University of Texas at Austin Title: Space-Time Analysis of Water Availability Model Data in GIS Statistical Analysis of Naturalized Flows in Texas’ Water Availability Model Considerations on the impact of trends in the naturalized flows of Texas’ Water Availability Model (WAM), as evidenced by statistical analysis. Clark Siler, 28 Apr 2009 CE 397 – Statistics in Water Resources University of Texas at Austin

Presentation Outline Introduction Correlation and Trend Analysis What is a WAM What are Naturalized Flows Correlation and Trend Analysis Pearson’s r Kendall’s Tau Results Conclusions and Possible Future Work Presentation Outline

Water Availability Model (WAM) Texas’ Water Availability Model consists of: Water Rights Analysis Package Suite of programs to digitally manage water rights Based on probabilistic and statistical analyses Developed by Dr. Ralph Wurbs at Texas A&M Input datasets for Texas’ 23 River Basins This includes the naturalized flow datasets Wurbs (2005)

Uses of WAM Texas Commission on Environmental Quality (TCEQ) Assigning new or adjusting existing water right permits Determine if sufficient water to meet proposed use Evaluate impact of use on other water users Consultants and Water Management Entities Local and regional planning studies Preparing permit applications Texas Water Development Board (TWDB) Statewide planning Wurbs (2005)

Naturalized Flows (Analytical) NF naturalized flow GF gaged flow D water supply diversions upstream RF return flow upstream EP reservoir evaporation minus precipitation DS change in storage in upstream reservoirs Goal is homogeneous flow dataset NF = GF + SDi – SRFi + SEPi + SDSi NF GF D RF EP DS

Naturalized Flows ?

Purpose of Study Goal of naturalized flows is homogeneity Trends may result from many causes Climate change El Niño-Southern Oscillation (ENSO) Incorrect calculations (not considered) Trended data are not homogeneous WRAP does not handle trended data Results from such are not considered representative

Neches Basin Naturalized Flows: 1940-1997 WRAP

Correlation (Pearson’s) to WRAP WRAP to WRAP WRAP

Trend Analysis: Kendall’s Tau, t Non-parametric Statistic (no assumptions on distribution) Based on rank; tests monotonicity H0: no correlation between time and flow Rejected (or not) based on comparison with standard normal calculations Can address seasonality through careful data separation and processing

Overall Kendall Results Regression using time Only one location rejected H0 WRAP to time

Seasonal Kendall Results Despite many individual seasons having H0 rejected, only one additional location is rejected overall WRAP to time

Conclusions Kendall’s Tau provides non-parametric statistic Failed to reject H0 for majority of Neches gages Suggests homogeneity of dataset Kendall’s test is for monotonicity; other trends may still exist (e.g. longer-term seasonality, ENSO)

Possible Future Work Working to detect cycles (such as ENSO) following process outlined by Wurbs Comparing Spearman to Kendall rank correlation Exploring applications of Pearson (parametric) correlation to Neches datasets Represent results effectively in GIS Analyzing other Texas basins

Personal Information Clark Siler Graduate Student University of Texas at Austin Environmental Water Resources Engineering (EWRE) Center for Research in Water Resources BS – Brigham Young University Civil Engineering MS – University of Texas at Austin EWRE clarksiler@mail.utexas.edu Personal Information (Presenter) Nov 2008

Additional Information

San Diego MSL

Codes

Tables WRAP to time WRAP to time