Review of the New York City Department of Environmental Protection Operations Support Tool for Water Supply (2018) / Chapter Skim
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2 Description of the Operations Support Tool
2 Description of the Operations Support Tool
Pages 43-96
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From page 43... ...
Operations Support Tool (OST) informs management decisions by formally quantifying flow and quality changes as water moves though the Catskill, Delaware, and Croton systems to New York City.
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From page 44... ...
, such as diversions, releases, reservoir levels, and water quality conditions, are used for decision making, communicating, and understanding various metrics of system performance. As previously noted, the origin of OST was the Catskill Turbidity Control Study conducted as part of NYC DEP's long-term Watershed Protection Program (see Chapter 3)
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From page 45... ...
. NWS = National Weather Service; USGS = U.S.
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From page 46... ...
The role of OST is to inform water system managers of the potential consequences of any particular set of decisions and to provide a basis for comparing multiple operational options. OST components include real-time, historical, and predicted data that describe water quantity and water quality attributes, models of water quantity and quality, and reporting of model outputs expressed as metrics of system performance (as shown in Figure 2-1)
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From page 47... ...
quantification of the current conditional probability of water supply shortages over the coming year and (2) quantification of how near-term (days to weeks)
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. FIGURE 2-1-1 Marginal probability distribution for Schoharie Creek flow on October 9.
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FIGURE 2-1-2 Relationship between Schoharie Creek discharge on October 9 and on October 5. continued
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This is a highly simplified example, but it represents the great importance of having conditional distributions of future streamflow in order to guide manage ment actions. In this example a very simple statistical model was used, whereas in the real examples of the methods considered in the New York City water supply watershed, much more complex approaches are taken, but they are all aimed at computing these kinds of conditional distributions of variables such as discharge or reservoir storage.
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, or (4) violating water quality goals in the reservoirs t or in the water delivered (resulting in the need to use large amounts of treatment chemicals or the need to make long-term investments in treatment works)
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SOURCE: Delaware River Basin Commission, www.drbc.gov. information on computing the conditional probability distributions of various outcome measures at various time horizons.
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From page 53... ...
OST DATA FLOW AND OUTPUTS As shown in Figure 2-1, OST uses water quantity and water quality models to combine forecasts of watershed tributary flows and water demands with various constraints and goals (such as physical/hydraulic attributes, operating rules, and weighting factors) to determine (1)
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54 FIGURE 2-3 Detailed summary of OST: Inputs, modeling, outputs, and data flow.
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(Top) Elevation in both basins of Ashokan Reservoir (west and east)
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An example is an evaluation of the implications of modifying existing operating rules (e.g., meet water supply demand and water quality needs while balancing the desire to refill all system reservoirs by June 1 of each year)
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From page 57... ...
As described in more detail in the following section, the OASIS operating rules related to water quality are based on institutional knowledge and various regulations. For example, the level of turbidity in the Schoharie Reservoir affects allowable transfers to Ashokan West Basin via the Shandaken T unnel and Upper Esopus Creek.
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From page 58... ...
The W2 models operate on much shorter time steps than the OASIS model and make use of tributary inflow and water quality, as well as meteorological conditions, to model reservoir water quality. More detailed descriptions of the OASIS and W2 models in OST, the uses of the models, and model data inputs are provided in the following section.
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From page 59... ...
These objectives have guided the formulation and configuration of the OASIS model, including the specification of key parameters that affect routing decisions, as described in the following section. OASIS simulates the physical attributes of a water supply system as a system of nodes (reservoirs, demand locations, junctions)
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From page 60... ...
, water quality (e.g., turbidity trigger to invoke goal to decrease diversion from Ashokan Reservoir to the Catskill Aqueduct) , and practical operational limits (e.g., frequent insertion and removal of stop shutters in the Catskill Aqueduct should be avoided)
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From page 61... ...
Initial condition variables include the current volume of storage in each of the system reservoirs, the status of "state variables1" in the hydrologic model, the flows of water in downstream river reaches that are influenced by releases from the system reservoirs, measures of critical water quality conditions (typically turbidity at various points in the system) , and any constraints on delivery of water through specific t unnels or aqueducts (e.g., due to system maintenance service outages)
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From page 62... ...
