U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2012–1122

Hydrologic Index Development and Application to Selected 

Coastwide Reference Monitoring System Sites and Coastal 

Wetlands Planning, Protection and Restoration Act Projects
 
Hydrologic Index Development and 

Application to Selected Coastwide 

Reference Monitoring System Sites and 

Coastal Wetlands Planning, Protection and 

Restoration Act Projects 

By Gregg A. Snedden and Erick M. Swenson

Open-File Report 2012–1122

U.S. Department of the Interior

U.S. Geological Survey
Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Snedden, G.A., and Swenson, E.M., 2012, Hydrologic index development and application to selected Coastwide 

Reference Monitoring System sites and Coastal Wetlands Planning, Protection and Restoration Act projects: U.S. 

Geological Survey Open-File Report 2012–1122, 25 p. 

U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

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U.S. Geological Survey

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U.S. Government.
iii

Contents

Abstract ..........................................................................................................................................................1

Introduction ....................................................................................................................................................1

Review of CWPPRA Monitoring Plans ......................................................................................................2

Tides in the Louisiana Coastal Zone ..........................................................................................................3

Tides and Harmonic Analysis .............................................................................................................3

Mean Sea Level ....................................................................................................................................6

Spectral Analysis .................................................................................................................................6

Marsh Elevation and Tidal Forcing ..................................................................................................10

Wetland Hydroperiod in the Louisiana Coastal Zone ...........................................................................10

Hydrologic Index .........................................................................................................................................11

Introduction .........................................................................................................................................11

Hydrologic Index Formulation ..........................................................................................................11

Application to CRMS Sites ...............................................................................................................13

Application to CWPPRA Projects ....................................................................................................17

Summary .......................................................................................................................................................24

References Cited .........................................................................................................................................24

Figures

1. Graph showing summary of restoration techniques to be used on the basis of a 

review of 111 monitoring plans written under the Coastal Wetlands Planning, 

Protection and Restoration Act .................................................................................................2

2. Graph showing summary of hydrologic goals based on a review of 40 projects 

funded by the Coastal Wetlands Planning, Protection and Restoration Act in 

which hydrologic manipulation is a project strategy..............................................................3

3. Graphical depiction of the amplitude (A) and phase (φ ) components of tidal 

constituents ..................................................................................................................................5

4. Tide signal resulting (bottom curve) from the addition of five separate tidal 

components (top five curves) obtained with harmonic analysis for the Grand Isle, 

Louisiana, tide station .................................................................................................................7

5. Illustration showing interaction of the O1 and K1 components to produce tropic 

and equatorial tides......................................................................................................................8

6. Illustration showing semiannual and annual cycles of sea level at Grand Isle, 

Louisiana .......................................................................................................................................8

7. Map showing the sites in Breton Sound, Louisiana, used to characterize the 

tidal forcing ...................................................................................................................................9

8. Graph showing variance spectra (upper) and cumulative variance spectra 

(lower) for intermediate (left) and saline (right) marsh sites in Breton Sound 

Basin, Louisiana ...........................................................................................................................9

9. Graph showing elevation of the marsh surface at the Breton Sound, Louisiana, 

marsh sites, in relation to tidal forcing ...................................................................................10

10. Graph showing hydrologic data and metrics from the intermediate marsh (left) 

and saline marsh (right) in Breton Sound ..............................................................................12

11. Graph showing proportion of maximum productivity by marsh type as a function 

of average annual salinity (left) and percent time flooded (right) .....................................13
iv

12. Proportion of maximum productivity (indicated with color axis) by marsh type as 

a function of the combined effects of average annual salinity and percent time 

flooded .........................................................................................................................................14

13. Map showing location of 173 Coastwide Reference Monitoring System sites 

where the hydrologic index was calculated .........................................................................15

14. Graph showing mean hydrologic indices, flooding indices, salinity indices, percent 

time flooded, and average annual salinity by marsh type for 173 Coastwide 

Reference Monitoring System sites .......................................................................................16

15. Map showing hydrologic index scores for 173 Coastwide Reference Monitoring 

System sites during the 2007–8 water year ...........................................................................17

16. Map showing locations of Coastal Wetlands Planning, Protection and Restoration 

Act projects and stations where hydrologic index was applied: CS-20 (top), PO-17 

(middle) and BA-03 (bottom) ....................................................................................................18

17. Graph showing time-series plots of the hydrologic indices from stations in the 

Coastal Wetlands Planning, Protection and Restoration Act projects CS-20 (top), 

PO-17 and BA-03 (bottom) ........................................................................................................21

18. Hydrologic indices for six locations associated with the CS-20 Coastal Wetlands 

Planning, Protection and Restoration Act project ...............................................................22

19. Comparison of hydrologic indices for six locations in the CS-20 Coastal Wetlands 

Planning, Protection and Restoration Act project area for different years .....................23

Tables

1. Periods and frequencies of major tidal constituents ............................................................6

2. Salinity and hydroperiod productivity algorithms by marsh classification ......................14

3. Seasonal productivity weights for each marsh classification ...........................................15

4. Hydrologic index, average salinity, and percent of time flooded for monitoring 

locations at three Coastal Wetlands Planning, Protection and Restoration Act 

projects ........................................................................................................................................19
Abstract

Hourly time-series salinity and water-level data are 

collected at all stations within the Coastwide Reference 

Monitoring System (CRMS) network across coastal Louisiana. 

These data, in addition to vegetation and soils data collected 

as part of CRMS, are used to develop a suite of metrics and 

indices to assess wetland condition in coastal Louisiana. This 

document addresses the primary objectives of the CRMS 

hydrologic analytical team, which were to (1) adopt standard 

time-series analytical techniques that could effectively assess 

spatial and temporal variability in hydrologic characteristics 

across the Louisiana coastal zone on site, project, basin, and 

coastwide scales and (2) develop and apply an index based 

on wetland hydrology that can describe the suitability of local 

hydrology in the context of maximizing the productivity of 

wetland plant communities.

Approaches to quantifying tidal variability (least squares 

harmonic analysis) and partitioning variability of time-series 

data to various time scales (spectral analysis) are presented. 

