Abstract of the Final Report

Transcription

1 Abstract of the Final Report Quantifying Potential Variations in Rain Gauge Precipitations Estimates In groundwater recharge estimates at the Yaphank Farm A Final Report Presented by Brian Pedersen In Partial Fulfillment of the Requirements for the Degree of Master of Science in Geosciences with Concentration in Hydrogeology Stony Brook University 2014 Stony Brook University i

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3 Abstract This study was undertaken to ascertain the potential variability of rain gauge precipitation measurements by installing and recording the results of 9 rain gauges placed at the Yaphank Farm located in Yaphank, New York over the period spanning a calendar year. All of the gauges were placed at varying locations within the farm (area of approximately 80 hectares) and precipitation totals were partitioned based on different meteorological variables (wind speed and direction, cold vs. warm season, average storm size, and frozen vs. liquid precipitation) to identify potential sources of variations. Another goal of this study was to quantify the potential variations of the yearly and cold season precipitation totals as it relates assessing potential groundwater recharge. The results of this study suggested that the most significant source of variation between the rain gauge measurements was location (i.e. distances from fence posts, trees, crops, etc.). This was most evident for Gauges 3, 6 and 9. Out of the 9 gauges these 3 had the most suspect location placement. Gauge 3 had a 7ft tall tree planted approximately 10ft from its location, Gauge 6 had high tension wires located about 100ft to the south and Gauge 9, which was installed on a fence, was approximately 5ft away from fence posts on either side that extended 3ft over the gauge height. The source of the variations were isolated by attempting to find precipitation variations based on differing meteorological variables. When the meteorological variables could not be identified as the source of the variation location was identified as the source. The variance based on the average yearly precipitation was 1.96 inches which constitutes a standard deviation of 1.40 inches. The variation in precipitation results yielded the greatest variance to occur during a north wind (6.5%) while the least variation (2.2%) occurred during a iii

4 south and west wind. As a function of increasing wind speed, the variation in precipitation increased from 2.1% to 2.6% for wind speeds of less than 10 miles per hour to greater than 30 miles per hour, respectively. There also was a greater variation in precipitation defined as frozen (5.5% compared to 2.2%) when compared to all liquid events. When examining only average storm size the maximum variation (6.4%) occurred for average storm sizes of 0.25 to 0.50 inches. However, as reported in other noted studies there was not an increase in variation as average storm size decreases. Cold Season precipitation totals saw greater variances (3.5% to 2.6%) when compared to the Warm Season. When viewing these variations as a whole all frozen precipitation occurred during the Cold Season, while most frozen precipitation storms had an average storm size of 0.25 to 0.50 inches and had an average north wind throughout the event. This leads to the conclusion that despite the smaller fraction of frozen to liquid precipitation totals potentially significant variations can exist under these meteorological conditions. These variations in precipitation when viewed through the area of the Yaphank Farm yielded potential maximum groundwater recharge variations of 2.1 inches, based on full season precipitation estimates, and 2.7 inches based on Cold Season estimates. These results further highlight the problems associated with finding appropriate reporting locations for rain gauges and suggest that single rain gauge estimates may be insufficient when quantifying potential groundwater recharge. iv

5 Table of Contents Chapter 1: Introduction 1 Chapter 2: Data Collection and Processing Rain Gauge Specifications Sampling Technique Rain Gauge Network Processing Results in ArcGIS Displaying Rain Gauge Locations Display Precipitation Results Display Precipitation Volume Display Dual Polarization Radar Results..17 Chapter 3: Results Total Precipitation Oct 2013 to Oct Wind Direction Precipitation Results Precipitation Based on Peak Winds Liquid vs. Frozen Precipitation Precipitation based on Average Storm Totals Precipitation based on Warm Season vs. Cold Season Spatial Analysis of Rainfall Data Potential Groundwater Recharge 47 Chapter 4: Discussion Recommendations Further Applications..51 Bibliography.53 Appendix A: Monthly and Total Precipitation Results. 56 Appendix B: Precipitation Results of the 9 Gauges based on Meteorological Parameters.57 v

6 List of Figures Figure 1.1 Long Island Aquifer System and Potential Groundwater Flow..1 Figure 1.2 Long-term annual mean recharge rates for Nassau and Suffolk County 3 Figure 1.3 Investigation Site-Suffolk County Farm Yaphank, New York 8 Figure 2.1 CoCoRaHS 4 diameter rain gauge..10 Figure 2.2 Location of the 9 rain gauges located at the Yaphank Farm.14 Figure 3.1 Monthly Precipitation Totals by Gauge 20 Figure 3.2 Total Precipitation by Gauge (12 month period)..21 Figure 3.3 Islip and Brookhaven Airport Locations..22 Figure 3.4 Yearly Precipitation Totals by Wind Direction.24 Figure 3.5 Yearly Precipitation Totals by Wind Speed..28 Figure 3.6 Yearly Liquid and Frozen Precipitation Totals.31 Figure 3.7 Yearly Precipitation Totals based on Average Storm Size 34 Figure 3.8 Yearly Warm and Cold Season Precipitation Totals.38 Figure 3.9 Yearly Rainfall Contours.44 Figure 3.10 Cold Season Rainfall Contours.45 Figure 3.11 Dual-Polarization Precipitation Estimates (August 13, 2014)...46 vi

7 List of Tables Table 3.1 Monthly and Yearly Precipitation Totals by Gauge 19 Table 3.2 Yearly North Wind Precipitation Totals and Variations..25 Table 3.3 Yearly East Wind Precipitation Totals and Variations.25 Table 3.4 Yearly South Wind Precipitation Totals and Variations..26 Table 3.5 Yearly West Wind Precipitation Totals and Variations 26 Table 3.6 Yearly Precipitation Totals and Variations for Winds <10 mph.28 Table 3.7 Yearly Precipitation Totals and Variations for Winds 10 to 20 mph...29 Table 3.8 Yearly Precipitation Totals and Variations for Winds 21 to 30 mph...29 Table 3.9 Yearly Precipitation Totals and Variations for Winds > 30 mph 30 Table 3.10 Yearly Frozen Precipitation Totals and Variations...32 Table 3.11 Yearly Liquid Precipitation Totals and Variations 32 Figure 3.12 Yearly Precipitation Totals based on Average Storm Size < Figure 3.13 Yearly Precipitation Totals based on Average Storm Size 0.25 to Figure 3.14 Yearly Precipitation Totals based on Average Storm Size 0.51 to Figure 3.15 Yearly Precipitation Totals based on Average Storm Size > Table 3.16 Yearly Warm Season Precipitation Totals and Variations.39 Table 3.17 Yearly Cold Season Precipitation Totals and Variations...40 Table 3.18 Comparison Results (Dual Polarization Radar vs. Rain Gauge)..43 Table 3.19 Potential Groundwater Recharge based on Total and Cold Season Precipitation 48 vii

