I. Title Page
Title:
Subtheme this proposal is
responding to
Principal Investigator and
Receiving Institution
Statistician & Programmer
Ecologist
Peer Reviewer
Agency Collaborators
Realigning the Lake Tahoe Interagency Monitoring Program for
Use as a Monitoring Tool
Integrating Science 4b: Identifying environmental indicators and
developing approaches for monitoring and evaluation
Robert Coats
Hydroikos Ltd.
2512 9th St. Ste. 7
Berkeley CA 94710
Phone: (510) 295-4094
Fax: (510) 845-0436
Email: Coats@Hydroikos.com
Jack Lewis
647 Elizabeth Dr
Arcata, CA 95521-9252
Phone: (707) 822-2652
Email: jacklewis@suddenlink.net
John Reuter
Tahoe Environmental Research Center
U.C. Davis
One Shields Avenue
Davis, CA 95616-8803
530-754-TERC (8372) or 530-304-1473
Email: jereuter@ucdavis.edu
Robert Thomas
bobt@bendcable.com
Nancy Alvarez, Hydrologist
U.S. Geological Survey
nalvarez@usgs.gov
Shane Romsos
Tahoe Regional Planning Agency
SRomsos@trpa.org
Jason Kuchnicki
Nevada Dep. Environmental Protection
jkuchnic@ndep.nv.gov
Sue Norman
USFS Lake Tahoe Basin Management Unit
snorman@fs.fed.us
Grants Contact Person
Funding requested:
Total cost share (Contributed
Labor)
Same as PI, above
$160,524
$6,236
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II. Proposal Narrative
A. Project Abstract
Since 1980, public agencies in the Tahoe Basin have supported the Lake Tahoe Interagency
Monitoring Program (LTIMP), acquiring streamflow and water quality data. The purpose of the
LTIMP is to provide data that can be used to monitor water quality conditions and trends. With recent
funding cutbacks, changes in regulatory programs, and advent of new technologies, it is time to realign
and redesign the LTIMP. The purposes of this project are 1) to plan the realignment and re-focusing
of the LTIMP to bring it into line with current needs, budgets constraints and technologies, and 2) to
address and correct some historic sources of bias in the data and load calculation methods that
jeopardize it usefulness as a long-term monitoring tool. The study team includes four experienced
scientists and statisticians presently or previously associated with UC Davis, and the USFS Pacific
Southwest Research Station. Deliverables will include 1) a statistically-based analysis for alternative
sampling programs (e.g. sites, sampling frequency, constituents sampled) along with their associated
confidence levels and costs; 2) recommendations for re-focusing and streamlining the LTIMP program
using agencies needs and data priorities as a framework; 3) a suite of computer programs for
correcting bias and calculating total loads and discharge-weighted mean concentrations, and testing for
time trends; and 4) a bias-corrected and carefully-vetted historic water quality data base for the LTIMP
stations; and 6) monthly progress reports and a written draft report and final report. The report will
answer “Key Monitoring Questions” that have been posed by agency staffs.
B. Justification Statement
The Lake Tahoe Interagency Monitoring Program (LTIMP) has provided water quality and discharge
data for 10 Tahoe basin streams for over 20 years, with some of these streams monitored for over 30
years. The data set is valuable because it is the only tool for monitoring long-term trends in water
quality in basin streams. The LTIMP data played a key role in the development of the TMDL program
(Lahontan and NDEP, 2010), and have also provided the basis for several peer-reviewed publications
on basin water quality (Coats & Goldman, 1993, 2001; Coats et al., 2002, 2008) and basin hydrology
(Coats, 2010; Rowe et al., 2002; Jeton, 1999). Over time, however, the development and
implementation of new programs in the Tahoe basin (such as the TMDL and the Tahoe Monitoring
and Evaluation program) have significantly changed the regulatory context in which the LTIMP
operates. In addition, the need for recent funding cuts (and the prospect of future cuts) require some
scaling back of the Program. These developments, taken together, require re-evaluation of the LTIMP
program, and its realignment to better meet the needs of regulatory agencies and the research
community. The Program Goals and Specific Objectives are laid out in a LTIMP programmatic draft
of October 6, 2011 from TRPA et al. (2011), and these Goals and Objectives are incorporated in this
proposal by reference.
