Browsing by Subject "Hydrologic sciences"

This dissertation investigates the limnology of the southwestern Peruvian Amazon centered on the Madre de Dios department by examining first the geomorphology and then the ecology and biogeochemistry of the region's fluvial systems.

Madre de Dios, Peru is world renowned for its prolific biodiversity and its location within the Andes biodiversity hotspot. It is also a site of study regarding the development of the Fitzcarrald Arch and that feature's geomorphological importance as the drainage center for the headwaters of the Madeira River - the Amazon's largest tributary and as well as its role as a physical divider of genetic evolution in the Amazon. Though each of these has been studied by a variety of prominent researchers, the ability to investigate all the aspects of this unique region is hampered by the lack of a regional geomorphological map. This study aims to fill that gap by using remote sensing techniques on digital elevation models, satellite imagery and soil, geology and geoecological maps already in publication to create a geomorphological map. The resulting map contains ten distinct landform types that exemplify the dominance of fluvial processes in shaping this landscape. The river terraces of the Madre de Dios River are delineated in their entirety as well as the various dissected relief units and previously undefined units. The demarcation of the boundaries of these geomorphic units will provide invaluable assistance to the selection of field sites by future researchers as well as insights into the origin of the high biodiversity indices of this region and aid in planning for biodiversity conservation.

Secondly this study examines 25 tropical floodplain lakes along 300 km of the Manu River within the Manu National Park in the Madre de Dios department. Alternative stable state and regime shifts in shallow lakes typically have been examined in lakes in temperate and boreal regions and within anthropogenically disturbed basins but have rarely been studied in tropical or in undisturbed regions. In contrast this study focuses on a tropical region of virtually no human disturbance and evaluates the effects of hydrological variability on ecosystem structure and dynamics. Using satellite imagery a 23 yr timeline of ecological regime shifts in Amazon oxbow lakes or "cochas" is reconstructed. The study shows that almost 25% of the river's floodplain lakes experience periodic abrupt vegetative changes with an average 3.4% existing in an alternative stable state in any given year. State changes typically occur from a stable phytoplankton-dominated state to a short lived, <3 yr, floating macrophytic state and often occur independent of regional flooding. We theorize that multiple dynamics, both internal and external, drive vegetative regime shifts in the Manu but insufficient data yet exists in this remote region to identify the key processes.

To complete the investigation of tropical limnology the third study compares and contrasts the nutrient-productivity ration of floodplain and non-floodplain lakes globally and regionally. For over 70 years a strong positive relationship between sestonic chlorophyll-a (Chl-a) and total phosphorus (TP) has been established with phosphorus generally viewed as the most limiting factor to productivity. Most of these studies, however, have focused on northern, temperate regions where the lakes are typically postglacial, isolated and fed by small streams. Relatively little work has been done on floodplain lakes which are semi or permanently connected to the river. This study examines the relationship between nutrients and productivity in floodplain lakes globally through an extensive literature synthesis. Values for total phosphorus, total nitrogen and chlorophyll-a were collected for 523 floodplain lakes, represented by 288 data points while 551 data points were collected for 5444 non-floodplain lakes. Analysis revealed that globally, floodplain lakes do not show any significant difference in the total phosphorus/chlorophyll-a relationship from that found in non-floodplain lakes but significant differences are seen between tropical and temperate lakes. We propose that the term `floodplain' lake should serve as purely a geographical descriptor and that it is lacking as an ecological indicator. Instead factors such as precipitation seasonality, hydrological connectivity and regional flooding regimes are better indicators of high or low productivity in floodplain lakes.

Society-induced changes to the environment are altering the effectiveness of existing management strategies for sustaining natural and agricultural ecosystem productivity. At the watershed scale, natural and agro-ecosystems represent complex spatiotemporal stochastic processes. In time, they respond to random rainfall events, evapotranspiration and other losses that are spatially variable because of heterogeneities in soil properties, root distributions, topography, and other factors. To quantify the environmental impact of anthropogenic activities, it is essential that we characterize the evolution of space and time patterns of ecosystem fluxes (e.g., energy, water, and nutrients). Such a characterization then provides a basis for assessing and managing future anthropogenic risks to the sustainability of ecosystem productivity.

To characterize the space and time evolution of watershed scale processes, this dissertation introduces a mean field approach to watershed hydrology. Mean field theory (also known as self-consistent field theory) is commonly used in statistical physics when modeling the space-time behavior of complex systems. The mean field theory approximates a complex multi-component system by considering a lumped (or average) effect of all individual components acting on a single component. Thus, the many body problem is reduced to a one body problem. For watershed hydrology, a mean field theory reduces the numerous point component effects to more tractable watershed averages resulting in a consistent method for linking the average watershed fluxes (evapotranspiration, runoff, etc.) to the local fluxes at each point.

The starting point for this work is a general point description of the soil moisture, rainfall, and runoff system. For this system, we find the joint PDF that describes the temporal variability of the soil water, rainfall, and runoff processes. Since this approach does not account for the spatial variability of runoff, we introduce a probabilistic storage (ProStor) framework for constructing a lumped (unit area) rainfall-runoff response from the spatial distribution of watershed storage. This framework provides a basis for unifying and extending common event-based hydrology models (e.g. Soil Conservation Service curve number (SCS-CN) method) with more modern semi-distributed models (e.g. Variable Infiltration Capacity (VIC) model, the Probability Distributed (PDM) model, and TOPMODEL). In each case, we obtain simple equations for the fractions of the different source areas of runoff, the spatial variability of runoff and soil moisture, and the average runoff value (i.e., the so-called runoff curve). Finally, we link the temporal and spatial descriptions with a mean field approach for watershed hydrology. By applying this mean field approach, we upscale the point description with the spatial distribution of soil moisture and parameterize the numerous local interactions related to lateral fluxes of soil water in terms of its average. With this approach, we then derive PDFs that represent the space and time distribution of soil water and associated watershed fluxes such as evapotranspiration and runoff.

