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3 Scientific and Technical Aspects of AHPS This chapter discusses the major scientific and technical aspects of the Advanced Hydrologic Prediction Service (AHPS) as they relate to the following four scientific and technical goals of AHPS: to produce more accurate products by incorporating advanced hydrologic science into the National Weather Service (NWS) model; to provide products with forecast horizons two weeks or further into the future; to create more information that is useful to assess risk to flooding; and to provide more specific and timely information on fast-rising floods with increased lead time. The chapter concentrates on the current models and techniques used by AHPS, with an emphasis on the NWS River Forecast System (NWSRFS), and it discusses limitations, research needs, and options to update those techniques so that AHPS can provide the "basic," "enhanced," and "partnered" hydrologic products that it promises. Major elements of the statement of task are addressed in this chapter, including discussions about operational flood forecasting and the overall strategy of AHPS to meet these needs, applying modern hydrology and modeling techniques and technologies to enhance hydrologic predictions, and an assessment of the research needs, priorities, and application of research into AHPS operations. The chapter opens with a description of precipitation inputs to hydrologic models. The next section discusses NWSRFS, its limitations, and its areas that need updating to achieve AHPS goals. A section on flash-flood guidance closes the chapter. PRECIPITATION INPUTS TO HYDROLOGIC MODELS Precipitation inputs are used in hydrologic runoff and snow-melt models to generate estimates of rates and stages of streamflow. AHPS is predicated on these hydrologic models; therefore, hydrometeorological inputs, generally, and precipitation inputs, specifically, strongly influence AHPS hydrologic forecasts. AHPS hydrometeorological inputs consist of quantitative precipitation estimations (QPEs), satellite-based precipitation measurements, and quantitative precipitation forecasts (QPFs). The skill ("skill" is used here as it is used by meteorological community to mean the accuracy of a forecast) of NWSRFS hydrologic products is largely dependent on the accuracy of QPE and skill of QPF. Quantitative Precipitation Estimations QPEs come from rain gages or a combination of gages and radar estimates. Historically, precipitation analysis operations have been based on interpolation of gage observations to mean areal precipitation within individual hydrologic basins. Certain functions of the radar-based precipitation processing system are controlled from Weather Forecast Offices (WFOs). Final control of NWS radar operation and the choice of adaptable parameters for most processing algorithms reside with WFO staff. These operations include decisions on the Z-R relationships (continental convective vs. tropical) used in processing reflectivity data from individual radars, and liaison with other agencies that maintain gages. During major tropical storm rain events, WFOs and River Forecast Centers (RFCs) consult on the choice of a Z-R relationship for radars within their areas of responsibility. 31

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32 Toward a New Advanced Hydrologic Prediction Service (AHPS) Recognizing the importance of accurate QPEs as input to hydrologic models, the NWS is developing AHPS techniques for multisensor precipitation estimation that include the use of the national network of Doppler weather radars (Seo and Breidenbach, 2002) and satellite and lightning data (Kondragunta, 2002). The NWS is also working towards quantifying the uncertainty of QPEs (McEnery et al., 2005). These activities are positive; however, better documentation is needed to evaluate the effectiveness of these efforts. Publication and dissemination of AHPS activity allow the academic community and others to learn about and participate in algorithm development, verification and uncertainty estimation. Publication, peer review, and information dissemination will help the continual advancement of hydrologic science in AHPS models. AHPS researchers should periodically publish progress of the development of and improvements to precipitation products. Satellite-Based Precipitation Estimations Researchers at the National Oceanic and Atmospheric Administration (NOAA)1, the National Aeronautics and Space Administration (NASA)2, and some universities3 have made remarkable progress in the development of satellite-based precipitation estimation algorithms. This new generation of algorithms is capable of merging and blending multiple types of observed information from both geostationary and low polar-orbiting satellites, and they generate estimates for precipitation at various spatial and temporal scales (Hong et al., 2004; Joyce et al., 2004). These research efforts progressively improve precipitation estimation over regions with limited ground- based observations. They also show much promise to improve coverage over mountainous terrains, especially in the western U.S., where gage and radar coverage are very sparse. Recent research (Yilmaz et al., 2005) shows encouraging results with respect to the use of high-resolution satellite- based precipitation as input to a hydrologic model. Like precipitation estimation algorithms, plans for a new generation of satellite systems are underway. NASA, with a group of international partners, is developing a constellation of satellite systems called the Global Precipitation Measurement4 for launch around 2010 that is capable of producing global coverage of precipitation every three hours. AHPS developers are strongly encouraged to work closely with satellite precipitation groups (NASA, NOAA, and those in the academic community) to ensure that AHPS hydrologic requirements for precipitation are included in the Global Precipitation Measurement mission. Quantitative Precipitation Forecasts A QPF is a prediction of the amount of precipitation that will fall at a given location in a given time interval. QPFs are issued routinely by the NWS as a part of meteorological forecasts. Intuitively, QPFs would be useful in producing hydrologic forecasts, but there is no strong evidence that QPFs are being used that way to extend flood and streamflow predictions. There may be several reasons why hydrologists do not use QPFs extensively. One reason could be that typical QPFs provide values averaged in 6-hour aggregations or blocks. Finer temporal scales would be 1http://www.cpc.ncep.noaa.gov/products/janowiak/cmorph.html. 2http://trmm.gsfc.nasa.gov/publications_dir/precipitation_msg.html. 3http://hydis8.eng.uci.edu/CCS. 4http://gpm.gsfc.nasa.gov.

