Desequilíbrios ambientais provocados pela combinação de queimadas, desmatamentos e os fenômenos de ENSO (Oscilação Sul de El Nino) podem ser os responsáveis pelo aumento de períodos de seca na região Amazônica. Com o propósito de compreender as conseqüências que longos períodos de seca podem causar na Floresta Nacional de Tapajós, foi desenvolvido um modelo numérico que simula a dinâmica da água no solo e o Balanço Hídrico para um latossolo (Haplustox, na taxonomia Americana), muito comum na região Amazônica. As simulações foram realizadas para o período de 1999 a 2003, utilizando dados de precipitação, evapotranspiração, umidade do solo, curvas de retenção, propriedades físicas do solo coletadas no local de estudo. Este estudo integra o Projeto Seca-Floresta do grupo de pesquisa do LBA (Experimento de Grande Escala da Biosfera-Atmosfera na Amazônia) para o experimento de exclusão parcial da chuva no projeto na Floresta Nacional Tapajós no que diz respeito a componente de modelagem hidrológica. Os resultados mostraram que, mesmo com a diminuição da quantidade de água disponível para a Floresta, não houve mudanças significativas em relação ao balanço hídrico da floresta, mostrando que a floresta provavelmente se adaptou, para sobreviver a longos períodos de seca / Environmental instability caused by the combination of fire, deforestation and the ENSO phenomena (El Nino South Oscillation) can be the responsible for increases of dry periods in the Amazonian region. With the purpose of understanding the consequences that long dry periods can cause on the National Forest of Tapajós, a mathematical model that determines the water dynamics in soil and the hydrological balance was developed for a typical soil of the Amazonian region the Amazonianlatossol (Haplustox). The simulation were performed for the period from 1999 to 2003, using precipitation, evapotranspiration, soil moisture, retention curves and soil physical properties data obtained in the study area. The present study integrates the Dry-Forest Project of the LBA (Large Scale Biosphere-Atmosphere experiment in the Amazon) research group for the experiment of rainfall exclusion in the National Forest of Tapajos and concerns the hydrological modeling component of the project. The results showed that even with a decrease in the amount of water available to the Forest, significant changes in the hydrological balance of the forest did not occur, showing that the forest had probably adapted itself to survive to longer periods of drought

Government Island, located in the Columbia River approximately 16 km (10 mi) upstream of the confluence with the Willamette River, is a wetland mitigation site prompted by expansion of the southwest quadrant of Portland International Airport. The purpose of the study is to predict water levels in two enclosed lowland areas, Jewit Lake and Southeast Pond, based on levels of the Columbia River, precipitation, and evapotranspiration. Mitigation is intended to convert 1.13 km2 (237 acres) of seasonally flooded wetland to 1.27 km2 (267 acres) of semi-permanently flooded wetland and seasonally flooded wetland. Flooding of the wetland is most likely to occur December through January and May through early June when Columbia River water levels at Government Island exceed 3.6 m (12 ft) m.s.l. Flooding of Jewit Lake occurs through a channel connecting the wetland to the Columbia River. A groundwater model (MODFLOW) was parameterized to simulate the hydrology of the wetland. Observations of the subsurface stratigraphy in 25 soil pits, bucket auger cores, and during installation of water monitoring devices were used to estimate thickness and lateral extent of a confining unit that overlies an aquifer. Climatological data for 1994 and water levels were entered into MODFLOW to calibrate rates of water movement through the subsurface. Periods of drying for Jewit Lake and Southeast Pond were predicted based on precipitation and actual evapotranspiration rates expected to be present in the study area between June and December. Results of groundwater modeling show that Jewit Lake will maintain surface water above 3.6 m (12 ft) in most years. Southeast Pond is expected to dry annually as mitigation is unlikely to change the hydrology of Southeast Pond. Groundwater modeling predicted the types of wetlands present at different elevations by evaluating periods of drying within the wetland using the U.S. Fish and Wildlife Service classification of wetlands method. Results suggest that Jewit Lake will be converted to semipermanently flooded wetland below 3.6 m (12 ft) in elevation. Southeast Pond will remain a seasonally flooded wetland as a result of mitigation.

