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Using Future Storm Statistics from Climate Models to Determine Flood Potential in the 1 Lehigh Valley, PA in the 21st Century 2 3 B.S. Felzer* and C. E. Withers 4 5 Earth and Environmental Sciences, Lehigh University, 1 W. Packer Ave., Bethlehem, PA 18017, 6 USA 7 8 *Corresponding author. Tel: +1 610 758 3536; fax: +1 610 758 3677 9 E-mail addresses: bsf208@lehigh.edu (B.S. Felzer), cew214@lehigh.edu (C.E.Withers). 10 11 12 Current approaches to estimate flood inundation rely on historical climate statistics, but 13 increasing extreme precipitation in the northeast U.S. has made it clear that historical trends can 14 no longer be used for future projections. Consequently, to more realistically assess flooding 15 potential in the Lehigh Valley, PA, we have developed a method for obtaining future storm 16 statistics from climate model projections. We apply a bias correction to the future 100 year flood 17 and convert this value to a river discharge for the Monocacy Creek. Water surface elevation and 18 flood inundation are then determined using the HEC-RAS model. This approach shows that the 19 current 100 year storm event will occur every 44 - 81years in the future, with the future storm 20 resulting in 173 - 783 ft3 s-1 more discharge and causing additional 165,650 - 990,236 ft2 of area 21 to be flooded along the upper reach of the Monocacy. 22 23 Keywords: flooding, extreme precipitation, HEC-RAS, recurrence interval, RCP8.5, NCAR 24 CESM 25 26 27 1. Introduction 28 29 The damaging effects of intense rainfall leading to flooding have become more widely 30 recognized around the US, as a result of recent intense storms. Impacts were felt along the 31 entire East Coast due to Hurricane Sandy in 2012, where over 7” of rainfall were dumped in 32 some areas (Gutro, 2012). Massive flooding has even occurred inland, such as in Colorado in 33 2013, in which over nine inches of rainfall occurred in one day and was exacerbated by 34 petroleum product spills that resulted from damage to fracking wells that were built within the 35 floodplain (Smith, 2013). Such problems are destined to play an increasingly prominent role in 36 the political and policy debate as coastal and river floodplain populations grow under conditions 37 of rapid climate change. And yet, the tendency for policy to lag behind the production of data 38 that could make it “proactive” rather than “reactive” is clearly evident in the area of flood 39 management. The challenge, in other words, is to create policy that is grounded in science, 40 before the flooding damage occurs. 41 42 Specifically, estimates of flood inundation used to determine flood insurance rates are based on 43 historical climate. However even during the 20th century, historical flooding has generally 44 increased in the eastern U.S. (Kundzewicz et al., 2005). The National Climate Assessment states 45 that the northeast has seen the largest increase of intense storms (71%) of any region in the U.S. 46

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over the 20th century (Melillo et al., 2014), and Intergovernmental Panel on Climate Change 47 (IPCC) Fifth Assessment (AR5) projections show that in the mid-Atlantic region of the U.S., 48 warming is expected to drive an increase in the annual mean amount of precipitation and cause 49 more frequent intense storm events (IPCC, 2013). Increased winter precipitation and runoff are 50 projected (Felzer and Sahagian, 2014), and have the potential to lead to more flooding in specific 51 watersheds. We develop a new approach to scale high resolution temporal data from General 52 Circulation Models (GCMs) to hydrological models commonly used by water managers and the 53 Federal Emergency Management Agency (FEMA) when developing water surface elevation 54 estimates, flood inundation maps, and flood insurance rate maps. 55 56 Amongst the most common models used by water managers and FEMA are the Hydrologic 57 Engineering Center Hydrologic Modeling System (HEC-HMS) and (HEC River Analysis 58 System) HEC-RAS models, which are generally used in a coupled way. HEC-HMS 59 (Scharffenberg and Fleming, 2006) is a lumped model that utilizes watershed-scale parameters to 60 determine the peak streamflow through time, whereas HEC-RAS (Brunner, 2001) is a distributed 61 model that uses the stream flow to determine the water surface elevation along the length of a 62 stream. These models are used to determine flood inundation areas that FEMA uses to produce 63 flood insurance rate maps (Merwade et al., 2008). The discharge data providing input for HEC-64 RAS is most commonly based on historical storm statistics, and does not account for future 65 changes in climate (e.g. (Hicks and Peacock, 2005, Horritt and Bates, 2002, Knebl et al., 2005, 66 Michaud and Gumtow, 2006, Papakos and Kristi Root, 2010, Stonestreet and Hogan, 2004)). 67 Thus, flood management policy, as contemporarily designed, runs the risk of underestimating 68 future water-related risks. At no time has this risk been more ominous then under conditions of a 69 rapidly changing global climate. 70 71 Climate scientists have developed various approaches to account for future climate change, but 72 these data are not easily translated into a form that can be used in common hydrological 73 modeling tools. A problem commonly encountered is the climate scenario data from GCMs are 74 often only stored as monthly means, so some method of producing more highly resolved 75 temporal data is required. HEC-HMS and HEC-RAS have been forced with data from regional 76 climate models (RCMs) (Grillakis et al., 2011, Moradkhani et al., 2010), which require computer 77 time-intensive runs for particular regions. Bias correction is required even from RCMs to correct 78 output directly from climate models in order to use more realistic values for studying climate 79 impacts of future climate change. Other downscaling methods include using stochastic weather 80 generators to downscale from monthly to daily (Minville et al., 2008) or applying the simple 81 delta approach to temperature and precipitation (Forbes et al., 2011), which does not account for 82 changed variability. 83 84 In this study, we use 100 years of hourly and daily data directly from a GCM for the 20th and 21st 85 centuries to calculate the 100-year storm statistic, which we then use to predict how the storm 86 statistic will change in the future, and apply this ratio to the observed storm statistic to allow for 87 bias correction. These data, observed and bias-corrected future storms, are then used to derive 88 streamflow from a curve of rainfall vs. discharge frequency developed for the Monocacy 89 watershed in the Lehigh Valley, PA. The streamflow is then used as input to derive water 90 surface elevation and horizontal flood inundation using HEC-RAS, for a section of the 91 Monocacy that has infrastructure in the floodplain. This approach provides a new method of 92

