Alluvial river channels are self-formed by the sediment-laden flow that is supplied to them from upstream and the interactions between this flow and the materials forming the channel bed and banks. Thus, any changes in the volumes of solid and liquid discharge or the resistance of the boundary materials can produce adjustments in the form of river channels over time. These shifts may increase or decrease the capacity of a channel to contain flood flows. However, despite a wealth of studies on the average geometry of river channels across different scales and climatic regimes, there has not yet been a systematic assessment of the rates and controls of trends in channel form. Using a combination of USGS data, including manual field measurements and mean daily streamflow data at hundreds of stream gages, this work is the first attempt to quantify how trends in channel geometry develop over decadal timescales and how they contribute to shifts in flood hazard, in comparison with trends in streamflow. Findings reveal that two-thirds of all channel cross-sections studied exhibit significant trends in channel geometry. The majority of the investigated US river channels are eroding, with widening and deepening trends partially offset by decreases in average flow velocity. Rates of change are principally controlled by the channel size. Although large channels develop larger trends, changes are proportionally greater in small channels in percentage terms. A secondary major control is hydrology: rates of change in channel geometry are heightened by the variability and flashiness of flow regimes. Finally, results show that changing flood frequencies can only be accurately quantified when both hydrologic and geomorphic trends are accounted for, and that flood hazard is significantly increasing across the studied sites. These documented trends in channel geometry, hydraulics, and flood hazard have important implications for the management of alluvial channels, navigation, and riverside infrastructure.

Surface transient storage (STS) in stream ecosystems serve an important function in retaining nutrients and refugia for aquatic communities. Unfortunately, they can retain contaminants as well. Therefore, it is of importance to determine the residence time distribution (RTD). A RTD of a particular STS zone encompasses the time it takes for the first pulse of water to leave the STS zone, and for the mean residence time of water in that zone, among other things. The RTD of STS is also useful to subtract from the RTD of the total transient storage in streams in order to determine the hyporheic transient storage (HTS) of streams, which is difficult to measure. Currently, there is no definitive method of determining the RTD of STS. They have been determined with tracer injection alone, though this is time consuming and subject to interference from HTS. A relationship between STS physical characteristics and a RTD would be desirable, as this would characterize the time of entrainment of STS based upon a few easily measured physical parameters. This exists for groyne fields and flumes, which both have artificial STS. However, direct application of these equations to natural STS leads to errors due to simplistic geometries. The focus of this study determines RTDs in lateral STS, which is adjacent to the main channel of a stream and a significant proportion of STS, and its relationship to physically measurable parameters of lateral STS. Twenty sites throughout Oregon were each injected with NaCl to determine four residence timescales: Langmuir time (��[subscript L]), negative inverse slope of the normalized concentration curve of the primary gyre (��[subscript 1]), negative inverse slope of the normalized concentration curve of the entire STS zone (��[subscript 2]), and the mean residence time (��[subscript STS]). The RTDs of these sites were then compared to the length, width, and depth of each lateral STS zone, as well as the velocity of the adjacent main channel. This data also was used to calculate dimensionless parameters submergence, a measure of bed roughness, and k, a measure of exchange that relates ��STS to lateral STS and associated parameters. ��[subscript 1] was found to be identical to ��[subscript STS], and ��[subscript 2] could not be defined. ��[subscript STS] was found to be approximately 1.35 times ��[subscript L], the ratio of which (��[subscript L]/��[subscript STS]) is positively correlated with lateral STS submergence. ��[subscript L] and ��[subscript STS] are positively correlated with lateral STS parameters, and inversely correlated with main channel velocity. The value of k from this study was comparable to the value of k from other studies in flumes, and so there is a relationship between RTDs and lateral STS parameters. / Graduation date: 2013

