General Circulation Models (GCMs), which are mathematical models based on principles of fluid dynamics, thermodynamics and radiative transfer, are the most reliable tools available for projecting climate change. However, the spatial scale on which typical GCMs operate is very coarse as compared to that of a hydrologic process and hence, the output from a GCM cannot be directly used in hydrologic models. Statistical Downscaling (SD) derives a statistical or empirical relationship between the variables simulated by the GCM (predictors) and a point-scale meteorological series (predictand). In this work, a new downscaling model called CRF-downscaling model, is developed where the conditional distribution of the hydrologic predictand sequence, given atmospheric predictor variables, is represented as a conditional random field (CRF) to downscale the predictand in a probabilistic framework. Features defined in the downscaling model capture information about various factors influencing precipitation such as circulation patterns, temperature and pressure gradients and specific humidity levels. Uncertainty in prediction is addressed by projecting future cumulative distribution functions (CDFs) for a number of most likely precipitation sequences. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework, and changes in the non-parametric distribution of precipitation and dry and wet spell lengths are projected. Application of the method is demonstrated with the case study of downscaling to daily precipitation in the Mahanadi basin in Orissa, with the A1B scenario of the MIROC3.2 GCM from the Center for Climate System Research (CCSR), Japan. An uncertainty modeling framework is presented in this work, which combines GCM, scenario and downscaling uncertainty using the Dempster-Shafer (D-S) evidence theory for representing and combining uncertainty. The methodology for combining uncertainties is applied to projections of hydrologic drought in terms of monsoon standardized streamflow index (SSFI-4) from streamflow projections for the Mahanadi river at Hirakud. The results from the work indicate an increasing probability of extreme, severe and moderate drought and decreasing probability of normal to wet conditions, as a result of a decrease in monsoon streamflow in the Mahanadi river due to climate change. In most studies to date, the nature of the downscaling relationship is assumed stationary, or remaining unchanged in a future climate. In this work, an uncertainty modeling framework is presented in which, in addition to GCM and scenario uncertainty, uncertainty in the downscaling relationship itself is explored by linking downscaling with changes in frequencies of modes of natural variability. Downscaling relationships are derived for each natural variability cluster and used for projections of hydrologic drought. Each projection is weighted with the future projected frequency of occurrence of that cluster, called ‘cluster-linking’, and scaled by the GCM performance with respect to the associated cluster for the present period, called ‘frequency scaling’. The uncertainty modeling framework is applied to a case study of projections of hydrologic drought or SSFI-4 classifications, using projected streamflows for the Mahanadi river at Hirakud. It is shown that a stationary downscaling relationship will either over- or under-predict downscaled hydrologic variable values and associated uncertainty. Results from the work show improved agreement between GCM predictions at the regional scale, which are validated for the 20th century, implying that frequency scaling and cluster-linking may indeed be a valid method for constraining uncertainty. To assess the impact of climate change on reservoir performance, in this study, a range of integrated hydrologic scenarios are projected for the future. The hydrologic scenarios incorporate increased irrigation demands; rule curves dictated by increased need for flood storage and downscaled projections of streamflow from an ensemble of GCMs and emission scenarios. The impact of climate change on multipurpose reservoir performance is quantified, using annual hydropower and RRV criteria, under GCM and scenario uncertainty. The ‘business-as-usual’ case using Standard Operating Policy (SOP) is studied initially for quantifying impacts. Adaptive Stochastic Dynamic Programming (SDP) policies are subsequently derived for the range of future hydrologic scenarios, with the objective of maximizing reliabilities with respect to multiple reservoir purposes of hydropower, irrigation and flood control. It is shown that the hydrologic impact of climate change is likely to result in decreases in performance criteria and annual hydropower generation for Hirakud reservoir. Adaptive policies show that a marginal reduction in irrigation and flood control reliability can achieve increased hydropower reliability in future. Hence, reservoir rules for flood control may have to be revised in the future.

