Paper No. JAWRA-07-0013-P of the Journal of the American Water Resources Association (JAWRA). Discussions are open until June 1, 2009.
Characterizing Storm Hydrograph Rise and Fall Dynamics With Stream Stage Data1
Version of Record online: 29 OCT 2008
© 2008 American Water Resources Association. No claim to original U.S. government works
JAWRA Journal of the American Water Resources Association
Volume 44, Issue 6, pages 1431–1440, December 2008
How to Cite
Shuster, W.D., Zhang, Y., Roy, A.H., Daniel, F.B. and Troyer, M. (2008), Characterizing Storm Hydrograph Rise and Fall Dynamics With Stream Stage Data. JAWRA Journal of the American Water Resources Association, 44: 1431–1440. doi: 10.1111/j.1752-1688.2008.00249.x
- Issue online: 1 DEC 2008
- Version of Record online: 29 OCT 2008
- Received January 24, 2007; accepted March 31, 2008.
- storm flow;
- rise rate;
- flow regime
Abstract: Storm-flow transients (i.e., hydrograph rise and fall dynamics) may represent an important aspect of understanding streamflow dynamics. However, little is known about how temporal resolution of transient data and climate variability may color these potential indicators of hydrologic pattern or condition. Warm-season stream stage and rainfall were monitored continuously (5 min) during the 2002 water year in eight tributaries of the Little Miami River (Ohio), which drain 17-58 km2 catchments. Rise rates generated using 5-min data were different than those generated with mean daily data [calculated with the Indicators of Hydrologic Alteration (IHA) software], though fall rates were similar for fine and coarse temporal data. This result suggests that data with low temporal resolution may not be adequate to fully represent the dynamics of storm rise rates. Conversely, fall rates based on daily stage data (via IHA) were similar to those based on the 5-min data, and so daily mean data may be appropriate for characterizing fall rates. We next analyzed the possible correlations between rainfall variability and storm-flow stage dynamics. We derived rise and recession rates from storm stage hydrographs by assuming exponential rise and decay of a runoff peak. We found that raw rise rates (Rraw) were correlated with both the maximum rainfall rate and the time to the centroid of a rain event. We subsequently removed the trend based on these rainfall characteristics, which yielded new representations of rise rates abbreviated as Rrate and Rtcent, respectively, and that had lower variability than the uncorrected (raw) data. Fall rates were found to be independent of rainfall characteristics. Due to the predominant influence of stream hydrology upon aquatic biota and nutrient fluxes, our work suggests that these stage data analysis protocols can refine or otherwise reduce variability in these indices by accounting for relevant factors such as rainfall forcing. These protocols for derivation of transient indices should be tested for their potential to improve correlations between stream hydrology and temporally aligned biotic data and dissolved nutrient fluxes in streams.