Abstract

Flow duration curve (FDC) is a hydrologically representation of the statistical distribution of daily streamflows. For a long time, hydrologists have sought deeper understanding of the process controls on the shape of FDC, which has been a challenge due to contrasting processes controlling the fast flow and slow flow components of streamflow and their interactions. This dissertation addresses the challenge by outlining a novel framework to explore the physical controls on FDC. The framework involves separating streamflow into fast flow and slow flow and studying their duration curves separately then combining them statistically to obtain FDC. Initially, the potential of framework in modeling FDC from fast flow duration curve (FFDC) and slow flow duration curve (SFDC), is assessed over 245 catchments from MOPEX dataset. The FFDC and SFDC are constructed from time series of fast flow and slow flow i.e. obtained from baseflow separation method applied on observed streamflow data. The dependence of FFDC and SFDC components in catchments are captured by the Gumbel copula. The modeled and observed FDC are compared using the Cramér-von Mises test. The high p value over 245 catchments (i.e., 0.2 in average) represents the ability of framework in modeling FDC from FFDC and SFDC. In second step, the controls of climate and catchment characteristics on FDC is assessed through the extended framework. In the extended framework, streamflow is initially partitioned in time domain (i.e. wet and dry days), and further partitioned in process domain (i.e., fast flow and slow flow). The time partitioning of streamflow addresses the challenge that fast flow intermittency poses in accounting the statistical dependence between fast and slow flows. During wet days streamflow has both fast flow and slow flow, whereas during dry days, there is only slow flow as fast flow is zero. The FDC during wet days (FDCw) is computed as the statistical sum of FFDC and slow flow duration curve (SFDCw), considering their dependency i.e. defined by Kendall's t. Then, FDC is modeled as the mixture distribution of FDCw and slow flow duration curve during dry days (SFDCd), by considering the fraction of wet days (d) for perennial streams and both d and the fraction of days of zero streamflow for ephemeral streams. The control of climate and catchment characteristics on FDC are explored through the streamflow components i.e. FFDC, SFDCw, SFDCd, and two parameters of Kendall's t and d. To characterize fast flow and slow flow components, the Kappa distribution is fitted on FFDC, SFDCw, and SFDCd over 300 catchments from MOPEX catchments across the U.S. The relationships between estimated Kappa distribution parameters and climate and catchment characteristics show that the climate aridity index (AI), the coefficient of variation of daily precipitation (CV_p), timing of precipitation in relation to evaporation (e.g., seasonality), time interval between storms, snow, topographic slope, and slope of recession slope curve are dominant controlling factors. These findings have improved our understanding in controls of climate and landscape on regional patterns of FDC, however, due to the impact of site-specific factors (e.g., topography, soil, and land cover/land use) on streamflow variability, the controls of climate and landscape on FDC cannot be easily generalized to other catchments. Moreover, no effort has made to explore the control of processes attributed to runoff processes on FFDC, SFDCw, and SFDCd and their contributions to FDC. In light of findings in second step, we go one further step to explore the control of climate variables and runoff generation processes on middle part of FDC which is affected by a suit of complex processes. The AI and timing of precipitation are considered as two key climatic variables and timescale ratio of fast flow generation to slow flow generation (a_t) is used to characterize runoff processes. A hydrologic model driven by synthetic rainfall from rainfall model is used to explore the control of climate and runoff generation processes on FFDC, SFDCw, SFDCd and their contributions to the middle part of FDC. Results show that, the increase of a_t leads to increasing dependency between fast flow and slow flow, increasing the contribution of FFDC to the middle part of FDCw, and decreasing contribution of SFDCd to the middle part of FDC. The AI and timing of precipitation also control the middle part of FDC. The higher AI leads to smaller contribution of FFDC to FDCw and larger contribution of SFDCd to the middle part of FDC. Under given AI, the contribution of FFDC to the FDCw and the contribution of SFDCd to the middle part of FDC in out-of-phase and in-phase seasonality are larger than that of uniform seasonality.

Notes

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Graduation Date

2021

Semester

Spring

Advisor

Wang, Dingbao

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental and Construction Engineering

Degree Program

Civil Engineering

Format

application/pdf

Identifier

CFE0008923

Language

English

Release Date

November 2021

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Campus-only Access)

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