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using future climate statistics to determine the flood inundation of flooding in an area that is 93 expected to be more prone to flooding in the future. This method accounts for the full 100 year 94 precipitation record from a GCM, without the need for temporal downscaling and without the 95 loss of changes in future variability. Given the general tendency of policy to evolve long after 96 the production of relevant scientific data, we propose this approach as a step toward creating 97 flood management policy that is more effective and proactive. However, given the preliminary 98 nature and considerable GCM bias, we discuss the approach as proof of concept, rather than a 99 particular assessment on the effect of increased flooding in this particular basin. 100 101 102 2. Material and Methods 103 104 The general approach (Figure 1) is to run the HEC-RAS model with the historical and future 105 discharge determined from the increase in the 100-year future storm event. HEC-RAS generates 106 water surface elevation and horizontal flood inundation, which are crucial to understanding 107 potential damage to infrastructure within the flood plain. We have selected the Monocacy Creek, 108 which has an area of 48.8 square miles (Figure 2). This river is an ideal test application of our 109 forward-modeling approach because the Lehigh Valley International Airport (LVIA) weather 110 station in Allentown, PA is located within the watershed and has a record of hourly precipitation 111 from 1948, and thus model results for the 21st century can be bias-corrected using model results 112 from the 20th century. Furthermore, studies (FEMA, 2001, Reese et al., 1989) are available to 113 provide an excellent source of historical information. Finally, major dams, weirs, and levees that 114 complicate accurate hydraulic modeling are infrequent along the Monocacy Creek. 115 116 The storm statistic (P) is calculated from hourly precipitation using a Log-Pearson Type III 117 Distribution (Equation 1, (Bedient et al., 2013)), 118 119

σ PPP logloglog Κ+= (1) 120 121

where P is the precipitation, Plog is the mean of the log P values, Κ is a frequency factor, and 122

σ is the standard deviation of the log P values. The frequency factor (Κ) is a function of the 123 skewness of log P and return period and is calculated by methods and table described in 124 (Prakash, 2004). The distribution of log P is taken from the maximum 24-hour running totals of 125 precipitation for each year over the respective historical (1948-2005) and future periods (2005-126 2099). The observed and modeled data (Figure 3) approximate a Log-Pearson Type III 127 distribution. While a case can be made for other distributions, previous studies of this and other 128 watersheds (Miller et al., 1981, Reese et al., 1989) rely on the Log-Pearson Type III distribution 129 for their analysis, so our study is consistent with these studies. 130 131 The storm statistic is calculated for historical data using National Climatic Data Center (NCDC) 132 hourly precipitation from Allentown from 1948-2011 (the extent of available data). Since hourly 133 data are not generally available from climate model distribution centers, we have done our own 134 runs with the National Center for Atmospheric Research (NCAR) Community Climate System 135 Model Version 3.0 (CCSM3.0) for the 20th century (1900-2005) and with the high scenario 136 Representative Concentration Pathways 8.5 (RCP8.5) for the 21st century (2006-2100). The 137

