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Home , East Canada Creek, Hudson River, Otsquago Creek

Final Report- Assessing Flood Risk in a Changing Climate in the Mohawk and Hudson Basins

Stephen Shaw SUNY College of Environmental Science and Forestry, Syracuse, NY

The report is divided into five different sections:

1. – Controls on Peak River Stage 2. Causative Processes of Flooding on Tributaries to the Main Stem of the Hudson and Mohawk 3. Controls on Precipitation Intensity in the U.S. and 4. Ice Jams on the – A simple model to predict future ice jams 5. Fact Sheet for Public: “Mohawk River Flooding: Will the future differ from the past?”

The first four sections reflect basic research that has gone into better understanding processes that dictate discharge and stage on the Hudson , Mohawk, and tributaries. Work from Sections 3 and 4 (precipitation intensity and ice jams) is ongoing. We plan to have publications on these two topics complete by the end of the spring. Work on Sections 1 and 2 is complete and provides some helpful conclusions that can provide insight into predicting potential future flooding.

Section 5 includes a draft fact sheet that draws from the basic research of Sections 1 to 4. The fact sheet is not a summary of the basic research, but it instead borrows bits and pieces of the basic research to make points as necessary. We are happy to incorporate any suggested revisions into this document as we presume it will be the deliverable from this project most suitable for distributing to the general public or policy makers.

Section 1 – Controls on Peak River Stage in the Hudson

Unlike many , the main channel of the Hudson River up to Albany has a very low channel slope and is sometimes likened to being similar to a . The river is effectively an extension of the . Thus, while in most rivers one thinks of stage (water height) as being dependent on inflow from the watershed, the stage in the mid-river of the Hudson can be dependent on variations in sea level due to tides, storm surge, and long-term changes.

While recent storm surge due to Tropical Storm Sandy made people acutely aware of the extent storm surge could raise water levels in the Hudson River, one could ask the question: Do other factors, such as inflow from the watershed, matter at all in controlling Hudson River stage? If watershed inflows matter little, then predictions of how the frequency of high stage may change is dependent only on the frequency of high ocean stage and not on precipitation intensities, watershed wetness, or other factors that influence generation of runoff in the watershed. If ocean stage dominates mid-river stage, then predicting future frequency of high stages is somewhat simplified, requiring only accurate predictions of extremes in sea level.

The only long-term measure of water level in the mid-Hudson is at Poughkeepsie. Therefore, to evaluate possible controls on mid-Hudson water stage we compared stage at Poughkeepsie to three possible controls: 1) ocean stage as indicated by the tidal gage at , ; 2) watershed inflows as represented by flow from the upper Hudson measured at Troy as well as from two gaged lower basin tributaries: the Walkill River and Creek; and 3) sustained wind in the Hudson Valley given its potential to lead to wave run- up. We initially attempted to construct a multivariate regression model to relate these factors to mid-Hudson stage. We found that there were too many possible interactions and possibly missing variables (such as changes in water density) and that a statistical model could not sufficiently predict mid-Hudson stage.

As an alternative, we used a qualitative approach to assess what factors were most important in leading to high mid-Hudson stage. This qualitative approach is shown in Tables 1 and 2. In Table 1 we indicate the 20 highest stage values at Poughkeepsie along with the concurrent values of the primary factors: Battery stage, watershed inflows, and wind speeds. Table 2 is similar but instead of using periods of highest mid-river stage we use the 20 dates with the lowest recorded stage value. Within each group of factors (e.g. stage at Battery, Troy Dam discharge, etc.) we established the range of the possible values and assigned percentiles. For instance we found that for daily discharge at the Troy dam, the 99th percentile value (the value that is met or exceeded 1 out of 100 days) was 60,000 cubic feet per second. Thus, we then color-coded the values of the primary factors to indicate the percentile of the value. Values at or exceeding the 99th percentile were colored red. Values between the 99th and 50th percentile were colored orange. The remainder were colored green.

