> The Distributed Model Intercomparison Project: Phase 2
>
> Science Plan
>
> Updated November 14, 2023

Mike Smith, Victor Koren, Seann Reed, Ziya Zhang,

Dong Jun Seo, Fekadu Moreda, Zhengtao Cui,

Hydrology Laboratory

Office of Hydrologic Development

NOAA National Weather Service

Executive Summary

The Hydrology Laboratory (HL) of the NOAA National Weather Service
(NOAA/NWS) proposes the second phase of the Distributed Model
Intercomparison Project (DMIP). The NOAA/NWS realizes the need for a
continued series of science experiments to guide its research into
advanced hydrologic models for river and water resources forecasting.
This need is accentuated by NOAA/NWS' recent progression into a broader
spectrum of water resources forecasting to complement its more
traditional river and flash flood forecasting mission. To this end, the
NOAA/NWS welcomes the input and contributions from the hydrologic
research community in order to better fulfill its mandate to provide the
Nation with valuable products and services.

Twelve groups participated in DMIP 1, resulting in a wealth of knowledge
for the scientific community and valuable guidance for the NOAA/NWS
research program. DMIP 2 is designed around two themes: 1) continued
investigation of science questions pertinent to the DMIP 1 test sites,
and 2) distributed and lumped model tests in hydrologically complex
basins in the mountainous Western US.

DMIP 2 will be supported by exciting, cross-cutting linkages to the
Oklahoma Mesonet, the Hydrometeorological Testbed program of NOAA
Environmental Technnology Laboratory, and the Sierra-Nevada Hydrologic
Observatory proposal to the Consortium of Universities for the
Advancement of Hydrologic Science, Incorporated (CUAHSI). As such, DMIP
2 will contribute to the goals of these partner institutions in a way
that will garner greater results than if these programs were executed in
an isolated manner.

NOAA 'Weather and Water Mission Goals' are directly addressed through
DMIP 2 by conducting experiments to guide the development, application,
and transition of advanced science and technology to operations and new
services and products. DMIP 2 also contributes to the NOAA
'Cross-Cutting Priority' of ensuring sound, state-of-the-science
research as a vigorous, forward-looking effort that invites
contributions from academia, other federal agencies, and international
institutions.

We expect that DMIP 2 will provide multiple opportunities to develop
data requirements for modeling and forecasting in hydrologically complex
areas. These requirements fall in the general categories of needed
spatial and temporal resolution and quality. From these, new sensor
platforms could be designed or appropriate densities of existing gages
could be specified to meet specific project goals. From the river
forecasting viewpoint, we think these data needs are particularly acute
in the mountainous west. In addition, DMIP 2 will serve as a
multi-institutional evaluation of the Oklahoma Mesonet sensors and data.
Such an evaluation may be able to promote an expansion of these sensors
to larger geographic domains. Or, DMIP 2 my point out a need for other
soil moisture sensors to meet the needs of NOAA/NWS water resources
forecasting mission.

> Table of Contents

1.  Introduction

    1.  Background 4

    2.  Need for DMIP 2 4

    3.  Relation to NOAA/NWS goals 6

    4.  Relation to NLDAS 6

2.  Science Questions 7

3.  Description of Proposed Sites 11

    1.  Overview 11

    2.  Oklahoma Region 11

    3.  Sierra-Nevada Region 13

4\. Overview of Proposed Experiments 18

5.  Proposed Schedule 23

6.  Expected Results 24

> References 26
>
> Appendices
>
> A. Additional Information for the Oklahoma Study Area 30
>
> B. Additional Information for the North Fork American River Basin 31
>
> C. Additional Information for the East Fork Carson River Basin 41
>
> D. The NOAA Hydrometeorological Testbed (HMT) Program 45

1.  Introduction

    1.  Background

The Hydrology Laboratory (HL) of the NOAA National Weather Service
(NOAA/NWS) proposes the second phase of the Distributed Model
Intercomparison Project (DMIP). The first phase of DMIP (hereafter
called DMIP 1) proved to be a landmark venue for the comparison of
lumped and distributed models in the southern Great Plains (Smith et
al., 2004a; Reed et al., 2004a). Twelve groups participated in DMIP 1,
including representatives from China, Denmark, Canada, New Zealand, and
universities and institutions in the US. Models ranged from conceptual
representations of the soil column applied to various computational
elements, to more comprehensive physically-formulated models based on
highly detailed triangulated representations of the terrain. DMIP 1
attracted the attention of many in the hydrologic research community,
resulting in the publication of a DMIP Special Issue of the Journal of
Hydrology in October, 2004. In addition, DMIP 1 provided valuable
guidance to the NWS HL research program for improved hydrologic models
for river and water resources forecasting.

The first phase of DMIP formally concluded in August, 2002 with a
meeting of all participants at NWS headquarters in Silver Spring,
Maryland. The purpose of this meeting was to present and discuss the
formal analyses of participants' results. At this meeting, the
participants eagerly discussed the need for a second phase of DMIP.
Ideas from this meeting were compiled and are presented herein along
with other science questions.

2.  Need for DMIP 2

While DMIP 1 served as a successful comparison of lumped and distributed
models, it also highlighted significant problems, knowledge gaps, and
topics that need to be investigated. First, DMIP 1 was limited by a
relatively short data period containing only a few significant
rainfall-runoff events in the verification period from which statistics
could be computed and inferences made. Thus, the need remains for
further DMIP 1-like testing in order to properly evaluate the hypotheses
related to lumped and distributed modeling. At this time, almost five
years of additional data are available to support such additional
comparisons. Also, DMIP 1 was somewhat hampered by the quality of the
radar estimates of observed precipitation. The quality of these data has
been oft-studied (e.g., Stellman et al., 2001; Young et al., 2000;
Johnson et al., 1999; Wang et al., 2000; Smith et al., 1999) and
includes problems such as underestimation and non-stationarity resulting
from changes in the processing algorithms. The effects of data errors
propagating through distributed models also need to be further explored.
The DMIP 1 participants discussed this need at the 2002 concluding DMIP
1 workshop.

Moreover, additional model comparisons must be performed in more
hydrologically complex regions. Most notably, experiments are needed in
the western US where the hydrology of most of the areas is dominated by
complexities such as snow accumulation and melt, orographic
precipitation, steep and other complex terrain features, and data
sparcity. The need for advanced models in mountainous regions is coupled
with the foundational requirements for more data in these areas. Experts
at NWS River Forecast Centers (RFCs) point to the need for explicit and
intense instrumentation programs to determine the required sensor
network density to improve forecast operations (Rob Hartman,
California-Nevada RFC, personal communication). Advanced models cannot
be implemented for RFC forecast operations without commensurate analyses
of the data requirements in mountainous regimes. Some argue that the
greatest knowledge gaps are in mountain hydrology, leading to the
proposed Sierra Nevada Hydrologic Observatory (SNHO) as a hydrologic
test area for the initiative established by the Consortium of
Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI).

Another unresolved question from DMIP 1 is: 'Can distributed models
reproduce processes at basin interior locations?' Included here is the
computation of spatial patterns of observed soil moisture. DMIP 1
attempted to address this question through blind simulations of nested
and basin interior observed discharges at a limited number of sites.
Investigations into this question have typically been hampered by a lack
of soil moisture observations organized in a high spatial resolution.
While much work has been done to estimate soil moisture from satellites,
these methods are currently limited to observing only the top few
centimeters of the soil surface. The test basins in DMIP 1 are mostly
contained in Oklahoma, offering an opportunity for the soil moisture
observations from the Oklahoma Mesonet to be used. Despite the
limitations of the Oklahoma Mesonet, (e.g., one sensor per county) it is
prudent to perform experiments to understand the real value of the
currently available data and work towards developing requirements for
future sensor deployment.

