Hydrologic and climate models predict variables through a simplification of the underlying complex natural processes. Model development involves minimising predictive uncertainty. Predictive uncertainty arises from three broad sources which are measurement error in observed responses, uncertainty of input variables and model structural error. This thesis introduces ways to improve predictive accuracy of hydroclimatic models by considering input and structural uncertainties. The specific methods developed to reduce the uncertainty because of erroneous inputs and model structural errors are outlined below. The uncertainty in hydrological model inputs, if ignored, introduces systematic biases in the parameters estimated. This thesis presents a method, known as simulation extrapolation (SIMEX), to ascertain the extent of parameter bias. SIMEX starts by generating a series of alternate inputs by artificially adding white noise in increasing multiples of the known input error variance. The resulting alternate parameter sets allow formulation of an empirical relationship between their values and the level of noise present. SIMEX is based on the theory that the trend in alternate parameters can be extrapolated back to the notional error free zone. The case study relates to erroneous sea surface temperature anomaly (SSTA) records used as input variables of a linear model to predict the Southern Oscillation Index (SOI). SIMEX achieves a reduction in residual errors from the SOI prediction. Besides, a hydrologic application of SIMEX is demonstrated by a synthetic simulation within a three-parameter conceptual rainfall runoff model. This thesis next advocates reductions of structural uncertainty of any single model by combining multiple alternative model responses. Current approaches for combining hydroclimatic forecasts are generally limited to using combination weights that remain static over time. This research develops a methodology for combining forecasts from multiple models in a dynamic setting as an improvement of over static weight combination. The model responses are mixed on a pair wise basis using mixing weights that vary in time reflecting the persistence of individual model skills. The concept is referred here as the pair wise dynamic weight combination. Two approaches for forecasting the dynamic weights are developed. The first of the two approaches uses a mixture of two basis distributions which are three category ordered logistic regression model and a generalised linear autoregressive model. The second approach uses a modified nearest neighbour approach to forecast the future weights. These alternatives are used to first combine a univariate response forecast, the NINO3.4 SSTA index. This is followed by a similar combination, but for the entire global gridded SSTA forecast field. Results from these applications show significant improvements being achieved due to the dynamic model combination approach. The last application demonstrating the dynamic combination logic, uses the dynamically combined multivariate SSTA forecast field as the basis of developing multi-site flow forecasts in the Namoi River catchment in eastern Australia. To further reduce structural uncertainty in the flow forecasts, three forecast models are formulated and the dynamic combination approach applied again. The study demonstrates that improved SSTA forecast (due to dynamic combination) in turn improves all three flow forecasts, while the dynamic combination of the three flow forecasts results in further improvements.

Transportation networks play a significant role in the economy of Canadians during winter seasons; thus, maintaining a safe and economic flow of traffic on Canadian roads is crucial. Winter contaminants such as freezing rain, snow, and ice cause reduced friction between vehicle tires and pavement and thus increased accident-risk and decreased road capacity. The formation of ice and frost caused by snowfall and wind chill makes driving a very difficult task. Pavement surface temperature is an important indicator for road authorities when they are deciding the optimal time to apply anti-icer/deicer chemicals and when estimating their effect and the optimal amounts to apply. By forecasting pavement temperature, maintenance crews can figure out road surface conditions ahead of time and start their operations in a timely manner, thereby reducing salt use and increasing the safety and security of road users by eliminating accidents caused by slipperiness. This research investigates the feasibility of applying simple statistical models for forecasting road surface temperatures at locations where RWIS data are available. Two commonly used modeling techniques were considered: time-series analysis and artificial neural networks (ANN). A data set from an RWIS station is used for model calibration and validation. The analysis indicates that multi-variable SARIMA is the most competitive technique and has the lowest number of forecasting errors.

Transportation networks play a significant role in the economy of Canadians during winter seasons; thus, maintaining a safe and economic flow of traffic on Canadian roads is crucial. Winter contaminants such as freezing rain, snow, and ice cause reduced friction between vehicle tires and pavement and thus increased accident-risk and decreased road capacity. The formation of ice and frost caused by snowfall and wind chill makes driving a very difficult task. Pavement surface temperature is an important indicator for road authorities when they are deciding the optimal time to apply anti-icer/deicer chemicals and when estimating their effect and the optimal amounts to apply. By forecasting pavement temperature, maintenance crews can figure out road surface conditions ahead of time and start their operations in a timely manner, thereby reducing salt use and increasing the safety and security of road users by eliminating accidents caused by slipperiness. This research investigates the feasibility of applying simple statistical models for forecasting road surface temperatures at locations where RWIS data are available. Two commonly used modeling techniques were considered: time-series analysis and artificial neural networks (ANN). A data set from an RWIS station is used for model calibration and validation. The analysis indicates that multi-variable SARIMA is the most competitive technique and has the lowest number of forecasting errors.