Water infrastructures have been implemented to support the vital activities of human society. The infrastructure developments at the same time have interrupted the natural catchment response characteristics, challenging society to implement effective water resources planning and management strategies. The Telemark area in southern Norway has seen a large number of water infrastructure developments, particularly hydropower, over more than a century. Recent developments in decision support tools for flood control and reservoir operation has raised the need to compute inflows from local catchments, most of which are regulated or have no observed data. This has contributed for the motivation of this PhD thesis work, with an aim of improving runoff estimation at ungauged catchments, and the research results are presented in four manuscript scientific papers.  The inverse distance weighting, inverse distance squared weighting, ordinary kriging, universal kriging and kriging with external drift were applied to analyse precipitation variability and estimate daily precipitation in the study area. The geostatistical based univariate and multivariate map-correlation concepts were applied to analyse and physically understand regional hydrological response patterns. The Sobol variance based sensitivity analysis (VBSA) method was used to investigate the HBV hydrological model parameterization significances on the model response variations and evaluate the model’s reliability as a prediction tool. The HBV hydrological model space transferability into ungauged catchments was also studied.  The analyses results showed that the inverse distance weighting variants are the preferred spatial data interpolation methods in areas where relatively dense precipitation station network can be found.  In mountainous areas and in areas where the precipitation station network is relatively sparse, the kriging variants are the preferred methods. The regional hydrological response correlation analyses suggested that geographic proximity alone cannot explain the entire hydrological response correlations in the study area. Besides, when the multivariate map-correlation analysis was applied, two distinct regional hydrological response patterns - the radial and elliptical-types were identified. The presence of these hydrological response patterns influenced the location of the best-correlated reference streamgauges to the ungauged catchments. As a result, the nearest streamgauge was found the best-correlated in areas where the radial-type hydrological response pattern is the dominant. In area where the elliptical-type hydrological response pattern is the dominant, the nearest reference streamgauge was not necessarily the best-correlated. The VBSA verified that varying up to a minimum of four to six influential HBV model parameters can sufficiently simulate the catchments' responses characteristics when emphasis is given to fit the high flows. Varying up to a minimum of six influential model parameters is necessary to sufficiently simulate the catchments’ responses and maintain the model performance when emphasis is given to fit the low flows. However, varying more than nine out of the fifteen HBV model parameters will not make any significant change on the model performance.  The hydrological model space transfer study indicated that estimation of representative runoff at ungauged catchments cannot be guaranteed by transferring model parameter sets from a single donor catchment. On the other hand, applying the ensemble based model space transferring approach and utilizing model parameter sets from multiple donor catchments improved the model performance at the ungauged catchments. The result also suggested that high model performance can be achieved by integrating model parameter sets from two to six donor catchments. Objectively minimizing the HBV model parametric dimensionality and only sampling the sensitive model parameters, maintained the model performance and limited the model prediction uncertainty.

Title: Spatiotemporal variability of global and regional climate models Author: RNDr. Lenka Crhová Department: Department of Atmospheric Physics Supervisor: RNDr. Eva Holtanová, Ph.D., Department of Atmospheric Physics Abstract: This thesis deals with variability of basic meteorological variables in global and regional climate models (GCMs and RCMs) outputs. Three different approaches were used in order to analyse climate models' ability to represent different aspects of variability of meteorological variables. The temporal variability with focus on its changes during a time and temporal scale components were studied. The relationship between air temperature and precipitation were employed in order to investigate the representation of spatiotemporal variability in climate models. Moreover, the influence of different characteristics of climate model simulations, such as the size of the RCM integration domain or differences between RCM and GCM simulations, were also considered. Two simulations of RCM ALADIN-Climate/CZ with different sizes of integration domain and their driving simulation of GCM ARPÉGE-Climat were used for analysis of the temporal changes in temperature mean and variability and selected simulations of RCMs and GCMs from the EURO-CORDEX and CMIP5 projects were employed for analyses of...

MESHWR / Department of Hydrology and Water Resources / This study dealt with the influence of climate variability on flood and drought cycles and implications on rainy season characteristics in Luvuvhu River Catchment (LRC) in Limpopo of South Africa. Extreme weather events resulting in hazards such as floods and droughts are becoming more frequent due to climate change. Extreme events affect rainy season characteristics and hence have an influence on water availability and agricultural production. Annual temperature was obtained from Water Research Commission for stations 0723485W, 0766628W and 0766898W from 1950-2013 were used to show/or confirm if there is climate variability in LRC. Daily rainfall data was obtained from SAWS for stations 0766596 9, 0766563 1, 0723485 6 and 0766715 5 were used to detect climate variability and determine the onset, duration and cessation of the rainy season. Streamflow data obtained from the Department of Water and Sanitation for stations A9H004, A9H012, and A9H001 for at least a period of 30 years for each station were used for climate variability detection and determination of flood and drought cycles. Influence of climate variability on floods and droughts and rainy season characteristic were determined in the area of study. Trends were evaluated for temperature, rainfall and streamflow data in the area of study using Mann Kendall (MK) and linear regression (LR) methods. MK and LR detected positive trends for temperature (maximum and minimum) and streamflow stations. MK and LR results of rainfall stations showed increasing trends for stations 0766596 9, and 0766563 1 whereas stations 0723485 6 and 0766715 5 showed decreasing trends. Standardized precipitation index (SPI) was used to determine floods and droughts cycles. SPI results have been classified either as moderately, severely and extremely dry or, moderately, very and extremely wet. This SPI analysis provides more details of dominance of distinctive dry or wet conditions for a rainy season at a particular station. Mean onset of rainfall varied from day 255 to 297, with 0766715 5 showing the earliest onset compared to the rest of the stations. Cessation of rainfall for most of the hydrological years was higher than the mean days of 88, 83 and 86 days in 0766596 9, 0766563 1 and 0723485 6 stations. Mean duration of rainfall varied from 102 to 128, with station 0766715 5 showing shortest duration of rainfall. The results of the study showed that the mean onset, duration and cessation were comparable for all stations except 0766715 5 which had lower values. The study also found that climate variability greatly affects onset, duration and cessation of rainfall during dry years. This led to late onset, early cessation and relatively short duration of the rainfall season. Communities within the catchment must be educated to practice activities such as conservation of indigenous plants, reduce carbon dioxide emissions.

