### APPENDIX 7-D. WEATHER DATA AND TEMPERATURE PARAMETERS

TABLE OF CONTENTS

7-D.1 INTRODUCTION .................................................................................................7-D-1 7-D.2 OUTDOOR TEMPERATURE DERIVATION ....................................................7-D-1 7-D.2.2 Imputation Method...............................................................................7-D-3 7-D.3 INLET WATER TEMPERATURE.....................................................................7-D-10

LIST OF TABLES
Table 7-D.2.1 Imputation Results ...............................................................................7-D-4 Table 7-D.3.1 Calculation of Water Inlet Offset for municipal treatment plants .....7-D-11

LIST OF FIGURES
Figure 7-D.2.1 Heating Degree Days (HDD) vs. Mean Temperature..........................7-D-2 Figure 7-D.2.2 Cooling Degree Days (CDD) vs. Mean Temperature..........................7-D-2 Figure 7-D.2.3 Heating Degree Days (HDD) vs. Imputed Mean Temperature Values ..................................................................................................7-D-3 Figure 7-D.2.4 Cooling Degree Days (CDD) vs. Imputed Mean Temperature Values ..................................................................................................7-D-4

7-D-i

### APPENDIX 7-D. WEATHER DATA AND TEMPERATURE PARAMETERS

7-D.1 INTRODUCTION The temperatures for water heater thermostat setpoint, inlet water temperature, and temperature of the air surrounding the water heater are based on the average annual outdoor air temperature, for each sample household. The outdoor air temperature for the households in the water heater RECS sample is not directly available. Similarly, the inlet water temperature is not available for the sample. This appendix describes DOE’s approach to develop data for these two parameters.
7-D.2 OUTDOOR TEMPERATURE DERIVATION RECS 2001 provides data on heating and cooling degree-days but not air temperatures for each household in the sample. To derive the outdoor air temperatures for the households in the RECS sample, DOE developed an approach to assign a physical location to each RECS household. The following steps were performed:

1.DOE assembled weather data from 282 weather stations that provide annual average outdoor air temperatures.1, 2

### The annual average outdoor air temperatures represent 30­ year averages. DOE also gathered the heating and cooling degree-days at base temperature 65o

### F for year 2004 for these weather stations.3

### The 2004 heating and cooling degree days match the period used to determine the degree-days in RECS 2001. 2.RECS reports both heating degree-days (HDD) and cooling degree-days (CDD) to base temperature 65o

### F for each housing record. DOE assigned each RECS household to one of the 282 weather stations by calculating which weather station (within the appropriate census region or large state) gave the best linear least squares fit of the RECS data to the weather data.
Figure 7-D.2.1 and Figure 7-D.2.2 show the relationship between the gathered mean temperature values and the HDD values.

7-D-1Mean Temperature (Fo)Mean Temperature (Fo)90

80

70
y = -0.0037x + 72.143 60 R2= 0.969

50

40

30

20

10

00 5000 10000 15000 20000 Heating Degree Days (HDD) 

### Figure 7-D.2.1 Heating Degree Days (HDD) vs. Mean Temperature

100

90

80

70

60

50

40 y = 0.0086x + 42.77 30 2 R = 0.7942 20

10

00 1000 2000 3000 4000 5000 Cooling Degree Days (CDD) 

### Figure 7-D.2.2 Cooling Degree Days (CDD) vs. Mean Temperature

7-D-2 6000

### 7-D.2.2 Imputation Method To calculate the mean outdoor air temperature, DOE matched the RECS 2001 combinations (255 individual combinations of census divisions plus 4 large states, together with the HDD and CDD data) to U.S. weather data. DOE used the U.S. weather station closest (or with minimum “distance”) from the RECS 2001 data combination. The following equation calculates the “distance” between the U.S. weather data and RECS 2001 data:



## "Distance"=(HDD2

## −HDD1

## )2

## +(CDD2

## −CDD1

## )2



### Where:

HDD1 

### = heating degree days from U.S. weather data, HDD2 

### = heating degree days from RECS 2001 data, CDD1 

### = cooling degree days from U.S. weather data, and CDD2 

### = cooling degree days from RECS 2001 data.
Figure 7-D.2.3 and Figure 7-D.2.4 show the relationship between the imputed mean outdoor air temperature values and the RECS 2001 HDD values.

y = -0.0038x + 72.163 R2= 0.948

0 10 20 30 40 50 60 70 80 90

0 2000 4000 6000 8000 10000 12000 HDD (days)Mean Temperature (Fo)



### Figure 7-D.2.3 Heating Degree Days (HDD) vs. Imputed Mean Temperature Values

7-D-3Mean Temperature (Fo)90

80

70

60

50

40

30

20

10

00 1000 2000 3000 4000 5000 6000 CDD (days) y = 0.0072x + 46.626 R = 0.7486

2



### Figure 7-D.2.4	 Cooling Degree Days (CDD) vs. Imputed Mean Temperature Values
Table 7-D.2.1 shows the imputation results for all RECS locations. Note that some weather station data matches with several of the RECS 2001 255 HDD & CDD combinations.
Table 7-D.2.1	 Imputation Results

RECS Data Combinations Weather Station Data Divisio

n + 4 HDD CDD Station Location Station ID Mean Temp HDD (2004) CDD (2004) 1 5053 903 BRIDGEPORT, CT 52.1 5358 808 1 5247 820 BRIDGEPORT, CT 52.1 5358 808 1 5256 613 BRIDGEPORT, CT 52.1 5358 808 1 5312 777 BRIDGEPORT, CT 52.1 5358 808 1 5397 760 BRIDGEPORT, CT 52.1 5358 808 1 5415 753 BRIDGEPORT, CT 52.1 5358 808 1 5455 583 BRIDGEPORT, CT 52.1 5358 808 1 5456 887 BRIDGEPORT, CT 52.1 5358 808 1 5549 769 BRIDGEPORT, CT 52.1 5358 808 1 5791 781 BOSTON, MA 51.6 5770 626 1 5872 711 PROVIDENCE, RI 376698 51.1 5802 613 1 5914 696 PROVIDENCE, RI 376698 51.1 5802 613 1 5939 708 PROVIDENCE, RI 376698 51.1 5802 613 1 5978 671 HARTFORD, CT 50.2 6157 645 1 6183 761 HARTFORD, CT 50.2 6157 645 1 6433 590 HARTFORD, CT 50.2 6157 645 1 6822 447 WORCESTER, MA 47.2 6800 413 1 8072 416 BURLINGTON, VT 431081 45.2 7831 369