8 Chapter 1: Introduction Long Island s aquifers are a sole source aquifer system (EPA, 2014) composed of 3 main aquifers. These are the Upper Glacial, Magothy and Lloyd aquifers. Precipitation recharges the aquifers and flows downward through the vadose zone toward the water table (Figure 1.1). It is also the sole source of fresh water to the aquifer system (Busciolano, 2002). Therefore, when evaluating potential groundwater recharge to the aquifers on Long Island it becomes apparent that accurate precipitation measurements are essential. The most common tools used to evaluate precipitation accumulation are radar derived estimates and rain gauge measurements. Figure 1.1 Long Island Aquifer System and Potential Groundwater Flow (Modified from Franke and Cohen, 1972 and by Busciolano 2002). 1

9 Radar estimates generally provide greater spatial and temporal resolution of rainfall precipitation estimates than those obtained from rain gauges (Wang et al., 2008). Despite the fact that radar estimates hold promise for hydrologic studies by providing data at high spatial and temporal resolution over extended areas they do suffer from biases due to several factors including hardware calibration, uncertain Z-R (radar reflectivity vs. rainfall rates) relationships (Winchell et al., 1998; Morin et al., 2003), ground clutter, bright band contamination, mountain blockage, anomalous propagation, and range-dependent bias (Smith et al., 1996). However according to sources at the National Weather Service in Upton, New York beam blockage rarely occurs here on Long Island and since the advent of dual polarization in 2012, radar estimated rainfall has significantly improved. Rain gauge measurements are point measurements. The three most common types of rain gauges are the tipping bucket, weighing gauge and the graduated cylinder. When the area being examined is small enough or the density of rain gauges is relatively high, good quality precipitation estimates can be expected. Rain gauge measurements however are not free from biases. Some problems associated with rain gauge measurements include wind speed (catch area), temperature (evaporation), gauge height, wetting losses, splashing, and human error (Legates and DeLiberty, 1993). The tipping bucket rain gauge is not as accurate as the standard rain gauge (graduated cylinder) because the rainfall may stop before the lever has tipped. When the next period of rain begins it may take no more than one or two drops to tip the lever. This would then indicate that a pre-set amount has fallen when in fact only a fraction of that amount had actually fallen. Tipping buckets also tend to underestimate the amount of rainfall, particularly in snowfall and heavy rainfall events (Groisman and Legates, 1994). 2

10 On Long Island groundwater recharge is approximately 50% of annual precipitation estimates (Petersen 1986 and Robbins, 1996). In Figure 1.2 groundwater recharge rate contours (Petersen 1986) are overlaid onto a shapefile of Long Island utilizing ArcGIS. Figure 1.2 Long-term annual mean recharge rates for Nassau and Suffolk County. However, groundwater recharge occurs in the fall to early spring when evapotranspiration rates are generally low. This is due to the cooler temperatures (lower evapotranspiration rates) and dormant plants. During the summer much of the rainfall is taken up by plants or evaporates due to the heat so there is little to no recharge (Busciolano, 2004). Another method for estimating groundwater recharge is that 75% to 90% of precipitation occurring from October 15th through May 15th gives the annual groundwater recharge (Steenhuis, 1985). Therefore, it is especially important to accurately assess precipitation during this time period. Graduated cylinder rain gauge measurements are superior to tipping bucket measurements during heavier or prolonged precipitation events that can occur in the fall or spring and during the colder snow events that occur during the winters. This is provided that the locations of the rain gauges are sufficiently dense and routine checking is done to reduce errors 3

11 associated with evaporation and to free the gauges from common obstructions such as leaves, grass and other foreign objects. When attempting to assess potential freshwater groundwater recharge via rainfall and snowfall it is important to ascertain the accuracy of rain gauge measurements. Therefore, one aim of this study is to provide an estimate for potential groundwater recharge by examining rainfall estimates via the construction of a rain gauge network at the Suffolk County Farm located in Yaphank, New York (Figure 1.3). Another factor that will be considered is the variability of the rain gauge measurements in relation to each other based on different meteorological parameters. Numerous studies and analyses have been done concerning rain gauge measurements. One study of significance titled Rainfall Relations on Small Areas in Illinois was authored by F. A Huff and J.C. Neill in This study which was sponsored by the Illinois State Water Survey Division, discussed the rainfall variability that resulted from an 18 gauge network with spacing that varied from 6 feet, 300 feet and 600 feet, located at the University of Illinois Airport. As in this study, wind data was also taken from an offsite location approximately 0.5 miles from the network. Rainfall data was collected from storms occurring from March through October during the years of 1953 and This study suggested that rainfall variation was greatest during showery weather and therefore the spring, summer and fall months would be analyzed. Results of the study yielded that relative variability based on overall rainfall totals for the 6 foot gauge spacing ranged from 6.1% to 1.3% with the highest variability on average precipitation totals less than 0.10 of an inch and the least variability occurring with precipitation totals at the highest observed range of 1.00 to 1.99 inches. It was also found that the relative variability based on wind speed ranged from 3.7% to 2.4%. The highest variability was for wind 4

