Low-flow frequency estimation using base-flow measurements

1985 ◽  
Author(s):  
J.R. Stedinger ◽  
W.O. Thomas
Keyword(s):  
Frequency Estimation ◽  
Base Flow ◽  
Low Flow ◽  
Flow Measurements ◽  
Flow Frequency

scholarly journals Multiple Causes of Nonstationarity in the Weihe Annual Low Flow Series

2017 ◽  
Author(s):  
Bin Xiong ◽  
Lihua Xiong ◽  
Jie Chen ◽  
Chong-Yu Xu ◽  
Lingqi Li
Keyword(s):  
Frequency Analysis ◽  
Driving Forces ◽  
Aridity Index ◽  
Base Flow ◽  
Low Flow ◽  
Flow Index ◽  
Time Varying ◽  
Explanatory Variables ◽  
Flow Generation ◽  
Flow Frequency

Abstract. Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced a variety of non-stationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, a nonstationary framework of low-flow frequency analysis has been developed on basis of the Generalized Linear Model (GLM) to consider time-varying distribution parameters. In GLMs, the candidate explanatory variables to explain the time-varying parameters are comprised of the eight measuring indices of the climate and catchment conditions in low flow generation, i.e., total precipitation (P), mean frequency of precipitation events (λ), temperature (T), potential evapotranspiration (ET), climate aridity index (AIET), base-flow index (BFI), recession constant (K) and the recession-related aridity index (AIK). This framework was applied to the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China. Stepwise regression analysis was performed to obtain the best subset of those candidate explanatory variables for the final optimum model. The results show that the inter-annual variability in the variables of those selected best subsets plays an important role in modeling annual low flow series. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that AIK is of the highest relative importance among the best subset of eight candidates, followed by BFI and AIET. The incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to predict future occurrences of low-flow extremes in similar areas.

scholarly journals Estimation of recharge in mountain hard-rock aquifers based on discrete spring discharge monitoring during base-flow recession

2021 ◽  
Author(s):  
Stefano Segadelli ◽  
Maria Filippini ◽  
Anna Monti ◽  
Fulvio Celico ◽  
Alessandro Gargini
Keyword(s):  
Hard Rock ◽  
Monitoring Program ◽  
Base Flow ◽  
Low Flow ◽  
Reliable Estimate ◽  
Spring Discharge ◽  
Aquifer Recharge ◽  
Clear Cut ◽  
Groundwater Circulation ◽  
Hard Rock Aquifers

AbstractEstimation of aquifer recharge is key to effective groundwater management and protection. In mountain hard-rock aquifers, the average annual discharge of a spring generally reflects the vertical aquifer recharge over the spring catchment. However, the determination of average annual spring discharge requires expensive and challenging field monitoring. A power-law correlation was previously reported in the literature that would allow quantification of the average annual spring discharge starting from only a few discharge measurements in the low-flow season, in a dry summer climate. The correlation is based upon the Maillet model and was previously derived by a 10-year monitoring program of discharge from springs and streams in hard-rock aquifers composed of siliciclastic and calcareous turbidites that did not have well defined hydrogeologic boundaries. In this research, the same correlation was applied to two ophiolitic (peridotitic) hard-rock aquifers in the Northern Apennines (Northern Italy) with well-defined hydrogeologic boundaries and base-outflow springs. The correlation provided a reliable estimate of the average annual spring discharge thus confirming its effectiveness regardless of bedrock lithology. In the two aquifers studied, the measurable annual outputs (i.e. sum of average annual spring discharges) could be assumed equal to the annual inputs (i.e. vertical recharge) based on the clear-cut aquifer boundaries and a quick groundwater circulation inferable from spring water parameters. Thus, in such setting, the aforementioned correlation also provided an estimate of the annual aquifer recharge allowing the assessment of coefficients of infiltration (i.e. ratio between aquifer recharge and total precipitation) ranging between 10 and 20%.

scholarly journals Multiple causes of nonstationarity in the Weihe annual low-flow series

2018 ◽  
Vol 22 (2) ◽  
pp. 1525-1542 ◽  
Author(s):  
Bin Xiong ◽  
Lihua Xiong ◽  
Jie Chen ◽  
Chong-Yu Xu ◽  
Lingqi Li
Keyword(s):  
Frequency Analysis ◽  
Driving Forces ◽  
Potential Evapotranspiration ◽  
Aridity Index ◽  
Base Flow ◽  
Low Flow ◽  
Distribution Model ◽  
Flow Index ◽  
Explanatory Variables ◽  
Distribution Parameters

Abstract. Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

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