In addition, interpretation of the existing LTIMP data may be complicated by historical changes in
sampling intensity and timing, constituents measured, sampling site location, analytical methods, and
methods of calculating relevant indicators, such as total annual nutrient and sediment loads and
discharge-weighted mean concentrations. If the long-term LTIMP data are to be used for monitoring
water quality trends in the basin, the inherent uncertainty associated with environmental monitoring
over the course of >30 years must be evaluated and reduced.
The project will include the following elements:
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•
Streamlining the LTIMP stream monitoring program. An appropriate sampling strategy based
on the methods used to calculate loads for the constituents will be developed and documented.
We will review the number and location of sampling sites, the number of samples collected at
each site, timing of sample collection during spring runoff and storm events, parameters measured
for each sample, and possible investments in new technology that would be appropriate to present
program needs. The primary objective of this review is to determine if there are aspects of the
LTIMP stream monitoring program that can be cut back or reduced, or ways in which the
sampling error and bias can be reduced. The agencies and decision-makers in the basin have
expressed a need for statistically-derived confidence limits on which to base answers to key
management questions. The review will include estimates of cost associated with different
programs designs and levels of confidence.
•
Testing for and removing possible bias in the LTIMP data set. In some basin streams, samples
were formerly collected at night as well as during the day. The shift to day-light only sampling in
some streams introduced a bias during the snowmelt period, when there is a strong diel shift in
stream discharge and concentration of some constituents. In addition, a change in the method of
nitrate analysis has also introduced potential bias that must be removed.
•
Developing and testing sampling designs and load calculation methods. Since estimates of
total constituent loads are sensitive to the method used to calculate them, it is essential to develop
consistent and theoretically-sound sampling and load calculation methods. The optimum method
may be different for different constituents. We will create a synthetic data base of discharge and
concentration for each constituent, and resample it in order to develop and test different methods.
Monte Carlo methods will be used to estimate confidence limits as a function of sample size, for
each constituent (See Coats et al., 2002). The most precise load calculation method will require
the fewest samples for a given level of confidence. Selecting the best method is a key to reducing
program costs as it is it will reduce uncertainty.
•
Updating the data base and developing an analytic toolbox. This step will include developing
a bias-corrected data set (to the extent possible), and a user-friendly programs to calculate the
proposed water quality indicators and test for time trends. The program will then be run with the
entire historic data set to develop time series, and apparent trends in the time series will be tested
for statistical significance. The program and data will be structured to allow yearly updating and
testing against pre-determined thresholds and time trend hypotheses.
Both aspects of the proposed work—planning for program realignment, and methodological
review—fit into the theme of “Integrating Science”, and the Subtheme of “Identifying
environmental indicators and development of approaches for monitoring and evaluation.”
C. Background and Problem Statement
Since 1978, LTIMP has been sampling basin streams for water quality constituents and measuring
stream discharge. Five gaging stations have been maintained near watershed mouths over most of the
sampling period, and by 1992, five more gaging stations near different watershed mouths were added.
Since about 1993, stations have been added on upper tributaries in five of the watersheds, and some
stations have been discontinued. The constituents measured (over at least part of the period of record)
include Suspended Sediment Concentration (SSC), nitrate+nitrite-nitrogen (hereinafter referred to as
nitrate-N (NO3-N)), ammonium-nitrogen (NH4-N), total Kjeldahl Nitrogen (TKN), Soluble Reactive
Phosphorus (SRP), Total Hydrolyzable Phosphorus (THP), Total Phosphorus (TP), Dissolved
phosphorus (DP), biologically-available iron (BAFe), water temperature and specific conductance.
Analysis of filtered and unfiltered samples permits separation of dissolved and total fractions.
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In recent years, the focus on water quality constituents has changed, however, and new technologies
have become available. The concentration (or number) of small (< 16µm) sediment particles is now
known to be an important factor influencing lake water clarity, and a number of instruments and
methods are available for measuring small particles. Continuous turbidity monitoring may permit
more accurate estimation of concentrations and loads of fine suspended sediment and particulate-borne
constituents. Programmable pumped samplers have been steadily improving and coming down in
price, presenting an alternative to dangerous night-time sampling of creeks at flood stage. The costs
and benefits of these new technologies need to be evaluated.
