Improving Water-Release Policies on the Delaware River Through ...

informs Vol. 41, No. 1, January–February 2011, pp. 18–34 issn 0092-2102 eissn 1526-551X 11 4101 0018

®

doi 10.1287/inte.1100.0536 © 2011 INFORMS

THE FRANZ EDELMAN AWARD Achievement in Operations Research

Breaking the Deadlock: Improving Water-Release Policies on the Delaware River Through Operations Research Peter Kolesar

Columbia University, New York, New York 10027, [email protected]

James Serio

Hancock, New York 13783, [email protected]

The Delaware River provides half of New York City’s drinking water, is a habitat for wild trout and American shad, and has suffered three major floods in the last five years. The water releases from three New York City dams on the Delaware River’s headwaters impact the reliability of the city’s water supply, the potential for floods, and the quality of the aquatic habitat. This project’s objective was to revise the release policies to benefit river habitat and fisheries without increasing New York City’s drought risk. Changes in the release policies were constrained by the dictates of two US Supreme Court Decrees and the need for unanimity among four states and New York City. We describe the analyses and the politics that led the Delaware River Basin Commission to implement our operations-research–based adaptive release framework in October 2007. In addition to meeting our habitat improvement goals and drought-risk constraint, our algorithm conservatively decreases end-ofsummer reservoir levels more in wet years, thereby modestly increasing flood protection during the hurricane season, and is substantially simpler to administer. Key words: environment; government: regulations; inventory/production: applications; natural resources: water resources; programming: quadratic; dams.

to devise the basis for new OR-based release rules, which were implemented in October 2007. We estimate that they increase critical summertime fish habitats by about 200 percent, while increasing the city’s drought risk by only 3 percent. The new release rules also mitigate flood risk and are significantly simpler to administer. Moreover, their intuitive structure has become the foundation of a new paradigm for managing the Delaware and is at the heart of recent developments that promise even more beneficial management of this valued resource (New York State Department of Environmental Conservation and Pennsylvania Fish and Boat Commission 2010, New York City Department of Environmental Protection 2010).

A river is more than an amenity, it is a treasure. Justice Oliver Wendell Holmes

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n January 2006, a coalition of four conservation organizations successfully undertook an operations research (OR) project to influence the four states and the federal government who jointly administer the Delaware River (the Delaware). The project’s objective was to change the policies governing water releases into the river from the three New York City (NYC) reservoirs on its headwaters to improve the aquatic habitat for several species of wild fish and other aquatic species that inhabit the upper Delaware without increasing the water-supply risk to New York City. The coalition collaborated with the river’s managers 18

Kolesar and Serio: Improving Water-Release Policies on the Delaware River Through OR

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Interfaces 41(1), pp. 18–34, © 2011 INFORMS

In this paper, we describe the technical and political paths the coalition took to achieve the successful implementation of the new Flexible FlowManagement Program (FFMP) (Office of the Delaware River Master 2007) by the Delaware River Basin Commission (DRBC). The project that led to this substantial policy change in what had been an adversarial setting is being studied in other regions in which acrimonious water-allocation disputes are currently raging (USA Today 2009, Jehl 2002).

The Delaware System and Its Water-Allocation Issues The Delaware originates in the Catskill Mountains in New York State (NYS). It successively forms the border between New York and Pennsylvania, Pennsylvania and New Jersey, and New Jersey and Delaware, and flows 330 miles past Trenton and Philadelphia into the Delaware Bay. Consequently, all four states have specific and sometimes competing claims on its water. Interstate disputes about the allocation of these waters have a long and complex history (Albert 1987, Galusha 2002), and the Delaware’s current water allocation is dictated by two decrees (U.S. Supreme Court 1931, U.S. Supreme Court 1954). Three NYC reservoirs on its headwaters provide nearly half of New York City’s drinking water and divert about half of the water that enters the reservoirs to the city. But, the upper Delaware is also one of the finest wild trout fisheries in the United States, is the spawning ground of the migratory American shad, and hosts several small colonies of the federally endangered dwarf wedge mussel. Managing the water releases from the dams is a regional and multidimensional problem. The magnitude and timing of the releases clearly impact the reliability of New York City’s water supply, affect the quality of the aquatic habitat for all three targeted species, and impact the propensity for flooding throughout the entire river valley. The Delaware system map and diagram (see Figures 1 and 2) illustrate the system’s complexity. The three NYC reservoirs—Cannonsville, Pepacton, and Neversink—each have significantly different storage capacities, drainage areas, and water qualities. The location of the Cannonsville Reservoir makes its

Cannonsville net inflows

Pepacton net inflows

Pepacton 140 BG

Cannonsville 96 BG

Neversink net inflows

Neversink 35 BG

West branch Hancock

East branch Total NYC diversions

Main stem

Lake Wallenpaupack 38 BG

Neversink Port Jervis

Schematic view with NYC diversions

Montague gage

Figure 1: The schematic shows the main elements of the upper Delaware River system.

releases particularly critical for the wild trout population; these releases were the focus of much of our work. Four regions of the upper Delaware—the West Branch, the East Branch, the upper mainstem of the Delaware (the section of river from where the East and West Branches join at Hancock, NY down some 30 miles to Callicoon, NY), and the Neversink River—each with distinct fauna, were of concern to environmental groups. Water releases from the dams are made in conformance with the aforementioned Supreme Court Decrees, which permitted New York City to build the dams, and allow it to divert up to 800 million gallons of water per day for its use, as long as a minimum flow of 1,750 cubic feet per second (cfs) is maintained at the US Geological Survey (USGS) gauge at Montague, New Jersey. The releases are administered by the USGS River Master under the purview of the DRBC, an intergovernmental agency that includes the states of New York, New Jersey, Pennsylvania, and Delaware, and the Federal Government. The USGS Office of the Delaware River Master is responsible for administering the provisions of the decree relating to releases of water from the reservoirs into the headwaters of the Delaware and the diversions from these reservoirs to New York City for water supply. New York City joins the four states as an equal party to the Supreme Court Decrees. According to the Delaware River Compact (i.e., the agreement that established the DRBC), any changes to the water-release rules must be unanimously approved by these “decree parties.” This

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Kolesar and Serio: Improving Water-Release Policies on the Delaware River Through OR Interfaces 41(1), pp. 18–34, © 2011 INFORMS

Figure 2: The map shows the upper Delaware River Basin.