12 speeds of 6 to 10 miles per hour while the least variation was observed during the highest wind speed category of 21 to 30 miles per hour. The study also examined the variation of rainfall as a function of distance. This part of the study showed that as average precipitation totals increased from 0.10 to 2.00 inches, the average difference of rainfall collected between the gauges increased from 0.06 to 0.23 inches. Lastly the 6ft, 300ft, and 600ft gauges were analyzed for the average difference collected and maximum difference collected. The results collected in 1953 showed that the average difference in rainfall collected at the gauges spaced 6ft, 300ft, and 600ft apart increased from inches to inches and inches, respectively. The results from 1954 showed greater variations in the gauges than in 1953 (0.113, and inches) but showed the same trend of increased rainfall variation with increasing distance between gauges. It is important to note that total rainfall during 1953 was inches and inches in 1954, which most likely contributed to the increased variations observed. Another study done in 1969 undertaken by John Sandsborg of the Agricultural College of Sweden sought to discuss the local rainfall variations over small flat cultivated areas. The study titled Local rainfall variations over small, flat, cultivated areas, consisted of 3 rain gauge networks observing rain totals for the period of May through October for the year 1957 in Ultuna located southeast Sweden. The sizes of the network under consideration were 20m 2 (5 gauges), 0.4km 2 (4 gauges) and 1.0km 2 (12 gauges). One part of this study sought to break down the precipitation totals for the 0.4km 2 and the 1.0km 2 networks by convective and non-convective precipitation. After which the coefficient of variance for these 2 networks were compared to the mean variance of the relatively small 20m 2 network. The results for the 0.4km 2 network yielded that the coefficient of variances for the non-convective precipitation ranged from around 11% to 2% and 8% to 3% for convective precipitation Lower variances were observed for higher 5

13 average rainfall totals. For the 1.0km 2 network the coefficient of variance for convective precipitation ranged from around 15% to 5% and 9% to 3% for non-convective precipitation, with lower variances observed for higher average rainfall totals. Meanwhile the mean variation for the 20m 2 network ranged from 4% to just fewer than 2%, with lower variances occurring during higher average rainfall totals. Therefore this study concluded that greater variances in precipitation occurred with lighter precipitation, overall convective precipitation varied greater than non-convective precipitation, rain gauge requirements increase as well as variances with increasing coverage and lastly that micro-variations though less significant are observed and follow similar trends to areas of greater coverage. This study further concluded that variations from a single rainfall may vary considerably whether by convective clouds, frontal precipitation, or precipitation bereft of convection, precipitation estimates increased downwind of the average wind direction and percentage variations in accumulated precipitation vary much less than that of single rainfalls. Also of note was a paper done by Floyd Huff in 1979 titled Spatial and Temporal Precipitation in Illinois. In this paper correlation patterns of annual, seasonal, monthly, storm and partial storm precipitation in Illinois, with an emphasis on the warm season (May through September) were analyzed. Data from 36 weather stations spaced from 25 miles to 150 miles were analyzed for spatial correlation patterns for annual precipitation. For monthly and seasonal precipitation spatial correlation patterns were studied with gauge distances ranging from 2 to 20 miles. This study concluded that the correlation of coefficient for gauge spacing at 25 miles was 0.90 for annual precipitation and gauge spacing of 2 miles was needed in the warm season and 6 miles for the cold season. 6

14 The importance of the aforementioned papers to this study vary from the area of study (micro-variation studied by Huff and Neill), to the type of land use (small cultivated land studied by Sandsborg) to common statistical analyses. However there are some are some notable differences. This study has a gauge network that has closer spacing than most studies so spatial variations may be influenced by micro-variations and/or gauge locations (i.e. distances from fence posts, trees, crops, etc.). Also of extreme importance is that the area of study in this paper is a working farm. While attempts to find ideal locations of gauges are paramount, this is increasingly difficult where obstructions are more ubiquitous in this setting. However, this is more comparable to where most rain gauge measurements are taken (high density locations) and may highlight the difficulties of rain gauge measurements in residential areas. Also this study will help to isolate and identify micro-variations vs. gauge locations through the examination of meteorological parameters. For example if a gauge consistently disagrees with all other gauge results under a specific meteorological parameter than the gauge location can be identified as the main source of the variation. As an example if Gauge X underestimates during a south wind and a known obstruction lies at a distance to the south or if a gauge under reports for most meteorological events it may be reasonable to assume that the gauge location is not ideal (possible poor location). By the same notion if a pattern emerges under specific meteorological conditions throughout all or most the network it is fair to assume that the variance is mostly governed by a realized micro-variance. To further support this assumption dual polarization radar will also be used as a comparison. This study will be comprehensive and broad. As opposed to focusing specifically on one or two variables variations will be examined for multiple meteorological variables. This broad study was chosen to better ascertain variations for a rain gauge or a local rain gauge network but not specifically for any one 7

15 given situation. The finer spatial scale was chosen to give specific emphasis on rain gauge variability over small distances and to determine the reliability or precision a single rain gauge precipitation estimate. The final step of this study is to examine this variability and assess the variations that result in determining potential groundwater recharge estimates. Figure 1.3 Investigation Site-Suffolk County Farm Yaphank, New York. 8

16 Chapter 2: Data Collection and Processing 2.1 Rain Gauge Specifications The 4 diameter rain gauge used in this research project is the standard rain gauge used by Community Collaborative Rain Snow and Hail Network (CoCoRsHS). This network is a non for profit community based network of volunteers that deploy rain gauges and report precipitation measurements throughout the United States and Canada. Data collected by CoCoRaHS is used by the National Weather Service, hydrologists, emergency management coordinators, city utilities (water supply, water conservation, storm water), insurance adjusters, USDA, engineers, mosquito control, ranchers and farmers, outdoor & recreation interests, teachers, students, and neighbors in the community. The gauge, which is made of plastic, is composed of an outer cylinder, inner cylinder and funnel (see Figure 2.1). The inner cylinder is 1 inch in diameter, has the capacity to measure 1 inch of precipitation and is graduated to the nearest one hundredth of an inch. The outer cylinder is 4 inches in diameter and has the capacity to measure 10 inches of precipitation. The total holding capacity of the gauge is therefore 11 inches of rain. 9

