Fig 2 - uploaded by Zsolt Jolankai
Content may be subject to copyright.
Source publication
A country scale analysis of diffuse source nutrient emissions have been undertaken previously on small catchments level using the MONERIS model, which needed a proper estimation of surface and subsurface runoff differentiation to support or contradict its own water budget based method. As reliable, country scale base flow estimation has not been av...
Context in source publication
Context 1
... the simple regressions between the variables and BFI, the best predicting variables seems to be the proportion of woodlands, proportion of agricultural land use, average eleva- tion and sand content, while climatic variables also have evi- dent connection with BFI values (Fig. 2, Table 2). Except for catchment size, some kind of linear relationship between the variables and the BFI is evident. In case of the catch- ment area it seems that there are other factors that influence this relationship, therefore in its own it is not a clear correlation. The direction of correlation (Table 2) is also clear in most cases, pro- portion of woodlands, evapotranspiration, solid rocks with poor permeability and average elevation are clearly acting against recharge hence underground flows, while increasing sand con- tent in the topsoil and the proportion of agriculture with much less interception capacity increase the recharge. Temperature is also positively correlated, which is probably because with lower elevation, temperature increases and slope decreases, hence the proportion of runoff will be smaller. Variables have been tested also with optimisation based variable ...
Similar publications
Rainfall and runoff relation is very important on efficient use of water resources and prevention of disasters. Nowadays, different methods of artificial intelligent techniques are applied to determine the rainfall-runoff relations. Artificial Neural Networks (ANN) is used for the present study. Also, Classical methods such as Multiple Linear Regre...
Citations
... Nathan and McMahon (1990) found a single parameter value (0.925) applicable to their study catchments using daily streamflow data. Many authors adopted this value since then (e.g., Jolánkai and Koncsos, 2018;Xie et al., 2020;Zheng, 2015), but other studies report a vastly expansive range of values. Even though many studies compared the performance of recursive digital filters (Nejadhashemi et al., 2009;Chapman, 1999;Eckhardt, 2005Eckhardt, , 2008Partington et al., 2012), only a very few attempted to calibrate the parameter of the LH filter using commonly available data, such as the discharge and precipitation time series. ...
... It is still not guaranteed that the separated base flow will match reality since the true value of base flow is never known (Szilagyi et al., 2003). The recursive filtering method was applied before for Hungarian catchments (Jolánkai & Koncsos, 2018) using a uniform β = 0.925 value, but the application of this value did not yield satisfactory results at every catchment examined during the present research. Therefore, the value of β was manually calibrated for every catchment using the ten largest flood events. ...
The estimation of catchment response time plays an essential role in several hydrological and civil engineering design problems. At ungauged catchments, its value is usually estimated by empirical equations that relate catchment response time to catchment characteristics. In order to facilitate the derivation of a well-performing empirical equation, measured values of catchment response time need to be assessed. The nonlinear relationship between catchment response time and rainfall intensity necessitates the estimation of an event-based set of catchment response time values instead of a characteristic constant value. However, there is no generally accepted method to define individual rainfall-runoff events from time series. Even if events are selected, several graphical definitions exist in the literature to estimate the values of catchment response time as time differences between specific points of the hyeto- and hydrograph.
The presented work provides a new method to select rainfall-runoff events from long time series while calculating the value of catchment response time at the event scale. The proposed method significantly facilitates the calculation of event-based catchment response time estimation of large datasets. The resulting values were compared with the catchment response time resulting from the widely used graphical definitions. The values yielded by the new method were considered as the characteristic, measured values of the catchment response time.
A new set of empirical equations were calibrated against the measured values. First, multiple catchment descriptors were collected and assessed for the study catchments. Second, this initial parameter space was reduced to a smaller set of catchment descriptors using different dimension-reduction techniques. Last, clustering methods were evaluated to improve the performance of the new empirical equation. Additionally, existing empirical equations were evaluated to quantify the improvement of catchment response time estimation.
As a result, a novel rainfall-runoff event selection method was developed and published, and the empirical estimation of catchment response time at Hungarian catchments was greatly improved.
... 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). ...
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.
... Daily baseflow (Q b ) and surface flow (Q s ) are separated from daily total streamflow (Q) using a digital filter technique, that is, the Lyne-Hollick (denoted as LH) method (Lyne & Hollick, 1979 Kelly et al., 2019;Tan et al., 2020;. 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). ...
