Urbanisation alters the hydrologic response of a catchment, resulting in increased runoff rates and volumes, and loss of infiltration and base flow. Quantification of uncertainties is important in hydrologic designs of urban infrastructure. Major sources of uncertainty in the Intensity Duration Frequency (IDF) relationships are due to insufficient quantity and quality of data leading to parameter uncertainty and, in the case of projections of future IDF relationships under climate change, uncertainty arising from use of multiple General Circulation Models (GCMs) and scenarios. The work presented in the thesis presents methodologies to quantify the uncertainties arising from parameters of the distribution fitted to data and the multiple GCMs using a Bayesian approach. High uncertainties in GEV parameters and return levels are observed at shorter durations for Bangalore City. Twenty six GCMs from the CMIP5 datasets, along with four RCP scenarios are considered for studying the effects of climate change. It is observed that the uncertainty in short duration rainfall return levels is high when compared to the longer durations. Further it is observed that parameter uncertainty is large compared to the model uncertainty. Disaggregation of precipitation extremes from larger time scales to smaller time scales when the extremes are modeled with the GPD is burdened with difficulties arising from varying thresholds for different durations. In this study, the scale invariance theory is used to develop a disaggregation model for precipitation extremes exceeding specified thresholds. A scaling relationship is developed for a range of thresholds obtained from a set of quantiles of non-zero precipitation of different durations. The disaggregation model is applied to precipitation datasets of Berlin City, Germany and Bangalore City, India. From both the applications, it is observed that the uncertainty in the scaling exponent has a considerable effect on uncertainty in scaled parameters and return levels of shorter durations. A Bayesian hierarchical model is used to obtain spatial distribution of return levels of precipitation extremes in urban areas and quantify the associated uncertainty. Applicability of the methodology is demonstrated with data from 19 telemetric rain gauge stations in Bangalore City, India. For this case study, it is inferred that the elevation and mean monsoon precipitation are the predominant covariates for annual maximum precipitation. For the monsoon maximum precipitation, it is observed that the geographic covariates dominate while for the summer maximum precipitation, elevation and mean summer precipitation are the predominant covariates. In this work, variation in the dependence structure of extreme precipitation within an urban area and its surrounding non-urban areas at various durations is studied. The Berlin City, Germany, with surrounding non-urban area is considered to demonstrate the methodology. For this case study, the hourly precipitation shows independence within the city even at small distances, whereas the daily precipitation shows a high degree of dependence. This dependence structure of the daily precipitation gets masked as more and more surrounding non-urban areas are included in the analysis. The geographical covariates are seen to be predominant within the city and the climatological covariates prevail when non-urban areas are added. These results suggest the importance of quantification of dependence structure of spatial precipitation at the sub-daily timescales, as well as the need to more precisely model spatial extremes within the urban areas. The work presented in this thesis thus contributes to quantification of uncertainty in precipitation extremes through developing methodologies for generating probabilistic future IDF relationships under climate change, spatial mapping of probabilistic return levels and modeling dependence structure of extreme precipitation in urban areas at fine resolutions.

Title: Frequency analysis of precipitation amounts Author: Mgr. Zuzana Rulfová Department: Department of Atmospheric Physics Supervisor: RNDr. Jan Kyselý, Ph.D., Institute of Atmospheric Physics CAS Abstract: This thesis deals with analysing characteristics of mean and extreme precipitation in observations and regional climate models (RCMs) with respect to their convective and stratiform origin. An algorithm for subdivision of precipitation amounts into predominantly convective and stratiform using station weather data is proposed and evaluated. The time series of convective and stratiform precipitation from the Czech Republic over 1982-2010 are used for analysing basic climatological characteristics of precipitation, including extremes, and evaluating RCMs from the ENSEMBLES project. Projected changes of convective and stratiform precipitation in Central Europe (the Czech Republic) are analysed using data from RCM simulations from the EURO-CORDEX project. The last part of the thesis introduces a new statistical model for analysing precipitation extremes. This model takes advantage from knowledge of origin of precipitation extremes. In future climate we could expect more convective and stratiform precipitation amounts in all seasons except summer, when climate models project decline in amounts of stratiform...

