Stochastic Weather Generators (SWG) try to reproduce the stochastic patterns of climatological variables characterized by high dimensionality, non-normal probability density functions and non-linear dependence relationships. However, conventional SWGs usually typify weather variables with unjustified probability distributions assuming linear dependence between variables. This research proposes an alternative SWG that introduces the advantages of the Copula modeling into the reproduction of stochastic weather patterns. The Copula based SWG introduces more flexibility allowing researcher to model non-linear dependence structures independently of the marginals involved, also it is able to model tail dependence, which results in a more accurate reproduction of extreme weather events. Statistical tests on weather series simulated by the Copula based SWG show its capacity to replicate the statistical properties of the observed weather variables, along with a good performance in the reproduction of the extreme weather events. In terms of its use in crop growth models for the ratemaking process of new insurance schemes with no available historical yield data, the Copula based SWG allows one to more accurately evaluate the risk. The use of the Copula based SWG for the simulation of yields results in higher crop insurance premiums from more frequent extreme weather events, while the use of the conventional SWG for the yield estimation could lead to an underestimation of risks.

Generally urban drainage systems are built to protect urban property and control runoff. Moreover, these systems collect the runoff for storage purposes to serve society through sufficient water supply to meet the needs of demand, irrigation, and drainage. Urban environments are exposed to risks of extreme hydrological events. Therefore, urban water systems and their management are critical. Precipitation data are crucial, but may be prone to errors due to the lack of information e.g., short length of records. In this thesis, a Monte Carlo simulation and regional frequency analysis based on L-moments approach were utilized during the research in order to estimate the uncertainty in the Intensity Duration Frequency (IDF) curves by using historical precipitation data from Environment Canada (EC) weather stations and simulating a new series of data through a weather generator (WG) model. The simulations were then disaggregated from daily into hourly data for extraction of the annual maximum precipitation for different durations in hours (1, 2, 6, 10, 12, and 24). Regional frequency analysis was used to form the sites into groups based on homogeneity test results, and the quantile values were computed for various sites and durations with the return periods (T) in years (2, 10, 20, and 100). As a result, the regional frequency analysis was used to estimate the regional quantile values based on L-moment approach. Moreover, the box and whisker plots were utilized to display the results. When the return periods and durations increased, the uncertainty slightly increased. The historical IDF curves of London site falls within the regional simulated IDF curves. Furthermore, 1000 runs have been generated by using the weather generator.

Generally urban drainage systems are built to protect urban property and control runoff. Moreover, these systems collect the runoff for storage purposes to serve society through sufficient water supply to meet the needs of demand, irrigation, and drainage. Urban environments are exposed to risks of extreme hydrological events. Therefore, urban water systems and their management are critical. Precipitation data are crucial, but may be prone to errors due to the lack of information e.g., short length of records. In this thesis, a Monte Carlo simulation and regional frequency analysis based on L-moments approach were utilized during the research in order to estimate the uncertainty in the Intensity Duration Frequency (IDF) curves by using historical precipitation data from Environment Canada (EC) weather stations and simulating a new series of data through a weather generator (WG) model. The simulations were then disaggregated from daily into hourly data for extraction of the annual maximum precipitation for different durations in hours (1, 2, 6, 10, 12, and 24). Regional frequency analysis was used to form the sites into groups based on homogeneity test results, and the quantile values were computed for various sites and durations with the return periods (T) in years (2, 10, 20, and 100). As a result, the regional frequency analysis was used to estimate the regional quantile values based on L-moment approach. Moreover, the box and whisker plots were utilized to display the results. When the return periods and durations increased, the uncertainty slightly increased. The historical IDF curves of London site falls within the regional simulated IDF curves. Furthermore, 1000 runs have been generated by using the weather generator.

