Browsing by Subject "Hydrology"

Large-scale coherent structures are systematically investigated in terms of their geometric attributes, importance toward describing turbulent exchange of energy, momentum and mass as well as their relationship to landscape features in the context of land-atmosphere interaction. In the first chapter, we present the motivation of this work as well as a background review of large-scale coherent structures in land-atmosphere interaction. In the second chapter, the methodology of large-eddy simulation (LES) and the proper orthogonal decomposition (POD) is introduced. LES was used to serve as a virtual laboratory to simulate typical scenarios in land-atmosphere interaction and the POD was used as the major technique to educe the coherent structures from turbulent flows in land-atmosphere interaction. In the third chapter, we justify the use of the LES to simulate the realistic coherent structures in the atmospheric boundary layer (ABL) by comparing results obtained from LES of the ABL and direct numerical simulation (DNS) of channel flow. In the fourth chapter, we investigate the effects of a wide range of vegetation density on the coherent structures within the air space within and just above the canopy (the so-called canopy sublayer, CSL). The fifth chapter presents an analysis of the coherent structures across a periodic forest-clearing-forest transition in the steamwise direction. The sixth chapter focuses on the role of coherent structures in explaining scalar dissimilarity in the CSL. The seventh chapter summarizes this dissertation and provides suggestions for future study.

Topography, vegetation, nutrient dynamics, soil features and hydroclimatic forcing are inherently coupled, with feedbacks occurring over a wide range of temporal and spatial scales. Vegetation growth may be limited by soil moisture, nutrient or solar radiation availability, and in turn influences both soil moisture and nutrient balances at a point. These dynamics are further complicated in a complex terrain, through a series of spatial interactions. A number of experiments has characterized the feedbacks between soil moisture and vegetation dynamics, but a theoretical framework linking short-term leaf-level to interannual plot-scale dynamics has not been fully developed yet. Such theory is needed for optimal management of water resources in natural ecosystems and for agricultural, municipal and industrial uses. Also, it complements the current knowledge on ecosystem response to the predicted climate change.

In this dissertation, the response of vegetation dynamics to unpredictable environmental fluctuations at multiple space-time scales is explored in a modeling framework from sub-daily to interannual time scales. At the hourly time scale, a simultaneous analysis of photosynthesis, transpiration and soil moisture dynamics is carried out to explore the impact of water stress on different photosynthesis processes at the leaf level, and the overall plant activity. Daily soil moisture and vegetation dynamics are then scaled up to the growing season using a stochastic model accounting for daily to interannual hydroclimatic variability. Such stochastic framework is employed to explore the impact of rainfall patterns and different irrigation schemes on crop productivity, along with their implications in terms of sustainability and profitability. To scale up from point to landscape, a probabilistic representation of local landscape features (i.e., slope and aspect) is developed, and applied to assess the effects of topography on solar radiation. Finally, a minimalistic ecosystem model, including soil moisture, vegetation and nutrient dynamics at the year time scale, is outlined; when coupled to the proposed probabilistic topographic description, the latter model can serve to assess the relevance of spatial interactions and to single out the main biophysical controls responsible for ecohydrological variability at the landscape scale.

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.

