Streams in small watersheds are often known to exhibit diel fluctuations, in which streamflow oscillates on a 24-hour cycle. Streamflow diel fluctuations, which we investigate in this study, are an informative indicator of environmental processes. However, in Environmental Data sets, as well as many others, there is a range of noise associated with individual data points. Some points are extracted under relatively clear and defined conditions, while others may include a range of known or unknown confounding factors, which may decrease those points' validity. These points may or may not remain useful for training, depending on how much uncertainty they contain. We submit that in situations where some variability exists in the clarity or 'Confidence' associated with individual data points – Notably environmental data – an approach that factors this confidence into account during the training phase is beneficial. We propose a methodological framework for assigning confidence to individual data records and augmenting training with that information. We then exercise this methodology on two separate datasets: A simulated data set, and a real-world, Environmental Science data set with a focus on streamflow diel signals. The simulated data set provides integral understanding of the nature of the data involved, and the Environmental Science data set provides a real-world case study of an application of this methodology against noisy data. Both studies' results indicate that applying and utilizing confidence in training increases performance and assists in the Data Mining Process.

Methods of forecasting streamflow using atmospheric ensembles and hydrologic routing have greatly improved over the past decades. These forecasts anticipate the timing and magnitude of streamflow peaks, enabling early warning of floods. Recent advances in atmospheric modeling have enabled production of forecasts months ahead, which are less precise but give a useful sense of trends. The purpose of this study is to produce and evaluate a seasonal streamflow forecast model using a Muskingum routing hydrologic model coupled with runoff from a land surface model, and atmospheric input from a medium-term atmospheric and precipitation model. To evaluate the skill of the coupled model, resultant streamflow forecasts with lead times up to 90 days are compared with stream gage data across the three-year historical period of interest. The coupled model system performed well in downstream reaches and with shorter lead times, but forecast skill degraded for lead times past 15 days.

Dams and reservoirs are important components of water resource management systems, but their operational sensitivity to streamflow variability may make them vulnerable to climate change. Climate change is likely to affect the magnitude and timing of streamflow, motivating the assessment of potential impacts on dams and reservoirs. Here I examine a case study of Cougar Dam, a multipurpose dam in Oregon, USA, to assess potential impacts of future climate change on operational performance. In the first portion of this study, I examine the historical operation of Cougar Dam, to understand (1), whether operational objectives have been achievable in the past despite operational variability, and (2) how climatic variation is expressed in operational trajectories. By analyzing historical streamflow and operations data using a set of metrics, I characterize variability in past operations and how that variability relates to streamflow. I also employ a reservoir model to distinguish operational differences due to streamflow variability from variability due to other factors that affect operations. I find that operational objectives have been achievable, despite variability in operations and departures from the ideal operational trajectory. Throughout the historical period, flood control operations have almost always kept reservoir outflows below the desired maximum outflow. Although filling occurs 9 days late on average, the reservoir has filled in all but 6 out of 37 years. Although drawdown occurs 47 days early on average, early drawdown does not generally impact recreation and allows minimum outflows to be met every day during all but the driest year. I also find that total seasonal inflow is correlated with measures of operational performance, and that other factors besides climate play an important role in determining operational trajectories. I conclude that operations of Cougar Reservoir are vulnerable to climate change, but that operational flexibility may mitigate some of the potential impacts. In the second portion of this study I assume that current operating rules will be kept in place and I aim to understand what types of operational impacts may be expected, when they may be expected to occur, and whether the operational impacts may necessitate changing operational rules. I employ both a traditional climate impacts assessment approach to assess changes over time as well as a scenario-neutral approach to generalize relationships between streamflow and operations of Cougar Dam. I find that projected increases in winter streamflow could result in up to twice the number of downstream high flows than in the past and that projected decreases in summer streamflow could result in earlier reservoir drawdown by up to 20 days on average. Additionally, filling of the reservoir may occur up to 16% more often or 11% less often than in the past, depending on spring flow magnitude and timing. I also find that there are strong general relationships between total inflow volume and flood control performance, and that there are total inflow thresholds for whether or not the reservoir will fill or will be full enough for recreation in late summer. I conclude that future modification of operating policies may be warranted, but that there will likely be tradeoffs between operating objectives in the future even if operating rules are modified. / Graduation date: 2013

