2022
DOI: 10.3390/w14101525
|View full text |Cite
|
Sign up to set email alerts
|

Identifying Spatial Patterns of Hydrologic Drought over the Southeast US Using Retrospective National Water Model Simulations

Abstract: Given the sensitivity of natural environments to freshwater availability in the Southeast US, as well as the reliance of many municipal and commercial water consumers on surface water supplies, specific issues related to low river streamflow are apparent. As a result, the need for quantifying the spatial distribution, frequency, and intensity of low flow events (a.k.a., hydrologic drought) is critical to define areas most susceptible to water shortages and subsequent environmental and societal risk. To that en… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
4
1

Year Published

2022
2022
2024
2024

Publication Types

Select...
6

Relationship

1
5

Authors

Journals

citations
Cited by 10 publications
(5 citation statements)
references
References 28 publications
0
4
1
Order By: Relevance
“…This indicates good agreement of the model data to real conditions. Some studies suggest the inclusion of at least fourth-order nodes in the analysis, due to lower NWM performance in lower-order nodes (Dyer et al, 2022;; however, results from this work indicate that third-order nodes are not significantly different from higher order nodes in terms of spatial and temporal patterns. As indicated by , NWM data-driven approaches might not be highly accurate in terms of point-to-point data values, but perform well in highlighting the regions likely to be at risk of high magnitude streamflow event occurrence.…”
Section: Trendscontrasting
confidence: 59%
“…This indicates good agreement of the model data to real conditions. Some studies suggest the inclusion of at least fourth-order nodes in the analysis, due to lower NWM performance in lower-order nodes (Dyer et al, 2022;; however, results from this work indicate that third-order nodes are not significantly different from higher order nodes in terms of spatial and temporal patterns. As indicated by , NWM data-driven approaches might not be highly accurate in terms of point-to-point data values, but perform well in highlighting the regions likely to be at risk of high magnitude streamflow event occurrence.…”
Section: Trendscontrasting
confidence: 59%
“…The maximal number of episodes increased from 200 for Q 10 to 300 for Q obj , which translates to an average of 8.4 days per episode for Q obj and 6.3 days per episode for Q 10 per year. The low flows identified by the objective method are longer, which allows for the inclusion of periods occurring in streamflow, even when additional criteria, of a minimal time of 7 days, are applied [12,38].…”
Section: Resultsmentioning
confidence: 99%
“…This sometimes leads to a large discretization of the flow values, i.e., the lowest flows can have repeated values in a dataset. If the data uniqueness is greater than 90%, then the use of the Q 10 as the threshold will represent statistical information; however, in some cases, for example with the National Water Model (NWM) retrospective data, minimum streamflow values are often repeated for extended periods [38]. In this case, the FDC flattens out on the lower flows, with the 10th percentile being equivalent to higher percentile values (i.e., 15th or 20th percentile).…”
Section: Introductionmentioning
confidence: 99%
“…On these, the US EPA 7Q10 approach was used to calculate the annual‐minimum 7‐day average flow discharge that occurs with a 10‐year recurrence interval (i.e. 7Q10) (Dyer et al, 2022). 7Q10 has been used in Australia, Europe and the United States (Petheram et al, 2008; Stanton et al, 2007).…”
Section: Regional Setting and Methodsmentioning
confidence: 99%