No description available.

This paper examines the effect of uncertain initial soil moisture on hydrologic response at the plot scale (1 m2) and the catchment scale (3.6 km2) in the presence of threshold transitions between matrix and preferential flow. We adopt the concepts of microstates and macrostates from statistical mechanics. The microstates are the detailed patterns of initial soil moisture that are inherently unknown, while the macrostates are specified by the statistical distributions of initial soil moisture that can be derived from the measurements typically available in field experiments. We use a physically based model and ensure that it closely represents the processes in the Weiherbach catchment, Germany. We then use the model to generate hydrologic response to hypothetical irrigation events and rainfall events for multiple realizations of initial soil moisture microstates that are all consistent with the same macrostate. As the measures of uncertainty at the plot scale we use the coefficient of variation and the scaled range of simulated vertical bromide transport distances between realizations. At the catchment scale we use similar statistics derived from simulated flood peak discharges. The simulations indicate that at both scales the predictability depends on the average initial soil moisture state and is at a minimum around the soil moisture value where the transition from matrix to macropore flow occurs. The predictability increases with rainfall intensity. The predictability increases with scale with maximum absolute errors of 90 and 32% at the plot scale and the catchment scale, respectively. It is argued that even if we assume perfect knowledge on the processes, the level of detail with which one can measure the initial conditions along with the nonlinearity of the system will set limits to the repeatability of experiments and limits to the predictability of models at the plot and catchment scales.

Wind transport and sublimation of snow particles are common phenomena across high altitude and latitude cold regions and play important roles in hydrological and atmospheric water and energy budgets. In spite of this, blowing snow processes have not been incorporated in many mesoscale hydrological models and land surface schemes. A physically based blowing snow model, the Prairie Blowing Snow Model (PBSM), initially developed for prairie environments was used to model snow redistribution and sublimation by wind over two sites representative of mountainous regions in Canada: Fisera Ridge in the Rocky Mountain Front Ranges in Alberta, and Granger Basin in the Yukon Territory. Two models were used to run PBSM: the object-oriented hydrological model, Cold Regions Hydrological Modelling Platform (CRHM) and Environment Canadas hydrological-land surface scheme, Modélisation Environmentale Communautaire Surface and Hydrology (MESH). PBSM was coupled with the snowcover energy and mass-balance model (SNOBAL) within CRHM. Blowing snow algorithms were also incorporated into MESH to create MESH-PBSM. CRHM, MESH and MESH-PBSM were used to simulate the evolution of snowcover in hydrological response units (HRUs) over both Fisera Ridge and Granger Basin.<p> To test the models of blowing snow redistribution and ablation over a relatively simple sequence of mountain topography, simulations were run from north to south over a linear ridge in the Canadian Rocky Mountains. Fisera Ridge snowcover simulations with CRHM were performed over two winters using two sets of wind speed forcing: (1) station observed wind speed, and (2) modelled wind speed from a widely applied empirical, terrain-based windflow model. Best results were obtained when using the site meteorological station wind speed data. The windflow model performed poorly when comparing the magnitude of modelled and observed wind speeds. Blowing snow sublimation, snowmelt and snowpack sublimation quantities were considerably overestimated when using the modelled wind speeds. As a result, end-of-winter snow accumulation was considerably underestimated on windswept HRUs. MESH and MESH-PBSM were also used to simulate snow accumulation and redistribution over these same HRUs. MESH-PBSM adequately simulated snow accumulation in the HRUs up until the spring snowmelt period. MESH without PBSM performed less well and overestimated accumulation on windward slopes and the ridge top whilst underestimating accumulation on lee slopes. Simulations in spring were degraded by a large overestimation of melt by MESH. The early and overestimated melt warrants a detailed examination that is outside the scope of this thesis.<p> To parameterize snow redistribution in a mountain alpine basin, snow redistribution and sublimation by wind were calculated for three winters over Granger Basin using CRHM. Snow transport fluxes were distributed amongst HRUs using inter-HRU snow redistribution allocation factors. Three snow redistribution schemes of varying complexity were evaluated. CRHM model results showed that end-of-winter snow accumulation can be most accurately simulated when the inter-HRU snow redistribution schemes take into account wind direction and speed and HRU aerodynamic characteristics, along with the spatial arrangement of HRUs in the catchment. As snow transport scales approximately with the fourth power of wind speed (u4), inter-HRU snow redistribution allocation factors can be established according to the predominant u4 direction over a simulation period or can change at each time step according to an input measured wind direction. MESH and MESH-PBSM were used to simulate snow accumulation and ablation over these same HRUs. MESH-PBSM provided markedly better results than MESH without blowing snow algorithms.<p> That snow redistribution by wind can be adequately simulated in computationally efficient HRUs over mountainous terrain has important implications for representing snow transport in large-scale hydrology models and land surface schemes. Snow redistribution by wind caused mountain snow accumulation to vary from 10% to 161% of seasonal snowfall within a headwater catchment in the Canadian Rocky Mountains, and blowing snow sublimation losses ranged from 10 to 37% of seasonal snowfall.