Lake surface temperature (LSTlake) can be obtained and studied in different ways: using in situ measurements, satellite imagery and modeling. Collecting spatially representative in situ data over lakes, especially for large and deep ones, is a real challenge. Satellite data products provide the opportunity to collect continuous data over very large geographic areas even in remote regions. Numerical modeling is also an approach to study the response and the role of lakes in the climate system. Satellite instruments provide spatial information unlike in situ measurements and one-dimensional (1-D) lake models that give vertical information at a single point or a few points in lakes. The advantage of remote sensing also applies to land where temperature measurements are usually taken at meteorological stations whose network is extremely sparse in northern regions. This thesis therefore examined the value of land/lake surface (skin) temperature (LSTland/lake) measurements from satellites as a complement to in situ point measurements and numerical modeling. The thesis is organized into two parts. The first part tested, two 1-D numerical models against in situ and satellite-derived LST measurements. LSTlake and ice phenology were simulated for various points at different depths on Great Slave Lake (GSL) and Great Bear Lake (GBL), two large lakes located in the Mackenzie River Basin in Canada’s Northwest Territories, using the 1-D Freshwater Lake model (FLake) and the Canadian Lake Ice Model (CLIMo) over the 2002-2010 period. Input data from three weather stations (Yellowknife, Hay River and Deline) were used for model simulations. LSTlake model results are compared to those derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observing System Terra and Aqua satellite platforms. The main goal was to examine the performance of the FLake and CLIMo models in simulating LSTlake and ice-cover under different conditions against satellite data products. Both models reveal a good agreement with daily average MODIS LSTlake from GSL and GBL on an annual basis. CLIMo showed a generally better performance than FLake for both lakes, particularly during the ice-cover season. Secondly, MODIS-derived lake and land surface temperature (LSTland/lake) products are used to analyze land and lake surface temperature patterns during the open-water and snow/ice growth seasons for the same period of time in the regions of both GBL and GSL. Land and lake temperatures from MODIS were compared with near-surface air temperature measurements obtained from nearby weather stations and with in situ temperature moorings in GBL. Results show a good agreement between satellite and in situ observations. MODIS data were found to be very useful for investigating both the spatial and temporal (seasonal) evolution of LSTland/lake over lakes and land, and for improving our understanding of thermodynamic processes (heat gains and heat loses) of the lake/land systems. Among other findings, the MODIS satellite imagery showed that the surface temperature of lakes is colder in comparison to the surrounding land from April-August and warmer from September until spring thaw.

Developing remote sensing instrumentation allows obtaining information about an area rapidly and with low costs. This fact offers a challenge to remote sensing algorithms aimed at extracting information about an area from the available re&not / mote sensing data. A very typical and important problem being interpretation of satellite images. A very efficient approach to remote sensing is employing discrim&not / inant functions to distinguish different landscape classes from satellite images. Various methods on this direction are already studied. However, the efficiency of the studied methods are still not very high. In this thesis, we will improve efficiency of remote sensing algorithms. Besides we will investigate improving boundary detection methods on satellite images.

The response of the tropical Atlantic Ocean to wind stress forcing on seasonal and interannual time scales is examined using an ocean data assimilation product from the Geophysical Fluid Dynamics Laboratory (GFDL), and an ocean general circulation model which incorporates a three dimensional flux correction technique to correct biases of the mean state of the ocean. On a seasonal time scale, we investigated the impact of the annual migration of the ITCZ on the exchange pathways of the northern tropical Atlantic. The results indicate that seasonal variation of the zonal slope of the thermal ridge along the boundary between the north equatorial countercurrent and north equatorial current in response to changes in the ITCZ controls, to a large extent, the amount of water participating in the equatorial circulation. These changes can be explained in terms of a simple dynamical model where local Ekman pumping dominates thermocline variation in the western part of the basin, and Rossby wave adjustment comes into play in the eastern basin. On an interannual time scale, we examined the upper heat budget of the equatorial Atlantic in order to identify the key mechanisms by which wind-driven ocean dynamics control SST variability during the onset and peak phases of the Atlantic zonal mode. It is found that, in contrast with Pacific ENSO, both Bjerknes and Ekman feedbacks act together to force the zonal mode, although their relative importance and dominance depend on season and location.