A recent survey of LTIMP stakeholders (agency staff, consultants and scientists) found unanimity of
opinion that the LTIMP program is extremely or very valuable (see: ftp://dotftp.co.eldorado.ca.us/RW081611/LTIMP%20Survey). Opinions varied, however, about (for example) the
relative value of stream discharge measurement vs. water quality, and the relative importance of
tributary monitoring to the implementation of the TMDL.
The “LTIMP Tributary Monitoring Draft Goals and Objectives” (10/06/11) set out the “key questions
that need to be addressed to inform the development of a tributary monitoring and evaluation plan.”
These questions (summarized) are:
1. What is the most cost-effective sampling and analysis plan to achieve the stated goals and
objectives? What are the relationships between sample size and our ability to detect differences
and trends in loads and concentrations?
2. What constituents should be measured, and how? What are the most cost-effective methods
for collecting and analyzing samples, and reporting results? What can new technologies such as
laser counting of fine sediment particles offer us?
3. At what frequency does stream discharge need to be measured in order to detect trends in
hydrology related to climate change?
4. What are the most cost-effective and reliable techniques for measuring flow, constituent
concentrations and constituent loads, and how are they different than current procedures? Can
automated (near-continuous) stream sampling improve sampling precision and reduce costs? Can
spatial regressions (e.g. Ward Creek vs. Blackwood Creek loads) reduce sampling costs while
maintaining data quality?
5. Can the agencies’ goals and objectives for a monitoring and evaluation plan be met within an
annual budget of $300,000? $550,000? $875,000.
The Draft Monitoring Goals and Objectives also posed some “Key Monitoring Questions”, most of
which concern possible time trends and in pollutant loads and concentrations. Some of the questions
posed by agency staffs can best be answered by analysis of the existing LTIMP data. Others,
however, will require creating a consistent bias-corrected data set of historic values, and a synthetic
data set of daily or hourly values. The synthetic data set will be used to test and compare different
sampling strategies and load calculation methods for their ability to estimate the “correct” values (See
Coats, Liu and Goldman, 2002).
In addressing the problem of bias in the existing LTIMP data, some issues that must be addressed
include:
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•
A change in the method of calculating total loads and mean concentrations. Formerly, annual
loads of nitrogen and phosphorus were calculated by plotting time series of discharge and
concentration, interpolating concentration values for each day, multiplying daily discharge by
concentration, and summing over days. Since 1988 (LTIMP Ninth Annual Report, 1988), a rating
curve method has been used for all constituent loads, which is both biased and less precise for
some constituents than the old method (Coats et al., 2002).
•
A change in sampling intensity. The number of annual samples at each station has declined
over the period of record, from around 100 samples/station/year to about 30 samples/station/year,
so the sampling error has increased.
•
A change in the time of daily sampling. Prior to 1988 when Tahoe Research Group (TRG)
was sampling the LTIMP streams, staff collected 2-4 samples per day (including nighttime) during
snowmelt runoff or storm events to help develop the concentration curve (Scott Hackley, oral
communication). Nighttime sampling was discontinued for safety reasons after the USGS started
collecting samples. During spring snowmelt, the daily diurnal peaks in discharge and
concentration (especially of nitrate-N, total phosphorus and suspended sediment) usually occur at
nighttime due to the long travel time of snowmelt runoff from the upper reaches of the basin. A
comparison of load calculations for nitrate-N in Blackwood Creek (May 12-13, 1988) found a 40
percent deficit in loads calculated with day-time only samples compared with loads calculated
from both day and nighttime samples.
•
A change in chemical analytic methods. In 2003, the chemical method for nitrate analysis was
improved, causing an apparent increase in stream-water nitrate-N concentrations (Kempers et al.,
1988; 1992). A data set of over 2300 analyses by both the old and improved methods allows us to
correct the pre-2003 data and remove the bias. Regression equations have already been developed
for some stations by the USGS, and the bias was removed from the data used in the development
of the TMDL. The corrected data, however, have not been published.