The Peruvian/Bolivian Altiplano is an important hydrologic system for paleoclimate reconstruction because it is unique in its ability to record climate variability associated with the near-continental scale South American summer monsoon (SASM), which is responsible for much of the precipitation over the Amazon basin and the southern subtropics. Over long timescales moisture on the Altiplano fluctuates in intensity due to changes in precessional insolation forcing as well as teleconnections to decadal-to-millennial scale abrupt temperature shifts in the Northern hemisphere Atlantic. These long-term changes in moisture transport to the Altiplano have been observed in multiple paleoclimate records, including drill core records and paleo-lake level records, as apparent advances and retreats of large lakes in the terminal basin occupied by the Salar de Uyuni and the Salar de Coipasa.

Presented here are the results from three studies that utilize different methods to create a refined reconstruction of paleohydrology, as well as paleoclimate, on the Altiplano. A major goal of this research is a more detailed understanding of millennial scale climate variability as it relates to insolation changes and abrupt warming and cooling in the north Atlantic. The first study discusses the creation of a paleohydrologic profile to reconstruct north-south hydrological history using previously reported lake core sediment records the northern and southern basins of the Altiplano, and a new 14 m core from the Salar de Coipasa representing the last ~45 ka. The second study uses a terrestrial hydrology model to simulate lake level changes through time given changes in precipitation and temperature. The third study uses strontium isotopic measurements of carbonates and halites in a 220-m core from the Salar de Uyuni to determine how source waters to the southern basin have changed through time.

The paleohydrologic profile in the first study is constructed using records from three major basins within the Altiplano: Lake Titicaca in the north, and Salar de Coipasa and Salar de Uyuni in the south. The new continuous sediment core from Salar de Coipasa indicates a lake that has fluctuated between deep and shallow phases for the last 45 ka. Lacking sufficient calcium carbonate, we instead take advantage of the general correlation between d18O and d13C in closed basin lakes to approximate water balance using d13C from organic carbon. This reconstruction is validated with diatom paleoecological records. The isotope measurements and diatom records indicate that from 45-36 ka Coipasa was moderately deep, consistent with paleoshoreline evidence of paleolake Minchin (46-36 ka). From 36-26 ka a shallow lake <10 m deep occupied the Coipasa basin. During the LGM (26-21 ka) the lake varied from moderate to shallow and during the Holocene (< 10 ka) the lake evolved from a shallow lake to a salt flat.

The hydrologic model in the second study was run through many scenarios including increases in precipitation, decreases in temperature, and combinations of the two. During the LGM southern Altiplano lakes fluctuated between 3,660 - 3,700 masl. Model results suggest that during this period basin wide precipitation increased up to 250 mm/yr compared to modern values dependent on a temperature decrease of 5 °C relative to modern values. To create a lake at elevation 3,760 masl consistent with the highest paleolake phase (Tauca, ~16 ka) the model requires an increase of 350 mm/yr compared to modern values dependent on a 5 °C decrease in temperature (relative to modern values). An increase in temperature alone of 2 °C above modern values causes Lake Titicaca water level to decrease ~30 m, creating a closed basin lake. Results indicate that Lake Titicaca outflow is necessary to sustain large lakes in the southern basin, providing ~40-60% of total input via the Rio Desaguadero.

Analysis of a 220 m core from the Salar de Uyuni suggests periods of alternating wet and dry phases (indicated by alternating mud and salt units respectively) at the salar. Evident in the record is a transition at ~60 ka from sediments consistent with dry conditions ("playa lakes") to sediments consistent with deep lakes ("great lakes"). It has been shown that rivers and lakes in the Bolivian and Peruvian Altiplano display a range of Sr isotopic ratios that can be connected to the lithologies of specific drainage basins. Measurements of Sr ratios of the alternating halites and carbonate sediments are used to determine when paleolakes in the Salar were supplied by flow from the northern and central basins of the Altiplano, and when they were more a product of increased precipitation in the Uyuni basin. The results from Sr isotope analysis suggest that prior to ~60 ka the primary source of Sr to the Uyuni was local runoff and direct precipitation. Following the state change from the "play lakes" phase to the "great lakes" phase Sr isotope measurements suggest a significant influence from more radiogenic waters originating in the central and northern Altiplano basins. The reason for this state change is attributed to a combination of a general increase in precipitation following the onset of the MIS-4 (~70 ka) glacial period and downcutting of the Laka Jahuira hydrologic divide, which connects Lago Poopó in the central basin to the Salar de Coipasa.

This approach of reconstructing hydrology using the combination of multiple paleolake records, hydrological modeling, and isotopic tracers allows for a better understanding of how precipitation and temperature changes affect the advance and retreat of large lakes on the Altiplano, and ultimately a more accurate understanding of how decadal-to-millennial forcings influence the climate of the subtropical Andes.

Due to its profound influence on various environmental processes and phenomena, the correctrepresentation of landscape physical heterogeneity in models is vital for applications spanning a wide range of scales, from global climate prediction to field-scale hydrological forecasting. Land Surface Models (LSM), Earth System Models (ESMs), and satellite remote sensing provide spatially distributed fields of surface fluxes and states, making them critical scientific tools for understanding the impact of physical heterogeneity. Enhanced understanding of heterogeneity's spatial and temporal effect can significantly improve our comprehension of hydrological, energy, and biogeochemical cycles at multiple scales. Under this framework, the dissertation focuses on optimizing, evaluating, and improving heterogeneity representations for LSM and ESM applications. Chapter 2 introduces a novel multi-objective optimization approach to efficiently determine optimal heterogeneity representation configuration for LSMs while considering the spatial structure of the generated fields, the accuracy of the representation of hydrological processes, and the computational trackability of the resulting structure. Chapter 3 builds upon the spatial nature of this approach and presents the Empirical Spatio-Temporal Covariance Function (ESTCF), a tool based on geostatistics that allows to efficiently and effectively characterize the spatio-temporal patterns observed in remotely sensed fields and relate them to physical characteristics of the environment. Intending to use remote sensing elevation data to its maximum, Chapter 4 proposes strategies to improve the coupling between river networks and heterogeneity representations in LSMs. Experiments demonstrate the sensitivity of spatiotemporal patterns in the land surface to the heterogeneity representation. Finally, the tool developed in Chapter 2 and the heterogeneity representation proposed in Chapter 4 are combined in Chapter 5, where the spacetime covariance is used to evaluate LSM simulated spatio-temporal patterns of land surface temperature. The proposed method efficiently summarizes complex patterns and offers valuable insights into model strengths and weaknesses. Overall, this dissertation contributes to a stricter description and assessment of the landscape heterogeneity representation in LSMs and ESMs, providing a foundation for a more comprehensive model development.