The plantation forestry is economically a very important industry in South Africa because it promotes the upliftment of many rural South African communities. However, afforestation has significant impacts on water use and biodiversity in a catchment. Thus, understanding the effects of afforestation on water resources at the catchment level is fundamental for optimal water resource allocation, long-term sustainable use, development and conservation. Much of the Limpopo Province is climatically and physiographically suitable for plantation forestry but it only contains approximately 4.7 % of the total existing plantation area in South Africa. For example, the size of the Letaba Catchment of the Limpopo Province is 13 669 km² but only approximately 484 km² of it is currently afforested. This study aims to identify potential areas for further afforestation in the Letaba Catchment using the Water Resources Modelling Platform (WReMP) model to determine if afforestation can be expanded here to promote development in South Africa‟s poorest Province. / Geography / M. Sc. (Geography)

Arizona is presently in the midst of a general adjudication for the Gila River system -- the watershed which comprises the southern two- thirds of the state. The purpose of the adjudication is to prioritize all water claims in the river system: both state -established and federally reserved rights. Arizona adheres to a bifurcated (or divided) system of water law which only recognizes a component of ground water -- called subflow -- to be appropriable. Wells which pump non-appropriable water -- called tributary flow -- are not to be included in the adjudication. The problem is that federal laws do not recognize this artificial bifurcation. The challenge lies in identifying a subflow zone which satisfies the hydrologic fiction of existing state precedents and the hydrologic reality of federal statutes. At the core of the problem lies the fate of Arizona's perennial stream water and the fulfillment of federally reserved tribal water rights. Thus, larger questions loom: can Arizona law reconcile its glutinous past with a water -scarce future, will the adjudication ever reach a finality, and even if it does, will it be a finality that all sides can live with?

Floods are one of the most devastating types of worldwide disasters in terms of human, economic, and social losses. If authoritative data is scarce, or unavailable for some periods, other sources of information are required to improve streamflow estimation and early flood warnings. Georeferenced social media messages are increasingly being regarded as an alternative source of information for coping with flood risks. However, existing studies have mostly concentrated on the links between geo-social media activity and flooded areas. This thesis aims to show a novel methodology that shows a way to close the research gap regarding the use of social networks as a proxy for precipitation-runoff and flood forecast estimates. To address this, it is proposed to use a transformation function that creates a proxy variable for rainfall by analysing messages from geo-social media and precipitation measurements from authoritative sources, which are then incorporated into a hydrological model for the flow estimation. Then the proxy and authoritative rainfall data are merged to be used in a data assimilation scheme using the Ensemble Kalman Filter (EnKF). It is found that the combined use of authoritative rainfall values with the social media proxy variable as input to the Probability Distributed Model (PDM), improves flow simulations for flood monitoring. In addition, it is found that when these models are made under a scheme of fusion-assimilation of data, the results improve even more, becoming a tool that can help in the monitoring of \"ungauged\" or \"poorly gauged\" catchments. The main contribution of this thesis is the creation of a completely original source of rain monitoring, which had not been explored in the literature in a quantitative way. It also shows how the joint use of this source and data assimilation methodologies aid to detect flood events. / As inundações são um dos tipos mais devastadores de desastres em todo o mundo em termos de perdas humanas, econômicas e sociais. Se os dados oficiais forem escassos ou indisponíveis por alguns períodos, outras fontes de informação são necessárias para melhorar a estimativa de vazões e antecipar avisos de inundação. Esta tese tem como objetivo mostrar uma metodologia que mostra uma maneira de fechar a lacuna de pesquisa em relação ao uso de redes sociais como uma proxy para as estimativas de precipitação e escoamento. Para resolver isso, propõe-se usar uma função de transformação que cria uma variável proxy para a precipitação, analisando mensagens de medições geo-sociais e precipitação de fontes oficiais, que são incorporadas em um modelo hidrológico para a estimativa de fluxo. Em seguida, os dados de proxy e precipitação oficial são fusionados para serem usados em um esquema de assimilação de dados usando o Ensemble Kalman Filter (EnKF). Descobriu-se que o uso combinado de valores oficiais de precipitação com a variável proxy das mídias sociais como entrada para o modelo distribuído de probabilidade (Probability Distributed Model - PDM) melhora as simulações de fluxo para o monitoramento de inundações. A principal contribuição desta tese é a criação de uma fonte completamente original de monitoramento de chuva, que não havia sido explorada na literatura de forma quantitativa.