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Scientific and Technical Aspects of AHPS 33 more useful to AHPS forecasts for fast-rising flood waters. The standard, 6-hour QPFs show great variation in accuracy in basins with complex topography or shorter hydrologic response times. Another reason that hydrologists may not use QPF extensively is that QPFs were developed for meteorological, not hydrologic, purposes, so verification aspects of accuracy and performance are neither consistent nor calibrated with other hydrologic models. The skill of QPF for hydrologic forecasting is relatively unknown and results from tests and applications of QPF to date have fallen short of convincing many hydrologists of their operational value. For these and maybe other reasons, QPE and QPF remain underutilized in generating hydrologic forecasts. AHPS could serve as a vehicle to connect the meteorologists who generate QPF to the hydrologists who have yet to embrace it (Droegemeier et al., 2000). In order to be useful in hydrologic products, QPE and QPF need systematic evaluation and verification for hydrologic applications. Systematic evaluation and verification would guide further development and refinement of the hydrometeorological QPE and QPF at the National Centers for Environmental Prediction, across the NWS, and potentially for use in AHPS. To ensure that QPE and QPF meet the nation's needs and needs of hydrologic forecasters, the NWS should strengthen QPE and QPF for hydrologic prediction through an end- to-end evaluation that assesses QPE/QPF quality and impacts on flood and streamflow products for basins of diverse size and topography. THE NWS RIVER FORECAST SYSTEM "Basic" services will upgrade static, diagnostic river gage hydrographs5 to show AHPS river forecasts in a range of predicted information, including forecasted river levels6, weekly flow probabilities7, and monthly flow probabilities8. Other hydrologic products and services are also envisioned, such as "enhanced" services of flash-flood guidance information presented graphically9, and "partnered" services, like an internet-based flood map service that uses geographic information systems (GIS; Figure 3-1). These, and all AHPS products and services, rely on the current NWS primary modeling engine, the NWSRFS. NWSRFS was developed in the 1970s and 1980s (NWS, 1972) in a modular framework, and it is based on FORTRAN code. Over the intervening years, it has been incrementally upgraded with new models, functions, and displays (McEnery et al., 2005). However, more updates are needed to: incorporate advanced hydrologic science into NWS models; provide forecasts for two weeks or further into the future; create information that is useful to assess risk to flooding; and provide other products and services promised by AHPS. NWSRFS Modules The basic structure of the NWSRFS includes a calibration system (CS), an operational forecast system (OFS), an interactive forecast program (IFP), and an ensemble streamflow 5http://www.crh.noaa.gov/ahps2/hydrograph.php?wfo=fgf&gage=lkbm5&view=1,1,1,1,1,1. 6http://newweb.erh.noaa.gov/ahps2/hydrograph.php?wfo=aly&gage=wtfn6&view=1,1,1,1,1,1. 7http://newweb.erh.noaa.gov/ahps2/weekly.php?wfo=aly&gage=wtfn6&view=1,1,1,1,1,1. 8http://newweb.erh.noaa.gov/ahps2/period.php?wfo=aly&gage=wtfn6&view=1,1,1,1,1,1. 9http://www.cnrfc.noaa.gov/flashFloodGuidance.php?cwa=RSA&hour=1.

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34 Toward a New Advanced Hydrologic Prediction Service (AHPS) FIGURE 3-1 Flood Internet Map Service prototype for Lewiston, PA. SOURCE: http://www.nws.noaa.gov/oh/ahps/floodims_news.html. prediction (ESP) system (Figure 3-2). The hydrologic model(s) is calibrated for basin-specific conditions in the CS; the calibrated model output is used to develop forecasts in the OFS, IFP, and ESP. Generating a hydrologic forecast using the NWSRFS involves a multi-step process that begins in the OFS. First, precipitation information is collected and analyzed. Next, the appropriate hydrologic model(s) are applied depending on whether the forecast area is due for rain or snow, and the precipitation data are input into the model. The IFP is then used to analyze the short-term streamflow forecasts generated by the OFS and to make system adjustments to improve the model simulations and forecast. Model states from the OFS are used by the ESP component, in conjunction with calibrated models and historical data, for the generation of longer-term probabilistic predictions. The functions, limitations, and research needs of each of the NWSRFS components are described in the following sections. Functions of NWSRFS Hydrologic Models NWSRFS allows hydrologists to combine the appropriate models in a manner that is descriptive of the basin, the available data, and the forecast products desired. NWSRFS hydrologic applications include conceptual rainfall-runoff models, snow, and the Antecedent Precipitation Index (API) model. The Sacramento Soil Moisture Accounting model (SAC-SMA; Burnash et al., 1973) is the primary conceptual rainfall-runoff model used at the RFCs. The main snow model is the Snow-17 model originally developed by Anderson (1968; 1973; 1976). Snow-17 is a temperature-index version of a full energy budget model. SAC-SMA and Snow-17 are lumped conceptual models that convert frozen and liquid precipitation into runoff. Conceptual models do not explicitly represent

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Scientific and Technical Aspects of AHPS 35 NWSRFS Components Calibration System(CS) EnsembleStreamflow Prediction (ESP) System Calibration Analysis (Hydrologic Hydrologic window Historical and Analysis and Data Hydraulic Hydraulic Models) Models time Operational Forecast System (OFS) Hydrologic and Statistical Hydraulic Models Analyses Real-Time Analysis and Data Observed short term Assimilation Probabilistic and forecasts Short term to Forecast Extended Data current states Interactive Forecast Interactive Adjustments Program (IFP) FIGURE 3-2 Main components of the NWSRFS. SOURCE: Adapted from McEnery et al., 2005. the measurable physical characteristics or processes of a basin, and are therefore limited to forecast locations equipped with river stage observations for calibration purposes. For this reason, conceptual models are typically run in lumped mode, compiling information over coarse spatial areas or homogenous conditions (i.e., similar elevation or topography). SAC-SMA and Snow-17 are sometimes used in a semi-distributed capacity, where a diverse basin is subdivided into smaller, more homogenous areas, so that the lumped models are better able to describe the dominant hydrological processes within each sub-basin area (McEnery et al., 2005). In some RFCs, the API model is used. The API empirical method estimates the amount of surface runoff that will occur in a basin from a given rainstorm based on an index of moisture stored within a drainage basin before a storm, physical characteristics of the basin, time of year, storm duration, rainfall amount, and rainfall intensity. Limitations of and Research Needs for NWSRFS Hydrologic Models The current lumped conceptual hydrologic models used by the NWS and in the NWSRFS are functional and relatively accurate (Reed et al., 2004), but have some limitations associated with their use. One limitation is that these models are based on the original empirical, lumped water balance accounting procedure and the FORTRAN computer coding standards of 20-30 years ago. The Office of Hydrologic Development's Hydrology Laboratory (HL) recognizes that the hydrologic modeling approaches used for AHPS products need to be updated from the current NWSRFS. HL has started making some modifications, such as addressing issues of modeling at

