Statistics in WR: Lecture 22

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Presentation transcript:

Statistics in WR: Lecture 22 Key Themes Review of concepts from Lecture 1 Patterns in space and time from class exercises Principles for thinking about spatial and temporal variation

Data Cube – What, Where, When Space, L Time, T Variable, V D “When” A data value “Where” “What”

Continuous Space-Time Data Model -- NetCDF Time, T Coordinate dimensions {X} D Space, L Variable dimensions {Y} Variables, V

Discrete Space-Time Data Model Time, TSDateTime TSValue Space, FeatureID Variables, TSTypeID

Hydrologic Statistics Time Series Analysis Geostatistics Multivariate analysis How do we understand space-time correlation fields of many variables?

Ex 3: Patterns of Discharge, Sediment Concentration and Load

Ex 4: Correlation in space and time

Ex 5: Trends through Time

Ex 6: Diurnal and Seasonal Patterns

Ex 7: SPARROW Modeling of Nitrogen Transport Mean annual load of Total N

288 USGS sites with flow and Nitrogen data These sites are ones that were used for the Sparrow model that continue to be operational to 2008 http://water.usgs.gov/nawqa/sparrow/

Ex 8: Flood frequency analysis 100 year flood discharge p = 0.01

USGS Peak-Flow Regression Equations for 100-year discharge 100 mile2 10 mile2 110 mile2 What happens to design flood discharge when a 10 square mile tributary joins a 100 square mile river?

Ex 9: Time Evolution of Spatial Patterns

Texas Water Data Services 10 services 15,870 sites 81,625 series 8,549,857 records

Salinity Layer for Texas