17 Figure 2.1 CoCoRaHS 4 diameter rain gauge. 2.2 Sampling Technique Sampling liquid precipitation requires the individual to visually inspect each rain gauge and read off the value to the nearest 0.01 of an inch. In the case of a reverse meniscus the bottom of the meniscus is to be read. Values less than 0.01 inches are recorded as a trace (T). In the event more than 1 inch of rain is received the contents of the inner cylinder are recorded (1.0 ), then emptied, and the contents of the outer cylinder are emptied into the inner cylinder and the subsequent values are added together. This process can be repeated until the total holding capacity of the gauge is reached (11 inches). When sampling for snow the inner tube and funnel are removed with snow collecting in outer cylinder. This is to prevent the snow from clogging the funnel and resulting in a decreased catch. Any snow that accumulates on the top of the outer cylinder can be pushed down by a 10

18 spatula over the edge of the gauge and what falls into the outer cylinder shall be part of the sample measured. After which a known volume of warm water is then measured within the inner cylinder and mixed with the snow contained in the outer cylinder. The sample is then allowed to melt completely and then poured back into the inner cylinder and the total contents are recorded to the nearest 0.01 of an inch. The total liquid equivalent is the total amount measured less the amount of warm water added to melt the sample. Rain gauge results are collected routinely after every precipitation event with results logged typically the day after the precipitation has ended. When precipitation totals for a singleday event occur the results are logged the day after the event and are recorded as a total for the last day the precipitation fell. When precipitation occurs for a multi-day event the results were generally logged the day after the precipitation event ended and are recorded as a total for the last day precipitation fell. Supplemental meteorological data obtained from the hourly observations reported at the airport in Brookhaven, New York that are also recorded are the maximum wind speed and direction during the precipitation event, and precipitation type (snow, rain, sleet, hail, etc.). The airport is approximately 2 miles east of the farm (refer to Figure 3.3). All meteorological data is stored on an Excel spreadsheet. The spreadsheet contains rain gauge identification numbers, GPS coordinates of each rain gauge, precipitation totals for each event, maximum wind speed and associated wind direction, precipitation type (frozen vs. liquid), average storm size, and warm and cold season precipitation totals. All aforementioned data can be found in the appendices. Further analysis of potential variations in precipitation estimates are done by examining the total precipitation collected when the predominant wind direction during the precipitation 11

19 event is North (315 degrees to 45 degrees), South (135 degrees to 225 degrees), East (45 degrees to 135 degrees) or West (225 degrees to 315 degrees). This is determined by examining hourly observations at the Brookhaven Airport and averaging the wind direction while precipitation is occurring during the event. Precipitation estimates are also grouped by peak wind speed or gust during an event. The categories are broken down by winds less than 10 miles per hour, 10 to 20 miles per hour, 21 to 30 miles per hour, and greater than 30 miles per hour. Wind speeds are also obtained from weather observations located at the Brookhaven Airport. Precipitation estimates are also broken down by liquid or frozen. In this study liquid was defined as precipitation that exists solely as rain. Freezing rain was considered liquid precipitation because it freezes upon contact with a surface but falls as a liquid. Frozen precipitation was said to have occurred when precipitation falls as snow, sleet, or hail at any point during the event. This distinction was made due to the fact that precipitation type can go back and forth between liquid and frozen and can vary over short spatial distances. Determination of frozen or liquid precipitation events are also made through examining the hourly observations at the Brookhaven Airport. Precipitation totals are also grouped by the average storm size. This is determined by calculating the average value collected from all 9 gauges during a precipitation event. The breakdown is from less than 0.25 inches, 0.25 to 0.50 inches, 0.51 to 1.00 inch, and greater than 1 inch. Furthermore, the total contribution of precipitation under each storm size category is accomplished by taking the sum of all precipitation events under each storm size category. Lastly, Warm Season vs. Cold Season precipitation estimates are tallied. Warm Season is defined as the months of April through September. The Cold Season is defined as October through March. The Warm Season is generally dominated by more convective precipitation 12

20 (Colle, 2006) while the Cold Season is generally dominated by dynamic Low Pressure systems (Miller and Friederick, 1969). 2.3 Rain Gauge Network The Suffolk County Farm located in Yaphank, New York was chosen as an ideal location for a rain gauge network because it is centrally located and far enough away from the coast to be within the recharge area of Long Island's aquifer system. It also has relatively open spaces and a convenient sampling location. The Suffolk County Farm area has an aerial extent of approximately 80 hectares. There are 9 rain gauges installed on the Suffolk County Farm (Figure 2.2). All rain gauge coordinates were plotted via a hand held GPS device. The construction of the rain gauge network was based on the following criteria: Standard Height: The bases of all the rain gauges are within a range of 3 to 4 feet from ground level. This is to ensure potential rainfall variations at each gauge are not influenced by differences with rain gauge height. The CoCoRaHS recommendation is within a 2 to 5 foot range (CoCoRaHS, 2014). Area Selection: Site locations for all rain gauges were chosen to reflect relatively open areas free from tall trees, buildings, high crops, and sprinkler lines. Minimum recommendations suggest that the rain gauge be placed as far from obstacles as they are high. While the minimum recommendations were met it is important to note that a 7 foot tree was planted around 10 feet away from Gauge 3, Gauge 6 had high tension wires approximately 100 feet to the south and Gauge 9 had fence posts 3 feet above the gauge height on either side, approximately 5 feet away. All other gauges had more open areas. Also the full spatial extent of the farm was to be considered while limiting areas where potential damage could occur due to farm equipment traffic, high crops and potential vandalism. Rain gauge spacing was not uniform because of the 13

21 above requirements mentioned. While there may be no perfect rain gauge location full spatial coverage of the farm was desired while minimizing the effects of obstructions, traffic and vandalism. Figure 2.2 Location of the 9 rain gauges located at the Yaphank Farm. 14