The relation between marsh elevation and the tidal frame 

of a given hydrograph is described. A hydrologic index that 

integrates water-level and salinity data, which are collected 

hourly, with vegetation data that are collected annually is 

developed. To demonstrate its utility, the hydrologic index is 

applied to 173 CRMS sites across the coast, and variability 

in index scores across marsh vegetation types (fresh, 

intermediate, brackish, and saline) is assessed. The index 

is also applied to 11 sites located in three Coastal Wetlands 

Planning, Protection and Restoration Act projects, and the 

ability of the index to convey temporal hydrologic variability 

in response to climatic stressors and restoration measures, as 

well as the effect that this community may have on wetland 

plant productivity, is illustrated.

Introduction

The Coastwide Reference Monitoring System (CRMS), 

a network of 392 monitoring stations across the Louisiana 

coast, was implemented under the Coastal Wetlands Planning, 

Protection and Restoration Act (CWPPRA; Steyer and others, 

2006). This network of monitoring stations enables the 

characterization of the Louisiana coastal wetland landscape 

across varying spatial and temporal scales. Because CRMS 

stations are located inside and outside of CWPPRA project 

boundaries, the stations can be used to assess CWPPRA 

project effectiveness. Comparisons can also be made at other 

spatial scales including marsh type and hydrologic basin 

(Steyer and others, 2003; 2006).

To evaluate the performance of CWPPRA projects with 

CRMS it is pertinent that existing knowledge and data are 

used to establish informed restoration targets. The existing 

knowledge includes an understanding of how vegetation 

communities respond to variations in local hydrology 

(Visser and others, 2003) and the extensive hydrologic and 

vegetation datasets maintained as part of the CRMS program. 

Individual indices for hydrology, vegetation, and soils are 

being developed to assess the condition of Louisiana’s coastal 

landscapes at various scales, and these indices, along with 

other community indicators, can be used to assess restoration 

project effectiveness.

This report gives an overview of standard time-series 

techniques that can be applied to hydrologic time-series data 

collected under CRMS to quantify local hydrologic conditions 

at CRMS sites. This report also describes the development 

of a “hydrologic index” used in the analysis of data collected 

under CRMS in use by the U.S. Geological Survey (USGS) 

and Louisiana Coastal Protection and Restoration Authority 

(CPRA), as part of CWPPRA activities, to monitor wetland 

restoration projects. The term “hydrologic index” is being 

used in a broad sense to include water levels and salinity. This 

index will be used, along with vegetation and soil indices, 

to assess the effectiveness of projects carried out under the 

Hydrologic Index Development and Application to 

Selected Coastwide Reference Monitoring System 

Sites and Coastal Wetlands Planning, Protection and 

Restoration Act Projects

By Gregg A. Snedden and Erick M. Swenson
2 Hydrologic Index Development and Application to Selected CRMS Sites and CWPPRA Projects

Figure 1. Summary of restoration techniques to be used on the basis of a review of 111 monitoring plans written under the Coastal 

Wetlands Planning, Protection and Restoration Act. 

CWPPRA restoration plan to create, restore, protect, and 

enhance the coastal wetlands of Louisiana. 

Review of CWPPRA Monitoring Plans

Figure 1 presents a summary of the restoration techniques 

being used under CWPPRA. Of the 111 monitoring plans 

reviewed, 40 (36 percent) use techniques that directly 

involved changes in hydrology (red bars). The specific 

hydrologic goals for those 40 monitoring plans are presented 

in figure 2. Changing the water level and/or salinity regime 

was the specific hydrologic goal in 52 (91 percent) of the 

57 hydrologic goals listed. The larger scale hydrologic 

questions that are of interest to these CWPPRA projects are 

as follows:

Question: Is CWPPRA effective in reducing the major 

stressors on wetlands? 

Answer: As shown in figure 2, objectives for most projects 

include the ability to reduce flooding duration and/or decrease 

salinity. This question can therefore be best answered in two 

parts:
Tides in the Louisiana Coastal Zone 3

Subquestion: Is CWPPRA effective in altering flooding in 

those projects that were intended to alter flooding? In other 

words, is the flooding reduced in the project sites relative to 

the flooding in the reference sites?

Answer: The comparison with the reference sites is necessary 

to account for changes in flooding caused by factors other 

than effects of CWPPRA projects, such as changes in sea 

level, river discharge, and rainfall. Variance components and 

changes in possible cycles and continuous behavior of the 

response will be assessed.

Subquestion: Is the CWPPRA effective in altering salinity in 

those projects that were intended to alter salinity? 

Answer: Typically, projects are intended to reduce overall 

mean salinity and/or salinity spikes relative to the selected 

reference sites. The comparison with the selected reference 

sites is necessary to remove changes in salinity caused by 

Figure 2. Summary of hydrologic goals based on a review of 40 projects funded by the Coastal Wetlands Planning, Protection and 

Restoration Act in which hydrologic manipulation is a project strategy.

other influences such as sea level, river discharge, and rainfall. 

Variance components and changes in possible cycles and 

continuous behavior of the response will be assessed.

Tides in the Louisiana Coastal Zone

Tides and Harmonic Analysis

The astronomical tides are the periodical rises and 

falls of the sea surface that result from the differences in 

the gravitational attraction of the sun and moon at various 

locations on the earth as the earth-moon system orbits the 

sun. The sun and the moon both generate tide-producing 

forces; however, the moon is 2.16 times more influential 

than the sun, and so the tide, in most locations, is primarily 
4 Hydrologic Index Development and Application to Selected CRMS Sites and CWPPRA Projects

controlled by the lunar tide-generating forces and modified 

by the solar tide-generating forces (Hicks, 2006). The 

numbers of configurations of the sun, moon, and earth that 

are possible during the course of time are numerous, and so 

they are repeated only roughly during successive months 

(von Arx, 1974). 