The W2 water quality models can be used to simulate in-reservoir turbidity and thus the turbidity of diversions and releases. Initial-condition turbidities are based on measured values in the water system.
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From page 63... ...
A water quality-based example would be to penalize and/ or constrain the diversion of Ashokan water to the Catskill Aqueduct if the Ashokan turbidity exceeds a certain set point (as defined, for example, by an OCL Constant, see Table 2-1)
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From page 64... ...
Calibrating and validating W2 models typically involves comparing predictions and measurements of spatial and temporal series of reservoir water surface elevation, water temperature, and often a nearly conservative water quality parameter such as total dissolved solids (as assessed by specific conductivity)
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DESCRIPTION OF THE OPERATIONS SUPPORT TOOL 65 FIGURE 2-5 CE-QUAL-W2 representation of Schoharie Reservoir.
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From page 66... ...
Also, turbidity is the regulated water quality parameter, so it is best to model it directly. The W2 model includes several optional "generic" water quality constituents that can have associated characteristics that affect their fate in the water column; for example, for turbidity, a settling velocity can be assigned, resulting in vertical transport to the reservoir bottom.
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From page 67... ...
The Ashokan W2 model allows for prediction of turbidity in water diversions to the Catskill Aqueduct and in water releases to the Ashokan Release Channel. Such predictions are important aspects of making operational decisions regarding Ashokan for drinking water supply as well as for ecosystem services, flood mitigation, and turbidity control in the Lower Esopus Creek.
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From page 68... ...
An important and challenging unique feature of the Kensico W2 model is the need to capture the impact of alum addition at the Catalum facility on the Catskill Aqueduct turbidity as it enters Kensico. The model developers successfully incorporated the impacts of alum addition and also included two separate state variables to model the spatial deposition of precipitated aluminum hydroxide and associated coagulated clay particles (referred to as "alum sludge" in NYC DEP documents)
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From page 69... ...
to produce an ensemble of output traces. The purpose of PA is to look ahead in time from the current status of the water supply system and consider the impacts of a plausible range of future meteorological conditions (most importantly tributary inflows)
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TOA runs also are useful for evaluating potential water quality changes. For example, the potential turbidity implications of different release channel and dividing weir operations at the Ashokan Reservoir can be evaluated.
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A black dashed line represents the seasonal PCN storage objective and the dashed blue line shows the drought watch curve. Republished with permission of World Scientific Publishing Co., Inc., from The New York City Operations Support Tool (OST)
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The individual run date coincides with the corresponding curve starting date. Republished with permission of World Scientific Publishing Co., Inc., from The New York City Operations Support Tool (OST)
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, including all ensemble members, showing that PCN would not continue deeper into the drought watch zone but would quickly recover to above the drought watch zone. Republished with permission of World Scientific Publishing Co., Inc., from The New York City Operations Support Tool (OST)
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From page 74... ...
. Republished with permission of World Scientific Publishing Co., Inc., from The New York City Operations Support Tool (OST)
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From page 75... ...
Other examples may include evaluating the impacts of changing demand on the ability of the system to continue to meet demand -- this includes safe yield analysis, in which different demand levels are tested to determine the point at which the system fails to meet demand. As discussed in detail in Chapter 5, climate change impacts on the water supply can be assessed by running OST in Sim mode, driven with multiple synthetic hydrology time series derived from climate change scenarios such as global climate model (GCM)
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Historical Records Approach In the historical records approach, all years in the historical record are considered to be equally likely inputs going forward. One advantage of 2 https://waterservices.usgs.gov/rest/IV-Test-Tool.html.
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It is a great advantage of the historical records approach that it does not require a set of simplifying assumptions about either the spatial or serial correlation structures. Any statistical model used for ensemble forecasting will never fully capture these patterns.
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At this time, there does not appear to be interest (within NYC DEP) in using the historical records approach to generating ensemble forecasts.
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It operates by initializing the model using flow data for the most recent months and then generating multiple realizations that preserve the observed month-to-month correlation structure and overall properties of the probability distribution for future months based on the historical records. The eHirsch method is simply an extension of the Hirsch method, defined for a shorter time step.