Sediment core MD02-2550 from Orca Basin located in the northern Gulf of Mexico (GOM) provides a high-resolution early Holocene record of climatic and hydrologic changes from ~10.5 to 7 thousand calendar years before present (ka). Paired analyses of Mg/Ca and δ18O on the planktonic foraminifer Globigerinoides ruber (white variety, 250-355 μm) sampled at ~ 20 year resolution were used to generate proxy records of sea surface temperature (SST) and the δ18O of seawater in the GOM (δ18OGOM). The Mg/Ca-SST record contains an overall ~1.5 °C warming trend from 10.5 to 7 ka that appears to track the intensity of the annual insolation cycle and six temperature oscillations (0.5-2 °C), the frequency of which are consistent with those found in records of solar variability. The δ18OGOM record contains six ~ 0.5 ‰ oscillations from 10.5 to 7 ka that bear some resemblance to regional hydrologic records from Haiti and the Cariaco Basin, plus a -0.8 ‰ excursion that may be associated with the “8.2 ka event” recorded in Greenland air temperatures. The δ18OGOM record, if interpreted as a salinity proxy, suggest large salinity fluctuations (> 2 ‰) reflecting changes in evaporation-precipitation (E-P) and Mississippi River input to the GOM. Percent Globigerinoides sacculifer records from three cores in the GOM exhibit remarkably coherent changes, suggesting episodic centennial-scale incursions of Caribbean waters. Spectral analysis of the Mg/Ca-SST and the δ18OGOM time series indicate that surface water conditions may be influenced by solar variations because they share significant periods of variability with atmospheric Δ 14C near 700, 200, and 80-70 years. Our results add to the growing body of evidence that the sub-tropics were characterized by significant decadal to centennial-scale climatic and hydrologic variability during the early Holocene.

Physical processes in the Gulf of Mexico (GOM) and mesoscale (10-300 km) processes associated with the Loop Current are fairly well known. However, little is known about the physical/ biological interactions of the frontal boundary system of the Loop Current. Zooplankton abundance and distribution was determined at 28 stations in the vicinity of the Loop Current. Species richness was high at all stations. Copepods comprised 60% of the total zooplankton collected. Oithona plumifera, Nannocalanus minor and Euchaeta marina were the most abundant copepods. Chaetognaths and ostracods were also very abundant and made up 11 and 5 % respectively of the zooplankton total. Total zooplankton abundance was higher at the boundary of the LC than it was inside the LC but not significantly different from abundances outside of the LC. Stations in the western Gulf of Mexico and on the western boundary had the highest abundances of zooplankton overall. The chlorophyll concentrations at the chlorophyll maximum were higher at the boundary of the LC than inside the LC. Physical-biological processes associated with the frontal boundary of the LC appear to influence the abundance and distribution of zooplankton in the GOM.

In the first phase of this project, four decades of monthly resolved geochemical variations from two massive heads of Montastraea were used to explore the reproducibility of the geochemical signal in these two corals from Looe Key, Florida. The coral d18O and d13C records of the two corals have statistically indistinguishable mean values, which is not the case for the coral Sr/Ca records implying that nonenvironmental factors are influencing coral Sr/Ca. Calibration equations relating coral geochemistry variations to environmental variations at Looe Key are different from previously published equations for Montastraea. These calibration differences are not related to growth-related kinetic effects, but may reflect variations in seawater chemistry in the coastal waters of the Florida Keys. Additional studies are needed to identify the causes of the observed geochemical variability. In the second phase of this study, fourteen decades of monthly resolved geochemical variations in another Montastraea coral from Looe Key, Florida were compared to records of sea-surface temperature (SST). Coral Sr/Ca and d18O variations have a weak relationship with variations in SST and skeletal extension rates; however, many events in the Sr/Ca and d18O records are coincident with anomalies in SST, growth, or precipitation. Strong coupling exists between Sr/Ca and d18O in both anomaly and mean annual perspectives, which reflects the combined influence of SST and growth related processes on the geochemical signal. Separating these impacts proved to be problematic due to modest agreements with each forcing variable. In the final phase of this study, geochemical records from three, mid-Holocene(~5 ka) fossil Montastraea corals from the Dry Tortugas, Florida were compared with geochemical records from modern Montastraea corals from the same region to investigate temporal changes in climate. Stable isotopic records show significant changes through time, which can be interpreted in terms of environmental variation; however, large inter-coral variability between modern specimens of Montastraea precludes meaningful assessment of Sr/Ca. The pattern and mean d18O values in the fossil corals reflects changes in both temperature and salinity are reminiscent of centennial-scale variability present in other records from this region.

