Prediction of flow is necessary for planning and management of water resources. The objective of this study is to estimate mean annual flows for the USA and mean monthly flows for the rivers of central Texas based on the precipitation and their watershed characteristics. Flow varies largely with topographic and climatic parameters and hence generalization of runoff models is difficult. This model aims at providing a prediction at ungaged locations with very few parameters that are easily available and measurable. Scatter in predicted data will be seen at the annual and monthly time scale in the range selected for each data. This model will work on annual and monthly means to reduce the scatter and produce better estimates.

The diploma thesis is focused on predictions of mean monthly flows for a purpose of control of storage functions when thinking differently positions of fictive reservoirs in the catchment area. One of the reservoir is situated in the upper part of the catchment area and the second is situated in the middle part of catchment area. Predictions are made by Support vector machine method in RStudio and with the use of R language. Predicted values of flows was evaluated by the correlation coefficient, coefficient of determination, Root mean square error and than was made the simulation of operation of storage function, which was evaluated by Total sum of squares modificated for problems of water management. In the end was made a comparison of both of the reservoirs for assessment of the suitability of the method.

The diploma thesis is focused on the average monthly flows forecast in the selected hydrometric profile. Aim of this work will be evaluation of the calculated values and the interpretation of the results in understandable form. The next step will be find an appropriate connection between randomly-generated inputs in the form of random real flow series using the standard hydrological prediction models. This models are based on the principles of artificial intelligence and probability model. The result of the work will be verification of procedures and compilation of mean monthly flow stochastic forecast in selected hydrometric profile, which would be used for a reservoirs management, respectively for water systems management.

<p>Hourly archived rainfall records are separated into individual rainfall events with</p> <p>an Inter-Event Time Denition. Individual storms are characterized by their depth,</p> <p>duration, and peak intensity. Severe events are selected from among the events for</p> <p>a given station. A lower limit, or threshold depth is used to make this selection,</p> <p>and an upper duration limit is established. A small number of events per year are</p> <p>left, which have relatively high depth and average intensity appropriate to small</p> <p>to medium catchment responses. The Generalized Pareto Distributions are tted</p> <p>to the storm depth data, and a bounded probability distribution is tted to storm</p> <p>duration. Peak storm intensity is bounded by continuity imposed by storm depth</p> <p>and duration. These physical limits are used to develop an index measure of peak</p> <p>storm intensity, called intensity peak factor, bounded on (0; 1), and tted to the Beta</p> <p>distribution. The joint probability relationship among storm variables is established,</p> <p>combining increasing storm depth, increasing intensity peak factor, with decreasing</p> <p>storm duration as being the best description of increasing rainstorm severity. The</p> <p>joint probability of all three variables can be modelled with a bivariate copula of</p> <p>the marginal distributions of duration and intensity peak factor, combined simply</p> <p>with the marginal distribution of storm depth. The parameters of the marginal</p> <p>distributions of storm variables, and the frequency of occurrence of threshold-excess</p> <p>events are used to assess possible shifts in their values as a function of time and</p> <p>temperature, in order to evaluate potential climate change eects for several stations.</p> <p>Example applications of the joint probability of storm variables are provided that</p> <p>illustrate the need to apply the methods developed.</p> <p>The overall contributions of this research combine applications of existing probabilistic</p> <p>tools, with unique characterizations of rainstorm variables. Relationships</p> <p>between these variables are examined to produce a new description of storm severity,</p> <p>and to begin the assessment of the eects of climate change upon severe rainstorm</p> <p>events.</p> <p>i</p> / Doctor of Philosophy (PhD)

Two measures of air temperature trends across southern Yukon and northern British Columbia were modelled based on measurements from 83 monitoring sites across seven areas, operating for up to 14 years. Both mean monthly air temperature (MMAT) and freezing and thawing degree days (FDD and TDD, respectively) were modelled across this area (59 °N to 64.5 °N) at elevations ranging from 330-1480 m asl. Lapse rates in this region show inversions in the winter months (November - March) varying in inversion strength and length in relation to degree of continentality. The spatial and elevation range of these sites allowed for regional lapse rate modelling at the monthly scale for MMAT and at the annual scale for FDD and TDD. Lapse rates below treeline were found to be correlated (p < 0.1) with degree of continentality in the colder months (November - April) and August. In these months, lapse rates were modelled using kriging trend surfaces. In months where degree of continentality was not found to have a significant impact on lapse rates (p > 0.1) (May - October, excluding August), an average lapse rate calculated from the seven study regions was used across the study region. A combination of lapse rate trend surfaces, elevation, and temperatures at sea level were used to model MMAT and F/TDD below treeline. A treeline trend surface was created using a 4th order polynomial, allowing for temperatures at treeline to be determined. MMAT and F/TDD above treeline were calculated using a constant lapse rate of -6 °C/km, elevation, and temperature at treeline. The above and below treeline models were combined to create continuous models of MMAT and F/TDD. Modelled MMAT showed a high degree of homogeneity across the study region in warmer months. Inversions in lapse rates are evident in the colder months, especially December through February, when colder temperatures are easily identified in valley bottoms, increasing to treeline, and decreasing above treeline. Modelled MMAT values were validated using 20 sites across the study region, using both Environment and Climate Change Canada and University of Ottawa sites. The RMSE between modelled and observed MMAT was highest in January (4.4 °C) and lowest in June (0.7 °C). Sites below treeline showed a stronger relationship between modelled and observed values than sites above treeline. Edge effects of the model were evident in the northeast of the study region as well as in the ice fields in the southwest along the Alaska border. The new MMAT maps can be used to help understand species range change, underlying permafrost conditions, and climate patterns over time. FDD values were found to be highly influenced by both degree of continentality as well as latitude, whereas TDD values were mainly dependent on elevation, with degree of continentality and latitude being lesser influences. FDD and TDD were validated using the same 20 sites across the study region, with FDD showing a larger RMSE (368 degree days) between modelled and observed values than TDD (150 degree days). TDD modelling performed better on average, with a lower average absolute difference (254 degree days) between modelled and observed values at the validation sites than FDD modelling (947 degree days). The models of FDD and TDD represent a component of temperature at top of permafrost (TTOP) modelling for future studies. Two mean annual air temperature (MAAT) maps were created, one calculated from the MMAT models, and the other from the F/TDD models. Most of the study region showed negative MAAT, mainly between -6 °C and 0 °C for both methods. The average MAAT calculated from FDD and TDD values was -2.4 ºC, whereas the average MAAT calculated from MMAT values was -2.8 ºC. Models of MAAT were found to be slightly warmer than in previous studies, potentially indicating warming temperature trends.

The diploma thesis is focused on the long term prediction of mean monthly flows in hydrometric profile for purposes of reservoir control optimization and optimization of reservoir systems. Discharges were predicted using by artificial neural network method. Predicted flows were statistically evaluated by relevant coefficients and then compared with the measured flows for given river hydrometric profiles.

The aim of the thesis is to introduce the concept of Monte Carlo method for incorporating the uncertainties into the all hydrological and operational data inputs, which are needed to design and operation of large open water reservoir. Incorporating uncertainties into data inputs during calculation of reservoir storage capacity, then the consequent active conservation storage capacity is loaded by uncertainties. In the same way the values of outflow water from reservoir and hydrological reliability are affected by these uncertainties as well. For these kind of calculations the reservoir simulation model has been used, which simulate behavior operation of reservoir and is able to evaluate the results of simulations and help to reduction risk of storage capacity failure, respectively reduction of water shortages during reservoirs operation during low water and dry periods.