Yet another major need highlighted by DMIP 1 experiments is the testing
of models in a 'pseudo-forecast environment' with forecast-quality
forcing data. Such tests are a logical complement to the process
simulation experiments in DMIP 1. The well-documented model
intercomparsion experiment of the WMO (WMO, 1992) highlighted the
testing of models in a forecasting environment. One of the conclusions
of this workshop was that good simulation (process) models are necessary
for longer lead-time forecasts. In DMIP 1, we tested process models in
simulation mode and thus satisfied this conclusion from the WMO
experiment. Now, we propose that DMIP 2 include a forecast test
component as a natural complement to the process experiments in DMIP 1.

Finally, as with DMIP 1, the NOAA/NWS realizes the need for an
accelerated venue of science experiments to guide its research into
advanced hydrologic models for river and water resources forecasting.
This need is accentuated by NOAA/NWS' recent progression into a broader
spectrum of water resources forecasting to complement its more
traditional river and flash flood forecasting mission (NWS, 2004b).
Moreover, the NOAA/NWS heeds the recommendations of the National
Research Council (NRC) that point to hydrologic forecasting as one of
the ten 'grand challenges' in environmental sciences in the next
generation. (NRC, 2000). To this end, the NOAA/NWS welcomes the input
and contributions from the hydrologic research community in order to
better fulfill its mandate to provide the Nation with meaningful
products.

[]{.mark}

3.  Relation to NOAA/NWS Goals

DMIP 2 is specifically designed to meet NOAA/NWS goals identified in the
NOAA 2005-2010 Strategic Plan (NOAA, 2004) and the NWS Strategic Plan
(NWS, 2004a). NOAA 'Weather and Water Mission Goals' are directly
addressed through DMIP 2 by conducting experiments to guide the
development, application, and transition of advanced science and
technology to operations and new services and products. DMIP 2 also
contributes to the NOAA 'Cross-Cutting Priority' of ensuring sound,
state-of-the-science research as a vigorous, forward-looking project
that invites contributions from academia, other federal agencies, and
international institutions.

[]{.mark}

Moreover, elements of DMIP 2 support the recommendations of the NWS
Integrated Water Science Plan (IWSP, 2004). One of the primary IWSP
objectives is to 'provide new water resources products and services' by
implementing a new comprehensive suite of high-resolution digital water
resources analysis and forecast products. DMIP 2 contributes to this via
a experiment designed to evaluate spatially-varied soil moisture
simulations. Georgakakos and Carpenter (2004) proved the value of such
distributed soil moisture estimates for irrigation scheduling. DMIP 2
will augment their work with agricultural benefits by providing multiple
computations and evaluations of soil moisture fields.

4.  Relation to NLDAS

The North American Land Data Assimilation System (NLDAS) (Mitchell et
al., 2004) was designed to provide enhanced soil moisture (and
temperature) initial conditions for numerical weather prediction models.
Four land surface models (LSMs) were run in NLDAS over a three-year
analysis period: NOAH model from the National Center for Environmental
Prediction (NCEP); the Mosaic model from Goddard Space Flight Center
(GSFC) of NASA, the Variable Infiltration Capacity (VIC), and the NWS
Sacramento Soil Moisture Accounting Model (SAC-SMA). The models were run
in retrospective, uncoupled mode, on a 1/8^th^ degree grid over the
continental US (CONUS). NLDAS models used a common linear channel
routing scheme and meteorological forcings. Interestingly, three of
these models (SAC-SMA, VIC, and NOAH) also participated in DMIP 1.

NLDAS provided valuable insight into model performance for predicting
land surface states and fluxes. While there is some level of overlap
between the NLDAS and DMIP experiments, there are major science
questions and issues that are central to DMIP apart from NLDAS. Amongst
these is the difference in project goals: the DMIP experiments are
designed to guide the NWS science direction for models and techniques
for improved water resources, river, and flash flood forecasting, at
current modeling scales as well as at increasingly finer spatial and
temporal scales. One of the dominant foci of the DMIP experiments is the
generation and evaluation of hydrographs. The focus of NLDAS was to
evaluate the models' ability to generate enhanced initial conditions for
weather models with an emphasis on fluxes. Another major differentiation
is the model scale. Many of the DMIP 1 models were run at finer scales
to assess the ability to predict small scale events at basin interior
points. In contrast, NLDAS models were run on a rather coarse 1/8^th^
degree scale.

2.  Science Questions

We present the following science questions to be addressed in DMIP 2.
Some of these are repeated from DMIP 1 in order to evaluate them given
longer archives of higher quality data than were available in DMIP 1. We
frame the science questions for the interest of the broad scientific
community and in most cases provide a corollary for the NOAA/NWS.

I.  Can distributed hydrologic models provide increased simulation
    accuracy compared to lumped models? If so, under what conditions?
    Are improvements constrained by forcing data quality? This question
    was one of the dominant questions in DMIP 1. Reed et al. (2004a)
    showed that only one of the DMIP basins showed improvements from
    deterministic distributed modeling. Furthermore, work by Carpenter
    and Georgakakos (2004a) indicates that even when considering
    operational parametric and radar-rainfall uncertainty, flow
    ensembles from lumped and distributed models are statistically
    distinguishable in the same basin where the deterministic model
    showed improvement. The specific question for the NOAA/NWS mission
    is: under what circumstances should NOAA/NWS use distributed
    hydrologic models rather than lumped models to provide hydrologic
    services?

II. What simulation improvements can be realized through the use of a
    more recent period of radar precipitation data than was used in DMIP
    1? One of the issues faced in DMIP 1 was the time-varying biases of
    the NEXRAD precipitation data (Reed et al., 2004a) which affected
    the simulations in the model calibration and verification periods.
    For DMIP 2, we propose to avoid the problematic 1993-1996 period of
    radar data. Simulations and analyses will be based on the period
    starting in 1996. For the NOAA/NWS, the question is whether this
    later (and less bias-prone) period of data can lead to improved
    calibrations and simulations.

III. What is the performance of (distributed) models if they are
     calibrated with [observed]{.underline} precipitation data but use
     [forecasts]{.underline} of precipitation? Georgakakos and
     Smith (1990) argued for such an experiment as follow-on work to the
     1980's WMO model comparisons. (In those tests, observed real-time
     mean areal precipitation values were used.) They stated that:

> 'It is imperative however that a follow-up workshop be planned during
> which forecasts of rainfall are utilized instead of actual future
> rainfall observations. It is the rainfall input component of the input
> uncertainty that contributes the most to prediction uncertainty
> ...........'
>
> While much work has been done to evaluate the improvements realized by
> distributed models in *simulation* mode, the NOAA/NWS also needs to
> investigate the potential gains when used for forecasting. For
> example, the following questions are relevant: is there a forecast
> lead time at which the distributed and lumped model forecasts
> converge? How far out into the future can distributed models provide
> better forecasts than currently used lumped models? Reed et al.
> (2004a) stated that because forecast precipitation data have a lower
> resolution and are much more uncertain than their observed
> counterparts, the benefits of distributed models may diminish for
> longer lead times.

IV. Can distributed models reasonably predict processes such as runoff
    generation and soil moisture re-distribution at interior locations?
    At what scale can we validate soil moisture models given current
    models and sensor networks? The soil moisture observations derived
    through the Oklahoma Mesonet provide a good opportunity to address
    the latter question over a large spatial domain. Koren et al. (2005)
    presents a comparison of computed and observed soil moisture using
    the Mesonet data. Fortin (1998) provided a good example of such
    experiments with the Sacramento model. Schaake et al. (2004)
    inter-compare CONUS-scale computed soil moisture values from four
    models and with available observations. They found better agreement
    between observed and simulated [ranges]{.underline} of water storage
    variability than between observed and simulated
    [amounts]{.underline} of total water storage. For the NOAA/NWS, the
    corollary question is: can distributed models provide meaningful,
    spatially-varied estimates of soil moisture to meet the US needs for
    an enlarging suite of water resources forecast products?