requirement for unanimity among parties with conflicting interests was a major challenge to our efforts to change the release policies. We began our OR project in January 2006; the extant release rules were set to expire in May 2007 (Office of the Delaware River Master 2006). The senior author, a fly fisherman and an OR professor, took the lead in unifying a coalition of conservation organizations that included The Nature Conservancy, Trout Unlimited, The Delaware River Foundation, and ultimately Theodore Gordon Fly Fishers. The coalition’s goal was to scientifically develop alternative release policies in time for adoption by the DRBC. Thus, we had a 16-month window in which to perform the

basic analysis, develop a factual understanding of the water-risk issues, understand potential fishery habitat benefits, develop a concrete alternative policy proposal, and persuade the decision makers to implement the results. Experience and expertise about the Delaware and its aquatic habitat needs came largely from the coalition members. OR and hydrology expertise were provided under the auspices of Columbia University’s Earth Institute, Department of Earth and Environmental Engineering, and Graduate School of Business. This pro bono project had no client or sponsor, and the conservation coalition provided only modest funding for analysis. Its broad initial goal, to improve


the aquatic environment of the upper Delaware River, was not clearly defined, partially because the members were not in full accord on what that meant. The “client,” although we never used the word during the project, was implicitly nature or the fish. Our challenge was to insert ourselves and our ideas into the political decision-making process. We should emphasize that the coalition’s goal was to improve aquatic habitat, not fishing, because what is good for the fish and what fishermen might prefer can differ. Heated disputes between the fishing community and the Delaware’s water managers had been going on for more than 30 years (The Binghamton Press and Sun Bulletin 1972, Cross 1976), and so we were entering a domain that was more adversarial than collaborative. Therefore, a significant aspect of this project was moving from the initial diverse positions within the coalition to a more coherent stance. We had to create a collaborative relationship between the coalition, the DRBC, and the decree parties, albeit without ever being in 100 percent agreement on all key issues. The scientifically based OR modeling helped to bridge the sometimes cavernous gaps between the disputing parties.

Specific Fishermen Issues For decades the fishing conservation community, focused mostly on trout, a coldwater fish and a good indicator of river health, had complained bitterly that DRBC release policies were needlessly conservative— both far too low and irrationally variable during critical summertime months. The politics were complex and contentious. A few vocal partisans dominated public discussions, arguing on the basis of personal experiences and private interests, rather than from science or a broad perspective on system costs, benefits, and risks. Critical reservoir data were not readily accessible to the coalition. To even present a proposal or an analysis, one needed to be invited by one of the decree parties; these invitations were not extended lightly. The trout fishery in the upper Delaware depends on coldwater releases from the bottom of the dams without which only warm water species would be supported. The conservationists’ basic problems with the extant release regime were that chronic low summertime flows led to loss of aquatic habitat and to high,

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potentially lethal water temperatures for both trout and the invertebrates on which trout feed. Rapid, severe, and unnatural oscillations between high and low flows amplified the destruction. Roughly every other year, summertime flows in the upper Delaware were inadequate to maintain the cold temperatures and water levels required to sustain a healthy trout population. At the outset of the project, these problems were not well documented. Using statistical analyses of historical river flows, our own simulation models of the river, and later on, custom models of the Delaware that had been developed for the DRBC, we were able to document the magnitude of the problems and eventually provide the DRBC with ORbased recommendations for improved release rules. The environmental event that precipitated the coalition’s unification illustrates the problem as fishermen saw it. On July 10, 2005, the reservoirs stood at 88 percent capacity—well above the DRBC’s “droughtwatch” level of 63 percent. Air temperatures in the upper Delaware Valley were in the upper 90s, and the release from the critical Cannonsville Reservoir was an inadequate 125 cfs. At this low flow, river water temperatures soared into the lethal range for trout. Streambeds were exposed, leading to destruction of the aquatic insect populations on which the trout depend. (A release of 300 cfs is needed to just wet the river bottom from bank to bank.) The fishing community was aghast. The Friends of the Upper Delaware River (FUDR), a vocal environmental organization, petitioned the DRBC to institute an emergency release regime to increase Cannonsville releases to 480 cfs. The DRBC rejected both the FUDR proposal and even a scaled-back emergency release of 300 cfs as being too risky. The chair of the DRBC’s Regulated Flow Advisory Committee called a public meeting to explain the DRBC decision to the fishing community. The first author’s detailed notes include two statements made by the commissioners at that meeting. These statements provoked the authors to action. The first was a candid admission that “We always knew these [designed] flows would only be adequate 50% of the time, so why do you fishermen come back here complaining every other year? You fishermen have to realize that I have landscapers who get paid to water lawns.” The second was that “As long as the fishing community speaks with

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multiple disjointed voices, you will not be taken very seriously.” Our statistical analysis showed that the DRBC’s numerical rationale against the emergency release was not accurate; this misguided rejection by the DRBC propelled the senior author to initiate serious OR-based water-release policy research with the dual intentions of using the research results to provide a factual basis for long-term policy revisions and to help unify the hitherto fractured environmental community. Heated as the arguments had already been, public disputes about the release policies became even more intense when, between 2004 and 2006, the upper Delaware Valley endured three major floods. Some groups blamed the downriver flooding on New York City’s practice of keeping its reservoirs as full as possible (Gluck 2009). Problems with the Structure of the Extant Rules Several fundamental flaws had permitted this lowflow situation to persist for many years. First, in the negotiations that led to the original 1931 Supreme Court Decree, the interests of New York City and the lower Delaware Basin were well represented; however, because no substantial environmental movement existed at the time of the decree, the upper river’s environment was not adequately protected. Second, because environmental science was in its infancy, the understanding of possible habitat benefits was minimal. By 2006, the time frame of our analyses and lobbying, the conservationists had gotten a seat at the table, environmental science had greatly advanced, and OR’s integrative models and computational capabilities made it possible to quantitatively analyze a once bafflingly complex problem. Third, in its overly conservative approach to water management, New York City had convinced the other decree parties that only worst-case water-usage scenarios should be considered when estimating risks and benefits and designing future release policies. Structural problems also existed. The operating rules, then in their eighth revision, had evolved over some 50 years. Based largely on experience and intuition, with only a modest scientific basis, they were a patchwork of compromises and rule-of-thumb approaches to an incredibly complex problem. The release rules, while adhering to the Supreme Court


River master estimates Montague releases

New York DEC looks up conservation releases

NYS DEC augments conservation release from the thermal bank

New York City DEP allocates releases to the reservoirs Figure 3: The flowchart illustrates the decision process of the extant release policy in 2006 (Revision 7).

dictates regarding NYC diversions and Montague flows, tried to balance competing concerns; these included New York City’s desire to hold back water to hedge against the possibility of future droughts, Delaware’s need to keep enough flow to protect the oysters of Delaware Bay, New Jersey’s concerns to have sufficient water to divert into its canals, and New York State’s desire to maintain the aquatic habitat in the upper river. Figure 3 illustrates the sequence of release decision making of the extant release policy (Delaware River Basin Commission 2004). Over the years, the New York State Department of Environmental Conservation, NYS-DEC, had defined minimum “conservation releases” to maintain some small flow in the upper Delaware regardless of what happened 100 miles south at Montague. From the fishermen’s perspective, the conservation release schedule was laughingly low. For example, even under normal reservoir storage, the extant rules permitted a release of as little as 60 cfs from the Cannonsville Reservoir in the summer; as we note above, a rate of about 300 cfs was needed just to cover the river from bank to bank. To somewhat mitigate these low flows, the existing rules included a set of “thermal” or “conservation” water banks—bookkeeping accounts of water that could be released into the river at the discretion of NYS-DEC to augment flows during high-temperature periods. But the conservation banks, which contained 12.9 billion gallons of water—only 4 percent of New York City’s


allocation—were insufficient to regularly meet environmental needs. On numerous occasions, the conservation water bank would be depleted early in the season, and critical high-temperature events would occur with no reserve on the books—although plenty of water might be in the reservoir. This had happened in July 2005. Administering the banks was problematic. Meeting temperature targets involved integrating weather forecasts, current stream conditions, and experience to predict how much water needed to be released. This required an experienced staff to constantly monitor and interpret stream conditions. Additionally, bank water sometimes went unused at the end of the season; such held-back water would typically be spilled early in the following season—to no one’s benefit. We concluded that the bank concept could not be made to work and needed to be replaced.