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RCP8.5 scenario results in 8.5 Wm-2 radiative forcing (1370 ppm CO2-equivalent) in 2100 (Rao 138 and Riahi, 2006, Riahi et al., 2007). The coarse resolution of the CESM (2.5ox1.9o) is roughly 139 17,424 square miles for a single grid. We then bias-corrected the future storm statistic using a 140 ratio approach, in which the 20th century observed storm statistic is multiplied by the ratio of the 141 21st century model storm statistic to the 20th century model storm statistic. This procedure was 142 repeated for each season (DJF, MAM, JJA, SON) as well as annually, to explore when extreme 143 precipitation is most relevant. We repeated this analysis using CMIP5 daily data from available 144 ensemble simulations (4 20th century and 6 RCP8.5) of the NCAR CESM at a higher spatial 145 resolution of 1.25o x 0.9375o. We therefore compared the implications of calculating the storm 146 statistic from daily data rather than hourly and the implications of using higher spatial resolution 147 but lower temporal resolution. 148 149 The future discharge is a function of the historical relationship between rainfall and discharge 150 based on USGS data. Given the historic relation of rainfall vs. return period, we find the model’s 151 future 100 year rainfall amount on the historic curve and the corresponding return period. The 152 future 100 year rainfall event year event will be larger than the historic one, so it will correspond 153 to a longer return interval historically, even though it will occur every 100 years in the future. 154 This larger rainfall amount can then be translated to a larger discharge using the relationship 155 between return period and discharge developed in the Act 167 report (Reese et al., 1989). Our 156 best estimate of how rainfall will relate to discharge in the future is based on present 157 relationships developed for this particular basin, rather than another modeling effort. While we 158 also did a calculation using HEC-HMS, it involves a very rough estimate of a design storm, as 159 well many assumptions (steady state, baseflow, snowmelt, evapotranspiration), and a calibration 160 procedure, that we deemed less accurate than our current approach. 161 162 HEC-RAS requires this peak stream discharge and information about the cross sections for each 163 stream. We focus on the upper reach of the Monocacy that historically experiences flooding 164 events (Reese et al., 1989). Eleven cross sections are constructed using the 1 m Digital Elevation 165 Model (DEM) of the Monocacy watershed as well as field measurements (Figure 2). These 166 DEMs, from the Pennsylvania Spatial Data Access (PASDA), are either corrected for beneath 167 the water surface or taken during relatively dry times of low stream depth. Further field work 168 was used to better estimate the below-stream depths. HEC-RAS automatically links these cross 169 sections and runs the given discharge value through them to create the resulting water surface 170 elevation. We verified the results of cross section interpolations by validating our rating curves 171 (discharge vs water surface elevation) and the slopes against those measured in the Act 167 172 report (Reese et al., 1989). We ran both the historical observed discharge and the future bias-173 corrected discharge (derived from the 100 year storm statistic) through HEC-RAS. 174 175 Further analysis enabled us to explore the biases within the CESM model results. Comparisons 176 were made against reanalysis data from the North American Regional Reanalysis (NARR) 177 (Mesinger et al., 2006). In particular, we analyzed both convective and non-convective 178 precipitation, as well as storm tracks, for each season, to better understand the source of the 179 biases. 180 181 182 183