Visando conciliar pressões antrópicas para o desenvolvimento das atividades sócio-econômicas com a conservação dos ecossistemas, os serviços ambientais associados aos recursos hídricos são claros benefícios e índices quantitativos que podem ser tomados como parâmetros na tomada de decisões para o uso da terra. No Brasil o Código Florestal prescreve as Áreas de Preservação Permanente (APPs) ao longo das margens dos rios, em topos de morro e áreas de alta declividade como setores onde estes serviços serão potencialmente expressivos. Os efeitos da extensão da vegetação ripária podem variar conforme as características geomorfológicas e extensão da bacia, com os ainda escassos resultados de estudos indicadores da faixa segura de vegetação ripária necessária para minimizar os efeitos das vazões máximas e prover a disponibilidade hídrica em bacias hidrográficas com relevância para o abastecimento humano, como é o caso da bacia do rio Piracicaba, e em bacias de pequena escala, onde também se concentra a agricultura familiar e são as efetivas áreas de mananciais dos grandes reservatórios. O objetivo desta tese foi avaliar o impacto causado pelas APPs ripárias e de áreas íngremes (reflorestamento ripário e de áreas íngremes) no regime hidrológico da bacia do rio Piracicaba ( ~12000 km²) e na sua sub-bacia, Ribeirão das Posses (12 km²) a partir de simulações com o modelo hidrológico distribuído SWAT. A avaliação do efeito das APPs nas bacias foi realizada a partir de simulações numéricas com o modelo SWAT calibrado e validado, com referência aos regimes de vazão e evapotranspiração dos principais ecossistemas dominantes. As simulações foram compostas de cenários variando-se as faixas de extensão do reflorestamento ripário e reflorestamento das áreas íngremes em pequena escala (Ribeirão das Posses, Extrema, MG) e mesoescala (Rio Piracicaba). Os cenários com reflorestamento (ripário e áreas íngremes) reduziram o escoamento superficial em ambas as bacias e aumentaram o escoamento básico em Posses, reduzindo este em Piracicaba. Os resultados dos cenários com maior reflorestamento ripário indicaram para as bacias do Ribeirão das Posses e do Rio Piracicaba, respectivamente, uma queda de aproximadamente 24% e 4% do escoamento superficial, e no escoamento básico um aumento de 2% e redução de 2%. A redução do escoamento superficial reduziu as vazões extremas máximas em aproximadamente 13% no exutório e 28% na cabeceira do Ribeirão das Posses, enquanto no Rio Piracicaba a redução foi de aproximadamente 4% em toda a bacia. As vazões de estiagem no Ribeirão das Posses aumentaram na cabeceira e no exutório em 19 e 9%, respectivamente, enquanto no Rio Piracicaba a vazão de estiagem teve uma redução de até 2%. Os resultados sugerem que as APPs, nas formas de reflorestamento ripário e nas áreas íngremes em pequena escala, mostram uma redução do escoamento superficial e da vazão média anual, mas que em termos de serviços ambientais reflete-se favoravelmente na redução dos eventos de inundação devido à diminuição dos pulsos hidrológicos extremos, e no aumento da vazão de estiagem devido ao aumento do escoamento básico. Na mesoescala mostrou-se analogamente uma qualidade favorável de serviço ambiental nas vazões máximas, mas desfavorável na vazão de estiagem, o que, todavia, deve ser analisada em conjunção com o ajuste dos parâmetros de recarga do aquífero raso e profundo do modelo nesta escala. O estudo indica as APPs como promotoras de serviços ambientais hidrológicos dominantemente favoráveis, a contar pela possibilidade real de recomposição florestal