Snow accumulation and snowmelt research is one of the most important hydrological issues in mountain areas World-wide. Spring snowmelt, usually in the combination with intensive rainfall or high air temperature, is one of the most common causes of flooding in the Czech Republic. Mathematical modeling of hydrological processes belongs to effective instruments of flood protection and finds its use in a variety of areas. For example, water management, hydrological forecasts for agriculture, information for dam regulation or for recreational areas and water sports are all affected. This thesis focuses on snow accumulation and snowmelt modeling with use of the empirical Degree-day method. This method is based on the relationship between snowmelt rate and air temperature. The American HAC-HMS programme is used for the simulation of hydrological processes. The main goal of this thesis is to calibrate the model and to simulate snow accumulation, snowmelt and run-off from the watershed. The experimental basin 'Ptačí potok' is situated in the central part of the Šumava Mountains, in altitude of about 1,200 m. Two winter seasons, 2011 and 2012, were simulated. Simulations showed high reliability and correct calibration of the Temperature index snowmelt model. Simulations of the snow water equivalent evolution...

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In order to support an integrated management policy and the rational use of water resources toward sustainable development, it is unavoidable the search of efficient instruments to measure the performance of hydrological and environmental systems. Indicators and indexes have the role of translating numerically one specific situation and point out to the decision maker the sustainability status of that region. The development of a hydro-environmental sustainability index complies a multidisciplinary analysis dealing with several interrelated aspects of hydrologic and environmental parameters, based on some important criteria such as: water availability, quality and use of water, people access to water, environment impact.To the development of such an index in the State of Cearà a representative area of the semiarid highland environment has been chosen: Baturità APA (Area of Environmental Protection).A model structure based on the Pression-State-Response approach has been considered, resulting in the proposition of a Hydro-Environmental Sustainability Index for the highlands in the Semiarid Brazilian Region. / No intuito de dar suporte à uma polÃtica de gestÃo integrada e uso racional dos recursos hÃdricos de forma a garantir um desenvolvimento sustentÃvel à imprescindÃvel a aplicaÃÃo de ferramentas capazes de medir o desempenho dos sistemas hÃdricos e ambientais. Os indicadores e Ãndices tÃm o papel de traduzir numericamente uma determinada situaÃÃo e apontar, ao tomador de decisÃo, o sentido da sustentabilidade de uma regiÃo. O desenvolvimento de um Ãndice de sustentabilidade hidroambiental (ISHA) corresponde a uma anÃlise multidisciplinar tratando de vÃrios aspectos de inter-relacionamento entre parÃmetros hÃdricos e ambientais tendo como base alguns critÃrios importantes como: disponibilidade hÃdrica, qualidade e uso da Ãgua, acesso à mesma e impacto no meio ambiente. Para o desenvolvimento de um Ãndice desta natureza no CearÃ, foi escolhida uma Ãrea representativa de Ambientes Serranos no SemiÃrido do Estado - a APA (Ãrea de ProteÃÃo Ambiental) do MaciÃo BaturitÃ. Foi considerado um modelo estrutural baseado na abordagem PressÃo-Estado-Resposta propondo uma metodologia de desenvolvimento de um Ãndice de Sustentabilidade Hidroambiental para Ãreas serranas do SemiÃrido brasileiro.

Diploma thesis describes the problem of quantitative expression of the quality of life. For this purpose is used one of the most widespread indicators of quality of life - Human Development Index. This paper describes its basic characteristics, method of calculation, its advantages and drawbacks. One of the objectives of this paper is the elimination of these shortcomings by alternative method of calculation. The methods of multi-criteria evaluation of alternatives and data envelopment analysis models are applied for these purposes. One part of this paper is a theoretical description of the tasks of multi-criteria decision making and their classification into the methods for multi-criteria evaluation of alternatives and into the methods of multi-criteria linear programming. In this paper are used methods WSA, TOPSIS and PROMETHEE, based on the definition of the theoretical characteristics of the methods of multi-criteria evaluation of alternatives. These methods are applied on the source data, obtained from a model of the human development index. After calculation, the results obtained are analyzed and written into the conclusion. Another alternative approach to obtain the level of human development is to evaluate the degree of efficiency of countries using data envelopment analysis models in comparison with the human development index. For these purposes is written the theoretical background, which defines the basic types of data envelopment analysis models. These theoretical findings serve as the basis for the practical analysis by the BCC output oriented model and by the corresponding model of super efficiency. The results of these calculations are analyzed and subsequently written into the final conclusion.