D. Goals and Objectives
The goal of this proposed project is two-fold: 1) to develop, in collaboration with the supporting
agencies, a realigned and re-focused LTIMP sampling and data analysis program that will meet agency
and research community needs in a cost-effective fashion, while yielding quantitative estimates of
uncertainty based on the need to track status and trends and EIP implementation (water quality
restoration), and 2) to develop the existing water quality data base into a long-term monitoring toolbox
that can be used to assess water quality indicator trends in basin streams.
The toolbox will include the original and (where necessary) bias-corrected data, along with one or
more user-friendly computer programs (with documentation and users’ guide) that can be used to
calculate time series of water quality indicators. Researchers and agency staffs will then be able to test
hypotheses about long-term water quality trends in basin streams.
E. Approach, Methodology and Location
The project will be carried out in the following steps:
1.
Program Planning and Realignment (Input phase)
1.1 Stakeholder workshops. Since the goal is to create a useful tool for monitoring water
quality trends, it will be important to incorporate input and participation from the potential
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user groups. To accomplish this, we will hold three workshops for staffs of the Lahontan
Regional Water Quality Control Board (LWRQCB), the Tahoe Regional Planning Agency
(TRPA), the U.S. Forest Service (USFS), the Nevada Division of Environmental Protection
(NDEP), and the UCD/UNR/DRI Tahoe Environmental Research Center (TERC). The first
will give staffs the opportunity to provide input to methodology, the second will be a progress
report and discussion of possible programmatic changes, and the third will be a training
session in use of the data base and calculation programs. In addition, the PI (Coats) will plan
two other trips to the basin to meet individually with agency staff (Primary: Coats/Reuter).
1.2
New technologies and water quality constituents. Since the inception of LTIMP, new
sampling and measurement technologies have been developed, and the relative importance of
different constituents has changed. In this step, we will research the cost, accuracy and
precision of new technologies, including laser measurement of particle size, continuous
measurement of turbidity and automated pumped sampling for constituent analysis. We will
investigate the possibility of using relatively inexpensive water quality surrogates, such as
continuous turbidity measurement to estimate fine sediment loads (Primary: Reuter/Coats).
2.
Bias Correction and Load Calculation
2.1 Extract instantaneous discharge at time of sampling for the entire record, and match the
date, time, and discharge with the reported concentrations. (Primary: Arneson (UCD);
Secondary: Coats)
2.2 Document, and where possible, remove bias from the LTIMP data set (Primary:
Lewis/Coats).
2.2.1 For nitrate-N, develop regression equations (for 5 years of overlapping methods)
relating the concentration measured with the pyrophosphate catalyst to concentrations without
the catalyst. Apply the equations for each up-watershed station to the “old” data to make it
consistent with current analytic methods. (Primary: Coats/Arneson)
2.2.2 Where necessary, relate TP to THP concentrations by linear regression, and estimate TP
concentrations (Primary: Coats/Arneson)
2.2.3 Quantify the possible bias introduced by changes in timing of sampling. The bias will
be measured by using a data set for the period during which samples were collected after dark.
We will calculate total loads and discharge-weighted means for each tributary, and then filter
the data to conform to the new sampling protocol. We will then relate the loads for day-only
sampling to the loads based on day/night sampling by linear regression, for each station
(where data permits), and correct the day-only results to conform to the results by the
day/night sampling protocol. (Primary: Coats/Lewis)
2.3 Create a synthetic data base of daily discharge and concentration for each constituent,
having the same statistical properties as the actual LTIMP data. The LSPC model, used in the
development of the TMDL and recent SNPLMA climate change project, produced hourly
discharge and concentration data (for SS, N and P) for the LTIMP streams. The LSPC was
calibrated and verified using the LTIMP data, so the output should work well for this project,
though it may need to be corrected for bias (Wood et al, 2002) (Primary: Lewis)
2.4 Review literature on total load calculation methods, investigate potential improvements to
these approaches, and select the best method for each individual constituent. This will include
6
evaluating the USGS LOADEST program, which uses multiple regression for estimating total
loads. Resample the synthetic data base to test different sampling strategies, load calculation
methods and confidence limits on estimates of load and discharge-weighted mean
concentration. Primary: Lewis; Secondary: Coats
2.5 Select the most precise and accurate load calculation methods (which may be different for
different constituents), and run the programs for all constituents, stations and water years.