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36 Toward a New Advanced Hydrologic Prediction Service (AHPS) finer spatial and temporal scales and incorporating more physically based process equations in future versions of the models (Koren et al., 2004; Smith et al., 2004a, b). Model resolution has been at the center of a debate in the hydrologic community about the advantages/disadvantages of lumped versus distributed models (Figure 3-3). One side of the debate notes that lumped models create coarse but accurate results, even though they do not effectively represent spatial variability of hydrologic processes, or intra-basin differences in elevation or terrain. Distributed models are designed to work at spatial and temporal scales finer than lumped models. The other side of the debate, the argument in favor of distributed models, posits that because distributed models can account for differences in site specific characteristics, including basin size, topography, land cover, they are more appropriate for AHPS products and services (McEnery et al., 2005). Fueling both of these arguments are recent research efforts that focus on downscaling and improving spatial and temporal resolution of hydrologic models (Reed et al., 2004; Zhang et al., 2001). The selection of lumped or distributed models for AHPS products is non-trivial because the type of hydrologic model(s) used in AHPS development will strongly impact AHPS products and services. AHPS currently uses lumped models, but the NWS is keenly aware of some of the benefits that distributed models may bring to AHPS. Therefore, the NWS is considering (1) making a switch from lumped models to distributed modeling for AHPS products; and (2) whether a single distributed model or a suite of distributed models will best achieve AHPS goals and purposes. The NWS must also determine how it will reconcile the incompatibility of the existing NWSRFS software structure with distributed modeling applications. To help address whether to switch to distributed models from the current lumped models, HL launched the Distributed Model Intercomparison Project (DMIP; Smith et al., 2004c) to guide AHPS' future distributed modeling research and applications. HL invited researchers from the academic and non-academic communities to participate in the DMIP project. DMIP Phase I has been completed and its results are summarized in Box 3-1; DMIP Phase II is being planned to address complex basin issues of snow and orography. The experimental design of DMIP Phase I was based on the comparison of distributed models applied to a common set of test data. Model simulations were compared to observed streamflow data as well as simulations generated from a lumped application of SAC-SMA. Results of DMIP Phase I have been published as a series in a special issue of Journal of Hydrology in 2004 (Box 3-1). Perhaps the first finding is the most critical and challenging for AHPS (Finding 1, Box 3- 1): overall, the lumped hydrologic models performed better than, or slightly inferior to, a well calibrated distributed model. The NWS initiated DMIP to assist with the distributed modeling choice for AHPS, but DMIP Phase I research results do not clearly delineate whether AHPS products should use lumped or distributed models. The NWS has expended resources for research on the issue of distributed models and comparison studies and now needs to make clear how the final model choice(s) will be made. A decision-making framework that will be used to select the next hydrologic model(s) is as important as the DMIP research efforts. A decision-making process should establish a template for the HL to select its model(s) and methods that standardize the mechanisms that will be used across RFCs and WFOs as they adjust hydrologic models to local conditions. An advisory group comprised of experts from outside of an internal to the NWS could help NWS develop this framework and guide its implementation across the NWS. From NWS personnel and written materials, it is difficult to

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Scientific and Technical Aspects of AHPS 37 Hydrologic Modeling Approaches 1. Rainfall, properties 1. Rainfall, properties in averaged over basin each grid 2. One rainfall/runoff model 2. Rainfall/runoff model in each grid 3. Prediction at only one 3. Prediction at many points point FIGURE 3-3 Differences between lumped and distributed model approaches. SOURCE: Adapted from Smith et al., 2004a. ascertain the mechanism used to guide AHPS model selection and implementation. DMIP has been a valuable effort to compare various models and has identified additional research questions that need to be addressed to provide a robust suite of AHPS models, but it is not clear how or when DMIP will converge to an ultimate decision about AHPS model choice(s). DMIP Phase II, like DMIP Phase I, has no stated strategy that outlines steps from DMIP results to the selection of the next generation of model(s) for AHPS. Therefore, the NWS should strengthen connections between DMIP Phase I/DMIP Phase II and AHPS goals. The NWS should clarify the criteria and decision-making process for selecting the next generation of hydrologic model(s) for AHPS, using an advisory group that involves modeling experts from inside and outside of the NWS to ensure that the state-of-the-art modeling advances are incorporated objectively into NWSRFS. Another limitation for DMIP and similar exercises is the lag time between research and implementation into AHPS operations that may be too long to be effective. Protracted intervening time may inhibit AHPS developers from fully exploiting new modeling capabilities and achieve the AHPS goal of producing advanced hydrologic products. Finally, there is a general observation about the mixed level of documentation of hydrologic models used in NWSRFS. While advancements and modifications to the SAC-SMA and NWSRFS have taken place over several decades and have been reported from time to time in conferences, proceedings, and in peer-reviewed journal papers, there needs to be more publication and documentation of the internal activities related to model development and decision making. The NWS has been proactive in publishing its work on some modeling research and development efforts (distributed modeling, cold seasons, and model calibration) but less in other areas (ESP, verification, etc.). A good example of documenting updates to hydrologic models is the U.S. Army Corps of

