in a binary fashion. This serves to provide a quick yes or no threshold. For aspect analysis, any orientation containing a form of west (W, SW, or NW) will receive a 1 (yes) while any other direction will receive a value of 0 (no). For the buffer analysis, any area contained by the buffer will receive a 1 while any area outside the mile threshold will receive a 0. The sum of the slope and watershed factors will be multiplied by these binary factors. This serves to eliminate any region that doesn’t face west or exceeds the defined distance from the coastline.
Map Algebra Expression:
                  Final Analysis = (“slope analysis” + “flow intensity”) * “aspect reclass” * “buffer reclass”
Analysis:
                  The four different factors that contribute to my overall analysis are each represented within individual data frames. These data frames were individually reclassified to best consider the problem of storm water runoff. The slope analysis reveals that the steepest regions are located within Palos Verdes. This area is dominated by high-ranking slope indexes. Torrance and Manhattan Beach both contain steep coastal regions as well. The flow intensity analysis reveals the highest watershed values are within Palos Verdes, but it’s also important to note the strip of orange coloring running along the entire South Bay region. This implies that there is a large volume of water running off during wet periods and high prospect for storm water capture systems. The aspect analysis is very simple and only contains two colors. The purple represents any land facing west, northwest, or southwest while the yellow contains all the remaining orientations. Overall, the strip closest to the coastline offers mostly all favorable aspect indicating a directional flow towards the ocean. Lastly, the buffer analysis serves to define the zone of interest. This zone doesn’t exceed a distance of one mile from the ocean. Through the map algebra expression (defined within the “methods” section) the final analysis reveals ultimate insight into the runoff hot spots within the South Bay. The highest scoring zones occur in Palos Verdes revealing very high potential for runoff diversion. The coastal zone of Torrance also received relatively high values as well. Redondo Beach and Hermosa Beach yielded the lowest scores indicating less storm water running off these beach communities. Moving north into Manhattan Beach and El Segundo, raster pixel values increase in the form of a consistent strip located very close to the coastline.
Conclusion:
                  By overlaying my final analysis calculations with a detailed street map of Los Angeles, I am able to define specific streets containing or adjacent to the top runoff hotspots within the South Bay of Los Angeles. Palos Verdes contained the highest index scores (purple coloring). The best candidate streets within this city are Paseo La Cresta, Via Fernandez, Via Del Monte, Paseo Del Sol, and Paseo Del Mar. These are the urban streets that would be responsible for catching the highest volume of storm water. The city of Torrance also contained solid prospects. Paseo De La Playa, Via Mesa Grande, and Via La Soledad all scored highly within my final analysis and therefore should be considered for catch basin implementation. Overall, Redondo Beach scored relatively low within the final analysis calculations, but most of the cities urban runoff could potentially be captured off Gertruda Avenue. Hermosa Beach yielded the lowest scores within the South Bay and therefore should be skipped when considering storm water capture. Manhattan Beach’s best candidate is located within the El Porto Beach parking lot. This region scored highest for the city. Finally, the city of El Segundo would capture most of their urban runoff by focusing on Vista Del Mar. This street runs parallel to the coast and therefore could serve as a solid diversion for polluting runoff. It is important to note that I did not factor in the toxicity of the individual runoff locations; this could potentially alter the focal points of my analysis. Overall, implementing a system to capture and treat storm water can juristically improve water quality throughout the entire Santa Monica Bay. My analysis yielded these specific streets as the top candidates for the South Bay region.

Wednesday, September 7, 2011

Lab 5 Interpolations


      Although these different methods of interpolation yielded slightly different estimations, the results remain constant and demonstrate that this season to date has experienced more rainfall than an average year. Every collecting station is registering rain collection that surpasses the norm. I believe the kriging method yielded more accurate, detailed results than the spline method. Comparing both maps to the original LADPW template demonstrates that the kriging method of interpolation estimated values closer to the actual measurements (for those I did not include in my table). Overall, interpolation is an easy form of estimation that reveals great spatial data.