On Table 1, we see that high stage at Poughkeepsie is always accompanied by greater than 99th percentile value of at least one factor and at least 50th to 90th percentile values of additional factors. The three highest Poughkeepsie stage values occur when the Battery stage is above 99th percentile. In some cases where Battery stage values are not 99th percentile but Poughkeepsie stage is high, watershed discharge values are 99th percentile (4/2/05, 10/13/05, 4/16/07, 3/13/10). However, on days when watershed inflows are below 50th percentile (12/11/92, 5/12/08, 9/2/06), Poughkeepsie stage can still be very high as long as the Battery stage is also high. Thus, it appears that Battery stage dominates in controlling Poughkeepsie stage, although it does appear that very high watershed inflows have some moderate ability to enhance Poughkeepsie stage. Notably, wind appeared to also have some moderate influence. While wind speed was typically high, the wind direction also seemed critical. Namely, in Table 1, higher mid-river stage often occurred when winds were from the north or northeast. Winds could possibly be seasonally correlated to some other factor, but the importance of north or north east wind is consistent with the possibility that a steady north wind could push water down the Hudson and increased wave heights in the mid to lower Hudson. When stage was at its lowest, the wind was consistently out of the west of northwest.

The fact that multiple processes interact to control river stage has also recently been detailed by Orton et al. 2012 (Detailed modeling of recent severe storm tides in of the City region, Geophysical Research Letters – Oceans, 117, C9) using a dynamic model of the . Orton et al. were able to run the model with certain processes removed to assess how the simulations of river stage were influenced. For Tropical Storm Irene, they found that removing watershed inputs under-predicted river stage at Poughkeepsie by approximately 18%, consistent with our assessment that watershed inputs have a moderate influence on mid- Hudson stage. They noted that this influence increased as one moved upriver and indicated (Orton et al. 2012 Figure 15) that near Albany watershed inflows could dictate up to 50% of stage height.

Table 1. Stage at Battery, watershed discharge, and winds at the same time as the twenty highest recorded mid- river stage values. Red shading indicates 99th percentile value. Orange shading indicates 99th to 50th percentile value. Green indicates a value below the 50th percentile.

Airport: Avg. Wind Avg. Wind Direction Stage (NGVD 1929 ft) Discharge (cfs) Wind Speed (mph) (deg. from N) Hudson @ Ocean @ Hudson Wallkill Wappinger Dutchess Stewart Dutchess Stewart Date of Peak Dir. Poughkeepsie Battery @ Troy River Creek Co. Int'l Co. Int'l 10/29/12 4.36 6.1965 - 1630 294 17.58 23.71 24.18 46 N/NE 12/11/92 4.33 4.6565 16000 2080 262 14.41 23.42 70 56.36 E/NE 4.32, 3.49, 8/28-30/11 3.0415 158000 21100 7320 14.65 21.32 n/a n/a n/a 3.15 4/2-3/05 3.23, 4.22 2.709 108000 17500 2210 11.77 14.94 n/a n/a n/a 4.2, 3.84, 4/16-19/07 2.389 104000 21300 7640 12.53 14.76 10.14 30.42 N 3.52, 3.36 10/28/06 3.65 2.4315 56500 6770 551 11.32 17.02 n/a n/a n/a 10/25/05 3.63 3.7665 80500 4910 2090 12.44 16.02 359.78 33.64 N 12/21/12 3.53 2.534 - 4830 796 13.13 18.25 n/a n/a n/a 10/19-20/96 3.5, 3.07 3.619 32000 9680 1950 12.27 21.30 75.24 76.25 E 3/13-14/10 3.11, 3.43 3.564 44000 12400 1270 12.93 19.48 39.6 75.71 NE/E 10/13/05 3.41 3.949 22300 15500 2180 7.14 13.00 355.5 48.75 N/NE 9/8-9/11 3.35, 3.33 2.739 107000 22200 4940 4.70 9.06 n/a 72.14 E 3/11/11 3.35 2.2915 76300 15300 3950 7.74 14.94 n/a n/a n/a 1/25/10 3.23 2.1915 56900 5530 1290 10.33 16.80 152.28 157.3 SE 5/12/08 3.22 3.5315 15500 1240 218 10.60 16.49 43 67.2 NE 9/2/06 3.21 3.329 9980 2020 146 10.74 16.04 n/a 76.82 E 1/18/06 3.17 1.179 87400 7890 2320 13.35 19.93 n/a n/a n/a 11/28/93 3.08 2.024 29800 4070 857 14.41 16.62 151 150.5 SE 12/14/92 3.05 2.919 13800 2000 230 n/a 8.30 n/a 49.17 NE 11/11/10 3.04 2.724 23800 725 204 8.54 9.06 17.39 58.10 N/NE 12/11/03 3.04 1.8715 59300 6730 1960 - - - - - 5/12/98 3.02 2.659 42400 9070 2030 12.73 17.16 46.875 61.74 NE

Table 2. Stage at Battery, watershed discharge, and winds at the same time as the twenty lowest recorded mid- river stage values. Red shading indicates 99th percentile value. Orange shading indicates 99th to 50th percentile value. Green indicates a value below the 50th percentile.

