We present a tree-ring based reconstruction of water-year (October–September) streamflow for the Manasi River in the northern Tien Shan mountains in northwestern China. We developed eight Tien Shan spruce (Picea schrenkiana Fisch. et Mey.) chronologies for this purpose, which showed a common climatic signal. The hydroclimatic forcing driving tree growth variability affected streamflow with a three- to four-year lag. The model used to estimate streamflow is based on the average of three chronologies and reflects the autoregressive structure of the streamflow time series. The model explains 51% of variance in the instrumental data and allowed us to reconstruct streamflow for the period 1629–2000. This preliminary reconstruction could serve as a basis for providing a longer context for evaluating the recent (1995–2000) increasing trends in Manasi River streamflow and enables the detection of sustained periods of drought and flood, which are particularly challenging for managing water systems. Several of the reconstructed extended dry (wet) periods of the Manasi River correspond to reconstructed periods of drought (flood) in Central Asia in general and in other Tien Shan mountain locations in particular, suggesting that the analysis of Tien Shan spruce could contribute significantly to the development of regionally explicit streamflow reconstructions.

Redes Neurais Artificiais (RNA's) vem sendo utilizada em diversas áreas do conhecimento inclusive para a previsão de séries temporais. O objetivo deste trabalho é utilizar uma RNA para o diagnóstico e prognóstico de vazão em bacias urbanas da Região Metropolitana de São Paulo (RMSP) com dados do radar meteorológio de São Paulo e os dados telemétricos da bacia do Alto Tietê. Uma RNA do tipo feedforward multicamadas, com aprendizado supervisionado e com o algoritmo de treinamento Linear Least Square SIMplex (LLSSIM, Hse et al. 1996) foram aplicados à bacia do Rio Tamanduateí. Dividiu-se os eventos disponíveis em três grupos; para o treinamento, verificação e previsão ideal com a RNA. Realizou o treinamento e verificação da rede com dados de vazão estimada e nível medido. Os erros de fase e amplitude foram utilizados para avaliar o desempenho da rede em cada uma das configurações empregadas. Estes indicam a importância da memória da bacia para o bom desempenho da RNA. Verifica-se também que nem sempre o aumento do número de camadas escondidas melhoram os resultados, bem como o aumento da quantidade de dados. Comparou-se ainda a performance da RNA contra um modelo auto regressivo sendo a primeira menos dependente da memória da bacia. Por último, realizou previsões do tipo ideal com resultados satisfatórios até 1 hora e 30 minutos de antecedência. Além deste período os erros crescem exponencialmente. / Artificial Neural Networks (ANN) have been widely used in several areas of science for many purposes, including time series forecasting. The objective of this work is to apply an ANN to simulate and to forecast streamflow at the outlet of the Tamanduateí basin. This urban basin is located within the Metropolitan Area of São Paulo (MASP). Radar and telemetric data are input to a multi-layer feedforward ANN. It is trained with the Linear Least Square SIMplex training algorithm (LLSSIM; Hse et al, 1996). Available flood events were divided up in three independent groups for training, verification and forecasting. The training and the verification of estimated streamflow and measured river stage were carried out. Phase and amplitude errors were used to evaluate the performance of the ANN for each configuration. The results indicate a better performance of the ANN when previous streamflow or river stage are input to the ANN. Furthermore, the increase of hidden layers not necessarily improve the results. The ANN was compared to an auto-regressive model. The former is less dependent on the basin memory. The ANN Forecasts yielded satisfactory results up to one and half-hour in advance. For longer time periods the errors tend to grow exponentially.

