Geology topics

David R. Legates

Publications and source records attributed to David R. Legates.

3 recordsLinked to original sources

USGS2012

A refined index of model performance: a rejoinder

Willmott et al. [Willmott CJ, Robeson SM, Matsuura K. 2012. A refined index of model performance. International Journal of Climatology , forthcoming. DOI:10.1002/joc.2419.] recently suggest a refined index of model performance ( d r ) that they purport to be superior to other methods. Their refined index ranges from − 1.0 to 1.0 to resemble a correlation coefficient, but it is merely a linear rescaling of our modified coefficient of efficiency ( E 1 ) over the positive portion of the domain of d r . We disagree with Willmott et al. ( 2012 ) that d r provides a better interpretation; rather, E 1 is more easily interpreted such that a value of E 1 = 1.0 indicates a perfect model (no errors) while E 1 = 0.0 indicates a model that is no better than the baseline comparison (usually the observed mean). Negative values of E 1 (and, for that matter, d r < 0.5) indicate a substantially flawed model as they simply describe a ‘level of inefficacy’ for a model that is worse than the comparison baseline. Moreover, while d r is piecewise continuous, it is not continuous through the second and higher derivatives. We explain why the coefficient of efficiency ( E or E 2 ) and its modified form ( E 1 ) are superior and preferable to many other statistics, including d r , because of intuitive interpretability and because these indices have a fundamental meaning at zero. We also expand on the discussion begun by Garrick et al. [Garrick M, Cunnane C, Nash JE. 1978. A criterion of efficiency for rainfall-runoff models. Journal of Hydrology 36 : 375-381.] and continued by Legates and McCabe [Legates DR, McCabe GJ. 1999. Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research 35 (1): 233-241.] and Schaefli and Gupta [Schaefli B, Gupta HV. 2007. Do Nash values have value? Hydrological Processes 21 : 2075-2080. DOI: 10.1002/hyp.6825.]. This important discussion focuses on the appropriate baseline comparison to use, and why the observed mean often may be an inadequate choice for model evaluation and development.

International Journal of Climatology↗

USGS2010

Variability and trends in dry day frequency and dry event length in the southwestern United States

Daily precipitation from 22 National Weather Service first-order weather stations in the southwestern United States for water years 1951 through 2006 are used to examine variability and trends in the frequency of dry days and dry event length. Dry events with minimum thresholds of 10 and 20 consecutive days of precipitation with less than 2.54 mm are analyzed. For water years and cool seasons (October through March), most sites indicate negative trends in dry event length (i.e., dry event durations are becoming shorter). For the warm season (April through September), most sites also indicate negative trends; however, more sites indicate positive trends in dry event length for the warm season than for water years or cool seasons. The larger number of sites indicating positive trends in dry event length during the warm season is due to a series of dry warm seasons near the end of the 20th century and the beginning of the 21st century. Overall, a large portion of the variability in dry event length is attributable to variability of the El Niño–Southern Oscillation, especially for water years and cool seasons. Our results are consistent with analyses of trends in discharge for sites in the southwestern United States, an increased frequency in El Niño events, and positive trends in precipitation in the southwestern United States.

Journal of Geophysical Research D: Atmospheres↗

USGS1999

Evaluating the use of “goodness‐of‐fit” measures in hydrologic and hydroclimatic model validation

Correlation and correlation‐based measures (e.g., the coefficient of determination) have been widely used to evaluate the “goodness‐of‐fit” of hydrologic and hydroclimatic models. These measures are oversensitive to extreme values (outliers) and are insensitive to additive and proportional differences between model predictions and observations. Because of these limitations, correlation‐based measures can indicate that a model is a good predictor, even when it is not. In this paper, useful alternative goodness‐of‐fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation‐based measures are discussed. Modifications to these statistics to aid in interpretation are presented. It is concluded that correlation and correlation‐based measures should not be used to assess the goodness‐of‐fit of a hydrologic or hydroclimatic model and that additional evaluation measures (such as summary statistics and absolute error measures) should supplement model evaluation tools.

Water Resources Research↗