7-D-41 8948 191 HOULTON, ME 173944 39.9 9539 128 2 4148 1225 PHILADELPHIA, PA 55.3 4683 1261 2 4168 1404 PHILADELPHIA, PA 55.3 4683 1261 2 4398 1271 PHILADELPHIA, PA 55.3 4683 1261 2 4484 1070 PHILADELPHIA, PA 55.3 4683 1261 2 4781 1045NEWARK, NJ 54.5 4904 1120 2 4807 1027NEWARK, NJ 54.5 4904 1120 2 4850 961 ATLANTIC CITY, NJ 280325 53.5 4990 993 2 4880 983 ATLANTIC CITY, NJ 280325 53.5 4990 993 2 4931 1083NEWARK, NJ 54.5 4904 1120 2 4940 1027 ATLANTIC CITY, NJ 280325 53.5 4990 993 2 5190 743 HARRISBURG, PA 363699 53.3 5101 917 2 5235 801 HARRISBURG, PA 363699 53.3 5101 917 2 5263 704 PITTSBURGH, PA 50.9 5510 650 2 5289 881 HARRISBURG, PA 363699 53.3 5101 917 2 5395 719 PITTSBURGH, PA 50.9 5510 650 2 5508 596 PITTSBURGH, PA 50.9 5510 650 2 5560 714 ALLENTOWN, PA 360106 50.6 5624 758 2 5573 589 PITTSBURGH, PA 50.9 5510 650 2 5631 714 ALLENTOWN, PA 360106 50.6 5624 758 2 5651 722 ALLENTOWN, PA 360106 50.6 5624 758 2 5826 608WILLIAMSPORT, PA 369728 49.8 5909 657 2 5831 515WILLIAMSPORT, PA 369728 49.8 5909 657 2 6340 377ERIE, PA 362682 50 6145 486 3 4683 1411EVANSVILLE, IN 56 4252 1265 3 4752 974 CINCINNATI, OH 54.2 4882 926 3 4806 1231 CINCINNATI, OH 54.2 4882 926 3 4824 1048 CINCINNATI, OH 54.2 4882 926 3 4889 1083 CINCINNATI, OH 54.2 4882 926 3 5046 1149 INDIANAPOLIS, IN 52.5 5093 938 3 5080 902 INDIANAPOLIS, IN 52.5 5093 938 3 5207 847 COLUMBUS, OH 52.9 5230 884 3 5297 740 COLUMBUS, OH 52.9 5230 884 3 5316 1154 COLUMBUS, OH 52.9 5230 884 3 5409 1072 PEORIA, IL 50.8 5446 824 3 5501 696 DAYTON, OH 51.5 5534 755 3 5513 740 DAYTON, OH 51.5 5534 755 3 5530 1064 PEORIA, IL 50.8 5446 824 3 5590 715 DAYTON, OH 51.5 5534 755 3 5598 697 DAYTON, OH 51.5 5534 755 3 5637 863MOLINE, IL 50.2 5722 816 3 5641 973MOLINE, IL 50.2 5722 816 3 5725 606FINDLAY, OH 332791 50.2 5880 624 3 5777 802MOLINE, IL 50.2 5722 816 3 5800 735MOLINE, IL 50.2 5722 816 3 5855 931MOLINE, IL 50.2 5722 816

7-D-53 5873 673FINDLAY, OH 332791 50.2 5880 624 3 5936 849MOLINE, IL 50.2 5722 816 3 6019 537 FORT WAYNE, IN 49.9 5994 598 3 6041 746 TOLEDO, OH 49.5 6050 646 3 6139 516 AKRON CANTON, OH 49.5 6144 500 3 6215 620 SOUTH BEND, IN 49.5 6120 642 3 6633 729 LACROSSE, WI 47.3 6810 580 3 6796 713LACROSSE, WI 47.3 6810 580 3 6877 693LACROSSE, WI 47.3 6810 580 3 6932 642 LACROSSE, WI 47.3 6810 580 3 7038 567MADISON, WI 46.1 6926 451 3 7076 722LACROSSE, WI 47.3 6810 580 3 7137 546 MADISON, WI 46.1 6926 451 3 7526 653 GREEN BAY, WI 44.4 7538 333 3 7927 590 HOUGHTON LAKE, MI 43.1 8167 192 4 4174 1419 SAINT LOUIS, MO 56.3 4135 1526 4 4198 1573WICHITA, KS 56.4 4216 1548 4 4249 1713WICHITA, KS 56.4 4216 1548 4 4404 1377 DODGE CITY, KS 55.2 4639 1290 4 4422 1545WICHITA, KS 56.4 4216 1548 4 4545 1637WICHITA, KS 56.4 4216 1548 4 4665 1294 DODGE CITY, KS 55.2 4639 1290 4 4720 1405 DODGE CITY, KS 55.2 4639 1290 4 4873 1114 KANSAS CITY, MO 54.2 4748 1122 4 4984 1270 CONCORDIA, KS 53.5 5022 1230 4 5106 1297 CONCORDIA, KS 53.5 5022 1230 4 5771 980 OMAHA, NE 50.7 5796 923 4 6456 940 SIOUX CITY, IA 48.3 6404 739 4 6727 711 PIERRE, SD 396597 47.5 6779 689 4 7153 922MINNEAPOLIS, MN 215435 45.4 7266 579 4 7506 647 MINNEAPOLIS, MN 215435 45.4 7266 579 4 7574 708MINNEAPOLIS, MN 215435 45.4 7266 579 4 7617 738MINNEAPOLIS, MN 215435 45.4 7266 579 4 7808 654WATERTOWN, SD 398932 42.8 8008 347 4 7988 777WATERTOWN, SD 398932 42.8 8008 347 4 8122 711 BISMARCK, ND 42.3 8183 381 5 2164 1936 CAPE HATTERAS, NC 311458 62.8 2573 1958 5 2251 1902 CAPE HATTERAS, NC 311458 62.8 2573 1958 5 2474 1669 ATLANTA, GA 62.1 2611 1846 5 2554 1535 ATLANTA, GA 62.1 2611 1846 5 2592 1648 ATLANTA, GA 62.1 2611 1846 5 2672 1770 ATLANTA, GA 62.1 2611 1846 5 2742 1669 ATHENS, GA 61.5 2766 1846 5 2932 1643 GREENVILLE, SC 383747 60 3007 1599 5 2957 1555 GREENVILLE, SC 383747 60 3007 1599 5 2971 1367 GREENVILLE, SC 383747 60 3007 1599