The topographical properties of a landscape and their time evolution are key features of the Earth's surface, regulating ecosystem functioning in terms of soil properties as well as water and energy budgets, and creating visually diverse and striking patterns across various spatial scales. Furthermore, the natural evolution of a topography under the influence of geologic erosion can be greatly altered by anthropogenic disturbances (e.g., through agriculture, mining, deforestation), with the potential of accelerating soil erosion, causing land degradation and soil fertility losses. Hence, understanding the geomorphological processes driving the evolution of landscapes under natural and disturbed conditions is key not only to define the main factors and feedbacks shaping the Earth's topography, but also to foresee the consequences of intensive land use and implement optimal strategies of land management and recovery.

This dissertation addresses some key aspects of landscape evolution and stability, with a focus on the statistical description and modeling of hillslope morphologies under natural and disturbed conditions, the theoretical definition of drainage area at regular and non-regular points of the watershed, and the formation of spatially organized ridge and valley patterns.

We start from the analysis of topographic slopes under natural and accelerated soil erosion. Using large topographic datasets from mountain ranges worldwide, we show that the approximate age of a landscape is fingerprinted in the tails of its slope distributions. We then explore the role of the different processes driving this dynamic smoothing over geologic time scales by means of numerical experiments, showing that the relaxation process is mainly dominated by diffusion. The effect of agricultural-driven soil erosion on hillslope morphology is then investigated, highlighting how the natural aging process can be altered by intensive land use which, at smaller scales, produces key differences in the slope distribution tails. Furthermore, theoretical solutions are derived for the hillslope profile and the associated soil creep and runoff erosion fluxes, and used to link the observed differences in the morphological features of disturbed and undisturbed areas to a disruption of the natural balance between soil creep and runoff erosion mechanisms.

We then move the analysis to the drainage area, an important nonlocal morphometric variable used in a large number of geomorphological and ecohydrological applications. A nonlinear differential equation whose validity is limited to regular points of the watershed is obtained from a continuity equation, and the theory is then extended to critical and singular points by means of both Gauss' theorem and dynamical systems concepts. Such a link between the drainage area and a continuity equation sets the basis for the subsequent analysis of organized ridge and valley patterns and channel forming instability. The formation of ridge/valley patterns is analyzed by means of numerical experiments in detachment limited conditions, with the identification of various regimes as a function of diffusive soil creep, runoff erosion, and tectonic uplift as well as the specific geomorphic transport law assumed. Lastly, a linear stability analysis of the coupled water and landscape evolution dynamics is outlined to investigate the critical conditions triggering channel formation and the emergence of characteristic valley spacings in relation to the main geomorphological processes involved.

This paper investigates climate change impact on the water resources in the Greenbrier basin using a distributed hydrological model VIC and future climate series. The GCM outputs under the SRES A2 greenhouse gas emission scenario is downscaled and bias-corrected by the BCCAQ method to obtain the future climate series. The VIC model performance is satisfactory with the Nash–Sutcliffe efficiency coefficient (NSE) of 0.62 and 0.58 in calibration and validation periods. The bias-corrected precipitation and temperature indicate a warmer and more humid climate with precipitation and temperature increase by 14% and 1.8°C in the future. Under climate change background, the mean annual cycles of water balance components keep similar seasonal fluctuation but have larger magnitudes in the future. The discharge in the future also has close monthly distribution with that in the historical observations. The results show that the future discharge is larger than historical observation, implying water resources would be more abundant in summer from 2046 to 2065. The hydrological simulations in the Greenbrier basin have a system error of underestimating the peak flows, and the extreme discharge would be larger and more frequent in the mid of 21st century.

Seasonally dry ecosystems cover large areas over the world, have high potential for carbon sequestration, and harbor high levels of biodiversity. They are characterized by high rainfall variability at timescales ranging from the daily to the seasonal to the interannual, and water availability and timing play key roles in primary productivity, biogeochemical cycles, phenology of growth and reproduction, and agricultural production. In addition, a growing demand for food and other natural resources in these regions renders seasonally dry ecosystems increasingly vulnerable to human interventions. Compounded with changes in rainfall regimes due to climate change, there is a need to better understand the role of climate variabilities in these regions to pave the way for better management of existing infrastructure and investment into future adaptations.

In this dissertation, the ecohydrological responses of seasonally dry ecosystem to climate variabilities are investigated under a comprehensive framework. This is achieved by first developing diagnostic tools to quantify the degree of rainfall seasonality across different types of seasonal climates, including tropical dry, Mediterranean, and monsoon climates. This global measure of seasonality borrows from information theory and captures the essential contributions from both the magnitude and concentration of the rainy season. By decomposing the rainfall signal from seasonality hotspots, increase in the interannual variability of rainfall seasonality is found, accompanied by concurrent changes in the magnitude, timing, and durations of seasonal rainfall, suggesting that increase in the uncertainty of seasonal rainfall may well extend into the next century. Next, changes in the hydrological partitioning, and the temporal responses of vegetation resulting from these climate variabilities, are analyzed using a set of stochastic models that accounts for the unpredictability rainfall as well as its seasonal trajectories. Soil water storage is found to play a pivotal role in regulating seasonal soil water hysteresis, and the balance between seasonal soil water availability and growth duration is found to induce maximum plant growth for a given amount of annual rainfall. Finally, these methods are applied in the context of biodiversity and the interplay of irrigation and soil salinity, which are prevailing management issues in seasonally dry ecosystems.

