Base Flow Index Estimation on Gauged and Ungauged Catchments in Hungary Using Digital Filter, Multiple Linear Regression and Artificial Neural Networks

Jolánkai, Zsolt; Koncsos, L.

doi:10.3311/ppci.10518

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Due to topographic and soil conditions, overland surface runoff from the catchment was neglected. This is supported by the fact, that the annual surface runoff is estimated to be ~2 % of annual P (550 mm/y) in the embodying catchment: i) the total (surface-subsurface) runoff is only ~7 % of P (data source: General Directorate of Water Management), and ii) the baseflow index (ratio of subsurface runoff to the total runoff) is more than 70 % [44].…”

Section: Boundary Conditions (Bcs)mentioning

confidence: 96%

Long-term Water Regime Studies of a Degraded Floating Fen in Hungary

Decsi

Ács

Kozma

2020

Period. Polytech. Civil Eng.

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Historical trends in water management and recent climatic variations put wetlands in the Carpathian basin under strong pressure and led to their degradation. The lack of extended site specific environmental data series inhibits the understanding of long-term eco-hydrological processes. This undermines the success of restoration and/or management efforts. As a precedent we analyzed a recently degraded Hungarian lowland wetland, the Nyárjas fen in order to identify the main cause of its drying. Our method is based on one-dimensional simulations of a variably saturated soil column representing the dominant hydrological conditions of the wetland. To properly define the necessary soil hydraulic parameters, soil sampling, laboratory measurements and inverse modelling were carried out. The hydrological simulations for the 1961-2010 period clearly suggest that (i) the wetland degraded due to a temporal unfavorable combination of regional groundwater depletion and decreased precipitation, (ii) and could not recover afterwards despite the improvement of hydrological conditions. The ecological water demand of the Nyárjas fen can be explicitly expressed in terms of groundwater level. However, water availability is a necessary, but not sufficient criteria of good habitat status. The elaborated methodology provides the basis of bottom-up type environmental water demand estimation on a regional/national scale.

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Section: Boundary Conditions (Bcs)mentioning

confidence: 96%

Long-term Water Regime Studies of a Degraded Floating Fen in Hungary

Decsi

Ács

Kozma

2020

Period. Polytech. Civil Eng.

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“…One of the standard methods in multivariate analysis is the multiple linear regression model (Kadam et al, 2019). A linear relationship is established between the independent variable and one or more dependent variables (Jolánkai and Koncsos, 2018). In the multiple linear regression, the parameters of a linear model are estimated using an objective function and the values of the variables (Zhang et al, 2020).…”

Section: The Multiple Linear Regression Modelmentioning

confidence: 99%

Estimation of seasonal and annual river flow volume based on temperature and rainfall by multiple linear and Bayesian quantile regressions

Modabber-Azizi¹,

Salarijazi²,

Ghorbani³

2022

Időjárás

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Investigation of river flow volume in different conditions as a function of temperature and rainfall variables can be quite effective in understanding the hydrological and hydro-climatic conditions of the watershed. Multiple linear regression models were applied in estimating river flow in several studies due to their straightforwardness and appropriate interpretation of results. In this study, to overcome the limitations of the multiple linear regression model, the Bayesian quantile regression model was used to estimate the river flow volume as a function of rainfall and temperature, and the results were compared. The data and information used for the Qareh-Sou basin in northern Iran are of substantial environmental and socio-economic importance. Five data series, including spring, summer, autumn, winter, and annual series, were created and used for this study. It was found that the Bayesian quantile regression model has considerable flexibility to model the volume of flow for different quantiles, predominantly upper and lower quantiles, and can be used to model high and low flows. With increasing the values of quantiles, a limited decreasing pattern in the effect of rainfall on the volume of flow was identified, which can be due to increasing the effect of other factors in the formation of extreme flows of the river. For summer data in high quantiles, the effect of rainfall on river flow volume shows an increasing pattern. This pattern is different from the other studied series, which may be due to the low base flow in summer. The results confirm that the application of Bayesian quantile regression compared to multiple linear regression leads to much more valuable information on the impact of rainfall and temperature on river flow volume.

