Credible projections of future local climate change are in demand. One way to accomplish this is to statistically downscale General Circulation Models (GCM’s). A new method for statistical downscaling is proposed in which the seasonal cycle is first removed, a physically based predictor selection process is employed and principal component regression is then used to train the regression. A regression model between daily maximum and minimum temperature at Shearwater, NS, and NCEP principal components in the 1961-2000 period is developed and validated and output from the CGCM3 is then used to make future projections. Projections suggest Shearwater’s mean temperature will be five degrees warmer by 2100.

The objective of the study was to investigate downscaling in South Africa’s mining industry and the manner in which it was managed with special reference to the gold mining sector. In this regard the study involved literature review, data and information gathering, participating in tripartite stakeholder forums and soliciting opinions from several role players in the industry. Driefontein Gold Mine, one of the largest gold mines in the world, was used as a case study to examine management of downscaling in the gold mining industry. Literature investigation showed that South Africa’s mining industry has always been cyclical and labour was vital for its development. A dichotomy was revealed in the study in that the early years of modern mining were besieged with a continuous shortage of labour; in recent years the opposite is true as the industry is continuously battling with downscaling and shedding of jobs. Historical data shows that the gold mining industry has gone through three periods: the Stable Period (Period 1: 1960-1975); Expansion Period (Period 2: 1976-1987); and Downscaling or Crisis Period (Period 3: 1988-2004). Sufficient evidence was presented to show that the downscaling period was triggered by a combination of political instability of the 1980s and economic factors such as declining gold grades and depressed gold prices. Stakeholders followed a three-phased approach to address negative impacts associated with downscaling. The approach involved holding two tripartite summits; Gold Mining Summit (Phase 1) and Mining Summit (Phase 2) and incorporating recommendations emanating from the summits into the legislative process (Phase 3). At mine level, mines such as Driefontein managed downscaling through a number of initiatives including productivity improvement, restructuring and providing redundant employees with the option of taking extended leave. Economic factors, such as gold grades and ore reserves, suggest that downscaling will continue well into the foreseeable future and will exacerbate the existing negative environmental and socio-economic legacies. It is against this background 3 that international experiences on the management of downscaling were investigated. The study concludes by proposing recommendations and a new strategy to manage downscaling in South Africa’s gold mining industry. The strategy proposes a number of measures that need to be put in place at national, local community and mine levels.

Precipitation is an important element in the hydrological cycle. To predict and simulate large-scale precipitation, Global Circulation Models (GCMs) are widely used. However, their grid scale is too big to apply to local hydrologic fields. In this study, non-homogenous hidden Markov models (NHMM) are explored as a means of generating the probability of precipitation occurrence in small scale given large-scaled weather patterns. Three different spatial models: (1) independent (2) auto-logistic (3) Chow-Liu tree, are also explored, along with methods and steps for parameter estimation. From this exploration, independent models with NHMM are recommended for very small precipitation networks, and the maximum likelihood method is found to be the most practical fitting method. If there are many points for downscaling, Chow-Liu tree models with the Expectation-Maximization (EM) algorithm are recommended. If more exact solutions are needed, auto-logistic models can be employed. If many points are considered in auto-logistic models, the (EM) algorithm should be used to estimate parameters separately and global optimization methods should be used for emission matrix. The major problem found with the NHMM model in this study is matching the rainfall amount for each year or month. This problem can be addressed by whether combining occurrence models with amount modes or by improving only occurrence models.