The cryosphere is defined as the portions of the earth where water is in solid form. It represents a very important part of the hydrologic cycle, affecting ecological, human and climate systems. A number of component models describing the energy and mass balances of a snowpack have been developed and these component models are finding their way into watershed models and land surface schemes. The purpose of this thesis is to examine the incorporation of a number of snow processes in the coupled land-surface-hydrological model WATCLASS. The processes under consideration were mixed precipitation, variable fresh snow density, maximum snowpack density, canopy interception and snow-covered area (SCA). The first four of these processes were based on similar work done by Fassnacht (2000) on a watershed in Southern Ontario. In the case of this thesis, the work was completed on a basin in Northern Manitoba. A theory of the relationship between snow-covered area and average snow depth was developed and an algorithm was developed to implement this theory in WATCLASS. Of the five snow processes considered, mixed precipitation was found to have the greatest impact on streamflow while the new canopy interception algorithm was found to have the greatest impact on sensible and latent heat fluxes. The development of a new relationship between SCA and average snow depth was found to have a minimal impact in one study case, but a significant impact on the sensible and latent heat fluxes when snow fell on a pack that had begun to melt and was partially free of snow. Further study of these snow processes in land-surface-hydrologic models is recommended.

Weather radar has long been recognized as a potentially powerful tool for hydrological modelling. A single radar station is able to provide detailed precipitation information over entire watersheds. The operational use of radar in water resources applications, however, has been limited. Interpretation of raw radar data requires several rigorous analytical steps and a solid understanding of the technology. In general, hydrologists’ lack of meteorological background and the persistence of systematic errors within the data, has led to a common mistrust of radar-estimated precipitation values. As part of the Enhanced Nowcasting of Extreme Weather project, researchers at McGill University’s J.S. Marshall Radar Observatory in Montreal have been working to improve real-time quantitative precipitation estimates (QPEs). The aim is to create real-time radar precipitation products for the water resource community that are reliable and properly validated. The validation of QPEs is traditionally based on how well observed measurements agree with data from a precipitation gauge network. Comparisons between radar and precipitation gauge quantities, however, can be misleading. Data from a precipitation gauge network represents a series of single-point observations taken near ground surface. Radar, however, estimates the average rate of precipitation over a given area (i.e. a 1-km grid cell) based on the intensity of reflected microwaves at altitudes exceeding 1 km. Additionally, both measurement techniques are susceptible to a number of sources of error that further confound efforts to compare the two. One of the greatest challenges facing radar meteorologists is the variation in the vertical profile of reflectivity (VPR). A radar unit creates a volumetric scan of the atmosphere by emitting microwave beams at several elevation angles. As a beam travels away from the radar, its distance from ground surface increases. Different precipitation types are sampled at a number of heights (i.e. snow above the 0º C elevation and rain below it) that vary with range. The difficulty lies in estimating the intensity of precipitation at the Earth’s surface, based on measurements