The transfer of sparingly soluble gases across the air-water interface has significant effects on the distribution of the constituents in aquatic ecosystems. Gas-liquid transfer rate determines the flux of the sparingly soluble gases driven by the concentration difference. Considerable stream-driven gas-liquid transfer rate formulae have been developed. They have reasonable predictions in one-dimensional uniform flows. However, their applications in more complex cases such as three-dimensional flows are problematic. Furthermore, the wind effects are not incorporated into these formulae. New models need to be developed for gas-liquid transfer rate in three-dimensional flows that incorporate the effects of both wind and streamflow. In this study, first, a model of gas-liquid transfer rate in non-isotropic turbulent flows is developed. Second, a general stream-driven gas-liquid transfer rate model is developed for the normal ranges of water depth and flow velocity in natural rivers. Third, a wind-stream-driven gas-liquid transfer rate model is developed. Fourth, a model of surface renewal rate caused by turbulence from transition location of shear flows is developed. Fifth, a gas-liquid transfer rate model for wind and dynamic three-dimensional flow systems is developed. A computer program is coded and applied to various cases from simple one-dimensional uniform flow systems to complex wind and dynamic three-dimensional flow systems. A specific model can be selected from the series models for a specific application based on the application requirements and the acceptable computation complexity.

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<p>Traditional methods for flood estimation can be categorized as (1) simplified methods, e.g., regression analysis, (2) frequency analysis of streamflow data, (3) design storm-based precipitation-runoff modeling, and ( 4) continuous precipitation-runoff simulation modeling. The new approach - the Analytical Probabilistic Stormwater Model - was developed as an alternative to provide an efficient way of getting realistic estimation of peak discharges of desired frequencies for use in stormwater management of urban areas. To extend APSWM's application to rural areas, a series of comparisons were made between the calibrated design storm-based OTTHYMO model results, frequency analysis results and APSWM results for the Ganaraska River watershed. Special considerations were given to the transformation of the input parameter values of OTTHYMO model to those of APSWM. Comparable results were obtained for large floods, while APSWM may underestimate peak discharges of low return periods. Upon further testing and development, APSWM may be used for large rural areas.</p> / Thesis / Master of Engineering (MEngr)

Several different approaches are applied in low flow frequency analysis. Each method's theory and application is explained. The methods are (1) physically based recession model dealing with time series, (2) log-Pearson type III and mixed log-Pearson type III using annual minimum series, (3) Double Bounded pdf using annual minimum series, (4) Partial Duration Series applying truncated and censored flows. Each method has a computer program for application. One day low flow analysis was applied to 15 stations, 10 perennial streams and 5 intermittent streams. The physically based method uses the exponential baseflow recession with duration, initial recession flow, and recharge due to incoming storm as random variables, and shows promise as an alternative to black box methods, and is appealing because it contains the effect of drought length. Log-Pearson is modified to handle zero flows by adding a point mass probability for zero flows. Another approach to zero flows is the Double Bounded probability density function which also includes a point mass probability for zero flows. Maximum likelihood estimation is used to estimate distribution parameters. Partial Duration Series is applied due to drawbacks of using only one low flow per year in annual minimum series. Two approaches were used in Partial Duration Series (i) truncation, and (ii) censorship which represent different low flow populations. The parameters are estimated by maximum likelihood estimation. / M.S.

