Derivation of low flow frequency distributions under human activities and its implications

Gao, Shanjun; Liu, Pan; Pan, Zhengke; Ming, Bo; Guo, Shenglian; Xiong, Lihua

doi:10.1016/j.jhydrol.2017.03.071

Cited by 16 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4(a)) examined in the first case study is a large subbasin of the Yellow River basin located on the Loess Plateau (Xu, 2011). Several studies have reported the anthropogenic impacts of this area and demonstrated the changing relationship between precipitation and runoff (Gao et al, 2017;Jiao et al, 2017).…”

Section: Study Area: Wuding River Basinmentioning

confidence: 99%

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

Zhang¹,

Liu²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Although the parameters of hydrological models are usually regarded as constant, temporal variations can occur in a changing environment. Thus, effectively estimating time-varying parameters becomes a significant challenge. Following a survey of existing estimation methodologies, this paper describes a new method that combines (1) the basic concept of split-sample calibration (SSC), whereby parameters are assumed to be stable for one sub-period, and (2) the parameter continuity assumption, i.e., the differences between parameters in consecutive time steps are small. Dynamic programming is then used to determine the optimal parameter trajectory by considering two objective functions: maximization of simulation accuracy and maximization of parameter continuity. The efficiency of the proposed method is evaluated by two synthetic experiments, one with a simple two-parameter monthly model and the second using a more complex 15-parameter daily model. The results show that the proposed method is superior to SSC alone, and outperforms the ensemble Kalman filter if the proper sub-period length is used. An application to the Wuding River basin indicates that the soil water capacity parameter varies before and after 1972, which can be interpreted according to land use and land cover changes. Further application to the Xun River basin shows that parameters are generally stationary on an annual scale, but exhibit significant changes over seasonal scales. These results demonstrate that the proposed method is an effective tool for identifying time-varying parameters in a changing environment.

show abstract

Section: Study Area: Wuding River Basinmentioning

confidence: 99%

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

Zhang¹,

Liu²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Streamflows in dry season exhibit high autocorrelation, which could be explained using the linear reservoir model for base flow generation. Base flow at the catchment scale could be represented as [36,37] Based on bivariate copulas and their conditional forms, the density distribution of an n-dimension canonical vine copula can be expressed as [22]:…”

Section: Monthly River Flow Simulation Using Canonical Vine Copulasmentioning

confidence: 99%

Section: Monthly River Flow Simulation Using Canonical Vine Copulasmentioning

confidence: 99%

A Stochastic Simulation Model for Monthly River Flow in Dry Season

Wang

Dong

Zhu

et al. 2018

Water

View full text Add to dashboard Cite

Streamflow simulation gives the major information on water systems to water resources planning and management. The monthly river flows in dry season often exhibit high autocorrelation.The headwater catchment of the Yellow River basin monthly flow series in dry season exhibits this clearly. However, existing models usually fail to capture the high-dimensional, nonlinear dependence. To address this issue, a stochastic model is developed using canonical vine copulas in combination with nonlinear correlation coefficients. Kendall's tau values of different pairs of river flows are calculated to measure the mutual correlations so as to select correlated streamflows for every month. Canonical vine copula is used to capture the temporal dependence of every month with its correlated streamflows. Finally, monthly river flow by the conditional joint distribution functions conditioned upon the corresponding river flow records was generated. The model was applied to the simulation of monthly river flows in dry season at Tangnaihai station, which controls the streamflow of headwater catchment of Yellow River basin in the north of China. The results of the proposed method possess a smaller mean absolute error (MAE) than the widely-used seasonal autoregressive integrated moving average model. The performance test on seasonal distribution further verifies the great capacity of the stochastic-statistical method. IntroductionWith the global population continuing to increase, water resources are becoming ever more vital by more demand for urbanization and agricultural intensification [1,2]. In water resources planning, streamflow simulation in dry season is a paramount process in water and drought management, determination of river water flow potentials, environmental flow analysis, agricultural practices, and hydro-power generation [3,4].Compared with models which consider relatively steady physiographic, geological, soil, land use, and plant cover attributes in a site or watershed, the statistical models are simpler and more reliable for their principle of identifying relations between output variables with their predictors without any explicit knowledge of the physical processes [5,6]. The traditional statistical model consists of the parametric and nonparametric models. The most famous parametric models are autoregressive moving average models and autoregressive integrated moving average models proposed by Box and Jenkins [7]. They are established based on the linear regression method with auto-correlation function and partial autocorrelation function. The models and their variants are widely used for their practical nature, but also show limits such as normal assumption and linearity or inaccuracy coursed by transformation [8]. Lall and Sharma [9] proposed the nonparametric model of nearest neighbor resampling method to fit the skewed marginal distribution and nonlinear structure of river flow. This kind of nonparametric model performs well in inheriting statistic features of historical record in case of the high-class dataset...

show abstract

“…Low-flow frequency analysis is a stochastic approach to characterize and predict low-flow events. It quantifies the likelihood of extended periods of low flow at specific sites and durations, aiding in estimating water availability during critical periods such as droughts [ [16] , [17] , [18] ]. Furthermore, regional flow analysis of ungauged catchments has garnered significant attention recently [ 8 , 9 , 14 , 19 ], highlighting the importance of understanding flow dynamics beyond monitored sites.…”

Section: Introductionmentioning

confidence: 99%

Quantifying regional low flows under data scarce conditions

Mengistu,

Chung,

Talpur

et al. 2024

Heliyon

View full text Add to dashboard Cite

Derivation of low flow frequency distributions under human activities and its implications

Cited by 16 publications

References 38 publications

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

A time-varying parameter estimation approach using split-sample calibration based on dynamic programming

A Stochastic Simulation Model for Monthly River Flow in Dry Season

Quantifying regional low flows under data scarce conditions

Contact Info

Product

Resources

About