Building Toward an Operations Research Solution Our approach viewed water-release policy from the perspective of inventory theory and feedback control. The key to success, we hypothesized, would be to change the timing of releases (not the amount) so that less water would be wasted by spills over the top of the dams. Our strategy was to quantify the risk and benefit trade-offs from increased conservation releases, and create a simple algorithm that, in feedback mode, keyed release quantities explicitly to reservoir storage levels, thus releasing more water when it was available and less in times of shortage. Such a policy had to be keyed to the seasonally changing needs of both the aquatic ecosystem and New York City. In this spirit, we formulated what became known as the adaptive release framework, a linked set of conservation release matrices, one for each reservoir in each season, which prescribed the conservation releases as a function of the amount of water actually in storage. We kept the algorithmic structure as simple as possible to enable us to identify feasible, near-optimal release parameters. We also wanted to facilitate the eventual implementation of our adaptive release framework. We kept the algorithmic structure as simple as possible to enable us to identify feasible, near optimal release parameters. We also wanted to

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facilitate the eventual implementation of our adaptive release framework. We did the research using a series of increasingly complex simulation models that were driven by a 73-year daily flow history of the Delaware and its tributaries. We conducted our simulation trials under a regime of balanced, orthogonal, statistically designed experiments. We then used regression analysis to derive a set of highly reliable predictive equations for key performance measures, including reservoir storages and refill probabilities, drought-day risks, and sizes of aquatic habitats by species, river segment, and season. We used these predictive equations, which became our main policy design tools, to zero in on the release policy that the DRBC ultimately adopted, almost in its entirety, in October 2007 as the FFMP (see Figure 4). A subsequent series of revisions by the DRBC in June 2009 brought the releases into nearly exact conformance with our original proposal.

Initial Release Research Let’s go back to 2004 and see how this research began. One suggested release proposal, which had received a good deal of publicity, focused on the West Branch of the Delaware and called for a minimum release of 600 cfs from the Cannonsville Reservoir throughout the summer. In its simplicity, this proposal ignored the other reservoirs in the system and the other seasons of the year. Could this intuitively based plan work? What would be its impact on reservoir storage? In fall 2004 at a conclave at the Catskill Fly Fishing Center and Museum in Livingston Manor, New York, the senior author volunteered to test this policy. Two undergraduate Columbia University environmental engineering students assisted. Using 11 years of daily data on diversions, reservoir release spills, and storage, which were newly obtained from the NYC Department of Environmental Protection (NYC-DEP), we created an Excel-based discrete-event simulation that compared the 600-cfs policy to the actual reservoir history over the 11 years from 1982 to 1992. For each year, we simulated releases for the 122 days from June 1 to September 30, starting each run with the actual storage behind the reservoirs on June 1 of that year. The simulated 600-cfs policy produced a 50 percent increase in average daily flows over the 11 summers. And, of great importance to the fishermen, the minimum daily flow increased from 16 to


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Table 3 schedule of releases (cfs) with 35 mgd available Winter

Spring

Summer

Fall

Dec 1–Mar 31 Apr 1–Apr 30 May 1–May 31 Jun 1–Jun 15 Jun 16–Jun 30 Jul 1–Aug 31 Sep 1–Sep 30 Oct 1–Nov 30 Cannonsville storage zone L1-a L1-b L1-c L2 L3 L4 L5 Pepacton storage zone L1-a L1-b L1-c L2 L3 L4 L5 Neversink storage zone L1-a L1-b L1-c L2 L3 L4 L5

1500 250 110 80 70 55 50

1500 ∗ 110 80 70 55 50

∗ ∗ 225 215 100 75 50

∗ ∗ 275 260 175 130 120

1500 ∗ 275 260 175 130 120

1500 350 275 260 175 130 120

1500 275 140 115 95 55 50

1500 250 110 80 70 60 50

700 185 85 65 55 45 40

700 ∗ 85 65 55 45 40

∗ ∗ 120 110 80 50 40

∗ ∗ 150 140 100 85 80

700 ∗ 150 140 100 85 80

700 250 150 140 100 85 80

700 200 100 85 55 40 30

700 185 85 60 55 40 30

190 100 65 45 40 35 30

190 ∗ 65 45 40 35 30

∗ ∗ 90 85 50 40 30

∗ ∗ 110 100 75 60 55

190 ∗ 110 100 75 60 55

190 125 110 100 75 60 55

190 85 75 70 40 30 25

190 95 60 45 40 30 25

Figure 4: The table shows the DRBC’s flexible flow management release matrices as implemented on September 27, 2007. Each matrix entry specifies a conservation release (in cfs) to be made from that reservoir when its storage is in the indicated band. ∗ Storage zone does not apply during this period. Releases will be made in accordance with zone L1-c.

600 cfs. However, these improvements came with a 10 percent decrease in end-of-summer reservoir storage. The year-by-year results showed that in dry summers the releases were only slightly above those of the extant policy; as a consequence, the simulated end-ofsummer reservoir storage in dry summers was only slightly below the actual. However, in wet summers, the upper river was typically starved of water because downstream flows were sufficient to meet the Montague requirement on their own so the River Master did not obligate New York City to release water from the dams. The substantial difference between the simulated and actual releases in wet summers was beneficial to the fishery and led to a significant reduction in end-of-summer storage; but, because of all the precipitation that flowed into the reservoirs, the end-of-summer reservoir levels, although lower than under the traditional policy, were still more than

sufficient. This observation prompted us to develop the feedback concept for water-release policy, which ultimately became the core of the FFMP. Still, we anticipated that the average 10 percent end-of-summer storage reduction would be unacceptable to New York City; therefore, we reran the simulation using conservation release values of 450, 300, and 150 cfs. The 300-cfs simulation run, which was 50 percent of what had originally been proposed, produced a slight 4 percent decrease in average flows, but still moved the minimum flow up from 16 to 300, and reduced end-of-summer reservoir storage by less than 2 percent. We conjectured that a minimum summertime release policy somewhat above 300 cfs could benefit the fishery at little increase in risk to New York City. More than a decade earlier, before the release valves on the Cannonsville reservoir were modified, the dam hardware had essentially dictated


a 325-cfs release policy, and many fishermen looked back fondly to that time. The knowledge and experience gained from these experiments prepared us for the more intensive OR analysis that followed the crisis of July 2005.

