Field layer:  
Select MCDA option 
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AHP Weights
Criteria  Weight  Subcriteria Min/Max Rel Weights Indifference (%) 

Value  Rank  Comment 

Criteria  Weight  Subcriteria  Min/Max  Relative Weights  Indifference (%) 

Computation Mode:  
DEM:  
Store results in folder:  
DEM input raster:  
Burn in stream layer (optional):  
Output raster: 
Filled raster:  
Flow direction raster:  
Slope raster: 
Flow direction:  
Flow accumulation raster: 
Flow direction:  
Longest flow path:  
Total length of flow paths:  
Grid network order: 
Filled DEM raster:  
PeukerDouglas Stream Network: 
Outlet point layer:  
Optional: 

Flow direction raster:  
Move outlets to stream raster:  
New outlet layer: 
Flow direction raster:  
PeukerDouglas skeleton stream network raster:  
Outlet points:  
Weighted contributing area raster: 
Filled DEM raster:  
Slope raster:  
Flow accumulation raster:  
Flow direction raster:  
Outlet points:  
Weighted flow accumulation raster:  
Drop table name: 
Estimated Area Threshold (acres)  Number of Upstream Curve Cells Threshold  DrainDen  NoFirstOrd  oHighOrd  MeanDFirstOrd  MeanDHighOrd  StdDevFirstOrd  StdDevHighOrd  Tval 

Weighted flow accumulation raster:  
Stream Raster Grid:  
Threshold: 
Filled DEM raster:  
Flow direction raster:  
Flow accumulation raster:  
Stream threshold raster:  
Stream grid order raster:  
Outlet points:  
Stream Network:  
Subwatersheds: 
Flow direction raster:  
Outlet points:  
Watershed: 
Store results in folder:  
Perform the analysis only for the zone layer specified below:  
Boundary Mask:  
Option 1: Trace the watershed on the map
Draw Tool:
Option 2: Use predefined NHD Hydrologic unit catalog:


Flow direction:  
Pour Point:  

Getting Started  
Station ID:  
Station Name:  
Supervising Agency:  
Latitude:  
Longitude:  
State:  
County:  
Hydrologic Unit Code:  
Drainage Area:  
Elevation:  
Elevation Datum:  
Upload Your Own Data 
Use Uploaded Data  
Select Analysis Begin Date:  End Date: 
Select Type of Parameter:  
Select Parameter to Graph: 
Specify Flow Type:  TimeStep:  Method: 
Use Uploaded Data  
Select Analysis Begin Date:  End Date: 
Select Flood Analysis Method: 
Use Default Regional Skewness 


Use UserDefined Regional Skewness 

Label the 5 largest floods 
Show returnperiod reference lines 
Legend located inside the graph 
Show Flood Analysis Summary 
Use Uploaded Data  
Select Analysis Begin Date:  End Date:  
Specify Annual Drought Limit: 
Use Longterm Average Annual Flow Rate  
Use UserDefined Value 

Apply BoxCox Transformation: 
Optimize Transformation Parameter  
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Select Regression Type: 
AR(p)  p 
ARMA(p, q)  p  q 
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Select Baseflow Separation Model: 
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Graph Results 
Select Baseflow Separation Method: 
Flow Duration Curve  Load Duration Curve 
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Statistical Analysis: 


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Select WaterQuality Test: 
Enter WaterQuality Target 
Specify Begin of Seasonal Analysis:  Season End: 
Include LowFlow Analysis: 