This dissertation is primarily concerned with the development and application of statistical models for analyzing ecological and hydrological data. A key technical achievement contained within is the deployment of Markov chain Monte Carlo methods leveraging log posterior gradients for dramatically speeding up inference for hybrid empirical-mechanistic models as well as for analyses of large ecological datasets. In order to utilize such methods, the environmental models had to be implemented in automatic differentiation frameworks originally designed for optimization of deep neural networks. These models are highly challenging to use with previously existing methods such as the Gibbs sampler and random walk Metropolis sampling. These findings enable the enumeration and estimation of an enormous variety of new models spanning a range of process specificities from purely empirical to purely mechanistic forms, all within the same coherent joint parameter estimation framework. Additionally, these methods were employed in an analysis of ongoing changes in hydrology in the Prairie Pothole Region of North America (PPR). Key contributions from this analysis include the identification of a major structural shift in the number and geometry of ponds and wetlands in the PPR likely exacerbated by shifts in agricultural practices. Observational data from this region were used to develop and assess the performance of the first Bayesian model of upland-embedded wetland water volumes. The utility of this approach is shown by conducting inference of model parameters using biased and highly noisy calibration data derived from remote sensing.

As the human population continues to increase, the effects of land use change on streams and their watersheds will be one of the central problems facing humanity, as we strive to find ways to preserve important ecosystem services, such as drinking water, irrigation, and wastewater processing. This dissertation explores the effects of land use change on watershed nitrate concentrations, and on several biogeochemical ecosystem functions in streams, including nitrate uptake, ecosystem metabolism, and heterotrophic carbon processing.

In a literature synthesis, I was able to conclude that nitrate concentrations in streams in forested watersheds tend to be correlated with soil solution and shallow groundwater nitrate concentrations in those watersheds. Watershed disturbances, such as ice storms or clear-cutting, did not alter this relationship. However both urban and agricultural land use change increased the nitrate concentrations in streams, soil solution, and groundwater, and altered the correlation between them, increasing the slope and intercept of the regression line. I conclude that although the correlation between these concentrations allows for predictions to be made, further research is needed to better understand the importance of dilution, removal, and transformation along the flowpaths from uplands to streams.

From a multi-site comparison of forested, urban, and urban restored streams, I demonstrated that ecosystem functions like nitrate uptake and ecosystem metabolism do not change in a linear unidirectional way with increasing urbanization. I also showed that Natural Channel Design stream restoration as practiced at my study sites had no net effect on ecosystem function, except those effects that came from clearing the riparian vegetation for restoration construction. This study suggested further consideration is needed of the ecosystem effects of stream restoration as it was practiced at these sites. It also suggested that more study was needed of the effects of urbanization on ecosystem metabolism and heterotrophic processes in streams.

In a 16-month study of ecosystem metabolism at four sites along an urbanization gradient, I demonstrated that ecosystem metabolism in urban streams may be controlled by multiple separate effects of urbanization, including eutrophication, light, temperature, hydrology, and geomorphology. One site, with high nutrients, high light, and stable substrate for periphyton growth but flashy hydrology, demonstrated a boom-bust cycle of gross primary production. At another site, high benthic organic matter standing stocks combined with low velocities and high depths to create hypoxic conditions when temperature increased. I propose a new conceptual framework representing different trajectories of these effects based on the balance of increases in scour, thermal energy and light, eutrophication, and carbon loading.

Finally, in a study of 50 watersheds across a landscape urbanization gradient, I show that urbanization is correlated with a decrease in particulate carbon stocks. I suggest that an increase in dissolved organic matter quality may serve to compensate for the loss of particulate carbon as fuel for heterotrophic microbial activity. Although I saw no differences among watershed landuses in microbial activity per gram of sediment, there was a strong increase in the efficiency of microbial activity per unit organic sediment with increasing watershed urbanization. Ultimately, I hope that this research contributes to our understanding of stream ecosystem functions and the way land use change can alter these functions, with the possibility of better environmental management of urban streams in the future.

This dissertation addresses feedbacks between vegetation dynamics and land surface response to rainfall events, particularly in Mediterranean climates. Specifically, we ask how a saturated hydraulic conductivity value (ks) that is tied to vegetation biomass affects how water is divided into infiltration and runoff under a range of conditions. First, a field campaign in Sardinia was conducted in which a 4 m by 4 m rainfall simulator was constructed and deployed on a number of dates. Measurements of surface runoff from the plot and soil moisture within the plot informed estimates of the effective ks for each experimental run, and a comparison between ks and vegetation height measurements revealed a monotonically increasing relationship between the two. We then fit a logistic equation to this relationship and incorporated it into the calculations of a coupled vegetation dynamics and land surface model. Using the model, which is calibrated for the Sardinia field site, we investigated the effect of the variable ks by comparing the model results of biomass, saturation, and runoff to results using a static ks. We then used the same model to investigate the effects of a variable ks on drought recovery by simulating drought severity through a range of biomass levels relative to a no-drought condition. Our modeling results revealed that the primary result of a variable ks is modification of the quantity and mechanism of surface runoff; specifically, runoff increased over the constant ks case and shifted from saturation excess runoff to infiltration excess runoff. These effects are more pronounced in drier conditions and when rainfall intensities are in a critical region similar to the ks value. We conclude that a dynamic ks value is relevant for prediction of surface runoff and may improve the performance of land surface models.