An Operations Research Perspective Managing water releases from dams is a type of inventory-control problem, and the analyses of such problems have a long history in OR. Indeed, the first OR dissertation, written at MIT (Little 1955), optimized water releases from hydroelectric dams and applied concepts from a foundation work on inventory theory (Dvoretsky et al. 1952). However, the Delaware’s water-release problem has unique features and a complexity that defy direct application of inventory theory. Water inputs to the system are stochastic, seasonal, and nonstationary—they cannot be readily modeled. Water diversions by New York City are also stochastic, seasonal, and partly dependent on policy decisions and water conditions outside the Delaware Basin. Each day, six interrelated decisions must be made: how much water is to be released into the river, and how much water is to be diverted to New York City from each of the three reservoirs. Although an “optimal release quantity” for any water-storage level, by reservoir and by day of the year, should theoretically exist, determining that quantity is another issue. Because the reservoirs had different capacities, drainage areas, water qualities, and unique impacts on four fishery regions, coupled with the infeasibility of modeling inflows, we were not able to optimize these release quantities. Moreover, taking a longer view, the system state should not simply be the amount of water behind each reservoir; it should include an estimate of future inflows and outflows over a reasonable planning horizon. The inflows, correlated stochastic processes, are the result of natural forces that are subject to macro environmental factors that have proven very difficult to forecast (Wang 2009). Bearing this complexity in mind, we began the next phase of our release research by working with a very simple model: we considered only two seasons—summer and “not summer” (winter for

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convenience)—and only three release levels—high, moderate, and low. Additionally, we simplified the system state to be the total aggregate reservoir storage across all three reservoirs. Our research question was: What should the aggregate release quantities be? Our objective was to improve the fishery; however, because we did not yet have a way to quantify this, we focused on the magnitude of river flows. Our chief constraint, imposed by the decree parties, was that our policy be drought-day neutral (i.e., have the same or fewer drought days as the current policy).

Risk and Drought-Day Neutrality Because the primary function of the reservoirs is to supply New York City with water, the dominant concern in managing the dams is to avoid running out of water. This is challenging because the dams hold only about one year’s supply of water and the city diverts half of the total reservoir inflow. The official risk measure that the city, the decree parties, and the DRBC use is drought days—days on which the total reservoir storage falls below a benchmark safe value for the day. For many decades, they had used benchmark “drought curves,” which had been derived in an undocumented rule-of-thumb fashion. Whatever criticisms one might make of the drought curves, from the DBRC’s and decree parties’ perspectives, they had worked for some 30 years. So, our strategy was to work with these curves; challenging them would be politically risky and consume so much time that we would never meet the May 2007 target date for a rules revision. Figure 5 illustrates reservoir capacity and the three drought curves—drought watch, drought warning, and drought emergency. To assess a release policy, the DRBC accumulated total drought days over a designated planning horizon and compared them to its benchmark. All candidate policies had to be evaluated using the DRBC’s “official” OASIS simulation model of the Delaware (HydroLogics 2002, Phillips 2004). OASIS is a one-day time-step discrete-event water flow simulation that models the Delaware River and all its tributaries from above the three New York City dams down to the Delaware Bay. It is capable of modeling a wide variety of release policies and produces, as output, daily time series of reservoir levels and


280 260 240 220 200 180 160 140 120 100 80 60 40 20 0

Watch Warning Drought Capacity 1-Jun 15-Jun 29-Jun 13-Jul 27-Jul 10-Aug 24-Aug 7-Sep 21-Sep 5-Oct 19-Oct 2-Nov 16-Nov 30-Nov 14-Dec 28-Dec 11-Jan 25-Jan 8-Feb 22-Feb 7-Mar 21-Mar 4-Apr 18-Apr 2-May 16-May 30-May

Storage in BG

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Figure 5: The graph shows DRBC/NYC drought curves.

water flows at critical points along the river. Although never fully validated scientifically, it was based on an earlier river simulation model that had been developed for the DRBC by the Army Corps of Engineers (Camp Dresser and McKee 1981). OASIS had become, by the time of our work, the DRBC’s policy-analysis benchmark. All OASIS simulations were driven by a constructed water inflow time series over the period from January 1, 1928 to September 30, 2000, and by decree party dictate, all simulation runs were to assume that New York City was diverting its full 800mgd allotment daily. In February 2006, the decree parties proclaimed that any revision to the rules would have to be droughtday neutral. During the course of our project, the drought-day standard evolved to become the number of drought days as computed in an OASIS simulation of the last permanent release rule, the so-called Revision 1, under the condition that New York City would be diverting 765 mgd. Thus computed, the droughtday benchmark became 5,531 days. With 26,573 days in the test time frame, this allowed for some level of drought day about 20 percent of the time. We should note that, although the Delaware system might experience a drought day by this definition, this did not imply an actual drought or that New York City did not have ample water. We had concerns with the drought-day constraint. First, the benchmark number was arbitrary; we believed it was excessively conservative. Second, we challenged the modeling presumption that New York

City would be diverting 800 mgd; it had recently been diverting closer to 500 mgd. This 300 mgd difference biased the drought risk measures upward, led to release polices that exposed the Delaware Valley to needlessly high flood risk, and punished the environment with needlessly low flows. We argued that although evaluating a release policy under worstcase conditions was important (an 800-mgd diversion was indeed a worst case), designing and evaluating a release policy under a forecast of what was likely to occur over the foreseeable future—500 mgd in our judgment—was also important. Moreover, we argued that although drought days were useful, they were not the only risk metric. Because the reservoirs store only about one year of water, considering the probability that they refill over the year is important. We also argued that the length and severity of drought periods should be considered. Although the decree parties never publicly agreed with our arguments, they did ultimately slightly soften the drought-day constraint. The Initial Coalition Research: Regressions on Drought Days Given the dominant role of drought days in the thinking of the decision makers, our first major research challenge was to quantify the relationship between releases and drought days. Clearly, higher releases would produce more drought days. But, how many? Details mattered; we needed to understand the differential impact on drought days of summer versus winter releases and releases from, for example, Cannonsville versus Neversink. (Because the winter season is longer, winter releases might have a stronger impact; however, most spilling occurs during the winter and, for a given amount of rainfall, these two reservoirs refill at very different speeds.) We built our initial understanding through a series of experiments in which we used our own Columbia “aggregate simulation model.” We started with a set of simple release policies that had only two seasons—summer and not summer (i.e., winter)— and only three release levels—normal, cautionary, and drought. To simplify the structure further, the cautionary release quantity was always set to one-half of the normal level, and the drought release levels mimicked the DRBC’s extant policy. The other parameter in the model, the trigger, was the percentage of