Airport: Avg. Wind Avg. Wind Direction Stage (NGVD 1929 ft) Discharge (cfs) Wind Speed (mph) (deg. from N) Cardinal Date of Hudson @ Ocean @ Hudson @ Wallkill Wappinger Dutchess Stewart Dutchess Stewart Dir. Peak Poughkeepsie Battery Troy River Creek Co. Int'l Co. Int'l 11/14/03 -1.95 -0.31 21700 1095.7 309.3 - - - - - 3/15/93 -1.94 -0.76 6033.3 706.7 200 11.79 11.77 267.39 277.78 W 1/17/00 -1.84 0.35 10800 433.3 160 - - - - - 3/6/07 -1.62 0.16 11966.7 920 243.3 15.59 22.77 307.08 286.09 NW 12/27/93 -1.16 -0.52 7966.7 866.3 279.7 10.56 10.02 273.64 279.09 W 12/22/08 -1.16 -0.73 19466.7 1486.7 510 14.83 20.67 262.61 266.82 W 12/17/07 -1.15 -0.08 10766.7 896 211 15.80 21.41 282 276.09 W 1/10/94 -1.13 -0.55 7600 540 150 n/a 8.55 n/a n/a n/a 2/26/12 -1.13 0 - 1213.3 198 10.47 17.29 278 303.5 NW 1/16/04 -1.09 -0.09 14133.3 706.7 263.3 11.52 - 318.57 - NW 2/11/01 -1.03 -0.76 16833.3 1300 325 - - - - - 3/9/05 -1.03 -1.28 13200 1813.3 339 17.45 20.56 279.2 280.91 W 1/23/03 -1 -0.54 8633.3 613.3 193.3 - - - - - 2/13/03 -1 -1.03 9100 620 140 - - - - - 12/11/99 -0.99 -0.04 - 553 218.3 16.15 21.79 310.5 299.47 NW 2/6/95 -0.98 -1.09 9066.7 396.7 253.3 14.70 18.28 273.91 282.61 W 4/5/95 -0.98 -0.64 8766.7 515.3 184 15.35 24.34 309.13 317.27 NW 12/25/00 -0.96 0.32 14900 860 370 - - - - - 2/5/07 -0.91 -0.77 14000 520 140 14.65 19.48 262.5 268.33 W 12/26/00 -0.89 0.16 14066.7 700 326.7 - - - - -

Section 2 - Causative Processes of Annual Maximum Discharge

It is often assumed that flooding will increase on all rivers in a changing climate, but this is not necessarily the case. When analyzing riverine flooding, it is important to consider the causative processes that influence the river because a warming climate will not have the same effect on all geographic areas. For instance, flooding in a place like North Carolina is dominated by hurricanes and will remain strongly linked to hurricane frequency and intensity into the future. However, on the Mohawk River, high flows can result from several processes, some of those such as snow melt and ice jamming being strongly dependent on temperature. Thus it is foreseeable that certain processes that currently cause flooding may simply disappear with warming associated with . The question remains, how important have these highly temperature dependent processes historically been in leading to flooding.

Traditionally, hydrologists have used statistical methods to predict the frequency of future flood events. The statistical models are formulated around historical observations, with little concern for the most part over the identifying different causative processes behind high flows in different years. To better predict how flooding could change in the future, the statistical models need to be adapted to separate flood frequencies and magnitudes associated with different causative processes. In this section, we have separated out different flood causing processes for each annual peak in the gaged historical record of the main stem of the Mohawk as well as several tributaries to the Mohawk and Hudson Rivers.

We consider three primary possible underlying causative processes: 1) high intensity precipitation event; 2) large snowmelt event; or 3) moderate precipitation on very wet soils. Causation due to high intensity precipitation is identified by considering whether peak annual streamflow occurs at the same time as the largest 2-day duration precipitation event in a given year. Causation due to a large snowmelt event is identified by considering whether peak annual streamflow occurs at the same time as the largest 3-day duration snowmelt event in a given year. Causation due to moderate precipitation on wet soils is for the most part identified as being the remainder of the events that do not fall into categories two or three.