V.  In what ways do routing schemes contribute to the simulation success
    of distributed models? In other words, can the differences in the
    rainfall-runoff transformation process be better understood by
    running computed runoff volumes from a variety of distributed models
    through a common routing scheme? Such experiments are necessary
    complements to validating distributed models with interior-point
    flow and soil moisture observations in that we are attempting to
    generate 'the right results for the right reasons.' Mitchell et
    al. (2004) present one large scale example of such a test. Such
    experiments also help the NOAA/NWS focus its research program.

VI. What is the nature of spatial variability of rainfall and basin
    physiograpic features, and the effects of their variability on
    runoff generation processes? What physical characteristics (basin
    shape, feature variability) and/or rainfall variability warrant the
    use of distributed hydrologic models for improved basin outlet
    simulations? The corollary question for the NOAA/NWS is: at what
    river forecast points can we expect distributed models to
    effectively capture essential spatial variability so as to provide
    better simulations and forecasts?

> While this question was not explicitly investigated via DMIP 1
> modeling instructions, it was nonetheless a good opportunity to
> explore these questions. Using the DMIP 1 data sets, Smith et al.
> (2004) attempted to derive quantitative indicators to determine the
> benefit of distributed models in an *[a priori]{.underline}* sense.
> Distinct differences in precipitation spatial variability and basin
> behavior were identified. Yet, no quantifiable indexes could be
> derived. At present, five more years of observed precipitation and
> streamflow data are available to continue the types of analyses
> performed by Smith et al. (2004) and others. This question was not
> part of the experiments explicitly called for by DMIP 1. However, it
> and others were investigated at the initiative of the DMIP 1
> participants.

VII. What is the potential for distributed models set up for basin
     outlet simulations to generate meaningful hydrographs at interior
     locations for flash flood forecasting? Inherent in this question is
     the hypothesis that better outlet simulations are the result of
     accurate hydrologic simulations at points upstream of the gaged
     outlet. This question is repeated from the DMIP 1 experiments. Reed
     et al. (2004a) identified reasonable performance for small ungaged
     areas. In DMIP 2, we will make available longer data periods as
     well as a few more gaged locations for such tests.

> For the NOAA/NWS, we restate this question as: can distributed runoff
> and flow predictions for small, ungauged locations be used to improve
> upon the existing NOAA/NWS flash flood forecasting procedure (i.e.
> Flash Flood Guidance)? Analysis tools that are now being developed as
> part of the statistical-distributed modeling investigation using
> HL-RMS (Reed et al. 2004b) can also be used to analyze participant
> uncalibrated simulations. Streamflow gauge data for 6 basins smaller
> than 157 km2 are available for DMIP 2 (5 of these were not available
> for DMIP 1).

VIII. What are the advantages and disadvantages associated with
      distributed modeling (versus lumped) in hydrologically complex
      areas using existing model forcings? DMIP 1 was limited to
      experiments in test basins in the southern Great Plains. These
      basins contain few complications such as snow accumulation and
      melt, forcing data scarcity, and orographic precipitation
      patterns. Many distributed hydrologic models have been developed
      to account for such complexities through accounting for slope,
      aspect, governing albedo, etc. (e.g., Wigmosta et al., 1994). The
      NOAA/NWS corollary is: what can be improved over the current
      lumped model (Snow-17) used in the NWSRFS?

IX. Is there a dominant constraint that limits the performance of
    hydrologic simulation and forecasting in mountainous areas? If so,
    is the major constraint the quality and/or amount of forcing data,
    or is the constraint related to a knowledge gap in our understanding
    of the hydrologic processes in these areas? In other words, given
    the current level of new and emerging data sets to drive advanced
    distributed models, can improvements be realized? Or, do we still
    not have data of sufficient quality in mountainous areas? As a
    corollary to the latter question, what data requirements can be
    specified for the NOAA/NWS to realize simulation and forecasting
    improvements in mountainous areas? Simpson et al. (2004) state that
    the primary limiting factors in the application of snow
    accumulation/melt models continue to be the 1) lack of spatially
    resolved meteorological inputs corresponding to the model
    computational units, and 2) lack of spatially relevant observations
    of hydrologic and snowpack conditions.

> A related corollary for the NOAA/NWS is: How can new observation sites
> that were not included in the calibration data set be incorporated
> into the hydrological modeling system? The NOAA HMT instrumentation
> effort provides the ideal forum to address this question. Presumably
> the hydrologic models - both distributed and lumped - will need to be
> calibrated from existing datasets that do not include the NOAA HMT
> dataset. How then, can these models best utilize these new sources of
> data? Answers to this question will have a wide application -
>
> specifically whenever RFC operations encounter a new sensor that did
> not exist during the calibration period.

X.  Can improvements to rain-snow partitioning be made? Partitioning
    between rainfall and snow fall plays a major role in determining
    both the timing and amount of runoff generation in high altitude
    basins (Kim et al., 1998). Advanced instrumentation such as
    vertically pointing wind profilers and S-Band radars have been used
    to detect freezing levels by locating the bright-band height (BBH)
    (White et al., 2002). This latter study reported that a 609m
    (2,000ft) rise in melting level can triple the amount of runoff. For
    the NOAA/NWS, such information is critical. In one case, these
    advanced techniques located the observed freezing level at 2700
    feet, which was 1300 feet lower than the forecast models suggested.
    This observed departure (lowering) from the forecast snow level led
    the Portland Weather Forecast Office to upgrade their Snow Advisory
    to a Winter Storm Warning.[^1] The question for the NOAA/NWS is: can
    advanced sensors planned for implementation via the NOAA HMT in the
    American River lead to improved simulations and forecasts?

XI. What are the dominant scales (if any) in mountainous area hydrology?
    Understanding the variations of snowpacks and the timing and volume
    of snowmelt that generate streamflow has grown in recent periods but
    is complicated by difficult scale issues (Simpson et al. 2004).
    Mismatches exist between the spatial and temporal scales of
    observations and the scales over which snowpacks and runoff vary. As
    stated by Simpson et al. (2004):

> '*The hydrologic results of these spatially and temporally varying
> land surface and climate conditions are complex differences and
> changes in snowmelt, soil moisture and streamflow......As a
> consequence, understanding, observing, and predicting such variations
> are central goals for hydrologists and resource managers alike in
> snow-dominated and snowfed regions....'*
>
> For the NOAA/NWS, the question can be restated as: is there an
> appropriate modeling scale in the mountainous areas that captures the
> essential rain/snow processes?

3.  Description of Proposed Sites

    1.  Overview

Figure 1 shows the two major geographic regions for the experiments to
be conducted in DMIP Phase 2. As seen in Figure 1, the Oklahoma region
and watersheds in DMIP 1 will be used. Second, we propose two
neighboring basins in the Sierra Nevada mountains as good candidates for
hydrologically complex areas. We present the basins here and provide
more specific information in Appendices A, B, and C.

![](media/image1.wmf)

Figure 1. The geographic scope of DMIP 2 experiments.

2.  Oklahoma Region

Here, we propose to use an area including the state of Oklahoma as shown
in Figures 1, 2 and 3. As in DMIP 1, we will use the Blue River and
Illinois River basinsfor specific tests regarding lumped and distributed
models. For tests related to the soil moisture, we propose to model a
'synthetic basin' encompassing the entire state of Oklahoma with its
Mesonet series of soil moisture observations. Smith et al. (2004)
present a description of the Illinois and Blue River basins and the
rationale for their selection for lumped and distributed model
comparisons.

![](media/image2.jpeg){width="6.342361111111111in" height="3.0375in"}

Figure 2. Location of Oklahoma Mesonet sites as they relate to the test
basins in DMIP 1.