Excluding nontidal effects such as winds, sea level 

y at a given time ti ∈{t

1,t2 ,,tN } can be resolved into M

simple sinusoidal periodic forces, or tidal constituents, with 

angular frequency ω=2πf where f is the frequency of the tidal 

constituent, or the inverse of the constituent’s period T:

{ } 1

( ) cos( ) sin( )

M

i o j j i j j i

j

y t Z C ω t S ω t == +∑ + (1)

where Zo is mean sea level. Obtaining C and S is achieved 

through harmonic analysis, where sine and cosine waves 

with periods corresponding to the periods of specific tidal 

constituents sought are fit through water-level datasets 

with a least squares model by finding the solution x to the 

matrix equation

-1 x = A y (2)

where y is the observation vector

1

1

( )

( )

( ) N

y t

y t

y t

           

y =  (3)

and A is the design matrix

1 1 1 1 2 1 2 1 1 1

1 2 1 2 2 2 2 2 2 2

1 1 2 2

1 cos( ) sin( ) cos( ) sin( ) cos( ) sin( )

1 cos( ) sin( ) cos( ) sin( ) cos( ) sin( )

1 cos( ) sin( ) cos( ) sin( ) cos( ) sin( )

M M

M M

N N N N M N M N

t t t t t t

t t t t t t

t t t t t t

ω ω ω ω ω ω

ω ω ω ω ω ω

ω ω ω ω ω ω

           

A =





       



(4)

Elements from the solution vector x

1

1

2

2

o

M

M

Z

C

S

C

S

C

S

            =              

x



(5)

can then be used to obtain amplitudes (Aj) and phases (φ j ) for 

each of the M tidal constituents according to 

2 2 Aj Cj j = + S (6)

and 

arctan j

j

j

S

C φ

  =      

(7)

Amplitude describes the height of the constituent, and 

phase is an indicator of the timing of the constituent (fig. 3). 

Generally, phase becomes meaningful only when the timing 

of tides between two stations is examined, or when the 

timing of two different tidal phenomena at a single station is 

investigated (for example, tide height and current velocity).

Although the number of constituents can be quite large, 

the principal features of the tide at a given location generally 

can be described by using five principal constituents (table 1). 

The “principal lunar semidiurnal” component (M2 ), which 

has a periodicity of 12.42 hours, and the “principal solar 

semidiurnal” component (S2), which has a periodicity of 12.0 

hours, together produce a semidiurnal tide which has two 

highs and two lows during a day. The orbit of the moon is 

elliptical, so the tidal-producing force varies on a 27.5 day 

period as the moon revolves around the earth. To account for 

this variation the “larger lunar elliptic semidiurnal” component 

(N2), which has a period of 12.66 hours, is used. The orbit of 

the moon is also inclined to the plane of the equator; thus, the 

declinations of the sun and the moon are constantly changing. 

To account for this effect two other tidal components the 

“Lunar diurnal” component, which has a periodicity of 25.82 

hours (referred to as O1), and the “luni-solar diurnal,” which 

has a periodicity of 23.93 hours component (referred to as K1) 

are used (Marmer, 1954). These components will produce a 

diurnal tide which has one high and one low during a day.

If they are added together, the separate tidal constituents 

will reproduce the astronomically forced tidal signal at a 

given location, as illustrated in figure 4. This figure presents 

the tidal constituents for Grand Isle, Louisiana. Note, it does 

not include effects related to nonastronomical conditions such 

as winds. It can be seen that the constituents with the largest 

amplitude are the K1 and O1 diurnal tides. These constituents 

are typical for areas, such as the Gulf of Mexico, that are 

dominated by diurnal tides (Marmer, 1954).

At times of the month when the O1 and K1 are in phase, 

the two components constructively interfere, and amplitude 

of the tide increases. This phenomenon occurs when the 

moon is over the Tropic of Cancer or the Tropic of Capricorn; 

thus, these higher tides are referred to as “tropic tides.” At 

times of the month when O1 and K1 are out of phase, the two 

components destructively interfere, and the amplitude of the 

tides decreases. This phenomenon occurs when the moon is 

over the Equator; thus, these lower ranges are referred to as 

“equatorial tides.” This tropic-equatorial cycle is the diurnal 
Tides in the Louisiana Coastal Zone 5

Figure 3. Graphical depiction of the amplitude (A) and phase (φ ) components of tidal constituents.

analog of the spring-neap cycle found in coastal areas with 

semidiurnal tidal regimes. The variation in range between 

tropic and equatorial tides represents the greatest change 

that occurs in the astronomical tidal amplitude for tides in 

Louisiana. This effect can be seen at the Grand Isle location 

(fig. 4) and is illustrated in greater detail in figure 5.

In addition to astronomically forced variations, sea level 

in the northern Gulf of Mexico is also strongly influenced 

by wind stress, both on synoptic (3–10 day) and seasonal 

timescales. The synoptic-scale variations result from periodic 

passages of meteorological fronts and are most prominent 

between October and April. The seasonal fluctuations produce 

a semiannual sea-level cycle that exhibits highs in the spring 

and fall when prolonged westward wind stresses (wind 

blowing toward west) push water against the coast via Ekman 

convergence. Reduced water levels occur during late summer 

when wind stresses diminish. During the winter, prolonged 

periods of southward (toward south) and eastward (toward 

east) wind stress tend to push water away from the coast, 

producing even lower water levels (fig. 6).

The tides within the Gulf of Mexico also exhibit the 

effect of the 18.6-year lunar epoch. This change in tidal range 

is due to the change of the inclination of the Moon’s orbit 

relative to the Earth’s equator. Baumann (1987) noted that 

for Barataria Basin, this change is greater than the seasonal 

change but is much less than the biweekly change. He also 

noted that marsh inundation frequency is positively related 

to this 18.6-year cycle. The water levels in the Gulf of 

Mexico also exhibit longer term (30 year) trends which are 

due to larger scale geologic processes (such as global sea-level 

rise, regional subsidence). The sea-level trends for the United 

States were summarized by Lyles and others (1988). Their 

analysis of stations in the Gulf of Mexico showed relatively 

stable conditions at the Florida stations with long-term trends 

ranging from 0.007 to 0.010 ft/year (2 to 3 mm/year). The 

Louisiana and northern Texas stations showed long-term 

trends ranging from 0.020 to 0.046 ft/year (6 to 14 mm/year). 

The southern Texas stations (south of Rockport) again 

showed stable conditions with trends ranging from 0.010 to 

0.013 ft/year (3 to 4 mm/year).