This thesis focuses on evaluation of joint occurrence of extreme precipitation and streamflow events at several hydrologic structures in South Florida. An analysis of twelve years storm events and their corresponding peak streamflow events during wet and dry season including annual peaks considering two seasons was performed first. Dependence analysis using time series data of precipitation and streamflow was carried out next. The analysis included use of storm events with different temporal lags from the time of occurrence of peak streamflow events. Bi-variate joint probability was found to be appropriate to analyze the joint occurrence of events. Evaluation of joint exceedence probabilities under two phases of Atlantic multidecadal oscillation (AMO) influencing south Florida was also evaluated. All methodologies are evaluated for application using observations at several structures in the case study region to provide advances and valuable insights on joint extremes of precipitation and streamflows. / by Chia-hung Lin. / Thesis (M.S.C.S.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

Water security requires that sufficient quantities of water be available at critical times. This is particularly challenging for high-intensity urban and agricultural settings. In underdeveloped nations, streamflow is commonly the preferred water source, as it is readily available and delivered cost-free to users. Yet, the sources of these critical streamflows are often unknown. This issue is salient in the Northern Andes, where basic knowledge of controlling factors for the quantity, quality, and timing of runoff is lacking. High-elevation headwaters are the primary catchment areas in the Northern Andes, but the extent of water providing to municipalities in the Northern Andes is unknown. In this study, the contribution of water derived from the upper watershed to the streamflow in the Tulúa River which supplies water to 200,000 people in the city of Tulúa was quantified. The river runs 72 km through urban, agricultural, and industrial land use in the Central Cordillera of the Colombian Andes. We collected 32 and 34 water samples in August and November, respectively. The water samples were representative of high-elevation headwaters runoff, shallow groundwater discharge, and streamflow throughout the watershed. Samples were analyzed for dissolved constituents and stable isotopes. The dissolved constituents were used in mass-balance mixing models to identify the source of streamflow in the lower watershed of the Tulúa River, where it the river supports a large municipality. Results indicate that approximately 50% surface runoff largely originates as high-elevation headwater runoff, including high-elevation settings where páramos dominate the land cover. These findings underscore the need for source-water protection efforts in the upper watershed, including the páramos. This project serves as a model for other páramo derived watersheds, where source-water protection is a critical challenge.

Natural stream systems contain a variety of flow geometries which contain flow separation, turbulent shear layers, and recirculation zones. This work focuses on streams with dead zones. Characterized by slower flow and recirculation, dead zones are naturally occurring cutouts in stream banks. These dead zones play an important role in stream nutrient retention and solute transport. Previous experimental work has focused on idealized dead zone geometries studied in laboratory flumes. This work explores the capabilities of computational fluid dynamics (CFD) to investigate the scaling relationships between flow parameters of idealized geometries and the time scales of transport. The stream geometry can be split into two main regions, the main stream flow and the dead zone. Geometric parameters of the dead zone as well as the bulk stream velocity were varied to determine a scaling relationship for the transport time scales. These flow geometries are simulated using the RANS turbulence model with the standard k-ω closure. The standard first order dead zone model is expanded to a two region model to accommodate the multiple time scales observed in the simulation results. While this model currently has limited predictive capability, it provides physical insight into the functional dependence of the dead zone time scales. LES is used to evaluate the performance of the Reynolds Averaged Navier-Stokes (RANS) turbulence model and to describe the anisotropic turbulence characteristics. The differences between the time averaged flow field for Large Eddy Simulation (LES) and RANS was determined to have a significant impact on passive scalar transport. / Graduation date: 2012

Uncertainties were analyzed in three areas (remote sensing, dendroclimatology, and climate modeling) relevant to current water resources management. First, the research investigated the relationships between remotely sensed and in situ Snow Water Equivalent (SWE) datasets in three western U.S. basins. Agreement between SWE products was found to increase in lower elevation areas and later in the snowpack season. Principal Components Analysis (PCA) revealed two distinct snow regions among the datasets and Singular Value Decomposition (SVD) was used to link both data products with regional streamflow. Remotely sensed SWE was found to be sufficient to use in statistically based forecast models in which magnitude did not affect results. Second, the research investigated the dendroclimatic potential of a critical flood control and hydropower region in the southeastern U.S. (Tennessee Valley) using climate division precipitation and regional tree-ring chronology datasets. Tennessee Valley May–July precipitation was reconstructed from 1692 to 1980 (289 years) using a stepwise linear regression model (R2 = 0.56). Weibull analysis illustrated that the Tennessee Valley reconstruction model developed generally underestimated extreme precipitation and overestimated average precipitation. The longest May–July drought occurred over 10 consecutive years (1827–1836). Instrumental records indicated that the two most recent droughts (1985–1988 and 2006–2008) ranked second and third in severity in the past three centuries. Third, past, present, and future patterns and extremes in streamflow within the North Platte River Basin were investigated. A streamflow reconstruction dating back to 1383 using tree rings was created to provide a proxy for the long-term variability in the region. Projected streamflow datasets from the Community Climate System Model (CCSM) were gathered to acquire future insight of the hydroclimatic variability within the North Platte River Basin (NRPB). Drought analysis revealed that 2002–2008 was one of the driest periods in the past 600 years. Multiple CCSM projections suggest that in the future, drier (5th percentile) years will become wetter relative to 1970–1999 CCSM hindcasts. Future average (50th percentile) and wet (95th percentile) years may yield statistically higher streamflow compared to those seen in the historical (1383–1999) record, suggesting potential anthropogenic influence beyond the historic natural variability.

