#	Stochastic Empirical Loading and Dilution Model (SELDM) output: 2013-03-10 13:24:15
#
#	SELDM Output Documentation File
#	
#	
#	Version: 1.0.0 
#	March 2013
#	Granato, G.E., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S. Geological
#	Survey Techniques and Methods, book 4, chap. C3, 112 p., CD–ROM. (Also available at http://pubs.usgs.gov/tm/04/c03/)
#	Initial Release
#	
#
#	SELDM
#	SELDM is a planning-level event-based runoff-quality model that uses Monte-Carlo methods to generate
#	a random sample of precipitation, stormflows, and runoff quality to assess the potential for adverse
#	effects of runoff on the water-quality of receiving waters. SELDM calculates potential effects of random
#	combinations of these input factors. The output values are a stochastic random sample generated using
#	input statistics, but because the outputs are a random sample, statistics calculated using SELDM outputs
#	may be different than input values. The user may input regional estimates, local estimates, or site-specific
#	data to estimate precipitation, flows, loads, and concentrations of the constituents of concern. The
#	representativeness of output values will depend on site-specific factors and the inputs that are selected.
#
#
#	Disclaimer
#	The information, data, and metadata in this application was prepared by the U.S. Geological Survey
#	(USGS) in cooperation with the Federal Highway Administration (FHWA); both are agencies of the U.S.
#	Government. This model was compiled for planning-level analysis of runoff data on a national, regional,
#	or local scale. Although the methods and data have been subjected to rigorous review and are substantially
#	complete and accurate, this information was generated from original data from other sources. The USGS
#	and the FHWA reserve the right to revise the data and methods pursuant to further analysis and review.
#	These data and this software have been used by the USGS and the FHWA, and are released on condition
#	that the USGS, the FHWA, or the U.S. Government may not be held liable for any damages resulting from
#	their use. Information and numerical methods used in this relational database are attributable to the
#	works cited in the technical method-development documents published by the FHWA and the USGS.
#
#
#	Annual and Storm Sequences
#	Individual storm events are identified by sequence number and annual-load accounting years. SELDM,
#	however, generates each storm randomly; there is no serial correlation. The order of storms does not
#	reflect seasonal patterns. Flows and loads are not propagated from storm to storm. The annual-load
#	accounting years are just random collections of events generated with storm durations and interevent
#	times that are less than or equal to a year. The sequence number and annual-load accounting years are
#	provided to facilitate generation of paired-value scatter plots and examination of the output to check/verify
#	the results of calculations.
#
#
#	Analyst(s):
#	Role: Primary analyst
#	Initials/Short Name:	Anonymous
#	Additional Information: 
#	The Anonymous profile is the default for hypothetical use
#
#
#	Project Information: 
#	Short Title: z01 Example Project
#	Title:	Example Project Z01
#	Number: FHWA-EIS-200X-XX-D
#	Organization: 
#	Federal Highway Administration
#	Abstract: 
#	This is an example project that can be used to experiment with the SELDM model
#
#
#	GIS Grid Information:
#	The grid is used to discretize geographic data for use within the database
#	Short Title: SELDM 0.25 degree Conterminous U.S.
#	Grid Information: 
#	A 0.25 degree grid for the conterminous U.S. developed for the SELDM model
#
#
#	Analysis Information: 
#	Example Analysis (I-81)
#	Description:	Example highway analysis I-81 Near Harrisburg PA
#	Type:	Stream and Lake Analysis
#	Status:	Draft
#	Master seed:	3166
#
#
#	Ecoregion Information: 
#	Group: USEPA Level III Ecoregion
#	Citation: 
#	U.S. Environmental Protection Agency, 2003, Level III ecoregions of the continental United States:
#	Washington, D.C., U.S. Environmental Protection Agency National Health and Environmental Effects Research
#	Laboratory, 1 pl.