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3. Results 184 185 3.1 Storm Statistic 186 187 To explore possible changes in the annual storm statistic, we analyze separately the two halves of 188 our local observed rainfall dataset period. There is an increase from 5.07” to 12.37” from the 189 period 1948-1979 to 1980-2012, with the results for the entire time period intermediate between 190 the two (Figure 4). This change confirms that intense precipitation has been increasing in the 191 Lehigh Valley. 192 193 The observed precipitation from the Allentown station from 1948-2005 has annual monthly 194 mean of 3.65” mo-1, with precipitation throughout the year, but maximum in July, August, and 195 September (Table 1). The model biases (observed/model) for the means are mostly positive and 196 go up to 1.88” mo-1. The storm statistic for the hourly distribution, based on the Log-Pearson 197 type III frequency, ranges from 3.01” in the winter to 8.71” in the fall, with an annual value of 198 8.45” (note that the annual value can be slightly less than a seasonal value due to fitting to a 199 distribution) (Table 2). Model biases in the 100-year storm statistic are higher than biases in the 200 means, with the largest bias of 3.09” during the fall, and an annual bias of 2.84” (Table 2). 201 When the storm statistic is calculated using daily data rather than the 24 hour running means, the 202 annual value is slightly lower (7.73), resulting in a higher bias (3.02”). Our analysis with HEC-203 RAS focuses on the annual values, so the observed 20th century value is 8.45 and the future 204 corrected model value is 9.02. 205 206 Using the suite of four 20th century CMIP5 ensemble runs (Table 3), the mean 20th century storm 207 statistic, based on daily precipitation, ranges from 2.59 to 3.51 throughout the seasons, with an 208 annual value of 3.55” and a standard deviation of 0.38”. The biases are less than the biases from 209 the lower resolution run for all seasons, but particularly in summer. The annual bias from the 210 improved spatial resolution, but decreased temporal resolution, improves from 2.84” (Table 2) to 211 2.38” (Table 3). Using the suite of 6 RCP8.5 ensemble runs (Table 3), the mean 21st century 212 storm statistic ranges from 3.01” to 4.18”, with an annual value of 4.54” and a standard deviation 213 of 0.56”. Based on the best estimate of the observed storm statistic (i.e. calculated from hourly), 214 the mean annual bias-corrected storm statistic for the 21st century is 10.8”. To estimate a lower 215 and upper bound, because the bias correction is based on the 21st century model value divided by 216 the 20th century model value, the upper bound was calculated by adding the appropriate standard 217 deviation to the numerator and subtracting the appropriate standard deviation to the denominator, 218 while the reverse was done for the lower bound. 219 220 The annual bias-corrected future values are therefore 9.02” from the Lehigh CESM runs and 221 10.8” from the ensemble CESM mean, which is an increase of 6.7% and 28%, respectively. . 222 Therefore, the current 100 year storm event will occur every 44 - 81 years in the future, or the 223 100 year storm event will contain an additional 0.57 - 2.35 inches of rainfall (Figure 5), based on 224 the Lehigh CESM and ensemble CESM runs, respectively. Alternatively, the future 100 year 225 events occur every 140 years (9.02”) and 324 years (10.8”) given the historical record. 226 227 228 229

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3.2 CESM Bias 230 231 Since the CESM biases for both low and high resolution runs is largest in the fall, we will focus 232 on this season to help understand the source of this bias. The following analysis is based on our 233 lower resolution simulation (Lehigh CESM). The CESM underestimates the total precipitation 234 in the fall when compared to reanalysis data from the NARR in both the larger northeast region 235 as well as the Lehigh Valley, generally by 1.35 mmd-1, though the overall pattern is correct 236 (Figure 6). The extreme precipitation is also underestimated, with observed storm maximum of 237 6-10” but only 4 – 6” in the Lehigh CESM (Figure 7). 238 239 Investigation of the standard deviation of the 500 mb geopotential height shows at every 240 longitude, CESM overestimates the number of storms passing through the area compared to the 241 NARR (Figure 8). The storminess in the Lehigh Valley simulated by CESM is more similar to 242 that of mid-Virginia 243 244 CESM has the correct pattern of convective precipitation for the Northeast, with more convective 245 precipitation occurring in the Southwest-Mid Atlantic states and less in New England (Figure 9). 246 However, the values are underestimated in CESM by roughly 50%. In the Lehigh Valley, 247 NARR shows 60 - 70% convective precipitation while CESM reports 30 - 40% for the Lehigh 248 Valley, also a 50% underestimation. Precipitation that is not convective in the model is 249 stratiform, so CESM simulates more stratiform precipitation, but less convective precipitation, 250 than actually occurs. 251 252 3.3 Hydrologic Modeling 253 254 Following the general method discussed above, the rainfall-discharge relationship (Figure 10) is 255 used to determine the increased future discharge. The historic 100-year rainfall of 8.45” 256 translates to 1745 ft3s-1 and the bias-corrected future rainfall of 9.02” translates to a discharge to 257 1918 ft3s-1, an increase of 173 ft3s-1. The ensemble CESM bias-corrected future rainfall of 10.8” 258 translates to a discharge of 2528 ft3s-1, which is an increase of 783 ft3s-1. 259 260 Historic discharge and future discharge values were run through the 11 cross sections in the 261 HEC-RAS model (Table 4). Based on our CESM run, the maximum additional flooding (cross-262 section 7, Figure 11) extends an additional 18 - 52 feet (7 – 59 ft mean all cross sections) onto 263 the existing floodplain, with an area of 59164 - 491421 ft2 (59354 - 452378 ft2 ) (Table 5). The 264 mean of the CESM ensembles results in a maximum increase in flooding of 52 ft and a mean of 265 58 ft, with an additional 491421 ft2 of flooding. The lower bound of the CESM ensembles 266 results in maximum flooding of only 6 ft, while the upper bound is greater than the current 500 267 year flood, which in itself would result in a maximum of 64 ft of flooding. 268 269 270 4. Discussion 271 272 Flooding in basins in this region was generally due to extratropical storms due to cold front 273 passage during the warmer months or warm front passage during the colder months. Cold front 274 storms were shorter and more intense than their warm front counterparts. There was also some 275