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38 Toward a New Advanced Hydrologic Prediction Service (AHPS) BOX 3-1 Summary of DMIP Phase I Findings 1. Although the lumped model outperformed distributed models in more cases than distributed models outperformed the lumped model, some calibrated distributed models can perform at a level comparable to or better than a calibrated lumped model (the current operational standard). The wide range of accuracies among model results suggest that factors such as model formulation, parameterization, and the skill of the modeler can have a bigger impact on simulation accuracy than simply whether or not the model is lumped or distributed. 2. Clear gains in distributed model performance can be achieved through some type of model calibration. On average, calibrated models outperformed uncalibrated models during both the calibration and validation periods. 3. Gains from applying a distributed simulation model at NWS forecast basin scales (on the order of 1,000 km2) will depend on the basin characteristics. 4. The Christie basin is a small basin nested in the Eldon Basin, and is distinguishable in the DMIP study because of its small size. Christie, compared with larger basins, showed improved calibrated, peak flow results likely because the lumped "calibrated" model parameters (from the parent basin calibration, Eldon) are scale dependent and distributed model parameters that account for spatial variability within Eldon are less scale dependent. The Christie results indicate that more studies on small, nested basins are needed to confirm and better understand these results. 5. Among calibrated results, models that combine techniques of conceptual rainfall-runoff and physically-based distributed routing consistently showed the best performance in all but the smallest basin. Gains from calibration indicate that determining reasonable a priori parameters directly from physical characteristics of a watershed is generally a more difficult problem than defining reasonable parameters for a conceptual lumped model through calibration. SOURCE: Reed et al., 2004. Engineers Hydrologic Engineering Center's series (see Box 4-1 in Chapter 4). The NWS needs to provide stronger documentation to allow the research community to learn about and contribute to AHPS research and development. Function of the NWSRFS Calibration System The NWSRFS hydrologic models use hydrometeorological inputs (precipitation and temperature) to generate hydrologic outputs (streamflow and evapotranspiration). These models contain empirical coefficients and parameters that require site-specific calibration and proper estimation of model parameters for the hydrologic model to work successfully. Calibration and parameterization occur in the CS phase of NWSRFS. Extensive research related to hydrologic model calibration has been reported in the literature (see Duan et al., 2003). In the NWSRFS, simulated streamflow is calibrated statistically and visually against the observed streamflow to determine which model parameters need adjustment to improve alignment. After the models have

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Scientific and Technical Aspects of AHPS 39 been calibrated for a specific basin, the optimal set of parameters can be combined with real-time hydrometeorological data in the OFS to predict streamflow (Koren et al., 2003; Smith et al., 2003). Limitations of and Research Needs for the Calibration System The primary limitation of the NWSRFS CS is a gap between the state-of-the-art calibration capabilities and what is used in operations in the NWS RFCs. The NWS is aware of the need for closing this gap and recently has made needed improvements along these lines. One such improvement is the interactive Calibration Assistance Program, which incorporates GIS and interactive user interfaces into the NWSRFS (Smith et al., 2003). Another improvement has been the development of a regional parameter estimation scheme that relates soil information to the parameters of the SAC-SMA (Koren et al., 2003). These efforts are commended. The benefits of these advancements to AHPS will be realized as they are translated into NWSRFS operations. RFC and WFO staff training on the purpose, protocol, and function of calibration and parameterization improvements will help ensure appropriate and consistent use of new techniques. The NWS should continue efforts to improve and expand AHPS calibration capabilities, accelerate the rate of transfer of the latest calibration techniques into its operational AHPS-NWSRFS version, and conduct adequate training of modeling personnel to ensure appropriate and consistent use of the new techniques. Like calibration advancements, model parameterization improvements in coupled climate/ hydrologic models need to be transferred into AHPS operation. With the increasing demand for longer-term hydrologic predictions, and the AHPS goal to provide longer range forecasts of two weeks or further into the future, it is necessary to improve the interface between climate/land-surface models and hydrologic rainfall runoff models. The HL has recently spearheaded the international Model Parameter Estimation Experiment (MOPEX) to develop enhanced a priori estimates of hydrologic model parameters for both gaged and ungaged basins (Duan et al., 2006). MOPEX would provide valuable support to the incorporation of more physically based modeling capabilities into AHPS. MOPEX, and efforts like it, are expected to have a strong connection to the DMIP effort, as model calibration was identified (see Box 3-1) as the possible and pivotal component of model performance in DMIP Phase I. The development of MOPEX is commended, and the goals of DMIP and MOPEX should be compatible with each other and with AHPS. Functions of the NWSRSF Operational Forecast System and the Interactive Forecast Program The elements of OFS and IFP are similar, but they perform slightly different functions. The OFS is larger than IFP and includes pre-processing data (computing areal and temporal averages), model setup (storing parameters in the data base), and model computations. OFS reads raw station data in near real-time, estimates missing data as required, and then it uses these data to calculate mean areal time series of precipitation, temperature and potential evapotranspiration. Calibrated models in the OFS are forced with these processed time series to generate river forecasts with lead times that typically range from one day to two weeks. The IFP is the graphical interface to the forecast component of OFS. Through the IFP interface, forecasters manually adjust the model simulations to match the current observations as closely as possible. The forecasters can adjust the model inputs, model states, model parameters (in a few cases) and model outputs. The forecasting component of the OFS maintains an account of the current model states that describe the hydrologic condition of the basin, including snow cover,