#	Ecoregion Number:	67
#	Ecoregion Name:	Central Appalachian Ridges and Valleys
#	Description: 
#	This northeast-southwest trending, relatively low-lying, but diverse ecoregion is sandwiched between
#	generally higher, more rugged mountainous regions with greater forest cover. As a result of extreme
#	folding and faulting events, the region’s roughly parallel ridges and valleys have a variety of widths,
#	heights, and geologic materials, including limestone, dolomite, shale, siltstone, sandstone, chert,
#	mudstone, and marble. Springs and caves are relatively numerous. Present-day forests cover about 50%
#	of the region. The ecoregion has a diversity of aquatic habitats and species of fish.
#
#
#	Precipitation Dataset Information: 
#	Name:	Federal Highway Administration Synoptic Analysis (FHWA 2010)
#	Period of Record:	1965-2006
#	Reference: 
#	Granato, G.E., 2010, Methods for development of planning-level estimates of stormflow at unmonitored
#	sites in the conterminous United States: Washington, D.C., U.S. Department of Transportation, Federal
#	Highway Administration, FHWA-HEP-09-005, 90 p. with CD-ROM
#	Precipitation Selection:	Avg of Rain Zone 3: Midatlantic DS: 3
#	Precipitation Calculation Method:	Rain Zone (average of station values)
#
#
#	Streamflow Dataset Information: 
#	Name:	Federal Highway Administration Streamflow Analysis (FHWA 2010)
#	Period of Record:	1961-2004
#	Reference: 
#	Granato, G.E., 2010, Methods for development of planning-level estimates of stormflow at unmonitored
#	stream sites in the conterminous United States: U.S. Department of Transportation, Federal Highway
#	Administration, FHWA-HEP-09-005, 90 p. CD-ROM.
#	Streamflow Selection:	Bixler Run near Loysville, PA
#	Streamflow Calculation Method:	Selected Stations (median of station values)
#
#
#	Highway Site Information: 
#	Site: I-81 Near Harrisburg PA
#	Name:	I-81 Near Harrisburg PA at intersection of RT-944 Wertzville Rd, p21 FHWA-RD-81-045
#	Site Location
#	Latitude:	40.292249
#	Longitude:	-76.980517
#	Drainage Basin Characteristics:
#	Drainage Area(Ac):	18.5
#	Drainage Length(ft):	2000
#	Mean Drainage Slope(ft/mi):	105
#	Impervious Fraction:	0.27
#	Basin Development Factor:	6
#	Highway Characteristics:
#	Number of lanes:	6
#	Lane Width (ft):	12
#	Average Daily Traffic (Vehicles per Day):	24000
#	Pavement Type:	Concrete
#	Type of Curbing:	flush/none
#	Site Information: 
#	This site was monitored in the 1970's for the FHWA Constituents of Highway Runoff Study
#
#	Upstream Basin Information: 
#	Site: Upstream Basin I-81 PA
#	Name:	Conodoguinet Creek Tributary near Enola PA
#	Drainage Basin Characteristics:
#	Drainage Area(mi^2):	0.5
#	Drainage Length(ft):	6500
#	Mean Drainage Slope(ft/mi):	669
#	Impervious Fraction:	0.007
#	Basin Development Factor:	0
#	Minimum hydrograph recession factor:	1
#	Most Probable Value hydrograph recession factor:	1.85
#	Maximum hydrograph recession factor: 	4.4
#	Basin Description: 
#	A forested (60 percent), rural/agricultural basin with two farm ponds. Developed area is 3.13 percent.
#	The basin's northern border is a steep straight ridge line.
#
#
#	BMP Information: 
#	BMP Selection: Flow Reduction & SSC BMP
#	BMP Full Name:	Hypothetical flow and suspended sediment reduction BMP
#	Description:
#	This hypothetical BMP should not be used for an actual analysis
#
#	BMP Flow Reduction Specifications:
#	Flow reduction is calculated using the unitless ratio of outflow to inflow volume.
#	If a non-zero rank correlation value is specified, flow reduction is a randomized function of inflow volume.
#	Specified Input Statistics:
#	Minimum Value:	0
#	Lower Bound of the Most Probable Value:	0.5
#	Upper Bound of the Most Probable Value:	0.5
#	Maximum Value:	1
#	Rank Correlation to Inflow:	-0.75
#	
#	Calculated Statistics:
#	Median:	0.5
#	Average:	0.5
#	Standard Deviation:	0.204
#
#	BMP Hydrograph Extension Specifications:
#	Hydrograph extension (in hours) is the time from the end of highway runoff to the end of BMP discharge.
#	If a non-zero rank correlation value is specified, hydrograph extension is a randomized function of inflow volume.
#	Specified Input Statistics:
#	Minimum Value:	0
#	Lower Bound of the Most Probable Value:	1
#	Upper Bound of the Most Probable Value:	4
#	Maximum Value:	6
#	Rank Correlation to Inflow:	0
#	
#	Calculated Statistics:
#	Median:	2.75
#	Average:	2.78
#	Standard Deviation:	1.38
#
#
#	Water-Quality Modification Definition(s):
#	Concentration reduction is calculated using the unitless ratio of outflow to inflow concentration.
#	If a non-zero rank correlation value is specified, concentration reduction is a randomized function of inflow concentration.