A generalized non-dimensional mathematical expression has been developed to describe the rating relation of depth and discharge for intermediate and high streamflow of natural and controlled streams. The expressions have been tested against observations from forty-three stations in West-Central Florida. The intermediate-flow region model has also been validated using data from thirty additional stations in the study area. The proposed model for the intermediate flow is a log-linear equation with zero intercept and the proposed model for the high-flow region is a log-linear equation with a variable intercept. The models are normalized by the depth and discharge values at 10 percent exceedance using data published by the U.S. Geological Survey. For un-gauged applications, Q10 and d10 were derived from a relationship shown to be reasonably well correlated to the watershed drainage area with a correlation coefficient of 0.94 for Q10 and 0.86 for d10. The average relative error for this parameter set shows that, for the intermediate-flow range, better than 50% agreement with the USGS rating data can be expected for about 86% of the stations and for the high-flow range, better than 50% for 44% of the stations. Testing the model outside West Central Florida, in some stations at North Florida, and South Alabama and Georgia, show some reasonable relative errors but not as good as the results obtained for West Central Florida. Using a model with a different slope, developed specific for those particular stations improved the results significantly.

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A changing climate can severely perturb regional hydrology and thereby affect human societies and life in general. To assess and simulate such potential hydrological climate change impacts, hydrological models require reliable meteorological variables for current and future climate conditions. Global climate models (GCMs) provide such information, but their spatial scale is too coarse for regional impact studies. Thus, GCM output needs to be downscaled to a finer scale either through statistical downscaling or through dynamic regional climate models (RCMs). However, even downscaled meteorological variables are often considerably biased and therefore not directly suitable for hydrological impact modeling. This doctoral thesis discusses biases and other challenges related to incorporating climate model output into hydrological studies and evaluates possible strategies to address them. An analysis of possible sources of uncertainty stressed the need for full ensembles approaches, which should become standard practice to obtain robust and meaningful hydrological projections under changing climate conditions. Furthermore, it was shown that substantial biases in current RCM simulations exist and that correcting them is an essential prerequisite for any subsequent impact simulation. Bias correction algorithms considerably improved RCM output and subsequent streamflow simulations under current conditions. In addition, differential split-sample testing was highlighted as a powerful tool for evaluating the transferability of bias correction algorithms to changed conditions. Finally, meaningful projections of future streamflow regimes could be realized by combining a full ensemble approach with bias correction of RCM output: Current flow regimes in Sweden with a snowmelt-driven spring flood in April will likely change to rather damped flow regimes that are dominated by large winter streamflows.

Three watersheds of Upper White River were chosen for this study. Stream samples were tested for total suspended solids and conductivity. Fourteen percent of the total suspended solids samples were above 80 mg L-1 which can cause negative effects to aquatic life. Total suspended solids results were tested with a general linear model and in linear regressions with discharge and nutrients. Conductivity results were tested with a general linear model. Concentrations of the total suspended solids were significantly related to watershed, Julian date, the interaction of watershed and Julian date, and discharge. Increasing total suspended solids concentrations were significantly related to increasing concentrations of orthophosphate, nitrate, and ammonia. Conductivity levels were significantly related to watershed, location within watershed, and Julian date. Total suspended solids, discharge, and nutrient concentrations were high in the spring, but decreased in the following seasons. / Department of Natural Resources and Environmental Management