7-D-65 2992 1352 GREENVILLE, SC 383747 60 3007 1599 5 2997 1330 GREENVILLE, SC 383747 60 3007 1599 5 3047 1420 GREENVILLE, SC 383747 60 3007 1599 5 3100 1479 GREENVILLE, SC 383747 60 3007 1599 5 3118 1497 GREENVILLE, SC 383747 60 3007 1599 5 3163 1536 CHARLOTTE, NC 61.4 3258 1568 5 3175 1422 CHARLOTTE, NC 61.4 3258 1568 5 3213 1414 CHARLOTTE, NC 61.4 3258 1568 5 3222 1290 CHARLOTTE, NC 61.4 3258 1568 5 3241 1463 CHARLOTTE, NC 61.4 3258 1568 5 3377 1356 CHARLOTTE, NC 61.4 3258 1568 5 3687 1424 GREENSBORO, NC 58.1 3578 1523 5 3885 1301 ROANOKE, VA 56.3 3825 1269 5 4068 1139 HUNTINGTON, WV 55 4181 1087 5 4095 1025 HUNTINGTON, WV 55 4181 1087 5 4263 1125 CHARLESTON, WV 54.5 4252 1044 5 4364 1105LYNCHBURG, VA 55.4 4276 1021 5 4394 1072LYNCHBURG, VA 55.4 4276 1021 5 4454 1030 LYNCHBURG, VA 55.4 4276 1021 5 4520 837 SALISBURY, MD 188000 56.6 4698 1055 5 4591 1133 BALTIMORE, MD 180470 54.6 4663 1219 6 1291 2716MOBILE, AL 66.8 1582 2807 6 2384 1955 BIRMINGHAM, AL 62.2 2504 2047 6 2565 1833 BIRMINGHAM, AL 62.2 2504 2047 6 2620 1797 TUPELO, MS 61.3 2790 1863 6 2760 1907 TUPELO, MS 61.3 2790 1863 6 3424 1430 KNOXVILLE, TN 58.4 3416 1440 6 3568 1206 BOWLING GREEN, KY 150909 57.2 3818 1298 6 3649 1315 BOWLING GREEN, KY 150909 57.2 3818 1298 6 3836 1626LOUISVILLE, KY 56.9 3862 1580 6 3851 1074JACKSON, KY 55.9 3874 1008 6 4047 1269 PADUCAH, KY 56.8 4142 1178 6 4193 981 BRISTOL, TN 54.9 4040 966 7 1437 2704 BATON ROUGE, LA 160549 67 1560 2858 7 1562 2502 BATON ROUGE, LA 160549 67 1560 2858 7 1718 2604 SHREVEPORT, LA 65.7 1939 2598 7 1816 2578 SHREVEPORT, LA 65.7 1939 2598 7 2629 2490FORT SMITH, AR 61.2 2886 1935 7 2915 2115FORT SMITH, AR 61.2 2886 1935 7 3287 2078 HOBART, OK 344204 59.8 3075 2022 7 3348 2374 HOBART, OK 344204 59.8 3075 2022 8 1093 5161 PHOENIX, AZ 74.2 963 4771 8 1212 4612 PHOENIX, AZ 74.2 963 4771 8 1615 3949LAS VEGAS, NV 68.1 1975 3755 8 1692 3509 TUCSON, AZ 28815 68.7 1586 3283 8 2277 2067 DOUGLAS, AZ 22659 61.5 2514 1808