![](media/image3.wmf){width="5.2444444444444445in"
height="4.604861111111111in"}

Figure 3 Location of DMIP test basins and interior computational points
in the Oklahoma, Missouri, Arkansas area. Note that additional gages
have been located for DMIP 2

Table 1 shows the USGS stream gages and basin drainage areas for the
Oklahoma region basins. Note that we have located additional gages that
were not used in DMIP 1.

Table 1. Data for USGS Stream Gages in the Oklahoma Region

  ------ ----------- ---------------------------------------- ------------
  No     USGS No     Name                                     Area(km2)

  1      7332500     Blue R. nr Blue, OK                      1233

  2      7196500     Illinois River near Tahlequah OK         2484

  3      7197000     Baron Fork at Eldon OK                   795

  4      7196973     Peacheater Creek at Christie OK          65

  5      7196000     Flint Creek near Kansas OK               285

  6      7195500     Illinois River near Watts OK             1645

  7      7194800     Illinois River at Savoy AR               433

  8      7189000     Elk River near Tiff City Mo              2258

  9      7188653     Big Sugar Creek near Powell MO           365

  10     7188885     Indian Creek near Lanagan MO             619

  11     7194880     Osage Creek near Cave Springs AR         90

  12     7195000     Osage Creek near Elm Springs AR          337

  13     7195430     Illinois River South of Siloam Springs   1489
                     AR                                       

  14     7195800     Flint Creek at Springtown AR             37

  15     7195865     Sager Creek near West Siloam Springs OK  49

  16     7196900     Baron Fork at Dutch Mills AR             105
  ------ ----------- ---------------------------------------- ------------

3.  Basins in the Sierra Nevada

    1.  Description

We propose to use sub-basins in the American and Carson River basins
located on the border of California and Nevada as shown in Figure 4.
Although these basins are geographically close, their hydrologic regimes
are quite different due to their mean elevation and location on either
side of the Sierran divide (Simpson et al. 2004). The Carson River basin
is a high altitude basin with a snow dominated regime, while the
American River drains an area that is lower in elevation with
precipitation falling as rain and mixed snow and rain (Jeton et al.
1996). Figure 5 shows the area-elevation curves of each basin and shows
that the East Fork Carson River is higher in elevation. Jeton et al.
(1996) present a similar figure. Figures B.3 and C.6 present expanded
versions of each areal elevation curves. These figures show differences
in the shape of the two curves, indicating that different hydrologic
responses may result.

![](media/image4.wmf){width="7.060416666666667in"
height="5.290972222222222in"}Figure 4. Location map of the American and
Carson River basins (after Jeton et al., 1996)

![](media/image5.wmf){width="5.322916666666667in"
height="3.8333333333333335in"}

Figure 5. Area-elevation curves for the East Fork and North Fork basins.

In the American River basin, we propose the North Fork sub-basin above
the North Fork dam forming Lake Clementine. Hereafter, we refer to this
test site as the American basin. This basin is 886 km^2^ in area and
rests on the western, windward side of the Sierran divide. The USGS gage
at the North Fork dam is number 11-417000. Precipitation is dominated by
orographic effects, with mean annual precipitation varying from 813mm at
Auburn (elev. 393m. above msl) to 1,651 mm at Blue Canyon (elev. 1,676
m. above msl) (Jeton et al., 1996). Precipitation occurs as a mixture of
rain events and rain-snow events. The mean annual precipitation is 60.3
in and the annual runoff is 33.5 in (Lettenmaier and Gan, 1990).
Streamflow is about two-thirds wintertime rainfall and snowmelt runoff
and less than one-third springtime snowmelt runoff (Dettinger et al.
2004). The basin is highly forested and varies from pine-oak woodlands,
to shrub rangeland, to ponderosa pine, and finally to sub-alpine forest
as one moves up in elevation. Much of the forests are secondary-growth
due to the extensive timber harvesting to support the mining industry in
the late 1800's. (Jeton et al.,1996). Soils in the basin are
predominately clay loams and coarse sandy loams. The geology of the
basin includes metasedimentary rocks and granodiorite (Jeton et
al.,1996). The American basin is designated as a Wild and Scenic River
(Dettinger et al., 2004).

In the Carson River basin, we propose the East Fork sub-basin. While the
American River and other west-facing Sierran basins are generally less
steep than the basins on the east side of the divide, the East Fork
Carson River generally flows from south to north so that its average
slope is not as steep as it could be if it were to face directly
east-west. As stated earlier, the East Fork of the Carson River is a
high altitude basin, with a drainage area of 714 km^2^ above USGS gage
10-308200 near Markleeville, CA and 922 km^2^ above USGS gage 10-309000
at Gardnerville, NV. Elevations in the East Fork basin range from
1,650m. near Markleeville to about 3,400m. at the basin divide. Mean
annual precipitation varies from 559mm at Woodfords (elev. 1,722) to
1,244mm near Twin Lakes (elev. 2,438m). Hereafter, we refer to this
basin as the Carson basin.

Table 2 presents a summary of the characteristics of the American and
Carson river basins.

Table 2. Summary of the characteristics of the Carson and American
Basins.

  ------------------ ------------------------- ---------------------------
                     Carson River              American River

  Area               922 km2                   886 km2

  Median altitude    2417 m                    1 270m

  Annual rainfall    560mm -1244mm             813mm -1651mm

  Min and max temp   0 ^0^C, 14                3^0^C 18

  Forcings           mostly snow               snow and rain

  Aspect             leeward                   windward

  Soil               Shallow sandy and Clay    clay loams and coarse sandy
                     soil                      loans

  Geology            volcanic rock and         metasedimentary rock and
                     granodiorite              granodiorite

  Vegetation         rangeland in lower        pine-oak woodlands, shrub
                     altitude and conifer      rangeland, ponderosa pine
                     forests upper altitude    forest, and subalpine
                                               forest

  USGS gage          1030900 near Garderville, 11427000 at North Fork Dam
                     NV                        
  ------------------ ------------------------- ---------------------------

3.3.2. Rationale for Basin Selection.

Several factors underscore the selection of the American and Carson
basins for use in DMIP 2. Numerous previous studies, largely unregulated
flows, and exciting linkages to cross-cutting initiatives will provide
the DMIP 2 participants with a multi-institutional venue for sound
scientific investigation.

First, both basins are largely unregulated (Jeton et al., 1996;
Dettinger et al., 2004), even though a few small reservoirs and
diversions exist in both basins. The American is largely unaffected by
upstream reservoirs and diversions (Jeton et al., 1996; Dettinger et
al., 2004). Figure B.10 in Appendix B shows a schematic of the small
reservoirs and diversions in this basin. None of the investigators found
it necessary to remove the effects of the small reservoirs to derive a
'natural' flow (Carpenter and Georgakakos, 2001). Also, the Corps of
Engineers studied reservoir effects in California basins and concluded
that the North Fork dam would not have significant effect on streamflow
hydrographs. (personal communication, Brett Whitin, USACE).

Second, these basins are geographically close, yet they present an
opportunity to study different hydrologic regimes. Moreover, their
proximity allows for more expedient data processing by DMIP 2 organizers
and participants.

Third, the selection of the American River for hydrologic analysis
dovetails with the planned deployment of the Hydrometeorological Testbed
(HMT) of NOAA's Environmental Technology Laboratory (ETL) in the same
basin for meteorologic analyses and development. Previously, NOAA
deployed the HMT in the Russian River, a flood prone basin also draining
to the Sacramento/San Francisco area. The Russian River HMT proved to be
a successful venture, providing a wealth of data and a sound footing for
subsequent HMTs. The American River HMT will allow advanced techniques
to address the problem of data scarcity in the mountainous west. DMIP 2
and the NOAA HMT would afford a multi-institutional evaluation of
hydro-meteorological observations gathered via advanced techniques.