The results are presented graphically for each watershed we considered (Figures 1 to 6). Each category is represented by a different symbol: solid diamonds for discharge resulting from 2-day annual maximum rainfall, crosses for discharge resulting from 3-day annual maximum snowmelt, and open squares for the annual maximum mean daily discharge. The events are plotted against the Julian Day (i.e. Julian Day one is January 1, Julian Day 365 is December 31st) to show what time of year they occurred during. When an open square aligns with one of the other symbols, the annual maximum discharge in that year is the same as the discharge associated with the maximum rainfall or snowmelt, respectively. When an open square does not align with a diamond of cross, the annual peak discharge could be attributed to moderate precipitation on wet soils, unless otherwise noted.

With the exception of , most watersheds display a similar pattern in terms of causation of annual peak flows. In most cases, large snowmelt or annual maximum precipitation are linked to only a small number of the annual maximum discharge values. Annual maximum snowmelt may lead to some peak flows but these tend to be on the low-end of the range of peak flow values. Annual maximum precipitation in many analyzed watersheds leads to approximately 10% of total annual maximum discharge events. For the most part, the magnitudes of the peaks are consistent with discharge peaks due to other causes. However, in some watersheds, there has been one or two years with very large maximum precipitation events (typically due to a hurricane or remnants of a hurricane) that results in the largest or near largest discharge on record (e.g. , , ). As an exception to the other basins, Wappinger Creek has its eight largest annual maximum discharges linked to annual maximum precipitation events. This reflects geographic differences in flood causation among basins that are not that far apart. Notably, the lower Hudson – where Wappinger Creek is located – is much more subject to tropical storm systems than the Mohawk watershed.

For all the watersheds, the annual maximum discharges most frequently occur in mid to late spring. In many cases, this peak flow falls within a span only a week or two after the last snowmelt. Soils are therefore still nearly saturated because of limited time to drain, low evapotranspiration rates, and recent recharge from snowmelt. Because of moderate temperatures and a shift toward summer circulation patterns, precipitation rates are greater than just several weeks before because warmer air can hold more moisture and more air is being transferred from the moisture-rich tropics. This interaction between soil wetness and precipitation quantity appears to dictate many moderate to high annual maximum discharge values. It remains to be seen how this interaction will change in a changing climate. While there is the possibility for greater intervals between precipitation events, there is also the possibility for more persistent precipitation patterns that may lead to extended periods of wet weather.

Schoharie Creek Annual Maximum Discharge 16.00 Max 2-day rainfall 14.00 3-day maximum snowmelt

12.00

Monthly Average (x10) 10.00

8.00

6.00 discharge(cm/day) 4.00

2.00

0.00 0 50 100 150 200 250 300 350 400 Julian

Figure 1. Summary of causation of annual peak flows on Schoharie Creek at Burtonsville.

Figure 2. Summary of causation of annual peak flows on the Hudson River at North Creek.

Figure 3. Summary of causation of annual peak flows on Wappinger Creek at Wappinger Falls.

Figure 4. Summary of causation of annual peak flows on West Canada Creek at Kast Bridge, NY.

Figure 5. Summary of causation of annual peak flows on East Canada Creek at…

Figure 6. Summary of causation of annual peak flows on the Mohawk River at Little Falls, NY.

Section 3 – Controls on Short-Duration Precipitation Intensity in the Hudson Valley

It is common to predict that precipitation intensity will increase in a warming climate. Particularly for short-duration rainfall events lasting on the order of an hour, most precipitation that falls over a watershed originates from the atmosphere directly over the watershed, as there would be insufficient time to transport moisture from more distant locations. The amount of moisture available in the atmosphere is dependent on the atmospheric temperature; at higher temperatures more moisture can be stored in the atmosphere. This linkage between atmospheric moisture content and temperature is represented by a mathematical expression referred to as the Clausius-Clapeyron Equation. The Clausius-Clapeyron equation predicts a 7% increase in moisture content per degree Celsius increase in temperature. For this reason, it is believed that rainfall intensity should increase as the climate becomes warmer.

In some cases, precipitation intensity increases in line with the Clausius-Clapeyron equation, near 7% per °C. But, in certain locales, precipitation increases at greater than 7% per °C, sometimes approaching 15% per °C. This stronger scaling between precipitation and temperature is referred to as super-CC (i.e. super Clausius-Clapeyron scaling). Understanding these geographic differences in how high intensity precipitation scales with temperature is important to predicting possible regional differences in climate change impacts. From combining and processing meteorological station data from across the U.S., in Figure 7 we show locations that have strong super-CC scaling (green dots), moderate super-CC scaling (orange dots), and only CC scaling (red dots).

Figure 7. Prevalence of super-CC scaling in different regions. Regions near green dots have strong super-CC scaling while regions with red dots have only CC scaling. Super-CC scaling means that precipitation intensity increases faster with temperature than with CC scaling.