Output to Excel (or SigmaPlot) and create selected time series graphics. (Primary: Lewis;
Secondary: Coats
2.6 Test for significance of time trends using the Mann-Kendall trend test (Helsel and Frans,
2006). Summarize trend statistics in tabular form (Primary: Coats).
2.7 Write a User Guide for the computer program(s) for distribution with the revised and
original data set, including description of sampling methodology and assumptions. The
appropriate sections of the TRPA document “Elements of a Monitoring and Evaluation Action
Plan” will be written into the User’s Guide. (Primary: Coats/Lewis
3.
Program Planning and Realignment (output phase)
3.1 Conduct a workshop with potential users of the LTIMP data, including staffs of the
LRWQCB, TRPA, NDEP, USFS, TERC, and USGS to present results and train staffs in the
use of the calculation programs and data base. (Primary: Coats//Lewis)
3.2 Review the design of the LTIMP stream monitoring program, including an evaluation of
the number and location of sampling sites, the number of samples collected at each site, timing
of sample collection during spring runoff and storm events, and the parameters measured for
each sample. Determine if there are aspects of the LTIMP stream monitoring program that can
be cut back or reduced, while simultaneously reducing bias and sampling error, and focusing
the program on measuring water quality trends and conditions. Appropriate sections of the
TRPA Monitoring and Evaluation Action Plan will be completed. (Primary: Coats/Reuter)
3.3 Answer the “Key Questions” posed in the “LTIMP Tributary Monitoring Draft Goals and
Objectives”. Primary: Coats
3.4 Estimate costs for program implementation. This will include gathering data from
agencies (USGS and UCD) on costs for operating gaging stations, collecting and analyzing
samples, working up and reporting data. Overhead costs will be shown separately. Costs of
new technologies (such as continuous sampling, laser counting of fine sediment particles, etc.)
will be included. Results will be presented as a matrix (spreadsheet) or graphic showing the
tradeoffs between cost, sampling intensity and confidence levels for load estimates by
constituent. The spreadsheet will show relationship between cost and the power of a test, that
is, its ability to detect real differences in constituent loads and concentrations between (for
example) different time periods or different sampling stations. Primary: Reuter/Coats
4.
Write and circulate draft report in format for a journal publication; circulate and revise in light
of agency comments. Primary: Coats
Peer review and oversight will be provided by Robert Thomas, a mathematical statistician recently
retired from the USFS Pacific Southwest Research Station. Thomas has published extensively on the
statistical approaches to estimating total sediment loads.
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Most of this project will be carried out in offices of Hydroikos Ltd. (Robert Coats) in Berkeley, and
Jack Lewis, in Arcata, the Tahoe Environmental Research Center in Davis and Incline Village.
Meetings will be held at the Tahoe Environmental Science Center in Incline Village, NV, and the
offices of the LRWQCB and TRPA.
F. Relationship to Previous and Current Research and Monitoring
This project will build on previous research on methods of calculating total constituent loads in Tahoe
basin streams (Coats et al., 2002). It will have immediate and direct benefits to monitoring of the
effects of the Tahoe basin TMDL, and the efficacy of agency policies and programs (such as the EIP).
It may also be useful in efforts to monitor and model the impacts of fuel management in basin
watersheds. It will enhance the usefulness of the LTIMP data base for basic research on water quality
in subalpine streams. The computer program will also enable USGS, TRPA and UCD to more
effectively provide an annual report on the LTIMP stream monitoring program that evaluates the
status and trend of nutrient and sediment loads.
G. Strategy for Engaging with Managers
We will conduct three workshops for potential users of the LTIMP data. These workshops will
provide stakeholder input at the outset and mid-project, and provide a forum for communicating
results when the project is nearly finished. In addition, the PI (Coats) will plan two other trips to the
basin to meet individually with agency staff. Team members will be accessible by phone and e-mail
throughout the duration of the project.