Fourth, these basins have been studied by numerous researchers,
providing substantial modeling experience and insight into their
hydrologic behavior. Moreover, we hope that these studies will encourage
participation in DMIP 2 by reducing project spin-up costs. Leavesley et
al., (2003) used the Carson basins for experiments on *a priori*
parameter estimation. Lettenmaier and Gan (1990) subjected these basins
to global warming scenarios to determine the resultant hydrologic
sensitivity. Jeton and Smith (1993) used these basins for GIS-based
parameter derivation for distributed model application. Using these
distributed models, Jeton et al. (1996) later modeled the potential
effects of climate change on the streamflow. Carpenter and Georgakakos
(2001) used the American River basin to investigate the effects of
climate scenarios on flood control, hydro-electric power generation, and
low flow augmentation. They were able to calibrate the North Fork basin
and other sub-basins of the American River to a satisfactory degree.
They did notice a slight over-simulation bias for the North Fork.
Lundquist and Cayan (2002) used the American river and others throughout
the West to study the seasonal and spatial patterns of diurnal
streamflow patterns. They found that the American River has a
rain-dominated power spectrum without a distinct diurnal cycle from
January to April, and a snowmelt-dominated diurnal peak from April to
July. Cayan and Riddle (1993) examined the influence of temperature and
precipitation on streamflow for a number of basins including the
American River across a range of elevations in California. Kim et al.
(1998) performed a numerical study of precipitation and streamflow for
the winter of 1994 and 1995. Simpson et al. (2004) examined issues of
scale and improved estimates of solar insolation for forecasting
snowmelt and streamflow in the American and Carson basins.

Several authors used these basins in the Sierra-Nevada mountains to
study the dynamics of the precipitation generation process in
mountainous areas. Reynolds and Dennis (1986) reported on cloud seeding
efforts to modify winter precipitation over the Sierra Nevada. Pandey et
al. (1999) studied the influences of upper air characteristics along the
California coast on wintertime precipitation. Shortly thereafter, Pandey
et al. (2000) used a hybrid physical-statistical scheme to resolve
fine-scale precipitation patterns in the same region. Hay and Clark
(2003) used statistically and dynamically downscaled weather model
output to force hydrologic simulation models in the Carson River Basin.
Tsintikidis et al. (2002) used the American river to examine the
estimation of hourly precipitation and related uncertainties given the
existing operational real-time network of gauges. Wang and Georgakakos
(2004) used the MM5 model to simulate 62 winter storms in the American
River basin. They investigated the dependence of model precipitation on
boundary and initial conditions and physical system parameterizations.
Dettinger et al. (2004) investigated the degree of orographic
enhancement in winter storms.

Finally, the American River basin is part of the Sierra-Nevada
Hydrologic Observatory (SNHO) proposal to the Hydrologic Observatory
initiative of the Consortium for the Advancement of Hydrologic Science,
Inc. (CUAHSI, see <http://www.cuahsi.org/> and
<http://www.cuahsi.org/HO/Prospectuses/prospectus_SNHO_080204.pdf>). One
of the primary aims of CUAHSI is to establish and maintain a set of
long-term hydrologic observatories (HO) at which research can be
conducted on pressing hydrologic problems by utilizing data generated by
CUAHSI as well as by other entities in the environs of the
observatories. Observatories will be selected on the basis of their
regional representation and their viability as laboratories to study
particular subsets of hydrologic problems from the master list, and data
networks will be designed and implemented to study these problems.
However, basic networks at each of the observatories will be implemented
to assure that cross-laboratory syntheses can be conducted.

These hydrologic observatories (HOs) are conceived to be large-scale
field facilities that will provide the coherent, multi-disciplinary
characterization of the landscape necessary to advance a number of
environmental sciences, including hydrology, biogeochemistry, ecology,
geomorphology and limnology.  The hydrologic cycle provides the
organizing principle for the design of these observatories.

4.0 Overview of Proposed Experiments

To address the science questions presented in Section 2.0, we propose
the following experiments. These are organized by geographic region,
although there is some overlap.

4.1 Oklahoma Region

[4.1.1 Simulation experiments: lumped and distributed
models]{.underline}.

These will essentially follow the DMIP 1 Project Design and Modeling
Instructions (see <http://www.nws.noaa.gov/oh/hrl/dmip/default.html>).
Calibrated and un-calibrated simulations from participants' distributed
models will be tested against observed streamflow and corresponding
lumped-model simulations. As in DMIP 1, such simulations help the
NOAA/NWS evaluate the effort and benefits of model calibration.

[4.1.1.a]{.underline} Data: We will make available data forcing data
from 1996 (or earlier) to the present, and will define appropriate
calibration and verification periods. We propose to use the archived
operational NOAA/NWS radar data. We propose to add additional interior
simulation points at USGS gage locations that were not used in DMIP 1.
Estimates of potential evaporation will be provided as was done in
DMIP 1. Data from the Oklahoma Mesonet may be used to derive PE.

[4.1.1.b]{.underline} Standard of Comparison: As in DMIP 1, we propose
to compare distributed model simulations (calibrated and uncalibrated)
to 1) corresponding simulations from a lumped model and 2) observed
hourly streamflow from the USGS.

[4.1.1.c]{.underline} Evaluation metrics: We propose to use essentially
the same criteria specified in Smith et al. (2004) that were used in
DMIP 1. We will make available our statistical analysis program to
participants.

[4.1.1.d]{.underline} HL will ask for two simulations: uncalibrated and
calibrated. Note: If the DMIP 2 participant also generated DMIP 1
simulations, then an additional simulation will be requested. Here, we
will ask the participant to run the DMIP 2 radar data through their
models calibrated with the DMIP 1 radar forcing. This test will provide
a meaningful analysis of the dependence of model parameters on
precipitation forcing.

[4.1.2 Forecast experiments]{.underline}

Here, we propose a 'pseudo' forecast experiment not unlike that
undertaken by the WMO (1992). Participants will use their calibrated
(with NEXRAD re-analysis data) distributed models. Forecast-quality data
from numerical weather models will be made available.

[4.1.2.a]{.underline} Data: we propose to use Eta model-derived forecast
fields from NCEP. These are not reanalysis fields. Observed forcing will
be used to run models up to the current time. An alternative would be to
use archives of precipitation forecasts archived in the National
Precipitation Verification Unit. See:
<http://www.hpc.ncep.noaa.gov/npvu/>

[4.1.2.b]{.underline} Standard of Comparison: Calibrated lumped model
forecasts, observed data. Evaluation Metrics: we propose standard
forecast metrics to be evaluated at various lead times (Kitanidis and
Bras, 1980).

[4.1.2.c]{.underline} Data Assimilation: The models in the WMO real-time
comparison (WMO, 1992) all used assimilation techniques. Here, we
propose that no data assimilation be used. Data assimilation for
distributed models still needs considerable development before use in an
experiment like DMIP 2.

[4.1.2.d]{.underline} Basins: We propose that only one or two basins be
used for the forecast experiments. A limited period containing a select
set of events is proposed. We will specify the forecast lead time to be
used.

[4.1.3 Comparisons of Computed and Observed Runoff Volumes and Water
Balance Components]{.underline}

We propose that participants set up their model to run over an area
encompassing the Oklahoma Mesonet shown in Figure 1. Models can be set
up at any resolution, but must convert the soil moisture estimates to
the 4km2 HRAP scale. We propose to compare computed and observed soil
moisture contents at the 0-25mm and 25-75mm depth ranges.

Models will not perform routing; only water balance computations. No
model calibration will be performed. We propose to evaluate state
variables: soil moisture and runoff volumes. In DMIP 2, we wish to build
on the NLDAS experience. In that experiment, Schaake et al. (2004)
intercompared NLDAS model-generated soil moisture fields with each other
and with available observations. The NLDAS soil moisture estimates were
generated on a 1/8^th^ degree grid, which is too coarse for the current
and expected NWS water resources forecast products. Observed soil
moisture data were taken from the Illinois State Water Survey. These
data were collected twice per month. We propose to use data from the
Oklahoma Mesonet which has a finer temporal resolution.