22 2.4 Processing Results in ArcGIS The rain gauge locations and precipitation results can be mapped and viewed utilizing ArcGIS ArcMap. After point values of rainfall have been determined at each rain gauge interpolation via the Inverse Distance Weighted (IDW) method is done. Inverse distance weighted (IDW) interpolation determines cell values using a linearly weighted combination of a set of sample points. The weight is a function of inverse distance. The surface being interpolated should be that of a locationally dependent variable. This method assumes that the variable being mapped decreases in influence with distance from its sampled location. Equation 1 In this formula x is an interpolated point, x i is a known point (e.g. rain gauge data) d, is a given distance from the known point x i to the interpolated point x, N is the total number of known points used in interpolation and p, is a positive real number, called the power parameter (ArcGIS Resources, 2014). IDW is applied in many precipitation mapping methods (e.g. Bedient and Huber, 1992; Burrough and McDonnell, 1998; Goovaerts, 2000; Li and Heap, 2008, Rudolf and Rubel, 2005; Ahrens, 2006). It is shown that statistical interpolation methods like multiple linear regression, optimal interpolation or Kriging can perform better, but only if data density is sufficient (Eischeidet al., 2000). Therefore due to the ease of utilizing IDW, the small number of gauges and extensive history of use IDW interpolation is the preferred method. 15

23 2.4.1 Displaying Rain Gauge Locations Launch ArcMap, add Suffolk County, New York: 2010 Ortho Imagery. Add Excel Spreadsheet containing GPS coordinates and meteorological data. Display X-Y data and set X data to Longitude and Y data to Latitude. Edit Coordinate System>Geographic Coordinate System>North American>NAD Data Management>Projections and Transformations>Define Projection>NAD State Plane Long Island Export Data to Personal Geodatabase, add exported data called Rain Gauge Points to map as layer Display Precipitation Results Properties>Labels>Change Label field to Precipitation >Check off Display Label Features in this layer. Arc Toolbox> Spatial Analyst Tools>Interpolation>IDW>Set Input Point Features to Rain Gauge Points >Set Z value field to Precipitation >Set Output Raster ( IDW Raster ) to Personal Geodatabase> Click OK Display Precipitation Volume Map Algebra>Raster Calculator> Precipitation Raster x cell size x cell size x 0.83 (in/ft)>set Output Raster to Volume. Map Algebra>Raster Calculator> int( Volume - Volume >Set Raster Output to RasterZone. This returns an integer raster of a constant value with the same size and shape as the original Precipitation raster, which can now be used to calculate a total volume over the whole area. 16

24 Spatial Analyst>Zonal>Zonal Statistics>Input Raster RasterZone >Input Value Volume Raster>Statistics Sum Display Dual Polarization Radar Results Download radar data from NCDC and choose dual-polar storm total precipitation ( Use NOAA Weather and Climate Toolkit to visualize list and load data. Export the data to a shapefile via the NOAA Weather and Climate Toolkit software. Display radar values by adding the feature class to ArcGIS using graduated symbology. 17

25 Chapter 3: Results 3.1 Total Precipitation Oct 2013 to Oct 2014 The precipitation estimates for the 9 rain gauges collected during the 12 month rolling period (October 21 st 2013 to October 20 th 2014) are shown in Figures 3.1 (monthly totals) and 3.2 (12 month total). A summary of these figures is also shown in Table 3.1. The average value obtained from all the gauges that fell over the farm was inches. Values ranged from a maximum of (Gauge 7) to a minimum of (Gauge 6) inches. The largest monthly average value occurred in March where 6.17 inches was observed at all 9 gauge locations while the smallest non-partial month (October 2013 and October 2014 are partial records) average occurred in July, with an average value of 1.71 inches. The variance and standard deviation for the total precipitation was 1.96 inches and 1.40 inches, respectively. Individually the highest monthly totals were recorded by only 4 of the 9 gauges. Gauge 4 received the maximum total 4 times. Gauge 5 also received the maximum total 4 times while Gauge 7 and Gauge 8 received it 3 and 1, respectively. The lowest monthly totals were received by only 3 of the 9 gauges. Gauges 3, 6, and 9 all received the lowest monthly total 4 times. As previously noted, these 3 gauges all have obstructions closer by than the other 6 gauges. It is also important to point out that none of the gauges that ever recorded the highest monthly total ever were recorded as a monthly minimum. Precipitation totals obtained from the NCDC were observed for the same time period from nearby airports at Brookhaven and Islip, for comparison purposes. The yearly totals were and inches, respectively. The spread between these sites are exaggerated most likely due to two main factors. According to the National Weather Service Brookhaven Airport s precipitation total under reports because they use a heated tipping bucket and as previously stated 18

26 the inaccuracy and subsequent underreporting of the use of this gauge is frequent during snow events but Islip uses a weighing gauge which is much more accurate. Also on August 13, 2014 a very narrow plume of moisture contributed to anomalously high precipitation totals (13.51 inches) over the Islip area while more modest totals (1.45 inches) were recorded at Brookhaven Airport and surrounding areas. The average value that fell over the farm falls in the range that fell over Islip and Brookhaven. This adds credence to the data as the location of the farm is between the two airports. For convenience Figure 3.3 shows the locations of Brookhaven and Islip Airport in relation to the Suffolk County Farm in Yaphank. Table 3.1 Monthly and Yearly Precipitation Totals by Gauge (inches) Month Gauge 1 Gauge 2 Gauge 3 Gauge 4 Gauge 5 Gauge 6 Gauge 7 Gauge 8 Gauge 9 Oct Nov Dec Jan Feb Mar Apr May June July Aug Sep Oct Total Total Variance 1.96 Std. Deviation

27 Precipitation (inches) 7.00 Monthly Precipitation Totals by Gauge Gauge 1 Gauge 2 Gauge 3 Gauge 4 Gauge 5 Gauge 6 Gauge 7 Gauge 8 Gauge Figure 3.1 Monthly Precipitation Totals by Gauge 20

28 Precipitation (inches) Total Precipitation by Gauge Gauge 1 Gauge 2 Gauge 3 Gauge 4 Gauge 5 Gauge 6 Gauge 7 Gauge 8 Gauge Total Precipitation Figure 3.2 Total Precipitation by Gauge (12 month period) 21

