i

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

ii

iii

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

iv

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.

v

Table of Contents

Chapter 1: Introduction……………………………………………………………………………………………1

Chapter 2: Data Collection and Processing….............................................................................................. ...........9

2.1. Rain Gauge Specifications…………………………………………………………………………………9

2.2. Sampling Technique……………………………………………………………………………………….10

2.3. Rain Gauge Network………………………………………………………………………………………13

2.4. Processing Results in ArcGIS……………………………………………………………………………...15

2.4.1 Displaying Rain Gauge Locations…………………………………………………………………16

2.4.2 Display Precipitation Results………………………………………………………………………16

2.4.3 Display Precipitation Volume………………………………………………………………………16

2.4.4 Display Dual Polarization Radar Results…………………………………………………………..17

Chapter 3: Results………………………………………………………………………………………………….18

3.1 Total Precipitation Oct 2013 to Oct 2014…………………………………………………………………..18

3.2 Wind Direction Precipitation Results……………………………………………………………………….23

3.3 Precipitation Based on Peak Winds…………………………………………………………………………27

3.4 Liquid vs. Frozen Precipitation……………………………………………………………………………..30

3.5 Precipitation based on Average Storm Totals……………………………………………………………….33

3.6 Precipitation based on Warm Season vs. Cold Season……………………………………………………...37

3.7 Spatial Analysis of Rainfall Data…………………………………………………………………………....41

3.8 Potential Groundwater Recharge……………………………………………………………………………47

Chapter 4: Discussion ……………………………………………………………………………………………..49

4.1 Recommendations ……………………… ………………………………………………………………….49

4.2 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

vi

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

vii

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 < 0.25”...…..………………………………35

Figure 3.13 Yearly Precipitation Totals based on Average Storm Size 0.25” to 0.50”…..….……………………...35

Figure 3.14 Yearly Precipitation Totals based on Average Storm Size 0.51” to 1.00”…..….……………………...36

Figure 3.15 Yearly Precipitation Totals based on Average Storm Size > 1.00”…………..……...…………………36

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

1

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).

2

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).

3

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

4

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 1957. 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 1954. 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

5

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 0.053 inches to 0.102 inches and 0.167 inches, respectively. The results

from 1954 showed greater variations in the gauges than in 1953 (0.113, 0.175 and 0.200 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 12.11 inches and 24.83 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 20m2 (5 gauges),

0.4km2 (4 gauges) and 1.0km

2 (12 gauges). One part of this study sought to break down the

precipitation totals for the 0.4km2 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 20m2 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

6

average rainfall totals. For the 1.0km2 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 20m2 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.

7

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

8

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.

9

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.

10

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

11

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 single-

day 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

12

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

13

(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

14

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.

15

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, xi is a known point (e.g. rain gauge data) d, is a given

distance from the known point xi 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.

16

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

1983.

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.

2.4.2 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.

2.4.3 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.

17

Spatial Analyst>Zonal>Zonal Statistics>Input Raster “RasterZone”>Input Value

“Volume” Raster>Statistics “Sum”.

2.4.4 Display Dual Polarization Radar Results

Download radar data from NCDC and choose “dual-polar storm total precipitation”

(http://www.ncdc.noaa.gov/nexradinv/chooseday.jsp?id=kokx).

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.

18

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 21st 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 43.24 inches. Values ranged from a

maximum of 45.28 (Gauge 7) to a minimum of 41.06 (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

38.79 and 53.39 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

19

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-13 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

Nov-13 2.87 2.88 2.64 2.92 2.98 2.77 2.92 2.93 2.84

Dec-13 5.88 5.92 5.92 6.00 5.78 5.63 6.02 6.03 5.61

Jan-13 2.85 3.26 2.62 3.21 2.53 2.50 3.56 2.79 2.38

Feb-13 4.46 4.61 4.43 4.75 3.93 3.59 4.83 4.37 3.81

Mar-13 6.26 6.32 6.04 6.43 6.13 6.05 6.13 6.12 6.00

Apr-13 2.85 2.90 2.82 2.91 2.94 2.79 3.05 2.96 2.76

May-13 5.22 5.22 4.97 5.23 5.48 5.08 5.44 5.19 5.04

June-13 1.68 1.66 1.57 1.75 1.95 1.60 1.79 1.65 1.71

July-13 2.35 2.39 2.33 2.49 2.37 2.24 2.38 2.33 2.25

Aug-13 2.90 2.68 2.65 3.06 3.07 2.73 3.00 3.01 2.93

Sep-13 2.28 2.27 2.26 2.35 2.31 2.25 2.29 2.33 2.25

Oct-14 3.84 3.78 3.77 3.92 3.84 3.81 3.85 3.88 3.79

Total 43.46 43.91 42.04 45.04 43.33 41.06 45.28 43.61 41.39 Total Variance 1.96

Std. Deviation 1.40

20

Figure 3.1 Monthly Precipitation Totals by Gauge

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

Pre

cip

itat

ion

(in

che

s)

Monthly Precipitation Totals by Gauge

Gauge 1

Gauge 2

Gauge 3

Gauge 4

Gauge 5

Gauge 6

Gauge 7

Gauge 8

Gauge 9

21

Figure 3.2 Total Precipitation by Gauge (12 month period)