Light rainfall is the baseline input to the annual water budget in mountainous landscapes through the tropics and at mid-latitudes. In the Southern Appalachians, the contribution from light rainfall ranges from 50-60% during wet years to 80-90% during dry years, with convective activity and tropical cyclone input providing most of the interannual variability. The Southern Appalachians is a region characterized by rich biodiversity that is vulnerable to land use/land cover changes due to its proximity to a rapidly growing population. Persistent near surface moisture and associated microclimates observed in this region has been well documented since the colonization of the area in terms of species health, fire frequency, and overall biodiversity. The overarching objective of this research is to elucidate the microphysics of light rainfall and the dynamics of low level moisture in the inner region of the Southern Appalachians during the warm season, with a focus on orographically mediated processes. The overarching research hypothesis is that physical processes leading to and governing the life cycle of orographic fog, low level clouds, and precipitation, and their interactions, are strongly tied to landform, land cover, and the diurnal cycles of flow patterns, radiative forcing, and surface fluxes at the ridge-valley scale. The following science questions will be addressed specifically: 1) How do orographic clouds and fog affect the hydrometeorological regime from event to annual scale and as a function of terrain characteristics and land cover?; 2) What are the source areas, governing processes, and relevant time-scales of near surface moisture convergence patterns in the region?; and 3) What are the four dimensional microphysical and dynamical characteristics, including variability and controlling factors and processes, of fog and light rainfall? The research was conducted with two major components: 1) ground-based high-quality observations using multi-sensor platforms and 2) interpretive numerical modeling guided by the analysis of the in situ data collection. Findings illuminate a high level of spatial – down to the ridge scale - and temporal – from event to annual scale - heterogeneity in observations, and a significant impact on the hydrological regime as a result of seeder-feeder interactions among fog, low level clouds, and stratiform rainfall that enhance coalescence efficiency and lead to significantly higher rainfall rates at the land surface. Specifically, results show that enhancement of an event up to one order of magnitude in short-term accumulation can occur as a result of concurrent fog presence. Results also show that events are modulated strongly by terrain characteristics including elevation, slope, geometry, and land cover. These factors produce interactions between highly localized flows and gradients of temperature and moisture with larger scale circulations. Resulting observations of DSD and rainfall patterns are stratified by region and altitude and exhibit clear diurnal and seasonal cycles.

Large areas of the world depend on snowmelt as a freshwater resource and for food production. Space-based remote sensing of seasonal snowpacks provides the only realistic means to monitor and quantify water availability (storage during the accumulation season, and release during the melt season) at global scales. The overarching goal of this study is to elucidate how meteorologic, topographic, and vegetation impact microwave remote-sensing measurements of high-elevation seasonal snowpacks. The working hypothesis is that changes in snowpack microwave behavior can be unambiguously attributed to snow physical processes modulated by meteorology, topography, and vegetation type. The research approach relies on the application of coupled snow hydrology and radiative transfer models to characterize the space-time evolution of snowpacks, and to support the interpretation of satellite-based microwave measurements toward enabling physically-guided estimation of Snow Water Equivalent (SWE). In the first part of this research, ensemble predictions of the seasonal snowpack over Grand Mesa, CO (~ 300 km2) for the hydrologic year 2016-2017 were conducted using a multilayer snow hydrology model. Snowpack ensembles were driven by gridded atmospheric reanalysis and evaluated against SnowEx’17 measurements. The multi-frequency microwave brightness temperatures and backscattering behavior of the snowpack (separate from soil and vegetation contributions) show that at sub-daily time scales, the ensemble standard deviation (i.e., weather variability at 3 × 3 km2) is < 3 dB for dry snow, and increases to 8-10 dB at mid-day when there is surficial melt that also explains the wide ensemble range (~20 dB). The linear relationship of SWE with the mean ensemble backscatter (R2 > 0.95) depends on weather conditions (e.g., 5-6 cm/dB in January; 2-2.5 cm/dB in late February as melt-refreeze cycles modify the microphysics in the top 50 cm of the snowpack). The nonlinear evolution of ensemble snowpack physics translates into seasonal hysteresis in the mesoscale microwave behavior. The backscatter hysteretic offsets between accumulation and melt regimes are robust in the L- and C-bands and collapse for wet, shallow snowpacks at Ku-band. The emissions behave as limit-cycles with weak sensitivity in the accumulation regime, and hysteretic behavior, with offsets increasing with frequency, is different for deep snowpacks at winter-spring transition and shallow ones at spring-summer transition. These findings suggest potential for multi-frequency active-passive remote sensing of high-elevation SWE depending on snowpack regime, particularly suited for data-assimilation via coupled snow hydrology-radiative transfer models extended to include the snow-soil and snow-vegetation interactions. To investigate snowpack microwave behavior in complex topography, an uncalibrated distributed multiphysics snow model driven by downscaled weather forecasts (30-m, 15-min) was implemented as a Radar Observing System Simulator (ROSS) in Senator Beck Basin (SBB), Colorado to elucidate topographic controls on C-, X- and Ku-bands active microwave sensing of mountain snowpacks. Phase-space maps of time-evolving grid-scale ROSS volume backscatter show the accumulation branch of the backscatter-snow water equivalent (σ-SWE) hysteresis seasonal loop that is the physical basis for radar retrieval (direct inference) of SWE and snowpack physical properties. There is good agreement in the accumulation season (R2 ~ 0.7) between Sentinel-1 and ROSS predictions corrected using average Sentinel-1 measurements under snow free conditions to estimate snow-ground backscatter, capturing well spatial patterns tied to elevation, slope, and aspect. Root Mean Square Deviations (RMSDs) do not exceed ±3.2 dB for ripening snowpacks in early spring and ±2.4 dB for dry snowpacks in the accumulation season when the mean absolute bias is < 1 dB for all land-cover types with topographic slopes ≤ 30°. Grid-point RMSDs are attributed to the underestimation of snowfall on upwind slopes compounded with forecast errors for the weather near the ground. Like Sentinel-1, ROSS backscatter fields exhibit frequency-independent single-scaling behavior in the 60-150 m scale range for dry snowpacks in the accumulation season, while frequency-dependent scaling behavior emerges in the ablation season. This study demonstrates skillful physical modeling capabilities to emulate Sentinel-1 observations in complex terrain. Conversely, it suggests high readiness to retrieve snow mass and snowpack properties in mountainous regions from radar measurements at high-spatial resolutions enabled by SAR technology. To estimate vegetation impacts on the snowpack microwave behavior, a coupled snow physics-radiative transfer forward-inversion modeling system was applied over snow-covered terrain in Grand Mesa to estimate vegetation contributions to the total backscatter from the ground-snow-vegetation system via referring to dual-frequency SnowSAR measurements. A simplified but comprehensive first-order microwave emission model (MEMLS-V) was iteratively inverted by a global optimizer – simulated annealing to retrieve unknown parameters and backscatter components from double-bounce, snowpack volume, and snow-ground interface. The retrieved parameters offered the simulations 100% correlation with the observed SnowSAR signal dynamics tied to vegetation and snowpack heterogeneities, which highlights that the forward-inversion system accounting for complex multiple scattering within the ground-snow-vegetation system reliably regulated compensation effects of vegetation and snow-ground interface. To the best of our knowledge, this is the first time that the system, with reduced computational requirements and ancillary data demands, has been successfully operated for SnowSAR data analysis, while maintaining robustness and interpretability. The findings have practical potentials of retrieving large-scale SWE in the northern hemisphere through Earth Observation radars and satellites.