Much attention in recent years has gone to the evaluation of teacher effectiveness, and some scholars have developed conceptual models to evaluate the effectiveness. The purpose of this study was to compare three teacher evaluation models – the Texas Professional Development Appraisal System (PDAS), the teacher index model (TI), and the value-added model (VAM) – to determine teacher effectiveness using student demographic and longitudinal academic data. Predictive data from students included economic disadvantage status, ethnicity, gender, participation in special education, limited English proficiency, and performance on Texas Assessment of Knowledge and Skills (TAKS). Data serving as dependent variables were scores from Scholastic Aptitude Test (SAT®) verbal/critical reasoning and mathematics. These data came from 1,714 students who were 9.7% Hispanic, 9.2% African American, and 81.2% White. The models were tested for 64 English language arts teachers and 109 mathematics teachers, using student examination scores from the SAT® verbal/critical reasoning and mathematics. The data were aligned for specific faculty members and the students whom they taught during the year of the study. The results of the study indicated that the TI and VAM explained approximately 42% of the variance in college entrance exam scores from the SAT® verbal/critical reasoning and mathematics (R2 = 0.418) across mathematics and English language arts teachers, whereas the TI model explained approximately 40% of the variance in the SAT® scores (R2 = 0.402). The difference, however, in the R-squared values between the VAM and the TI model was not statistically significant (t (169) = 1.84, p > 0.05), suggesting that both models provided similar results. The least effective model used to predict student success on college entrance exams was the PDAS, which is a state-adopted model currently in use in over 1,000 school districts in Texas, The teacher PDAS scores explained approximately 36% of the variance in student success on the SAT® (R2 = 0.359). The study provides school leadership with information about alternative methods of evaluating teacher effectiveness without difficult formulas or high costs associated with hiring statisticians. In addition, results indicate that the models vary significantly in the extent to which they can predict which teachers are most effective in preparing students for college. This study also emphasizes the critical need to provide teacher evaluations that align with student achievement on college entrance exams.

Snowmelt is one of the major sources of surface water supply and ground-water recharge in high elevation areas and can also cause flooding in snow dominated watersheds. Direct estimation of daily snowmelt requires daily snow water equivalent (SWE) measurements that are not always available, especially in places without monitoring stations. There are two alternative approaches to modeling snowmelt without using direct measurements of SWE, temperature-based and energy-based models. Due to its simplicity, computational efficiency, and less input data requirement, the temperature-based method is commonly used than the energy-based method. In the temperature-index approach snowmelt is estimated as a linear function of average air temperature, and the slope of the linear function is called the degree-day factor (DDF). Hence, the DDF is an essential parameter for utilizing the temperature-based method to estimate snowmelt. Thereby, to analyze the spatial properties of DDF, 10 years DDF from the entire state of Colorado was calculated for this research. Likewise, to study the temporal properties, DDFs for 27 years from the White Yampa water basin and the Colorado Headwaters water basin were calculated. As a part of the spatial analysis, the calculated DDFs were correlated with spatial variables (slope, aspect, latitude and elevation) and a spatial correlation graph was created to observe the possibility of predicting DDF. Also a multivariate regression model was prepared using these spatial variables to predict the DDF using spatial variables. Further, the DDFs calculated from Colorado headwaters and the White Yampa water basins were correlated for annual temporal variation, daily variation, variation with peak snow water equivalent and variation with important temporal cycles like accumulation period and melting period of snowmelt. The result obtained from this study showed that the variability of DDF is more dependent upon temporal factors compared to the spatial factors. Also, the results showed that predicting DDF is a difficult process and requires complex methods than simple linear models or multivariate models.

Dans cette thèse, nous étudions le problème de réduction de la dimension dans le cadre du modèle de régression suivant Y=g(B X,e), où X est un vecteur de dimension p, Y appartient à R, la fonction g est inconnue et le bruit e est indépendant de X. Nous nous intéressons à l'estimation de la matrice B, de taille dxp où d est plus petit que p, (dont la connaissance permet d'obtenir de bonnes vitesses de convergence pour l'estimation de g). Ce problème est traité en utilisant deux approches distinctes. La première, appelée régression inverse nécessite la condition de linéarité sur X. La seconde, appelée semi-paramétrique ne requiert pas une telle condition mais seulement que X possède une densité lisse. Dans le cadre de la régression inverse, nous étudions deux familles de méthodes respectivement basées sur E[X f(Y)] et E[XX^T f(Y)]. Pour chacune de ces familles, nous obtenons les conditions sur f permettant une estimation exhaustive de B, aussi nous calculons la fonction f optimale par minimisation de la variance asymptotique. Dans le cadre de l'approche semi-paramétrique, nous proposons une méthode permettant l'estimation du gradient de la fonction de régression. Sous des hypothèses semi-paramétriques classiques, nous montrons la normalité asymptotique de notre estimateur et l'exhaustivité de l'estimation de B. Quel que soit l'approche considérée, une question fondamentale est soulevée : comment choisir la dimension de B ? Pour cela, nous proposons une méthode d'estimation du rang d'une matrice par test d'hypothèse bootstrap. / In this thesis, we study the problem of dimension reduction through the following regression model Y=g(BX,e), where X is a p dimensional vector, Y belongs to R, the function g is unknown and the noise e is independent of X. We are interested in the estimation of the matrix B, with dimension d times p where d is smaller than p (whose knowledge provides good convergence rates for the estimation of g). This problem is processed according to two different approaches. The first one, called the inverse regression, needs the linearity condition on X. The second one, called semiparametric, do not require such an assumption but only that X has a smooth density. In the context of inverse regression, we focus on two families of methods respectively based on E[X f(Y)] and E[XX^T f(Y)]. For both families, we provide conditions on f that allow an exhaustive estimation of B, and also we compute the better function f by minimizing the asymptotic variance. In the semiparametric context, we give a method for the estimation of the gradient of the regression function. Under some classical semiparametric assumptions, we show the root n consistency of our estimator, the exhaustivity of the estimation and the convergence in the processes space. Within each point, an important question is raised : how to choose the dimension of B ? For this we propose a method that estimates of the rank of a matrix by bootstrap hypothesis testing.