It is thus clear that water level can vary over a broad 

range of timescales including those with daily (astronomical 

tides), weekly (synoptic weather forcing), annual and 

interannual periods. Because the goal of many hydrologic 

restoration projects is to reduce water-level variability, it 

can be useful to partition the variability according to the 

timescale over which it occurs in order to determine what 

processes are driving the variability. A powerful method for 

partitioning time-series variability in such a manner is spectral 

analysis.
Mean Sea Level

Mean sea level (MSL) is defined as the average of the 

hourly values of water levels measured over a 19-year tidal 

datum epoch (Hicks, 1989). In practice mean tide level 

(MTL), a plane midway between high and low water that is 

computed by averaging the high and low water levels over a 

19-year period of observation (Swanson, 1974), is often used 

in place of MSL because it is easier to compute. MSL and 

MTL approximate each other along the open coast (Swanson, 

1974). The National Geodetic Vertical Datum (NGVD), a fixed 

(relative to the center of the earth) datum based upon the best 

fit over a large area, does not take into account local variations 

or changing stands in sea level and should not be confused 

with MSL (Hicks, 1989). The relation between MTL (or MSL) 

and NGVD is not consistent from one location to another in 

either time or space (Swanson, 1974); thus, to standardize 

MSL estimates local tide data are tied into a specified National 

Tidal Datum Epoch, which is a specific 19-year time period 

over which observations are to be averaged to compute means 

(Hicks, 1989). 

It is possible, however, to compute means based upon 

short-term datasets. This short-term mean may or may not 

be an accurate representation of the accepted value of MTL 

depending upon location. Swanson (1974) compared MSL 

from short-term records to MTL calculated from a 19 year 

record. His results indicated that for the Gulf Coast, with 1 

month of observations, the accuracy of the estimate of MTL 

is ~0.181 ft (5.5 cm) but the accuracy improves nonlinearly to 

~0.098 ft (3 cm) with 12 months of observations.

Spectral Analysis

As shown previously, periodic variability present in 

time series data can be represented as a sum of periodic terms 

involving combinations of sines and cosines. In a general 

sense, this principle applies not only to strictly periodic 

phenomena such as astronomical tides but also quasi-periodic 

phenomena such as meteorologically forced fluctuations that 

occur over longer timescales. Thus, 

{ } 1

( ) cos(2 ) sin(2 ) ( )

M

i o j j i j j i i

j

y t Z C π f t S π f t ε t == +∑ + + (8)

still applies, though a noise term (ε(ti)) is included to account 

for stochasticity, or noise, because the data are not strictly 

periodic. Unlike harmonic analysis, periods of variability are 

sought, rather than known a priori, and coefficients Cj and Sj

are obtained for periods Tj that are harmonics of a fundamental 

period To such that Tj = To / Tj, where j = 0, 1, ..., N / 2 and N is 

the total number of observations in the time series. Generally, 

To is taken equal to the length of the data series. The variance 

(Syy (f)) associated with each period Tj (or frequency f

j

 = 1

Tj

) 

can be obtained as 

2 2 ( ) ( ) / 2 yy f f S f = N C + S (9)

where /2 0 N ≤ f ≤ f . 

A variance spectrum can then be obtained by plotting 

variance Syy as a function of frequency f (or period T). 

Because water-level variability driven by different processes 

(for example, astronomical tides, wind-driven variations) 

often occur over widely disparate timescales (diurnal versus 

weekly), variance spectra can provide insight as to what 

processes are dominant in forcing water-level variability at a 

given site or time period. 

Two contrasting sites (saline and intermediate) in 

Breton Sound (fig. 7) were selected to investigate water-level 

variability via spectral analysis. The water-level spectrum of a 

strongly tidal saline marsh looks considerably different from 

that of an intermediate marsh where the tide is strongly muted 

(fig. 8). Whereas variability associated with the O1 and K1

tidal constituents dominates the water-level spectrum from the 

saline marsh (upper right panel), this variability is filtered out 

by the frictional wetland landscape by the time the tidal wave 

reaches the intermediate marshes (fig. 8, upper left panel). 

Variability across 20-50 day timescales is more prominent in 

the intermediate marsh site. This marsh site is located in the 

outfall vicinity of the Caernarvon Diversion and its variability 

may result from Caernarvon operations, which tend to occur 

over similar timescales (Snedden and others, 2007). The 

bottom panels of figure 8 simply show the cumulative variance 

of the time series accounted for as a function of decreasing 

timescale. Including all timescales through the diurnal tide 

accounts for 0.15 ft2 of variance at the fresh marsh site and 

0.41 ft2 of variance at the saline marsh site. These values are 

nearly equal to the variances of each time series indicating 

that intra-daily timescale variability (timescales < 1 day) is 

insignificant at each site. It is clear from the spectra that the 

saline marsh site is much more energetic from a water-level 

standpoint, as evidenced by its higher variance.

6 Hydrologic Index Development and Application to Selected CRMS Sites and CWPPRA Projects

Table 1. Periods and frequencies of major tidal constituents. 

Constituent Name Period

(hours)

Frequency

(cycles per hour)

M2 principal lunar semidiurnal 12.42 0.0805

S2 principal solar semidiurnal 12.00 0.0833

N2 larger lunar elliptic semidiurnal 12.66 0.0789

O1 lunar diurnal 25.82 0.0387

K1 luni-solar diurnal 23.93 0.0418
Tides in the Louisiana Coastal Zone 7

Figure 4. Tide signal resulting (bottom curve) from the addition of five separate tidal components (top five curves) obtained with 

harmonic analysis for the Grand Isle, Louisiana, tide station. 
8 Hydrologic Index Development and Application to Selected CRMS Sites and CWPPRA Projects

Figure 5. Interaction of the O1 and K1 components to produce tropic and equatorial tides.

Figure 6. Semiannual and annual cycles of sea level at Grand Isle, Louisiana. The cycle was obtained by harmonic least squares 

regression with periods of 180 and 365 days on 11 years of data from the Grand Isle water-level gage.
Wetland Hydroperiod in the Louisiana Coastal Zone 9

Figure 7. The sites in Breton Sound, Louisiana, used to characterize the tidal forcing.

Figure 8. Variance spectra (upper) and cumulative variance spectra (lower) for intermediate (left) and saline (right) marsh sites in 

Breton Sound Basin, Louisiana. The intermediate site is located in the outfall vicinity of the Caernarvon Diversion.
10 Hydrologic Index Development and Application to Selected CRMS Sites and CWPPRA Projects

Marsh Elevation and Tidal Forcing

The hourly water-level data from the Breton Sound 

stations (shown on fig. 7) were subjected to harmonic analysis 

to extract the tidal constituents and the mean water level. 