Watersheds are complex, three dimensional structures that partition water between the components of the water balance and multiple storage pools within the watershed. This central function, however, remains poorly understood in a broadly transferable way despite decades of research. Perhaps one reason for this is the disciplinary bias towards studying pristine, mountainous watersheds with steep terrain and shallow soil. Although the relative simplicity of such systems has made them ideal hydrologic laboratories, understanding how watersheds function globally will require the incorporation of new types of landscapes into the studies of hillslope and watershed hydrology.

The Southern Piedmont region of the United States is situated between the Appalachian mountains and Atlantic coastal plains and stretches from Alabama to Maryland. It’s generally rolling terrain if underlain by deeply weathered and highly stratified soil characterized by relatively shallow argillic Bt horizons while weathered saprolite can extend tens of meters deep. Although it is a low-relief landscape, headwaters are often highly dissected with steep narrow valleys containing temporary streams surrounded by diverse topography. The region represents an ideal opportunity to incorporate more diverse landscapes into our studies of watershed hydrology.

As part of the NSF funded Calhoun Critical Zone Observatory, we intensively instrumented a 6.9 ha headwater (watershed 4, WS4), along with other targeted sensor locations including discharge in the 322 ha watershed that contains it (Holcombe’s Branch, HLCM), a nearby meteorological station, a deep groundwater well on a relatively flat interfluve, and a small network of wells in the buried floodplain. Sensors were continuously monitored for over 3 years while logging at 5 minute intervals. This sensor network allowed us to quantify the timing and magnitude of runoff, precipitation, deep and shallow groundwater levels distributed across a watershed, and soil moisture at multiple depths and hillslope positions. By doing so we were able to 1) describe the interactions between water balance components in WS4, 2) compare these watershed-scale measurements to internal hydrologic dynamics to determine what parts of the watershed are responsible for distinct watershed functions, and 3) explore how headwaters connect to higher order streams.

Using the monthly water balance in WS4, we calculated changes in integrated watershed storage and then derived a cumulative monthly storage time series from its running integral. We found that storage changes within the year by hundreds of millimeters (~25% of annual precipitation) in conjunction with seasonal peaks in evapotranspiration. Additionally, of all the potential variables that correlated to runoff magnitudes at the watershed scale, we found storage to be the best, particularly above a threshold value which remained remarkably consistent across all three years even with substantial differences in precipitation.

However, despite the storage threshold dependence of runoff, when we calculated daily storage we found that while runoff increased primarily in response to major precipitation events and then decreased again shortly thereafter, storage primarily wet up once from its low point at the end of the growing season and then drained starting at the growing season and continuing through the summer. Similarly, individual measurements of internal watershed hydrology like soil moisture or water table level displayed either seasonal or event-scale changes. We determined that measurements taken at watershed positions with more convergent hillslopes, or farther from the watershed divide, or installed deeper in the soil are more likely to display seasonal changes, and vice versa for event-scale changes. These three gradients are essentially proxies for vertical, lateral, and longitudinal distances, and so it appeared that the underlying gradient being measured was actually contributing volume. We determined the functions of different landscape components based on this analysis, and came to understand that storage-linked sites wet up first and then stay consistently so, making conditions for runoff. Subsequently, when runoff-linked sites wet-up, they mobilize significant runoff fluxes either by hydraulic displacement, or interflow, or a transmissivity feedback, or likely some combination of them all. During these times a substantial portion of the watershed is connected before drying down again with the exception of more storage-linked locations.

The result of this threshold setting followed by large runoff events is extremely flashy outputs from WS4. In contrast, we found HLCM to be far less flashy and relatively less sensitive to year to year fluctuations in precipitation. Further, we observed that except in the most extreme storms, surface flow from WS4 across the former floodplain in between it and HLCM always fully infiltrates into the sandy, legacy sediments deposited along the entire former floodplain. These sediments are the legacy of centuries of intensive and poorly managed agriculture across the Southern Piedmont. Wells in these sediments revealed a highly dynamic water table that was very responsive to outflow from WS4. A simple geometric simplification of the shape of these sediments and an estimate of their porosity revealed that these sediments had ~900 m3 of available storage space, space that was constantly filling and draining. Interestingly, that available storage volume level was sufficient to absorb discharge from WS4 on 97% of the days we measured. Through most of WS4 flow states, this storage served to buffer HLCM from flashier runoff coming from WS4, and then subsequently releasing it much more slowly and drawn out as shallow subsurfaceflow. However, when it reached volumes within 15% of maximum, usually in conjunction with large fluxes coming from WS4, runoff in HLCM reacted closely with WS4. So the storage volume in legacy sediments serves as an effective buffer from flashy upstream hydrology, but when the reach or approach saturation they become effective at transmitting surface flow, likely via saturation excess. Although we observed this phenomenon in only one alluvial fan, we have reason to think that such features are quite common locally and regionally, and represent a heretofore underappreciated legacy of historic agriculture.

Taken together, these findings describe a hydrologic system that is much more dynamic than its abundant rainfall and surface water resources would suggest. Further, they indicate that even a century or more after agricultural land abandonment and forest regrowth, legacies of the 18th and 19th century remain in the landforms and soils of the region. We feel that these findings are strong support for continued and expanded hydrologic study at the CCZO and in the Southern Piedmont in general.