38.00

39.00

40.00

41.00

42.00

43.00

44.00

45.00

46.00

Total Precipitation

Pre

cip

itat

ion

(in

che

s)

Total Precipitation by Gauge

Gauge 1

Gauge 2

Gauge 3

Gauge 4

Gauge 5

Gauge 6

Gauge 7

Gauge 8

Gauge 9

22

Figure 3.3 Islip and Brookhaven Airport Locations

23

3.2 Wind Direction Precipitation Results

The precipitation estimates collected at the 9 rain gauges during the 12 month rolling

period (October 21st 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 10.28 inches based on

23 events. Maximum and minimum values ranged from 11.35 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 23.49 from 29

events. Maximum and minimum values ranged from 24.23 to 23.05 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 (3rd

Column/ number of storms), and the last column represents the percent variation

per storm size (4th

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

24

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%.

Figure 3.4 Yearly Precipitation Totals by Wind Direction

0.00

5.00

10.00

15.00

20.00

25.00

30.00

Gauge 1 Gauge 2 Gauge 3 Gauge 4 Gauge 5 Gauge 6 Gauge 7 Gauge 8 Gauge 9

Pre

cip

itat

ion

(in

)

Precipitation vs Wind Direction

North Wind

East Wind

South Wind

West Wind

25

Table 3.2 Yearly North Wind Precipitation Totals and Variations Rain Gauge Precipitation

(in) abs dev per year

abs dev per storm

% Variation per storm size

Rain Gauge 1 10.51 0.231 0.010 2.2%

Rain Gauge 2 11.03 0.751 0.033 7.3%

Rain Gauge 3 10.34 0.061 0.003 0.6%

Rain Gauge 4 11.18 0.901 0.039 8.8%

Rain Gauge 5 9.56 0.719 0.031 7.0%

Rain Gauge 6 9.03 1.249 0.054 12.2%

Rain Gauge 7 11.35 1.071 0.047 10.4%

Rain Gauge 8 10.00 0.279 0.012 2.7%

Rain Gauge 9 9.51 0.769 0.033 7.5%

average/mean deviations

10.28 0.670 0.029 6.5%

Variance 0.59

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 1 7.16 0.040 0.005 0.6%

Rain Gauge 2 6.94 0.260 0.033 3.6%

Rain Gauge 3 6.87 0.330 0.041 4.6%

Rain Gauge 4 7.46 0.260 0.033 3.6%

Rain Gauge 5 7.41 0.210 0.026 2.9%

Rain Gauge 6 6.86 0.340 0.043 4.7%

Rain Gauge 7 7.51 0.310 0.039 4.3%

Rain Gauge 8 7.57 0.370 0.046 5.1%

Rain Gauge 9 7.02 0.180 0.023 2.5%

average/mean deviations

7.20 0.256 0.032 3.5%

Variance 0.07

26

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 1 23.62 0.134 0.005 0.6%

Rain Gauge 2 23.77 0.284 0.010 1.2%

Rain Gauge 3 22.71 0.776 0.027 3.3%

Rain Gauge 4 24.23 0.744 0.026 3.2%

Rain Gauge 5 23.93 0.444 0.015 1.9%

Rain Gauge 6 23.05 0.436 0.015 1.9%

Rain Gauge 7 24.16 0.674 0.023 2.9%

Rain Gauge 8 23.47 0.016 0.001 0.1%

Rain Gauge 9 22.43 1.056 0.036 4.5%

average/mean deviations 23.49 0.507 0.017 2.2%

Variance 0.36

Table 3.5 Yearly West Wind Precipitation Totals and Variations Rain Gauge Precipitation

(in) abs dev per

year abs dev per storm % Variation

per storm size Rain Gauge 1

1.78 0.006 0.001 0.6% Rain Gauge 2

1.78 0.006 0.001 1.2% Rain Gauge 3

1.73 0.056 0.006 3.3% Rain Gauge 4

1.76 0.026 0.003 3.2% Rain Gauge 5

2.01 0.224 0.025 1.9% Rain Gauge 6

1.74 0.046 0.005 1.9% Rain Gauge 7

1.87 0.084 0.009 2.9% Rain Gauge 8

1.74 0.046 0.005 0.1% Rain Gauge 9

1.66 0.126 0.014 4.5% average/mean deviations

1.79 0.069 0.008 2.2% Variance

0.01

27

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 21st 2013- 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

18.76 inches based on 15 events. Maximum and minimum values ranged from 19.67 to 17.99

located at Gauge 7 and Gauge 3 respectively. For winds ranging from 21 to 30 mph the average

value was 11.42 inches based on 16 events. Maximum and minimum values were 12.0 and 10.71

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.

