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Analysis of runoff in selected urban watershed
Kučera, Vít ; Máca, Petr (advisor) ; Petr, Petr (referee)
The work deals with schematization area with sewage networks in the Rainfalls-runoff model urbanized watershed modeling and changes in the Manning roughness coefficient of the pipe to the main sewer, which drains most of the wastewater. The comparison was made on two schematization basin Bohnice collector the main sewer F in the city. Prague. For the simulation program was used Mike URBAN DHI Inc.
The main variable was observed outflow hydrograph on the selected sealing profile watershed and its development depending on schematization and change the Manning roughness coefficient.
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Effect of snowpack on runoff generation during rain on snow event.
Juras, Roman ; Máca, Petr (advisor) ; Ladislav , Ladislav (referee)
During a winter season, when snow covers the watershed, the frequency of rain-on-snow (ROS) events is still raising. ROS can cause severe natural hazards like floods or wet avalanches. Prediction of ROS effects is linked to better understanding of snowpack runoff dynamics and its composition. Deploying rainfall simulation together with hydrological tracers was tested as a convenient tool for this purpose. Overall 18 sprinkling experiments were conducted on snow featuring different initial conditions in mountainous regions over middle and western Europe.
Dye tracer brilliant blue (FCF) was used for flow regime determination, because it enables to visualise preferential paths and layers interface. Snowpack runoff composition was assessed by hydrograph separation method, which provided appropriate results with acceptable uncertainty. It was not possible to use concurrently these two techniques because of technical reasons, however it would extend our gained knowledge. Snowmelt water amount in the snowpack runoff was estimated by energy balance (EB) equation, which is very efficient but quality inputs demanding. This was also the reason, why EB was deployed within only single experiment.
Timing of snowpack runoff onset decrease mainly with the rain intensity. Initial snowpack properties like bulk density or wetness are less important for time of runoff generation compared to the rain intensity. On the other het when same rain intensity was applied, non-ripe snowpack featuring less bulk density created runoff faster than the ripe snowpack featuring higher bulk density. Snowpack runoff magnitude mainly depends on the snowpack initial saturation. Ripe snowpack with higher saturation enabled to generate higher cumulative runoff where contributed by max 50 %. In contrary, rainwater travelled through the non-ripe snowpack relatively fast and contributed runoff by approx. 80 %.
Runoff prediction was tested by deploying Richards equation included in SNOWPACK model. The model was modified using a dual-domain approach to better simulate snowpack runoff under preferential flow conditions. Presented approach demonstrated an improvement in all simulated aspects compared to the more traditional method when only matrix flow is considered.
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Application of optimization methods in hydrological modeling
Jakubcová, Michala ; Máca, Petr (advisor) ; Hanel, Martin (referee)
Finding the optimal state of reality is the main purpose of the optimization process. The best variant from many possibilities is selected, and the effectiveness of the given system increases. Optimization has been applied in many real life engineering problems as in hydrological modelling. Within the hydrological case studies, the optimization process serves to estimate the best set of model parameters, or to train model weights in artificial neural networks.
Particle swarm optimization (PSO) is relatively recent optimization technique, which has only a few parameters to adjust, and is easy to implement to the selected problem. The original algorithm was modified by many authors. They
focused on changing the initialization of particles in the swarm, updating the population topology, adding new parameters into the equation, or incorporating shuffling mechanism into the algorithm.
The modifications of PSO algorithm improve the performance of the optimization, prevent the premature convergence, and decrease computation time. Therefore, the main aims of the presented doctoral thesis consist of proposal of a new PSO modification with its implementation in C++ programming language. More PSO variants were compared and analysed, and the best methods based on benchmark problems were applied in two hydrological case studies.
The first case study focused on utilization of PSO algorithms in inverse problem related to estimation of parameters of rainfall-runoff model Bilan. In the second case study, combination of artificial neural networks with PSO methods was introduced for forecasting the Standardized precipitation evapotranspiration drought index.
It was found out, that particle swarm optimization is a suitable tool for solving problems in hydrological modelling. The most effective PSO modifications are the one with adaptive version of parameter of inertia weight, which updates the velocity of particles during searching through the multidimensional space via feedback information. The shuffling mechanism and redistribution of particles into complexes, at which the PSO runs separately, also significantly improve the performance.
