Geology topics

Elena Crowley-Ornelas

Publications and source records attributed to Elena Crowley-Ornelas.

3 recordsLinked to original sources

USGS2023

Generalized additive model estimation of no-flow fractions and L-moments to support flow-duration curve quantile estimation using selected probability distributions for bay and estuary restoration in the Gulf States

Censored and uncensored generalized additive models (GAMs) were developed using streamflow data from 941 U.S. Geological Survey streamflow-gaging stations (streamgages) to predict decadal statistics of daily streamflow for streams draining to the Gulf of Mexico. The modeled decadal statistics comprise no-flow fractions and L-moments of logarithms of nonzero streamflow for six decades (1950–2009). These statistics represent metrics of decadal flow-duration curves (dFDCs) derived from about 10 million daily mean streamflows. The L-moments comprise the mean, coefficient of L-variation, and the third through fifth L-moment ratios. The GAMs were fit to the statistics from 941 streamgages and 2,750 streamgage-decades by using watershed properties such as basin area and slope, decadal precipitation and temperature, and decadal values of flood storage and urban development percentages. The GAMs then estimated decadal statistics for 9,220 prediction locations (stream reaches) coincident with outlets of level-12 hydrologic unit codes. Both entire dataset (whole model) and leave-one-watershed-out model results are reported. No-flow fractions are censored data, and Tobit extensions to GAMs were used to model ephemeral streamflow conditions. Conversely, uncensored GAMs were used for estimation of the L-moments. The GAMs are shown, by coverage probabilities, to construct reliable 95-percent prediction limits. An example shows how no-flow fractions and L-moments may be used to approximate dFDCs by using selected probability distributions (mathematical formulas) including the asymmetric exponential power, generalized normal, and kappa distributions.

Alabama, Arkansas, Florida, Georgia, Kentucky, Lou↗

USGS2023

Investigating hydrologic alteration in the Pearl and Pascagoula River basins using rule-based model trees

Anthropogenic hydrologic alteration threatens the health of riverine ecosystems. Machine learning algorithms that employ the use of model trees to predict hydrologic alteration are underrepresented in related literature. This study assesses hydrologic alteration in the Pearl and Pascagoula River basins using modeled daily streamflow . Hydrologic alteration was determined by hypothesis testing and the computation of the net change across 60 years. Cubist models were developed for both basins to predict hydrologic alteration and to identify important basin characteristics. Results from net change and the hypothesis test indicated the basins were essentially identical with respect to the amount of hydrologic alteration. Cubist models for the basins successfully made accurate predictions of hydrologic alteration and demonstrated that the importance of basin geomorphology and land cover on alteration differed in both basins. The results of the study demonstrate the feasibility of model trees in assessing hydrologic alteration.

Mississippi↗

USGS2020

An analysis of streamflow trends in the southern and southeastern US from 1950-2015

In this article, the mean daily streamflow at 139 streamflow-gaging stations (sites) in the southern and southeastern United States are analyzed for spatial and temporal patterns. One hundred and thirty-nine individual time-series of mean daily streamflow were reduced to five aggregated time series of Z scores for clusters of sites with similar temporal variability. These aggregated time-series correlated significantly with a time-series of several climate indices for the period 1950–2015. The mean daily streamflow data were subset into six time periods—starting in 1950, 1960, 1970, 1980, 1990, and 2000, and each ending in 2015, to determine how streamflow trends at individual sites acted over time. During the period 1950–2015, mean monthly and seasonal streamflow decreased at many sites based on results from traditional Mann–Kendall trend analyses, as well as results from a new analysis (Quantile-Kendall) that summarizes trends across the full range of streamflows. A trend departure index used to compare results from non-reference with reference sites identified that streamflow trends at 88% of the study sites have been influenced by non-climatic factors (such as land- and water-management practices) and that the majority of these sites were located in Texas, Louisiana, and Georgia. Analysis of the results found that for sites throughout the study area that were influenced primarily by climate rather than human activities, the step increase in streamflow in 1970 documented in previous studies was offset by subsequent monotonic decreases in streamflow between 1970 and 2015. View Full-Text

Alabama, Arkansas, Florida, Georgia, Louisiana, Mi↗