29 Figure 3.3 Islip and Brookhaven Airport Locations 22

30 3.2 Wind Direction Precipitation Results The precipitation estimates collected at the 9 rain gauges during the 12 month rolling period (October 21 st 2013 to October 20 th 2014) based on wind direction are shown in Figure 3.4. When a north wind occurred the average value that fell over the farm was inches based on 23 events. Maximum and minimum values ranged from to 9.03 inches at Gauge 7 and Gauge 6 respectively. For an east wind the average value that fell over the farm was 7.20 from 8 events. Maximum and minimum values ranged from 7.57 to 6.86 inches at Gauge 8 and Gauge 6 respectively. For a south wind the average value that fell over the farm was from 29 events. Maximum and minimum values ranged from to inches at Gauge 4 and 9, respectively. Lastly for a west wind the average value that fell over the farm was 1.79 inches from 9 events. Maximum and minimum values ranged from 2.01 to 1.66 inches at Gauge 5 and Gauge 9, respectively. A graphical representation of the precipitation values by wind direction are shown in Figure 3.4. Tables 3.2 through 3.5 show a summary of the total precipitation and variations of precipitation per gauge as a function of wind direction. The first column is the rain gauge number, the second column represents the total precipitation that fell during a given wind direction, the third column is the absolute deviation per year (absolute value of the total rain gauge average individual rain gauge total), the fourth column represents the absolute deviation per storm (3 rd Column/ number of storms), and the last column represents the percent variation per storm size (4 th Column/(average precipitation/total number of storms). Lastly averages and means were calculated as well as yearly variances. When a north wind occurred the greatest total variance of 0.59 occurred. However, when viewing the variation as a function of average storm size the greatest variation occurred during a 23

31 Precipitation (in) north wind (6.5%). The south wind total variance was 0.36 followed by values 0.07 and 0.01 for an east and south wind, respectively. However, again when viewing the variations as a function of average storm size the variance of both the south and west wind had a variation of 2.2% while an east wind produced a value of 3.5% Precipitation vs Wind Direction North Wind East Wind South Wind West Wind Gauge 1 Gauge 2 Gauge 3 Gauge 4 Gauge 5 Gauge 6 Gauge 7 Gauge 8 Gauge 9 Figure 3.4 Yearly Precipitation Totals by Wind Direction 24

32 Table 3.2 Yearly North Wind Precipitation Totals and Variations Rain Gauge Precipitation (in) abs dev per year abs dev per storm Rain Gauge Rain Gauge Rain Gauge Rain Gauge Rain Gauge Rain Gauge Rain Gauge Rain Gauge Rain Gauge average/mean deviations Variance 0.59 % Variation per storm size 2.2% 7.3% 0.6% 8.8% 7.0% 12.2% 10.4% 2.7% 7.5% 6.5% Table 3.3 Yearly East Wind Precipitation Totals and Variations Rain Gauge Precipitation abs dev per year abs dev per storm % Variation per storm size Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % average/mean % deviations Variance

33 Table 3.4 Yearly South Wind Precipitation Totals and Variations Rain Gauge Precipitation (in) abs dev per year abs dev per storm % Variation per storm size Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % average/mean deviations % Variance 0.36 Table 3.5 Yearly West Wind Precipitation Totals and Variations Rain Gauge Rain Gauge 1 Rain Gauge 2 Rain Gauge 3 Rain Gauge 4 Rain Gauge 5 Rain Gauge 6 Rain Gauge 7 Rain Gauge 8 Rain Gauge 9 average/mean deviations Variance Precipitation (in) abs dev per year abs dev per storm % Variation per storm size % % % % % % % % % %

34 3.3 Precipitation Based on Peak Wind Speeds The precipitation estimates based on peak wind speeds for the 9 rain gauges collected during the 12 month rolling period (October 21 st October 20 th 2014) are shown in Figure 3.4. Overall the average value that fell over the farm with a wind speed in excess of 30 mph was inches based on 15 events. Maximum and minimum values ranged from to located at Gauge 7 and Gauge 3 respectively. For winds ranging from 21 to 30 mph the average value was inches based on 16 events. Maximum and minimum values were 12.0 and inches located at Gauge 7 and Gauge 6 respectively. For winds ranging from 10 to 20 mph the average value was 6.94 based on 15 events. Maximum and minimum values ranged from 5.81 to 5.35 inches located at Gauge 4 and Gauge 6 respectively. Lastly for wind speeds under 10 mph, the average value was 6.94 inches based on 23 events. The maximum and minimum values ranged from 7.20 to 6.62 inches located at Gauge 4 and Gauge 9 respectively. When analyzing the trends in Figure 3.4 it clearly shows that the gauges behave the same regardless of wind direction with the main difference being the amplitude or difference between the gauge measurements increases as precipitation increases. A summary of the statistical analysis of precipitation based on peak winds are shown from Table 3.6 through 3.9. The greatest total variance of 0.34 occurred with winds in excess of 30 miles per hour. The next highest total variance (0.14) occurred with winds ranging from 21 to 30 miles per hour followed by winds less than 10 mph (0.03) and then winds that ranged from 10 to 20 miles per hour (0.02). However when viewing the percent variation as a function of storm size there is a slight increase in variation as a function of wind speed ranging from 2.1% for winds less than 10 miles per hour to 2.6% for both winds ranging from 21 to 30 mph and winds in excess of 30 miles per hour. 27

35 Figure 3.5 Yearly Precipitation Totals by Wind Speed Table 3.6 Yearly Precipitation Totals and Variations for Winds <10 mph Rain Gauge Precipitation abs dev per abs dev per % Variation per (in) year storm storm size Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % average/mean % deviations Variance

36 Table 3.7 Yearly Precipitation Totals and Variations for Winds 10 to 20 mph Rain Gauge Precipitation abs dev abs dev % Variation per (in) per year per storm storm size Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % average/mean deviations % Variance 0.02 Table 3.8 Yearly Precipitation Totals and Variations for Winds 21 to 30 mph Rain Gauge Precipitation abs dev abs dev % Variation per storm (in) per year per storm size Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % average/mean deviations % Variance