28

Figure 3.5 Yearly Precipitation Totals by Wind Speed

Table 3.6 Yearly Precipitation Totals and Variations for Winds <10 mph Rain Gauge Precipitation

(in) abs dev per

year abs dev per

storm % Variation per

storm size Rain Gauge 1 6.92 0.017 0.001 0.2%

Rain Gauge 2 6.96 0.023 0.001 0.3%

Rain Gauge 3 6.82 0.117 0.005 1.7%

Rain Gauge 4 7.20 0.263 0.011 3.8%

Rain Gauge 5 7.15 0.213 0.009 3.1%

Rain Gauge 6 6.74 0.197 0.009 2.8%

Rain Gauge 7 7.04 0.103 0.004 1.5%

Rain Gauge 8 6.98 0.043 0.002 0.6%

Rain Gauge 9 6.62 0.317 0.014 4.6%

average/mean deviations

6.94 0.144 0.006 2.1%

Variance 0.03

29

Table 3.7 Yearly Precipitation Totals and Variations for Winds 10 to 20 mph Rain Gauge Precipitation

(in) abs dev per year

abs dev per storm

% Variation per storm size

Rain Gauge 1 5.70 0.094 0.006 1.7%

Rain Gauge 2 5.68 0.074 0.005 1.3%

Rain Gauge 3 5.43 0.176 0.012 3.1%

Rain Gauge 4 5.81 0.204 0.014 3.6%

Rain Gauge 5 5.68 0.074 0.005 1.3%

Rain Gauge 6 5.35 0.256 0.017 4.6%

Rain Gauge 7 5.71 0.104 0.007 1.9%

Rain Gauge 8 5.63 0.024 0.002 0.4%

Rain Gauge 9 5.46 0.146 0.010 2.6%

average/mean deviations 5.61 0.128 0.009 2.3%

Variance 0.02

Table 3.8 Yearly Precipitation Totals and Variations for Winds 21 to 30 mph Rain Gauge Precipitation

(in) abs dev per year

abs dev per storm

% Variation per storm size

Rain Gauge 1 11.47 0.048 0.003 0.4%

Rain Gauge 2 11.45 0.028 0.002 0.2%

Rain Gauge 3 11.35 0.072 0.005 0.6%

Rain Gauge 4 11.85 0.428 0.027 3.7%

Rain Gauge 5 11.31 0.112 0.007 1.0%

Rain Gauge 6 10.71 0.712 0.045 6.2%

Rain Gauge 7 12.00 0.578 0.036 5.1%

Rain Gauge 8 11.66 0.238 0.015 2.1%

Rain Gauge 9 11.00 0.422 0.026 3.7%

average/mean deviations 11.42 0.293 0.018 2.6%

Variance 0.14

30

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 1 18.85 0.088 0.006 0.5%

Rain Gauge 2 19.05 0.288 0.019 1.5%

Rain Gauge 3 17.99 0.772 0.051 4.1%

Rain Gauge 4 19.50 0.738 0.049 3.9%

Rain Gauge 5 18.87 0.108 0.007 0.6%

Rain Gauge 6 18.10 0.662 0.044 3.5%

Rain Gauge 7 19.67 0.908 0.061 4.8%

Rain Gauge 8 18.82 0.058 0.004 0.3%

Rain Gauge 9 18.01 0.752 0.050 4.0%

average/mean deviations 18.76 0.486 0.032 2.6%

Variance 0.34

3.4 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 21st 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

29.03 inches based on 49 events. Maximum and minimum values ranged from 30.00 and 28.2

inches located at Gauge 5 and Gauge 3 respectively. For frozen precipitation the average value

was 14.21 inches for 21 events and values ranged from 15.48 to 12.86 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 3rd

least of frozen precipitation. This main variation seems to be a

function of precipitation type and wind direction. When examining individual storms Gauge 5

31

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.

Figure 3.6 Yearly Liquid and Frozen Precipitation Totals

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

Gauge 1 Gauge 2 Gauge 3 Gauge 4 Gauge 5 Gauge 6 Gauge 7 Gauge 8 Gauge 9

Pre

cip

itat

ion

(in

)

Liquid and Frozen Precipitation Totals

Frozen

Liquid

32

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 1 14.51 0.301 0.014 2.1%

Rain Gauge 2 15.09 0.881 0.042 6.2%

Rain Gauge 3 14.17 0.039 0.002 0.3%

Rain Gauge 4 15.23 1.021 0.049 7.2%

Rain Gauge 5 13.33 0.879 0.042 6.2%

Rain Gauge 6 12.86 1.349 0.064 9.5%

Rain Gauge 7 15.48 1.271 0.061 8.9%

Rain Gauge 8 14.24 0.031 0.001 0.2%

Rain Gauge 9 12.97 1.239 0.059 8.7%

average/mean deviations 14.21 0.779 0.037 5.5%

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 1 29.0 0.077 0.002 0.3%

Rain Gauge 2 28.8 0.207 0.004 0.7%

Rain Gauge 3 27.9 1.157 0.024 4.0%

Rain Gauge 4 29.8 0.783 0.016 2.6%

Rain Gauge 5 30.0 0.973 0.020 3.4%

Rain Gauge 6 28.2 0.827 0.017 2.8%

Rain Gauge 7 29.8 0.773 0.016 2.7%

Rain Gauge 8 29.4 0.343 0.007 1.2%

Rain Gauge 9 28.4 0.607 0.076 12.5%

average/mean deviations 29.0 0.639 0.013 2.2%

Variance 0.52

33

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 21st 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 11.79 to 10.69 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.