Put Paragraph Here: 
Analysis Summary: 
 Total Observations:  
 Start Date:  
 End Date:  
 Max:  
 Upper Quartile:  
 Mean:  
 Median:  
 Lower Quartile:  
 Min:  
 Standard Deviation: 
Comments: 
References: 
Put References Here: 
Disclaimer: 
The primary purpose of these graphs is to help indentify possible flow and pollutant problems. The developers of eRAMS are not liable for use of this model (indlucing but not limited to information extracted and results). 
Flood Analysis Results: 
The flood analysis follows the USGS Bulletin 17B (IACWD 1982) methodology for fitting a LogPearson Type III distribution to available annual flood data. The graph contains the Bulletin 17B fitted data and its corresponding 95% confidence interval, as well as the historic annual flood values. The flood analysis then estimates flood flow value (cfs) for standard return periods, which are summarized in the table below. 
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Analysis Summary: 
 Total Observations:  
 Start:  
 End:  
 Regional Skewness: 
Comments: 
References: 
Put References Here: 
Interagency Advisory Committee on Water Data (IACWD). 1982. "Guidelines for determining flood flow frequency." Bulletin No. 17B (revised and corrected), Hydrology Subcommittee, Washington, D.C. 
Water Resources Council, Hydrology Committee. 1967. "A Uniform Technique for Determining Flood Flow Frequencies." Bulletin No. 15, Washington, D.C. 
Disclaimer: 
The primary purpose of these graphs is to help indentify possible flow and pollutant problems. The developers of eRAMS are not liable for use of this model (indlucing but not limited to information extracted and results). 
The drought anlaysis first converts the annual flow data to only its stochastic component (stochatic data = (annual data  average) / standard deviation). Thereafter, a BoxCox transformation, Equation 1, converts the stochastic data into a normally distributed dataset.
Equation 1: BoxCox Transformation 
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Drought Regression Coefficients: 
References: 
Put References Here: 
Disclaimer: 
The primary purpose of these graphs is to help indentify possible flow and pollutant problems. The developers of eRAMS are not liable for use of this model (indlucing but not limited to information extracted and results). 
Regression Optimization: 
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Equation 2: Akaike Information Criterion (AIC) Equation  Equation 3: Bayesian Information Criterion (BIC) Equation 
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Drought Regression Coefficients: 
References: 
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Disclaimer: 
The primary purpose of these graphs is to help indentify possible flow and pollutant problems. The developers of eRAMS are not liable for use of this model (indlucing but not limited to information extracted and results). 
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Figure 1: Annual Flow Rate and Drought Limit 
Figure 2 contains a second time series containing the annual surplus or deficit between the supplied annual flow and the drought demand limit; this is meant to highlight the occurrence of droughts. 
Figure 2: Annual Flow Deficit/Surplus 
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Figure 3 contains a plot of the original annual data verses the predicted model data to illustrate the correlation between the datasets. If the correlation is poor then further modifications need to be made to the regression model in order to improve the reliability of the drought analysis. 
Figure 4 contains a plot of the original data and the first portion of the 100,000 year projected dateset used to analyze the drought impacts. This projected dataset is large to allow sufficient 'droughts' to occur illustrating high recurrence interval droughts that cannot be calculated from minimal observed data. The first 100 years of this dataset are not used in the analysis and a dropped as a model warmup period. This allows for the model to operate independent of initial conditions. 
