Estimation of baseflow considering recession characteristics of hydrograph

Jung, Younghun; Lim, Kyoung Jae; Kim, Hung-Soo

doi:10.17663/jwr.2014.16.2.161

Cited by 3 publications

(5 citation statements)

References 18 publications

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“…There is a limitation from using single preset parameters for entire study period when separating the baseflow from streamflow because the characteristics of streamflow cannot be represented with one a parameter − BFI max set for long‐term period. Also the recession trend could vary depending on the season and flow condition (Yang et al., 2019)

b_{k} goodbreak= \frac{()(, 1 goodbreak- {BFI}_{\max}) {ab}_{k - 1} + ()(, 1 goodbreak- a) {BFI}_{\max} y_{k}}{1 - a {BFI}_{\max}},

where

a

is the a parameter,

b_{k}

is the quantity of baseflow at Day k , and

y_{k}

is the quantity of direct runoff at Day k .…”

Section: Methodsmentioning

confidence: 99%

“…Baseflow is typically associated with aquifer characteristics, so the MRC is utilized for calculating the a parameter. For constructing MRCs, the adapted matching strip method (Posavec et al., 2006; Yang et al., 2019) was used to grasp the representative recession trends. The parameters estimation has consisted of three automated processes: Extract recession curves from continuous streamflow data. Array recession curves using the matching strip method. Derive MRC and calculate recession coefficient ( k ) using the gradient descent algorithm. …”

Section: Methodsmentioning

confidence: 99%

Section: A Parametermentioning

confidence: 99%

“…According to Yang et al. (2019), as a base material for this study, the necessity of seasonality and flow condition considerations has been demonstrated regarding that the recession analysis result using MRC was varied depending on the seasons and flow conditions. Therefore, using one parameter set (e.g., a = 0.98, BFI max = 0.80, which are default) for separating baseflow from RDF can derive an underestimated and overestimated baseflow for the flood wet season and dry season respectively.…”

Section: Introductionmentioning

confidence: 99%

“…If precipitation is concentrated in a certain period such as the rainy season, the hydrologic behavior (i.e., streamflow, groundwater recharge, soil water, and lateral flow) can be influenced by the precipitation pattern, such as rainfall intensity, that can be regarded as the streamflow seasonality (Gnann et al, 2019). According to Yang et al (2019), as a base material for this study, the necessity of seasonality and flow condition considerations has been demonstrated regarding that the recession analysis result using MRC was varied depending on the seasons and flow conditions. Therefore, using one parameter set (e.g., a = 0.98, BFI max = 0.80, which are default) for separating baseflow from RDF can derive an underestimated and overestimated baseflow for the flood wet season and dry season respectively.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Development of web‐based hydrograph analysis tool considering seasonality and flow condition

Yang,

Lee,

Kim

et al. 2023

J American Water Resour Assoc

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Baseflow was proven to be the most unpredictable component of streamflow through various research. However, the recent method for estimating baseflow is due to the development of theoretical and computational techniques. This paper attempted to develop a fully automated baseflow separation system based on a recursive digital filter with an optimization algorithm for the single separation. Most of the previous baseflow separation methods use a single set of a parameter and BFImax (the maximum value of baseflow index), which can underestimate or overestimate the baseflow; however, the system developed in this study estimates multiple optimized a parameters using seasonality and flow conditions and uses them for BFImax calculation and baseflow separation. This system derived baseflow results in better understanding of watershed and streamflow tendency characteristics. This study developed a Web‐based Hydrograph Analysis Tool 2020 with a user‐friendly interface and new separation method regarding the seasonality and flow conditions with a fully automated python module to optimize a parameters and BFImax. The application to the two area show diverse parameter sets and different baseflow according to seasonality and flow conditions representing the flow characteristics. This study could be a fundamental tool for detailed watershed management decisions regarding water security in the dry season or environmental water for aquatic ecosystems.

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b_{k} goodbreak= \frac{()(, 1 goodbreak- {BFI}_{\max}) {ab}_{k - 1} + ()(, 1 goodbreak- a) {BFI}_{\max} y_{k}}{1 - a {BFI}_{\max}},

where

a

is the a parameter,

b_{k}

is the quantity of baseflow at Day k , and

y_{k}

is the quantity of direct runoff at Day k .…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: A Parametermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Development of web‐based hydrograph analysis tool considering seasonality and flow condition

Yang,

Lee,

Kim

et al. 2023

J American Water Resour Assoc

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Analysis of Spatiotemporal Changes in Groundwater Recharge and Baseflow using SWAT and BFlow Models

Lee¹,

Park²,

Jung³

et al. 2014

Journal of Korean Society on Water Environment

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Occurrence frequency of flood and drought tends to increase in last a few decades, leading to social and economic damage since the abnormality of climate changes is one of the causes for hydrologic facilities by exceedance its designed tolerance. Soil and Water Assessment Tool (SWAT) model was used in the study to estimate temporal variance of groundwater recharge and baseflow. It was limited to consider recession curve coefficients in SWAT model calibration process, thus the recession curve coefficient was estimated by the Baseflow Filter Program (BFLOW) before SWAT model calibration. Precipitation data were estimated for 2014 to 2100 using three models which are GFDL-ESM2G, IPSL-CM5A-LR, and MIROC-ESM with Representative Concentration Pathways (RCP) scenario. SWAT model was calibrated for the Soyang watershed with NSE of 0.83, and R 2 of 0.89. The percentage to precipitation of groundwater recharge and baseflow were 27.6% and 17.1% respectively in 2009. Streamflow, groundwater recharge, and baseflow were estimated to be increased with the estimated precipitation data. GFDL-ESM2g model provided the most large precipitation data in the 2025s, and IPSL-CM5A-LR provided the most large precipitation data in the 2055s and 2085s. Overall, groundwater recharge and baseflow displayed similar trend to the estimated precipitation data.

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A Method to Filter Out the Effect of River Stage Fluctuations using Time Series Model for Forecasting Groundwater Level and its Application to Groundwater Recharge Estimation

Yoon¹,

Park²,

Kim³

et al. 2015

Journal of Soil and Groundwater Environment

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A method to filter out the effect of river stage fluctuations on groundwater level was designed using an artificial neural network-based time series model of groundwater level prediction. The designed method was applied to daily groundwater level data near the Gangjeong-Koryeong Barrage in the Nakdong river. Direct prediction time series models were successfully developed for both cases of before and after the barrage construction using past measurement data of rainfall, river stage, and groundwater level as inputs. The correlation coefficient values between observed and predicted data were over 0.97. Using the time series models the effect of river stage on groundwater level data was filtered out by setting a constant value for river stage inputs. The filtered data were applied to the hybrid water table fluctuation method in order to estimate the groundwater recharge. The calculated ratios of groundwater recharge to precipitation before and after the barrage construction were 11.0% and 4.3%, respectively. It is expected that the proposed method can be a useful tool for groundwater level prediction and recharge estimation in the riverside area.

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Estimation of baseflow considering recession characteristics of hydrograph

Cited by 3 publications

References 18 publications

Development of web‐based hydrograph analysis tool considering seasonality and flow condition

Development of web‐based hydrograph analysis tool considering seasonality and flow condition

Analysis of Spatiotemporal Changes in Groundwater Recharge and Baseflow using SWAT and BFlow Models

A Method to Filter Out the Effect of River Stage Fluctuations using Time Series Model for Forecasting Groundwater Level and its Application to Groundwater Recharge Estimation

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