taken aloft. Scientists at McGill University have incorporated VPR correction techniques into algorithms used to automatically convert raw radar data into quantitative hydrological products. This thesis evaluates three real-time radar precipitation products from McGill University’s J.S. Marshall Radar Observatory in the context of hydrological modelling. The C0 radar product consists of radar precipitation estimates that are filtered for erroneous data, such as ground clutter and anomalous precipitation. The C2 and C3 radar products use different VPR correction techniques to improve upon the C0 product. The WATFLOOD hydrological model is used to assess the ability of each radar product to estimate precipitation over several watersheds within the McGill radar domain. It is proposed that using a watershed as sample area can reduce the error associated with sampling differences between radar and precipitation gauges and allow for the evaluation of a precipitation product over space and time. The WATFLOOD model is run continuously over a four-year period, using each radar product as precipitation input. Streamflow hydrographs are generated for 39 gauging stations within the radar domain, which includes parts of eastern Ontario, south-western Quebec and northern New York and Vermont, and compared to observed measurements. Streamflows are also modelled using distributed precipitation gauge data from 44 meteorological stations concentrated around the Montreal region. Analysis of select streamflow events reveals that despite the non-ideal placement of precipitation gauges throughout the study area, distributed precipitation gauge data are able to reproduce hydrological events with greater accuracy and consistency than any of the provided radar products. Precipitation estimates within the McGill radar domain are found to only be useful in areas within the Doppler range (120-km) where the radar beam is unobstructed by physiographic or man-made features. Among radar products, the C2 VPR-corrected product performed best during the greatest number of the flood events throughout the study area.

This thesis examines the feasibility of assimilating space borne remotely-sensed microwave data into WATClass using the ensemble Kalman filter. WATClass is a meso-scale gridded hydrological model used to track water and energy budgets of watersheds by way of real-time remotely sensed data. By incorporating remotely-sensed soil moisture estimates into the model, the model’s soil moisture estimates can be improved, thus increasing the accuracy of the entire model. Due to the differences in scale between the remotely sensed data and WATClass, and the need of ground calibration for accurate soil moisture estimation from current satellite-borne active microwave remote sensing platforms, the spatial variability of soil moisture must be determined in order to characterise the dependency between the remotely-sensed estimates and the model data and subsequently to assimilate the remotely-sensed data into the model. Two sets of data – 1996-1997 Grand River watershed data and 2002-2003 Roseau River watershed data – are used to determine the spatial variability. The results of this spatial analysis however are found to contain too much error due to the small sample size. It is therefore recommended that a larger set of data with more samples both spatially and temporally be taken. The proposed algorithm is tested with simulated data in a simulation of WATClass. Using nominal values for the estimated errors and other model parameters, the assimilation of remotely sensed data is found to reduce the absolute RMS error in soil moisture from 0.095 to approximately 0.071. The sensitivities of the improvement in soil moisture estimates by using the proposed algorithm to several different parameters are examined.