Catchment baseflow is jointly controlled by climate and landscape properties. Previous studies have recognized that spatial variability of mean annual baseflow coefficient (BFC = (Formula presented.), 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.
... 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]. ...
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.
... In this study, groundwater flow proportion values calculated by the MONERIS equations were compared to those calculated by the digital-filter-based method [41] developed by Arnold [42]. For this purpose, catchments with a flow gauge (daily discharge data) described by monthly TN/TP measurements and consisting of fewer than three AUs were selected. ...
The contamination of waters with nutrients, especially nitrogen and phosphorus originating from various diffuse and point sources, has become a worldwide issue in recent decades. Due to the complexity of the processes involved, watershed models are gaining an increasing role in their analysis. The goal set by the EU Water Framework Directive (to reach "good status" of all water bodies) requires spatially detailed information on the fate of contaminants. In this study, the watershed nutrient model MONERIS was applied to the Hungarian part of the Danube River Basin. The spatial resolution was 1078 water bodies (mean area of 86 km 2); two subsequent 4 year periods (2009-2012 and 2013-2016) were modeled. Various elements/parameters of the model were adjusted and tested against surface and subsurface water quality measurements conducted all over the country, namely (i) the water balance equations (surface and subsurface runoff), (ii) the nitrogen retention parameters of the subsurface pathways (excluding tile drainage), (iii) the shallow groundwater phosphorus concentrations, and (iv) the surface water retention parameters. The study revealed that (i) digital-filter-based separation of surface and subsurface runoff yielded different values of these components, but this change did not influence nutrient loads significantly; (ii) shallow groundwater phosphorus concentrations in the sandy soils of Hungary differ from those of the MONERIS default values; (iii) a significant change of the phosphorus in-stream retention parameters was needed to approach measured in-stream phosphorus load values. Local emissions and pathways were analyzed and compared with previous model results.
Linear regression is widely used in flood quantile study that consists of meteorological and physiographical variables. However, linear regression does not capture the complex nonlinear relationship between predictor and target variables. It is rare to find a hydrological application using the group method of data handling (GMDH) model, artificial bee colony (ABC) algorithm, and ensemble technique, precisely predicting ungauged sites. GMDH model is known to be an effective model in complying with a nonlinear relationship. Therefore, in this paper, we enhance the GMDH model by implementing the ABC algorithm to optimize the parameter of partial description GMDH model with some transfer functions, namely polynomial, radial basis, sigmoid and hyperbolic tangent function. Then, ensemble averaging combines the output from those various transfer functions and becomes the new ensemble GMDH model coupled with the ABC algorithm (EGMDH-ABC) model. The results show that this method significantly improves the prediction performance of the GMDH model. The EGMDH-ABC model satisfies the nonlinearity in data to produce a better estimation. Also, it provides more robust, accurate, and efficient results.
Among the foremost frequent and vital tasks for hydrologist is to deliver a high accuracy estimation on the hydrological variable, which is reliable. It is essential for flood risk evaluation project, hydropower development and for developing efficient water resource management. Presently, the approach of the Group Method of Data Handling (GMDH) has been widely applied in the hydrological modelling sector. Yet, comparatively, the same tool is not vastly used for the hydrological estimation at ungauged basins. In this study, a modified GMDH (MGMDH) model was developed to ameliorate the GMDH model performance on estimating hydrological variable at ungauged sites. The MGMDH model consists of four transfer functions that include polynomial, hyperbolic tangent, sigmoid and radial basis for hydrological estimation at ungauged basins; as well as; it incorporates the Principal Component Analysis (PCA) in the GMDH model. The purpose of PCA is to lessen the complexity of the GMDH model; meanwhile, the implementation of four transfer functions is to enhance the estimation performance of the GMDH model. In evaluating the effectiveness of the proposed model, 70 selected basins were adopted from the locations throughout Peninsular Malaysia. A comparative study on the performance was done between the MGMDH and GMDH model as well as with other extensively used models in the area of flood quantile estimation at ungauged basins known as Linear Regression (LR), Nonlinear Regression (NLR) and Artificial Neural Network (ANN). The results acquired demonstrated that the MGMDH model possessed the best estimation with the highest accuracy comparatively among all models tested. Thus, it can be deduced that MGMDH model is a robust and efficient instrument for flood quantiles estimation at ungauged basins.