7-D-78 2686 2255 CARLSBAD, NM 291469 62.8 2778 1948 8 3266 1911 ROSWELL, NM 297610 60.8 3188 1718 8 5356 1018 BOISE, ID 51.9 5227 1008 8 5447 1109 GRAND JUNCTION, CO 51.8 5640 1166 8 5566 905LOVELOCK, NV 264698 50.1 5428 785 8 5576 663LOVELOCK, NV 264698 50.1 5428 785 8 5675 912 SALT LAKE CITY, UT 52 5693 1102 8 6022 789WINNEMUCCA, NV 269171 49.3 6057 606 8 6074 592WINNEMUCCA, NV 269171 49.3 6057 606 8 6262 703WINNEMUCCA, NV 269171 49.3 6057 606 8 6583 426 CHEYENNE, WY 481675 44.9 6603 260 8 6781 1037MILES CITY, MT 245690 46.3 7143 546 8 7075 859 MILES CITY, MT 245690 46.3 7143 546 8 7134 431 HELENA, MT 244055 44 7146 341 8 7205 235 HELENA, MT 244055 44 7146 341 8 7379 298 GREAT FALLS, MT 243751 43.7 7407 240 8 7418 346WORLAND, WY 489770 45.4 7414 366 8 7419 790MILES CITY, MT 245690 46.3 7143 546 8 7594 538LANDER, WY 45 7509 311 9 0 4912 HONOLULU-OAHU, HI 77.5 0 5130 9 4195 357 SALEM, OR 52.6 4320 396 9 4670 106 ASTORIA, OR 350328 51 4383 36 9 4993 71 BELLINGHAM, WA 450587 50.8 5037 94 9 5821 35 YAKIMA, WA 48.9 5568 644 9 5897 531 YAKIMA, WA 48.9 5568 644 9 6860 357 BAKER, OR 350412 45.4 7193 193 9 10045 0 ST PAUL ISLAND, AK 35 9926 0 10 4242 1251NEW YORK, NY 305801 55.1 4730 1180 10 4298 1216NEW YORK, NY 305801 55.1 4730 1180 10 4575 1029NEW YORK, NY 305801 55.1 4730 1180 10 5947 789 BUFFALO, NY 301012 47.9 6641 401 10 5957 638 BUFFALO, NY 301012 47.9 6641 401 10 6191 684 BUFFALO, NY 301012 47.9 6641 401 10 6217 653 BUFFALO, NY 301012 47.9 6641 401 10 6243 571 BUFFALO, NY 301012 47.9 6641 401 10 6820 325 ROCHESTER, NY 307167 47.6 6761 355 10 6932 400 SYRACUSE, NY 308383 47.4 6781 461 10 7675 145WATERTOWN, NY 309000 45.7 7960 253 11 1258 1314 SAN DIEGO, CA 64.4 997 975 11 1281 1058 SAN DIEGO, CA 64.4 997 975 11 1290 1050 SAN DIEGO, CA 64.4 997 975 11 1359 1704 SAN DIEGO, CA 64.4 997 975 11 1413 544 LOS ANGELES, CA 63.3 1239 734 11 1539 446LOS ANGELES, CA 63.3 1239 734 11 1576 960LOS ANGELES, CA 63.3 1239 734 11 1669 311LOS ANGELES, CA 63.3 1239 734