The contribution of this doctoral thesis lies in creation of new PSO modification, which was tested on benchmark problems, and was successfully applied in two hydrological case studies. The results of this thesis also extended the utilization of PSO methods in real life engineering optimization problems. All analysed PSO algorithms are available for later use within other research projects.
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Using Weather Generators for the Assessment of the Impact of Climate Change in Catchments
Martínková, Marta ; Hanel, Martin (advisor) ; Máca, Petr (referee)
The main objective of this dissertation is to provide a novel approach to downscaling of outputs from regional climate models and to simulation of future climate. The resulting method consists of rain generator that operates in 6-hour time step. The generator performs well for the observational data.
It consists of following steps: disaggregation of 6-hour cumulative precipitation into convective and stratiform types, fitting of first order 3-state discrete time Markov chain to the data and simulation of long time series of precipitation. Then the mixture of log-normal and Generalized Pareto distribution is fitted to stratiform events and the Generalized extreme value distribution is fitted to convective events.
The impact of climate change on precipitation is evaluated by using change factors that are identified for precipitation occurrence (by comparing the transition matrices for the future and control period) and for precipitation amount (by comparing the scale and location parameters of distributions fitted for the future and control period). The observational data are then altered with obtained change factors.
From evaluation of observational data it stems that the average volume of an convective event is higher for the western region than for eastern region of the Czech Republic. Additionally, statistically significant trends in number and volume of convective events were identified for the region.
The relative portion of convective precipitation is the highest in summer for observational data.
From analysis of RCMs simulations, it stems that even though the overall precipitation is projected to be lower in future, the proportion of convective events (versus stratiform ones) would be higher. The number of convective events is projected to be lower in the future, while the volume of a convective event to be bigger.
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Mathematical modelling in basin of Litavka in the framework integrated system water
Hejduk, Tomáš ; Pech, Pavel (advisor) ; Máca, Petr (referee)
These thesis brings new findings in flood issues, which importance has been sharply increasing in the light of last years experience. Hypotheses about the usefulness of hydrological measurements in the creation of computational geometry watercourses, as well as using the data from aerial laser scanning in the preparation of computer tracks water flows have been confirmed. Presented papers introduce the use of new technologies, knowledge and other results of applied research in the field of preparation of input data for hydrodynamic models, geographic information systems, personal identification and early warning and information sharing to support the elimination of consequences of natural disasters or traffic accidents. The above presented findings about the use of airborne laser scanning data and synthesis these data with hydrological measurements are of great importance for the improvement of flood prevention. Another practical use of these findings lies in urbanism planning and flood forecasts. The effort is to increase the security of citizens in the case of threats to their security through early warning - ie. Preventative protection by results of conducted research in the field of mathematical modelling rainfall-runoff and passage of flood flows in the river system providing new knowledge for the identification and registration of persons. During the research conducted, the attention was paid to define tools supporting integrated activities of the state security and rescue forces, including increased education and communication between state administration, local governments and the public. However the main goal of this work is to prevent the effects of natural and anthropogenic risks to human health and property of citizens. The attention is paid especially on the most common natural hazard represented by the floods.
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Using artifical neural network for ice phenomena prediction on the lower Berounka
Šebestová, Lucie ; Máca, Petr (advisor) ; Havlíček, Vojtěch (referee)
Ice phenomena on watercourses are commonly occurring effect in winter period. In most places do not cause any complication, but in certain places their occurrence is more frequent and in conjunction with forming ice phenomena into dangerous, as a break-up ice jam or a freeze-up ice jam, can lead to the formation of ice flood. Such places is affected lower Berounka watercourse in section Křivoklát - Vltava confluence. Occurrence and formation of ice phenomena depends on ice regime, which lower Berounka causing frequent problems. Ice regime is the interplay of many factors and ice phenomena are thus generally very difficult to predict because of strongly nonlinear relationships. Artificial neural networks excel in ability to learn on examples, in this case historical data, and ability to apply the knowledge gained on the data present and the future. This work uses multilayer perceptron neural network to realization of ice phenomena prediction based on historical flow and temperature data from the years 1887 - 1940 from the measuring station Křivoklát, which is a place of frequent occurrence of dangerous ice phenomena. The results provided by the learned neural network are comparable to the standard model used in modeling of ice phenomena. Obtained outputs confirmed the possibility of the successful application of neural networks in this area. Their use is possible as e.g. a part of information (warning) system or a system for predicting the occurrence of ice phenomena during winter season, which may lead to the alleviation of their impact on watercourse, surrounding area and residents.