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Weather Model-Driven Forecast Approach The third approach is driven by weather-model outputs used in conjunction with a model of watershed hydrology. This approach overcomes both of the drawbacks mentioned for the historical records approach.
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That conditional distribution captures what is known about the natural variability of precipitation and the skill of the forecast. However, hydrologic models require an actual time series of precipitation and temperature for each modeled basin.
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When asked why the two different model systems were used in the two different parts of the overall New York City watershed, the NWS representatives answered that these were the models that each of the two river forecast centers was most familiar with. There was no indication that any testing had occurred to determine if the use of these two different models results in any type of differential bias between the two parts of the watershed.
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From page 83... ...
, while the stitching uses observed data alone. The data available for these two exercises are constrained by what the two River Forecast Centers of the National Weather Service that cover the NYC water supply watershed can provide.
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A new tool for ensemble streamflow forecasts that has come into existence since OST was developed is the National Water Model.4 The National Water Model, which is evolving rapidly, simulates weather at local scales throughout the United States and currently operates four different versions that vary in forecast range and the frequency of updates. Should NYC DEP decide to consider alternative ensemble forecasts that offer more state-ofthe-art models than those provided by the River Forecast Centers, it should only do so after validation efforts have been undertaken for National Water Model outputs for the New York City watershed region.
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From page 85... ...
Water Quality Input Data Water quality information used in OST is collected in real time, in near real time, and through grab samples analyzed in a laboratory. Real-time data are collected at critical locations within the watershed through the New York City Keypoint Monitoring Program (NYC DEP, 2010)
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From page 86... ...
This relationship was developed during the Catskill Turbidity Control Study. Figure 2-11 shows the turbidity-flow relationship for the Upper Esopus Creek at Coldbrook as used within OST in PA mode, also developed as part of the Catskill Turbidity Control Study.
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From page 87... ...
are collected through NWS surface weather forecasts and are utilized in the CE-QUAL-W2 water quality
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VALIDATION OF OST Validating an end-to-end system such as OST is no simple matter, and it is not clear that the statistical literature has a lot of advice to give about this process. Moving away from single-value forecasts and rigid use of rule curves is clearly the right thing to do for informing water management and operations.
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The question is, how does one judge the quality of models designed to estimate conditional probability distributions? Most atmospheric and hydrologic forecast modeling has been focused on the question of the accuracy of a point estimate.
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From page 90... ...
Rather, the validation approaches need to focus on the degree to which the ensemble forecasts correctly capture both the central tendency and the variability of the conditional probability distributions. In presentations to the Committee in late 2017 there were descriptions of work in progress aimed at validation of HEFS forecasts in general (not limited to those for the New York City watersheds)
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CONCLUSIONS AND RECOMMENDATIONS OST is one of the most advanced and complex support tools for water supply operations in the world. The Committee regards the NYC DEP as a leader among water resource agencies nationwide at applying these types of tools to inform system operations.
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NYC DEP and the NWS, which operates the weather and hydrologic forecasting model components of the system, need to critically evaluate the appropriateness of each of the key components of the ensemble streamflow forecasts. The hydrologic model used by the Middle Atlantic River Forecast Center, Continuous API, is not state of the art, and serious consideration should be given to replacing it with the Sacramento Soil Moisture Accounting Model (SAC-SMA)
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The Committee encourages the NYC DEP to examine the sensitivity of OST simulations to the values of the weights and penalties assigned within the OASIS model. The capability of OASIS to simulate the decisions that would be made by expert system operators is likely to depend on the values of the weights assigned to different flows and reservoir volumes, which reflect relative priorities of system performance (e.g., water storage volume, water quality, and water releases)
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: A new era of water forecasting at the National Weather Service. Presentation to the NASEM Committee to Review the New York City Department of Environmental Protection Operations Support Tool for Water Supply.
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Presentation to the NASEM Committee to Review the NYC DEP Operations Support Tool. September 25.
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2013. Integrated water quality-water supply modeling to support long-term planning.
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