The O1 and K1 constituents were then used to determine the 

tropical and equatorial high and low tides by adding them to 

the means by using the following formula:

Tropic high = mean water level + (O1 + K1)

Tropic low = mean water level – (O1 + K1)

Equatorial high = mean water level + ( [O1 - K1] )

Equatorial low = mean water level – ( [O1 - K1] )

where [ ] indicates absolute value.

This analysis is limited only to the astronomical tidal 

forcing but is an indicator of the water levels experienced at a 

site on an average daily basis. The main interest is the extreme 

water levels, so only the tropic highs and lows were used. 

The results (fig. 9) show that the saline marsh exhibits about a 

2.0-ft increase in water levels from tropic low to tropic high, 

that the marsh is inundated by over 1 ft of water at high tide, 

and that the mean water levels exceed the marsh elevation 

(suggesting that the marsh elevation is low relative to the 

tidal frame). On the other hand, the fresh marsh exhibits only 

about a 0.1-ft increase in water levels from tropic low to tropic 

high. The combination of the difference in marsh elevation, 

coupled with this difference in water levels between tropic low 

and tropic high tides can result in a fairly large range of tidal 

inundation. 

Wetland Hydroperiod in the Louisiana 

Coastal Zone

Wetland inundation regimes, or hydroperiods, are 

determined by the interaction between local water levels and 

marsh elevations. There are three fundamental attributes of 

hydroperiods: frequency, duration, and depth of inundation. 

Frequency is simply the number of inundation events per 

unit time; duration is the total time a wetland is inundated 

for a given time period, often expressed as a percentage 

of the time period in question; and depth is a measure of 

how deep the inundation events are. In this application, we 

determined inundation depth by obtaining the peak depth of 

each inundation event over the course of a year and taking an 

average of all peak depths. The hydroperiods were computed 

for the Breton Sound stations (fig. 7).

Figure 9. Elevation of the marsh surface at the Breton Sound, Louisiana, marsh sites, in relation to tidal forcing. Indicated are the 

marsh surface elevation in relation to the mean water level, the tropic high water levels, and the tropic low water levels.
Hydrologic Index 11

Water-level variability is one of the drivers of wetland 

hydroperiods. Because high-frequency water-level variability 

(for example, diurnal tides) tends to be removed by frictional 

attenuation as one progresses inland from the coast, 

hydroperiods between fresh and intermediate marshes tend to 

be remarkably different from those in saline marshes near the 

coast. Relatively high diurnal tide amplitudes at saline marshes 

produce inundation frequencies that often exceed 200 events 

per year. In contrast, the diurnal tide is of minimal importance 

in many intermediate and most fresh marsh locations where 

seasonal and meteorologically driven processes produce fewer 

flooding events, each lasting much longer. Though the percent 

time flooded is very similar for both habitats (59 percent in the 

intermediate marsh, 54 percent in the saline marsh), how long 

each event lasts varies substantially. Flooding events average 

8.9 days in duration at the intermediate site but only 0.9 days 

at the saline site.

Marsh elevation can also strongly influence wetland 

hydroperiods. The relations among marsh elevation and 

inundation frequency, inundation depth, average event 

duration, and percent time inundated for intermediate and 

saline marshes are shown in figure 10. Both habitats show 

similar patterns. Inundation frequency shows a Gaussian-like 

response to elevation, where intermediate elevations maximize 

the number of flooding events. Marshes that are exceedingly 

high rarely flood, and marshes that are low in the tidal frame 

rarely drain. Either extreme leads to a low number of discrete 

inundation events. The remaining three metrics, depth, percent 

time flooded, and average event duration, all show inverse 

relationships with elevation.

Hydrologic Index

Introduction

According to Mitsch and Gosselink (2000) salinity 

and water levels are the major driving forces controlling the 

distribution of coastal wetland types. The degree of inundation 

has a significant effect on plant production (Conner and Day, 

1976; Broome and others, 1995; Webb and Mendelssohn, 

1996; Höppner, 2002), but the level of this forcing function 

can differ depending on the environment. For example, much 

higher inundation levels are required to convert established 

vegetation into open water than can be tolerated by vegetation 

on created mudflats (Visser and others, 2003). Changes in 

the salinity regime may force community shifts in fresh, 

intermediate, brackish and saline habitats, with extreme 

salinities possibly leading to the conversion of fresh and 

intermediate marshes to open water (Flynn and others, 1995, 

as cited by Visser and others, 2003, Steyer and others 2010). 

The challenge in developing a hydrologic index is being 

able to create an index that integrates the water level and 

salinity, which are collected continuously (hourly), with soil 

and vegetation data that are collected annually. Although 

the exact relation between salinity and water-level regime is 

not known, the combination of these two forcing functions 

is one of the critical controls on the vegetation distribution. 

Sasser (1977) described the broad conceptual limits of plant 

distribution in terms of flooding and salinity, in which species 

shifts such as transitions from Spartina patens to Spartina 

alterniflora may result from increased flooding, increased 

salinity, or a combination of the two.

Hydrologic Index Formulation

The hydrologic index combines the temporal behavior in 

water level and salinity into a single annual number that can 

then be related to the end of growing season vegetation data. 

In the CRMS program, water level and salinity are 

recorded hourly, providing over 8,000 records for each site 

in a given year. Although any single water level or salinity 

data point by itself conveys very little information about the 

hydrology of the site, various properties of data streams (for 

example, percent time flooded, flooding frequency, average 

salinity) collected over extended time periods (for example, 

months, years) can be valuable in assessing abiotic stressors 

or subsidies to a given location. Other CRMS data related 

to geologic processes (for example, subsidence, erosion) 

or biotic structure (for example, vegetation abundance and 

species composition) are collected much less frequently (for 

example, annual sampling of emergent vegetation). The hourly 

water level and salinity data collected under CRMS thus need 

to be characterized over longer timescales in a manner that 

(1) characterizes important hydrologic characteristics of the 

hourly data, (2) provides an indication of the suitability of 

these hydrologic characteristics to specific wetland habitat 

classifications, and (3) does so over annual timescales to allow 

for the integration of other CRMS data collected over longer 

sampling intervals. 