Next the drought analysis uses the projected dateset to calculate the average recurrence interval of the 1yr, 2yr, 3yr, etc. droughts. These droughts are then categorized by their amount of drought deficit (supplied annual flow  drought demand limit) and illustrated in Figure 5. The original data and its corresponding recurrence intervals are included in Figure 5 as well to illustrate the fit of the predicted data to that of the observed data. If the fit is poor, a better correlation of the regression model will likely improve the fit of the drought recurrence intervals. 
Figure 5: Predicted Drought Recurrence Interval, Length, and Deficit(relative to the drought limit) 
Analysis Summary: 
 Total Observations:  
 Start:  
 End: 
Comments: 
References: 
Put References Here: 
Salas, Jose D., Chongjin Fu, Antonino Cancelliere, Dony Dustin, Dennis Bode, Andy Pineda, and Esther Vincent. 2005. "Characterizing the Severity and Risk of Drought in the Poudre River, Colorado." Journal of Water Resources Planning and Management 131(5): 383393. 
Salas, Jose D. 1993. "Chapter 19: Analysis and Modeling of Hydrologic Time Series." The McGraw Hill handbook of hydrology. D. R. Maidment, ed., McGrawHill New York 
The primary purpose of these graphs is to help indentify possible flow and pollutant problems. The developers of eRAMS are not liable for use of this model (indlucing but not limited to information extracted and results). 
Baseflow Analysis Results: 
The baseflow analysis retrieves the available flow data for the specified station and analysis period. It then executes the baseflow separation program BFLOW (Arnold et. al. 1995, Arnold and Allen 1999) on the collected data. The 3pass baseflow separation results are then graphed on top of the original flow data in the below figure, and a summary table of the analysis is displayed below as well. 
Pass 1 Baseflow Fraction  Pass 2 Baseflow Fraction  Pass 3 Baseflow Fraction  Number of Recessions  Alpha Factor  Baseflow Days 
Baseflow Passes: 
The automated baseflow filter is passed over the streamflow data three times. First forwards, then backwards, then forwards again. Each successive pass will result in less baseflow as a percentage of total flow (Arnold et. al. 1995). The value in the table indicates the average baseflow amount divided by the average flow amount to indicate a relative fraction. The first or second pass is usually sufficient to extract a baseflow similar to that reached by manual separation techniques. 
Alpha Factor: 
The alpha factor is a recession coefficient derived from the properties of the aquifer in question contributing to baseflow. Large alpha factors siginify steep recession indicative of rapid drainage and minimal storage. Conversely low alpha values indicate very slow drainage (Arnold et. al. 1995). 
Analysis Summary: 
 Total Observations:  
 Start:  
 End: 
Comments: 
References: 
Put References Here: 
Arnold, J.G., P.M. Allen, R. Muttiah, and G. Bernhardt. 1995. "Automated base flow separation and recession analysis techniques." Ground Water 33(6): 10101018. 
Arnold, J.G. and P.M. Allen. 1999. "Automated methods for estimating baseflow and ground water recharge from streamflow records." Journal of the American Water Resources Association 35(2): 411424. 
Disclaimer: 
The primary purpose of these graphs is to help indentify possible flow and pollutant problems. The developers of eRAMS are not liable for use of this model (indlucing but not limited to information extracted and results). 
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Analysis Summary: 
 Discharge Observations:  
 Start Date:  
 End Date: 
 Discharge Observations:  
 Water Quality Observations:  
 Water Quality Target:  
 Start Date:  
 End Date:  
 Start of 'Season':  
 End of 'Season': 
 Low Flow Value (mQn flow): 
Comments: 
References: 
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Disclaimer: 
The primary purpose of these graphs is to help indentify possible flow and pollutant problems. The developers of eRAMS are not liable for use of this model (indlucing but not limited to information extracted and results). 
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This site does not have 'Flood' data available. Would you like to use the annual maximum of daily data as 'Flood' values for this model? 