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flooding due to the incursion of tropical storms. Floods along these smaller tributaries and in the 276 headwater regions of the large streams were mostly due to cold front storms. Our historical data 277 confirm the results of Easterling et al. (2000) and Karl and Knight (1998) that there are fewer 278 weak storms and more intense storms, especially during the Fall season. The longer return 279 interval events are those that are relevant to major flooding, so flood events will get more 280 intense. 281 282 Groisman et al. (1999) and Karl and Knight (1998) found that mean annual precipitation has 283 increased by at least 5% in the last century and that heavy precipitation events contribute to the 284 increase disproportionately more than light events, which is likely a result of global warming 285 (Shiu et al. (2012), Groisman et al. (2005)). A multi-model analysis (Meehl et al., 2005) 286 revealed that more intense precipitation in northeastern North American under a greenhouse-287 warmed climate scenario is due primarily to advective effects, but also to increased water vapor. 288 Our projected future storm statistic of around 10 inches (9.02 – 10.8 inches) is currently typical 289 of Georgia, while Louisiana, the rainiest state in the contiguous USA, experiences just over14 290 inches (Bonnin et al., 2006). 291 292 From fieldwork, it is known that at least 5 homes, 1 hotel, and 3 businesses are vulnerable to the 293 100-year event, so damage to them would increase under our projections. In a broader study of 294 the effects of increased flooding on structural damage, Wright et al. (2012) used the 100 year 295 return period from multiple climate models and scenarios to show that the resulting increase in 296 peak discharge could be detrimental to the structural integrity of 27% of highway bridges in the 297 mid-Atlantic region by the end of the century for the mid-range A1B scenario. Data from this 298 type of study can be used to produce flood insurance maps that are relevant to future climate 299 (Hafliger and Lim, 2012, Tatiana Hernandez and Bin Zhang, 2007). 300 301 Uncertainty in this type of “progressive” modeling propagates from the discharge model to the 302 hydraulic model (HEC-RAS) to the terrain level (Merwade et al., 2008). The Log-Pearson Type 303 III Distribution is a simplistic method of determining a general storm, so there would be more 304 accuracy in applying the actual time series of hourly precipitation values. We have, however, in 305 this study evaluated the effect of using daily vs hourly data to calculate this statistic. The bias 306 and the percent change are higher using the daily data, while the absolute flood value is higher 307 using the hourly data. However, the bias from coarser spatial resolution is larger than the bias 308 from coarser temporal resolution. Clearly, better resolving the individual storms through 309 improved spatial resolution is more important than using more frequent data. We use steady 310 flow assumptions in HEC-RAS, and so do not account for unsteady flow due to channel 311 geomorphology or bed discontinuities (Hicks and Peacock, 2005). However, some studies 312 (Michaud and Gumtow, 2006) have shown more accurate results using constant flow rates, but 313 given the limited data on channel profiles, our steady state assumptions are appropriate for our 314 proof-of-concept study. Merwade et al. (2008) found that discharge uncertainty propagates 315 nonlinearly to the water surface elevation.. The largest uncertain factor within the hydraulic 316 model is the Manning’s n channel roughness (Hicks and Peacock, 2005, Merwade et al., 2008), 317 which is a measure of how the roughness of the channel bed slows down the flow. We have used 318 pre-determined values from the Northampton County flood insurance study (FEMA, 2001). 319 320