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“…Different digital filter techniques have no significant influence on the annual and mean annual estimation of Q b and Q s (L. Cheng et al., 2012, 2016; Kelly et al., 2019; Tan et al., 2020; J. Zhang et al., 2017). The LH method has the advantage of being minimally parameterized, and thus is easily applied to a large sample of catchments (Jolánkai & Koncsos, 2015). Here the LH method was applied in a traditional way, that is, baseflow was separated from total flow with three passes (forward, backward, and forward) and the filter parameter f 1 was set to 0.925 as suggested by Nathan and McMahon (1990).…”

Section: Catchments and Datamentioning

confidence: 99%

“…However, the identified dominant factors controlling baseflow have been different in different studies. These factors include precipitation and potential evapotranspiration (Ahiablame et al., 2013; Beck et al., 2013; Peña‐Arancibia et al., 2010; Van Dijk, 2010), geology, topography, and soil properties (Bloomfield et al., 2009; Brandes et al., 2005; Gebert et al., 2007; Jolánkai & Koncsos, 2015; Longobardi & Villani, 2008; Rumsey et al., 2015; Singh et al., 2019), and vegetation (L. Cheng et al., 2017; Huseby Karlsen et al., 2016). A universal method for explaining the underlying mechanisms of climate and physiography that control the spatial variability of baseflow is still lacking (Price, 2011).…”

Section: Introductionmentioning

confidence: 99%

An Analytical Baseflow Coefficient Curve for Depicting the Spatial Variability of Mean Annual Catchment Baseflow

Cheng

Liu

et al. 2021

Water Resources Research

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Catchment baseflow is jointly controlled by climate and landscape properties. Previous studies have recognized that spatial variability of mean annual baseflow coefficient (BFC = Qb/P, ratio of baseflow to precipitation) is primarily controlled by aridity index and storage capacity. However, an analytical solution of BFC in terms of the dominant controlling factors has not yet been established. The objective of this study was to develop an analytical BFC curve to depict spatial variability of BFC based on the “limit” concept of the Budyko framework. The BFC curve relates the baseflow coefficient to aridity index and storage capacity without resolving complex interactions between evapotranspiration and baseflow generation. The proposed BFC curve showed that, in the arid catchments, baseflow coefficient was primarily limited by available water (precipitation, P) and, in the humid catchments, was jointly controlled by both the available energy (potential evapotranspiration, Ep) and catchment retention capability (ratio of catchment storage capacity to P, i.e., Sp/P). Observed hydrological data from 950 catchments in Australia, the conterminous United States and the United Kingdom with diverse hydro‐climatic conditions (BFC = 0.001–0.650) were collected to demonstrate the capability of the developed curve. Results showed that the BFC curve captured the spatial variability of observed BFC in the 950 study catchments (R2 = 0.75, RMSE = 0.058). Mean annual baseflow estimated by the BFC curve agreed well with observed baseflow (R2 = 0.86, RMSE = 0.19 mm). The developed analytical curve provides an analytical solution for understanding how aridity index and storage capacity control mean annual catchment baseflow, and will improve predictability of baseflow at ungauged basins.

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Base Flow Index Estimation on Gauged and Ungauged Catchments in Hungary Using Digital Filter, Multiple Linear Regression and Artificial Neural Networks

Cited by 5 publications

References 14 publications

Long-term Water Regime Studies of a Degraded Floating Fen in Hungary

Long-term Water Regime Studies of a Degraded Floating Fen in Hungary

Estimation of seasonal and annual river flow volume based on temperature and rainfall by multiple linear and Bayesian quantile regressions

An Analytical Baseflow Coefficient Curve for Depicting the Spatial Variability of Mean Annual Catchment Baseflow

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