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40 Toward a New Advanced Hydrologic Prediction Service (AHPS) soil moisture and channel storage, by storing these values in the operational database. The same models used in the forecast component of the OFS are used in the IFP. The model states stored by the OFS reflect the modifications made by the forecasters in IFP. The updated model states are needed as starting points for the subsequent forecasts made with the ESP system. Limitations of and Research Needs for OFS and IFP From the information available about the OFS/IFP component of the AHPS NWSRFS, a few observations and recommendations are noted. First, site visit interviews and other interviews with NWS forecasters indicate that the current design of the OFS/IFP is difficult to use. This difficulty is attributed to missing or hard-to-use- graphical interfaces. The NWS should review the current suite of operational software and develop a comprehensive plan for refreshing that software. Two other, related concerns with the current configuration of OFS/IFP include the lack of a "model only" forecast run and automatic data assimilation. The OFS/IFP model state updating process is done manually, and at least two problems are associated with the manual approach. First, a strong possibility exists for an individual forecaster to introduce error or bias when manually adjusting models. Manual adjustments are based primarily upon forecaster expertise, which will vary among individuals. The resulting ad-hoc, inconsistent methods do not constitute a robust scientific approach to assimilating observations into a model simulation in real-time. Second, forecasters' manual control of model output may obscure or thwart scientific advances that improve forecast skill and certainty. An NWS staff member stated that, "you might improve the [hydrologic] models 100 fold, and never see any improvement [in forecasts] because the forecasters are always sticking their fingers into the mix." To avoid these problems, AHPS developers could adopt current practices from the meteorological forecast side of the NWS. NWS meteorologists run their models, "hands off" or "model only," and then transfer the forecast outputs "hands off" to the forecast offices. NWS meteorologists use post-processing techniques to make forecast adjustments prior to public issuance and they document these adjustments for future verification purposes. Like their meteorological counterparts in the NWS, hydrologic forecasters should run hydrologic models primarily in a "model only" mode, make forecast adjustments with post-processing techniques, and document these adjustments for future verification purposes. Elements of current real-time hydrologic data assimilation are recognized as problematic. There are many sources of error with routing, snow, runoff, and precipitation, and current data assimilation uses only a single data point (typically river stage) for updating. The current lack of a fully automated, robust data assimilation component precludes "model only" forecast runs. Without a "model only" forecast system, it is not possible to assess the impact of a new calibration or other scientific advancements. These limitations, in addition to the problems associated with manually produced forecasts, suggest that the NWS should automate real-time hydrologic data assimilation. AHPS developers should consider automating the OFS/IFP component of the AHPS- NWSRFS and develop a systematic mechanism to include new research results and error analysis techniques into the operational OFS/IFP component. A switch from a manual to "model only" and an automated data assimilation process will impact current forecasters' responsibilities. Although the responsibilities of the individual forecasters would change with an automated process, the role fulfilled by forecasters in the forecast process is essential and will continue to be important with automated data assimilation. In no way should forecasters be removed from the forecast process, and the NWS is urged to redefine the role of the hydrologic forecaster in a fully automated data assimilation process.

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Scientific and Technical Aspects of AHPS 41 Function of the NWSRFS Ensemble Streamflow Prediction System The final component of the NWSRFS is the ESP system. The current version of the ESP component of NWSRFS has a modular design (Figure 3-4) and allows future streamflow traces to be analyzed for peak flows, minimum flows, and flow volumes. ESP assumes that historical meteorological data are representative of possible future conditions and uses past traces for the same-season and location as input data to produce probabilistic hydrologic forecasts. Knowledge of the current climatology is often used to weight the years of simulated streamflow based on the similarity between the climatological conditions of each historical year and the current year. More specifically, the ESP component blends together historical temperature and precipitation data sequences and deterministic meteorological forecasts to form ensemble inputs to hydrologic models that produce forecasts out to several months (Werner et al., 2005). A well designed and implemented ESP would progress AHPS towards fulfilling its goal of providing products with forecast horizons of two weeks or further into the future. Limitations of and Research Needs for ESP In the past decade, there have been significant advances in the development of ensemble tools in the fields of atmospheric and hydrologic sciences. Based on a recent spate of published works by NWS personnel or about ESP improvements for the NWSRFS, AHPS researchers appear to be developing and presenting these advancements for incorporation into AHPS products and forecasts. Published or written documentation exists for some, but not all, ESP sub-systems in the NWSRFS. ESP sub-systems with published documentation include: the ESP pre-processor (Schaake et al., 2005); ESP verification (Bradley et al., 2004); medium-range forecasts (Werner et al., 2005); climate index weighting (Werner et al., 2004); and the ESP post-processor (Seo et al., 2006). These recent publications reflect strong advancements of ESP tools and their potential incorporations into the NWSRFS and by extension to AHPS products and forecasts. Through these and other research efforts, AHPS aims to improve the ESP system and produce seamless and consistent probabilistic forecasts. Probabilistic forecasts explicitly quantify levels of uncertainty associated with each ensemble forecast. Quantified uncertainty for hydrologic forecasts offers several advantages, including the ability to archive forecasts and assess the overall skill of hydrologic forecasts over time based on comparisons against observed conditions. NWS meteorological forecasts consistently have associated, quantified uncertainties, but hydrologic forecasts historically have not. Fortunately, recent developments in hydrologic modeling and tools steadily increase the number and percentage of probabilistic hydrologic forecasts. The NWS and AHPS researchers are commended for advancing the ESP tools for possible incorporation into the NWSRFS, and for publishing and documenting their results. Still, the NWS needs to more strongly connect these advancements to the overall AHPS Development and Implementation plan and specify how they fit into the envisioned sequence of implementation of the ESP (Figure 3-5). Figure 3-5 presents the proposed sequence of the enhancements for short- to long-term forecasting services with the approximate delivery to RFCs. From this documentation (NWS, 2004),