The hydrologic cycle may be described in essence as the process of water rising and falling in its various phases between land and atmosphere. In this minimal description of the hydrologic cycle two features come into focus: intermittency and irreversibility. In this dissertation intermittency and irreversibility are investigated broadly in the soil-plant-atmosphere system. The theory of intermittency and irreversibility is addressed here in three ways: (1) through its effect on components of the soil-plant-atmosphere system, (2) through development of a measure of the degree of irreversibility in time-series, and (3) by the investigation of the dynamical sources of this intermittency. First, soil infiltration and spring frost risk are treated as two examples of hydrologic intermittency with very different characters and implications for the soil plant system. An investigation of the water budget in simplified soil moisture models reveals that simple bucket models of infiltration perform well against more accurate representation of intra-storm infiltration dynamics in determining the surface water partitioning. Damaging spring frost is presented as a ``biologically-defined extreme event'' and thus as a more subtle form of hydrologic intermittency. This work represents the first theoretical development of a biologically-defined extreme and highlights the importance of the interplay between daily temperature mean and variance in determining the changes in damaging frost risk in a warming climate. Second, a statistical measure of directionality/asymmetry is developed for stationary time-series based on analogies with the theory of nonequilibrium thermodynamics. This measure is then applied to a set of DNA sequences as an example of a discrete sequence with limited state-space. The DNA sequences are found to be statistically asymmetric and further that the local degree of asymmetry is a reliable indicator of the coding/noncoding status of the DNA segment. Third, the phenomenology of rainfall occurrence is compared with canonical examples of dynamical intermittency to determine whether these simple dynamical features may display a dominant signature in rainfall processes. Summer convective rainfall is found to be broadly consistent with Type-III intermittency. Following on this result we studied daytime atmospheric boundary layer dynamics with a view toward developing simplified models that may further elucidate the interaction the interaction between land surface conditions and convective rainfall triggering.

The overall objective of this thesis is to characterize the space-time variability of snowpack physical properties at high spatial and temporal resolution for downscaling of remote-sensing products of snow cover, snow depth and snow water equivalent. The hypothesis is that the temporal evolution of the sub grid-scale statistical structure of relative permittivity fields and other snow properties can be related to the temporal evolution of the areal averages obtained from remote-sensing, thus enabling downscaling of snow water equivalent and snow depth even in the absence of ground-based measurements. For this purpose, research was conducted on ground-based measurements of subgrid-scale properties, and on the development and evaluation of a microwave simulation system consisting of coupled snow hydrology and radiative transfer models.

First, an L-band TX-RX wireless sensor to monitor snow accumulation and snow wetness was designed, fabricated, and tested under laboratory conditions. The sensor was designed to operate at 39 discrete frequencies (39 channels) in the 1.00-1.76-GHz frequency range (0.02-GHz increments). Full-system testing of the first-generation system was conducted using commercial attenuators up to 20.0 dB to test the prototypes against design specifications. It was determined that performance was nearly optimal in the 1-1.2-GHz range. Next, snow layers of varying snow wetness were physically modeled under controlled laboratory conditions. This was achieved by adding varying amounts of water to a layer of fixed porosity foam inside a rectangular tank placed above the transmitter. The attenuation and relative phase shift of the RF signal propagating through the experimental "snowpack" and through the laboratory "atmosphere" were subsequently analyzed as a function of volumetric water content equivalent to snow wetness. Under the space and geometry limitations of the laboratory setup, the data show that the single-frequency measurements exhibit high sensitivity for wetness values up to 24%, whereas multifrequency retrieval is necessary for higher liquid water contents. Measurements from a field deployment during snowfall in January 2009 are also presented. The results suggest that there is potential for using the RF sensor to measure cumulative snowfall for short-duration events.

Second, a land-surface hydrology model (LSHM) [Devonec 2002] with one-layer snowpack physics was coupled to a microwave emission model (MEMLS, [Wiesmann 1999], [Matzler 1999]) including and atmospheric attenuation correction algorithm. The objective is to develop a parsimonious and autonomous framework for monitoring snow water equivalent in remote regions where ancillary data and ground-based observations for model calibration and, or data assimilation are lacking. Two case-studies were conducted to evaluate the coupled hydrology-emission model in forward mode: 1) the intercomparison of a multi-year simulation of snowpack radio-brightness behavior at Valdai, Russia, against Scanning Multi-channel Microwave Radiometer (SMMR) observations at three frequencies (18, 21, and 37 GHz, V and H polarizations) for a six year period, 1978-1983; and 2) an intercomparison against Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) during February 2003 in Colorado as part of CLPX (Cold Land Processes Field Experiment). The results show that the model captures well the radiative behavior of the snowpack, especially for vertical polarization in the winter accumulation season January-March for all years of simulation and all cases. Large biases for the Valdai case study were identified for intermittent snowpack conditions at the beginning of the cold season (e.g. fall) which can be explained by uncertainty in fractional snow cover and spatial variability of skin liquid water content at the large spatial scale of SMMR products. Nevertheless, the modeling system uncertainty range remains below the known biases of SMMR products as compared to SSM/I [Derksen 2003]. Using meteorological data from NASA CLPX [Cline 2003], the simulated brightness temperatures agree well with SSM/I and AMSR-E during February, 2003. Overall, the coupled LSHM-MEMLS forward model also performs well at smaller scales and where more ancillary data are available, and its performance is consistent with that of more complex snow models. This research suggests there is therefore potential of this modeling framework model that does not require calibration for useful physically-based estimation of snow water equivalent from remote sensing observations over large areas at multiple spatial resolutions.

