Salt marshes are some of the most productive and valuable landscapes on earth, but they are vulnerable to the effects of sea-level rise, erosion and eutrophication. These processes act on a wide range of temporal and spatial scales, which complicate assessments of the health and stability of marsh ecosystems. High-frequency monitoring using in situ sensors captures the complete range of these dynamics, but extracting meaningful physical and ecological information from these signals requires process-based models coupled with statistical techniques. I develop and apply such methods to study two coastal landscapes, a coastal pine forest on the Eastern Shore of Virginia and a mesotidal salt marsh complex in the Plum Island Estuary, Massachusetts. Observations from groundwater wells in the Virginia pine forest indicate that storms are the dominant controls on the hydrology of the forest and that tidal influence is nonexistent. This forest exhibits a distinct spatial pattern in age structure in which young trees do not grow at low elevations. This pattern can be explained by a model that includes the interaction of sea-level rise, storms and the age-dependent variation in tree stress response, which predicts that the long-term evolution of the boundary is an ecological ratchet. Stresses due to sea-level rise slowly push the boundary at which young trees can survive upslope. Powerful storms then kill the mature, persistent forest at low elevations, which quickly pushes the forest boundary up to the regeneration boundary. Salt marshes need to accumulate sediment to replenish material lost as sea-level rises and creek banks erode. Fluxes of sediment can be monitored with simultaneous high-frequency observations of flow from acoustic Doppler current profilers and turbidity from optical backscattering sensors. I first investigate the relationship between water level and flow in marsh channels and develop predictive stage-discharge models to simplify the monitoring of fluxes. I then construct sediment budgets for eleven salt marshes in the Plum Island Estuary. The observed budgets depend strongly on the unique hydrodynamic conditions of each marsh channel. Variability in these conditions leads to the observed spatial and temporal variability in sediment fluxes from these marshes.

A rating curve provides a functional relationship between water height (i.e. stage) and discharge at a specified cross-section in a river. Used in combination with a time series of stage, rating curves become one of the central components for generating continuous records of streamflow. Since developing and maintaining rating curves can be time consuming, hydraulic models have shown potential to reduce the effort required for developing rating curves. A central challenge with modeling procedures, however, is the acquisition of accurate stream channel and floodplain topography. From this perspective, this thesis focuses on the real-world application of close-range remote sensing techniques such as laser-based ranging technologies (i.e. Light detection and ranging or LiDAR) or acoustic based ranging technologies (i.e. acoustic Doppler current profiler or ADCP) to capture topographic information for hydraulic modeling applications across various spatial scales. First, a review of the current LiDAR literature was carried out to identify potential ways to take full advantage of these novel data and technologies in the future. This was followed by four interconnected studies whereby: (i) a low-cost custom laser scanning system was designed to capture grain size distributions for a small stream; (ii) synthetically thinned airborne laser scanning (ALS) data was applied in a physically-based hydraulic modelling framework to develop rating curves; (iii) low-resolution national-scale ALS was coupled with ADCP bathymetry to be used in conjunction with a hydraulic model to develop rating curves; and (iv) the impact of measurement uncertainties on generating rating curves with a hydraulic model were investigated. This thesis highlights the potential of close-range remote sensing techniques for capturing accurate stream channel topography and derive from these data, the necessary parameters required for hydraulic modeling applications. / En avbördningskurva tillhandahåller ett funktionellt förhållande mellan vattendjup (dvs. vattenstånd) och flöde vid ett specifikt tvärsnitt i ett vattendrag. Avbördningskurvan blir en central komponent för generering av kontinuerliga tidsserier av vattenföring från tidsserier av vattenstånd. Eftersom det är tidskrävande att utveckla och underhålla avbördningskurvor erbjuder hydrauliska modeller attraktiva möjligheter att minska den insats som krävs för att utveckla avbördningskurvorna. En central utmaning för sådana modelleringsförfaranden är emellertid tillgången till noggrann topografidata av strömfåran och de omgivande stränderna. Den här avhandlingen fokuserar på tillämpningen av fjärranalystekniker för avståndsmätning på nära håll, såsom laserbaserade teknik (dvs. Light detection and ranging eller LiDAR) och akustisk baserat teknik (dvs. acoustic Doppler current profiler eller ADCP), för att fånga topografisk information för hydraulisk modellering av vattendrag i olika rumsliga skalor. Först presenteras en litteraturstudie av den nuvarande LiDAR-litteratur för att identifiera potentiella sätt att dra full nytta av dessa nya data och tekniker i framtiden. Detta följs av fyra sammanlänkade studier: (i) tillämpning av ett lågkostnads-laseravsökningssystem för att fånga kornstorleksfördelningar i ett litet vattendrag, (ii) syntetiskt förtunnad flygburen laserskanningsdata (ALS) applicerad i en fysiskt baserad hydraulisk modell för att utveckla avbördningskurvor, (iii) lågupplösta ALS från Svensk nationell höjdmodell kopplade med ADCP-batymetri för att ta fram en avbördningskurva med en hydraulisk modell, och (iv) undersökning av effekterna av osäkerheter på mätdata för att generera avbördningskurvor med en hydraulisk modell. Denna avhandling belyser potentialen för fjärranalystekniker för avståndsmätning på nära håll, för att fånga strömfårans exakta topografi och ifrån dessa data härleda de parametrar som krävs för hydrauliska modelleringstillämpningar. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 5: Manuscript.</p>

We analyzed recent total suspended solids (TSS) data from 45 gages on 36 rivers throughout the state of Minnesota. Watersheds range from 32 to 14,600 km2 and represent a variety of distinct settings in terms of topography, land cover, and geologic history. Our study rivers exhibited three distinct patterns in the relationship between discharge and TSS: simple power functions, threshold power functions, and peaked or negative power functions. Differentiating rising and falling limb samples, we generated sediment rating curves (SRC) of form TSS = aQb, Q being normalized discharge. Rating parameters a and b describe the vertical offset and steepness of the relationships. We also used the fitted SRCs to estimate TSS values at low flows and to quantify event-scale hysteresis. In addition to quantifying the watershed-average topographic, climatic/hydrologic, geologic, soil and land cover conditions, we used high-resolution lidar topography data to characterize the near-channel environment upstream of gages. We used Random Forest statistical models to analyze the relationship between basin and channel features and the rating parameters. The models enabled us to identify morphometric variables that provided the greatest explanatory power and examine the direction, form, and strength of the partial dependence of the response variables on individual predictor variables. The models explained between 43% and 60% of the variance in the rating curve parameters and determined that Q-TSS relation steepness (exponent) was most related to near-channel morphological characteristics including near-channel local relief, channel gradient, and proportion of lakes along the channel network. Land use within the watershed explained most variation in the vertical offset (coefficient) of the SRCs and in TSS concentrations at low flows.