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42 Toward a New Advanced Hydrologic Prediction Service (AHPS) Architecture Management Meteorological Forecasts Ensemble Pre- Processor Hydrologic Ensemble Processed Reservoir, River Ensemble Forcing Input Ensemble Streamflow Post- Streamflow Regulation, & Product Ensembles Processor Ensembles Processor Ensembles Hydraulic Models Dissemination Probabilistic Forecasts Probabilistic Verification Verification Information FIGURE 3-4 System components for the ESP. SOURCE: NWS, 2004. Pre-processor 1 Pre-processor 1 Short-term Smoothing Pre-processor 2 Short-term Smoothing Verification 1 (Short- and Med-term) Pre-processor Verification 1 (Med-term) Verification 2 Verification 3 CY 2004 CY 2005 CY 2006 CY 2007 Initial Condition Post-Processor 1 Hydraulic Model Model Parameters (Ensemble) Model Structure Post-processor 3 Post-processor 1 River Regulation Initial Conditions River Regulation Reservoirs Post-Processor 2 Reservoirs Evaluation On-going Activities: Implementation - Enhancements for Fielded Systems FIGURE 3-5 Envisioned sequence of implementation of the ensemble system. SOURCE: NWS, 2004. it is unclear what experimental design and methods are being used to develop the ESP sub-systems and whether a prototype of this framework is being tested before implementation in this succession (Figure 3-5). Furthermore, this schedule seems incomplete because it omits important elements such as research, analysis, and operational development, and the supporting text (NWS, 2004) does not fully describe all development activities associated with implementing this sequence. Therefore, AHPS should document its overall strategy about ESP, including priorities for the ESP system and sub-system development, testing, and implementation. The AHPS approach to

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Scientific and Technical Aspects of AHPS 43 quantifying uncertainties in operational forecasts must be articulated. In addition, AHPS should clarify connections between current and future research activities and the AHPS overall development and implementation and ESP sequencing plans. Function of NWSRFS Verification Verification includes documenting the uncertainty expected for each forecast and monitoring over time the accuracy of the forecasts against observed conditions. AHPS developers are commended for including verification in the NWSRFS, which could provide long-term statistics on the skill of AHPS and all NWSRFS forecasts. Quantification of uncertainty in forecasts should include measures of bias or accuracy and measures of variability of the ensemble forecasts. Bias measures can derive from comparisons of forecasts and observed field conditions; variance measures can be calculated from the statistical variability of the forecast. Inclusion of verification sub-systems in the ESP system design (Bradley et al., 2004), as well as in the OFS, is needed and long overdue. Unlike meteorological forecasts, little is known about hydrologic forecasts and actual river forecast skill. The assumption that forecasts have been improving over time may not be true because it is not documented whether the forecasts have skill over simple persistence forecasts. The importance of verification was highlighted in a recent Ph.D. dissertation (Welles, 2005) when 10 years of NWS river stage forecasts for 5 locations and 20 years of NWS forecasts for 11 locations were evaluated using standard verification metrics. The improvement in the forecast skill was not as great as had been anticipated (Welles, 2005), although these results are not definitive due to the limited sample size of the study. This research underscores the need to implement a long-term verification strategy and maintenance of a forecast archive for future forecast verification and NWSRFS evaluation. Limitations of and Research Needs for Verification in the NWSRFS Hydrometeorologists need to understand the skill characteristics of their forecasts, and this can only be accomplished through rigorous verification of the forecasts, including quantification of uncertainty of the forecasts, and quantifying the accuracy and variability of ensemble forecasts compared to observed field conditions. It is possible to make available verification information, such as river forecast skill, as an AHPS product for each forecast point. While the inclusion of a verification sub-component in AHPS NWSRFS is commended, there is a pressing need for a long-term strategy and maintenance of a forecast archive for future verification and NWSRFS evaluation. Overarching Limitations of NWSRFS The software architecture of NWSRFS is a major limiting factor in the development and implementation of AHPS. The NWSRFS software is based on antiquated FORTRAN-based algorithms of the 1970s and 1980s, and current distributed, statistical, and probabilistic models that are in various stages of research for use in AHPS products are not aligned with it.

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44 Toward a New Advanced Hydrologic Prediction Service (AHPS) The NWS recently started to address the NWSRFS system software problems through the Community Hydrologic Prediction System (CHPS)10. CHPS is an effort to redesign forecast system architecture based on a web-service architecture. CHPS would enable sharing of modeling applications across the hydrologic "community," which is composed of people from research, government, and academic organizations. As described, CHPS would provide a modular modeling framework for the development, enhancement, integration, and application of a wide variety of models and associated analysis and forecasting tools. CHPS is made up primarily of NWS employees and it has a strong NWS focus. Similar frameworks to CHPS are in development at other federal agencies and research centers. The U.S. Geological Survey's Modular Modeling System11, the U.S. Department of Agriculture's Object Modeling System12, the U.S. Department of Energy's (DOE) Framework for Risk Analysis in Multimedia Environmental Systems (Whelan, et al., 1997), and the DOE's Dynamic Information Architecture System13 are examples of major software development efforts. The experiences gained from other federal modeling collaborations should be considered in the development of CHPS. Even with CHPS and other NWS efforts to address the NWSRFS software problems, the current NWSRFS system must operate until new AHPS functionality is developed and implemented. The NWS will either fit new AHPS capabilities into the existing framework or abandon NWSRFS for a new, redesigned approach. The addition of new hydrologic methods into NWSRFS in some cases, such as in distributed modeling, may be impossible given the current structure. Furthermore, site specific hydrologic conditions may require alternative or even multiple models and techniques to be applied at a particular location in order to optimize forecast skill. The NWSRFS is a barrier to the AHPS goal of producing more accurate products by incorporating advanced hydrologic science into the NWS model. The existing forecast system severely limits the ability to: (1) test research advances within the NWSRFS framework; (2) add new and diverse hydrologic features to the system; and (3) accelerate the transfer of new technology to operations. To incorporate the state-of-the-art hydrologic modeling capabilities, the NWS should invest in the next generation of NWSRFS that includes a flexible framework that allows alternative models, methods, or features that can be tested, verified and implemented expediently. A total redesign of the NWSRFS is needed for AHPS to fulfill its scientific and technical goals. A redesign would involve updating NWSRFS to current state-of-the-art software and hardware standards and using software that is more modular in design to support future modifications and enhancements of AHPS. FLASH-FLOOD GUIDANCE Flash-floods occur within a few short hours from the onset of heavy precipitation, and rank among the top natural hazards in the U.S because they cause major losses of life and property. Like the NWSRFS, the scientific foundations of current NWS flash-flood guidance and flash-flood warnings generation are derived from 1970s and 1980s techniques. Primarily, flash-flood forecasting has remained in the meteorological domain, and few, if any, hydrologic tools and models have been developed to forecast flash-floods (Droegemeier et al., 2000). AHPS has a goal to provide more specific and timely information on fast-rising floods with increased lead time. In order to achieve this goal, AHPS will 10http://www.nws.noaa.gov/om/water/ahps/BAMS_Article.pdf. 11http://wwwbrr.cr.usgs.gov/projects/SW_precip_runoff/mms/. 12http://oms.ars.usda.gov/. 13http://www.dis.anl.gov/DIAS/.

