Modelling the Climate Change Impact On Monthly Runoff in Central Slovakia

Hlavčová, Kamila; Štefunková, Zuzana; Valent, Peter; Kohnová, Silvia; Výleta, Roman; Szolgay, Ján

doi:10.1016/j.proeng.2016.08.804

Cited by 5 publications

(4 citation statements)

References 1 publication

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“…Scenarios for the variables have mainly been prepared: the daily means, maximum and minimum of the air temperature, the daily means of the relative air humidity, daily precipitation total, daily means of the wind speed, and daily totals of the global radiation. (Hlavčová et al, 2016). The latest climate change scenarios for the territory of Slovakia were processed on the basis of outputs from climatic atmospheric models at the Department of Astronomy, Earth Physics and Meteorology at the Faculty of Mathematics, Physics and Informatics of Comenius University (Lapin et al, 2012).…”

Section: The Climate Change Scenariosmentioning

confidence: 99%

The impact of climate change on the hydropower potential: A case study from Topľa River basin

Rončák¹,

Šurda²,

Vitková³

2021

AHS

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The contribution presents the impact of climate change on the hydropower potential in the Topľa River basin. There are various methodological approaches for determining the impact of climate change on the hydrological regime. One of them is the assessment of the impact of climate change on the hydropower potential. Changed climatic conditions, characterized mainly by changes in precipitation, potential evapotranspiration, and air temperature in future decades were predicted by recent outputs of the KNMI and MPI regional climate change models and the A1B emission scenario. To specify changes in long-term mean monthly runoff in comparison with the reference period 1981-2010 and future time horizons, the physically based WetSpa rainfall-runoff model was used. As a basic indicator of the potential for water energy utilization, hydropower potential (HPP) was calculated. An assessment of possible adaptation strategies for water management with respect to the hydropower potential and its utilization for energy production in Slovakia was attempted. The hydropower potential of small, run-of-river and storage hydropower plants is strongly related to the distribution of runoff over the year and can therefore affect not only the total change in runoff but also changes in its distribution in the future.

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Section: The Climate Change Scenariosmentioning

confidence: 99%

The impact of climate change on the hydropower potential: A case study from Topľa River basin

Rončák¹,

Šurda²,

Vitková³

2021

AHS

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“…Brezianska et al (2018) analyzed the occurrence of drought and reduced soil water supplies in the localities of the Záhorská lowlands during the period 1961-2010. Hlavčová et al (2016) expected an increase in the flows in the spring and winter months. On the other hand, in the summer and autumn months was expected a decreasing trend in the flows on the upper Hron River basin.…”

Section: Introductionmentioning

confidence: 99%

Analysis of a Topsoil Moisture Regime Through an Effective Precipitation Index for the Locality of Nitra, Slovakia

Rončák

Šurda

Vitková

2021

Slovak Journal of Civil Engineering

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The aim of this work was to analyze and statistically confirm the relationship between the computed daily values of the Effective Precipitation Index (EP365) and the measured moisture content of the topsoil of a research site near Nitra, Slovakia. The statistical analysis (coefficients of determination) confirmed the strong correlation. Subsequently each day’s mean of EP365 (MEP) was used to quantify the long-term deficit or surplus of the soil water supply in the individual years. The daily values of the EP365 index provide a more detailed view of the development of the topsoil’s moisture regime. According to the deviation of EP (DEP) from the 14-year MEP, the most extreme dry periods prevailed during the years 2015, 2017 and 2018. Therefore, the 2012–2018period was evaluated as dry, and the topsoil moisture regime described through the MEP index was compared with the course of the MEP index from the normal (slightly humid) period of the years 2005 – 2011.

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“…These models are primarily based on rainfall and runoff data and seek to characterize nonlinearity and non-stationary behaviour from those data by the use of transfer functions [8][9][10]. Among the system theoretic models, Artificial Neural Network (ANN) based models for rainfall-runoff modelling have received global attention because of their capability to capture high degree of non-2121 linearity and complex nature of relationship between the hydrological variables without fully understanding the processes beneath [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) Approach for Rainfall-Runoff Modelling

2019

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Reliable method of rainfall-runoff modeling is a prerequisite for proper management and mitigation of extreme events such as floods. The objective of this paper is to contrasts the hydrological execution of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) for modelling rainfall-runoff in the Sone Command, Bihar as this area experiences flood due to heavy rainfall. ENN is a modified version of ANN as it includes neural parameters which enhance the network learning process. Selection of inputs is a crucial task for rainfall-runoff model. This paper utilizes cross correlation analysis for the selection of potential predictors. Three sets of input data: Set 1, Set 2 and Set 3 have been prepared using weather and discharge data of 2 raingauge stations and 1 discharge station located in the command for the period 1986-2014. Principal Component Analysis (PCA) has then been performed on the selected data sets for selection of data sets showing principal tendencies. The data sets obtained after PCA have then been used in the model development of ENN and ANN models. Performance indices were performed for the developed model for three data sets. The results obtained from Set 2 showed that ENN with R= 0.933, R2 = 0.870, Nash Sutcliffe = 0.8689, RMSE = 276.1359 and Relative Peak Error = 0.00879 outperforms ANN in simulating the discharge. Therefore, ENN model is suggested as a better model for rainfall-runoff discharge in the Sone command, Bihar.

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Modelling the Climate Change Impact On Monthly Runoff in Central Slovakia

Cited by 5 publications

References 1 publication

The impact of climate change on the hydropower potential: A case study from Topľa River basin

The impact of climate change on the hydropower potential: A case study from Topľa River basin

Analysis of a Topsoil Moisture Regime Through an Effective Precipitation Index for the Locality of Nitra, Slovakia

A Comparison of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) Approach for Rainfall-Runoff Modelling

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