Headwater streams serve multiple important biogeochemical, hydrologic, and ecological functions, including: transporting solutes from the terrestrial landscape to downstream fluvial ecosystems; providing a surface for gas evasion to the atmosphere; integrating terrestrial, riparian and aquatic ecosystems, amalgamating surface and groundwater; accumulating and storing sediment; and transforming and retaining solutes. The numerous mechanisms mediating these physical and biological processes remain poorly understood despite their prominent influence on catchment outlet biogeochemical dynamics.

In light of this research need, this study sought to determine the influence of hydrologic, ecological, and biogeochemical processes on solute (specifically carbon and nitrogen) concentrations and fluxes in a paired set of headwater stream networks.

This research was conducted at the Tenderfoot Creek Experimental Forest in Montana. An empirical, field-based approach that combined observational monitoring using a network of high temporal resolution sensors and experimental solute additions was used to quantify carbon and nitrogen uptake, metabolism, and export across the snowmelt and baseflow recession periods.

Based on analysis of this data set, we determined that headwater streams show strong demand for carbon and nitrogen across a range of concentrations from ambient to saturating concentrations; that the variation in uptake kinetics seasonally and between sites is driven by substrate availability; that this retention capacity is linked to the magnitude of metabolic demand; and that through the metabolism of the biological community carbon and nitrogen cycles are coupled. We then demonstrate that these biological processes can have variable roles in mediating carbon and nitrogen export at the catchment scale, but during some periods of the year they can be as influential as physically driven fluxes in mediating watershed export.

This study integrates disparate ecological and hydrologic perspectives to address how energy and macronutrients move through headwater stream networks. We believe the findings presented here begin to reconcile the seemingly incompatible paradigms of streams as highly retentive biogeochemical reactors and streams as “passive pipes” that reflect and integrate the terrestrial landscape.

Human-induced changes in climate and landscape characteristics are driving the coupled climate-hydrological-ecological system (CHES) into unchartered territories, with major implications on natural resource availability and sustainability at both local and global scales. Given that soil-plant-atmosphere are part of a hydrologic continuum, the variability and changes in climate may impact hydrological states and fluxes, which in turn can increase vegetation stress potentially resulting in an abrupt regime shift in the ecohydrological system. Describing and predicting the non-linear dynamics of CHES is challenging in part due to uncertainties in the parameters that describe the system and insufficient understanding of the physical mechanisms that control these responses. This dissertation strives to bridge these gaps through synergistic use of data analytics and physically-based modeling so as to characterize a spectrum of dimensionality, nonlinearity, and stochasticity of CHES across a range of spatial-temporal scales. Three overarching questions frame the direction and scope of this dissertation: Q1 – how do meteorological conditions affect groundwater dynamics in forested wetlands? Q2 – how to evaluate forest mortality risk under long-term climate change, and predict near-term forest mortality? Q3 – how does plant hydraulics regulate plant water use under hydro-climatic stress across biomes? Addressing these questions will improve the understanding of CHES dynamics and representations of hydrologic and vegetation dynamics in Earth System Models. The findings and methodologies developed here can be leveraged for devising mitigation and adaptation strategies for water resource management and ecosystem conservation under current and future climate regimes.

Flooding is the leading cause of losses from natural disasters in the United States, responsible for over 140 deaths a year (United States Geological Survey [USGS], 2006 and 2012). Dynamic inundation mapping, or using continuous modeling in conjunction with a GIS-system to create maps of flooded areas under different scenarios, has the possibility to better predict flooding by taking into account environmental conditions preceding and during the flood ( such as soil moisture, rainfall spatial heterogeneity and timing). This study examines the effect of hydrologic-hydraulic model linkage schemes in generating inundation maps and understanding the uncertainties involved, with an eye towards improving operational use.

The Hydrology Laboratory's Research Distributed Hydrologic Model (HL-RDHM) was used to generate inputs to the Army Corps of Engineers Hydrologic Engineering Center's River Analysis System (HEC-RAS) hydraulic model for two simulation time periods (September to December 1999 for the simulation of flooding due to Hurricane Floyd and from June to October 2006 for flooding due to Tropical Storm Alberto) in the Lower Tar River sub-basin for four different hydrologic-to-hydraulic model connection scenarios of increasing complexity and increasing number of inflow points. The HEC-GeoRAS, an extension built for use with ArcGIS to pre-process and post-process HEC-RAS geometric data, was used to generate inundation maps from the maximum water surface produced during each simulation time period.

The stage and flow simulated by the hydraulic model were compared to observed data recorded by USGS gauges and the goodness of simulation was assessed using the Nash-Sutcliffe Efficiency (NSE). The most detailed scenario slightly outperformed the less detailed scenarios for simulation of flow and stage at gauges on the main stem of the Tar River for the both simulation periods (NSE >0.89). No scenario did a good job of simulating the stage or flow on the tributaries of the Tar River (NSE <0.52). For the 1999 simulation, the flood elevation predicted by the inundation map was compared to high water marks collected by the Federal Emergency Management Agency (FEMA) and the USGS after Hurricane Floyd. All scenarios generally predicted flooding well, with mean error of around 1 foot for the less complex, more calibrated models, around 1-1.5 feet for the more complex models, and mean absolute error for all scenarios around 3.5-4 feet. The time required to generate these simulations and maps was not greatly increased with increasing modeling complexity, though the models did require careful set-up to ensure stability. The results of this work suggest modest benefits in accuracy of modeling stage and flow by increasing the number of inputs from a hydrologic model to a hydraulic model. Mapping results show limited statistical gain, but some importance of a multi-stem model in order to create inundation maps which include flooding effects on tributaries. Lack of observed data to provide boundary conditions, and reliance on input data with its own set of uncertainties seem to dominate uncertainty in tributary models unforced by observed data.