[4.1.3.a]{.underline} Data: More recent NEXRAD radar data and other
tested forcings will be made available.

[4.1.3.b]{.underline} Standard of Comparison: Mesonet soil moisture
observations

[4.1.3.c]{.underline} Evaluation Metrics: For soil moisture, we propose
that a subset of the following measures could be used to evaluate the
goodness of fit of computed vs observed values of soil moisture over a
region. We will also use these for noting intermodel
differences[:]{.mark}

> 1\. Visual Agreement (Perica and Foufoula-Georgiou, 1996)
>
> 2\. Compare time series of computed soil moisture at various depths to
> corresponding observations. These time series comparisons will be
> performed at the locations of the OK. Mesonet soil moisture sites.
>
> 3\. Pattern correlation (Huang et al. 1996)
>
> 4\. Frequency Scaling Ratio (Guetter et al. 1996)
>
> 5\. 2-d wavelet transforms (Briggs and Levine, 1997)
>
> 6\. 'Figure of Merit'. (Perica and Foufoula-Georgiou, 1996). This is a
> dimensionless index defined as the area of the intersection of the
> observed and predicted areas, divided by the union of these two areas.
> Theoretical range is 0.0 (no agreement) to 1.0 (perfect agreement).
>
> 7\. Hausdorf Norm (Marron and Tsybakov, 1995). Qualitatively, this is
> a metric for the 'visual notion' of distance between curves or shapes.
> Tcherednichenko et al. (2004) used this metric to compute agreement of
> computed spatially variable distributed model outputs. The problem
> with this metric is that it is very computationally expensive (Luis
> Bastidas, personal communication, 2004).
>
> 8\. A test of the frequency at which a model soil moisture deficit
> exceeds a threshold (e.g., Georgakakos and Carpenter, 2004).
>
> 9\. Methods used by Schaake et al. (2004). Intermodel differences were
> described through the dimensionality of the correlation matrix.
> Comparisons of modeled to observed soil moisture were not made between
> point soil moisture measurements and area average model estimates at
> the corresponding grid points. Instead, a composite average of
> observed total column soil water content was compared to an average of
> the total water content at the corresponding grid points.

4.  [Common Channel Routing Scheme]{.underline}

In this series of experiments, we propose that we rout participants'
runoff time series through a common channel routing scheme. This will
help discern differences amongst the participants' rainfall-runoff
mechanisms. We propose that participants generate runoff volumes
(aggregated to one hour time step) at the HRAP scale. Here, participants
provide the runoff that they use in their models before hillslope and
channel routing. The participants will be free to use whatever basin
discretization is appropriate for their models, but then must average
the runoff volumes to the 4km2 HRAP scale. We will ingest the runoff
volumes and route them through the HL distributed model using kinematic
hillslope and channel routing. We will then compute goodness-of-fit
statistics. We propose to run such simulations for a 2-3 year period on
the Blue and Tahlequah River basins.

4.1.4.a Data: We propose to use the more recent NEXRAD precipitation
data as the primary forcing.

4.1.4.b Standard of comparison: USGS hourly discharge data at selected
points.

4.1.4.c Evaluation Metrics: We propose to use essentially the same
criteria specified in Smith et al. (2004) that were used in DMIP 1.

1.  Sierra Nevada Basins

In the American and Carson sites, we propose a general multi-model
inter-comparison of lumped and distributed models similar to DMIP 1.
Models will be parameterized and set up to generate calibrated and
uncalibrated simulations of streamflow, snow cover, and soil moisture,
depending on the basin.

1.  Data:

    1.  Precipitation. We propose to first make available several
        precipitation forcings at an hourly time step. Several
        preliminary options are available and are listed below. In all
        cases, we will evaluate the forcings to have the proper long
        term areal mean precipitation. The primary format/spatial
        resolution will be the nominal 4km HRAP grid used in DMIP-1.
        Other resolutions may be made available.

4.2.1.1.a MPE derived rain-gage only field.

4.2.1.1.b MPE derived rain gage -- satellite merged product. Note that
analyses by Kondragunta et al. (2005) show that in the Sierras, use of
satellite-sensed precipitation does not provide significant improvement
over a gauge-only field due to the high density gauge network.

4.2.1.1.c MM5 output. There are potentially two alternatives here. The
first is to use MM5 results from George Leavesly; the second is through
PhD work by Art Henkel (NWS Sacramento) at the University of California
at Davis under Lavent Kavvas and John Schaake. These data sources are
proposed for FY06.

4.2.1.1.d Gridded precipitation estimates derived using the procedure of
Shuzheng Cong and John Schaake in HL.

4.2.1.1.e Operational data produced via the 'Mountain Mapper'
application.

4.2.1.1.f Gridded precipitation amounts from the National Mosaic QPE
(NMQ) being developed at the National Severe Storms Lab (NSSL).

4.2.1.1.g Following this and in participation with the NOAA
Environmental Technology Lab Hydromet Testbed in the American River, we
will make available revised precipitation estimates derived from the
X-band polarimetric radars and other advanced sensors described in
Appendix D. These data will be used to evaluate the simulation
improvements possible via advanced observation sensors.

2.  Temperature: We propose to use one or more data sets of temperature.
    As with precipitation, we will ensure that temperature data
    corresponds to the proper long term areal mean. The primary
    format/spatial resolution will be the nominal 4km HRAP grid used in
    DMIP 1. Other resolutions may be made available. We propose to
    provide these data at an hourly time step. We have not yet finalized
    the method for generating the gridded temperatures. Operational data
    from the "Mountain Mapper" application may be used.

3.  Snow: snow data collected by the State of California are available
    at <http://cdec.water.ca.gov/snow/> (also precipitation and
    temperature similar to SNOTEL sites).

4.  Soil Moisture: We will make available soil moisture measurements in
    the North Fork as part of the NOAA HMT.

5.  PE: We will provide an estimate of PE for both basins. One
    possibility would be to provide an estimate of PE versus elevation
    for each basin.

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1.  Standard of Comparison: We propose to use 1) USGS observed (hourly
    and daily) discharges and 2) simulations from a lumped or
    semi-lumped modeling approach that is the same as run by the River
    Forecast Center. In the American basin, we will also perform
    comparisons of computed and observed soil moisture as well as snow
    depth, snow water equivalent, and areal extent of snow as these data
    become available via the NOAA/ ETL Hydromet Testbed (HMT) in the
    cold seasons of 2005-6, 2006-7, and 2007-8. All models will be run
    at the same time step. We propose to investigate the data
    requirements for mountainous areas via model simulations with and
    without the HMT advanced data.

2.  Metrics: We propose to use essentially the same criteria specified
    in Smith et al. (2004) that were used in DMIP 1 for discharge
    comparisons. Computed spatial fields of soil moisture and snow
    characteristics will be evaluated using the proposed criteria
    discussed earlier.

5.0 Proposed schedule

Table 3 presents the propose schedule for the major DMIP 2 activities.
We have the opportunity to re-run the simulations in the American basin
with enhance data anticipated from the ETL Hydromet Test Bed data
collection activities in that basin. We plan to have a summary workshop
in the October 2007 time frame to discuss the results from both the
Oklahoma and Sierra Nevada regions. After that, the participants can run
more tests using the HMT data from the 2007-2008 cool season.

