Watershed Hydrology Modeling: What is Considered Calibrated?

1. Watershed Hydrology Modeling: What is Considered Calibrated? Presented by: Jeremy Wyss, HIT Tetra Tech 27th Annual Alabama Water Resources Conference Orange Beach, Alabama

2. Watershed Models • Play an important role in linking sources of pollutants to receiving waterbodies as point and nonpoint source loads. • Are driven by precipitation, land use, impervious area, slope, soil type and drainage area. • Are simplified mathematical representations of complex real world systems which make use of known interrelationships to predict change in response to perturbation of independent variable or forcing function from its current state. • To be considered credible, the ability of the model to represent real world conditions should be demonstrated through calibration/corroboration. • LSPC, HSPF, SWAT, WAM, SWMM, WARMF, etc…

3. Model Calibration/Corroboration • Calibration – parameter adjustment to achieve the best fit between model prediction and field observations. • Explicit focus on available stream gages at multiple locations in the watershed (each has it’s own uniqueness to capture). • Calibration is for a specific period of time. • Corroboration – utilizes the calibrated model for a different period of time at multiple locations in the watershed. • Investigates whether a models predictive capability is similar to that achieved over the calibration period. • Statistical and Graphical comparison of observed and simulated.

4. Hydrology Graphical Comparison Daily Average Flow (log scale) Daily Average Water Balance Monthly Average Regression Monthly Average Flow Balance Monthly Average Flow Average by Month Regression Average by Month Distribution Monthly Distribution Box Plot Daily Average Flow (normal scale) Daily Average Flow Duration Daily Average Flow Accumulation

5. Hydrology Statistical Comparison E = Nash-Sutcliffe Coefficient of Efficiency E’ = Garrick Baseline Adjusted Coefficient of Efficiency Used to assess the predictive power of hydrological models Range from -∞ to 1 -infinity < E < 0: The observed mean is a better predictor than the model Equal to 0: The long-term average of the observed data is just as good a predictor as the model Between 0 and 1: The closer the model efficiency is to 1, the more accurate the model is Obtaining a value of 0.7 or better is a reasonable indicator of adequate fit.

6. Hydrology Calibration Pitfalls

7. Hydrology Acceptance Criteria • How accurate is the model? • Is the model good enough for this evaluation?

8. Our “Rating” Approach • Assign weight to each statistical measure • Assign value to “Very Good”, “Good”, “Fair” and “Poor” ranges • Multiply weight by value and sum results to obtain quantitative score • Compare quantitative score to range to produce qualitative grade • Produce a summary of statistical bias (all +, all -, or mixed) • Process completed for Calibration/Validation on Period of Record and for each individual year

9. Next Steps • Add/Incorporate/Create and Standard Operation Procedure for visual component • Determine how to incorporate Modeling Efficiencies into Rating

10. Watershed Hydrology Modeling: What is Considered Calibrated? Comments/Questions?