A method advancing concepts put forward by the 

productivity component of the Louisiana Coastal Area 

Habitat Switching Module (LCA HSM) (Visser and others, 

2003) is proposed here. Under the LCA HSM, productivity 

algorithms were produced on the basis of extensive literature 

reviews of field and laboratory studies to obtain relationships 

between percent maximum emergent plant productivity of 

dominant species in each habitat type (Panicum hemitomon,

Sagittaria lancifolia, Spartina alterniflora, and Spartina 

patens, Taxodium distichum), percent time flooded, and 

average annual salinity. Those relations are presented in 

figure 11, and parameters for those relationships are given in 

table 2. Here psal is proportion of maximum productivity as 

a function of average annual salinity, and pfld is proportion 

of maximum productivity as a function of percent time 

flooded. Because salinity effects can be exacerbated by 

excessive flooding (Spalding and Hester, 2007), it was 

assumed that psal and pfld interact multiplicatively. Thus, the 

overall proportion of maximum productivity caused by the 
12 Hydrologic Index Development and Application to Selected CRMS Sites and CWPPRA Projects

Figure 10. Hydrologic data and metrics from the intermediate marsh (left) and saline marsh (right) in Breton Sound. The top panel is 

the hourly water level values (NAVD 88), the next three panels present the relation between marsh elevation and flooding frequency, 

percent of time flooded, flood event depth, and flood event duration, respectively. The dashed vertical lines in the lower three panels 

indicate the actual marsh elevation at the site where the water levels were taken (also shown by the red horizontal line in the top 

panels). The dashed horizontal lines show the actual parameter values for the two sites.
Hydrologic Index 13

combined effects of salinity and flooding (poverall) is simply 

psal × pfld. For each marsh type, poverall can be obtained for all 

combinations of average annual salinity and percent time 

flooded, and displayed as a surface. The swamp, fresh marsh, 

and intermediate marsh are quite similar with salinity exerting 

a stronger influence than flooding (fig. 12). The effect of 

prolonged flooding in lowering productivity, particularly in the 

swamp and intermediate marsh, is also evident. The flooding 

exhibits a stronger control in the brackish marsh and saline 

marsh resulting in a locus (circular in the brackish marsh and 

elongated in the saline marsh) of optimal conditions compared 

to the narrow band (along the flooding axis) that was evident 

in the swamp, fresh marsh, and intermediate marsh. The saline 

and brackish hydrologic index response is what one would 

expect based on the marsh flooding and salinity data already 

presented.

Because a high degree of intra-annual variation in 

productivity occurs in wetlands of the northern Gulf of 

Mexico, poverall is seasonally weighted according to seasonal 

productivity rates for each marsh type according to LCA HSM 

(table 3). The seasonally weighted poverall takes on a value 

between 0 and 1 and is used as the score for the hydrologic 

index. 

Application to CRMS Sites

Hydrologic (hourly water level and salinity covering the 

time period covering October 01, 2007 through September 30, 

Figure 11. Proportion of maximum productivity by marsh type as a function of average annual salinity (left) and percent time flooded 

(right).
14 Hydrologic Index Development and Application to Selected CRMS Sites and CWPPRA Projects

Table 2. Salinity and hydroperiod productivity algorithms by marsh classification.

Marsh type Salinity Percent time flooded

Swamp 3

1

1

4

sal p sal =

  +    

2 0.000082 0.0029 0.972474 fld p = − fld + fld +

Fresh 3

1

1

2

sal p sal =

  +    

2 0.00124 0.012354 0.69229 fld p = − fld + fld +

Intermediate 3

1

1

4

sal p sal =

  +    

2 0.000124 .012354 0.69229 fld p = − fld + fld +

Brackish 5

1

1

10

sal p sal =

  +    

2 0.000344 0.02613 0.500639 fld p = − fld + fld +

Saline 5

1

1

35

sal p sal =

  +    

2 0.000273 0.026532 0.35536 fld p = − fld + fld +

Figure 12. Proportion of maximum productivity (indicated with color axis) by marsh type as a function of the combined effects of 

average annual salinity and percent time flooded.
Hydrologic Index 15

2008) and marsh elevation data from 173 CRMS sites (fig. 13) 

were analyzed to investigate the general hydrologic patterns 

at each site and compute the hydrologic indices. Because 

the water levels and the surveyed marsh elevations at each 

site were on the same datum (NAVD88), the location of the 

marsh surface in relation to the water level distribution can be 

documented. The marsh elevations for the 173 CRMS sites 

range from ~0.6 to ~1.8 ft NAVD88. The average elevations 

of the brackish marshes (1.22 ft NAVD88) and intermediate 

marshes (1.27 ft NAVD88) are slightly higher than the 

elevations of the saline marshes (1.04 ft NAVD88), although 

the variability is fairly high, on the order of half a foot. 

Figure 14d shows the percent of time flooded for each marsh 

type calculated from the 173 CRMS sites. The 10 percent 

and 90 percent quantiles for percent time flooded values are 

21 and 87, respectively. This range of flooding occurs over a 

~1.2 foot range in elevation across the Louisiana coastal zone, 

indicating how sensitive inundation regimes are to minor 

changes in marsh elevations. The seasonally weighted salinity 

for the 173 CRMS sites (fig. 14e), although variable, exhibits 

a coast-inland gradient, with the average salinity progressing 

from 13.1 practical salinity units (psu) in the saline marshes 

to 5.9 psu in the brackish marshes, 2.8 psu in the intermediate 

marshes, and 0.6 psu in the fresh marshes.

The calculated flooding index (pfld), salinity index (psal) 

and hydrologic index (poverall) for the 173 CRMS sites are 

also presented in figure 14. The saline marshes exhibit high 

values for all of the indices, with very little variation. This 

low variability is largely attributable to the high tolerance 

exhibited in the saline marsh salinity index curve (fig. 11, 

lower left panel) across a broad range of salinities. The 

brackish and intermediate marshes show a much greater 

range in all of the indices. The variability in hydrologic index 

scores for brackish sites is primarily driven by flooding index 

variability, whereas variability in hydrologic index scores 

for intermediate sites mainly results from salinity index 

variability. Overall, hydrologic index scores were greatest for 

saline and fresh marshes and reduced for intermediate and 

brackish marshes.

Table 3. Seasonal productivity weights for each marsh classification. 