The cryosphere is defined as the portions of the earth where water is in solid form. It represents a very important part of the hydrologic cycle, affecting ecological, human and climate systems. A number of component models describing the energy and mass balances of a snowpack have been developed and these component models are finding their way into watershed models and land surface schemes. The purpose of this thesis is to examine the incorporation of a number of snow processes in the coupled land-surface-hydrological model WATCLASS. The processes under consideration were mixed precipitation, variable fresh snow density, maximum snowpack density, canopy interception and snow-covered area (SCA). The first four of these processes were based on similar work done by Fassnacht (2000) on a watershed in Southern Ontario. In the case of this thesis, the work was completed on a basin in Northern Manitoba. A theory of the relationship between snow-covered area and average snow depth was developed and an algorithm was developed to implement this theory in WATCLASS. Of the five snow processes considered, mixed precipitation was found to have the greatest impact on streamflow while the new canopy interception algorithm was found to have the greatest impact on sensible and latent heat fluxes. The development of a new relationship between SCA and average snow depth was found to have a minimal impact in one study case, but a significant impact on the sensible and latent heat fluxes when snow fell on a pack that had begun to melt and was partially free of snow. Further study of these snow processes in land-surface-hydrologic models is recommended.

Weather radar has long been recognized as a potentially powerful tool for hydrological modelling. A single radar station is able to provide detailed precipitation information over entire watersheds. The operational use of radar in water resources applications, however, has been limited. Interpretation of raw radar data requires several rigorous analytical steps and a solid understanding of the technology. In general, hydrologists’ lack of meteorological background and the persistence of systematic errors within the data, has led to a common mistrust of radar-estimated precipitation values. As part of the Enhanced Nowcasting of Extreme Weather project, researchers at McGill University’s J.S. Marshall Radar Observatory in Montreal have been working to improve real-time quantitative precipitation estimates (QPEs). The aim is to create real-time radar precipitation products for the water resource community that are reliable and properly validated. The validation of QPEs is traditionally based on how well observed measurements agree with data from a precipitation gauge network. Comparisons between radar and precipitation gauge quantities, however, can be misleading. Data from a precipitation gauge network represents a series of single-point observations taken near ground surface. Radar, however, estimates the average rate of precipitation over a given area (i.e. a 1-km grid cell) based on the intensity of reflected microwaves at altitudes exceeding 1 km. Additionally, both measurement techniques are susceptible to a number of sources of error that further confound efforts to compare the two. One of the greatest challenges facing radar meteorologists is the variation in the vertical profile of reflectivity (VPR). A radar unit creates a volumetric scan of the atmosphere by emitting microwave beams at several elevation angles. As a beam travels away from the radar, its distance from ground surface increases. Different precipitation types are sampled at a number of heights (i.e. snow above the 0º C elevation and rain below it) that vary with range. The difficulty lies in estimating the intensity of precipitation at the Earth’s surface, based on measurements taken aloft. Scientists at McGill University have incorporated VPR correction techniques into algorithms used to automatically convert raw radar data into quantitative hydrological products. This thesis evaluates three real-time radar precipitation products from McGill University’s J.S. Marshall Radar Observatory in the context of hydrological modelling. The C0 radar product consists of radar precipitation estimates that are filtered for erroneous data, such as ground clutter and anomalous precipitation. The C2 and C3 radar products use different VPR correction techniques to improve upon the C0 product. The WATFLOOD hydrological model is used to assess the ability of each radar product to estimate precipitation over several watersheds within the McGill radar domain. It is proposed that using a watershed as sample area can reduce the error associated with sampling differences between radar and precipitation gauges and allow for the evaluation of a precipitation product over space and time. The WATFLOOD model is run continuously over a four-year period, using each radar product as precipitation input. Streamflow hydrographs are generated for 39 gauging stations within the radar domain, which includes parts of eastern Ontario, south-western Quebec and northern New York and Vermont, and compared to observed measurements. Streamflows are also modelled using distributed precipitation gauge data from 44 meteorological stations concentrated around the Montreal region. Analysis of select streamflow events reveals that despite the non-ideal placement of precipitation gauges throughout the study area, distributed precipitation gauge data are able to reproduce hydrological events with greater accuracy and consistency than any of the provided radar products. Precipitation estimates within the McGill radar domain are found to only be useful in areas within the Doppler range (120-km) where the radar beam is unobstructed by physiographic or man-made features. Among radar products, the C2 VPR-corrected product performed best during the greatest number of the flood events throughout the study area.