37 Table 3.9 Yearly Precipitation Totals and Variations for Winds > 30 mph Rain Gauge Precipitation (in) abs dev per year abs dev per storm % Variation per storm size Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % average/mean deviations % Variance Liquid vs. Frozen Precipitation The precipitation estimates based on frozen and liquid values occurred for the 9 rain gauges collected during the 12 month rolling period (October 21 st 2013 to October 20 th 2014) are shown in Figure 3.6. Overall the average value of liquid precipitation that fell over the farm was inches based on 49 events. Maximum and minimum values ranged from and 28.2 inches located at Gauge 5 and Gauge 3 respectively. For frozen precipitation the average value was inches for 21 events and values ranged from to inches located at Gauge 7 and Gauge 6 respectively. When examining the trend of the curves located in Figure 3.6 a general agreement in pattern is observed between the frozen and liquid precipitation. However, this agreement is more subtle than those based on wind speed or direction. The most notable difference is exhibited by Gauge 5 which recorded the most liquid precipitation out of all the gauges however exhibited the 3 rd least of frozen precipitation. This main variation seems to be a function of precipitation type and wind direction. When examining individual storms Gauge 5 30

38 Precipitation (in) under reported when the precipitation type was frozen and there was a north wind. This was shown prior via the wind direction analysis section and later during the Warm Season/Cold Season analysis. Statistical analysis of the precipitation data shown in Tables 3.10 and 3.11 yielded a greater variance 0.85 to 0.52 for frozen precipitation over liquid precipitation. This is not surprising due to the decreased accuracy associated with collecting snow. The less dense snow particle is more vulnerable to wind and variations in path. The average percent variation per storm size exhibited the same behavior with variations averaging 5.5% for snow and 2.2% for liquid Liquid and Frozen Precipitation Totals Frozen Liquid Gauge 1 Gauge 2 Gauge 3 Gauge 4 Gauge 5 Gauge 6 Gauge 7 Gauge 8 Gauge 9 Figure 3.6 Yearly Liquid and Frozen Precipitation Totals 31

39 Table 3.10 Yearly Frozen Precipitation Totals and Variations Rain Gauge Precipitation (in) abs dev per year abs dev per storm % Variation per storm size Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % average/mean deviations % Variance 0.85 Table 3.11 Yearly Liquid Precipitation Totals and Variations Rain Gauge Precipitation (in) abs dev per year abs dev per storm % Variation per storm size Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % average/mean deviations % Variance

40 3.5 Precipitation based on Average Storm Totals The precipitation estimates based on average storm totals occurred for the 9 rain gauges collected during the 12 month rolling period (October 21 st 2013 to October 20 th 2014) are shown in Figure 3.7. Overall the average value that fell over the farm was 2.36 inches when the average storm size was less than 0.25 inches). Maximum and minimum values ranged from 2.63 to 2.14 inches located at Gauge 5 and Gauge 3. Overall the average value that fell over the farm was 5.68 inches when the average storm size ranged from 0.25 to 0.50 inches. Maximum and minimum values ranged from 6.38 to 5.17 inches located at Gauge 7 and 6. Overall the average value that fell over the farm was 2.36 inches when the average storm size ranged from 0.51 to 1.00 inches. Maximum and minimum values ranged from to inches located at Gauge 7 and 6. Overall the average value that fell over the farm was 2.36 inches when the average storm size was less than 0.25 inches. Maximum and minimum values ranged from 2.63 to 2.14 inches located at Gauge 5 and 3. Overall the average value that fell over the farm was 2.36 inches when the average storm size was greater than 1.00 inches. Maximum and minimum values ranged from 24.3 to 22.8 inches located at Gauge 4 and 9. When analyzing the trends located in Figure 3.6 the pattern represented by average storm size are quite similar with increased yearly variations exhibited with increased average storm size. 33

41 Figure 3.7 Yearly Precipitation Totals based on Average Storm Size Statistical analysis as a function of storm size is shown in Tables 3.12 through From these tables the greatest variance (0.3) occurred when average storm totals exceeded one inch, while the smallest variance occurred for storm totals less than 0.25 inches. However when viewed as a function of storm size the greatest variance (6.4%) occurred for the intermediate storm size category of 0.25 to 0.50 inches and the least variance (2.0%) occurred for storm sizes greater than 1 inch. This variation may be explained by the nature that the lower the average storm total the greater the potential variation. This is due to the fact that an equal variation versus decreasing storm size will constitute a greater percent variation. However, this explanation does not explain why the less than 0.25 inch average storm precipitation variations are so low. Most 34

42 likely what is also increasing the percent variation is the fact that many snow events were within the 0.25 to 0.50 inches range and thus decreasing the overall agreement between gauges. Table 3.12 Yearly Precipitation Totals based on Average Storm Size < 0.25 Rain Gauge Precipitation (in) abs dev per year abs dev per storm % Variation per storm size Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % average/mean deviations % Variance 0.02 Figure 3.13 Yearly Precipitation Totals based on Average Storm Size 0.25 to 0.50 Rain Gauge Precipitation (in) abs dev per year abs dev per storm % Variation per storm size Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % average/mean deviations % Variance

43 Table 3.14 Yearly Precipitation Totals based on Average Storm Size 0.51 to 1.00 Rain Gauge Precipitation abs dev abs dev (in) per year per storm Rain Gauge Rain Gauge Rain Gauge Rain Gauge Rain Gauge Rain Gauge Rain Gauge Rain Gauge Rain Gauge average/mean deviations Variance 0.12 % Variation per storm size 0.58% 1.20% 1.72% 3.95% 0.05% 5.18% 4.57% 0.58% 4.03% 2.43% Table 3.15 Yearly Precipitation Totals based on Average Storm Size > 1.00" Rain Gauge Precipitation abs dev abs dev % Variation per storm (in) per year per storm size Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % average/mean deviations % Variance

44 3.6 Precipitation based on Warm Season vs. Cold Season The precipitation estimates based on the warm season vs. the cold season occurred for the 9 rain gauges collected during the 12 month rolling period (October 21 st 2013 to October 20 th 2014) are shown in Figure 3.8. Overall the average value that fell over the farm during the warm season was inches. Maximum and minimum values ranged from to 16.6 inches at Gauge 5 and Gauge 3. For the cold season the average value recorded was inches of precipitation. Maximum and minimum values ranged from to inches at Gauge 7 and Gauge 6, respectively. Furthermore the patterns represented in Figure 3.7 demonstrate similar trends. This is similar to all the other meteorological parameters examined with increased variation in the Cold Season due to more precipitation when compared to the Warm Season. This further indicates that dominant variation is related to gauge location over meteorological parameters. 37