Flow Data  Water Quality Data 
The graph of flood data plots the results of a Bulletin 17 (B17) flood analysis of the current station, in skewed probability space. Plotting in skewed space results in a straight line frequency curve, even if the data is not normally distributed.
The Flood Analysis Model automatically highlights and labels the year of the largest flood and the most recent year's flood return period. If the user desires, checking the checkbox corresponding to "Label the 5 largest floods," labels the years of the to five floods after the model runs.
This method also summarizes the flow values for standard return periods like the 25, 50, 100, and 200 year floods calculated using the B17 method and the confidence intervals for these flood values.
Station skew is calculated based on available flood data while the generalized skew a value is obtained from Bulletin 17B Plate 1, a map of generalized station skew values for the country. This map of generalized regional skewness was then supplemented using general skewness values for stations on a state level interpolated using a Kriging method and combined with the Plate 1 map, see "Regional Skewness" for more information.
A discrepancy may arise between the station skew value and generalized skew value. This discrepancy is important because the LogPearson TypeIII variant, used by B17, is sensitive to small changes in the skewness parameter. Due to frequent differences between the skew values, a weighted average of the two is used in this B17 Flood Analysis Model.
References:
Interagency Advisory Committee on Water Data (IACWD). 1982. "Guidelines for determining flood flow frequency." Bulletin No. 17B (revised and corrected), Hydrology Subcommittee, Washington, D.C.
Water Resources Council, Hydrology Committee. 1967. "A Uniform Technique for Determining Flood Flow Frequencies." Bulletin No. 15, Washington, D.C.
Disclaimer:
The primary purpose of these outlines, the tables, and the graphs is to help indentify possible flow and pollutant problems. The developers of eRAMS are not liable for use of this model (indlucing but not limited to information extracted and results).
Adapted from: Interagency Advisory Committee on Water Data (IACWD). 1982. "Guidelines for determining flood flow frequency." Bulletin No. 17B (revised and corrected), Hydrology Subcommittee, Washington, D.C. 
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The drought analysis begins by calculating annual flow values from available average daily flow data. Then if the user has specified a drought limit, it uses this to identify drought years from the annual dataset. If no drought limit is provided the average of the annual flow data is taken to be the drought limit.
Then the annual flow data is converted to only its stochastic component (stochastic data = (annual data  average)/standard deviation). Subsequently a BoxCox transformation, Equation 1, converts the stochastic data into a normally distributed dataset. There after a regression model is applied to the dataset to allow forecasting of the minimial observed data to a larger sample size.
Equation 1 
Either an AutoRegressive, AR(p), model is fitted to the data using the form of Equation 2 below or an AutoRegressiveMovingAverage(p, q) model of the form seen in Equation 3.
Equation 2  Equation 3 
The number of parameters of each model, p and q, are choosen by the user, or can be optimized by fitting multiple models. The optimal model is choosen by selecting the one which best fits the data, minimal errors, with the least complexity, fewest parameters. The optimal model is selected by using the Akaike Information Criterion (AIC), Equation 4, and the Bayesian Information Criterion (BIC), Equation 5, are used as predictors of goodness of model fit
Equation 4  Equation 5 
Once the optimal model is selected, a 100,000 year projection of the model is performed to create a sufficiently large dataset to observe rare occurences of droughts. Then using the previously defined drought limit, the projected data is analyzed to determine the average occurence interval of various 1year, 2year, etc. droughts. The projected data droughts lengths are then graphed against their average recurrence interval along with the recurrence interval of the original historic data.
References:
Salas, Jose D., Chongjin Fu, Antonino Cancelliere, Dony Dustin, Dennis Bode, Andy Pineda, and Esther Vincent. 2005. "Characterizing the Severity and Risk of Drought in the Poudre River, Colorado." Journal of Water Resources Planning and Management 131(5): 383393.
Salas, Jose D. 1993. "Chapter 19: Analysis and Modeling of Hydrologic Time Series." The McGraw Hill handbook of hydrology. D. R. Maidment, ed., McGrawHill New York
Disclaimer:
The primary purpose of these outlines, the tables, and the graphs is to help indentify possible flow and pollutant problems. The developers of eRAMS are not liable for use of this model (indlucing but not limited to information extracted and results).
The baseflow analysis performs a threepass baseflow separation of the flow data using BFLOW (1995 J.G. Arnold, et. al, 1999 Arnold, J.G. and P.M.Allen) then graphs the results, if desired, on a timeseries graph. The baseflow of a river is usefull when identifying the runoff contribution from a storm to streamflow.
Arnold, J.G., P.M. Allen, R. Muttiah, and G. Bernhardt. 1995. "Automated base flow separation and recession analysis techniques." Ground Water 33(6): 10101018.
