Google Earth and Statistical Trends Analysis Tools Brandon Bergenroth, Jay Rineer, Breda Munoz and William Cooter (RTI) Dwane Young (EPA OW) Dwight Atkinson (EPA OW/AWPD) RTI International is an exchange name of Research Triangle Institute 3040 Cornwallis Road �� P.O. Box 12194 �� Research Triangle Park, North Carolina, USA 27709 Phone 919-316-3537 email bbergenroth@rti.org
Slide 2Statistical Trend Analysis for STORET DATA
Slide 3New STORET Tools (Services) Simplify Pulling Data for Trend Analysis Trends investigation recognizes corruption patterns for waters that warrant security to maintain a strategic distance from 303(d) posting Trend examination additionally report incremental upgrades indicating progress in reestablishing weakened waters
Slide 4Seasonal Kendall tests a typical apparatus to affirm evident pattern designs Seasonal Kendall tests supported by the USGS, EPA ORD, and numerous college scientists Valuable where parameter demonstrate inconstancy identified with regular changes in temperature or changes in streams Can oblige some level of "controlled perceptions (underneath identification confines or missing qualities)
Slide 5Trend investigation capacities/modules like ESTREND (USGS) and Kendall (S-PLUS) officially actualized in the open source R.
Slide 6R is bolstered by EPA through EMAP and through activities, for example, NCEA's CADDIS
Slide 7R-based Trend Analysis utilizing STORET waterway/stream station information Scatter plots for information arrangement of customary and poisonous parameters Seasonal Kendall test can be utilized to survey regular patterns
Slide 8Non Parametric Statistic Tests Non parametric measurement tests allude to the accumulation of measurement tests that don't require any presumption on the circulation of the information. They are likewise referred to in the measurement writing as circulation free tests and dispersion autonomous tests. Besides, non parametric tests have couple of fundamental presumptions and tend to amass in the relative qualities (e.g. positions) of the perceptions rather than the greatness of the perceptions. Most non parametric tests were intended to survey the nearness or nonappearance of a given measurement trademark (e.g. incline) and thusly don't give the extent of the measurement normal for intrigue. Therefore, a few analysts characterize them as exploratory information methodology. They are frequently utilized as a part of theory testing (e.g. presence of patterns) and accordingly considered as corroborative information investigation devices.
Slide 9MannKendall Let: be a succession of estimations after some time, to test the invalid speculation, : originate from a populace where the arbitrary factors are free and indistinguishably appropriated, : take after a monotonic (e.g. expanding or diminishing) incline after some time. The Mann-Kendall test measurement is figured as where S is asymptotically regularly dispersed. The mean and fluctuation of S are given by where p is the quantity of tied gatherings in the information set and is the quantity of information focuses in the j th tied gathering.
Slide 10MannKendall A positive estimation of S shows that there is an upward (expanding) incline (e.g. perceptions increment with time). A negative estimation of S implies that there is a descending (diminishing) slant. On the off chance that S is essentially unique in relation to zero, then in light of the information can be rejected at a pre-chosen importance level and the presence of a monotonic pattern can be acknowledged. Take note of that S is a tally of the quantity of times for j k , more than . The most extreme estimation of S (called it D ) happens when . Kendall's tau is characterized as where
Slide 11MannKendall The appropriation of Kendall's tau can be effectively acquired from the dissemination of S. A positive estimation of tau demonstrates that there is an upward (expanding) drift (e.g. perceptions increment with time). A negative estimation of tau implies that there is a descending (diminishing) drift. In the event that tau is altogether not quite the same as zero (e.g. esteem under 0.05 at the 5% hugeness level or under 0.01 at the 1% criticalness level), then in light of the information, can be rejected at a pre-chosen centrality level (e.g. alpha = 5%) and the presence of a monotonic pattern can be acknowledged. Take note of that the test just permits the product client to finish up about the presence not about the extent of the pattern.
Slide 12Getting Results Using STORET Data Warehouse STORET Station Descriptions Stations by Geographic Location http://iaspub.epa.gov/stormoda/DW_stationcriteria Stations by Organization and Station ID http://iaspub.epa.gov/stormoda/DW_stationcriteria_STN
Slide 13Visualizing Results Transform content results to KML Keyhole Markup Language (KML) is a XML based dialect for portraying three-dimensional geospatial information and its show in application programs. KML is upheld in GoogleEarth, GoogleMaps and Microsoft VirtualEarth http://code.google.com/apis/kml/documentation
Slide 14Visualizing Results
Slide 15Visualizing Results
Slide 16Report Results http://iaspub.epa.gov/storpubl/storet_wme_pkg.Display_Station?p_station_id=SP-64&p_org_id=MWRD
Slide 17Report Results http://iaspub.epa.gov/stormoda/DW_resultcriteria_station http://iaspub.epa.gov/webservices/StoretResultService/index.html?operation=getResults
Slide 18Kendall Trend Analysis for pH
Slide 19Kendall Trend Analysis for Temperature
Slide 20Kendall Trend Analysis for Dissolved Oxygen
Slide 21Kendall Trend Analysis for Total Suspended Solids
Slide 22Kendall Trend Analysis for Turbidity
Slide 23Kendall Trend Analysis for Cadmium
Slide 24Kendall Trend Analysis for Zinc
Slide 25Indexing STORET stations to the NHD can expand refinement of pattern investigations Group destinations in respect to upstream NPDES releases Group utilizing Horton-Strahler stream orders Group as far as landcover examples utilizing NHDPlus LU/LC raster information
Slide 26Indexing and consolidating station comes about
Slide 27Next Steps Additional work on "pre-preparing" STORET station information to center consideration on stations with enough "information thickness" to bolster incline examinations Develop an "information shop" of R pattern examination comes about – including spared pictures of dissipate plots after some time from R Consider ways slant examinations can bolster either ace dynamic investigation of against debasement impacts [ Goal: recognize corruption slant at an opportune time and consider administration ventures to abstain from ending up with extra 303(d) lists] Also – utilize slant investigations as an instrument to record incremental advance in meeting targets built up under WQ Standards or the TMDL program
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