In nearly all regions, precipitation is a result of either convective systems (thunderstorms) or frontal systems. Convective rainfall is spatially more isolated and occurs when there is localized atmospheric instability that causes vertical movement and thus rainfall. Frontal system rainfall occurs as large –scale air masses move past each other. Because of fundamentally different mechanisms, convective rainfall tends to be more intense but shorter lived than frontal system rainfall. Super-CC relationships can occur for differing reasons depending on the type of rainfall. In some regions, convective or frontal system rainfall itself scales as super-CC. In other places, convective and frontal system rainfall are nearly CC, but the transition between them leads to the appearance of super-CC scaling (illustrated in Figure 8). Across the Hudson basin, short duration extreme precipitation appears to have super-CC scaling (Figure 7). However, in the upper part of the basin, the scaling seems to be due to super-CC scaling of convective rainfall. In the lower portion of the basin, super-CC scaling basin appears to be due to the transition between prevalence of convective and frontal system precipitation (as in Figure 8). In the context of understanding differences in super-CC scaling across the contiguous US, we are continuing to do work focusing on how transitions in processes controlling CC-scaling can change in relatively short distances.

Figure 8. Super-CC scaling (black line) due to transition in prevalence of convective (blue dots) and frontal system precipitation (red dots). One can see that the slope of the black line is greater than that for the red line or blue line alone. Section 4 - Ice Jams on the Mohawk River

The geomorphology of the Mohawk River and the location of bridges along the river make it very vulnerable to ice jams (Lederer & Garver, 2001). The ice jams cause water to back-up in certain segments of the river and they result in localized flooding. Because this flooding is due to an increase in river stage but not necessarily in discharge, flooding due to ice jamming is not always apparent in USGS gage records. Differing from Section 2, here we undertake a focused analysis of only a single causing of flooding, that due to ice jams. While historical ice jams have been documented (i.e. Lederer & Garver 2001) there has been little work to systematically identify factors that lead to ice jams. Here, we develop a simple model that can be used predict occurrence of past ice jam floods and hopefully be useful in evaluating the potential for future ice jam floods in a changing climate.

As an initial hypothesis, we assumed that ice jamming may be dependent on ice thickness. Historical ice thickness data for the Mohawk is not available able but temperature data is. We used a previously developed equation to calculate ice thickness (hi) at the time of thaw events as only a function of temperature:

hi = k√푆퐹 where SF is the accumulated freezing degree days and k is a coefficient that “accounts for differing surface insulation as supplied by overlying snow and varying degrees of exposure to atmospheric heat fluxes,” (Beltaos & Prowse, 2009). A freezing degree day is the calculated as the difference between any negative mean daily temperature and 0° C. Freezing degree days only occur when temperatures are below 0° C. According to the

U.S. Army Corps of Engineers, the SF value for the Mohawk River would be between 0.12 and 0.15 since it is an “average river with sno” . The values for lakes or sheltered rivers would be higher. Based on the equation, the higher the number of cumulative freezing degree days, the thicker the ice will be. Ice thickness is reset if mean daily temperature exceeds 6° C.

With the equation for ice thickness, we were able to compare approximated ice thickness in select years with and without reported ice jams (Figure 9). For instance in 1936 and 1945, modeled ice thickness reached above 14 inches in both cases, before a thaw event occurred. However, only an ice jam occurred in 1936. The thaw period in 1945 had little precipitation and temperatures rose gradually over approximately a week. In comparison, the thaw period in 1936 saw a rapid jump in temperature and was accompanied by large rainfall amounts. Thus, in comparing years such as 1945 to 1936, it becomes apparent that the occurrence of ice jams due to flooding is dependent on multiple factors.

Figure 9. Ice thickness and temperature in 1936 and 1945. Ice thickness (inches) is represented by the solid blue and the temperature (°C) is represented by the solid black line.

To incorporate the multiple factors that could lead to ice jams, a rule-based model was developed using thresholds evident in historically observed ice jam years: 1832, 1865, 1888, 1891, 1893, 1910, 1914, 1936, 1964, 1979, 1981, 1996, 2007, 2010, and 2011 (Garver and Cockburn 2009 and NOAA). The model sequentially assesses characteristics of a given year against given threshholds. For an ice jam to occur: ice thickness must be > 8 in., the average temperature during the thaw period must be > 4° C, the warming degree days during the thaw period must exceed 15, and the precipitation amount 1 week prior to the that period must exceed 0.5 inches (Figure 10).