H. Deliverables and Products
The final products of this project will include: 1) a written report with recommendations for
modifications to the LTIMP program (based on a statistical evaluation) that will improve its focus and
utility as a tool for managers; 2) a set of data files for the LTIMP stations that represent consistent
sampling, analytic and calculation methods, so that time trends in the data can be examined; 3) a
computer program (or programs) that can be run annually to update the long-term records for selected
water quality indicators, such as total annual load and discharge-weighted mean concentration; 4)
time series analysis of the updated records, with tests for statistical significance of time trends; 5) a
user guide for the data set and program(s) that can be used by agency staff, that will include
appropriate sections of TRPA’s Monitoring and Evaluation Action Plan. These products will be
distributed to relevant agencies in the Tahoe basin on CDs, or through the Tahoe Integrated
Information Management System (http://www.tiims.org/ ); 6) an easy-to-read matrix or Excel
spreadsheet showing the tradeoffs between project cost vs. number of stations, constituents, number
of samples per year, and confidence levels in estimates of constituent loads.
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III. Schedule and major milestones
REALIGNING LTIMP FOR USE AS A MONITORING TOOL
III. Project Schedule
TASK
1.
Stakeholder workshops
2.
Investigate and price new technologies
3.
Set up discharge and concentration data
4.
Write & apply programs to correct for bias
5.
Write and test and programs for Load and Q-wtd. Conc.
6.
Run programs; output graphics and summary tables
7.
Test for time trends in loads and Q-wtd means
9.
Write User Guide for programs and vetted data
10.
Develop alternatives (with cost estimates) for refocused LTIMP
11.
Write draft report; revise User Guide
12.
Respond to comments on draft report; write draft paper for publication
13.
Administrative (quarterly and ann. progress reports, invoices & contracts
1
9
2
3
4
5
6
Month from Written Notice to Proceed
7 8 9 10 11 12 13 14 15 16
17
18
19
20
IV. Literature Cited
Coats RN, and CR Goldman. 1993. Nitrate transport in subalpine streams, Lake Tahoe basin,
California-Nevada, U.S.A. Applied Geochemistry Supplement 2: 17-21.
—. 2001. Patterns of nitrogen transport in streams of the Lake Tahoe basin, CaliforniaNevada. Water Resour. Res. 37: 405-415.
Coats RN, F. Liu, and CR Goldman. 2002. A Monte Carlo test of load calculation methods,
Lake Tahoe Basin, California-Nevada. Jour. Am. Water Resour. Assoc. 38: 719-730.
Coats RN, M. Larsen, A. Heyvaert, J. Thomas, M. Luck, and J. Reuter. 2008. Nutrient and
sediment production, watershed characteristics, and land use in the Tahoe Basin,
California-Nevada. J. Am. Water Resour. Assoc. 44: 754-770.
Coats, RN. 2010. Climate change in the Tahoe basin: regional trends, impacts and drivers.
Climatic Change 102:435-466.
Helsel DR, and Frans, L. 2006. Regional Kendall Test for Trend. Environ. Sci. Tech. 40:
4066-4073.
Jeton, A. 1999. Precipitation-Runoff Simulations for the Lake Tahoe Basin, California and
Nevada. U.S. Geol. Surv. Water-Resources Investigations Report 99-4110. Carson
City, NV. 61 pp.
Kempers AJ, and Luft, A. G. 1988. Re-examination of the Determination of Environmental
Nitrate as Nitrite by Reduction with Hydrazine. Analyst 113:1117-1120.
Kempers AJ, and G. Van Der Velde. 1992. Determination of nitrate in eutrophic coastal
seawater by reduction to nitrite with hydrazine. Jour. Environ. Anal. Chem. 47: 1-6.
Lahontan and NDEP. 2010. Lake Tahoe Total Maximum Daily Load Technical Report. June
2010. California Regional Water Quality Control Board, Lahontan Region, Nevada
Division of Environmental Protection.
Rowe T.G., D.K. Saley, S.A. Watkins, and CR Kratzer. 2002. Streamflow and water-quality
data for selected watersheds in the Lake Tahoe Basin, California and Nevada,
Through September 1998, U.S. Geological Survey Water-Resources Investigations
Report 02-4030, 118 p.
TRPA et al. 2011. LTIMP –Tributary Monitoring Draft Goals and Objectives. Oct. 6. 4 pp.
Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long-range experimental hydrologic
forecasting for the eastern United States. J. Geophys. Res. 107:4429 doi:
4410.1029/2001JD000659
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