7-D-811 1718 3745 BLYTHE, CA 40924 71.3 1064 4444 11 1730 646LOS ANGELES, CA 63.3 1239 734 11 1734 1137LOS ANGELES, CA 63.3 1239 734 11 1737 1066LOS ANGELES, CA 63.3 1239 734 11 1849 2041 BAKERSFIELD, CA 65 1993 2398 11 1912 1856FRESNO, CA 43257 63.2 2227 2243 11 1967 152 SAN FRANCISCO, CA 57.3 2282 275 11 1971 579 SAN FRANCISCO, CA 57.3 2282 275 11 2013 1787FRESNO, CA 43257 63.2 2227 2243 11 2050 1107 SACRAMENTO, CA 61.1 2358 1322 11 2094 1820FRESNO, CA 43257 63.2 2227 2243 11 2223 234 SAN FRANCISCO, CA 57.3 2282 275 11 2273 1528 STOCKTON, CA 61.8 2402 1356 11 2427 1522 STOCKTON, CA 61.8 2402 1356 11 2443 828 PASO ROBLES, CA 46730 58.5 2687 942 11 2642 135 SAN FRANCISCO, CA 57.3 2282 275 11 2705 1676 STOCKTON, CA 61.8 2402 1356 11 2869 2256 REDDING, CA 47304 61.6 2450 2141 11 2935 290 SAN FRANCISCO, CA 57.3 2282 275 11 5228 310MT SHASTA, CA 45983 49.2 5299 299 11 5524 256MT SHASTA, CA 45983 49.2 5299 299 11 5968 463MT SHASTA, CA 45983 49.2 5299 299 11 6083 106MT SHASTA, CA 45983 49.2 5299 299 12 1189 3086 SAN ANTONIO, TX 417945 68.7 1278 3078 12 1342 2806 SAN ANTONIO, TX 417945 68.7 1278 3078 12 1376 2947 SAN ANTONIO, TX 417945 68.7 1278 3078 12 1542 2995 AUSTIN, TX 69 1633 2774 12 1545 2521 AUSTIN, TX 69 1633 2774 12 1568 2875 AUSTIN, TX 69 1633 2774 12 1791 2510WACO, TX 66.6 1827 2708 12 2022 2916 DALLAS FT WORTH, TX 65.5 1998 2654 12 2294 2642 DALLAS FT WORTH, TX 65.5 1998 2654 12 2340 2580EL PASO, TX 412797 64.7 2519 2344 12 2619 2574EL PASO, TX 412797 64.7 2519 2344 12 2852 2242 MIDLAND ODESSA, TX 63.4 2605 2163 12 3402 1937LUBBOCK, TX 59.7 3170 1699 12 4180 1622 AMARILLO, TX 57 3955 1269 12 4280 1828 AMARILLO, TX 57 3955 1269 13 83 4553FT LAUDERDALE, FL 83163 75.4 112 4528 13 138 4546FT LAUDERDALE, FL 83163 75.4 112 4528 13 224 4151WEST PALM BEACH, FL 75.3 189 4096 13 416 3458 VERO BEACH, FL 73.2 433 3420 13 421 3675 TAMPA, FL 73.1 517 3563 13 508 3665 TAMPA, FL 73.1 517 3563 13 516 3494 TAMPA, FL 73.1 517 3563 13 522 3308 ORLANDO, FL 72.8 536 3425 13 545 3294 ORLANDO, FL 72.8 536 3425 7-D-913 550 3661 TAMPA, FL 73.1 517 3563 13 849 2806 DAYTONA BEACH, FL 71 741 3087 13 992 2726 GAINESVILLE, FL 68.6 1237 2846 13 1010 2723 GAINESVILLE, FL 68.6 1237 2846 13 1061 2628 GAINESVILLE, FL 68.6 1237 2846 13 1480 2225 TALLAHASSEE, FL 88758 68 1661 2704