34

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 3.15. 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

35

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 1 2.38 0.0085 0.0003 0.4%

Rain Gauge 2 2.36 0.0000 0.0000 0.0%

Rain Gauge 3 2.14 0.0932 0.0033 4.0%

Rain Gauge 4 2.46 0.0424 0.0015 1.7%

Rain Gauge 5 2.63 0.1144 0.0041 4.8%

Rain Gauge 6 2.18 0.0763 0.0027 3.2%

Rain Gauge 7 2.50 0.0593 0.0021 2.5%

Rain Gauge 8 2.30 0.0254 0.0009 1.1%

Rain Gauge 9 2.29 0.0297 0.0011 1.3%

average/mean deviations 2.36 0.0499 0.0018 2.1%

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 1 5.70 0.018 0.001 0.3%

Rain Gauge 2 6.09 0.408 0.025 7.2%

Rain Gauge 3 5.39 0.292 0.018 5.1%

Rain Gauge 4 6.20 0.518 0.032 9.1%

Rain Gauge 5 5.33 0.352 0.022 6.2%

Rain Gauge 6 5.17 0.512 0.032 9.0%

Rain Gauge 7 6.38 0.698 0.044 12.3%

Rain Gauge 8 5.67 0.012 0.001 0.2%

Rain Gauge 9 5.21 0.472 0.030 8.3%

average/mean deviations 5.68 0.365 0.023 6.4%

Variance 0.18

36

Table 3.14 Yearly Precipitation Totals based on Average Storm Size 0.51” to 1.00” Rain Gauge Precipitation

(in) abs dev per year

abs dev per storm

% Variation per storm size

Rain Gauge 1 11.34 0.066 0.0044 0.58%

Rain Gauge 2 11.41 0.136 0.0090 1.20%

Rain Gauge 3 11.08 0.194 0.0130 1.72%

Rain Gauge 4 11.72 0.446 0.0297 3.95%

Rain Gauge 5 11.28 0.006 0.0004 0.05%

Rain Gauge 6 10.69 0.584 0.0390 5.18%

Rain Gauge 7 11.79 0.516 0.0344 4.57%

Rain Gauge 8 11.34 0.066 0.0044 0.58%

Rain Gauge 9 10.82 0.454 0.0303 4.03%

average/mean deviations 11.27 0.274 0.0183 2.43%

Variance 0.12

Table 3.15 Yearly Precipitation Totals based on Average Storm Size > 1.00" Rain Gauge Precipitation

(in) abs dev per year

abs dev per storm

% Variation per storm size

Rain Gauge 1 23.7 0.113 0.009 0.5%

Rain Gauge 2 23.7 0.083 0.006 0.4%

Rain Gauge 3 23.1 0.527 0.041 2.2%

Rain Gauge 4 24.3 0.703 0.054 3.0%

Rain Gauge 5 23.8 0.163 0.013 0.7%

Rain Gauge 6 22.8 0.767 0.059 3.2%

Rain Gauge 7 24.3 0.663 0.051 2.8%

Rain Gauge 8 24.0 0.363 0.028 1.5%

Rain Gauge 9 22.8 0.797 0.061 3.4%

average/mean deviations 23.6 0.464 0.036 2.0%

Variance 0.3

37

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 21st 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 17.13 inches. Maximum and minimum values ranged from 18.12 to 16.6 inches at

Gauge 5 and Gauge 3. For the cold season the average value recorded was 26.06 inches of

precipitation. Maximum and minimum values ranged from 27.33 to 24.37 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.

38

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 3.17. 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 (3rd

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.

0.00

5.00

10.00

15.00

20.00

25.00

30.00

Gauge 1 Gauge 2 Gauge 3 Gauge 4 Gauge 5 Gauge 6 Gauge 7 Gauge 8 Gauge 9

Pre

cip

itat

ion

(in

) Warm Season vs. Cold Season

WarmSeason

ColdSeason

39

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 (3rd

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 1 17.28 0.048889 0.00188034 0.3%

Rain Gauge 2 17.12 0.208889 0.00803419 1.2%

Rain Gauge 3 16.60 0.728889 0.02803419 4.2%

Rain Gauge 4 17.79 0.461111 0.01707819 2.6%

Rain Gauge 5 18.12 0.791111 0.03042735 4.6%

Rain Gauge 6 16.69 0.638889 0.02457265 3.7%

Rain Gauge 7 17.95 0.621111 0.02388889 3.6%

Rain Gauge 8 17.47 0.141111 0.00542735 0.8%

Rain Gauge 9 16.94 0.388889 0.01495726 2.2%

average/mean deviations 17.33 0.447654 0.01714449 2.6%

Variance 0.24

40

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 1 26.18 0.123 0.003 0.5%

Rain Gauge 2 26.79 0.733 0.017 2.8%

Rain Gauge 3 26.79 0.733 0.017 2.8%

Rain Gauge 4 27.25 1.193 0.028 4.6%

Rain Gauge 5 25.21 0.847 0.020 3.2%

Rain Gauge 6 24.37 1.687 0.039 6.5%

Rain Gauge 7 27.33 1.273 0.030 4.9%

Rain Gauge 8 26.14 0.083 0.002 0.3%

Rain Gauge 9 24.45 1.607 0.037 6.2%

average/mean deviations 26.06 0.920 0.021 3.5%

Variance 1.03

41

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%

42

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 0.3km2. 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 3.18.