PhD (Geography) / Department of Geography and Geo- Information Sciences / One of the major challenges facing all categories of farmers globally is climate change. African smallholder farmers are the most vulnerable to changes in climate. In most parts of South Africa, empirical evidence indicates the level to which climate change has impacted negatively on agricultural production. Rising temperatures, prolonged drought and decreasing rainfall have affected local farmers’ livelihood and crop production. In the Vhembe District of South Africa’s Limpopo Province, smallholder farming predominates and its vulnerability to climate change has increased for the past decades. This study, therefore, assesses the impact of climate change and adaptation strategies on smallholder farming systems in the Vhembe District To achieve this aim, qualitative and quantitative research methodologies were employed. A questionnaire was administered to a sample of 224 smallholder farmers to elicit data on perceptions; climate change impacts, adaptation and IKS based strategies to deal with climatic shocks. Focus group discussions (FGDs), semi-structured interviews with the extension officers elicited thematic data that complemented the interview survey. Climate data were obtained from the South Africa Weather Service (SAWS) for the period 1980 to 2015. Smallholder farmers’ perceptions about climate change were validated by an analysis of climatic trends from 1980-2015. A thematic analysis of qualitative data and the Multi Nominal Logit (MNL) regression model was used based on socio-economic and biophysical attributes such as access to climate knowledge, gender, farm size, education level, and farmers’ experience, decreasing rainfall and increasing temperature as farmers’ determinants of their adaptation options to climate change. Furthermore, farmers’ perceptions tallied well with climatic trends that showed flood and drought cycles. Most of the smallholder farmers were aware of climate change and its impacts over the past decades. The study further indicated that, due to the marked climate change over this period, farmers have adopted different coping strategies at on-farm and off-farm levels. In terms of adaptation, the major adaptive strategies used by smallholder farmers included the use of drought-tolerant seeds; planting of short-seasoned crops; crop diversification; changing planting dates; irrigation and migrating to urban areas. The study recommends a framework that would include water conservation (rainfall harvesting); investment in irrigation schemes and other smart technologies that integrate indigenous knowledge systems and modern scientific knowledge to enhance crop production. / NRF

Indiana University-Purdue University Indianapolis (IUPUI) / Precipitation trends and their driving mechanisms are examined over a variety of spatial and temporal scales using a multi-proxy, decadally-resolved sediment record from Martin Lake that spans the last 2300 years. This unique archive from a northern Indiana kettle lake documents significant climate variability during the last 2 millennia and shows that the Midwest has experienced a wide range of precipitation regimes in the late Holocene. Three independent proxies (i.e., oxygen and carbon isotopes of authigenic carbonate and %lithics) record variations in synoptic, in-lake and watershed processes related to hydroclimate forcing, respectively. Together, these proxies reveal enhanced summer conditions, with a long period of water column stratification and enhanced summer rainfall from 450 to 1200 CE, a period of time that includes the so-called Medieval Climate Anomaly (950-1300 CE). During the Little Ice Age, from 1260 to 1800 CE, the three proxy records all indicate drought, with decreased summer rainfall and storm events along with decreased lake stratification. The Martin Lake multi-proxy record tracks other Midwest climate records that record water table levels and is out-of-phase with hydroclimate records of warm season precipitation from the High Plains and western United States. This reveals a potential warm season precipitation dipole between the Midwest and western United States that accounts for the spatial pattern of late Holocene drought variability (i.e., when the Midwest is dry, the High Plains and the western United States are wet, and vice versa). The spatiotemporal patterns of late Holocene North American droughts are consistent with hydroclimate anomalies associated with mean state changes in the Pacific North American teleconnection (PNA). Close associations between late Holocene North American hydroclimate and records of Northern Hemisphere temperatures and the Pacific Ocean-atmosphere system suggests a mechanistic linkage between these components of the global climate system that is in line with observational data and climate models. Based on our results, predominantly –PNA conditions and enhanced Midwestern summer precipitation events are likely to result from continued warming of the climate system. In the western United States, current drought conditions could represent the new mean hydroclimate state.