General Circulation Models (GCMs) are tools designed to simulate time series of climate variables globally, accounting for effects of greenhouse gases in the atmosphere. They attempt to represent the physical processes in the atmosphere, ocean, cryosphere and land surface. They are currently the most credible tools available for simulating the response of the global climate system to increasing greenhouse gas concentrations, and to provide estimates of climate variables (e.g. air temperature, precipitation, wind speed, pressure etc.) on a global scale. GCMs demonstrate a significant skill at the continental and hemispheric spatial scales and incorporate a large proportion of the complexity of the global system; they are, however, inherently unable to represent local subgrid-scale features and dynamics. The spatial scale on which a GCM can operate (e.g., 3.75° longitude x 3.75° latitude for Coupled Global Climate Model, CGCM2) is very coarse compared to that of a hydrologic process (e.g., precipitation in a region, streamflow in a river etc.) of interest in the climate change impact assessment studies. Moreover, accuracy of GCMs, in general, decreases from climate related variables, such as wind, temperature, humidity and air pressure to hydrologic variables such as precipitation, evapotranspiration, runoff and soil moisture, which are also simulated by GCMs. These limitations of the GCMs restrict the direct use of their output in hydrology. This thesis deals with developing statistical downscaling models to assess climate change impacts and methodologies to address GCM and scenario uncertainties in assessing climate change impacts on hydrology. Downscaling, in the context of hydrology, is a method to project the hydrologic variables (e.g., rainfall and streamflow) at a smaller scale based on large scale climatological variables (e.g., mean sea level pressure) simulated by a GCM. A statistical downscaling model is first developed in the thesis to predict the rainfall over Orissa meteorological subdivision from GCM output of large scale Mean Sea Level Pressure (MSLP). Gridded monthly MSLP data for the period 1948 to 2002, are obtained from the National Center for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) reanalysis project for a region spanning 150 N -250 N in latitude and 800 E -900 E in longitude that encapsulates the study region. The downscaling model comprises of Principal Component Analysis (PCA), Fuzzy Clustering and Linear Regression. PCA is carried out to reduce the dimensionality of the larger scale MSLP and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is performed to derive the membership of the principal components in each of the clusters and the memberships obtained are used in regression to statistically relate MSLP and rainfall. The statistical relationship thus obtained is used to predict the rainfall from GCM output. The rainfall predicted with the GCM developed by CCSR/NIES with B2 scenario presents a decreasing trend for non-monsoon period, for the case study. Climate change impact assessment models developed based on downscaled GCM output are subjected to a range of uncertainties due to both ‘incomplete knowledge’ and ‘unknowable future scenario’ (New and Hulme, 2000). ‘Incomplete knowledge’ mainly arises from inadequate information and understanding about the underlying geophysical process of global change, leading to limitations in the accuracy of GCMs. This is also termed as GCM uncertainty. Uncertainty due to ‘unknowable future scenario’ is associated with the unpredictability in the forecast of socio-economic and human behavior resulting in future Green House Gas (GHG) emission scenarios, and can also be termed as scenario uncertainty. Downscaled outputs of a single GCM with a single climate change scenario represent a single trajectory among a number of realizations derived using various GCMs and scenarios. Such a single trajectory alone can not represent a future hydrologic scenario, and will not be useful in assessing hydrologic impacts due to climate change. Nonparametric methods are developed in the thesis to model GCM and scenario uncertainty for prediction of drought scenario with Orissa meteorological subdivision as a case study. Using the downscaling technique described in the previous paragraph, future rainfall scenarios are obtained for all available GCMs and scenarios. After correcting for bias, equiprobability transformation is used to convert the precipitation into Standardized Precipitation Index-12 (SPI-12), an annual drought indicator, based on which a drought may be classified as a severe drought, mild drought etc. Disagreements are observed between different predictions of SPI-12, resulting from different GCMs and scenarios. Assuming SPI-12 to be a random variable at every time step, nonparametric methods based on kernel density estimation and orthonormal series are used to determine the nonparametric probability density function (pdf) of SPI-12. Probabilities for different categories of drought are computed from the estimated pdf. It is observed that there is an increasing trend in the probability of extreme drought and a decreasing trend in the probability of near normal conditions, in the Orissa meteorological subdivision. The single valued Cumulative Distribution Functions (CDFs) obtained from nonparametric methods suffer from limitations due to the following: (a) simulations for all scenarios are not available for all the GCMs, thus leading to a possibility that incorporation of these missing climate experiments may result in a different CDF, (b) the method may simply overfit to a multimodal distribution from a relatively small sample of GCMs with a limited number of scenarios, and (c) the set of all scenarios may not fully compose the universal sample space, and thus, the precise single valued probability distribution may not be representative enough for applications. To overcome these limitations, an interval regression is performed to fit an imprecise normal distribution to the SPI-12 to provide a band of CDFs instead of a single valued CDF. Such a band of CDFs represents the incomplete nature of knowledge, thus reflecting the extent of what is ignored in the climate change impact assessment. From imprecise CDFs, the imprecise probabilities of different categories of drought are computed. These results also show an increasing trend of the bounds of the probability of extreme drought and decreasing trend of the bounds of the probability of near normal conditions, in the Orissa meteorological subdivision. Water resources planning requires the information about future streamflow scenarios in a river basin to combat hydrologic extremes resulting from climate change. It is therefore necessary to downscale GCM projections for streamflow prediction at river basin scales. A statistical downscaling model based on PCA, fuzzy clustering and Relevance Vector Machine (RVM) is developed to predict the monsoon streamflow of Mahanadi river at Hirakud reservoir, from GCM projections of large scale climatological data. Surface air temperature at 2m, Mean Sea Level Pressure (MSLP), geopotential height at a pressure level of 500 hecto Pascal (hPa) and surface specific humidity are considered as the predictors for modeling Mahanadi streamflow in monsoon season. PCA is used to reduce the dimensionality of the predictor dataset and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is carried out to derive the membership of the principal components in each of the clusters and the memberships thus obtained are used in RVM regression model. RVM involves fewer number of relevant vectors and the chance of overfitting is less than that of Support Vector Machine (SVM). Different kernel functions are used for comparison purpose and it is concluded that heavy tailed Radial Basis Function (RBF) performs best for streamflow prediction with GCM output for the case considered. The GCM CCSR/NIES with B2 scenario projects a decreasing trend in future monsoon streamflow of Mahanadi which is likely to be due to high surface warming. A possibilistic approach is developed next, for modeling GCM and scenario uncertainty in projection of monsoon streamflow of Mahanadi river. Three GCMs, Center for Climate System Research/ National Institute for Environmental Studies (CCSR/NIES), Hadley Climate Model 3 (HadCM3) and Coupled Global Climate Model 2 (CGCM2) with two scenarios A2 and B2 are used for the purpose. Possibilities are assigned to GCMs and scenarios based on their system performance measure in predicting the streamflow during years 1991-2005, when signals of climate forcing are visible. The possibilities are used as weights for deriving the possibilistic mean CDF for the three standard time slices, 2020s, 2050s and 2080s. It is observed that the value of streamflow at which the possibilistic mean CDF reaches the value of 1 reduces with time, which shows reduction in probability of occurrence of extreme high flow events in future and therefore there is likely to be a decreasing trend in the monthly peak flow. One possible reason for such a decreasing trend may be the significant increase in temperature due to climate warming. Simultaneous occurrence of reduction in Mahandai streamflow and increase in extreme drought in Orissa meteorological subdivision is likely to pose a challenge for water resources engineers in meeting water demands in future.