### 7-D.3 INLET WATER TEMPERATURE The daily hot water use and the water heater energy use depend on average inlet water temperature. The inlet water comes to the water heater either from a municipal treatment plant or from ground well sources. DOE determined the water source using RECS 2001, which provides information about the households in the sample that have a well. The households that don’t have a well presumably use water from the municipal treatment plants.
DOE derived the inlet water temperature using an approach developed by the National Renewable Energy Laboratory.4, 5



### +ratio∗⎛Δ ⎜ ⎜ ⎝ ,max

### ⎞ ⎟ ⎠

⎟∗ T 

# ( T 

# ) air 

### 2 sin

# (0.986

# (30 

# )90

# ) T

mains

### = offset ∗ ∗month#

−30

−lag

− + air,avg



### Where:

T

mains 

### = mains (supply) temperature to domestic hot water tank, °F, T

air,avg 

### = annual average outdoor air temperature, °F, ΔT air,max 

### = maximum difference between monthly average outdoor air temperatures (i.e.MAX(T air,month

### ) - MIN(T air,month

### )), °F, 0.986 = degrees/day (360/365), month# = number of month of the year (e.g. January = 1), offset = given by a distribution of values as shown below, °F, ratio = 0

.4+0

.01×(T amb,avg

### -44), and lag = 35

−1.0×(T amb,avg

### -44), °F.
DOE calculated the offset term separately for the households using municipal treatment plants and ground well sources.
For households with wells DOE calculated offset value using a triangular distribution with min value of 0, average value of 2, and maximum value of 4.
The municipal plants offset was calculated using typical long-term water temperature 6 

### average values for households with water coming from municipal treatment plants. as well as annual air temperature data1, 2

### and population by state7

### for 75 cities. The difference between these two averages was then calculated. The average population weighted average offset was

7-D-10

### calculated to be on average 3.72 with a standard deviation of 5.94. These values where used in the LCC spreadsheet to create a normal distribution for the offset. (See Table 7-D.3.1).


### Table 7-D.3.1 Calculation of Water Inlet Offset for municipal treatment plants

Site Average Water Inlet Temp(°F) Average Annual Air Temp(°F) Difference (Offset), Temp(°F) Population by State (Census