em bacias de menores escalas, com ênfase na influência dos processos nas imediações da rede de drenagem onde habitam as populações rurais que poderiam se beneficiar dos serviços mais direta e frequentemente. / Aimed at reconciling human pressures for the development of socio-economic activities with the conservation of ecosystems, ecosystem services associated with water resources are benefits and quantitative indices that can be taken as parameters in decision-making for land use. In Brazil, its Forest Code prescribes the Permanent Preservation Areas (PPAs) along rivers banks, steep areas and areas of high slope as sectors where these services will be potentially significant. The effects of the size of riparian vegetation may vary depending on geomorphological characteristics and extension of the basin. There are few studies indicators of the safe range of riparian vegetation necessary to minimize the effects of peak flows and provide water availability in relevant river basins for water supply, like Piracicaba river basin, and in small scale watershed, which also focuses on the family farms and are also effective watershed areas of large reservoirs. The objective of this thesis was to evaluate the impact of the riparian and steep PPAs areas in the hydrological regime of the Piracicaba river basin (~ 12000 km²) and its sub-basin, Ribeirão das Posses (12 km²), in Extrema, MG, by means of numerical simulations. Evaluation of the effect of PPAs in the watersheds was carried out using the hydrological model SWAT (Soil and Water Assessement Tool) calibrated (for streamflow and evapotranspiration of the main ecosystems of the basin) and validated (for streamflow). Scenarios were composed of varying extension of riparian reforestation and afforestation of steep areas in small scale (Ribeirão das Posses) and in mesoscale (Piracicaba). The (riparian and steep area) reforestation scenarios reduced surface runoff in both watersheds and increased baseflow in Posses but reduced it in Piracicaba. The results of the annual average scenarios with greater riparian reforestation indicated a runoff decrease of 24% in Ribeirão das Posses and of 4% in Piracicaba. As for the baseflow, it increased by 2% and decreased by 2%, respectively in the two watersheds. The reduction of runoff reduced the maximum streamflows in the mouth in 13% and in 28% at the head of the Ribeirão das Posses. While in Piracicaba, these were reduced to approximately 4% in the entire watershed. Low flows in Posses increased the head and mouth in, respectively, 19% and 9%, while in Piracicaba drought streamflow had a modest reduction of up to 2%. The results suggest that the PPAs, in the forms of riparian and steep reforestation on a small scale, show a reduction of runoff and mean annual flow, but in terms of environmental services reflects favorably on the reduction of flood events due the reduction of extreme hydrological pulses, and the increase of low flow due to increased base flow. The mesoscale showed analogously a favorable quality in providing environmental services in peak flows, but unfavorable as to dry season flow, which however should be analyzed in conjunction with adjusting the parameters of the model of low and deep aquifer recharge at this scale. The study suggest the PPAs as promoters of environmental hydrological services dominantly favorable, emphasizing the benefits of reforestation in smaller scale watersheds, influencing processes nearby the drainage area, where rural populations could be benefited from services more directly and frequently.