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Scientific and Technical Aspects of AHPS 45 need to update the hydrologic scientific basis for flash-flood guidance. The needs of updating the scientific basis for flash-flood guidance have been identified before (NRC, 1996) and the following recommendation is repeated here to help guide the NWS to fulfill the AHPS flash-flood goal: The NWS should improve the scientific basis that underpins the forecasting of floods that occur in the zero to six-hour time frame. WFO and RFC staff should be enabled to contribute to this effort by facilitating their access to adequate training, continuing education, and university cooperative programs. Furthermore, they should be able to access state-of-the-art geographic information systems, digital elevation models, and drainage and land-use data. There is no evidence of any significant progress in development of hydrologically-based flash-flood modeling systems; hence, the issues identified in 1996 (NRC, 1996) still apply today. According to NWS presentations, there are plans to develop site-specific SAC-SMA to determine local hydrologic preconditions for flash-flooding for AHPS. The NWS is considering using this "semi-distributed" model at RFCs (see earlier section, Hydrologic Models of NWSRFS), and perhaps eventually employing a statistical distributed model to replace the current flash-flood guidance. The statistical model would be based on developing the frequency distribution of flooding at ungaged locations based on retrospective QPE data. The technical basis for these various approaches to flash-flood guidance and the flash-flood problem are not well documented. The utility and importance of the choice of hydrologic model for AHPS products is noted (see earlier section, Hydrologic Models of NWSRFS), and the same issues discussed with respect to NWSRFS models apply to flash-flood forecasting because model selection will be central to flash- flood forecasting envisioned for AHPS. Like with hydrologic models in NWSRFS, the step-by-step testing and evaluation plans are not defined for transitioning to distributed modeling for flash-flood forecasting. The NWS plans to implement a national verification program for its flash-flood monitoring and prediction (FFMP) effort (NWS, 2004). Verification is welcome, but more detail about deliverables and milestones needs to accompany it. The plans for improvements in the production and use of QPE and QPF in FFMP are mentioned as well, and again, a schedule for deliverables and milestones along with an evaluation plan is required. Therefore, the NWS should provide adequate documentation within AHPS of the scientific details and the implementation strategy for its end-to-end flash-flood hazards forecast generation and dissemination. Coordination between RFCs and WFOs in hydrological and hydrometeorological analyses and modeling will be required for producing and delivering forecasts, warnings and watches at local scales where the information is useful and actionable for the public. AHPS should include the forecast of flash-flood hazards and generation of warnings (dissemination) at the local WFO-level in its suite of activities. As a core capability, AHPS should include support for the forecast of flash-flood hazards and generation of warnings at the local WFO-level. CHAPTER SUMMARY This chapter describes and evaluates the scientific, technical, and modeling aspects of AHPS. The NWSRFS is the primary modeling engine for hydrologic forecasts, and each of the modular elements of NWSRFS is discussed, and recommendations are made to improve NWSRFS and help the NWS to fulfill the scientific and technical goals of AHPS. The evaluation of these scientific and

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46 Toward a New Advanced Hydrologic Prediction Service (AHPS) technical aspects of AHPS resulted in numerous, specific recommendations throughout the chapter, and the major recommendations are noted here as three broad observations. First, improvements are needed to the precipitation inputs to the hydrologic models that are used to generate AHPS hydrologic forecasts. The quality of the hydrologic forecast depends on the quality of its precipitation inputs, and improvements to precipitation inputs will work towards fulfilling the AHPS goal of creating information that is useful to assess risk to flooding through better, more accurate forecasts. Therefore, AHPS should strengthen the QPE and QPF through an end-to-end evaluation that assesses QPE/QPF quality and impacts on flood and streamflow products for basins of diverse size and topography. In addition to improving QPE and QPF, AHPS developers are encouraged to work with satellite precipitation groups to ensure the AHPS hydrologic requirements for precipitation are considered in other federal activities, such as NASA's Global Precipitation Measurement mission. Second, the modeling capability needs improvements for AHPS to produce more accurate products and incorporate advanced hydrologic science in the NWS hydrologic models. Also noted in the modeling evaluation was a gap between the state-of-the-art hydrologic modeling capabilities and those used in AHPS product development. The current AHPS model, the NWSRFS, is described as needing updates, better verification, and better alignment with models that have finer spatial and temporal resolution. Therefore, AHPS should invest in the next generation of NWSRFS that includes a flexible framework that allows alternative models, methods, or features that can be tested, verified, and implemented expediently. CHPS, DMIP, and other collaborative activities to address the modeling capability of AHPS are commended, and the committee recommends that the NWS strengthen connections between DMIP Phase I/DMIP Phase II and AHPS goals. The committee also recommends that the NWS clarify the criteria and decisionmaking process for selecting the next generation hydrologic model(s) for AHPS, using an advisory board that involves modeling experts from inside and outside of the NWS to ensure that the state-of-the-art modeling advances are incorporated objectively into NWSRFS. Finally, a recurrent finding in this evaluation was that very few scientific and technical aspects of AHPS are documented. The program will benefit from greater publication, peer review, and dissemination of its current and recent activities to improve the hydrologic science and technology used in AHPS product development and operation. The full list of this chapter's recommendations is presented in Box 3-2. BOX 3-2 Recommendations AHPS researchers should periodically publish progress of the development of and improvements to precipitation products. AHPS developers are strongly encouraged to work closely with satellite precipitation groups (NASA, NOAA, and those in the academic community) to ensure that AHPS hydrologic requirements for precipitation are included in the Global Precipitation Measurement mission. The NWS should strengthen QPE and QPF for hydrologic prediction through an end- to-end evaluation that assesses QPE/QPF quality and impacts on flood and streamflow products for basins of diverse size and topography. continues