Abstract An automated base flow separation technique has been developed and tested. Base flow is considered to be the groundwater contribution to stream flow. Estimates of the amount of base flow can be derived from stream flow records. Such estimates are critical in the assessment of low flow characteristics of streams for use in water supply, water management, and pollution assessment. An automated base flow separation technique using a digital filter has been tested against three other automated techniques and manual separation methods. The filter appears to be comparable to other automated techniques in its ability to reproduce the results produced from graphical separation techiques. The filter technique is easy to use and has the added advantage in that it can be adjusted by the user to take into account personnel preferences in separation of stream flow into surface flow and base flow. The slope of the base flow recession has been used to estimate the volume of water in storage in the basin above the level of the stream channel, the amount of recharge to the shallow aquifer, and as an input into water budget models. A second automated technique was developed to calculate the slope of the base flow recession curve from stream flow record. This technique is an adaptation of the Master Recession Curve procedure. The results of this method were compared to manual estimates with an efficiency of 74 percent. 
Arnold, J.G. and P.M. Alen. 1999. "Automated methods for estimating baseflow and ground water recharge from streamflow records." Journal of the American Water Resources Association 35(2): 411424.
Abstract To quantify and model the natural ground water recharge process, six sites located in the midwest and eastern United States where previous water balance observations had been made were compared to computerized techniques to estimate: (1) base flow and (2) ground water recharge. Results from an existing automated digital filter technique for separating baseflow from daily streamflow records were compared to baseflow estimates made in the six water balance studies. Previous validation of automated baseflow separation techniques consisted only of comparisons with manual techniques. In this study, the automated digital filter technique was found to compare well with measured field estimates yielding a monthly coefficient of determination of 0.86. The recharge algorithm developed in this study is an automated derivation of the Rorabaugh hydrograph recession curve displacement method that utilizes daily streamflow. Comparison of annual recharge from field water balance measurements to those computed with the automated recession curve displacement method had coefficients of determination of 0.76 and predictive efficiencies of 71 percent. Monthly estimates showed more variation and are not advocated for use with this method. These techniques appear to be fast, reproducible methods for estimating baseflow and annual recharge and should be useful in regional modeling efforts and as a quick check on mass balance techniques for shallow water table aquifers. 
Disclaimer:
The primary purpose of these outlines, the tables, and the graphs is to help indentify possible flow and pollutant problems. The developers of eRAMS are not liable for use of this model (indlucing but not limited to information extracted and results).
A flow duration curve (FDC) is the ranked graphing of river flows on a scale of percent exceedence. For example a flow value associated with the flow interval of 15% means that particular flow value is met or exceeded only 15% of the time. This graph is meant to give a quick overview of the flow ranges, variability, and probability of flows of a river segment during the different flow periods of a river; which are High Flows from 0 to 10 percent flow interval, Moist Conditions 1040, MidRange Flows 4060, Dry Conditions 6090, and Low Flows 90100 (Cleland 2003).
A load duration curve (LDC) is a flow duration curve multiplied by the user's chosen target pollutant concentration that is wished not to be exceeded. This LDC for the selected river segment is graphed along with points of observed pollution concentrations and box plots of the observed points within their respective flow intervals. The interpretation of this graph is if the observed points are below the LDC line, there is no excess problem for that particular pollutant. If, however, the observed points lie above the LDC line this is indicative that there are observations which exceed the maximum specified target pollution concentration. A more in depth study of the particular watershed should be done to completely identify the proper pollution sources and remediation solutions.
For further clarification on probable sources a more in depth look needs to be taken as to the flow interval of target exceedence. Based on which interval(s) contain the exceeded observations different pollution sources are most likely the cause. Below is a table of probable pollution sources based on flow interval location of exceedence:
Table1:
Flow Interval and Probable Contributing Source (adapted from Cleland 2003, Table 2.)
Contributing Source Area  Duration Curve Zone 
  High Flow  Moist  Midrange  Dry  Low Flow 
Point Source        Med.  High 
Onsite wastewater systems      High  Med.   
Riparian Areas    High  High  High   
Stormwater: Impervious Areas  High  High  High    
Combined sewer overflows  High  High  High     
Stormwater: Upland  High  High  Med.     
Bank erosion  High  Med.       
For example if the LDC is exceeded under the low flow interval with a couple exceedences under the dry condition interval it is likely that point sources are the cause. It is important to note that the type of watershed/river section will greatly determine the type and effectiveness of pollutant remediation. It is imperative to not just rely on this data for solutions but to also study the whole watershed to better understand the possible pollutant delivery systems. The pollutant of interest is also an important variable to keep in mind; for example solutions to excess nitrogen problems probably will not work well to reduce excessive sediment concentrations.
Once the source has been identified, by where in the graph intervals the exceedence of pollution concentration occurs, a solution needs to be identified. Depending on which interval the problem occurs different solutions are possible to correct the exceedence. Below is a table of some possible solutions based on pollution type and where the solution will have the greatest impact on excessive pollutant concentrations. Again special attention should be paid to the type of watershed because, for example, in a wilderness segment of river septicsystemproblemremediation probably will not improve water quality problem greatly because there are few or no septic systems in the wilderness area. Some common sense and an actual study of the watershed segment of interest are needed when identifying possible solutions based on these tables.