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

43

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 1 2.16 2.33

Gauge 2 1.84 2.10

Gauge 3 2.00 2.10

Gauge 4 1.92 2.48

Gauge 5 1.92 2.50

Gauge 6 3.04 2.18

Gauge 7 1.60 2.44

Gauge 8 1.76 2.46

Gauge 9 1.84 2.37

Average 2.22 2.32

44

Figure 3.9 Yearly Rainfall Contours

45

Figure 3.10 Cold Season Rainfall Contours

46

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

47

3.8 Potential Groundwater Recharge

When assessing the potential differences between rain gauge measurements it also

important to examine applications that utilize these measurements. In this section an examination

of the potential ground water recharge over the farm is examined using 4 different

measurements. These measurements will include the highest recorded gauge value (Gauge 7), the

lowest reported gauge value (Gauge 6), the central gauge value (Gauge 4), and the total predicted

value utilizing all rain gauges together.

The total predicted value utilizing all rain gauges was completed using ArcGIS. This

method computes the total volume of water accumulated over each cell over an area of 2964ft x

4594ft. This rectangular area was determined via the raster that was created during the IDW

method while incorporating all rain gauges. This rectangular area is the same area that was used

in Figure 3.9 and Figure 3.10. After each cell value has been obtained the sum of all the cells is

the total volume over the given area. For individual gauge measurements the area is multiplied

by the single gauge value to obtain the volume. The next step in assessing potential groundwater

recharge is to use the two known methods for assessing potential recharge. The two methods

which were referenced earlier demonstrate that the potential groundwater recharge may be

quantified by approximately 50% of the annual precipitation or 75% to 90% of the Cold Season

precipitation totals. It is important to note that the Cold Season is defined by this study as the 6

months from October through March, while the approximation of groundwater recharge is from

October 15th

to May 15th

. While the Cold Season values of October through March are more in

line with values obtained from the 50% of the annual precipitation, 90% of the Cold Season total

will be the metric used to account for this difference in time period. Table 3.19 shows the

predicted values from the 4 different measurements.

48

Table 3.19 Potential Groundwater Recharge based on Total and Cold Season Precipitation Measurement Total Volume

(ft3)

Total Season Recharge (in)

Cold Season Volume (ft

3)

Cold Season

Recharge (in)

All Gauges 4.89 x 107 ft3 21.6 2.95 x 10

7 ft3 23.5

Gauge 7 5.18 x 107 ft3 22.6 3.09 x 10

7 ft3 24.6

Gauge 4 5.09 x 107 ft3 22.5 3.08 x 10

7 ft3 24.5

Gauge 6 4.64 x 107 ft3 20.5 2.75 x 10

7 ft3 21.9

When comparing Figure 1.2 to the potential recharge seen through the total season and

cold season recharge estimates the values compare relatively well. Visual estimates from Figure

1.2 yield approximately long term average for the area of study to be around 22 to 24 inches.

This coincides well with the average values ranging from 20.5 to 22.6 for total season and 21.9

to 24.6 based on cold season recharge estimates. The difference in total volume and recharge

from the maximum value seen at Gauge 7 and the minimum value seen at Gauge 6 is

approximately 10% for the total season totals and 11% for the Cold Season. It is apparent that

values from all the gauges are a reasonable approximation for all gauges because it reduces the

uncertainties inherent in basing your totals on a single gauge. These uncertainties may result due

to variations seen due to gauge locations or meteorological parameters. It is also possible that a

central gauge could be a reasonable approximate for potential groundwater recharge given that

it’s value it very close to the high value while noting that the average could be skewed by

possibly potential bad gauge locations (Gauge 3 and 6).

49

Chapter IV: Discussion

4.1 Recommendations

As previously stated rain gauge provide point measurements of rainfall totals. When the

area being examined is sufficiently small in scale, placed in appropriate locations, and the

relative density is high rain gauge measurements do hold promise for accurate measurements.

However, this task is not easily accomplished in the real world. Obstructions commonly and do

occur especially as urbanization and population density increase. Even if the gauges are within

appropriate distances from obstructions they still may be close enough to cause eddies in the

wind (Legates and DeLiberty, 1993) which may cause different catch rates of precipitation,

especially during snow events. While snow generally accounts for a small fraction of

precipitation of the precipitation here on Long Island it is important to note that during a very

cold winter or Cold Season a greater variability in precipitation totals (Groisman and Legates

1994) and thus potential groundwater recharge may be observed. This variation was most

exhibited within Gauge 5, which showed a variation of 14% (13.33 to 15.48 inches) when

compared the highest gauge total for solid precipitation. This variation was minimized when

looking at the Cold Season (8%) and was completely absent when Gauge 5 recorded the highest

total for the Warm Season.