2007 Est) Anchorage, AK 38.6 36.2 2.4 683,478 Birmingham, AL 71.7 62.2 9.5 4,627,851 Montgomery, AL 66.4 65 1.4 Little Rock, AR 63.9 62.1 1.8 2,834,797 Phoenix, AZ 82.3 72.8 9.5 6,338,755 Los Angeles, CA 72.8 63.3 9.5 36,553,215 San Diego, CA 76.2 64.4 11.8 San Francisco, CA 67.7 57.3 10.4 Denver, CO 61.3 50.1 11.2 4,861,515 Hartford, CT 56.6 50.2 6.4 3,502,309 Washington, DC 63.9 57.5 6.4 588,292 Dover, DE 61.9 56.8 5.1 864,764 864,764 Wilmington, DE 59.6 54.4 5.2 Miami, FL 75 76.7 -1.7 18,251,243 Tallahassee, FL 76.7 68 8.7 Atlanta, GA 62 62.1 -0.1 9,544,750 Savannah, GA 68.1 66.2 1.9 Honolulu, HI 76.8 77.5 -0.7 1,283,388 Des Moines, IA 60.3 50 10.3 2,988,046 Boise, ID 45.3 51.9 -6.6 1,499,402 Chicago, IL 53.9 49.1 4.8 12,852,548 Indianapolis, IN 49 52.5 -3.5 6,345,289 Topeka, KS 59 54.3 4.7 2,775,997 Louisville, KY 56.3 56.9 -0.6 4,241,474 New Orleans, LA 64.9 68.8 -3.9 4,293,204 Boston, MA 59.3 51.6 7.7 6,449,755 Baltimore, MD 56.8 54.6 2.2 5,618,344 Portland, ME 63.5 45.7 17.8 1,317,207 Detroit, MI 49.9 49.7 0.2 10,071,822 Minneapolis, MN 45.8 45.4 0.4 5,197,621 Kansas City, MO 51.1 54.2 -3.1 5,878,415 St. Louis, MO 61.3 56.3 5 Biloxi, MS 64.9 67.4 -2.5 2,918,785 Jackson, MS 67.8 64.1 3.7 Helena, MT 41.8 44 -2.2 957,861 Raliegh, NC 71.8 59.6 12.2 9,061,032 Bismark, ND 51 42.3 8.7 639,715 Lincoln, NE 53.5 51.1 2.4 1,774,571 Concord, NH 65.6 45.9 19.7 1,315,828 7-D-11Trenton, NJ 55.3 52.9 2.4 8,685,920 Albuquerque, NM 76.2 56.8 19.4 1,969,915 Carson City, NV 58 50.6 7.4 2,565,382 Albany, NY 51.5 47.5 4

19,297,729 Binghamton, NY 59.9 45.8 14.1 Buffalo, NY 49.2 47.9 1.3 Long Island, NY 53 52.4 0.6 New York, NY 57.6 55.1 2.5 Plattburgh, NY 50.6 44.5 6.1 Potsdam, NY 48.5 43.3 5.2 Poughkeepsie, NY 56.8 48.8 8 Rochester, NY 57 47.6 9.4 Rome, NY 51.3 46.6 4.7 Syracuse, NY 54.7 47.4 7.3 Watertown, NY 51.7 44.7 7 Columbus, OH 54.8 52.9 1.9 11,466,917 Oklahoma City, OK 58.8 60.1 -1.3 3,617,316 Portland, OR 51.6 53.5 -1.9 3,747,455 Philadelphia, PA 56 55.3 0.7 12,432,792 Pittsburgh, PA 58 50.9 7.1 Providence, RI 49.7 51.1 -1.4 1,057,832 Columbia, SC 59.2 63.6 -4.4 4,407,709 Sioux Falls, SD 55.3 45.1 10.2 796,214 Chattanooga, TN 67.7 60 7.7 6,156,719 Knoxville, TN 55 58.4 -3.4 Memphis, TN 55 62.3 -7.3 Dallas, TX 68.3 65.5 2.8 23,904,380 Houston, TX 65.9 70.5 -4.6 Salt Lake City, UT 70.5 52 18.5 2,645,330 Richmond, VA 59.1 57.6 1.5 7,712,091 Montpelier, VT 47.8 42.9 4.9 621,254 Seattle, WA 41.1 52.3 -11.2 6,468,424 Greenbay, WI 44.7 44.4 0.3 5,601,640 Milwaukee, WI 46 47.5 -1.5 Charleston, WV 62.8 54.5 8.3 1,812,035 Cheyenne, WY 51.7 44.9 6.8 522,830 Mean 3.72 Standard Deviation 5.94

7-D-12

### REFERENCES

1. National Oceanic and Atmospheric Administration,Normal Daily Mean Temperature, Deg F,2004. <http://lwf.ncdc.noaa.gov/oa/climate/online/ccd/meantemp.html>

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3. National Oceanic And Atmospheric Administration,Degree Days Statistics,2005.
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