Table 3. Major DMIP 2 milestones and proposed completion dates

<table>
<colgroup>
<col style="width: 15%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 2%" />
<col style="width: 3%" />
<col style="width: 3%" />
<col style="width: 3%" />
<col style="width: 3%" />
<col style="width: 3%" />
<col style="width: 3%" />
<col style="width: 4%" />
<col style="width: 5%" />
<col style="width: 25%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 7%" />
</colgroup>
<tbody>
<tr class="odd">
<td></td>
<td></td>
<td>Oct 2005</td>
<td><p>Jan</p>
<p>06</p></td>
<td><p>April</p>
<p>06</p></td>
<td><p>July</p>
<p>06</p></td>
<td><p>Oct</p>
<p>06</p></td>
<td><p>Jan</p>
<p>07</p></td>
<td><p>April</p>
<p>07</p></td>
<td><p>July</p>
<p>07</p></td>
<td><p>Oct</p>
<p>07</p></td>
<td><p>Jan</p>
<p>08</p></td>
<td><p>April</p>
<p>08</p></td>
<td><p>July</p>
<p>08</p></td>
<td><p>Oct</p>
<p>08</p></td>
<td><p>April</p>
<p>08</p></td>
</tr>
<tr class="even">
<td>Project Start</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td><p>Data for OK region</p>
<p>Available Oct 1</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td><p>Generate simulations:</p>
<p>Oklahoma region</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Soil moisture Tests Oklahoma region</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td><p>Forecast tests</p>
<p>Oklahoma region</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Unified routing Ok.</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Analyze results</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>HL summary workshop</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Basic Data available for western basins (DEM, etc)</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td><p>Basic forcing data available</p>
<p>For Western areas.</p></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Generate basic simulations</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>’06-’07 HMT collected, QC’d, made available</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Generate updated simulations</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>HL Summary Workshop</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>’07-’08 HMT data collected, QC’d, made available</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Generate updated simulations</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>Additional analyses by participants: papers, etc.</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>

6.0 Expected Results

We envision that DMIP 2 will provide a wealth of results that can help
fill the identified knowledge gaps.

First, based on updated and revised radar precipitation data sets, we
expect to confirm the primary results of DMIP 1 (Reed et al., 2004a)
regarding lumped and distributed models in hydrologically simple
terrain. NEXRAD radar precipitation from a later and less bias-prone
period will lead to reduced uncertainty and thus more appropriate
conclusions. The longer archive of data will also contain more
rainfall-runoff events, a problem that plagued the short verification
period in DMIP 1.

Large-scale comparison of simulated and observed soil moisture will
undoubtedly add to our understanding of distributed modeling to
correctly model interior processes. Such testing is also necessary to
generate results that are spatially coherent and consistent.
Furthermore, such large scale tests will provide much experience as the
NOAA/NWS moves forward with CONUS runs to generate soil moisture and
other water resources forecasts.

DMIP 2 should serve as a natural complement to the growing number of
other model comparison projects such as the well-known efforts by WMO
(e.g., WMO, 1992). In particular, the forecast component of DMIP 2
should underscore the issues surrounding operational river and flash
flood forecasting. As occurred in DMIP 1, DMIP 2 will provide a positive
opportunity for developers to evaluate their models in yet another
arena, potentially uncovering needed algorithmic and/or science
corrections or enhancements.

We also expect that DMIP 2 will provide multiple opportunities to
develop data requirements for modeling and forecasting in hydrologically
complex areas. These requirements fall in the general categories of
needed spatial and temporal resolution and quality. From these, new
sensor platforms could be designed or appropriate densities of existing
gages could be specified to meet specific project goals. From the river
forecasting viewpoint, we think these data needs are particularly acute
in the mountainous west. In addition, DMIP 2 will serve as a
multi-institutional evaluation of the Oklahoma Mesonet sensors and data.
Such an evaluation may be able to promote an expansion of these sensors
to larger geographic domains. Or, DMIP 2 may point out a need for other
soil moisture sensors to meet the needs of NOAA/NWS water resources
forecasting mission.

Moreover, we envision that DMIP 2 will contribute to meeting the goals
of partner agencies and initiatives such as the NOAA HMT and the
Sierra-Nevada HO of CUAHSI. We foresee that such combined, cross-cutting
efforts will provide results not possible to achieve if the same
programs were executed in an isolated manner. For example, we will work
closely with NOAA/ETL personnel to plan the siting of soil moisture and
other sensors in the American River HMT. Such cross-cutting
collaboration will facilitate an end-to-end evaluation of the new data
in a multi-institutional framework.

As with DMIP 1, we hope that scientists will take advantage of the DMIP
2 project to investigate ideas not explicitly identified. For example,
several DMIP 1 participants investigated uncertainty issues related to
model structure (Butts et al., 2004), parametric and radar-rainfall
uncertainty (Carpenter and Georgakakos, 2004b), and quantifying
uncertainty via multimodal ensembles (Georgakakos et al., 2004).

We expect DMIP2 to positively impact forecasting operations at the
relevant RFCs through successful technology transfer. Many aspects of
the forecasting enterprise could be improved through DMIP2. Potentially,
candidate models could be transferred to the RFCs and run in parallel
with their existing models. Research into the questions posed by this
plan could be applied to either existing RFC tools and data sources or
to new tools and data sources developed for DMIP2. We expect both RFCs
involved in this study to be included in the research findings. We also
expect to work with the RFCs to develop methods to best apply the
lessons learned from this plan.

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Appendix A.

Additional Descriptive Information for the Oklahoma region

![](media/image6.wmf){width="6.364583333333333in"
height="4.758333333333334in"}

Figure A.1 Location of NEXRAD radars and extent of coverage. The red
circles indicate the extent of coverage of each radar. The yellow areas
are the river basins from the DMIP 1 experiment.

Appendix B.

Additional Description of the American Basin

![](media/image7.jpeg){width="5.485416666666667in"
height="3.783333333333333in"}

Figure B.1 USGS basin number for the American River and location of the
North Fork Dam

![](media/image8.wmf)

Figure B.2 Elevation variability in the American River Basin

![](media/image9.wmf)

Figure B.3 Area Elevation Curve for the North Fork basin

![](media/image10.wmf){width="5.007638888888889in"
height="3.7472222222222222in"}

Figure B.4 Distribution of elevation and long-term mean precipitation in
the North Fork basin

![](media/image11.wmf){width="5.007638888888889in"
height="3.7472222222222222in"}

Figure B.5 Distribution of Long Term PE in the North Fork basin

![](media/image12.wmf){width="5.007638888888889in"
height="3.7472222222222222in"}

Figure B.6 Forest type and percent coverage in the North Fork basin

Description of North Fork Dam and Lake Clementine

The North Fork dam seen in Figures B.7 and B.8 is a concrete arch dam
with an ogee weir overflow spillway. The dam was built as a debris
retention dam and is partially full. The USGS gage is 50 feet upstream
of the crest of the dam and is a water-stage recorder. The North Fork
dam was built in 1939 by the Corps of Engineers. It rises 155 feet above
the foundation and its crest is at elevation 718. It forms Lake
Clementine, a 12,800 acre-foot lake. Lake Clementine has a surface area
of 280 acres and is approximately 3.5 miles long, having a very narrow
shape with steep canyon walls as shown in Figures B.8 and B.9.

The California Comprehensive Study modeled the regulation effects of
many headwater reservoirs in the Central Valley of California including
five in the American River Basin (Hell Hole, French Meadows, Loon Lake,
Union Valley, and Ice House). Reservoirs selected for explicit modeling
had to satisfy one of two criteria:

1\) They have existing flood damage reduction functions, or

2\) They maintain an active storage greater than 10,000 acre-feet and
regulate a significant natural drainage area.

North Fork Dam original capacity is 14,700 acre-feet and its drainage
area is 342 square miles. Its drainage area is fairly substantial
(approximately 18% of the drainage upstream of

Folsom Dam), however, the capacity today is much less than the original
due to the fact that its primary purpose is debris control. Because of
its reduced capacity, it was assumed by the Comprehensive Study that the
North Fork Dam had little effect on hydrograph attenuation. Based on
this, we believe that we can assume the North Fork dam will not
negatively affect the comparisons outlined in DMIP 2.