reservoir storage above the drought-watch curve, at which releases were reduced from the normal to the cautionary level. We ran an initial series of 33 experiments with summertime normal release level set successively at 800, 600, 400, and 200 cfs, normal winter release levels set at 300, 200, and 100 cfs, and three trigger levels: 100, 110, and 120 percent of drought watch. Figure 6 shows the plot of the resulting regression equation. With an R-square of 0.97, it predicted DD, total drought days, as DD = 3199 + 016 ∗ summer + 015 ∗ winter − 1182 ∗ trigger indicating that summer and winter releases had almost the same highly statistically significant influence; the trigger was not statistically significant. The insights from this regression model enabled us to design more realistic policies in our next steps; these policies would become candidates for actual implementation. Subsequently, we created more complex regression models for a variety of performance metrics, including end-of–summer storage, reservoir refill probability, and trout habitat. We integrated these predictive equations into a simple Excel-based releasepolicy design tool. Developing and Proselytizing the Coalition’s Moderate Adaptive Release Policy We formed the coalition in January 2006 in response to advice from the DRBC’s Regulated Flow Advisory Scatterplot of drought days vs. total normal release 225

Drought days

200 175 150 125 100 75 50 200

300

400

500

600

700

800

900

1,000 1,100

Total normal release (summer + winter) CFS

Figure 6: The graph is a regression plot from the drought-day vs. releases calibration trials.

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Committee (RFAC) that the conservation groups needed to speak with a more unified voice. The RFAC chair was also the Assistant Director, Division of Water of NYS-DEC in charge of water policy for the Delaware and also the DRBC’s NYS representative. Because the dams and most of the coldwater fisheries were in New York State, the decree parties and the DRBC had charged NYS-DEC with designing an alternative to the expiring release rules. Thus, NYS-DEC was the natural venue for us to seek collaboration. We reached an informal understanding with NYS-DEC leadership that if we developed a credible policy proposal, they would secure an invitation for us to present our proposal to the other decree parties. In late March 2006, our first informal briefing to the NYS-DEC, including the regressions, garnered enough interest that cooperation began in earnest. These early discussions clarified our mutual objectives and concerns, and narrowed the areas of disagreement. The coalition formulated a set of four release-level, two-season policies, and NYS-DEC agreed to collaborate with us in evaluating them. Because of our theory that releases from Cannonsville provided the most benefit to the fishery, the differences among the candidate policies were predominantly about the levels of the Cannonsville summer normal releases. Although our aggregate analysis had sufficed to build a basic understanding of the system, we now needed to represent the details of the individual reservoirs and the decisions on each of them. To this end, we created our new Columbia “QP simulation,” which modeled daily decisions on releases and diversions from each reservoir. Programmed in Visual Basic for Excel, the model captured the physical characteristics of the system (e.g., reservoir capacities, drainage areas, maximal pipe discharges). Similar to the aggregate simulation model, it was driven by historical daily inflows and diversions for 1982–2004. Although the simulation duplicated New York City’s historical aggregate daily diversions and made aggregate conservation releases according to our adaptive release framework, it remained to allocate these aggregate releases and diversions across the three reservoirs. Our intention was to imitate New York City’s decision process, which placed a premium on diverting

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water of high drinking quality to the city, releasing lower-quality water into the river, and managing the reservoirs to avoid shortage. Research on optimal reservoir operations had shown that to minimize the probability of shortage, the reservoirs should be kept in “refill balance” by equalizing the probability of spillage; this could be translated to equalizing reservoir voids relative to the reservoir drainage areas (Clark 1950, Lund and Guzman 1999, Sand 1984). To do this, we formulated a six-variable, 17-constraint quadratic program, implemented it using Excel’s solver, and imbedded it in the simulation. The quadratic program’s constraints kept the decisions physically and logically feasible, while the quadratic objective function balanced maximizing the water quality of the NYC diversions, minimizing the water quality of releases to the river, and minimizing reservoir imbalance as measured by the sum of squared deviations from perfect reservoir balance (see the appendix). Among the set of candidate release rules, our “moderate” policy seemed particularly attractive. It increased drought days by about 15 percent and promised substantial fishery benefits. With NYS-DEC, we made some modifications and evaluated it further, now using the OASIS Delaware simulation model. The DRBC’s OASIS (HydroLogics 2002) simulation model was the critical analytical tool because the decree parties and the DRBC had agreed that they would only entertain analyses carried out using it. Moreover, several aspects of OASIS supported its use. By modeling the entire basin from the dams to the Delaware Bay, OASIS included relevant details of Delaware River operations and metrics that had not been included in our simpler simulations. It measured drought days exactly as the DRBC had legally defined them. Although our simulations were driven by actual NYC reservoir data for 1982–2004, the only such data publicly available, OASIS was driven by a constructed data set spanning 1928– 2000 and included the 1960s drought of the century, a period of vital interest to the decision makers (Thatcher and Mendoza 1990). Finally, the Delaware River fishery habitat model that would ultimately be central to our analysis was driven by OASIS output (Bovee et al. 2007).


In our initial evaluations of the moderate release policy spanning 1982–2004, historical NYC diversions had averaged 636 mgd; therefore, when we simulated it in OASIS, we wanted to use 636-mgd diversions (OASIS assumed constant daily diversions). Our rationale was that this was a reasonable projection of what was likely to happen over the foreseeable future. However, the NYS-DEC objected because the decree parties permitted only simulations that diverted 800 mgd (this dispute is still unresolved). Still, we ran simulations at both diversion levels, and the results were eye-opening. First, because we felt that 636-mgd diversions better reflected reality, we compared the moderate policy to the existing DRBC policy at 636-mgd diversions. The number of drought days increased by about 10 percent, which was within our original coalition target (although we knew the decree parties were certain to resist such an increase). Conservation releases, our best fishery measure at the time, increased by a remarkable 220 percent. Because average and minimum reservoir storage hardly changed, the moderate release policy seemed to be achieving most of its benefits via a concomitant spill reduction of 22 percent. The results of the simulation of the 800-mgd worstcase scenario highlighted the burden imposed on the non-NYC stakeholders from formulating a policy based solely on this worst case. The diversion difference of 250 mgd was, we argued, water that could have been allocated for conservation purposes, but was held back, only to be spilled later when it did little good for the environment. Worse, under such an 800-mgd-based policy, reservoir storages would be needlessly high, exposing the river valley to increased flood risk. Additionally, drought-day risk was exaggerated by 300 percent. Our detailed analysis of the moderate policy included day-by-day analyses of the most critical period, the 1960s “drought of the century,” and confirmed our position that the moderate policy or a variant would handle the great drought and still substantially improve fishery. The tool we required for ecological-evaluation research, the USGS Delaware habitat model, was soon forthcoming. We also had to examine and question other aspects of the system, which space does not permit us to detail here. These included destructive weekly yo-yo–like swings in releases that were