Floods are one of the most devastating types of worldwide disasters in terms of human, economic, and social losses. If authoritative data is scarce, or unavailable for some periods, other sources of information are required to improve streamflow estimation and early flood warnings. Georeferenced social media messages are increasingly being regarded as an alternative source of information for coping with flood risks. However, existing studies have mostly concentrated on the links between geo-social media activity and flooded areas. This thesis aims to show a novel methodology that shows a way to close the research gap regarding the use of social networks as a proxy for precipitation-runoff and flood forecast estimates. To address this, it is proposed to use a transformation function that creates a proxy variable for rainfall by analysing messages from geo-social media and precipitation measurements from authoritative sources, which are then incorporated into a hydrological model for the flow estimation. Then the proxy and authoritative rainfall data are merged to be used in a data assimilation scheme using the Ensemble Kalman Filter (EnKF). It is found that the combined use of authoritative rainfall values with the social media proxy variable as input to the Probability Distributed Model (PDM), improves flow simulations for flood monitoring. In addition, it is found that when these models are made under a scheme of fusion-assimilation of data, the results improve even more, becoming a tool that can help in the monitoring of \"ungauged\" or \"poorly gauged\" catchments. The main contribution of this thesis is the creation of a completely original source of rain monitoring, which had not been explored in the literature in a quantitative way. It also shows how the joint use of this source and data assimilation methodologies aid to detect flood events. / As inundações são um dos tipos mais devastadores de desastres em todo o mundo em termos de perdas humanas, econômicas e sociais. Se os dados oficiais forem escassos ou indisponíveis por alguns períodos, outras fontes de informação são necessárias para melhorar a estimativa de vazões e antecipar avisos de inundação. Esta tese tem como objetivo mostrar uma metodologia que mostra uma maneira de fechar a lacuna de pesquisa em relação ao uso de redes sociais como uma proxy para as estimativas de precipitação e escoamento. Para resolver isso, propõe-se usar uma função de transformação que cria uma variável proxy para a precipitação, analisando mensagens de medições geo-sociais e precipitação de fontes oficiais, que são incorporadas em um modelo hidrológico para a estimativa de fluxo. Em seguida, os dados de proxy e precipitação oficial são fusionados para serem usados em um esquema de assimilação de dados usando o Ensemble Kalman Filter (EnKF). Descobriu-se que o uso combinado de valores oficiais de precipitação com a variável proxy das mídias sociais como entrada para o modelo distribuído de probabilidade (Probability Distributed Model - PDM) melhora as simulações de fluxo para o monitoramento de inundações. A principal contribuição desta tese é a criação de uma fonte completamente original de monitoramento de chuva, que não havia sido explorada na literatura de forma quantitativa.

Les précipitations et les débits des cours d'eau constituent les deux variables hydrométéorologiques les plus importantes pour l'analyse des bassins versants. Ils fournissent des informations fondamentales pour la gestion intégrée des ressources en eau, telles que l’approvisionnement en eau potable, l'hydroélectricité, les prévisions d'inondations ou de sécheresses ou les systèmes d'irrigation.Dans cette thèse de doctorat sont abordés deux problèmes distincts. Le premier prend sa source dans l’étude des débits des cours d’eau. Dans le but de bien caractériser le comportement global d'un bassin versant, de longues séries temporelles de débit couvrant plusieurs dizaines d'années sont nécessaires. Cependant les données manquantes constatées dans les séries représentent une perte d'information et de fiabilité, et peuvent entraîner une interprétation erronée des caractéristiques statistiques des données. La méthode que nous proposons pour aborder le problème de l'imputation des débits se base sur des modèles de régression dynamique (DRM), plus spécifiquement, une régression linéaire multiple couplée à une modélisation des résidus de type ARIMA. Contrairement aux études antérieures portant sur l'inclusion de variables explicatives multiples ou la modélisation des résidus à partir d'une régression linéaire simple, l'utilisation des DRMs permet de prendre en compte les deux aspects. Nous appliquons cette méthode pour reconstruire les données journalières de débit à huit stations situées dans le bassin versant de la Durance (France), sur une période de 107 ans. En appliquant la méthode proposée, nous parvenons à reconstituer les débits sans utiliser d'autres variables explicatives. Nous comparons les résultats de notre modèle avec ceux obtenus à partir d'un modèle complexe basé sur les analogues et la modélisation hydrologique et d'une approche basée sur le plus proche voisin. Dans la majorité des cas, les DRMs montrent une meilleure performance lors de la reconstitution de périodes de données manquantes de tailles différentes, dans certains cas pouvant allant jusqu'à 20 ans.Le deuxième problème que nous considérons dans cette thèse concerne la modélisation statistique des quantités de précipitations. La recherche dans ce domaine est actuellement très active car la distribution des précipitations exhibe une queue supérieure lourde