Snow and glaciers are considered the most important sources of the estimated 32-60% of global water resources which are provided by mountains. Consequently, snow and glaciers have regularly been the focus of climate change studies in mountain regions. Rock glaciers are a type of ice-debris landform characterized by creeping ice-rich permafrost. Recognition of the hydrological significance of rock glaciers is increasing and is of particular relevance to the Arid Andes, where rock glaciers cover greater area than glaciers by an order of magnitude. Little research exists on the hydrological significance of rock glaciers beyond potential water storage capacities and their runoff pathways. Additional knowledge and research approaches pertaining to the seasonal hydrological contributions and climatic sensitivities of rock glaciers are necessary for improved water resource planning in many regions around the world. This work explored the feasibility of utilizing the energy and water balance model GEOtop to quantify the thermal and hydrological dynamics of rock glaciers under several climate scenarios. Weather data was generated with the intermediate-stochastic weather generator AWE-GEN for a site in the Southeast Swiss Alps, which marked a novel approach in studies of rock glaciers. Weather data for a reference (REF) scenario was generated which approximates conditions during the observation period (1985 to 2012). AWE-GEN produced time series of weather data for the REF scenario with statistical properties of precipitation in close agreement with observations. Air temperature had substantial inaccuracies with mean annual air temperature (MAAT) cooler by 1.82 C due to negative temperature biases in summer months which are attributed to difficulties in estimating parameters of the weather generator model caused by local climatic factors. The influence of climate change was also examined. Data for 8 climate change scenarios were generated by specifying change factors for mean monthly air temperature. MAAT in the climate change scenarios was within +/-0.12 C of the speci ed change factor from MAAT in the REF scenario. The thermal and hydrological evolution of rock glacier soils were simulated for 50 years under the climatic forcing of the REF scenario followed by 50 years under each climate change scenario. Mean annual ground surface temperature (MAGST), active layer depth (Dal), permafrost total ice content (IWEtot), and the potential summer runoff contribution (MELTsum) were quanti ed and compared before and after the onset of the climate change conditions. Air temperature increases in the climate change scenarios were amplified in MAGST. Stable rock glacier points were resistant to changes in Dal and IWEtot under any annual, summer, and winter mean air temperature increase of 1 C, and summer and winter mean air temperature increases of 3 C despite notable changes in MAGST and MELTsum. Under warming scenarios, the greatest increase in MELTsum occurred for high elevation rock glacier points with the mean possible runoff contribution increasing 88% under 3 C of warming, which corroborates with increased runoff from high elevation permafrost in the Colorado Rockies in recent decades.

Estimating the impacts of climate change on groundwater represents one of the most difficult challenges faced by water resources specialists. One difficulty is that simplifying the representation of the hydrological system, or using too simple climate change scenarios often leads to discrepancies in projections. Additionally, these projections are affected by uncertainties from various sources, and these uncertainties are not evaluated in previous studies. In this context, the objective of this study is to provide an improved methodology for the estimation of climate change impact on groundwater reserves, including the evaluation of uncertainties. This methodology is applied to the case of the Geer basin catchment (480 km²) in Belgium. A physically-based surface-subsurface flow model has been developed for the Geer basin with the finite element model HydroGeoSphere. The simultaneous solution of surface and subsurface flow equations in HydroGeoSphere, as well as the internal calculation of the actual evapotranspiration as a function of the soil moisture at each node of the defined evaporative zone, improve the representation and calibration of interdependent processes like recharge, which is crucial in the context of climate change. Fully-integrated surface-subsurface flow models have recently gained attention, but have not been used in the context of climate change impact studies. This surface-subsurface flow model is combined with advanced climate change scenarios for the Geer basin. Climate change simulations were obtained from six regional climate model (RCM) scenarios assuming the SRES A2 greenhouse gases emission (medium-high) scenario. These RCM scenarios were statistically downscaled using two different methods: the 'Quantile Mapping Biased Correction' technique and a 'Weather Generator' technique. Both of them are part of the most advanced downscaling techniques. They are able to apply corrections not only to the mean of climatic variables, but also across the statistical distributions of these variables. This is important as these distributions are expected to change in the future, with more violent rainfall events, separated by longer dry periods. The 'quantile mapping bias-correction' technique generate climate change time series representative of a stationary climate for the periods 2011-2040, 2041-2070 and 2071-2100. The 'CRU' weather generator is used to generate a large number of equiprobable scenarios simulating full transient climate change between 2010 and 2085. All these scenarios are applied as input of the Geer basin model. The uncertainty is evaluated from different possible sources. Using a multi-model ensemble of RCMs and GCMs enables to evaluate the uncertainty linked to climatic models. The application of a large number of equiprobable climate change scenarios, generated with the 'weather generator', as input of the hydrological model allows assessing the uncertainty linked to the natural variability of the weather. Finally, the uncertainty linked to the calibration of the hydrological model is evaluated using the computer code 'UCODE_2005'. The climate change scenarios for the Geer basin model predict hotter and drier summers and warmer and wetter winters. Considering the results of this study, it is very likely that groundwater levels and surface flow rates in the Geer basin will decrease. This is of concern because it also means that groundwater quantities available for abstraction will also decrease. However, this study also shows that the uncertainty surrounding these projections is relatively large and that it remains difficult to state on the intensity of the decrease.