In many environmental and climate applications, extreme data are spatial by nature, and hence statistics of spatial extremes is currently an important and active area of research dedicated to developing innovative and flexible statistical models that determine the location, intensity, and magnitude of extreme events. In particular, the development of flexible sub-asymptotic models is in trend due to their flexibility in modeling spatial high threshold exceedances in larger spatial dimensions and with little or no effects on the choice of threshold, which is complicated with classical extreme value processes, such as Pareto processes. In this thesis, we develop new flexible sub-asymptotic extreme value models for modeling spatial and spatio-temporal extremes that are combined with carefully designed gradient-based Markov chain Monte Carlo (MCMC) sampling schemes and that can be exploited to address important scientific questions related to risk assessment in a wide range of environmental applications. The methodological developments are centered around two distinct themes, namely (i) sub-asymptotic Bayesian models for extremes; and (ii) flexible marked point process models with sub-asymptotic marks. In the first part, we develop several types of new flexible models for light-tailed and heavy-tailed data, which extend a hierarchical representation of the classical generalized Pareto (GP) limit for threshold exceedances. Spatial dependence is modeled through latent processes. We study the theoretical properties of our new methodology and demonstrate it by simulation and applications to precipitation extremes in both Germany and Spain. In the second part, we construct new marked point process models, where interest mostly lies in the extremes of the mark distribution. Our proposed joint models exploit intrinsic CAR priors to capture the spatial effects in landslide counts and sizes, while the mark distribution is assumed to take various parametric forms. We demonstrate that having a sub-asymptotic distribution for landslide sizes provides extra flexibility to accurately capture small to large and especially extreme, devastating landslides.

A consequence of climate change may be higher frequencies and higher intensities of extreme climate events all over the world. This paper takes a closer look at the Northern Vietnam climate conditions. The area of interest are the geographical regions North East, North West, Red River Delta and North Central Coast. For research of extreme climate, the data from 72 meteorological stations for the time period from 1975 to 2006 were used and tested for the rainy season with the method of indices for climate change research created by Expert Team on Climate Change Detection (ETCCDI). Apparently, there is a linkage between the indices and topics of social and economic impacts, but this is not a clear fact. The climate change and extreme precipitation indices of the annual total precipitation above the 95th percentile (R95p), the annual total precipitation above the 99th percentile (R99p), the simple precipitation intensity amount (SDII), the annual total precipitation on wet days (PRCPTOT) and a modified annual total precipitation above 50 mm (R50mm) are used in this study. The question, whether there are statistically significant trends is answered using the Mann-Kendall Trend test. The results show that the indices are strongly influenced by the variations of the Vietnamese climate. Hence many stations have no significant trends. For the investigated time period, most of significance trends were decreasing. But there is a positive correlation between the total precipitation in the rainy season (PRCPTOT) and the frequencies of extreme climate events above the indices thresholds from R95p and R99p. Concluding, climate models show that higher total precipitations are likely for the area of interest. Therefore, it can be expected that, in a changing climate, more extreme climate events with higher intensities will occur. / Biến đổi khí hậu có thể dẫn đến sự gia tăng về tần số và cường độ của các hiện tượng thời tiết cực đoan trên toàn thế giới. Nghiên cứu này sẽ xem xét kỹ hơn về các điều kiện khí hậu ở miền Bắc Việt Nam. Địa điểm nghiên cứu bao gồm các khu vực địa lý Đông Bắc, Tây Bắc, Đồng bằng sông Hồng và Bắc Trung Bộ. Để nghiên cứu về khí hậu cực đoan, các dữ liệu trong khoảng thời gian từ 1975 đến 2006 đã được thu thập từ 72 trạm khí tượng. Những dữ liệu này được dùng để kiểm chứng đối với mùa mưa theo phương pháp chỉ số nghiên cứu biến đổi khí hậu của Nhóm chuyên gia về phát hiện biến đổi khí hậu (ETCCCDI). Hiển nhiên có một mối liên hệ giữa các chỉ số với các chủ đề về tác động kinh tế và xã hội, tuy nhiên thực tế này vẫn chưa rõ ràng. Các chỉ số biến đổi khí hậu và mưa cực đoan của tổng mưa hằng năm trên 95 phần trăm (R95p), tổng mưa hằng năm trên 99 phần trăm (R99p), chỉ số cường độ mưa trên ngày (SDII), tổng mưa hằng năm vào những ngày ẩm ướt – mùa mưa (PRCPTOT) và tổng mưa hằng năm biến đổi trên 50mm (R50mm) được sử dụng trong nghiên cứu này. Câu hỏi về sự tồn tại của các xu hướng quan trọng về mặt thống kê được trả lời bằng phương pháp Mann-Kendall Trend. Các kết quả chỉ ra rằng các chỉ số chịu ảnh hưởng lớn từ sự biến đổi của khí hậu Việt Nam. Do vậy, ở một số trạm khí tượng không có các xu hướng có ý nghĩa. Trong khoảng thời gian nghiên cứu, các xu hướng quan trọng đều giảm. Tuy nhiên, có một mối tương quan thuận giữa tổng lượng mưa trong mùa mưa (PRCPTOT) và cường độ của các hiện tượng thời tiết cực đoan trên các cực của chỉ số từ R95P và R99p. Kết luận, các mô hình thời tiết cho thấy tổng lượng mưa lớn hơn có khả năng sẽ xảy ra trên địa bàn nghiên cứu. Vì vậy, có thể phỏng đoán rằng khi thay đổi khí hậu, sẽ diễn ra nhiều hiện tượng thời tiết cực đoan với cường độ cao.