Season Swamp Fresh Intermediate Brackish Saline

March 1–June 30 0.75 0.38 0.40 0.35 0.29

July 1–October 31 0.25 0.48 0.39 0.35 0.47

November 1–February 28 0.00 0.14 0.21 0.30 0.24

Figure 13. Location of 173 Coastwide Reference Monitoring System sites where the hydrologic index was calculated.
16 Hydrologic Index Development and Application to Selected CRMS Sites and CWPPRA Projects

Figure 14. Mean hydrologic indices, flooding indices, salinity indices, percent time flooded, and average annual salinity by marsh type 

for 173 Coastwide Reference Monitoring System sites. 
Hydrologic Index 17

Figure 15. Hydrologic index scores for 173 Coastwide Reference Monitoring System sites during the 2007–8 water year. Blue indicates 

high index score; red indicates low index score.

Examination of the spatial distribution of hydrologic 

index scores across the coast shows (fig. 15) that, in general, 

the marshes located at the lower reaches of Terrebonne, 

Barataria, and Breton Sound Basins exhibit high values. In 

contrast, many of the sites located in the Chenier Plain in 

southwest Louisiana exhibit much lower scores. These sites 

can generally be characterized by impounded hydrology, 

which can prolong the duration of saltwater intrusion or 

flooding events.

Application to CWPPRA Projects

The annual hydrologic index, average annual salinity, 

and percent of time flooded were calculated for CWPPRA 

monitoring sites in the following CWPPRA projects: East 

Mud Lake (CS-20; Castellanos, 2005), Naomi Siphon (BA-03; 

Boshart and Richard, 2008), and LaBranch Wetland Marsh 

Creation (PO-17; Boshart, 2008; fig. 16). CS-20 is a marsh 

management project designed to limit hydrologic exchange 

between the marshes of the project area and high saline 

waters that are found in nearby Calcasieu Lake. BA-03 is a 

freshwater diversion project designed to deliver Mississippi 

River water into the marshes immediately flanking the river. 

PO-17 is a marsh creation project on the southwest shore 

of Lake Pontchartrain. Detailed project descriptions and 

boundaries can be found at http://www.lacoast.gov.

The results are tabulated in table 4 and plotted in figure 

17. The indices range from 0.01 to 0.99, and all sites show 

similar patterns through time in that there is a decrease from 

1998 to 2000 followed by an increase in 2001. This similarity 

may have resulted from the coastwide drought that affected 

the Louisiana coastal zone during this late period which 

resulted in some of the highest salinities observed. The index 

scores for BA-03 are relatively high between 1999 and 2007, 

with the exception of strong minima in 2000 and 2006. The 

2000 minimum likely reflects a severe drought that impacted 

much of the Louisiana coast, and this minimum is also present 

in hydrologic index scores for CS-20 and PO-17. The 2006 

minimum may reflect Hurricane Katrina impacts, which 

resulted in prolonged elevated salinities during the 2006 water 

year. The indices from CS-20, which has the greatest number 

of sites, are presented as a time series and on the hydrologic 
18 Hydrologic Index Development and Application to Selected CRMS Sites and CWPPRA Projects

Figure 16. Locations of Coastal Wetlands Planning, Protection and Restoration Act projects and stations where hydrologic index was 

applied: CS-20 (top), PO-17 (middle) and BA-03 (bottom).
Hydrologic Index 19

Table 4. Hydrologic index, average salinity, and percent of time flooded for monitoring locations at three Coastal Wetlands Planning, 

Protection and Restoration Act projects.

Hydrologic index score

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Mean

PO17-13 0.41 -- 0.59 0.25 0.05 0.28 0.52 -- -- -- -- -- 0.35

PO17-44R 0.52 0.83 0.68 0.26 0.07 0.40 0.60 -- -- -- -- -- 0.48

Mean 0.45 0.83 0.63 0.25 0.07 0.34 0.56 -- -- -- -- -- 0.45

CS20-03 -- -- 0.24 0.05 0.01 0.40 0.40 0.24 -- -- -- -- 0.22

CS20-07 -- -- 0.39 0.07 0.01 0.46 0.56 0.29 -- -- -- -- 0.30

CS20-17 -- -- 0.53 0.12 0.02 0.37 0.52 0.59 -- -- -- -- 0.35

CS20-106 -- -- 0.74 0.18 0.02 0.64 0.92 0.82 -- -- -- -- 0.55

CS20.14R -- -- 0.09 0.03 0.01 0.14 0.12 0.06 -- -- -- -- 0.08

CS20-15R -- -- 0.54 0.45 0.03 0.64 0.77 0.50 -- -- -- -- 0.49

Mean -- -- 0.42 0.15 0.02 0.44 0.55 0.41 -- -- -- -- 0.33

BA03-16 -- -- -- 0.79 0.26 0.88 0.88 0.88 0.98 0.98 0.54 0.99 0.8

BA03-60 -- -- -- 0.85 0.19 0.91 0.99 0.98 0.91 0.94 0.64 0.99 0.82

BA03-61 -- -- -- 0.23 0.03 0.4 0.61 0.58 0.69 0.61 0.09 0.70 0.44

Mean -- -- -- 0.62 0.16 0.73 0.82 0.81 0.86 0.84 0.42 0.89 0.69

Average annual salinity, in practical salinity units

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Mean

PO17-13 4.51 3.48 5.50 9.53 5.39 3.87 -- -- -- -- -- 5.38

PO17-44R 3.85 1.44 2.96 5.53 8.56 4.38 3.38 -- -- -- -- -- 4.30

Mean 4.18 1.44 3.22 5.52 9.05 4.89 6.63 -- -- -- -- -- 4.56

CS20-03 -- -- 11.69 17.5 21.79 10.31 10.45 12.18 -- -- -- -- 13.99

CS20-07 -- -- 10.8 15.89 22.24 10.29 9.50 11.15 -- -- -- -- 13.31

CS20-17 -- -- 8.39 14.6 20.48 10.02 7.53 8.70 -- -- -- -- 11.62

CS20-106 -- -- 8.01 12.46 20.54 8.88 5.29 5.94 -- -- -- -- 10.19

CS20.14R -- -- 15.66 19.60 24.21 14.42 14.71 17.29 -- -- -- -- 17.64

CS20-15R -- -- 9.62 10.34 19.79 8.75 6.95 9.95 -- -- -- -- 10.9

Mean -- -- 9.72 15.11 21.56 9.87 8.19 9.49 -- -- -- -- 12.28

BA03-16 -- -- -- 1.89 5.41 0.92 0.81 0.66 0.69 1.09 3.75 0.46 1.74

BA03-60 -- -- -- 2.07 6.46 1.62 0.90 1.03 1.71 1.56 3.12 0.86 2.15

BA03-61 -- -- -- 5.19 12.43 3.96 2.64 2.63 1.71 2.66 7.94 2.05 4.58

Mean -- -- -- 3.05 8.10 2.17 1.45 1.44 1.37 1.77 4.94 1.13 2.82
20 Hydrologic Index Development and Application to Selected CRMS Sites and CWPPRA Projects