There has been an increasing demand for describing, predicting, and drawing inferences for various environmental processes, such as air pollution and precipitation. Environmental statistics plays an important role in many related applications, such as weather-related risk assessment for urban design and crop growth. However, modeling the spatio-temporal dynamics of environmental data is challenging due to their inherent high variability and nonstationarity. This dissertation is composed of four signi cant contributions to the modeling, simulation, and prediction of spatiotemporal processes using statistical techniques and machine learning algorithms. This dissertation rstly focuses on the Gaussian process emulators of the numerical climate models over a large spatial region, where the spatial process exhibits nonstationarity. The proposed method allows for estimating a rich class of nonstationary Mat ern covariance functions with spatially varying parameters. The e cient estimation is achieved by local-polynomial tting of the covariance parameters. To extend the applicability of this method to large-scale computations, the proposed method is implemented by developing software with high-performance computing architectures for nonstationary Gaussian process estimation and simulation. The developed software outperforms existing ones in both computational time and accuracy by a large margin. The method and software are applied to the statistical emulation of high-resolution climate models. The second focus of this dissertation is the development of spatio-temporal stochastic weather generators for non-Gaussian and nonstationary processes. The proposed multi-site generator uses a left-censored non-Gaussian vector autoregression model, where the random error follows a skew-symmetric distribution. It not only drives the occurrence and intensity simultaneously but also possesses nice interpretations both physically and statistically. The generator is applied to 30-second precipitation data collected at the University of Lausanne. Finally, this dissertation investigates the spatial prediction with scalable deep learning algorithms to overcome the limitations of the classical Kriging predictor in geostatistics. A novel neural network structure is proposed for spatial prediction by adding an embedding layer of spatial coordinates with basis functions. The proposed method, called DeepKriging, has multiple advantages over Kriging and classical neural networks with spatial coordinates as features. The method is applied to the prediction of ne particulate matter (PM2:5) concentrations in the United States.

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Master of Science / Department of Biological & Agricultural Engineering / Kyle R. Douglas-Mankin / The Great Plains once encompassed 160 million hectares of grassland in the central United States. In the last several decades, conversion of grassland to urban and agricultural production areas has caused significant increases in runoff and erosion. Past attempts to slow this hydrologic system degradation have shown success, but climate change could once again significantly alter the hydrology. The Intergovernmental Panel on Climate Change (IPCC) studies the state of knowledge pertaining to climate change. The IPCC has developed four possible future scenarios (A1, A2, B1 and B2). The output temperature and precipitation data for Northeast Kansas from fifteen A2 General Circulation Models (GCMs) were analyzed in this study. This analysis showed that future temperature increases are consistent among the GCMs. On the other hand, precipitation projections varied greatly among GCMs both on annual and monthly scales. It is clear that the results of a hydrologic study will vary depending on which GCM is used to generate future climate data. To overcome this difficulty, a way to take all GCMs into account in a hydrologic analysis is needed. Separate methods were used to develop three groups of scenarios from the output of fifteen A2 GCMs. Using a stochastic weather generator, WINDS, monthly adjustments for future temperature and precipitation were applied to actual statistics from the 1961 – 1990 to generate 105 years of data for each climate scenario. The SWAT model was used to simulate watershed processes for each scenario. The streamflow output was analyzed with the Indicators of Hydrologic Alteration program, which calculated multiple hydrologic indices that were then compared back to a baseline scenario. This analysis showed that large changes in projected annual precipitation caused significant hydrologic alteration. Similar alterations were obtained using scenarios with minimal annual precipitation change. This was accomplished with seasonal shifts in precipitation, or by significantly increasing annual temperature. One scenario showing an increase in spring precipitation accompanied by a decrease in summer precipitation caused an increase in both flood and drought events for the study area. The results of this study show that climate change has the potential to alter hydrologic regimes in Northeast Kansas.