Despite the conclusion of this study which is to identify location as the main contributor

to the variation seen throughout the gauges one can not deny that meteorological conditions also

played a role. Unlike the study done by A Huff and J.C. Neill in 1957 percent variation did not

always decrease with increased average storm size. This was most likely due to the fact that most

of snow storms occurred within the storm size range of 0.25 to 0.50 inches and the

aforementioned study only examined precipitation in the form of rain. However like the study

50

conducted by Huff and Neill and as expected increasing wind speed increased the variation

between the gauges. The highest variability was for wind 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. Also in contrast to the paper by Huff in 1979 the variation in precipitation was greater in

the winter than the summer. This is most likely explained by two obvious factors. Air mass

thunderstorms and convective showers were not as prevalent during this period as years past

while snow totals and frozen precipitation was anomalously high. So while the results of this

study may vary it does suggest the importance of improving the accuracy of quantifying snow

fall totals.

Another valid concern is while the average value of all the gauges may help reduce the

uncertainties associated with a single or even a few gauge points it does not eliminate bad gauge

points. In this study there it was noted that Gauges 3, 6, and 9 all under reported when compared

to other gauges for all meteorological parameters. This furthers the notice that these points

suffered due to location. A more reasonable assumption of an average could be the elimination of

these points altogether. However, one must be prudent when making this distinction because if

only the Cold Season was examined Gauge 5 may have been excluded but this variation did not

exist during the Warm Season as previously stated. Overall however it has been suggested that

higher density gauge locations increase the overall accuracy of the measurements (Mishra,

2013). That is why this study recommends using multiple rain gauges to help minimize the

potential uncertainties when assessing precipitation amounts or potential groundwater recharge

due to not only the sporadic nature of showers and thunderstorms but to snow as well.

51

4.2 Further Applications

Future applications could consist of providing rain gauge networks in areas of varying

sizes and locations. Also when possible, equal spacing between the gauges can provide further

analysis of the disparity between measurements as a function of distance. The equal spacing can

provide the necessary rain gauge density requirement per given area. This could allow future

users to assess whether rain gauges are even the appropriate tool to use or whether radar may be

more appropriate. Another consideration could be the use of wind shields around the gauge to

reduce potential uncertainties related to wind.

Another potential application involves the emerging field of GIS. As previously stated

there are numerous interpolation methods that are used to assess the rain gauge estimates

between the gauge locations. While not directly analyzed it is known that different methods may

yield potentially significant results. The merits of each method could be analyzed by single storm

type (synoptic or mesoscale) or as part of a longer (monthly or seasonal) accumulation study.

This study could offer validity as to which method offers the most promise for a specific area of

study.

Lastly, for the importance of assessing potential groundwater recharge precipitation

estimates from the rain gauges could be compared to radar derived results. As suggested

previously (L. Zou, 2008) the prospect of quantifying potential groundwater recharge with radar

shows some promise. This is due to the fact that with the greater spatial and temporal resolution

of the radar when compared to the density of most rain gauge networks and overall costs. It is

also further enhanced by the potential promise in the dual polarization radars that have been

recently deployed nation-wide. This idea suggests a direct comparison between the two

technologies to assess the strengths and weaknesses of each under different meteorological

52

conditions. However the problem for Long Island is the lack of continuous data for the

approximately 15 gauges operated by the FAA (L. Zou, 2008). However, with the emergence and

possible incorporation of data from national community based rain gauge networks like

CoCoRaHS data from gauges may be able to be used solely or incorporated with radar

depending on the nature and requirements of future studies.

53

Bibliography

Ahrens, B. 2006, Distance in spatial interpolation of daily rain gauge data, Hydrol. Earth Syst.

Sci., 10, 197–208, http://www.hydrol-earth-syst-sci.net/10/197/2006/.

ArcGIS Resources 2014, ArcGIS Help:

http://resources.arcgis.com/en/help/main/10.1/index.html#//009z00000075000000

Bedient PB, Huber WC 1992, Hydrology and floodplain analysis, 2nd edn. Addison-Wesley,

Reading

Burrough PA, McDonnell RA 1998, Principles of geographical information systems, Oxford

University Press, Oxford

Busciolano, R., 2002, Water-Table and Potentiometric-Surface Altitudes of the Upper Glacial,

Magothy, and Lloyd Aquifers on Long Island, New York, U.S. Geological Survey, Water-

Resources Investigations Report 01-4165.

Busciolano, R., 2004, Statistical Analysis of Long-Term Hydrologic Records for selection of

Drought-Monitoring Sites on Long Island, New York, U.S. Geological Survey, Scientific

Investigations Report 2004-5152.

CoCoRaHS 2014, Training Slide Shows:

http://www.cocorahs.org/media/docs/CoCoRaHS_Training_8.1.pdf

Colle, B. A., Yuter, S. E., 2006, The Impact of Coastal Boundaries and Small Hills on the

Precipitation Distribution across Southern Connecticut and Long Island, New York.

MONTHLY WEATHER REVIEW. Vol (135). 933-954.