Based on these criteria, all ice jams events after 1893 (when sufficient meteorological data is present) can be identified with the exception of the 2010 event. From randomly picking 20 additional non-ice jam years in the historic record, only two were identified as ice jam years. Thus the approach seems to minimize errors in positively identifying ice jam flood years while also identifying only a small number of false positives. We are in the process of validating this model against other datasets of ice jamming from other river systems. Once validated, we can apply the rule-based model to climate model output and project the future prevalence of ice jams on the Mohawk River.

References: Beltaos, S. and T. Prowse. 2009. River-ice hydrology in a shrinking cryosphere. Hydrological Processes, 23: 122-144.

Garver, J.I., Cockburn, J.M.H. 2009, A historical perspective of Ice Jams on the lower Mohawk River. In: Cockburn, J.M.H. and Garver, J.I., Proceedings from the 2009 Mohawk Watershed Symposium, , Schenectady NY, p. 25-29.

Lederer, J.R., and Garver, J.I., 2001, Ice jams on the lower Mohawk River, New York: Lessons from recent breakup events. Geological Society of America, Abstracts with Programs v. 33, n. 1, p. 73.

Figure 10. Schematic of rule-based model for predicting ice jam flooding on the Mohawk River.

Section 5 – Fact Sheet on Flooding in the Mohawk

I presume the formatting of this fact sheet will change depending on where or how it is published (i.e. website, stand-alone document, article in gray literature, etc.). Please feel free to contact me with any suggestions on formatting or modified content.

Mohawk River Flooding: Will the future differ from the past?

In 2006, 2011, and 2103, the Mohawk Valley was struck by several large floods. There is general concern that due to climate change, the future will bring more flooding. These last few years would seem to indicate that, indeed, flooding is becoming more frequent.

However, the story is likely not that simple. Despite the dramatic nature of the floods of recent years, they are not that unusual when considered in the nearly 100 year flow record of the basin. For flow recorded at Cohoes, NY (near where the Mohawk empties into the Hudson), the 8/29/2011 event due to the remnants of was large, but it was still exceeded by two prior events in 1977 and 1936 (see Figure 1). Similarly, very recent high flows in June 2013, were exceeded by 27 previous events. As part of the complication in analyzing flooding, this is not to say there was not localized severe flooding in June 2013 in other parts of the Mohawk Valley. For instance, parts of the 2,300 person town of Fort Plain, NY were inundated by flood waters. But this flooding came from , a small tributary to the main stem of the Mohawk River. This flooding on Otsquago Creek was caused by very intense but isolated thunderstorms that did not impact the Mohawk Basin as a whole.

Figure 1. Sixty largest daily flow values at Cohoes, NY since 1919. While the 8/29/2011 flow was large, there were two previous events that were larger. Similarly, while the very recent 6/14/2013 caused flooding, there have been 27 other previous events with equal or higher flows. Note, discharge has been divided by watershed area so that it is presented as a discharge per unit area.

Thus, to make reasonable judgments whether flood risk in the Mohawk Valley is changing, one must carefully weigh information on the areal extent of reported high flows, the magnitude of the flooding relative to events often far back in the historical record, and the actual underlying cause of the flooding. In doing so, one may not reach a definitive answer of what will happen in the future, but one will have a better sense of what we can reasonably know and what likely remains beyond our current scientific understanding of this river system.

This fact sheet will provide insight on three topics related to flooding in the Mohawk River Basin:

• Even if high intensity precipitation does increase, does that mean that there will be an increase in the number of peak flow events? • Have reports of increased high flows been consistent across the entire areal extent of the Mohawk River Basin? • What have been the causes of historical floods and how are these causative processes anticipated to change in the future?

Does more high intensity precipitation mean more flooding? There is well documented evidence that the number of high intensity precipitation events are increasing (Degaetano 2009). For instance, in Figure 2 we see that for a rain gage located in the central part of the Mohawk Valley at Tribes Hill, NY, intense rainfall has certainly increased in recent . Since 1970, on average, a precipitation event greater than 2.5 inches/day has occurred every third year. Prior to 1970, such a precipitation event only occurred a little more than once every ten years.

Figure 2. Circles indicate precipitation events with greater than 2.5 inches of rain in 24 hours. A red square surrounding a circle indicates that the rainfall event resulted in a flow event exceeding the 99.5th percentile discharge (the largest 190 discharge events since 1918). Discharge data is for Cohoes, NY and precipitation is from Tribes Hill, NY.