Table 4. Hydrologic index, average salinity, and percent of time flooded for monitoring locations at three Coastal Wetlands Planning, 

Protection and Restoration Act projects.—Continued

Percent time flooded

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Mean

PO17-13 58.79 34.84 19.24 21.03 34.39 43.75 -- -- -- -- -- 35.34

PO17-44R 58.79 16.81 30.77 29.42 10.10 27.03 34.29 -- -- -- -- -- 29.60

Mean 58.78 16.81 32.81 24.33 15.57 30.71 39.02 -- -- -- -- -- 32.47

CS20-03 -- -- 11.39 20.14 2.39 18.85 55.09 20.23 -- -- -- -- 21.35

CS20-07 -- -- 26.63 11.29 3.09 38.85 36.37 14.09 -- -- -- -- 21.72

CS20-17 -- -- 64.93 52.93 16.83 65.35 70.10 58.61 -- -- -- -- 54.79

CS20-106 -- -- 43.51 10.24 5.44 33.09 49.01 19.48 -- -- -- -- 26.79

CS20.14R -- -- 42.92 32.83 22.33 40.17 44.70 42.97 -- -- -- -- 37.65

CS20-15R -- -- 44.92 41.30 15.87 29.93 54.97 33.64 -- -- -- -- 36.77

Mean -- -- 36.62 21.48 6.94 39.04 52.64 28.10 -- -- -- -- 30.80

BA03-16 -- -- -- 81.87 78.45 29.22 80.57 80.67 62.50 54.26 36.77 56.65 67..88

BA03-60 -- -- -- 66.43 53.98 34.33 47.99 42.91 36.44 43.66 30.00 48.19 44.88

BA03-61 -- -- -- 95.95 84.05 90.29 91.41 94.59 95.54 91.65 88.90 91.10 91.50

Mean -- -- -- 81.42 72.16 67.94 73.32 72.72 64.82 63.19 51.89 65.31 68.09

index response surface in figure 18. This presentation shows 

trajectories of movement over the response surface (from ideal 

to less ideal conditions) that are very similar at all stations, and 

indicates a large scale forcing that affects the whole project 

area is the dominant driver of hydrology at CS-20. In the case 

of a project that is improving over time, the data should begin 

to converge toward the more favorable conditions indicated 

on the response surface (blue area) with occasional movement 

toward less favorable conditions. The duration of the present 

dataset is insufficient for proper assessment of the long-term 

(decadal) trend.

The index can also be used to compare years in greater 

detail as shown in figure 19. This figure presents the clustering 

of all six stations in CS-20 for 2000 and 2002. It is clear that 

the 2000 conditions were less ideal at all stations (index value 

of ~0.10) in response to the coastwide drought conditions. 

The 2002 data show that all of the sites improved, with two 

of the sites having high (~0.8 or greater), three sites having 

moderate (0.6–0.7), and one site still having a low (~0.2) 

index value.
Hydrologic Index 21

Figure 17. Time-series plots of the hydrologic indices from stations in the Coastal Wetlands Planning, Protection and Restoration Act 

projects CS-20 (top), PO-17 and BA-03 (bottom).
22 Hydrologic Index Development and Application to Selected CRMS Sites and CWPPRA Projects

Figure 18. Hydrologic indices for six locations associated with the CS-20 Coastal Wetlands Planning, Protection and Restoration Act 

project. The time series of the index is presented in the line plot and on the hydrologic index response surface.
Hydrologic Index 23

Figure 19. Comparison of hydrologic indices for six locations in the CS-20 Coastal Wetlands Planning, Protection and Restoration Act 

project area for different years.
24 Hydrologic Index Development and Application to Selected CRMS Sites and CWPPRA Projects

Summary

Nearly 40 percent of existing Coastal Wetlands Planning, 

Protection and Restoration Act (CWPPRA) projects use 

techniques to directly influence hydrology, and changing water 

level and salinity regimes are explicit goals in the majority 

of projects that fall under this category. Many projects, 

particularly marsh management and hydrologic restoration 

efforts, aim to reduce tidal exchange between project areas and 

their surroundings, and these projects are deemed successful 

if the overall variance in water level or salinity is significantly 

reduced. This approach is problematic in that there are many 

processes other than tidal exchange that can influence salinity 

or water level (for example, precipitation, seasonal variability, 

meteorology, river inputs) that typically occur over different 

time scales. This document points to several techniques 

such as harmonic and spectral analysis that are effective at 

partitioning time-series variability across different time scales. 

Although these techniques are commonplace in estuarine 

physical oceanography, to date they have rarely been applied 

to the assessment of coastal wetland restoration.

The hydrologic index presented in this document 

provides an avenue to apply the Louisiana Coastal Area 

Habitat Switching Model (LCA HSM) productivity component 

put forth by Visser and others (2003) with hourly hydrologic 

data collected under the Coastwide Reference Monitoring 

System (CRMS) program. The utility of the index rests 

in its ability to take all the hourly salinity and water level 

observations at a given CRMS site and provide an indication 

as to how suitable the hydrology is for emergent marsh 

vegetation productivity for a given marsh type. The hydrologic 

index can be used as a planning tool, identifying regions of 

the coast where index scores are low (indicating hydrology 

that is not conducive to high productivity) that may benefit 

from hydrologic restoration projects or river diversions. It 

can also be used to assess the effects of restoration efforts by 

comparing project area scores with those in nearby nonproject 

areas.

Though the hydrologic index provides a useful approach 

for assessing site hydrology, the salinity algorithms that drive 

it are based on relatively few studies that were performed in 

a laboratory setting, and the flooding algorithms were based 

entirely on expert opinion. Improving the index will require 

testing and refining these algorithms with data collected in 

a field setting. There is an immediate need to empirically 

determine the relation between productivity and percent time 

flooded. 

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Louisiana—field manipulation of salinity and submergence: 

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Snedden and Swenson—Hydrologic Index Development and Application to Selected CRMS Sites and CWPPRA Projects—OFR 2012–1122

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