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The choice of GCM is also crucial to the final results. While Minville et al. (2008) found some 321 generalities from 5 GCMs and 2 scenarios in a Quebec watershed, there were very different 322 GCM responses in snowmelt peak discharge due to different GCM temperatures in 323 winter/spring. Other studies (Grillakis et al., 2011, Moradkhani et al., 2010) using RCMs found 324 that even these RCM data require bias-correction before applying to hydrological models. The 325 delta downscaling used by some (e.g. (Forbes et al., 2011)) does not account for GCM changes 326 in variability except on the monthly timescale (Leavesley, 1994). In this study we have been 327 able to show the uncertainty inherent in natural variability through use of the ensemble analysis, 328 but the uncertainty between different GCMs will be even larger. 329 330 The issue of bias correction and downscaling is ultimately the key to applying output from 331 GCMs to watershed models like HEC-HMS or HEC-RAS. In this study we have used high 332 temporal resolution (hourly or daily) over 100 year time periods directly from the GCM, so have 333 obviated the need for weather generator-downscaling approaches. The small basin in this study 334 fits within a single GCM grid, in an area of moderate topographic relief. The bias correction of 335 the storm statistic is a first order, simple approach to spatial downscaling. Since the GCM grid 336 encompasses a large area relative to the study area, there is the question of how much the 337 measured storm statistic varies within the grid. Using NOAA (2014) data for PA and NJ, the 338 annual 100 year recurrence interval of the 24 hour storm event ranges from 7 - 9”, with larger 339 values the further east and closer to the ocean. Our calculated annual value of 8.45” from the 340 Allentown station is within that range. A larger value would lead to a larger bias, whereas a 341 lower value would lead to a reduced bias, because the CESM value lies below this range. 342 343 The particular CESM low storm bias seems most likely due to an underestimate of convective 344 precipitation, which may indicate that the most intense tropical storms are not being transmitted 345 inland far enough in the model projections, and is consistent with the findings of Shiu et al. 346 (2012). The storm analysis actually shows an excessive number of storms in CESM, but these 347 are most likely storms from the West carrying little precipitation. If more of the CESM storms 348 are stratiform rather than convective, they would not contribute as much precipitation (IPCC, 349 2001, Tremblay, 2005). Also, since stratiform rainfall events tend to last longer, even more than 350 1 day, the 24 hour period may not capture the precipitation from each storm event. 351 352 353 5. Conclusions 354 355 Flood maps currently used to determine flood insurance rates are based on historical climate 356 data, rather than future climate projections. Policies based on historical data do not sufficiently 357 incentivize individual preparedness and precaution in flood management policy. Climate change 358 is leading to an increase in extreme events such as floods, particularly in the northeastern U.S. 359 Many future projections predict more winter precipitation, summer drying, and increased 360 flooding throughout the northeast region. These changes in climate statistics should be 361 considered when providing flood maps for the coming decades. Policy that is based on historic 362 climate data cannot prepare people for the more dramatic variation expected under conditions of 363 predicted rapid climate change. Effective policy, in this instance, requires a more prudent 364 approach to flood management—an approach that grounds policy in available evidence of future 365 climate change. 366

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367 We introduce a new method that relies on hourly or daily precipitation data directly from GCM 368 output, bias-corrected, and applied to measured discharge curves and a model commonly used to 369 produce flood insurance rate maps, HEC-RAS. The precipitation data for 100 years is used to 370 produce the 100 year recurrence interval of the 24 hour storm event for the 20th and 21st 371 centuries. This storm statistic is then bias-corrected using the same statistic from historical 372 hourly precipitation data. We have applied this approach as a proof-of-concept to a small basin 373 in the Lehigh Valley, PA. Future peak discharge in this basin is projected to increase by 7 - 374 28%, which could lead to 18 - 52 feet more horizontal flooding. This method could be applied to 375 a larger watershed known to be flood-prone to investigate floodplain increases with more costly 376 consequences. In addition, other factors such as an increase in urbanization and amount of 377 impervious surface can drastically increase discharge values at all precipitation levels, so these 378 factors should also be considered when estimating potential flooding in the future (Bedient et al., 379 2013). 380 381 Other methods to predict flooding future climate change impacts have involved downscaling 382 GCM data using regional climate models, which then require further bias-correction, 383 downscaling/bias-correcting GCM data using a simple delta approach, or using bias-corrected 384 monthly GCM data in a weather generator to determine daily precipitation. The method 385 developed in this study relies on the high resolution data directly from the GCM to the discharge 386 increase from a 100 year flood event, and so retains the high frequency temporal variability in 387 the original GCM data. It would be worthwhile to test this approach with hourly or daily data 388 directly from an RCM to determine if that reduces the bias, but previous studies with regional 389 downscaling show that further bias-correction is still necessary. 390 391 The technique advances in this paper can be used to help generate flood maps based on statistics 392 from expected climate change. The information can then be used to generate flood insurance 393 rate maps that account for future climate impacts, or to offer communities a more realistic 394 assessment of flood plains in the coming decades, so that they can prepare better for the risks 395 they face. 396 397 Residents of contemporary floodplain communities will navigate a settlement pattern that public 398 policy makes available to them. If we are going to avoid the historic pattern of suffering 399 enormous losses before managing land based on the uses appropriate to actual risk and 400 possibility, then we will need to ground policy on predictions rather than historical data. 401 Nowhere is this more important than in the area of flood management, which concerns the most 402 common, costly, and deadly form of natural disaster in the United States (FEMA, 2010). Policy 403 that is grounded in model projections, rather than historical data, has the potential to save lives as 404 well as monetary costs. While this paper is intended as a proof of concept for one method of 405 integrating such projections into existing river modeling tools, it nonetheless points toward the 406 creation of policy that is precautionary and proactive, rather than risky and reactive. 407 408 409 410 411 412 413