This research presents a new multivariate statistical approach to downscale hydroclimatic variables associated with global climate signals, from low-resolution Global Climate Models (GCMs) to high-resolution grids that are appropriate for regional and local hydrologic analysis. The approach uses Principal Component Analysis (PCA) and Multichannel Singular Spectrum Analysis (MSSA) to: 1) evaluate significant variation modes among global climate signals and spatially distributed hydroclimatic variables within certain spatial domain; 2) downscale the GCMs' projections of the hydroclimatic variables using these significant modes of variation and 3) extend the results to other correlated variables in the space domain. The approach is applied to the Colorado River Basin to determine common oscillations among observed precipitation and temperature patterns in the basin and the global climate signals El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). These common oscillations serve as a basis to perform the downscaling of ENSO-related precipitation and temperature projections from GCMs, using a new gap-filling algorithm based on MSSA. The analysis of spatial and temporal correlations between observed precipitation, temperature and vegetation activity (represented by the Normalized Difference Vegetation Index, NDVI) is used to extend the downscaling of precipitation to vegetation responses in ten ecoregions within the basin. Results show significant common oscillations of five and 15-year between ENSO, PDO and annual precipitation in the basin, with wetter years during common ENSO and PDO positive phases and dryer years during common negative phases. Precipitation also shows an increase in variability in the last 20 years of record. Highly correlated responses between seasonally detrended NDVI and precipitation were also identified in each ecoregion, with distinctive delays in vegetation response ranging from one month in the southern deserts (in the fringe of the monsoon precipitation regime), to two months in the mid latitudes and three months to the north, affected by seasonal precipitation. These results were used to downscale precipitation and temperature from two GCMs that perform well in the basin and have a distinctive ENSO-like signal (MPI-ECHAM5 and UKMO-HADCM3) and to extend the downscaling to estimate vegetation responses based on their significant correlations with precipitation.

This diploma thesis deals with statistical downscaling of extréme temperature values. In first section describes two type sof downscaling- dynamical and statistical. All the examples are listed and described variol methods to simulation chmate elements, in particular temperatures and precipitation. Then there asre the linea rand non-linear methods were compared and the results of previous studies deals with this problem. These studies address not only daily or monthly average values, but also extréme. Extreme values are more difficult to simulate. In my thesis, I focus on downscaling of extréme temperature using linear regression. I focused on the are sof Europe, where I chose 10 stations, which cover variol chmate of Europe. Extreme values to every season, the lowest in winter and the highest in summer. The aim of this thesis was determine whether i tis appropriate to use to simulate extreme temperature seasonal average values in the free atmosphere. Key words: downscaling, statistical downscaling, extreme temperature, climate simulation

The statistical prediction of local sea surface winds at a number of locations over the global ocean (Northeast Pacific, Northwest Atlantic and Pacific, tropical Pacific and Atlantic) is investigated using a surface wind statistical downscaling model based on multiple linear regression. The predictands (mean and standard deviation of both vector wind components and wind speed) calculated from ocean buoy observations on daily, weekly and monthly temporal scales are regressed on upper level predictor fields (derived from zonal wind, meridional wind, wind speed, and air temperature) from reanalysis products. The predictor fields are subject to a combined Empirical Orthogonal Function (EOF) analysis before entering the regression model. It is found that in general the mean vector wind components are more predictable than mean wind speed in the North Pacific and Atlantic, while in the tropical Pacific and Atlantic the difference in predictive skill between mean vector wind components and wind speed is not substantial. The predictability of wind speed relative to vector wind components is interpreted by an idealized Gaussian model of wind speed probability density function, which indicates that the wind speed is more sensitive to the standard deviations (which generally are not well predicted) than to the means of vector wind component in the midlatitude region and vice versa in the tropical region. This sensitivity of wind speed statistics to those of vector wind components can be characterized by a simple scalar quantity theta=arctan(mu/sigma) (in which mu is the magnitude of average vector wind and sigma is the isotropic standard deviation of the vector winds). The quantity theta is found to be dependent on season, geographic location and averaging timescale of wind statistics. While the idealized probability model does a good job of characterizing month-to-month variations in the mean wind speed based on those of the vector wind statistics, month-to-month variations in the standard deviation of speed are not well modelled. A series of Monte Carlo experiments demonstrates that the inconsistency in the characterization of wind speed standard deviation is the result of differences of sampling variability between the vector wind and wind speed statistics. / Graduate