Rain_Seas Rain_Norm Rain_Diff
























Name Latdeg Latmin Latsec LatDD Longdeg Longmin Longsec LongDD Rain_Seas Rain_Norm Rain_Diff
North Lancaster Precip   34 45 41 34.761 118 7 30 -118.125 5.31 5.19 0.12
Redman Precip   34 45 52 34.764 117 55 30 -117.925 6.85 5.18 1.67
Avek Precip   34 32 21 34.539 117 55 23 -117.92306 8.26 5.78 2.48
Rocky Buttes Precip   34 39 0 34.65 117 51 48 -117.86333 8.43 4.84 3.59
Palmdale Water Dist Precip   34 35 41 34.595 118 5 31 -118.09194 9.49 6.93 2.56
Acton Camp Precip   34 27 2 34.451 118 11 54 -118.19833 10.71 10.03 0.68
Scott Ranch Precip   34 46 59 34.783 118 28 10 -118.46944 12.24 8.25 3.99
Mescal Smith Precip   34 28 3 34.468 117 42 40 -117.71111 13.19 7.8 5.39
Quartz Hill Precip   34 38 53 34.648 118 14 24 -118.24 13.29 7.97 5.32
La Mirada Precip   33 52 59 33.883 118 1 0 -118.01667 13.58 13.45 0.13
Ballona Crk Precip   33 59 55 33.999 118 24 5 -118.40139 14.17 12.39 1.78
Neenach Check 43 Precip   34 47 40 34.794 118 37 15 -118.62083 14.32 15.1 -0.78
Little Rock Crk Above Dam Percip   34 28 41 34.478 118 1 24 -118.02333 15.28 9.16 6.12
Sanberg Airways Station Precip   34 44 47 34.746 118 43 27 -118.72417 15.71 12.85 2.86
Bouquet Cyn @ Urban Precip   34 26 54 34.448 118 30 21 -118.50583 15.79 7.98 7.81
Gorman-Sheriff Precip   34 47 47 34.796 118 51 27 -118.8575 16.18 12.87 3.31
Mint Cyn @ Fitch Precip   34 26 48 34.447 118 25 39 -118.4275 16.26 9.05 7.21
Signal Hill City Hall Precip   33 47 48 33.797 118 10 3 -118.1675 16.38 12.18 4.2
Pnt Vicen Ligh Precip   33 44 30 33.742 118 24 38 -118.41056 16.5 11.07 5.43
El Segondo Yard Precip   33 55 1 33.917 118 23 14 -118.38722 16.57 13.23 3.34
Castaic Junct Precip   34 26 17 34.438 118 36 42 -118.61167 16.89 15.78 1.11
Del Valle Training Center Precip   34 25 46 34.429 118 40 1 -118.66694 17.36 15.67 1.69
Domin Wat Co Precip   33 49 53 33.831 118 13 29 -118.22472 17.76 12.11 5.65
LA Ducommun St Precip   34 3 9 34.053 118 14 12 -118.23667 17.8 15.63 2.17
RollingHills Fire Station Precip   33 45 25 33.757 118 21 16 -118.35444 18.03 16.97 1.06
Imperial Yard South FMD Precip   33 55 49 33.93 118 10 22 -118.17278 18.94 15.34 3.6
Electric Ave Pumping Plnt Precip   33 59 35 33.993 118 28 23 -118.47306 19.19 17.34 1.85
Fire Station 149 Precip   34 29 48 34.497 118 36 47 -118.61306 19.25 16.38 2.87
La Tuna DB Precip   34 14 12 34.237 118 19 36 -118.32667 19.8 16.44 3.36
Agoura Precip   34 8 8 34.136 118 45 7 -118.75194 20.16 17.84 2.32
Northridge- LADPW Precip   34 13 52 34.231 118 32 28 -118.54111 20.93 15.3 5.63
La Habra Hgts Precip   33 56 54 33.948 117 57 51 -117.96417 21.1 15.85 5.25
Eaton Wash Precip   34 4 29 34.075 118 3 17 -118.05472 21.26 12.4 8.86
Eagle Rock Rsvr Precip   34 8 44 34.146 118 11 24 -118.19 22.05 18.14 3.91
San Gab Pow House Precip   34 9 20 34.156 117 54 28 -117.90778 22.28 22.98 -0.7
Tujunga Spreading Ground Precip   34 14 6 34.235 118 24 27 -118.4075 22.45 19.36 3.09
Walnut Crk Precip   34 4 14 34.071 117 52 14 -117.87056 22.95 13.29 9.66
Castic Powerhouse Precip   34 35 17 34.588 118 39 24 -118.65667 22.99 20.05 2.94
Sepulv cyn @ Mulholland Precip   34 7 50 34.131 118 29 25 -118.49028 24.09 21 3.09
Big Rock Mesa Precip   34 2 21 34.039 118 37 0 -118.61667 24.37 16.58 7.79
San Gabriel East Fork Percip   34 14 9 34.236 117 48 18 -117.805 25.55 26.04 -0.49
Chiloa-St Hwy Precip   34 19 4 34.318 118 0 29 -118.00806 25.67 22.9 2.77
Pine Canyon Patrol Station Pcp   34 40 24 34.673 118 25 45 -118.42917 25.87 19.14 6.73
Pacoima Dam Precip   34 19 48 34.33 118 23 58 -118.39944 25.87 19.54 6.33
School House D.B. Precip   34 19 32 34.326 118 27 29 -118.45806 25.98 20.33 5.65
Road Mant. Yard 417 Precip   33 59 42 33.995 117 52 3 -117.8675 27.1 23.41 3.69
Brown's Canyon Precip   34 18 42 34.312 118 36 26 -118.60722 27.95 19.55 8.4
San Dimas Dam Precip   34 9 8 34.152 117 46 17 -117.77139 29.41 22.78 6.63
Lechuza Pat Sta Precip   34 4 37 34.077 118 52 46 -118.87944 29.49 22.4 7.09
Aliso Canyon Precip   34 19 42 34.328 118 33 17 -118.55472 30.24 22.82 7.42
Flintridge Precip   34 10 54 34.182 118 11 8 -118.18556 32.36 22.09 10.27
Cedar Springs Precip   34 21 21 34.356 117 52 34 -117.87611 36.14 29.95 6.19
Santa Anita Dam Precip   34 11 3 34.184 118 1 11 -118.01972 36.54 26.21 10.33
Cogswell Dam Precip   34 14 36 34.243 117 57 48 -117.96333 36.69 34.21 2.48
San Gabriel Dam Precip   34 12 20 34.206 117 51 38 -117.86056 37.6 28.86 8.74
Clear Crk School Precip   34 16 37 34.277 118 10 12 -118.17 40.98 30.54 10.44
Tanbark Precip   34 12 19 34.205 117 45 39 -117.76083 54.33 28.04 26.29




























































































































































































































































































































