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Acknowledgements 414

This study was funded by the National Science Foundation Macrosystems Biology (NSF 10-415 555), the Westwind Foundation, and the Strohl Grant of Lehigh University. We especially thank 416 Lauren Schneck for her initial work on several other watersheds, and Breena Holland and Dork 417 Sahagian for their helpful review comments. We thank George Yasko and Bruce Hargreaves for 418 helping with computer technical issues, and Joan Ramage and Stephen Peters for their input in 419 this project. 420

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Table 1: Monthly precipitation means and Lehigh CESM biases for 1948-2005. 533 534 Time Observed (inmo-1) Modeled (inmo-1) Bias January 3.16 2.83 1.12 February 2.78 2.68 1.04 March 3.62 3.54 1.02 April 3.70 3.58 1.03 May 3.97 3.64 1.09 June 3.64 3.97 0.92 July 4.30 4.22 1.02 August 4.11 3.72 1.10 September 4.19 2.23 1.88 October 3.28 2.06 1.59 November 3.63 2.73 1.33 December 3.49 2.99 1.17 annual 3.65 3.19 1.14 535 536 537 538 Table 2: 100 year return interval of 24 hour storm event based on hourly precipitation (and 539 Annual-D is daily) from 1948-2012 for observed and modeled historical and future (2012-2099) 540 in inches. 541 542 Season Observed Modeled

Historical Modeled Future

Bias Bias-Corrected Future

% change

Fall 8.71 2.56 3.00 3.09 9.27 6.38 Spring 4.01 2.09 2.83 1.92 5.43 35,41 Summer 5.71 1.90 2.00 3.01 6.01 5.26 Winter 3.01 2.56 2.48 1.18 2.92 -3.13 Annual 8.45 2.97 3.17 2.84 9.02 6.73 Annual-D 7.73 2.56 2.92 3.02 8.84 14.39 543

544

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Table 3: Ensemble historical, future, observed, and future corrected for each season and annual. 545 Annual ranges are based on standard deviations as described in the text. 546 547 1906-2005 djf jja mam son ann his_r1i1p1 2.45 3.22 2.34 2.82 3.51 his_r1i2p1 3.43 3.45 2.74 3.01 3.98 his_r1i1p2 2.63 4.41 2.56 3.29 3.65 his_r2i1p1 2.67 2.95 2.72 3.23 3.07 mean 2.80 3.51 2.59 3.09 3.55 stdev 0.43 0.63 0.19 0.22 0.38 2006-2100 rcp_r1i1p1 3.21 4.10 3.18 3.58 4.03 rcp_r2i1p1 2.97 4.11 2.73 2.88 4.71 rcp_r3i1p1 2.60 3.97 2.53 3.56 4.04 rcp_r4i1p1 3.63 4.80 3.28 3.31 5.42 rcp_r5i1p1 3.53 4.17 3.13 2.76 3.98 rcp_r6i1p1 3.24 3.92 3.19 2.99 5.02 mean 3.20 4.18 3.01 3.18 4.54 stdev 0.34 0.29 0.28 0.32 0.56 future change

1.14 1.19 1.16 1.03 1.28 (1.61,1.01)

1948-2005 observed 3.01 5.71 4.01 8.71 8.45 bias 1.08 1.63 1.55 2.82 2.38 future corrected

3.44 6.80 4.65 8.98 10.8 (13.58, 8.56)

548 549

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Table 4: HEC-RAS results at each cross section for Lehigh CESM run with future 100 year 550 storm equal to 9.02 inches. 551 552 # Cross section

gap distance Water surface elevation increase (ft)

Top width increase (ft)

Increase in flooded area (ft2)

1 1547 0.16 7.71 7124 2 1231 0.18 5.23 15088 3 652 0.07 8.30 19499 4 810 0.19 4.59 24720 5 1654 0.11 3.29 31236 6 2626 0.15 17.98 59164 7 1457 0.08 3.1 74521 8 2353 0.17 10.52 90545 9 2334 0.16 2.72 105996 10 1671 0.04 6.05 165650 average 0.15 6.45 553 554 555 556 557 558 559 Table 5a: HEC-RAS results for each scenario with comparisons to observed hourly. These 560 results are for the cross-section (section 7) with maximum flooding. Descriptions are obs_hrly = 561 historical observed based on hourly precipitation, CESM_hrly = Lehigh CESM future based on 562 hourly precipitation, ens_dly = CESM ensemble future based on daily precipitation, ens_min = 563 bias correction with ensemble standard deviation subtracted from the numerator and added to the 564 denominator based on daily precipitation, ens_max = bias correction with ensemble standard 565 deviation added to the numerator and subtracted from the denominator, CESM_dly = Lehigh 566 CESM future based on daily precipitation. 567 568 Storm value (in)

Desciption Current 100 year storm (years)

Additional Rainfall (in)

Streamflow (cfs)

Elevation increase (ft)