45 Precipitation (in) Warm Season vs. Cold Season Warm Season Cold Season Gauge 1 Gauge 2 Gauge 3 Gauge 4 Gauge 5 Gauge 6 Gauge 7 Gauge 8 Gauge 9 Figure 3.8 Yearly Warm and Cold Season Precipitation Totals Statistical analysis of the Warm and Cold season is shown in Tables 3.16 and The Warm Season had a significantly lower variance when compared to that of the Cold Season (0.24 to 1.03). This variation held through when analyzed by average storm total (2.6% to 3.5%). This variation is mostly attributed to the significant amounts of frozen precipitation that occurred during the period of examination. The discrepancy noted earlier with Gauge 5 in regards to the variation of precipitation during solid and liquid precipitation is seen once again during this analysis. When examining Figure 3.7 Gauge 5 which collected poorly in the Cold Season (3 rd lowest) was the highest collector or precipitation during the Warm Season. This variation further indicates that Gauge 5 under reports during snow or cold season events. 38

46 The Warm Season had a significantly lower variance when compared to that of the Cold Season (0.24 to 1.03). This variation held through when analyzed by average storm total (2.6% to 3.5%). This variation is mostly attributed to the significant amounts of frozen precipitation that occurred during the period of examination. The discrepancy noted earlier with Gauge 5 in regards to the variation of precipitation during solid and liquid precipitation is seen once again during this analysis. When examining Figure 3.8 Gauge 5 which collected poorly in the Cold Season (3 rd lowest) was the highest collector or precipitation during the Warm Season. This variation further indicates that Gauge 5 under reports during snow or cold season events. Table 3.16 Yearly Warm Season Precipitation Totals and Variations Rain Gauge Precipitation (in) abs dev per year abs dev per storm % Variation per storm size Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % average/mean deviations % Variance

47 Table 3.17 Yearly Cold Season Precipitation Totals and Variations Rain Gauge Precipitation (in) abs dev per year abs dev per storm % Variation per storm size Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % Rain Gauge % average/mean deviations % Variance

48 3.7 Spatial Analysis of Rainfall Data Spatial analysis of the total yearly and cold season rainfall data was accomplished through manual interpolation of isohyets. The total rainfall and cold season were analyzed because these two time periods are used to estimate potential groundwater recharge. Figures 3.8 and 3.9 show the spatial distribution of rainfall over the course of a year and during the Cold s Season to the nearest 0.25 inch via precipitation contour lines. When viewed spatially the total precipitation over the course of the year show a general decrease from northwest down toward the southeast with Gauge 7 reporting the highest annual total and Gauge 6 reporting the lowest. The two most southern gauges reported the least precipitation (Gauges 3 and 6) while the most northern gauge (Gauge 7) and the most central gauge reported the highest amounts. It is also important to note that in between Gauge 1 and 2, which are less than 150 feet apart, there is a lack of density of precipitation contours. This shows that the catchment between the two gauges was very similar and due to the small distance was expected. Also unlike the study by Sandsborg precipitation generally does not increase with prevailing wind direction (south wind was dominant). Overall the pattern shows a randomness that further suggests location was the dominant factor in variance. The Cold Season shows a similar pattern from the northwest to the southeast again with the highest precipitation values again seen at Gauge 7 followed by Gauge 4 and the two lowest were reported at Gauge 6 followed by Gauge 9. What is also important to note is the similarity of the precipitation gradient around Gauge 5 during the one year of study vs. the cold season period. Again, but now seen spatially the precipitation gradient (greater than 2 inches) is actually slightly greater for the Cold Season than the total yearly precipitation (just under 2 inches) when compared to the closest gauges (Gauge 4 and 7). This is also despite the fact that around 45% 41

49 more precipitation was recorded for the total year when compared to the Cold Season. This shows that Gauge 5 suffered a diminished catch of precipitation during the Cold Season and further demonstrates that this gauge may be prone to greater variation during snow events. Although not completely understood a possible explanation could be that during the Cold Season or during snow events a north wind was observed. This north wind would travel over a greater flat area thereby decreasing the wind shear in the vertical and possibly causing precipitation to overshoot the gauge. Lastly the Cold Season possibly shows more of an increasing precipitation pattern with a north wind, which was seen mostly on snow events. However since the same general precipitation pattern exists with the total precipitation (south wind dominant) it is a reasonable assumption to conclude that this pattern suffers from the same bias and further lends to the notion that gauge location was the primary factor in variance. To examine further whether the spatial variation is rainfall is due to location or possibly real variations it was prudent to examine results from a specific storm with the Dual-Polarization Radar located at Brookhaven National Lab. The premise being that if the patterns between the two technologies are the same it may be reasonable to assume that the variations are legitimate and not based on location. Therefore the gauge values will be compared to the storm total radar measurements. The example that is used is the extreme rain event that occurred on August 13, 2014 (Figure 3.11). At this distance from the radar the approximate area of each grid space of the radar is 0.3km 2. Each gauge is contained within a separate grid space and therefore each gauge location will have a unique precipitation total. A comparison of the totals between the dual polarization radar and the gauges are shown in Table The results from the Table 3.18 further indicate possible locational issues in regards to the gauge locations. While the average values are 0.1 inches the patterns at each location do not 42

50 match. The highest reporting gauge location for dual polarization and the rain gauges were at Gauge 6 and Gauge 5, respectively. The lowest reporting gauge locations for the dual-pol radar and the gauges were located at Gauge 7 and Gauge 6. While it is possible that the radar suffered problems as well the preponderance of evidence suggests that gauge location was the main contributor to the variations seen between the gauges. Table 3.18 Comparison Results (Dual Polarization Radar vs. Rain Gauge) Location Dual Polarization (in) Rain Gauge (in) Gauge Gauge Gauge Gauge Gauge Gauge Gauge Gauge Gauge Average

51 Figure 3.9 Yearly Rainfall Contours 44

52 Figure 3.10 Cold Season Rainfall Contours 45

53 Figure 3.11 Dual-Polarization Precipitation Estimates (August 13, 2014) 46