Third, the snow hydrology model was modified to include a multi-layer transient representation of the evolution of snowpack properties with time. The coupled multi-layer snow hydrology and emission model was implemented and tested independently for two very different climatic and physiographic regions (Valdai and CLPX2002-2003) for both wet and dry snow regimes over multiple years with good results both in terms of capturing the evolution of snowpack physical properties, and the radiometric signature consistent with SMMR, SSM/I and AMSR-E observations at 18-19, 22-23, and 36-37 GHz V and H polarizations. These applications show transferability of the modeling system, and its potential utility in large-scale retrieval over large areas with limited if any ground-based observations to constrain the model or for data-assimilation. Despite overall good skill as demonstrated by relatively low errors, one weakness was identified with respect to the simulation of the emission behavior of the snowpack, especially for horizontal polarization induced by liquid water in the snowpack, when ice layers (ice lenses) form due to freezing of liquid water either due to daytime melting, or due to rain-on-snow events. Furthermore, it was established that a more accurate estimation of snow density especially in the case of wet snow regimes would be important to improve skill for vertical polarization. Consequently, a multi-layer snow hydrology model (MLSHM) that can capture the events and snowpack gradients in water content and structure through accumulation, ripening and melting phases was developed and coupled to MEMLS. Significant differences between the simulations using the single and multilayer model formulations were found in the ripening and melting phases when wet snow regimes are more frequent. These differences result from differences in snow density, with the single-layer formulation exhibiting higher density (shallower snow depths) and faster melting rates. Whereas there are no significant changes in the microwave brightness temperatures in the vertical polarization from single to multilayer simulations, there is dramatic improvement in the results for horizontal polarization in Valdai, but not in the case of the more complex snow regimes in CLPX. Further work is required to improve parameterizations of snow density and snow structure including evolution of grain size distribution.

Overall error statistics and detailed analysis of physical behavior show that the coupled MLSHM-MEMLS is apt to be used in data-assimilation in snow retrieval.

Human and natural disturbance affect the Amazon basin at several spatial and temporal scales. In this thesis, I used satellite-detected hot pixels to examine patterns of human-caused disturbance and protected areas in the Brazilian Amazon from 1996-2006. Deforestation fires, as measured by hot pixels, declined exponentially with increasing distance from roads. Fewer deforestation fires occurred within protected areas than outside and this difference was greatest near roads. However, even within reserves, more deforestation fires occurred in regions with high human impact than in those with lower impact. El Niño-related droughts affected deforestation fires most outside of reserves and near roads. There was no significant difference in fire occurrence among inhabited and uninhabited reserve types.

Within this context of disturbance in the Brazilian Amazon basin, I examined relatively undisturbed savanna-like `campina' ecosystems. I reviewed the literature on campinas and discussed their variation and their significance for beta diversity. As one of two case studies, I assessed spatio-temporal patterns of disturbance (fire and blowdowns), and vegetation change from 1987 to 2007 in campinas in the central Brazilian Amazon using Landsat imagery. In 2001 images, an increase in open areas corresponded with significantly more visible signs of disturbance, likely precipitated by the 1997-98 El Niño. Bird community data indicated a trend of more generalist/savanna species in more frequently disturbed campinas.

As the second case study, I used daily 500 m resolution MODIS reflectance data to assess seasonal and inter-annual flooding in ~33,000 km2 of campinas in the Negro river basin. Flooding cycles of these wetland campinas critically influence regional ecosystem processes. Flooded areas ranged from 15,000 km2 at the end of the rainy season (August-Oct) to little, if any, open water in the driest times (Jan-Mar). Predictable seasonal flood pulses occurred, but also displayed high inter-annual variability. This variability was weakly correlated with the Multivariate El Niño Southern Oscillation Index (MEI).