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Drought Indices in Panama Canal
Gutiérrez Hernández, Julián Eli ; Máca, Petr (advisor)
Panama has a warm, wet, tropical climate. Unlike countries that are farther from the equator, Panama does not experience seasons marked by changes in temperature. Instead, Panama's seasons are divided into Wet and Dry. The Dry Season generally begins around mid-December, but this may vary by as much 3 to 4 weeks. Around this time, strong northeasterly winds known as "trade winds" begin to blow and little or no rain may fall for many weeks in a row. Daytime air temperatures increase slightly to around 30-31 Celsius (86-88 Fahrenheit), but nighttime temperatures remain around 22-23 Celsius (72-73 Fahrenheit). Relative humidity drops throughout the season, reaching average values as low as 70 percent. The Wet Season usually begins around May 1, but again this may vary by 1 or 2 weeks. May is often one of the wettest months, especially in the Panama Canal area, so the transition from the very dry conditions at the end of the Dry Season to the beginning of Wet Season can be very dramatic. With the arrival of the rain, temperatures cool down a little during the day and the trade winds disappear. Relative humidity rises quickly and may hover around 90 to 100% throughout the Wet Season. Drought forecasts can be an effective tool for mitigating some of the more adverse consequences of drought. The presented thesis compares forecast of drought indices based on seven different models of artificial neural networks model. The analyzed drought indices are SPI and SPEI-ANN Drought forecast, and was derived for the period of 1985-2014 on Panama Canal basin; I've selected seven of sixty-one Hydro-meteorological networks, existing in the Panama Canal basin. The rainfall is 1784 mm per year. The meteorological data were obtained from the PANAMA CANAL AUTHORITY, Section of Water Resources, and Panama Canal Authority, Panama. The performance of all the models was compared using ME, MAE, RMSE, NS, and PI. The results of drought indices forecast, explained by the values of seven model performance indices, show, that in Panama Canal has problem with the drought. Even though The Panama is generally seen as a wet country, droughts can cause severe problems. Significant drought conditions are observed in the index based on precipitation and potential evaporation found in this thesis; The Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), were used to quantify drought in the Panama Canal basin, Panama Canal, at multiple time scales within the period 1985-2014. The results indicate that drought indices based on different variables show the same major drought events. Drought indices based on precipitation and potential evaporation are more variable in time while drought indices based on discharge. Spatial distribution of meteorological drought is uniform over Panama Canal.
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The Bathymetry of Němčice reservoir
Prchal, Jan ; Máca, Petr (advisor) ; Bašta, Petr (referee)
Bathymetric measurements are important for mapping relief of current reservoirs and streams. Due to the lack of similar measurements in the Czech Republic is presented diploma thesis focused on the implementation of the first bathymetric survey on the water reservoir located on the basin Němčice - Sedlický stream. As a part of the work was done bathymetric data collection device River Surveyor M9 data processing interpolation procedures and evaluate the mutual comparison of estimation results. It was also evaluated the effect of the resolution to the retention volume of the tank and its sedimentations regime. Based on the evaluation of the measurement is based on a minimum average error method Universal Kriging. Processed data were measured during the year 2015. The result of the work can serve as a guide for subsequent measurements and estimates bathymetric data on a similar small reservoirs in the catchment areas of the Czech Republic.
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Short duration precipitation extremes in the Czech Republic
Roub, Tomáš ; Hanel, Martin (advisor) ; Máca, Petr (referee)
The aim of this paper is to assess precipitation records from 182 monitoring stations. For every station intensities of short duration rain events are evaluated and compare with intensities presented in the work of Josef Trupl Intensity krátkodobých dešťů v povodích Labe, Odry a Moravy. For comparison always nearest monitoring station with an appropriate altitude was selected. The comparison is done for 3 data groups based on quality code of the records. Furthermore, stations with overlapping records are compared.
The review part deals with rainfall and the methods of assessment of short duration rainfall intensities. The following part describes the selected historical rainfall series, which serve as a basis for estimation of design rainfall.
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