![](media/image13.jpeg){width="4.35625in" height="5.816666666666666in"}

Figure B.7. Ogee weir at North Fork Debris Dam forming Lake Clementine.
USGS gage 11427000 is on the bank of the lake approximately 50 feet
upstream of the dam. Apparently, there are no low-flow outlets. (Photo
used with permission from Leon Turnbull, see also
www.waterfallswest.com)

![](media/image14.jpeg){width="5.729166666666667in"
height="3.7708333333333335in"}

Figure B.8 View of lower end of Lake Clementine and the North Fork dam.

![](media/image15.png){width="6.052777777777778in" height="7.90625in"}

Figure B.9 Contour map of the region around Lake Clementine in the North
Fork basin

Several small impoundments and diversions exist in the North Fork basin
as shown in Figure B.10. A short description of each is provided
(Source: USGS California Water Resources Data, 1994, Volume 4)

> ![](media/image16.wmf){width="6.263888888888889in" height="4.6875in"}

Figure B.10 Schematic of the small reservoirs and diversion in the North
Fork American River basin

1.  **USGS Gage 1142700** North Fork American River. Drainage area 342
    square miles. Remarks: No estimated daily discharge. Records good.
    Minor regulation by Lake Clementine, usable capacity, 12,800
    acre-ft, formed by North Fork Dam. Storage in Big Reservoir and Lake
    Valley Reservoir (station 11426170), combined capacity, 10,300
    acre-ft upstream from station. Lake Valley Canal (station 11426190)
    diverts from North Fork of North Fork American River into Bear River
    Basin for power development in power plants of Pacific Gas and
    Electric Co. Combined storage and diversion have small effect on
    natural flow. See schematic diagrams of Bear and Lower Sacramento
    River basins. (page 320, USGS Ca. No. 4 1994)

2.  **USGS Gage 114126190** Lake Valley Canal. Remarks: No estimated
    daily discharge. Canal diverts from right bank of the North Fork of
    the North Fork American River, 2.0 miles downstream from Lake Valley
    Reservoir (station 11426170) to the Drum Canal in the Bear River
    Basin.

3.  **USGS Gage 11426180.** Kelly Lake near Cisco, Ca. Drainage area:
    0.58 square miles. Remarks: Reservoir is formed on natural lake by
    rock-fill dam completed in 1928. Usable capacity, 336 acre-feet
    between gage heights 0.0 ft invert of outlet, and 17.1 feet, top of
    flashboards. Water is used for Power development downstream.
    Records, including extremes, represent useable contents at 2400
    hours. See schematic of Bear River Basin.

4.  **USGS Gage 11426170**. Lake Valley Reservoir. Drainage area: 4.54
    square miles. Remarks: Lake is formed by an earthfill dam; storage
    began in 1911. Usable capacity, 7,960 acre-ft. between gage heights
    6.2 feet (natural rim of lake) and 57.5 feet (top of flashboards).
    Released water is diverted downstream to Lake Valley Canal
    (station 11426190) and then to several power plants. Records,
    including extremes, represent usable contents at 2400 hours.

Appendix C.

Additional Information for the Carson River Basin

![](media/image17.png){width="6.489583333333333in"
height="4.939583333333333in"}

Figure C.1 Spatial variability of forest type in the Carson River Basin

![](media/image18.wmf){width="5.007638888888889in"
height="3.7472222222222222in"}

Figure C.2 Elevation distribution in the Carson River basin

![](media/image19.wmf){width="5.007638888888889in"
height="3.7472222222222222in"}

Figure C.4 Percent of forest cover in the Carson River Basin

![](media/image20.wmf){width="6.854861111111111in"
height="5.133333333333334in"}

Figure C.5 Spatial variability of annual potential evaporation

![](media/image21.wmf){width="5.007638888888889in"
height="3.7472222222222222in"}

Figure C.6 Area-elevation curve for the Carson River basin

![](media/image22.wmf){width="5.007638888888889in"
height="3.7472222222222222in"}

Figure C.7 Location of NRCS SNOTEL sites: Carson River basin

Appendix D

The NOAA/OAR/ETL Hydrometeorological Testbed (HMT) Program

Overview

A national Hydrometeorological Testbed (HMT) program is being developed
by NOAA for the purpose of advancing water resources data assimilation.
The general strategy of this effort is to conduct research and
development to deploy advanced systems for observed information to
support critical decision making and fresh/salt water forecasting. More
specifically, high resolution atmospheric and hydrometeorologic
observations (precipitation, soil moisture, snowpack, winds,
temperature, and moisture) will be collected and analyzed for several
key water resource applications such as distributed hydrologic model
validation, quantitative precipitation forecast (QPF) and estimation
(QPE) validation, and improved understanding of key physical processes
such as atmospheric rivers, orographic effects, air mass transformation,
soil moisture variability and streamflow response to precipitation. In
turn, these analyses will be integrated into water management decision
support systems for purposes of flood mitigation, hydropower energy
generation, water resources control, and fisheries management.

The HMT program will ultimately be implemented incrementally in
different regions of the U.S. where distinct hydrometeorological
forecasting issues are unresolved. In broad terms, hurricanes are a
major focus in the eastern part of the country, warm-season mesoscale
convective systems are a major focus in the central part of the country
and cool-season extratropical cyclone systems are a major focus in the
western part of the country. These focci have driven the first
realizations of HMT and will provide the basis for migration of HMT to
meet national priorities in water management. The first realizations was
established in the western United States during the 2002-03 and 2003-04
cool seasons through pilot studies on the flood-prone Russian River of
northern California.[^2] These studies have laid the groundwork for
improving cool season QPF in an area where researchers and forecasters
have worked closely with key forecast users. The enhanced predictability
of major precipitation events created by the orographic forcing in the
western U.S. during the cool season makes this area and season the most
tractable to demonstrate improved user decision making. Lessons learned
during these pilot studies are being applied in the planning of the
first major HMT effort (HMT-WEST), a more comprehensive study centered
on the American River basin of the western Sierra Nevada during cool
seasons 2005-06 through 2007-08. The American River basin was selected
because of its huge impact on water management within the state of
California, mitigating risks of floods that can produce billions of
dollars in damage and serious loss of life, and optimizing the
production of hydro-electric power.

Instrumentation

The suite of ground-based observing systems to be deployed by NOAA in
the American River basin will be patterned after those used in the pilot
studies. These include a scanning X-band polarimetric Doppler radar, 915
MHz wind profilers, vertically pointing S-band Doppler radars, GPS
integrated water vapor sensors, GPS rawinsondes, soil moisture sensors,
surface meteorology stations (e.g., temperature, moisture, wind),
all-weather precipitation gauges, and liquid and frozen hydrometeor
disdrometers. Airborne observing systems for soil moisture and snowpack
mapping (onshore) and precipitation and water vapor mapping (offshore)
will also be deployed by NOAA occasionally during HMT-WEST. These
systems include GPS dropsondes, imaging radiometers for soil moisture
and snowpack mapping, Doppler radars for precipitation mapping and wind
field derivation, and microphysical probes for determining hydrometeor
size, shape and mass characteristics. Some of the above instrumentation
has been developed under the support of the NASA Terrestrial Hydrology
Program for the AMSR-E calibration and validation effort, and will be
reused to support HMT. Statistics from the verification will be used to
improve the specification of the WRF-NMM error covariance matrix.  

For soil moisture, measurements will start in November 2004 with
observations at 2 depths using the Campbell Scientific 616L probe at
Blue Canyon in the North Fork. The burial depths will depend on the soil
conditions found at the site. Probes are typically inserted horizontally
at depths from 5 to 15 cm, and deeper (root zone) if located inside of a
canopy.

![](media/image23.wmf){width="5.007638888888889in"
height="3.7472222222222222in"}

Figure D-1. Planned NOAA Hydromet Test Bed for the American River

[^1]: Personal communication: David Kingsmill, NOAA/ETL, Boulder, CO.

[^2]: See <http://www.etl.noaa.gov/programs/2004/hmt/> .