Campina ecosystems are an important, but largely overlooked, component of the biodiversity of the Amazon basin. My research shows that climate, particularly ENSO-associated droughts, strongly affects campinas even in remote areas, just as it increases fire frequencies in more populated regions of the Amazon.

Over 50% of Western Hemisphere shorebird species are in decline due to ongoing habitat loss and degradation. Many shorebird species require flooded habitat to rest and feed during migratory movements spanning thousands of miles between breeding and wintering grounds every spring and fall. In particular, shorebirds require shallowly flooded habitat (water depth <15cm deep)—due to their morphology (i.e., bill and tarsus length), many species are excluded from exploiting invertebrate prey resources in deeper waters. While habitat-associations for shorebirds are relatively well understood from observational studies, the distribution of suitable shorebird habitat over the broad areas used by these species during migration is not well described. In some regions of high wetland loss, shorebirds are heavily reliant on a core network of remaining human-managed wetlands and flood-irrigated agricultural fields. Refuges also provide substantial flooded habitat resources; however, these have typically been designed and managed to match the habitat needs of waterfowl, which can use much deeper water than shorebirds. Effective conservation strategies for migratory shorebirds will require improved understanding of flooded habitat suitability patterns over large migratory pathways, as well as knowledge of how species respond to habitat fluctuations over time.

We analyzed water extent dynamics across the Sacramento Valley of California, a globally important shorebird stopover site, for a 1983-2015 Landsat time series, and evaluated the effect of climate on water extent. Satellite measurements of surface water offer promise for understanding wetland habitat availability at broad spatial and temporal scales. A range of methods can detect open water from imagery, including supervised classification approaches and thresholds for spectral bands and indices. Thresholds provide a time advantage; however, there is no universally superior index, nor single best threshold for all instances. We used random forest to model the presence or absence of water from >6,200 reference pixels, and derived an optimal water probability threshold for our study area using receiver operating characteristic curves. An optimized mid-infrared (1.5–1.7 µm) threshold identified open water in the Sacramento Valley of California at 30-m resolution with an average of 90% producer’s accuracy, comparable to approaches that require more intensive user input. SLC-off Landsat 7 imagery was integrated by applying a customized interpolation that mapped water in missing data gaps with 99% user’s accuracy. On average we detected open water on ~26,000 ha (~3% of the study area) in early April at the peak of shorebird migration, while water extent increased five-fold after the migration rush. Over the last three decades, late March water extent declined by ~1,300 ha per year, primarily due to changes in the extent and timing of agricultural flood-irrigation. Water within shorebird habitats was significantly associated with an index of water availability at the peak of migration. Our approach can be used to optimize thresholds for time series analysis and near-real-time mapping in other regions, and requires only marginally more time than generating a confusion matrix.

Two dimensional representations of flooded habitat are insufficient to capture dynamic changes within the narrow water depth range that is effectively accessible to migratory shorebirds. We developed a method to quantify shallow water habitat distributions in inland non-tidal wetlands, and assessed how water management practices have affected the amount of shorebird habitat in Sacramento National Wildlife Refuge Complex (SNWRC), California. We produced water depth distributions and modeled optimal habitat (<10 cm deep) within 23 managed wetlands using high-resolution topography and fixed-point water depth records. We also demonstrated that habitat availability, specifically suitable water depth ranges, can be tracked from satellite imagery and high-resolution topography. We found that wetlands with lower topographic roughness may have a higher potential to provide shorebird habitat and that strategically reducing water levels could increase habitat extent. Over 50% of the wetlands measured provided optimal habitat across <10% of their area at the peak of migration in early April, and most provided a brief duration of shallow water habitat. Reducing water volumes could increase the proportion of optimal habitat by 1– 1,678% (mean = 294 %) compared to actual volumes measured at peak spring migration in 2016. For wetlands with a high habitat potential, beginning wetland drawdown earlier and extending drawdown time could dramatically improve habitat conditions at the peak of shorebird migration. Our approach can be adapted to track dynamic hydrologic changes at broader spatial scales as additional high-resolution topographic (e.g., lidar, drone imagery photogrammetry) and optical remote sensing data (e.g., Planet imagery, drone photography) become available.

