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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.
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Representative terms from entire chapter:
hydrologic models