From the Proceedings of the 1971 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 22-23, 1971, Tempe, Arizona / Streamflows of monthly or shorter time periods, are, in most parts of the world, conditionally dependent. In studies of planning, commitment and operation decisions concerning reservoirs, it is probably most computationally efficient to use simulation routines for decisions of low dimensions, as planning and commitment, and optimization routines for the highly dimensional operation rule decisions. This presents the major problem of combining the 2 routines, since streamflow dependencies in simulation routines are continuous while the direct stochastic optimization routines are discrete. A stochastic streamflow synthesis routine is described consisting of 2 parts: streamflow probability distribution and dependency analysis and a streamflow generation using the relationships developed. A discrete dependency matrix between streamflow amounts was then sought. Setting as the limits of interest the class 400-500 thousand acre ft in January and 500-600 thousand acre ft in February, and using the transforms specified, the appropriate normal deviates were determined. The next serious problem was calculating the conditional dependency based on the bivariate normal distribution. In order to calculate the joint probability exactly, double integrations would be required and these use too much computer time. For the problem addressed, therefore, the use of 1-dimensional conditional probabilities based on the flow interval midpoint is an adequate and effective procedure.

From the Proceedings of the 1972 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - May 5-6, 1972, Prescott, Arizona / Chaparral covers 4 million acres in Arizona. There is interest in determining how much these lands contribute to surface water supply, and how this contribution could be changed by conversion of chaparral cover to grass or grass forb. Results from treatment in the Whitespar watersheds are interpreted. Live oak and true mountain mahogany dominate the study area, which averages 22.7 inches of annual precipitation. Whitespar B watershed was converted to grasses in 1967, and litter was not disturbed. The 246 acre watershed produced more streamflow than the untreated, 303-acre control which tended to remain intermittent. Prior to treatment, streamflow in both watersheds was quite well synchronized. Watershed b has since had continual flow. Winter flows contribute about 77 percent of the increased streamflow volume. The degree of effect is still under study, but a new rainfall-runoff relationship for the treated watershed is necessitated.

From the Proceedings of the 1972 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - May 5-6, 1972, Prescott, Arizona / A critique of statistical properties of monthly flows on an ephemeral stream in Arizona is given. A subjective procedure, justified for managerial purposes not concerned with the variability of flow within the month, is proposed for sequential generation of monthly flow data. Ephemeral flows should be modeled by starting with at least historical daily flows for more meaningful monthly flow models. Stochastic properties of monthly streamflows and state transition probabilities are reviewed with regard to ephemeral streams. A flow chart for a streamflow model geared to digital computers, with a simulation of streamflow subroutine, is developed. Meaningful monthly flow models could serve as a check on alternative models (subjective matrix, lag-one auto regressive, harmonic, bivariate normal, bivariate log-normal models). Rules and guidelines are presented in developing meaningful probability matrices.