Characterizing streamflow trends is important for water resources management. Streamflow conditions, and trends thereof, are critical drivers of all aspects of stream geomorphology, sediment and nutrient transport, and ecological processes. Using the non-parametric modified Mann-Kendall test, we analyzed streamflow trends from 1996 to 2022 for the Southern Appalachian (SA) region of the U.S. The forested uplands of the SA receive high amounts of rain and act as a "water tower" for the surrounding lowland area, both of which have experienced higher than average population growth and urban development. For the total of 127 USGS gages with continuous streamflow measurements, we also evaluated precipitation and land change rates and patterns within the upstream contributing areas. Statistical methods (i.e., generalized linear models) were then used to assess any linkages between land cover change (LCC) and streamflow trends. Our results show that 42 drainage areas are experiencing increasing trends in their precipitation, and 1 is experiencing a negative trend. A total of 71 drainage areas are experiencing increasing trends in either their annual streamflow minimums, maximums, medians, or variability, with some experiencing changes in multiple. From our models, it is suggested that agricultural expansion is associated with increasing minimum streamflow trends, but increasing precipitation is also positively linked. With this information, water managers would be aware of which areas are experiencing changes in streamflow amounts from LCC or precipitation and could then apply this in planning and predictions. / Master of Science / Water availability is important for resources management. Streamflow is a measure of available surface water and is an important component in the hydrological cycle. Using the non-parametric modified Mann-Kendall test, we analyzed streamflow trends from 1996 to 2022 for the Southern Appalachian (SA) region of the U.S. The forested uplands of the SA receive high amounts of rain and act as a "water tower" for the surrounding lowland area, both of which have experienced higher than average population growth and city expansion. For the total of 127 USGS gages with continuous streamflow measurements, we also evaluated precipitation and land cover change rates within the area upstream of the gage (or drainage/contributing area). Statistical methods (i.e., generalized linear models) were then used to assess any linkages between land cover change (LCC) and streamflow trends. Our results show that 42 drainage areas are experiencing increasing trends in their precipitation, and 1 is experiencing a negative trend. A total of 71 drainage areas are experiencing increasing trends in either their annual streamflow minimums, maximums, medians, or variability, with some experiencing changes in multiple. From our models, it is suggested that agricultural expansion is associated with increasing minimum streamflow trends, but increasing precipitation is also positively linked. With this information, water managers would be aware of which areas are experiencing changes in streamflow amounts from LCC or precipitation and could then apply this in planning and predictions.

Tennessee has a long history of meteorological hazards that have caused property damage and loss of life. Given climate change and variability, it is imperative to look at trends to ascertain changes spatiotemporally. Space-time cubes, a novel geographic tool, were used to analyze historical heavy precipitation (1-, 2-, and 5-year returns), floods, and flash flood data in Tennessee counties to assess the trends, identify emerging hotspots/cold spots and display changes over space and time. For all return periods, trends analysis revealed that heavy precipitation events are increasing in several counties across the state, with middle Tennessee identified as a hotspot. While floods and flash flood event trends are mixed (with both increases and decreases) across the state counties, related property damages are increasing, especially in middle Tennessee. This study is an important step to understanding spatiotemporal trends and will be useful in federal, state, and county hazard mitigation planning.

Post-processing bias correction of streamflow forecasts can be useful in the hydrologic modeling workflow to fine-tune forecasts for operations, water management, and decision-making. Hydrologic model runoff simulations include errors, uncertainties, and biases, leading to less accuracy and precision for applications in real-world scenarios. We used random forest regression to correct biases and errors in streamflow predictions from the U.S. National Water Model (NWM) long-range streamflow forecasts, considering U.S. Geological Survey (USGS) gauge station measurements as a proxy for true streamflow. We used other features in model training, including watershed characteristics, time fraction of year, and lagged streamflow values, to help the model perform better in gauged and ungauged areas. We assessed the effectiveness of the bias correction technique by comparing the difference between forecast and actual streamflow before and after the bias correction model was employed. We also explored advances in hydroinformatics and cloud computing by creating and testing this bias correction capability within the Google Cloud Console environment to avoid slow and unnecessary data downloads to local devices, thereby streamlining the data processing and storage within the cloud. This demonstrates the possibility of integrating our method into the NWM real-time forecasting workflow. Results indicate reasonable bias correction is possible using the random forest regression machine learning technique. Differences between USGS discharge and NWM forecasts are less than the original difference observed after being run through the random forest model. The main issue concerning the forecasts from the NWM is that the error increases further from the reference time or start of the forecast period. The model we created shows significant improvement in streamflow the further the times get from the reference time. The error is reduced and more uniform throughout all the time steps of the 30-day long-range forecasts.