Regional assessments of droughts are limited and meticulous assessment of droughts over larger spatial scales are often not substantial. Understanding drought variability on a regional scale is crucial for enhancing resiliency and adaptive ability of water supply and distribution systems. Moreover, it can be essential for appraising the dynamics and predictability of droughts based on regional climate across various spatial and temporal scales. The drought analysis of the past was carried out with the development of a high-resolution dataset (1km×1km) for three drought-prone regions of India between 1950 and 2016. In the study the monthly values of self-calibrating Palmer Drought Severity Index (scPDSI), incorporating Penman–Monteith (PM) approximation, which is physically based on potential evapotranspiration. Climate data were statistically downscaled using the delta downscaling method and was formulated to form a timeline for characterizing major drought events that occurred in the past. The downscaled climate data were validated with the station observations. Major severe drought events that occurred between 1950 and 2016 were identified and studied with greater emphasis to the drought situation in smaller spatial extent such as districts, villages or localities. A timeline of drought events within the period of study was also prepared to have an understanding of the severity of drought in all three regions.Likewise, the future drought durations are projected for droughts of different levels of severity and assessed in the same regions of India. Coupled Model Intercomparison Project Phase 6 (CMIP6) simulated precipitation and climate data were used for near‐future (2015–2044) for different shared socio-economic pathways (SSPs). scPDSI, was used again based on its fairness in identifying drought conditions which accounts for the temperature as well. Gridded rainfall and temperature data of spatial resolution of 1km were used to bias correct the multi-model ensemble (MME) mean of 7 Global Climatic Models (GCMs) from CMIP6 project. Equidistant quantile-based mapping was adopted to remove the bias in the rainfall and temperature data and were corrected at the monthly scale. The downscaled climate data exhibited good statistical agreement with station data with correlation coefficient (R) ranging from 0.83 to 0.93 for both precipitation and temperature. Drought analysis indicated several major incidences over the analysis time period considered in this work, which truly adheres to the droughts recorded in qualitative reports of meteorological institutions in those regions. The drought study of the past helped to understand the situation in local levels and understand the necessities that can be opted for the future by proper management of water resources. While the outcome of the future prediction of drought duration suggests multiple severe to extreme drought events in all three study areas of appreciable durations mostly during the mid-2030s under the SSP2-4.5 scenario. The severe drought durations under the SSP2-4.5 scenario were found to be ranging around 25 to 30 months in 30 years period of the near future. The high-resolution drought index proved to be key to assess the drought situation for both the past and the future in three different drought-prone regions of India.

Soil moisture is an essential climate variable and provides critical state information for hydrological applications. The state of soil moisture influences the exchange of water and energy between the earth surface and the atmosphere, partitions infiltration and runoff, can limit the net primary productivity of a region and govern the dynamics of geochemical processes. Satellite observations can be used to provide information about this important variable but are often available at a scale that is far greater than most hydrological processes. The scope of the research presented in this dissertation was to identify practical methods to facilitate the use of coarse scale satellite soil moisture information in higher resolution hydrological and land-surface modelling applications. Research was primarily conducted in the Hamilton-Halton watershed of Southern Ontario, Canada, although other watersheds and datasets were periodically used in some chapters. A comprehensive review was conducted on the use of high resolution soil moisture information for hydrological applications, and data assimilation was identified as the most common method for integrating soil moisture information into a hydrological model. It was also identified that most watersheds displayed the property of temporal persistence and that root-zone soil moisture was of greater importance than surface soil moisture (Appendix B). In light of this information, the focus of this research was the downscaling of soil moisture and brightness temperature (TB) observations from the Soil Moisture and Ocean Salinity (SMOS) passive microwave satellite. Satellite observations are sensitive to surface soil moisture, while rootzone soil moisture provides the greatest benefit to hydrological and land surface applications. To overcome this discrepancy, artificial neural networks (ANN) were evaluated as a method to estimate rootzone soil moisture from surface observations that accounted for the known non-linearities of soil moisture processes. The ANN model was trained with a numerical soil moisture physics model and validated using in situ observations from the McMaster Mesonet and USDA SCAN sites. The ANN was capable of accurately depicting the rootzone soil moisture based on its training data at multiple sites, but was limited when the temporal distribution of soil moisture at a particular site was considerably different than the training data. Therefore, with the appropriate training data, ANNs are a viable method for predicting rootzone soil moisture from surface observations such as those available from satellites. To provide high resolution soil moisture information from coarse resolution satellite data, bias correction was proposed and evaluated as a downscaling method for both soil moisture and TB. Using in situ data from two well instrumented USDA watersheds and a hydrological land-surface scheme (HLSS), it was found that temporal evolution of both soil moisture and TB at fine scale (~1 km) could be well characterized by the temporal evolution of the coarse scale (~20 km) soil moisture and TB. The fine scale spatial distribution of soil moisture could be predicted with a high degree of skill by correcting the bias between the coarse and fine scale soil moisture/TB. In studying the correction of biases, it was found that naïve application of bias correction methods could result in the introduction of multiplicative biases in the bias corrected dataset. The theoretical implications of this for a data assimilation system were discussed although not yet evaluated. A bootstrap resampling approach was evaluated as a solution to this problem and it was found that resampled data could result in a robust bias correction that eliminated additive bias in most instances while limiting the induction of multiplicative bias. This new method was found to significantly outperform the standard bias correction techniques. / Thesis / Doctor of Philosophy (PhD) / Soil moisture is an important hydrological variable. The state of soil moisture controls the partition between the runoff and infiltration as well as the exchange of heat from the surface to the atmosphere. Therefore, an accurate depiction of the state of soil moisture is important for producing accurate flood and drought forecasts, numerical weather prediction and agricultural forecasts. The state of soil moisture can be observed from space using microwave remote sensing measurements. However, the resolution of most passive microwave observations, such as those from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) satellite are at a resolution of approximately 40 km which is far more coarse than the approximately 1 km resolution of most hydrological processes. The work in this thesis presented bias correction methods as a mean to match the spatial scale of the satellite observations to high resolution hydrological and land surface models. These data were generated and compared using an advanced land surface hydrological scheme under development at Environment Canada. It was found that simple bias correction methods were capable of effectively downscaling SMOS observations to the a scale of 1 km without the loss of information from the satellite. A new bias correction method was also presented that was found to significantly outperform standard techniques.