Climate change is predicted to have a global effect on temperatures and precipitation rates throughout the world. The UK Climate projections expect that in the United Kingdom this will lead to warmer, drier summers and wetter winters, where events of extreme rainfall are more common. These changes are expected to impact on slope hydrology, and concurrently slope stability. In the United Kingdom this impact is expected to be negative, whereas in other countries, such as Italy and France it could lead to slopes being more stable. Infrastructure slopes in the UK range in age and construction quality, they are susceptible to serviceability problems, characterised by heterogeneous material properties and can fail unexpectedly due to progressive reduction in soil shear strength. In this thesis the effects of climate change on a highway cutting in the south of England are modelled, using numerical methods. A finite element model is created and developed in the software package GeoStudio VADOSE/W. The model has been validated against observed pore water pressure trends and magnitudes and is shown to be able to accurately replicate the behaviour. By incorporating the effects of desiccation cracking on the soil s material properties, by the means of bimodal soil water characteristic curve and hydraulic conductivity function, the replication of these trends is improved even further. A series of future climate series were created using the UKCP09 Weather Generator 2.0. These series were implemented with the VADOSE/W model as climate boundary conditions and models were run, and the results compared to control, current climate results. The results were investigated by the means of statistical analyses which revealed that climate change will have some significant effects on the slope s hydrology, increasing magnitudes of evapotranspiration greatly which can have further significant effects on the magnitude of suctions developing in the slope throughout the summer. It is thought that the results suggest that climate change will not have significant negative effects on slope stability. However it is important to remember that the results only apply with certainty to the specific slope and climate change scenario investigated here. The methods used and developed within this thesis can be extended to other locations, in the UK and internationally, analysing the effects of different climate change scenarios.

The conventional approach to the frequency analysis of extreme rainfall is complicated by non-stationarity resulting from climate change. In this study significant trends in extreme rainfall are detected using statistical trend tests (Mann-Kendall test and t-test) for all over the Korean Peninsula. The violation of the stationarity for 1 hour annual maximum series is detected for large part of the area especially for southwestern and northeastern regions. For stations showing non-stationarity, the non-stationary generalized extreme value (GEV) distribution model with a location parameter in the form of linear function of time makes significant improvement in modeling rainfall extremes when compared to the stationary GEV model. The Bartlett-Lewis rainfall model is used to generate annual maximum series for the purpose of generating the Intensity-Duration-Frequency (IDF) curve. Using 100 sets of 50 year synthetic annual maxima, it is found that the observed annual rainfall maximum series are reasonably represented by the model. The observed data is perturbed by change factors to incorporate the climate change scenario from the WRF (Weather Research and Forecasting) regional climate model into IDF estimates. The IDF curves for the future period 2040-2079 show highest estimates for all return periods and rainfall durations. The future IDF estimates show significant difference from the IDF estimates of the historical period (1968-2000). Overall, IDF curves show an increasing tendency over time. A historical and future climate simulation is evaluated over the Colorado River Basin using a 111-year simulation (1969-2079) of the WRF climate change scenario. We find the future projections show statistically significant increases in temperature with larger increases in the northern part of the basin. There are statistically insignificant increases in precipitation, while snowfall shows a statistically significant decrease throughout the period in all but the highest elevations and latitudes. The strongest decrease in snowfall is seen at high elevations in the southern part of the basin and low elevations in the northern part of the basin.