Algumas abordagens para o problema de Planejamento Ótimo da Operação de Sistemas Hidrotérmicos (POOSH) utilizam modelos estocásticos para representar as vazões afluentes dos reservatórios do sistema. Essas abordagens utilizam, em geral, técnicas de Programação Dinâmica Estocástica (PDE) para resolver o POOSH. Por outro lado, muitos autores têm defendido o uso dos modelos determinísticos ou, particularmente, a Programação Dinâmica Determinística (PDD) por representar de forma individualizada a interação entre as usinas hidroelétricas do sistema. Nesse contexto, esta dissertação tem por objetivo comparar o desempenho da solução do POOSH obtida via PDD com a solução obtida pela PDE, que emprega um modelo Markoviano periódico, com distribuição condicional Log-Normal Truncada para representar as vazões. Além disso, é realizada a análise com abordagem bayesiana, no modelo de vazões, para estimação dos parâmetros e previsões das vazões afluentes. Comparamos as performances simulando a operação das usinas hidroelétricas de Furnas e Sobradinho, considerando séries de vazões geradas artificialmente / Some approaches for problem of Optimal Operation Planning of Hydrothermal Systems (OOPHS) use stochastic models to represent the inflows in the reservoirs that compose the system. These approaches typically use the Stochastic Dynamic Programming (SDP) to solve the OOPHS. On the other hand, many authors defend the use of deterministic models and, particularly, the Deterministic Dynamic Programming (DDP) since it individually represents the interaction between the hydroelectric plants. In this context, this dissertation aims to compare the performance of the OOPHS solution obtained via DDP with the one given by SDP, which employs a periodic Markovian model with conditional Truncated Log-Normal distribution to represent the inflows. Furthermore, it is performed a bayesian approach analysis, in the inflow model, for estimating the parameters and forecasting the inflows. We have compared the performances of the DDP and SDP solutions by simulating the hydroelectric plants of Furnas and Sobradinho, employing artificially generated series

Study region Nyangores River watershed, headwater catchment of Mara River basin in Kenya. Study focus Climate variability and human activities are the main drivers of change of watershed hydrology. The contribution of climate variability and land use change to change in streamflow of Nyangores River, was investigated. Mann Kendall and sequential Mann Kendall tests were used to investigate the presence and breakpoint of a trend in discharge data (1965–2007) respectively. The Budyko framework was used to separate the respective contribution of drivers to change in discharge. Future response of the watershed to climate change was predicted using the runoff sensitivity equation developed. New hydrological insights for the region There was a significant increasing trend in the discharge with a breakpoint in 1977. Land use change was found to be the main driver of change in discharge accounting for 97.5% of the change. Climate variability only caused a net increase of the remaining 2.5% of the change; which was caused by counter impacts on discharge of increase in rainfall (increased discharge by 24%) and increase in potential evapotranspiration (decreased discharge by 21.5%). Climate change was predicted to cause a moderate 16% and 15% increase in streamflow in the next 20 and 50 years respectively. Change in discharge was specifically attributed to deforestation at the headwaters of the watershed.

Algumas abordagens para o problema de Planejamento Ótimo da Operação de Sistemas Hidrotérmicos (POOSH) utilizam modelos estocásticos para representar as vazões afluentes dos reservatórios do sistema. Essas abordagens utilizam, em geral, técnicas de Programação Dinâmica Estocástica (PDE) para resolver o POOSH. Por outro lado, muitos autores têm defendido o uso dos modelos determinísticos ou, particularmente, a Programação Dinâmica Determinística (PDD) por representar de forma individualizada a interação entre as usinas hidroelétricas do sistema. Nesse contexto, esta dissertação tem por objetivo comparar o desempenho da solução do POOSH obtida via PDD com a solução obtida pela PDE, que emprega um modelo Markoviano periódico, com distribuição condicional Log-Normal Truncada para representar as vazões. Além disso, é realizada a análise com abordagem bayesiana, no modelo de vazões, para estimação dos parâmetros e previsões das vazões afluentes. Comparamos as performances simulando a operação das usinas hidroelétricas de Furnas e Sobradinho, considerando séries de vazões geradas artificialmente / Some approaches for problem of Optimal Operation Planning of Hydrothermal Systems (OOPHS) use stochastic models to represent the inflows in the reservoirs that compose the system. These approaches typically use the Stochastic Dynamic Programming (SDP) to solve the OOPHS. On the other hand, many authors defend the use of deterministic models and, particularly, the Deterministic Dynamic Programming (DDP) since it individually represents the interaction between the hydroelectric plants. In this context, this dissertation aims to compare the performance of the OOPHS solution obtained via DDP with the one given by SDP, which employs a periodic Markovian model with conditional Truncated Log-Normal distribution to represent the inflows. Furthermore, it is performed a bayesian approach analysis, in the inflow model, for estimating the parameters and forecasting the inflows. We have compared the performances of the DDP and SDP solutions by simulating the hydroelectric plants of Furnas and Sobradinho, employing artificially generated series