Table2:
Management Practice and Likely Flow Interval Effectiveness (adapted from Cleland 2003, Table 4.)
Management Practice  Duration Curve Zone 
  High Flow  Moist  Midrange  Dry  Low Flow 
Point Source controls      Med.  High  High 
Septic system inspection    Med.  High  High  Med. 
Combined Sewer Overflow (CSO) repair / abatement  High  High  High     
Sanitary Sewer Overflow (SSO) repair / abatement      Med.  High  High 
Riparian buffers    High  High  High   
Pasture management  High  High  Med.     
Pet waste education and ordinances      Med.  High  High 
Hobby farm livestock education      High  High  High 
References:
Cleland, B. R. November 2003. "TMDL Development from the 'Bottom Up'  Part III: Duration Curves and WetWeather Assessments." National TMDL Science and Policy 2003.
Cleland, B. R. August 2007. "An Approach for Using Load Duration Curves in the Development of TMDLs." National TMDL Science and Policy 2007.
Disclaimer:
The primary purpose of these outline, the tables, and the graphs is to help indentify possible flow and pollutant problems. The developers of eRAMS are not liable for use of this model (indlucing but not limited to information extracted and results).
A time series graph is a straight scale graphing of available data with the oldest date on the bottom left and the most recent date on the bottom right with flows on the y axis. This can be useful to identify flow hydrographs from storm runoff for small time frames (ie. less than a couple days worth of data points), or to get a general feel for temporal changes in the data.
Flood Analysis:The Flood Analysis Tool graphs the results of a Bulletin 17 (B17) flood analysis of the current station, in skewed probability space. Plotting in skewed space results in a straight line frequency curve, even if the data is not normally distributed.
The Flood Analysis Tool automatically highlights and labels the year of the largest flood and the most recent year's flood return period. If the user desires, checking the checkbox corresponding to "Label the 5 largest floods," labels the years of the to five floods after the model runs.
This method also summarizes the flood flow values for return periods like the 25, 50, 100, and 200 year floods calculated using the B17 method. These flood values are then summarized in a table with the confidence intervals for the flood values.
Drought Analysis:The drought anlsyis calculates annual flow data and then fits a regression model, AR(p) or ARMA(p, q), to the annual data in order to project it forward 100,000 years to create a sufficiently large sample size.
Then using a user defined, the projected data is analyzed to determine the average occurence interval of various 1year, 2year, etc. droughts. The projected data droughts lengths are then graphed against their average recurrence interval along with the recurrence interval of the original historic data.
Baseflow Analysis:Depending on the user's selction the baseflow analysis can perform a threepass baseflow separation of daily flow data using BFLOW (1995 J.G. Arnold, et. al, 1999 Arnold, J.G. and P.M.Allen) then graphs the results, if desired, on a timeseries graph.
Or if desired the baseflow analysis can perform a baseflow separation of daily flow data using USGS's HYSEP (1996 Ronald A. Sloto and Michele Y. Crouse) then graphs the results, if desired, on a timeseries graph. The baseflow of a river is usefull when identifying the runoff contribution from a storm to streamflow.
Duration Curve Analysis:A flow duration curve (FDC) is the ranked graphing of river flows on a scale of percent exceedence. For example a flow value associated with the flow interval of 15% means that particular flow value is met or exceeded only 15% of the time. This graph is meant to give a quick overview of the flow ranges, variability, and probability of flows of a river segment during the different flow periods of a river; which are High Flows from 0 to 10 percent flow interval, Moist Conditions 1040, MidRange Flows 4060, Dry Conditions 6090, and Low Flows 90100 (Cleland 2003).
A load duration curve (LDC) is a flow duration curve multiplied by the user's chosen target pollutant concentration that is wished not to be exceeded. This LDC for the selected river segment is graphed along with points of observed pollution concentrations and box plots of the observed points within their respective flow intervals. The interpretation of this graph is if the observed points are below the LDC line, there is no excess problem for that particular pollutant. If, however, the observed points lie above the LDC line this is indicative that there are observations which exceed the maximum specified target pollution concentration. A more in depth study of the particular watershed should be done to completely identify the proper pollution sources and remediation solutions.
Disclaimer:
The primary purpose of these graphs is to help indentify possible flow and pollutant problems. The developers of eRAMS are not liable for use of this model (indlucing but not limited to information extracted and results).
The station you have selected is a streamflow monitoring station from either the USGS streamflow database or the EPA's STORET database.
The the first tab is simply a brief summary of available information about the station you have selected.
If you have any further questions about how each individual model works select the "Further Model Information" button on each of the tabs for the different analysis types. Or click the help button for instructions on each analysis tab.
This tab will allow you to create a time series graph of the selected parameter.
This tab will begin a flood analysis on the available flood data for USGS stations or on the annual maximum of averagedailyflows from nonUSGS stations.
This tab will begin a drought analysis based on annualized flow data.
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