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Scientific and Technical Aspects of AHPS 47 BOX 3-2 Continued The NWS should strengthen connections between DMIP Phase I/DMIP Phase II and AHPS goals and clarify the criteria and a decision-making process for selecting the modeling engine for AHPS. To do so, the NWS should form an advisory structure that involves modeling experts from inside and outside of the NWS to ensure that the state-of-the-art modeling advances are incorporated into NWSRFS operations. The NWS needs to provide stronger documentation to allow the research community to learn about and contribute to AHPS research and development. The NWS should continue efforts to improve and expand AHPS calibration capabilities, accelerate the rate of transfer of the latest calibration techniques into its operational AHPS- NWSRFS version, and conduct adequate training of modeling personnel to ensure appropriate and consistent use of the new techniques. The goals of DMIP and MOPEX should be compatible with each other and with AHPS. The NWS should review the current suite of operational software and develop a comprehensive plan for refreshing that software. Like their meteorological counterparts in the NWS, hydrologic forecasters should run hydrologic models primarily in a "model only" mode, make forecast adjustments with post-processing techniques, and document these adjustments for future verification purposes. AHPS developers should consider automating the OFS/IFP component of the AHPS-NWSRFS and develop a systematic mechanism to include new research results and error analysis techniques into the operational OFS/IFP component. In no way should forecasters be removed from the forecast process, and the NWS is urged to redefine the role of the hydrologic forecaster in a fully automated data assimilation process. AHPS should document its overall strategy about ESP, including priorities for the ESP system and sub-system development, testing, and implementation. The AHPS approach to quantifying uncertainties in operational forecasts must be articulated. In addition, AHPS should better specify connections between current and future research activities and the AHPS overall development and implementation and ESP sequencing plans. While the inclusion of a verification sub-component in AHPS NWSRFS is commended, there is a pressing need for a long-term strategy and maintenance of a forecast archive for future verification and NWSRFS evaluation. The experiences gained from other federal modeling collaborations should be considered in the development of CHPS. To incorporate the state-of-the-art hydrologic modeling capabilities, the NWS should invest in the next generation of NWSRFS that includes a flexible framework that allows alternative models, methods, or features that can be tested, verified and implemented expediently. A total redesign of the NWSRFS is needed for AHPS to fulfill its scientific and technical goals. continues

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48 Toward a New Advanced Hydrologic Prediction Service (AHPS) BOX 3-2 Continued The NWS should improve the scientific basis that underpins the forecasting of floods that occur in the zero to six-hour time frame. WFO and RFC staff should be enabled to contribute to this effort by facilitating their access to adequate training, continuing education, and university cooperative programs. Furthermore, they should be able to access state-of-the-art geographic information systems, digital elevation models, and drainage and land-use data. The NWS should provide adequate documentation within AHPS of the scientific details and the implementation strategy for its end-to-end flash-flood hazards forecast generation and dissemination. As a core capability, AHPS should include support for the forecast of flash-flood hazards and generation of warnings at the local WFO-level. REFERENCES Anderson, E. 1968. Development and testing of snow pack energy balance equations. Water Resources Research 4(1):19-37. Anderson, E. 1973. National Weather Service River Forecast System: Snow Accumulation and Ablation Model. NOAA Technical Memorandum NWS Hydro-17. Silver Spring, MD: NWS. Anderson, E. 1976. A Point Energy and Mass Balance Model of a Snow Cover. NOAA Technical Report: NWS 19. Silver Spring, MD: NWS. Bradley, A., S. Schwartz, and T. Hashino. 2004. Distributions-oriented verifications of ensemble streamflow predictions. Journal of Hydrometeorology 5: 532-45. Burnash, R., R. Ferral, and R. McGuire. 1973. A generalized streamflow simulation system: Conceptual modeling for digital computers. Technical Report, Joint Federal and State River Forecast Center. Sacramento, CA.: NWS and California Department of Water Resources. Droegemeier, K., J. Smith, S, Businger, C. Doswell, J. Doyle, C. Duffy, E. Foufoula-Georgiou, T. Graziano, L. James, V. Krajewski, M. LeMone, D. Lettenmaier, C. Mass, R. Pielke, P. Ray, S. Rutlegde, J. Schaake, and E. Zipser. 2000. Hydrological aspects of weather prediction and flood warnings: Report of the ninth prospectus development team of the U.S. Weather Research Program. Bulletin of the American Meteorological Society 81(11): 2665-80. Duan, Q., H. Gupta, S. Sorooshian, A. Rousseau, and R. Turcotte, eds. 2003. Calibration of Watershed Models: Water Science and Application Series Volume 6. Washington, DC: American Geophysical Union Duan, Q., J. Schaake, V. Andrassian, S. Franks, G. Goteti, H. Gupta, Y. Gusev, F. Habets, A. Hall, L. Hay, T. Hogue, M. Huang, G. Leavesley, X. Liang, O. Nasonova, J. Noilhan, L. Oudin, S. Sorooshian, T. Wagener, E. Wood. 2006. Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops. Journal of Hydrology 320(1-2): 3-17. Hong, Y., K. Hsu, X. Gao, and S. Sorooshian. 2004. Precipitation estimation from remotely sensed information using an artificial neural network--cloud classification system. Journal of Applied Meteorology 43:1834-52.

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