Periods of drought and low streamflow can have profound impacts on both human and natural systems. People depend on a reliable source of water for numerous reasons including potable water supply and to produce economic value through agriculture or energy production. Aquatic ecosystems depend on water in addition to the economic benefits they provide to society through ecosystem services. Given that periods of low streamflow may become more extreme and frequent in the future, it is important to study the factors that control water availability during these times. In the absence of precipitation the slower hydrological response of groundwater systems will play an amplified role in water supply. Understanding the variability of the fraction of streamflow contribution from baseflow or groundwater during periods of drought provides insight into what future water availability may look like and how it can best be managed. The Mills River Basin in North Carolina is chosen as a case-study to test this understanding. First, obtaining a physically meaningful estimation of baseflow from USGS streamflow data via computerized hydrograph analysis techniques is carried out. Then applying a method of time series analysis including wavelet analysis can highlight signals of non-stationarity and evaluate the changes in variance required to better understand the natural variability of baseflow and low flows. In addition to natural variability, human influence must be taken into account in order to accurately assess how the combined system reacts to periods of low flow. Defining a combined demand that consists of both natural and human demand allows us to be more rigorous in assessing the level of sustainable use of a shared resource, in this case water. The analysis of baseflow variability can differ based on regional location and local hydrogeology, but it was found that baseflow varies from multiyear scales such as those associated with ENSO (3.5, 7 years) up to multi decadal time scales, but with most of the contributing variance coming from decadal or multiyear scales. It was also found that the behavior of baseflow and subsequently water availability depends a great deal on overall precipitation, the tracks of hurricanes or tropical storms and associated climate indices, as well as physiography and hydrogeology. Evaluating and utilizing the Duke Combined Hydrology Model (DCHM), reasonably accurate estimates of streamflow during periods of low flow were obtained in part due to the model’s ability to capture subsurface processes. Being able to accurately simulate streamflow levels and subsurface interactions during periods of drought can be very valuable to water suppliers, decision makers, and ultimately impact citizens. Knowledge of future droughts and periods of low flow in addition to tracking customer demand will allow for better management practices on the part of water suppliers such as knowing when they should withdraw more water during a surplus so that the level of stress on the system is minimized when there is not ample water supply.

Tropical Cyclones (TCs) intensity and frequency are expected to be impacted by climate change. Despite their destructive potential, these phenomena, which can produce heavy precipitation, are also an important source of freshwater. Therefore any change in frequency, seasonal timing and intensity of TCs is expected to strongly impact the regional water cycle and consequently the freshwater availability and distribution. This is critical, due to the fact that freshwater resources in the US are under stress due to the population growth and economic development that increasingly create more demands from agricultural, municipal and industrial uses, resulting in frequent over-allocation of water resources.

In this study we concentrate on monitoring the impact of hurricanes and tropical storms on vegetation activity along their terrestrial tracks and investigate the underlying physical processes. To characterize and monitor the spatial organization and time of recovery of vegetation disturbance in the aftermath of major hurricanes over the entire southeastern US, a remote sensed framework based on MODIS enhanced vegetation index (EVI) was developed. At the SE scale, this framework was complemented by a water balance approach to estimate the variability in hurricane groundwater recharge capacity spatially and between events. Then we investigate the contribution of TCs (season totals and event by event) to the SE US annual precipitation totals from 2002 to 2011. A water budget approach applied at the drainage basins scale is used to investigate the partitioning of TCs' precipitation into surface runoff and groundwater system in the direct aftermath of major TCs. This framework allows exploring the contribution of TCs to annual precipitation totals and the consequent recharge of groundwater reservoirs across different physiographic regions (mountains, coastal and alluvial plains) versus the fraction that is quickly evacuated through the river network and surface runoff.

Then a Land surface Eco-Hydrological Model (LEHM), combining water and energy budgets with photosynthesis activity, is used to estimate Gross Primary Production (GPP) over the SE US The obtained data is compared to AmeriFlux and MODIS GPP data over the SE United States in order to establish the model's ability to capture vegetation dynamics for the different biomes of the SE US. Then, a suite of numerical experiments is conducted to evaluate the impact of Tropical Cyclones (TCs) precipitation over the SE US. The numerical experiments consist of with and without TC precipitation simulations by replacing the signature of TC forcing by NARR-derived climatology of atmospheric forcing ahead of landfall during the TC terrestrial path. The comparison of these GPP estimates with those obtained with the normal forcing result in areas of discrepancies where the GPP was significantly modulated by TC activity. These areas show up to 10% variability over the last decade.

In this thesis, an intermediate-level complexity model of a coupled land-surface and atmospheric boundary layer is developed. The model is used to investigate the controls on soil moisture-precipitation feedbacks and vegetation biomass-precipitation feedbacks. The diurnal evolution of a well-mixed boundary layer with piece-wise linear profiles of potential temperature and specific humidity is modeled. Rainfall can (though is not guaranteed to) occur if the boundary layer height crosses the lifting condensation level and if the boundary layer height is sufficiently high relative to the Obukhov length. The model illustrates that precipitation occurrence is more sensitive to the free atmospheric humidity profile than to the free atmospheric temperature profile. The precipitation model is also coupled to a model of spatial vegetation patterns in the Sahel to investigate the timescales of vegetation pattern response to changes in rainfall regime. Land surface-precipitation feedbacks increase the sensitivity and rate of change of vegetation pattern morphology.

Accurate modeling of land-atmosphere interaction would help us understand the persistent weather conditions and further contribute to the skill of seasonal climate prediction. In this study, seasonal variations in radiation and precipitation forcing are included in a stochastic soil water balance model to explore the seasonal evolution of soil moisture probabilistic structure. The theoretical results show soil moisture tends to exhibit bimodal behavior only in summer when there are strong positive feedback from soil moisture to subsequent rainfall. Besides the statistical analysis of soil moisture – rainfall feedback, simplified mixed-layer models, coupled with soil-plant-atmosphere continuum, are also used to study heat flux partitioning, cloud initiation, and strength of moist convection. Approximate analytical solutions to the mixed-layer model are derived by applying Penman-Monteith approach, which help explain the roles of equilibrium evaporation and vapor pressure deficit in controlling the diurnal evolution of boundary layer. Results from mixed-layer model also define four regimes for possible convection in terms of cloud/no-cloud formation and low/high convection intensity. Finally, cloud-topped mixed-layer model is developed to simulate the boundary-layer dynamics after the cloud formation, when the evaporative and radiative cooling other than surface heat flux may significantly contribute to the growth of the boundary layer.