La Habra Hgts Precip   33 56 54 33.948 117 57 51 -117.96417 21.1 15.85 5.25
Eaton Wash Precip   34 4 29 34.075 118 3 17 -118.05472 21.26 12.4 8.86
Eagle Rock Rsvr Precip   34 8 44 34.146 118 11 24 -118.19 22.05 18.14 3.91
San Gab Pow House Precip   34 9 20 34.156 117 54 28 -117.90778 22.28 22.98 -0.7
Tujunga Spreading Ground Precip   34 14 6 34.235 118 24 27 -118.4075 22.45 19.36 3.09
Walnut Crk Precip   34 4 14 34.071 117 52 14 -117.87056 22.95 13.29 9.66
Castic Powerhouse Precip   34 35 17 34.588 118 39 24 -118.65667 22.99 20.05 2.94
Sepulv cyn @ Mulholland Precip   34 7 50 34.131 118 29 25 -118.49028 24.09 21 3.09
Big Rock Mesa Precip   34 2 21 34.039 118 37 0 -118.61667 24.37 16.58 7.79
San Gabriel East Fork Percip   34 14 9 34.236 117 48 18 -117.805 25.55 26.04 -0.49
Chiloa-St Hwy Precip   34 19 4 34.318 118 0 29 -118.00806 25.67 22.9 2.77
Pine Canyon Patrol Station Pcp   34 40 24 34.673 118 25 45 -118.42917 25.87 19.14 6.73
Pacoima Dam Precip   34 19 48 34.33 118 23 58 -118.39944 25.87 19.54 6.33
School House D.B. Precip   34 19 32 34.326 118 27 29 -118.45806 25.98 20.33 5.65
Road Mant. Yard 417 Precip   33 59 42 33.995 117 52 3 -117.8675 27.1 23.41 3.69
Brown's Canyon Precip   34 18 42 34.312 118 36 26 -118.60722 27.95 19.55 8.4
San Dimas Dam Precip   34 9 8 34.152 117 46 17 -117.77139 29.41 22.78 6.63
Lechuza Pat Sta Precip   34 4 37 34.077 118 52 46 -118.87944 29.49 22.4 7.09
Aliso Canyon Precip   34 19 42 34.328 118 33 17 -118.55472 30.24 22.82 7.42
Flintridge Precip   34 10 54 34.182 118 11 8 -118.18556 32.36 22.09 10.27
Cedar Springs Precip   34 21 21 34.356 117 52 34 -117.87611 36.14 29.95 6.19
Santa Anita Dam Precip   34 11 3 34.184 118 1 11 -118.01972 36.54 26.21 10.33
Cogswell Dam Precip   34 14 36 34.243 117 57 48 -117.96333 36.69 34.21 2.48
San Gabriel Dam Precip   34 12 20 34.206 117 51 38 -117.86056 37.6 28.86 8.74
Clear Crk School Precip   34 16 37 34.277 118 10 12 -118.17 40.98 30.54 10.44
Tanbark Precip   34 12 19 34.205 117 45 39 -117.76083 54.33 28.04 26.29