#
#	Parameter:	p80154	Suspended sediment concentration, milligrams per liter
#	Irreducible Minimum Concentration:	1
#	Concentration Ratios (MPV is most probable value):
#	Minimum:                	0
#	Lower bound of the MPV: 	0.5
#	Upper bound of the MPV: 	0.5
#	Maximum:                	1
#	Rank Correlation to Inflow Concentration: 	-0.75
#	Calculated Statistics:
#	Median:	0.5
#	Average:	0.5
#	Standard Deviation:	0.204
#
#
#	ExampleAnalysisI81-PS.txt
#
#	Prestorm Streamflow
#	SELDM uses streamflow statistics for generating a random sample of prestorm streamflows from the basin
#	upstream of the highway-runoff outfall. The mean, standard deviation, and skew of the logarithms of
#	nonzero streamflows and the proportion of zero flows relative to all streamflow values are used to
#	generate a population of prestorm flows. Prestorm streamflow may be a substantial proportion of the
#	total upstream stormflow during a storm event. The methods for estimating prestorm streamflows are
#	described in Federal Highway Administration Report FHWA-HEP-09-005.
#	
#	The population of prestorm flows is well represented by statistics for the complete population of mean
#	daily streamflows. Mean daily flow statistics represent the full range of prestorm flows for many basins
#	because of differences in the timing of discrete rainfall-runoff events and the storm runoff hydrograph.
#	Prestorm flows commonly are associated with base flow (generally defined as groundwater discharge)
#	because the occurrence of storm runoff defines the end of the base-flow recession period. In reality,
#	however, prestorm streamflow commonly includes base flow and some residual stormflow from previous
#	storms. Uncertainties in using streamflow statistics to model prestorm flows are well within other
#	runoff modeling uncertainties because results from different base flow-separation techniques can vary
#	substantially and because estimation of streamflow at unmonitored sites is considered to be one of
#	the most difficult unsolved problems in hydrology.
#	
#	The prestorm flow file includes the prestorm flow rate from the upstream basin for each storm event
#	in the random sample generated by SELDM.
#
#	ExampleAnalysisI81-PE.txt
#
#	Precipitation Events
#	SELDM uses synoptic precipitation statistics for generating a random sample of precipitation events.
#	Storms commonly are defined as independent statistical events using synoptic precipitation statistics
#	for the purposes of planning, analysis, and sampling efforts. Synoptic precipitation statistics include
#	the interval between storm-event midpoints, the precipitation volume, and duration for each storm event.
#	The interval between storm-event midpoints is modeled using the average and coefficient of variation
#	(COV--the standard deviation divided by the average) of intervals and a minimum interevent time. The
#	precipitation volume is modeled using the average and COV of storm-event precipitation volumes and
#	a minimum total storm volume, which is commonly defined as 0.1 inch for runoff producing events. The
#	storm event duration is modeled using the average and COV of durations. A minimum duration of one hour
#	is used because synoptic precipitation statistics are calculated using hourly precipitation data. The
#	number of storms per year are calculated using values generated for the interval between storm-event
#	midpoints. The methods for estimating precipitation statistics for use in SELDM are described in Federal
#	Highway Administration Report FHWA-HEP-09-005.
#	
#	The precipitation file includes the interval between storm event midpoints, the volume, and the duration
#	of precipitation for each storm event in the random sample generated by SELDM.
#
#	ExampleAnalysisI81-SF.txt
#
#	Stormflows
#	SELDM uses prestorm flow statistics, precipitation statistics, and runoff coefficient statistics for
#	generating a random sample of stormflows from the upstream basin; the model uses precipitation statistics,
#	runoff coefficient statistics, and best management practice (BMP) flow-modification statistics for
#	generating a random sample of stormflows from the highway. SELDM uses basin characteristics and precipitation-event
#	duration statistics to calculate the duration of upstream stormflow, highway runoff, and BMP discharge.
#	The flow durations determine the amount of upstream stormflow that is available to dilute highway runoff
#	during the highway and BMP discharge period. The methods for estimating runoff-coefficient statistics
#	and stormflow duration statistics for use in SELDM are described in Federal Highway Administration
#	Report FHWA-HEP-09-005.
