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 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.

Low flow frequency analysis for stream with mixed populations

2015 ◽  
Vol 42 (8) ◽  
pp. 503-509 ◽  
Author(s):  
Mike Hulley ◽  
Colin Clarke ◽  
Ed Watt
Keyword(s):  
Frequency Analysis ◽  
Worked Examples ◽  
Low Flow ◽  
Flow Estimation ◽  
Low Flows ◽  
Record Length ◽  
Mixed Populations ◽  
Flow Frequency ◽  
Recommendations For Assessment

A methodology is developed for the estimation of annual low-flow quantiles for streams with annual low flows occurring in both the summer and winter. Since the low flow generating processes are different in summer and winter, independent seasonal analyses are required. The methodology provides recommendations for assessment of record length, randomness, homogeneity, independence and stationarity, as well as guidelines for distribution selection and fitting for seasonal distributions. The seasonal distributions are then used to develop the combined distribution for annual low flow estimation. Four worked examples of long-term Canadian hydrometric stations are provided.

scholarly journals Controls on hydrologic drought duration in near-natural streamflow in Europe and the USA

2016 ◽  
Vol 20 (10) ◽  
pp. 4043-4059 ◽  
Author(s):  
Erik Tijdeman ◽  
Sophie Bachmair ◽  
Kerstin Stahl
Keyword(s):  
Large Scale ◽  
Aridity Index ◽  
Base Flow ◽  
Classification Systems ◽  
Flow Index ◽  
Climate Classification ◽  
Drought Duration ◽  
Base Flow Index ◽  
The Usa ◽  
Hydrologic Drought

Abstract. Climate classification systems, such as Köppen–Geiger and the aridity index, are used in large-scale drought studies to stratify regions with similar hydro-climatic drought properties. What is currently lacking is a large-scale evaluation of the relation between climate and observed streamflow drought characteristics. In this study we explored how suitable common climate classifications are for differentiating catchments according to their characteristic hydrologic drought duration and whether drought durations within the same climate classes are comparable between different regions. This study uses a dataset of 808 near-natural streamflow records from Europe and the USA to answer these questions. First, we grouped drought duration distributions of each record over different classes of four climate classification systems and five individual climate and catchment controls. Then, we compared these drought duration distributions of all classes within each climate classification system or classification based on individual controls. Results showed that climate classification systems that include absolute precipitation in their classification scheme (e.g., the aridity index) are most suitable for differentiating catchments according to drought duration. However, differences in duration distributions were found for the same climate classes in Europe and the USA. These differences are likely caused by differences in precipitation, in catchment controls as expressed by the base flow index and in differences in climate beyond the total water balance (e.g., seasonality in precipitation), which have been shown to exert a control on drought duration as well. Climate classification systems that include an absolute precipitation control can be tailored to drought monitoring and early warning systems for Europe and the USA to define regions with different sensitivities to hydrologic droughts, which, for example, have been found to be higher in catchments with a low aridity index. However, stratification of catchments according to these climate classification systems is likely to be complemented with information of other climate classification systems (Köppen–Geiger) and individual climate and catchment controls (precipitation and the base flow index), especially in a comparative study between Europe and the USA.

scholarly journals A method for low-flow estimation at ungauged sites: a case study in Wallonia (Belgium)

2013 ◽  
Vol 17 (4) ◽  
pp. 1319-1330 ◽  
Author(s):  
M. Grandry ◽  
S. Gailliez ◽  
C. Sohier ◽  
A. Verstraete ◽  
A. Degré
Keyword(s):  
Return Period ◽  
Management Practice ◽  
Global Model ◽  
Low Flow ◽  
Quality Analysis ◽  
Flow Index ◽  
Physical Parameters ◽  
Integrated Water Management ◽  
Explanatory Variables ◽  
Ungauged Sites

Abstract. Well-integrated water management can notably require estimating low flows at any point of a river. Depending on the management practice, it can be needed for various return periods. This is seldom addressed in the literature. This paper shows the development of a full analysis chain including quality analysis of gauging stations, low-flow frequency analysis, and building of a global model to assess low-flow indices on the basis of catchment physical parameters. The most common distributions that fit low-flow data in Wallonia were two-parameter lognormal and gamma. The recession coefficient and percolation were the most explanatory variables, regardless of the return period. The determination coefficients of the models ranged from 0.51 to 0.67 for calibration and from 0.61 to 0.80 for validation. The regression coefficients were found to be linked to the return period. This was used to design a complete equation that gives the low-flow index based on physical parameters and the desired return period (in a 5 to 50 yr range). The interest of regionalisation and the development of regional models are also discussed. Four homogeneous regions are identified, but to date the global model remains more robust due to the limited number of 20-yr-long gauging stations. This should be reconsidered in the future when enough data will be available.

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