et, au début de cette thèse, il n'existait aucune méthode satisfaisante permettant de modéliser toute la gamme des précipitations. Récemment, une nouvelle classe de distribution paramétrique, appelée distribution généralisée de Pareto étendue (EGPD), a été développée dans ce but. Cette distribution exhibe une meilleure performance, mais elle manque de flexibilité pour modéliser la partie centrale de la distribution. Dans le but d’améliorer la flexibilité, nous développons, deux nouveaux modèles reposant sur des méthodes semiparamétriques.Le premier estimateur développé transforme d'abord les données avec la distribution cumulative EGPD puis estime la densité des données transformées en appliquant un estimateur nonparamétrique par noyau. Nous comparons les résultats de la méthode proposée avec ceux obtenus en appliquant la distribution EGPD paramétrique sur plusieurs simulations, ainsi que sur deux séries de précipitations au sud-est de la France. Les résultats montrent que la méthode proposée se comporte mieux que l'EGPD, l’erreur absolue moyenne intégrée (MIAE) de la densité étant dans tous les cas presque deux fois inférieure.Le deuxième modèle considère une distribution EGPD semiparamétrique basée sur les polynômes de Bernstein. Plus précisément, nous utilisons un mélange creuse de densités béta. De même, nous comparons nos résultats avec ceux obtenus par la distribution EGPD paramétrique sur des jeux de données simulés et réels. Comme précédemment, le MIAE de la densité est considérablement réduit, cet effet étant encore plus évident à mesure que la taille de l'échantillon augmente. / Precipitation and streamflow are the two most important meteorological and hydrological variables when analyzing river watersheds. They provide fundamental insights for water resources management, design, or planning, such as urban water supplies, hydropower, forecast of flood or droughts events, or irrigation systems for agriculture.In this PhD thesis we approach two different problems. The first one originates from the study of observed streamflow data. In order to properly characterize the overall behavior of a watershed, long datasets spanning tens of years are needed. However, the quality of the measurement dataset decreases the further we go back in time, and blocks of data of different lengths are missing from the dataset. These missing intervals represent a loss of information and can cause erroneous summary data interpretation or unreliable scientific analysis.The method that we propose for approaching the problem of streamflow imputation is based on dynamic regression models (DRMs), more specifically, a multiple linear regression with ARIMA residual modeling. Unlike previous studies that address either the inclusion of multiple explanatory variables or the modeling of the residuals from a simple linear regression, the use of DRMs allows to take into account both aspects. We apply this method for reconstructing the data of eight stations situated in the Durance watershed in the south-east of France, each containing daily streamflow measurements over a period of 107 years. By applying the proposed method, we manage to reconstruct the data without making use of additional variables, like other models require. We compare the results of our model with the ones obtained from a complex approach based on analogs coupled to a hydrological model and a nearest-neighbor approach, respectively. In the majority of cases, DRMs show an increased performance when reconstructing missing values blocks of various lengths, in some of the cases ranging up to 20 years.The second problem that we approach in this PhD thesis addresses the statistical modeling of precipitation amounts. The research area regarding this topic is currently very active as the distribution of precipitation is a heavy-tailed one, and at the moment, there is no general method for modeling the entire range of data with high performance. Recently, in order to propose a method that models the full-range precipitation amounts, a new class of distribution called extended generalized Pareto distribution (EGPD) was introduced, specifically with focus on the EGPD models based on parametric families. These models provide an improved performance when compared to previously proposed distributions, however, they lack flexibility in modeling the bulk of the distribution. We want to improve, through, this aspect by proposing in the second part of the thesis, two new models relying on semiparametric methods.The first method that we develop is the transformed kernel estimator based on the EGPD transformation. That is, we propose an estimator obtained by, first, transforming the data with the EGPD cdf, and then, estimating the density of the transformed data by applying a nonparametric kernel density estimator. We compare the results of the proposed method with the ones obtained by applying EGPD on several simulated scenarios, as well as on two precipitation datasets from south-east of France. The results show that the proposed method behaves better than parametric EGPD, the MIAE of the density being in all the cases almost twice as small.A second approach consists of a new model from the general EGPD class, i.e., we consider a semiparametric EGPD based on Bernstein polynomials, more specifically, we use a sparse mixture of beta densities. Once again, we compare our results with the ones obtained by EGPD on both simulated and real datasets. As before, the MIAE of the density is considerably reduced, this effect being even more obvious as the sample size increases.

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.