This thesis presents the development and application of a downscaling framework for multi site simulation of daily rainfall. The rainfall simulation is achieved in two stages. First, rainfall occurrences at multiple sites are downscaled, which is followed by the generation of daily rainfall amounts at each site identified as wet. A continuous weather state based nonparametric downscaling model conditional on atmospheric predictors and a previous day average rainfall state is developed for simulation of multi site rainfall occurrences. A nonparametric kernel density approach is used for simulation of rainfall amounts at individual sites conditional on atmospheric variables and the previous day rainfall amount. The proposed model maintains spatial correlation of rainfall occurrences by simulating concurrently at all stations and of amounts by using random innovations that are spatially correlated yet serially independent. Temporal dependence is reproduced in the occurrence series by conditioning on previous day average wetness fraction and assuming the weather states to be Markovian, and in the amount series by conditioning on the previous day rainfall amount. The seasonal transition is maintained by simulating rainfall on a day-to-day basis using a moving window formulation. The developed downscaling framework is calibrated using the relevant atmospheric variables and rainfall records of 30 stations around Sydney, Australia. Results indicate a better representation of the spatio-temporal structure of the observed rainfall as compared to existing alternatives. Subsequently, the framework is applied to predict plausible changes in rainfall in warmer conditions using the same set of atmospheric variables for future climate obtained as a General Circulation Model simulation. While the case studies presented are restricted to a specific region, the downscaling model is designed to be useful in any generic catchment modelling and management activity and/or for investigating possible changes that might be experienced by hydrological, agricultural and ecological systems in future climates.

A statistical downscaling approach based on multiple linear-regression is used to investigate the predictability of land surface winds over the Canadian prairies and Ontario. This study's model downscales mid-tropospheric predictors (wind components and speed, temperature, and geopotential height) from reanalysis products to predict historical wind observations at thirty-one airport-based weather surface stations in Canada. The model's performance is assessed as a function of: season; geographic location; averaging timescale of the wind statistics; and wind regime, as defined by how variable the vector wind is relative to its mean amplitude. Despite large differences in predictability characteristics between sites, several systematic results are observed. Consistent with recent studies, a strong anisotropy of predictability for vector quantities is observed, while some components are generally well predicted, others have no predictability. The predictability of mean quantities is greater on shorter averaging timescales. In general, the predictability of the surface wind speeds over the Canadian prairies and Ontario is poor; as is the predictability of sub-averaging timescale variability. These results and the relative predictability of vector and scalar wind quantities are interpreted with theoretically- and empirically-derived wind speed sensitivities to the resolved and unresolved variability in the vector winds. At most sites, and on longer averaging timescales, the scalar wind quantities are found to be highly sensitive to unresolved variability in the vector winds. These results demonstrate limitations to the statistical downscaling of wind speed and suggest that deterministic models which resolve the short-timescale variability may be necessary for successful predictions. / Graduate