Eischeid, J., Pasteris, P., Diaz, H., Plantico, M., Lott, N., 2000, Creating a serially complete,

national daily time series of temperature and precipitation for the western United States.

Journal of Applied Meteorology 39, 1580–1591.

Goovaerts P., 2000, Geostatistical approaches for incorporating elevation into the spatial

interpolation of rainfall. J Hydrol 228:113–129.

Groisman, P. and Legates, D., 1994, The Accuracy of United States Precipitation Data. Bull.

Amer. Meteo. Society 75, 215-275.

Huff, F. A., 1979, Spatial and Temporal Precipitation in Illinois, Illinois State Water Survey,

Urbana, 17 pp.

Huff, F.A., and Neil, J.C., 1957, Illinois State Water Survey, Champaign, IL., 61p

Legates, D.R., 2000, Real-time calibration of radar precipitation estimates, Professional

Geographer 52 (2), 235–246.

54

Legates, D.R., and DeLiberty, T.L., 1993, Precipitation measurement biases in the United States,

Water Resources Bulletin 29 (5), 855–861.

Li J, Heap AD, 2008, Spatial interpolation methods: a review for environmental scientists,

Geoscience Australia, Record. Geoscience Australia, Canberra

Miller, J.F. and Frederick, R.H., 1969. The Precipitation regime of Long Island, New York, New

York Geological Survey Professional Paper 627-A

Mishra, A., Effect of Rain Gauge Density over the Accuracy of Rainfall: A case study over

Bangalore, India. Springer Plus 2013, 2:311.

Morin, E., W. F. Krajewski, D. C. Goodrich, X. Gao, and S. Sorooshian, 2003:

Estimating Rainfall Intensities from Weather Radar Data: The Scale-Dependency

Problem. J. Hydrometeor., 4, 782-797.

Peterson D. S., 1986. Groundwater Recharge Rates in Nassau and Suffolk Counties, New York,

U.S.Geological Survey. Water-Resources Investigations Report 86-4181

Robbins, S and Mary Anne Taylor, 1996, Cooperative development and application of a

groundwater modeling Suffolk County, New York.

Rudolf, B. and Rubel, F. 2005, Global precipitation, in: Observed Global Climate, edited by: M.

Hantel, Landolt-Bornstein: Numerical Data and Functional Relationships in Science and

Technology –New Series, Group 5: Geophysics, 6(A), 11.1–11.53, Springer, Berlin, 2005.

Sandsborg, J., 1969, Local Rainfall variations over small, flat, cultivated areas. Department of

Agricultural Hydrotechnics., Agricultural College of Sweden, 12p.

Smith, J.A., D. J. Seo, M. L. Baeck, and M. D. Hudlow, 1996: An intercomparison study

of NEXRAD precipitation estimates, Water Resour. Res., 32 (7), 2035–2045.

Steenhuis, T. S., Jackson, C.D., Kung, S.K. and Brutsaert, W., 1985, Measurement of

groundwater recharge on eastern Long Island, New York, U.S.A. Journal of Hydrology. 79

(1985) 145-169.

U.S. Environmental Protection Agency 2014, Nassau-Suffolk Aquifer System:

http://www.epa.gov/region02/water/aquifer/nasssuff/nassau.htm#I18

Wang, X., Xie, H., Sharif, H. and Zeitler, J., 2008, Validating NEXRAD MPE and Stage III

precipitation products for uniform rainfall on the upper Guadalupe river basin of the Texas

Hill Country. Journal of Hydrology Vol. 348(1-2): 73-86.

Winchell, M., H. V. Gupta, and S. Sorooshian, 1998: On the simulation of infiltration and

55

saturation-excess runoff using radar-based rainfall estimates: Effects of algorithm uncertainty

and pixel aggregation. Water Resour. Res., 34(10), 2655–2670.

Zhou, L., 2008, Annual precipitation pattern over Long Island based on Radar Data,

Department of Geosciences, Stony Brook University. Available from:

http://pbisotopes.ess.sunysb.edu/reports/lisha-zhou-thesis.pdf [Accessed 8 November 2014]

56

Appendix A: Monthly and Total Precipitation Results

Precipitation Totals (in)

Month Gauge 1 Gauge 2 Gauge 3 Gauge 4 Gauge 5 Gauge 6 Gauge 7 Gauge 8 Gauge 9

Oct-13 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

Nov-13 2.87 2.88 2.64 2.92 2.98 2.77 2.92 2.93 2.84

Dec-13 5.88 5.92 5.92 6.00 5.78 5.63 6.02 6.03 5.61

Jan-14 2.85 3.26 2.62 3.21 2.53 2.50 3.56 2.79 2.38

Feb-14 4.46 4.61 4.43 4.75 3.93 3.59 4.83 4.37 3.81

Mar-14 6.26 6.32 6.04 6.43 6.13 6.05 6.13 6.12 6.00

Apr-14 2.85 2.90 2.82 2.91 2.94 2.79 3.05 2.96 2.76

May-14 5.22 5.22 4.97 5.23 5.48 5.08 5.44 5.19 5.04

June-14 1.68 1.66 1.57 1.75 1.95 1.60 1.79 1.65 1.71

July-14 2.35 2.39 2.33 2.49 2.37 2.24 2.38 2.33 2.25

Aug-14 2.90 2.68 2.65 3.06 3.07 2.73 3.00 3.01 2.93

Sep-14 2.28 2.27 2.26 2.35 2.31 2.25 2.29 2.33 2.25

Oct-14 3.84 3.78 3.77 3.92 3.84 3.81 3.85 3.88 3.79

Total 43.46 43.91 42.04 45.04 43.33 41.06 45.28 43.61 41.39

Month Brookhaven Airport Islip Normals Islip Normals Brookhaven (1981-2010)