#	
#	The stormflow file includes runoff coefficients, stormflows and stormflow durations, for the highway
#	site (with or without a BMP) and the upstream basin.
#
#	ExampleAnalysisI81-DF.txt
#
#	Dilution Factor
#	SELDM uses the random samples of highway discharge, BMP discharge, and concurrent upstream stormflows
#	to calculate dilution factors for each storm. The dilution factor is the ratio of highway runoff to
#	downstream flow. This file includes the dilution factors for each storm with and without BMP modification
#	of the highway-runoff hydrograph.
#
#	ExampleAnalysisI81-HQ.txt
#
#	Highway Runoff Quality
#	SELDM uses event mean concentration (EMC) statistics for generating a random sample of concentrations
#	and loads of selected constituents in highway runoff. In theory, the event mean concentration is the
#	total load of a constituent that discharges from the highway site divided by the total flow of runoff
#	that discharges from the highway site. In practice, a series of small samples are collected throughout
#	a storm event and composited to measure or calculate the event mean concentration (EMC). Concentrations
#	of water-quality constituents in runoff may be random or may be correlated with another water-quality
#	constituent. The highway-runoff database (HRBD) documented in Federal Highway Administration Report
#	FHWA-HEP-09-004 can be used to estimate these event mean concentration (EMC) statistics from available
#	data.
#	
#	The highway runoff quality file includes the concentration and load of highway runoff and BMP discharge
#	for every selected constituent for each storm event in the random sample of storms generated by SELDM.
#
#	ExampleAnalysisI81-Annual.txt
#
#	Annual Highway Runoff Loads
#	SELDM uses the storm event output for generating a random sample of annual flows and loads from a highway
#	site with or without use of a BMP. The annual-load accounting years in this file are just random collections
#	of events generated with storm durations and inter-event times that are less than or equal to a year.
#	The annual highway-runoff file includes annual precipitation volumes, highway-runoff flows and loads,
#	and BMP discharge flows and loads.
#
#	ExampleAnalysisI81-UQ.txt
#
#	Upstream Water Quality
#	SELDM uses water-quality statistics for generating a random sample of event mean concentrations (EMCs)
#	and loads of selected constituents in streamflow from the basin upstream of the highway runoff outfall.
#	Concentrations of water-quality constituents in upstream streamflow may be random, may be a function
#	of upstream streamflow, or may be correlated with another water-quality constituent. Methods for estimating
#	upstream concentrations are described in documented in Federal Highway Administration Report FHWA-HEP-09-003.
#	
#	The upstream water quality file includes the concentration and load of every selected constituent for
#	each storm event in the random sample of storms generated by SELDM. An upstream water-quality file
#	will only be generated if one or more downstream water-quality selections are made.
#
#	ExampleAnalysisI81-DQ.txt
#
#	Downstream Water Quality
#	SELDM uses the random samples of highway discharge loads (with or without BMP treatment) highway discharge
#	flows, upstream loads and upstream flows to calculate the sample of downstream concentrations and loads.
#	The upstream loads and flows used for the calculations are the values that are concurrent to discharge
#	from the highway site. The downstream water quality file includes the concentration and load of every
#	selected constituent for each storm event in the random sample of storms generated by SELDM. The downstream
#	water quality file also includes adverse effect concentrations, which are the proportion of the total
#	downstream concurrent concentration thought to have an adverse effect on water quality. The adverse
#	effect concentrations are calculated as a random fraction of the total downstream concentration on
#	the basis of user-defined statistics.
#
#	ExampleAnalysisI81-Lake.txt
#
#	Lake Analysis
#	SELDM uses the random samples of highway discharges and loads with random samples of discharges and
#	loads from the entire lake basin to generate a random sample of total annual loads from the entire
#	lake basin and average annual concentrations in the lake. The lake-basin output uses the highway runoff
#	results for storm events, but calculates daily loads from the rest of the lake basin for the entire
#	simulation period. This is because a substantial proportion of many water-quality constituents can
#	be transported during dry-weather baseflows.
#