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responses to fluctuations in downstream power generation releases, and arbitrary and wasteful excess release quantity (ERQ) releases, which were part of the Supreme Court’s 1954 decree. Three major floods in 2004, 2005, and 2006 brought flooding issues to the forefront; in response to public pressure, the NYCDEP and NYS-DEC collaborated in designing a spill [flood] mitigation program that was integrated into the adaptive release framework. Even as we continued to experiment with alternatives, we took the moderate policy on the road. The turning point in the coalition’s efforts to influence the decree parties was our public presentation of the moderate policy at the Delaware River Foundation’s August 2006 “Day on the Delaware.” This all-day event was attended by several hundred persons, including local politicians, representatives of the decree parties, the DRBC, major conservation organizations, and the National Park Service—and many fishermen, canoeists, local residents, and business people who had been impacted by the recent flooding. The dramatic moment came when we showed that the moderate policy would have handled the drought of the 1960s as well as the DRBC’s current policy. Figure 7 shows a time plot of reservoir

storage in the early 1960s, proving that during this critical period the reservoir levels of the moderate policy and the DRBC’s existing policy were essentially indistinguishable. There was a hush in the room. A DRBC commissioner stood and asked, “But these comparisons were done with a 636-mgd diversion, and, as you know, New York City is entitled to take 800 mgd. What would the comparison look like under an 800 mgd diversion?” We had anticipated the question, and flashed on the screen a chart comparing the two policies at an 800 mgd diversion, which again showed no appreciable difference. The nature of our relationship with the decision makers had changed; the decree parties would soon move informally towards adoption of the adaptive release concept and algorithmic structure, although they still did not accept our release quantity recommendations.

Assessing Habitat Impacts at Last Our team had been working on the assumption that increased coldwater flows would provide a benefit for the fish; however, when we began, a quantitative link between the releases and habitat did not exist. In October 2006, after we were 10 months into our

Total Delaware system storage 1964–1968 Comparing moderate adaptive policy and revision 1 250,000

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research, the USGS-developed Delaware River Decision Support System model (DRDSS) (Bovee et al. 2007) became available. The DRDSS produces seasonal habitat estimates for several fish species in 11 distinct river segments based on extrapolations from detailed on-stream studies. Given an OASISgenerated time series of daily stream flows, the DRDSS uses internal USGS-created translation functions, one for each habitat segment, to compute habitat estimates, and adjusts them to account for temperature variations. Moving Toward Our Release Proposal: CP2 By early fall 2006, the decree parties had informally adopted the adaptive release framework as a basis for a new release policy. In addition, the NYS-DEC– and NYC-DEP–designed spill-mitigation component was completed and fit nicely into the adaptive release framework. Without agreeing on all points, an informal consensus had developed between the coalition and the DEC to move forward together on developing an adaptive release policy that, including spillmitigation releases, would have seven release levels and five seasons. NYS-DEC fisheries experts took the initiative of defining the drought-condition release levels, which were higher than what we had been anticipating; we immediately signed on. We took the initiative throughout the early fall to define the normal release levels. Our regression equations permitted us to relate release levels to risk and habitat benefit; we begin our risk and benefit trade-off analysis. Although a number of issues had been resolved, differences of opinion remained within the coalition about the emphasis that should be given to the different branches of the river. The most contentious were about releases from Cannonsville into the West Branch, as compared to releases from Pepacton into the East Branch. A generally shared opinion was that releases into the West Branch had higher potential benefit to the river as a whole, but the leverage was unknown. To help resolve these issues and further explore the risk habitat trade-offs, we again used the principles of statistical experimental design. Our results from the DRDSS model confirmed that Cannonsville releases were at about twice the habitat benefit per cfs as Pepacton releases. In November 2006, the NYS-DEC proposed an adaptive release policy, which it called THDPP.


Although the policy included many of our ideas, its normal release levels were short of what we felt was needed and feasible. We met with the NYSDEC experts in late January 2007 to share the extensive OASIS experiments and DRDSS analyses we had done to identify a risk-feasible, more ecologically beneficial THDPP variant as a candidate for joint presentation to the decree parties. On a strategic level, we proposed that future release policies should be adaptive in three senses. First, the releases should be keyed to reservoir storage via seasonally adaptive release matrices. This was already the agreed working premise. Second, we introduced a concept that was viewed as revolutionary (and is still not fully accepted by the decree parties)—that one policy (adaptive release matrix) should be adopted for normal NYC diversion levels (e.g., 550 mgd) and another policy for extreme NYC diversion levels (e.g., 765 mgd). Which policy would be in effect at a given time would be dictated by forecasts of NYC diversions over the coming period. Third, we advocated that the overall policy itself should be revised and adjusted periodically as knowledge and experience were gained from the implementation. We used the DEC’s THDPP proposal as a starting point for a series of variants in which we increased the Cannonsville summer normal release from 250 to 450 in steps of 50 cfs, and the Cannonsville spring normal release from 250 to 450 in steps of 50 cfs. We varied NYC diversions from 500, to 636, and to 765 mgd, and we turned both spill mitigation and the ERQ on and off. Based on these trials, we built regression models linking releases, diversion levels, mitigation, and the ERQ to drought days and habitat. This example illustrates a regression equation with an R-square of 0.97. Total Drought Days = −6925 + 197 CanSummerNormal + 0339 CanSpringNormal + 149 Diversions + 727 SpillMitigation + 382 ERQ The important insight here is that diversions have a huge impact on risk. Modeling diversions at 765 mgd when they would actually be only 550 overstates drought days by about 70 percent. Spill mitigation, which was justified from a flood perspective, added


73 drought days; however, we felt that this burden was being imposed unfairly on the fishery. Important from the fisheries perspective was that summer normal reservoir level releases from Cannonsville had almost six times the drought-day impact as spring releases. The ERQ also had a huge impact, adding 382 drought days, and we questioned its inclusion in the FFMP. The ERQ policy had originated in the US Supreme Court decrees as a way to ensure that the city would not hold back large amounts of water during the summer, only to spill it later. We concluded that, from a fishery perspective, the ERQ water could be better used to increase Cannonsville summer L2 flows by about 125 cfs. March 6, 2007 and Onward Our research and design efforts continued through the next two months. By mid-February, NYS-DEC had formulated and gotten tentative approval from the decree parties for its FFMP design (see Figure 4). We had developed our own proposal, CP2, which was in agreement with the FFMP on all but seven of the 105 entries in the release matrix. The contentious differences were in the normal Cannonsville summer and spring releases, which our habitat research had shown needed to be higher to support adequate trout habitat in the upper mainstem. The DEC’s position was that trout in the upper mainstem could not really be protected, and that the decree parties would not approve the drought-day cost of higher releases. Meanwhile, we worked to build outside support; however, many in the fishing community, taking an all-or-nothing position, saw CP2 as too much of a compromise. The coalition, believing strongly that CP2 was an ecologically superior policy with only slightly more drought days than the FFMP, petitioned the decree parties to reconsider. We were granted a public hearing, which was held on March 6, 2007 in a packed hearing room at DRBC headquarters. The principle policy differences between our CP2 proposal and the FFMP were (1) the FFMP called for a 250-cfs summer release from Cannonsville, while we called for 350, and (2) the FFMP called for a Cannonsville spring release of 180 cfs, while we called for 250. Relying on the DRDSS habitat results, we characterized CP2 as ecologically superior in that it protected