Oct-13 0.05 0.03 3.79 4.05

Nov-13 2.7 2.81 3.67 3.86

Dec-13 5.67 5.8 4.06 3.8

Jan-14 1.78 3.98 3.64 3.77

Feb-14 3.45 4.36 3.26 3.08

Mar-14 6.22 5.9 4.44 4.39

Apr-14 4.7 4.84 4.34 4.68

May-14 3.87 2.66 3.78 4.16

June-14 1.1 1.79 4.27 4.16

July-14 1.92 2.96 3.43 3.74

Aug-14 2.13 14.07 3.98 3.82

Sep-14 2.26 1.62 3.58 3.62

Oct-14 2.94 2.53 - -

Total 38.79 53.35 46.24 47.13

57

Appendix B: Precipitation Results of the 9 Gauges based on Meteorological Parameters

Name Latitude Longitude Total Precip North Wind East Wind South Wind West Wind

Gauge 1 40.828690 -72.920170 43.46 10.51 7.16 23.62 1.78

Gauge 2 40.827990 -72.919280 43.91 11.03 6.94 23.77 1.78

Gauge 3 40.825050 -72.920560 42.04 10.34 6.87 22.71 1.73

Gauge 4 40.827910 -72.924980 45.04 11.18 7.46 24.23 1.76

Gauge 5 40.828220 -72.929950 43.33 9.56 7.41 23.93 2.01

Gauge 6 40.819110 -72.919700 41.06 9.03 6.86 23.05 1.74

Gauge 7 40.831740 -72.926160 45.28 11.35 7.51 24.16 1.87

Gauge 8 40.825140 -72.925360 43.61 10.00 7.57 23.47 1.74

Gauge 9 40.830300 -72.921940 41.39 9.51 7.02 22.43 1.66

average

43.24 10.28 7.20 23.49 1.79

Name Latitude Longitude Precip < 0.25 Precip 0.25-0.50 Precip 0.51-1.0 Precip > 1.00 <10

Gauge 1 40.828690 -72.920170 2.38 5.7 11.34 23.71 6.92

Gauge 2 40.827990 -72.919280 2.36 6.09 11.41 23.68 6.96

Gauge 3 40.825050 -72.920560 2.14 5.39 11.08 23.07 6.82

Gauge 4 40.827910 -72.924980 2.46 6.2 11.72 24.3 7.2

Gauge 5 40.828220 -72.929950 2.63 5.33 11.28 23.76 7.15

Gauge 6 40.819110 -72.919700 2.18 5.17 10.69 22.83 6.74

Gauge 7 40.831740 -72.926160 2.5 6.38 11.79 24.26 7.04

Gauge 8 40.825140 -72.925360 2.3 5.67 11.34 23.96 6.98

Gauge 9 40.830300 -72.921940 2.29 5.21 10.82 22.8 6.62

average

2.36 5.68 11.27 23.60 6.94

58

Name Latitude Longitude 10-20 21-30 >30 Frozen Liquid

Gauge 1 40.828690 -72.920170 5.7 11.47 18.85 14.51 28.95

Gauge 2 40.827990 -72.919280 5.68 11.45 19.05 15.09 28.82

Gauge 3 40.825050 -72.920560 5.43 11.35 17.99 14.17 27.87

Gauge 4 40.827910 -72.924980 5.81 11.85 19.5 15.23 29.81

Gauge 5 40.828220 -72.929950 5.68 11.31 18.87 13.33 30

Gauge 6 40.819110 -72.919700 5.35 10.71 18.1 12.86 28.2

Gauge 7 40.831740 -72.926160 5.71 12 19.67 15.48 29.8

Gauge 8 40.825140 -72.925360 5.63 11.66 18.82 14.24 29.37

Gauge 9 40.830300 -72.921940 5.46 11 18.01 12.97 28.42

average

5.61 11.42 18.76 14.21 29.03

Name Latitude Longitude Warm Season Cold Season

Gauge 1 40.828690 -72.920170 17.28 26.18

Gauge 2 40.827990 -72.919280 17.12 26.79

Gauge 3 40.825050 -72.920560 16.6 26.79

Gauge 4 40.827910 -72.924980 17.79 27.25

Gauge 5 40.828220 -72.929950 18.12 25.21

Gauge 6 40.819110 -72.919700 16.69 24.37

Gauge 7 40.831740 -72.926160 17.95 27.33

Gauge 8 40.825140 -72.925360 17.47 26.14

Gauge 9 40.830300 -72.921940 16.94 24.45

average

17.33 26.06