From the Proceedings of the 1972 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - May 5-6, 1972, Prescott, Arizona / Numerous reports from the southwest claim that soil moisture prior to rainfall-runoff event has no influence on the resulting flow volumes and peak rates. Runoff occurs from many storms that would not be expected to produce runoff, and an explanation lies in the occurrence of antecedent rains. This hypothesis is tested by dividing runoff events into 2 subsets--one with no rain within the preceding 120 hours, and the other with some rain within the preceding 24 hours--and to test the null hypothesis. The hypothesis was tested with rainfall and runoff data from a 40-acre agricultural research service watershed west of Albuquerque, New Mexico, using the Wilcoxon's rank sum test. Various levels of statistical significance are discussed, and shown graphically, to conclude conclusively that antecedent rainfall influences runoff from a semiarid watershed.

From the Proceedings of the 1973 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - May 4-5, 1973, Tucson, Arizona / The Lake Powell Research Project is investigating the effects of man's activities on the Southeastern Utah-Northeastern Arizona region. A major portion of this project is devoted to the hydrology of Lake Powell, the largest recent modification in the region. This hydrologic research is separated into the following subprojects and administrative institutions: Subprojects: Streamflow Trends, Evaporation, Bank Storage / Institution: University of California at Los Angeles. Subprojects: Sedimentation, Physical Limnology, Lake Geochemistry / Institution: Dartmouth College. The project is now concluding its first year of full-scale research effort. The UCLA subprojects are aimed at developing an overall water budget for the lake, both on an annual and long -term basis. The Streamflow_trends study indicates that the Upper Colorado River Basin (UCRB) has shifted from a few extraordinarily wet decades in the early 1900's to several relatively dry decades up to the present. Evaporation efforts so far are toward installing a data collection system capable of furnishing data for mass-transfer and energy-budget calculations. The bank-storage study indicates that bank storage constitutes a large fraction of the impounded waters. Secondary as well as primary permeability may be of major importance in bank storage. The Evaporation and Bank Storage subprojects are working in close coordination with the Bureau of Reclamation. The Sedimentation subproject has shown that the rate may be in general agreement with earlier estimates from river flow and suspended sediment data. However, the distribution is affected by sediment dams formed by slumping of canyon wall material. Physical limnology studies indicate the presence of stratifications resulting from thermal and turbidity layers causing complex movements within the lake waters. Field and laboratory efforts in lake geochemical analyses indicate that the precipitation of calcium carbonate may be the most important chemical process in changing the water quality of the lake.

From the Proceedings of the 1977 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 15-16, 1977, Las Vegas, Nevada / Riverflow modeling is believed useful for purposes of decision making with respect to reservoir control, irrigation planning, and flood forecasting and design of structures to contain floods. This author holds the view that present riverflow models in vogue are unsatisfactory because, for one thing, sample simulations according to these models do not resemble observed southwestern river records. The purpose of this paper is to outline a general Markov model which assumes only that rivers have a finite memory. We show how to calibrate the model from river records and then present evidence to support our contention that some success has been realized in mimicking typical flows by our simulation procedure.

The Little Bow River Project was implemented in 2003 and includes Alberta’s newest dam. The Project involves tripling the diversion of water from the Highwood River to the Little Bow River and subsequently storing the water in the Twin Valley Reservoir. This MSc Thesis provided part of the environmental monitoring for that Project and particularly investigated the impacts of augmented flows on the river channel and riparian vegetation along the upper reach of the Little Bow River. An initial component of the long-term study was to determine the existing associations between fluvial geomorphic characteristics and riparian plant communities. Poplar (Populus balsamifera L.), willow (Salix bebbiana Sargent and S. exigua Nutt.) and wolf-willow (Elaeagnus commutata Bernh.) communities were located along the upper section of the river, where the channel had a steeper gradient and was narrower and more sinuous. Cattail (Typha latifolia L.) and grass (grasses and sedges) communities were generally located along the lower section of the river that was shallower in gradient, wider and straighter. Plant community distribution also reflected impacts from cattle grazing. Initial channel and vegetation responses in the first two years following the increase in flow augmentation were slight and included bank slumping, sediment scour and inundation of flooded zones. The initial responses are consistent with the primary prediction of channel widening and this will probably be associated with some changes in the adjacent riparian plant communities. / xiv, 139 leaves : ill. (some col.) ; 29 cm.