本研究主要是探討台灣股票市場 79 至 81 年各分類指數的報酬率，以比 較各產業間報酬的多寡與風險的大小。並介紹投資組合的觀念以降低非系 統風險，而系統風險的部分可由單一指數模式估計。再以主成分分析法， 找出影響八個分類指數報酬率的主要成分，得到了第一個主成分做單一指 數模式的迴歸估計，以比較與加權指數為因變數的估計結果。最後探討這 三年來分類指數的報酬率，是否具有隨機性以及分類指數的行為。

In an experiment to understand colon carcinogenesis, all animals were exposed to a carcinogen while half the animals were also exposed to radiation. Spatially, we measured the existence of aberrant crypt foci (ACF), namely morphologically changed colonic crypts that are known to be precursors of colon cancer development. The biological question of interest is whether the locations of these ACFs are spatially correlated: if so, this indicates that damage to the colon due to carcinogens and radiation is localized. Statistically, the data take the form of binary outcomes (corresponding to the existence of an ACF) on a regular grid. We develop score??type methods based upon the Matern and conditionally autoregression (CAR) correlation models to test for the spatial correlation in such data, while allowing for nonstationarity. Because of a technical peculiarity of the score??type test, we also develop robust versions of the method. The methods are compared to a generalization of Moran??s test for continuous outcomes, and are shown via simulation to have the potential for increased power. When applied to our data, the methods indicate the existence of spatial correlation, and hence indicate localization of damage. Assuming that there are correlations in the locations of the ACF, the questions are how great are these correlations, and whether the correlation structures di?er when an animal is exposed to radiation. To understand the extent of the correlation, we cast the problem as a spatial binary regression, where binary responses arise from an underlying Gaussian latent process. We model these marginal probabilities of ACF semiparametrically, using ?xed-knot penalized regression splines and single-index models. We ?t the models using pairwise pseudolikelihood methods. Assuming that the underlying latent process is strongly mixing, known to be the case for many Gaussian processes, we prove asymptotic normality of the methods. The penalized regression splines have penalty parameters that must converge to zero asymptotically: we derive rates for these parameters that do and do not lead to an asymptotic bias, and we derive the optimal rate of convergence for them. Finally, we apply the methods to the data from our experiment.

In an experiment to understand colon carcinogenesis, all animals were exposed to a carcinogen while half the animals were also exposed to radiation. Spatially, we measured the existence of aberrant crypt foci (ACF), namely morphologically changed colonic crypts that are known to be precursors of colon cancer development. The biological question of interest is whether the locations of these ACFs are spatially correlated: if so, this indicates that damage to the colon due to carcinogens and radiation is localized. Statistically, the data take the form of binary outcomes (corresponding to the existence of an ACF) on a regular grid. We develop score??type methods based upon the Matern and conditionally autoregression (CAR) correlation models to test for the spatial correlation in such data, while allowing for nonstationarity. Because of a technical peculiarity of the score??type test, we also develop robust versions of the method. The methods are compared to a generalization of Moran??s test for continuous outcomes, and are shown via simulation to have the potential for increased power. When applied to our data, the methods indicate the existence of spatial correlation, and hence indicate localization of damage. Assuming that there are correlations in the locations of the ACF, the questions are how great are these correlations, and whether the correlation structures di?er when an animal is exposed to radiation. To understand the extent of the correlation, we cast the problem as a spatial binary regression, where binary responses arise from an underlying Gaussian latent process. We model these marginal probabilities of ACF semiparametrically, using ?xed-knot penalized regression splines and single-index models. We ?t the models using pairwise pseudolikelihood methods. Assuming that the underlying latent process is strongly mixing, known to be the case for many Gaussian processes, we prove asymptotic normality of the methods. The penalized regression splines have penalty parameters that must converge to zero asymptotically: we derive rates for these parameters that do and do not lead to an asymptotic bias, and we derive the optimal rate of convergence for them. Finally, we apply the methods to the data from our experiment.