There is often the assumption that because the frequency of intense precipitation events is increasing, flooding must also increase. However, in Figure 2, we see that this isn’t necessarily the case. The red squares indicate a rainfall event that generated the highest flow event in a given year. As we see, of the 25 intense precipitation events, only nine result in a discharge exceeding the 99.5th percentile value ( equivalent to being one of the top 190 values in the nearly 100 year record). And, as seen in Figure 1, there is no obvious upward trend in discharge on the Mohawk despite the increase in intense rainfall. Thus, as suggested here, there is often only a partial connection between intense precipitation and river flow.

This partial connection between peak rainfall and peak flow occurs because factors in addition to rainfall intensity control high flows. For a 2.5 inch rainfall event, there is often sufficient storage capacity within the soils of the watershed to adsorb and slowly release a large portion of the incoming rainfall, particularly in the summer when the landscape has been drying down. Of the 25 precipitation events greater than 2.5 inches, only 7 occur between November and May when the watershed would be very wet and rainfall would most effectively lead to river flow. So, while rainfall intensities may be increasing, this intense rainfall most often comes in the summer, at the wrong time of the year to generate large flows. Thus, instead of looking at rainfall intensity alone, it is often more important to look at the connection between rainfall and very wet conditions, a fact we will discuss later.

How do high flows vary across the entire Mohawk River Watershed? The Mohawk River watershed is comprised of several subbasins with distinctly different features. To the south it encompasses high elevation land in the Catskills Mountains. To the north, portions of the watershed fall on the lower flanks of the Adirondacks. The central part of the watershed consists of a region of rolling hills in a broad valley bottom. Thus, it would seem possible different meteorological and watershed processes control high flows in different locales.

If one repeats the analysis done for the flow at Cohoes as in Figure 1, but instead for Schoharie Creek (which drains from the Catskills) one sees a much more dramatic shift in the number of large discharge events occurring in recent years. Of the 65 largest peak flows since 1940, 47 have occurred since 1970. Given that we do not see this dramatic shift in all subbasins, it appears that there have been distinct shifts in streamflow and precipitation over some parts of the Mohawk River basin but not necessarily across the entire basin.

To more systematically map spatial changes in peak flow across the Mohawk River basin and beyond, we assessed the presence of a trend in peak annual discharges on 16 different rivers and streams. All the waterbodies we assessed had at least forty years of recorded spanning at least until 2005. We calculated the slope of a trendline fit to these data points of peak flow. A large upward slope in the trend indicates a sizable recent increase in the magnitude of peak flows. A small slope indicates little to no measureable change in the magnitude of recent peaks. As seen in Figure 3, some locales have large changes in trend, but many do not. This suggests that the different subbasins are not always subject to the same meteorological inputs and that it may be possible for different subbasins within the Mohawk watershed to experience different trends in flow and precipitation.

Possible differences in meteorological inputs are evident when examining different influences of tropical storm trajectories on different parts of the basin. Using trajectories compiled in the National Climate Data Center IBTrACS database (http://www.ncdc.noaa.gov/ibtracs/index.php), one finds that the likelihood of a tropical storm or its remnants directly tracking over the northern portion of the Mohawk basin is much less than for the southern portion of the basin. For instance, based on historical storm tracks, the area over the Catskills has been subject to upwards of 10 major tropical storms since 1842. The area over the central and northern parts of the basin has been subject to only four. Two of the streams with large slopes in trend line of peaks drained from the Catskills (Schoharie Creek and Beaver Kill). This is not to say that all high stream flows in the Catskills have been due to tropical storms (many have not), but it is meant to suggest that even across relatively small distances there can be large differences in meteorological processes. Notably, these localized changes over the Catskills suggests that recent high flows on Schoharie Creek and Beaver Kill may be due to natural variability in certain storm patterns, not a wide-ranging change in climatology. Figure 3. Trends in annual peaks for rivers and streams with more than 40 years of data. While a few sites have had significant increases in peak flows, many others have not. The lack of consistent increases across all sites in the region suggests that there is no one cause of these trends. The trend is calculated using the non- parametric Sen slope method and units are in mm day-1 yr-1.