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aquatic habitat into the mainstem Delaware, not only the tributaries. Both trout and shad mainstem habitat would be 150 percent higher under CP2. We also argued that shad were important to Pennsylvania and New Jersey. Even under the decree parties’ mandated worst-case diversions scenario, CP2’s higher releases would increase drought days over the FFMP by only 4 percent. Although we cited other risk metrics, including the number of drought events and reservoir refill probabilities, to show that CP2 was not riskier, and we were supported in these arguments by Hydrologics Inc., the designers of the OASIS model, we could not convince the decree parties; the unmodified FFMP was implemented in October 2007. We had won the war: our framework was adopted; but, we lost this battle: the releases were lower than needed. The FFMP implementation over the next two years effectively demonstrated that our estimates had been right; actual experience on the river showed that more water was indeed needed for the environment, and it could be released with little risk. In June 2009, the FFMP was modified and the release quantities were brought into near exact conformance with our CP2 recommendations. Impact Assessment The FFMP operates in an environment subject to high weather-driven variation and was designed for long-term performance over an historical 73-year time frame. Thus, it is difficult to make a definitive judgment about its impact on the basis of short-run performance. In its first nine months, the FFMP performed essentially as expected. Overall, its increased coldwater releases increased habitat on most river segments. But, as we had predicted, at the FFMP’s initial release levels, thermal protection would be inadequate in the mainstem; the FFMP produced lower flows than Revision 7 would have during August 2008. This was the result of an unusual “not quite dry” late summer in which precipitation in the lower reaches of the river was sufficient to meet the Montague flow target, without the usual late summer River Master-directed releases. This situation attracted substantial public criticism and led NYS-DEC and the decree parties to raise the FFMP’s summer releases to nearly coincide with the coalition’s recommended CP2 release policy

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in the FFMP’s second year of implementation. Summer 2009 was exceedingly wet, and the FFMP performed better than expected. Because of the FFMP’s spill-mitigation component, releases stayed above the normal standard for much of the summer of 2009. The spring of 2010 was exceedingly wet throughout the northeastern United States and flooding was widespread in the region, but not along the Delaware. We argue that the FFMP’s spill mitigation releases contributed. The initial implementation demonstrated that the FFMP works, but substantial improvements are possible. We now attempt to quantify the expected long-term potential benefits from the FFMP implementation, the foundation of which is the estimated long-run OASIS and DRDSS performance metrics. We examine, in turn, contributions to fishing, flood mitigation, recreational boating, and administrative simplicity. Fishing. To estimate economic impact of an increase in trout habitat, we rely on a study (Maharaj et al. 1998) by Trout Unlimited (TU), which estimates the 1996 economic contribution to Delaware County, New York, from trout-fishing activities. The report estimates that in 1996 about 32,000 anglers spent 266,000 days fishing on the upper Delaware in New York State. Angler expenditures resulted in about $18 million in business revenue, supported some 350 local jobs with wages of about $3.7 million, and produced about $700,000 in local taxes. The report estimates that the overall annual impact of the $18 million in spending was about $30 million in economic activity. Using these 1996 estimates for one New York county as our base case, we developed a 2010 extrapolation to the upper Delaware in four steps: 1. The quality and number of fishable days during the current, essentially two-month fishing season would increase somewhat because of the improved releases. We estimate this as +20 percent. 2. Markedly increased summertime releases could extend the current two-month fishing season to a five-month season, as in Montana. A full five-month season would imply a 250 percent increase in expenditures. Because we think this is too optimistic, we estimate this impact at a more conservative onemonth increase, or +50 percent. 3. Increased releases extend the trout habitat further downriver, increasing angling opportunities and


expenditures. At current FFMP levels, the habitat model suggests a modest increase down to Lordville, New York, on the mainstem of the Delaware. We estimate this impact at +10 percent. 4. The TU study covered only Delaware County. The counties of Sullivan and Broome in New York and Wayne in Pennsylvania also have considerable trout fishing activity on the Delaware. We estimate this impact at +50 percent. The consumer price index has increased by about 138 percent since 1996, the date of the TU study data; therefore, we apply an inflation factor of +138 percent to the above estimates. Based on the above assumptions, we estimate the overall annual increase in economic activity because of improved fishing at $84 million. Flood mitigation. FEMA has made payments of $226 million for property damage to insured structures resulting from the 2004, 2005, and 2006 floods (Delaware River Basin Commission 2007). Moreover, the DRBC has been unable to estimate the massive costs for infrastructure replacement and community cleanup all along the river. In addition, determining flood losses by uninsured individuals and businesses, and gathering information on private insurance payments is difficult. Indeed, the owner of a major Delaware River canoe livery, David Jones, reported $1.5 million in uninsured losses from the recent floods (D. Jones, pers. comm.). Thus, $226 million is a gross underestimate of flood losses. The FFMP results in lower average seasonal reservoir levels, thereby buffering against flooding at certain seasons. The impact of these buffers on flooding is impossible to determine at present. We note that the decree parties and the DRBC have artfully used the term “spill mitigation” in their policy statements, which avoids the appearance of promising something they are not sure they can deliver. But, without a solid quantification in hand, one can fairly say that the FFMP has mitigated flood danger in the Delaware Basin; how much cannot be readily quantified. Recreational boating. According to the National Park Service, an estimated 150,000 people make trips on the river each summer with canoes, kayaks, or rafts rented from nearly a dozen commercial enterprises (National Park Service 2009). 2008 direct revenues of these establishments are estimated at about


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$20 million, and the industry generates approximately 400 seasonal and 100 full-time jobs (D. Jones, pers. comm.). Low summertime flows, which make the canoeing experience unpleasant and sometimes nearly impossible, are a major problem for canoe livery owners, who state that many clients, when coming off the river on a low-flow day, say “never again!” By reducing the number of extremely low-flow days, the FFMP modestly enhances potential for a pleasant day on the river. We estimate this at a +10 percent impact, and apply it to the $20 million in estimated related economic activity, yielding a benefit of about $2 million annually. Administrative simplicity. We believe that the FFMP’s administrative simplicity has allowed the Department of Environmental Conservation and the NYC DEP to reduce staff. Our rough estimate is one fulltime-equivalent staff reduction in each agency. Estimating $100,000 for a professional employee yields savings of about $200,000 annually (P. Rush, M. Klotz, pers. comm.).