Attempting to model the response of a community of shorebird species to flooded habitat dynamics from local to landscape scale necessitates a rich dataset including field observations of shorebird habitat use as well as information regarding regional habitat conditions over multiple time periods. Bringing together these data sources results in several challenges for classical statistical approaches, including overdispersion, fixed and random effects due to repeated measures, irregular temporal intervals, and missing data. We investigated how spring migration habitat use by 19 shorebird species at 327 wetland survey locations across SNWRC responded to flooded habitat fluctuations at multiple spatial scales from 1997-2015 using a generalized joint attribute modelling approach. In this analysis, we integrated shorebird census records and habitat conditions documented in the field with a suite of landscape-level habitat measurements derived from satellite imagery, as well as water availability, water allocation and land use information. We found that abundance by species peaked in late March and early April at SNWRC. Shorebird abundance responded positively to the amount of flooded habitat at a given wetland survey location. The total amount of water detected was the most important landscape habitat measure; shorebirds were less likely to be observed at high abundance at SNWRC wetlands when greater flooded habitat extent was present on the surrounding landscape. We found that human land and water management were influential drivers of shorebird habitat use. Water allocation information and reservoir storage resulted in better model fit (i.e., lower DIC) than including measures of surface water availability or drought conditions. Furthermore, the amount of landscape flooded habitat on agricultural land produced a better fit than considering all flooded habitat, or flooded habitat detected in wetlands. We found that the most relevant scale for measuring landscape flooded habitat was within 2-10 km of wetland survey locations; this distance could be a useful guideline for monitoring habitat conditions and targeting creation of supplemental habitat to bolster the existing wetland network in the Sacramento Valley.

Global climate change is largely due to human induced increases in the emission of greenhouse gases to the atmosphere. Although this fact does not directly motivate this research, it does set the backdrop for the impressive increase in research that topic has garnered across disciplines over the past 30 plus years. The goals of the research presented herein were to investigate the spatial and temporal dynamics of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) flux dynamics in a snowmelt dominated, semi-arid watershed in central Montana and to assess if and how these fluxes were related to patterns imposed by the topographic structure of the watershed. In the process, it has become apparent that a conceptual model that incorporates all three of these important GHGs, and their relationships with environmental variables does not exist. This is certainty at least in part due to the high variability of these fluxes within and between ecosystems. However, a concise conceptual model is necessary to compare empirical evidence and test alternative scaling methods across systems. Explicitly incorporating hydrologic processes into a conceptual framework will not only be important, but imperative, to predicting and assessing responses of these biogeochemical fluxes to a changing climate. This will be particularly relevant in locations that are likely to experience a change in the timing of precipitation such as snow versus rain dominated in sub-alpine zones or change in the timing and frequency of rain events.

In this study, we assessed the spatial and temporal dynamics of three major GHGs using a spatially distributed sampling campaign over two growing seasons. Real time sensors (5 locations) and local spatial variability plots (700 m2, n = 7, with 30 samples in each) were nested within a landscape scale sampling design (n=52). The sites that were distributed across the landscape (n = 52) were organized by transects that either exemplified specific landscape elements (e.g. uplands vs riparian area) or crossed significant environmental gradients (e.g. riparian – uplands or clearcut – forest). Total annual precipitation was similar between the two focal years (2012 = 764 mm 2013 = 749 mm). However, the contribution from rain versus snow shifted from 76% snow and 24% rain in 2012 to 56% snow and 44% rain in 2013. The influential rain events in 2013 began on 17 July and were observed through 14 August.

In this study, we observed that the strength of the relationship between soil water content and topographic metrics of water redistribution increased as the average wetness of the watershed declined. Soil water content and CO2 flux (fCO2) exhibited distinct spatial and temporal variability at the plot and landscape scales in 2013. The legacy effects of clearcutting remained prevalent with regards to fCO2 (which was significantly higher in the forest than in the clearcut regrowth), while differences in the spatial and temporal variability of \theta were not evident between the two landcover types.