What has caused flooding in the past and how might this change in the future? Peak flows are not always caused by the same processes from year to year. In Table 1 below, we identify the cause of high flows for the 15 highest daily flows observed at Cohoes, NY since the 1920’s. These are the highest flows depicted in Figure 1. We assigned the cause of each high flow by analyzing historical precipitation and snow cover data for a number of meteorological stations in the Mohawk Valley. The causes of high flow can be broken down into five categories: tropical storms (i.e. hurricanes or their remnants), snowmelt, moderate rainfall on very wet soils, ice jams, and rain on snow events. Tropical storm events bring very large rainfall amounts, often greater than 5 inches per day and sometimes more than 10 inches over several days. Snowmelt events entail the melting of a sizable snowpack. In some cases, this melt is accompanied by low to moderate amounts of precipitation (< 2 inches over several days). Moderate rainfall on wet soils occur when event rainfall amounts are often less than 3 inches but the rain falls on very wet soils due to very recent snowmelt or other recent sizable rains. As an example, for the 1977 event, only 2.5 inches of rain fell over three days, but it occurred only days after snow had melted according to meteorological station records. Ice jamming events occur when accumulated ice impedes flow on the river. At the site of the damming, there is often localized flood. Presumably when the ice dam gives way, this can lead to a brief flood pulse downstream of the ice dam location. Rain on snow events occur when sizable amounts of rain fall on a snowpack that does not greatly diminish by the end of the storm event. In this rain on snow event, snow is assumed to enhance the transformation of rainfall into runoff.

Table 1. Fifteen highest daily flows on Mohawk River at Cohoes, NY since 1919. Note, as in Figure 1, flows are given per unit area instead of as volume.

Event Date Flow at Cohoes, Cause of High Flow NY (in. /day) Prior Ice Jam; Moderate Rain on Wet 3/19/1936 1.21 Soils 3/14/1977 1.05 Moderate Rain on Wet Soils 8/29/2011 1.04 Tropical Storm 1/20/1996 1.00 Ice Jam; Snow Melt with Minor Rainfall 9/22/1938 0.97 Tropical Storm 6/29/2006 0.97 Moderate Rain on Wet Soils 10/17/1955 0.94 Tropical Storm 1/9/1998 0.88 Moderate Rain on Wet Soils 3/22/1980 0.85 Moderate Rain on Wet Soils 4/3/2005 0.85 Moderate Rain on Wet Soils 3/6/1964 0.84 Ice Jam; Snow Melt with Minor Rainfall 4/5/1960 0.82 Moderate Rain on Wet Soils 4/17/2007 0.81 Moderate Rain on Wet Soils 4/10/2001 0.75 Rain on Snow 4/6/1956 0.74 Snow Melt

In acknowledging that a range of different processes can lead to high flows, it becomes harder to simply conclude that a changing climate will increase the frequency of floods. That is, one would not expect all processes to necessarily be enhanced because of a changing climate. For instance, events linked to snow or ice jams would likely decrease in risk because of a likely reduction in snowpack depth and ice formation.

Of events not linked to snow or ice accumulation, the direction of change in a changing climate remains somewhat unclear. The largest flow events have been caused by tropical storms and moderate rainfall on wet soils. Neither of these causative events is yet known to change in a definitive way in a changing climate. While warmer ocean water may increase the energy in a hurricane, this doesn’t dictate other factors important to flood causation in inland regions such as hurricane trajectory, speed of passage, and precipitation quantity. The recent Intergovernmental Panel on Climate Change Report notes that there remains low confidence in changes in intense hurricane activity in the next several decades (IPCC 2013). Large flow events that occur from moderate rainfall on wet soils sometimes follow extensive snowmelt, but other times they occur due to periods of persistent rainfall. In the mid-latitudes, such persistent rainfall often occurs when the jet stream has larger southern divergences that slowly propagate. Scientists are just beginning to investigate how a changing climate may influence variations in movement of the jet stream (Francis and Vavrus 2012).

Conclusions This fact sheet is not meant to provide a definitive answer on the magnitude or frequency of future floods. Instead, it is meant to provide some insight into how recent large discharge events fit into the bigger picture of variations in hydrology in the Mohawk watershed over time. Additionally, it is meant to provide some context for understanding why future flooding remains difficult to predict even if there are robust global scale changes in climate. In short, high flows on the Mohawk and its tributaries result from a number of different processes which can in some cases vary by location within the Mohawk watershed. Because of the diversity of these processes, the possible interactions between these processes, and the complexity of atmospheric controls on these processes, there remains a large deal of uncertainty in how flood frequency may change in the future in the Mohawk River watershed.

References:

Degaetano, A.T. 2009. Time-Dependent Changes in Extreme-Precipitation Return-Period Amounts in the Continental . Journal of Applied Meteorology and Climatology, 48, 2086-2099.

Frances, J.A. and S.J. Vavrus. 2012. Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophysical Research Letters, 39: L06801.

IPCC, 2013: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.