Appendix. A Quadratic Programming Allocation Model This quadratic program computes optimal directed releases (DRs) and diversions (Divs) by reservoir: Can = Cannonsville, Nev = Neversink, and Pep = Pepacton, given that conservation releases (CRs) and total NYC diversions (TotDiv) and Montague flow requirements (MonReq) have already been specified. The decision variables are shown in italic. Find CanDR, NevDR, PepDR, CanDiv, NevDiv, PepDiv to maxvCan×CanDiv+vNev×NevDiv+vPep×PepDiv

−cCan×CanDR+cNev×NevDR+cPep×PepDR +ErrCanNev+ErrCanPep+ErrPepNev subject to CanDiv + NevDiv + PepDiv = TotDiv CanDiv < 500 NevDiv < 500 PepDiv < 700 CanDR + NevDR + PepDR = MonReq

A Postscript

CanDR + CanDiv < CanSto − CanMin − CanCR

Although the FFMP is an improvement over the old release policies, we are not satisfied. Its fundamental shortcoming stems from its design premise that New York City diverts its full 800-mgd allotment each day—far more water than the city is actually using. The current diversion gap is more than 200 million gallons per day; holding onto this water as if it were to be diverted tomorrow results in much of it being wasted. We designed a policy modification, the “augmented FFMP,” which would incorporate structured forecasts of current city needs into the FFMP (Kolesar and Serio 2008). This augmented FFMP variant has an additional series of release matrices that add another level of flexibility by changing releases based on anticipated NYC diversions. Such a policy would result in enormous benefits to other river stakeholders, most notably in habitat improvements and possibilities for increased flood mitigation. These ideas are beginning to gain traction (New York State Department of Environmental Conservation and Pennsylvania Fish and Boat Commission 2010). As we write the work continues, because the current FFMP implementation expires in October 2011.

NevDR + NevDiv < NevSto − NevMin − NevCR PepDirR + PepDiv < PepSto − PepMin − PepCR CanCR + CanDR < 1400 NevCR + NevDR < 220 PepCR + PepDR < 490 CanPctVoid = CanCap − CanSto − CanCR − CanDR − CanDiv/CanDrain NevPctVoid = NevCap − NevSto − NevCR − NevDR − NevDiv/NevDrain PepPctVoid = PepCap − PepSto − PepCR − PepDR − PepDiv/PepDrain ErrCanNev = CanPctVoid − NevPctVoid ∧ 2 ErrCanPep = CanPctVoid − PepPctVoid ∧ 2 ErrPepNev = PepPctVoid − NevPctVoid ∧ 2 Acknowledgments

We particularly thank Rick Fromuth and Hernan Quinodoz of the DRBC; Paul Rush and Bob Mayer from NYC

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DEP; Fred Nuffer, D. Muralidhar, Jim Daley, Mark Klotz, Robert Klosowski, and Steve Lorence from NYS DEC; Gary Paulachok from the Office of the Delaware River Master, and Ken Bovee of USGS. The Theodore Gordon Fly Fishers joined the coalition later on in our work; Steve Lieb of that organization was very helpful, and Kurt Huhner coded most of our QP simulation model. Jack Stauffer at the Delaware River Foundation and Leon Szeptycki of Trout Unlimited played significant roles at the beginning of the project. Larry Miri, formerly of the Friends of the Upper Delaware, and Nat Gillespie of Trout Unlimited played significant roles throughout the project. Upmanu Lall of Columbia University provided continuing guidance on water resource issues and hydrology. And last, but so far from least, Alastair Shearman and Luke Wang, Columbia University environmental engineering students, bore the brunt of running thousands of Delaware simulations, hundreds of regressions, and preparing too many PowerPoint presentations—you have our grateful thanks. This project was supported in part by the Earth Institute of Columbia University, The Nature Conservancy, and Trout Unlimited, and by a research grant from the Climate Research Program of the National Oceanic and Atmospheric Administration.

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HydroLogics, Inc. 2002. Modeling the Delaware River Basin with OASIS. Report, Delaware River Basin Commission, HydroLogics, Inc., Columbia, MD. Jehl, D. 2002. Atlanta’s growing thirst creates water war. Accessed September 27, 2002, http://www.nytimes.com/2002/05/27/ us/atlanta-s-growing-thirst-creates-water-war.html. Kolesar, P., J. Serio. 2008. An augmented flexible flow management plan. Presentation, January 16. Delaware River Basin Commission, Trenton, NJ. Little, J. D. C. 1955. The use of storage in a hydroelectric system. Oper. Res. 3(2) 187–197. Lund, J. R., J. Guzman. 1999. Some derived operating rules for reservoirs in series or in parallel. J. Water Resources Planning Management 125(3) 143–153. Maharaj, V., J. McGurrin, J. Carpenter. 1998. The economic impact of trout fishing on the Delaware tail waters in New York. Report, American Sportfishing Association and Trout Unlimited, Alexandria, VA. National Park Service. 2009. Upper Delaware scenic and recreational river, monthly public use report. U.S. Department of the Interior, Washington, DC. New York City Department of Environmental Protection. 2010. New York City’s Operations Support Tool White Paper. Prepared for the Delaware River Basin Supreme Court Decree Parties, October 8, 2010. Accessed December 3, 2010, http://water.usgs.gov/ osw/odrm/documents/OST_White_Paper.pdf. New York State Department of Environmental Conservation, Pennsylvania Fish and Boat Commission. 2010. Recommended improvements to the flexible flow management program for coldwater ecosystem protection in the Delaware River tailwaters. Accessed April 1, 2010, http://www.fish.state.pa.us/ water/rivers/delaware/dela_flex_flow.pdf. Office of the Delaware River Master. 2006. History of the reservoir releases program in the upper Delaware River Basin. Accessed September 27, 2010, http://water.usgs.gov/osw/odrm/releases .html. Office of the Delaware River Master. 2007. Agreement of the parties to the 1954 U.S. Supreme Court decree, September 27, 2007. Accessed September 27, 2010, http://water.usgs.gov/osw/odrm/ document_archive/FFMP_original.pdf. Phillips, J. C. 2004. Upgrade of the Delaware River Basin OASIS model, simulation of operations of Cannonsville, Pepacton, and Neversink reservoirs for downstream fisheries management. Report, Delaware River Basin Commission, West Trenton, NJ. Sand, G. M. 1984. An analytical investigation of operating policies for water-supply reservoirs in parallel. Doctoral dissertation, Cornell University, Ithaca, NY. Thatcher, L. M., C. Mendoza. 1990. Extension and testing of the daily flow model. Report, Department of Civil Engineering and Engineering Mechanics, Columbia University, New York. U.S. Supreme Court. 1931. New Jersey v. New York, 283 U.S. 805. U.S. Supreme Court. 1954. New Jersey v. New York, 347 U.S. 995. USA Today. 2009. Judge rules against Atlanta regional water wars. Accessed November 1, 2009, http://www.usatoday.com/news/ washington/2009-07-17-lake-lanier_N.htm. Wang, L. C. 2009. Forecasting seasonal stream flow in the upper Delaware River Basin using climate informed statistical methods. Master’s thesis, Columbia University, New York.