Since most of the inhabitants in West Africa is working with, or are dependent on agricultural work, accurate weather forecasts are important in their daily work. Knowledge of when to start to sow is one of the most important features from a farmer’s point of view. It can be devastating for the farmers if the soil is not moist enough when planting since the crops risks to dry out, but also planting too late needs to be avoided since it will affect the growing time and therefore might reduce the production. In this thesis, investigations whether the start of the rainy season in Ghana and parts of Burkina Faso is predictable, only with the use of patterns in rainfall and changes in sea surface temperature in the Gulf of Guinea. The region of interest has been divided into four equally sized areas with a latitudinal width of 2  from south to north. The models are first of all predicting the start of the rainy season in the southernmost area by use of four different methods, three that are based on precipitation patterns and one based on changes in sea surface temperature. Thereafter, the three northerly areas are predicted with a linear function based on when the rainy season started in the southernmost area. The results shows that the model is acceptable in its predictability but is very good in indicating if the rainy season will start earlier or later than the year before. This is of major benefits for the farmers in the region. On a long‐range average, the rainy season starts in the southernmost area first and then it starts further north, but this is not always the case in individual years, which makes the models complicated to use in some years. In order to give reliable forecasts to the farmers, the rainy season needs to be defined so it fulfils the conditions that are needed for plants to grow. Therefore, the start of the rainy season is defined as when 40 mm of precipitation is received during a five‐day period with at least 16 mm in one of these five days. Thereafter, the next 30 days cannot contain more than 18 days without precipitation. / Eftersom de flesta invånarna i Västafrika arbetar med, eller är beroende av jordbruksarbete så är väderprognoser till stor hjälp i det dagliga arbetet. Att ha kännedom om när det är lämpligt att börja så är en av de viktigaste aspekterna ur böndernas perspektiv. Att börja så innan marken är tillräckligt fuktig kan leda till förödande konsekvenser för bönderna då grödorna riskerar att torka ut och dö, men även att vänta för länge med att så bör undvikas eftersom det påverkar längden på skördesäsongen och därmed också produktionen. I den här studien har det gjorts undersökningar om det är möjligt att göra prognoser för när regnperioden börjar i Ghana och delar av Burkina Faso med hjälp av nederbördsfördelningen och förändringar i ytvattentemperaturen i Guineabukten. Regionen har delats in i fyra lika stora områden med latitudinell bredd på 2 ° från söder till norr. Modellerna börjar med att göra en prognos för regnperiodens början i det sydligaste området med hjälp av fyra olika metoder, tre som är baserade på nederbördsfördelningen och en som är baserad på ändringar i ytvattentemperaturen. Därefter görs prognoser för de tre nordligare områdena med hjälp av en linjär funktion baserad på när regnperioden började i det sydligaste området. Resultaten visar att modellen är acceptabel när det gäller att komma så nära den verkliga starten som möjligt, men är väldigt bra på att indikera om regnperioden kommer att börja tidigare eller senare än året innan. Detta är till stor nytta för bönderna i området. Över ett längre perspektiv så börjar regnperioden först i det sydligaste området för att sedan börjar längre norrut, men så ser det inte ut i varje enskilt år, vilket gör att modellerna inte är användbara alla år. För att kunna ge bönderna så bra prognoser som möjligt så behöver regnperioden definieras så att den uppfyller de villkor som krävs för att de ska kunna börja så. Därför har regnperiodens början definierats som när 40 mm nederbörd mottagits under en femdagarsperiod med minst 16 mm under en av dessa fem dagar. Därefter får de närmaste 30 dagarna inte innehålla mer än 18 dagar utan nederbörd.