Width increase (ft)

Increase flooded area (ft2)

8.45 obs_hrly 100 1745 9.02 CESM_hrly 140 0.57 1918 0.15 17.98 59164 10.8 ens_dly 324 2.35 2528 0.61 52.27 491421 8.56 ens_min 107 0.11 1775 0.02 6.29 13289 12.11 ens_max 500* 3.66 3000 0.94 64.21 698747 8.84 CESM_dly 128 0.39 1875 0.12 16.35 49409 569 570

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Table 5b: Similar to Table 5a, except results are for mean of all the cross sections. 571 572 Storm value (in) Elevation

increase (ft)

Width increase (ft)

Increase flooded area (ft2)

9.02 0.13 6.95 59354 10.8 0.58 58.91 452378 8.56 0.02 1.49 12416 12.11 0.88 81.36 620419 8.84 0.10 5.88 49325 573 574 575 576 577 578 579 580 581 582 583 584 585 586

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587 Figure 1: Experimental Approach 588 589

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590 Figure 2: A digital elevation model of the Monocacy Creek Watershed. It has moderate relief, 591 with elevations ranging from 962.72 to 112.75 feet. The watershed boundary and cross sections 592 used in the project are delineated in thick black. 593 594

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595 596 Figure 3: Frequency data compared to Log-Pearson Type III distribution for a) observed latter 597 half of 20th century, b) modeled latter half of 20th century, and c) modeled 21st century. Modeled 598 results are based on hourly precipitation data from the Lehigh CESM run. 599 600 601

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602 Figure 4: The Allentown International Airport data is parsed in half to investigate changing 603 storms. The dashed gray line depicts the storm statistics that result from the 1948-1979 data set, 604 while the dashed black line depicts the storm statistics generated from the 1980-2012 data set, 605 and the bold solid line depicts the full record. It is clear that rainfall in the more recent past 606 reflects a pattern of larger storm events, with values for the 100-year storm jump from 5.07” to 607 12.37”. The longer period is depicted by the bold solid line and hence has an intermediate value. 608 609

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610 611 Figure 5: Comparison of historic vs future storm return intervals, based on the Lehigh CESM 612 and mean ensemble CESM runs. Future storms have shorter return period than historical storms, 613 or equivalent storms are more intense in the future. 614 615 616 617

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618 (a) (b) 619

620 621 Figure 6: Total SON precipitation (mm/day) from 1948-2005 for a) NARR and b) Lehigh 622 CESM for the Northeast U.S. Overall, NARR precipitation is underestimated by CESM. 623 Rainfall estimates are lower by about 0.9 mmd-1 for the Northeast region and by 1.35 mmd-1 in 624 the Lehigh Valley. 625 626

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627

628 (a) (b) 629

630 Figure 7: Maximum precipitation during September from 1948-2005 with a) historical data and 631 b) Lehigh CESM projection. The historical record of rainfall totals in PA ranges from 6 - 10 632 inches, while all of PA is in the 4 - 6 inch category in the CESM run. 633 634

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635 (a) (b) 636

637 Figure 8: The number of storms passing through the area is approximated by the standard 638 deviation of the 500mb pressure surface for a) NARR and b) Lehigh CESM. The NARR 639 displays much less storminess than the Lehigh CESM model. 640 641 642 643 644

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645 (a) (b) 646

647 648 Figure 9: Convective precipitation in the Northeast region for a) NARR and b) Lehigh CESM. 649 NARR shows that 40–90% of precipitation is convective for the region, while CESM reports a 650 regional range of 20-50%. 651 652 653

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654 655

656 657 (a) (b) 658 659 Figure 10: a) Relationship between precipitation and return interval showing that the high 660 precipitation associated with future 100 year storms would occur every 140 years (for Lehigh 661 CESM, 9.02 inches) and 324 years (for ensemble CESM, 10.8 inches) based on historical data. 662 Likewise, the current 8.45 inches would occur every 81 years in the future for the Lehigh CESM 663 and every 44 years in the future for the ensemble CESM. b) Converting rainfall to discharge is 664 based on the relationship between return interval and discharge, such that the historic 100-year 665 event was 1745 ft3s-1 but will become 1918 ft3s-1 in the future given the Lehigh CESM and 2528 666 ft3s-1 given the ensemble CESM. 667 668 669 670 671 672

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673 674 675 Figure 11: a) Cross section 7, water surface elevation differences between a) present storm 676 statistic (8.45 in) and future from Lehigh CESM run (9.02) and mean of daily ensembles (10.80) 677 and b) present storm statistic based on hourly (8.45) vs daily (7.73) data and future maximum 678 (12.11) based on current 500 year storm. 679 680

(a)

(b)