Relationships between fluxes of CO2, CH4, and N2O and \theta were variable. The relationship between each gas and soil water content was not consistent between riparian and upland landscape elements. Although the transition zone between riparian and upland locations has been a focal point in watershed biogeochemistry, it appears that focusing on the shifting hydrodynamics, or the dominant hydrologic processes themselves, might be more important than focusing on specific, pre-defined locations in the landscape.

We capitalized on the significant relationships between terrain mediated \theta in the uplands and cumulative seasonal flux of CH4 to empirically scale our weekly measurements of CH4 flux to the watershed scale. We determined that incorporating multiple terrain metrics in the model produced the strongest fit between modeled and observed CH4 flux. This scaling exercise showed that the best fit model predicted over twice as much CH4 consumption in the uplands than predicted with an individual topographic wetness index or by extrapolating the mean/median CH4 flux to the watershed. Additionally, we determined that even if we used the maximum value of seasonal CH4 efflux in the riparian area to estimate riparian contributions, the riparian CH4 efflux only constituted 1– 4% of the net watershed CH4 flux (depending on which value of net influx is used).

While searching for the mechanisms that create biogeochemical optima can interesting and valuable, moving forward, it seems equally important to investigate the spatiotemporal dynamics of fluxes (or times/ places) that we expect to exhibit more landscape scale characteristic levels of a given flux/pool/process. It is also critical that we do not treat the hydrologic dynamics that can influencing those pools/fluxes as a “black box”. Field studies that measure these hydrologic dynamics can provide rich data sets to test accepted and proposed conceptual models and provide useful calibration data for process-based models. The combination of these techniques will most certainly advance our understanding of the spatial and temporal dynamics of greenhouse gas fluxes across given systems. However, evolved conceptual models will be key to assessing how each unique field site or modeling exercise contributes to greater process understanding and predictive capacity. Here we contribute to an updated conceptual model of the relationship between the processes that influence these GHG fluxes and soil water content. We hope that these conceptual contributions will spur new research questions that span systems and scales, while the empirical contributions highlight a few ways that this can be done in practice.

Drylands, comprising arid and semi-arid areas and the dry subtropics, over some 40% of the world's land area and support approximately 2 billion people, including at least 1 billion who depend on dryland agriculture and grazing. 10-20% of drylands are estimated to have already undergone degradation or desertification, and lack of monitoring and assessment remains a key impediment to preventing further desertification. Change in vegetation cover, specifically in the spatial organization of vegetation may occur prior to irreversible land degradation, and can be used to assess desertification risk. Coherent spatial structures arise in the distribution of dryland vegetation where plant growth is localized in regular spatial patterns. Such "patterned vegetation" occurs across a variety of vegetation and soil types, extends over at least 18 million ha, occurs in 5 continents and is economically and environmentally valuable in its own right.

Vegetation patterning in drylands arises due to positive feedbacks between hydrological forcing and plant growth so that the patterns change in response to trends in mean annual rainfall. Mathematical models indicate that vegetation patterns collapse to a desertified state after undergoing a characteristic set of transformations so that the condition of a pattern at any point in time can be explicitly linked to ecosystem health. This dissertation focuses on the mathematical description of vegetation patterns with a view to improving such predictions. It evaluates the validity of current mathematical descriptions of patterning for the specific case of small-scale vegetation patterns and proposes alternative hypotheses for their formation. It assesses the significance of seed dispersal in determining pattern form and dynamics for two cases: vegetation growing on flat ground with isotropic patterning, and vegetation growing on slopes and having anisotropic (i.e. directional) patterning. Thirdly, the feedbacks between local biomass density and infiltration capacity, one of the positive feedbacks believed to contribute to patterning, are quantified across a wide range of soil and climatic conditions, and new mathematical descriptions of the biomass-infiltration relationship are proposed. Finally the influence of land surface microtopography on the partitioning of rainfall into infiltration and runoff is assessed.