1,721,104 research outputs found
An Ecohydrological Framework to Explain Shifts in Vegetation Organization Across Climatological Gradients: Vegetation pattern in dry environments
No full textSpatial patterns found in vegetated ecosystems exhibit different degrees of organization in stand density that can be interpreted as an indicator of ecosystem health. In semi- arid environments, it is possible to observe transitions from over-dispersed individuals (e.g. an ordered lattice) to under-dispersed individuals (e.g. clumped points). These configurations correspond to different strategies of adaptation or optimization, whose understanding may help to predict some of the consequences of environmental changes for both ecosystem services and water resources. For this reason, we have developed a theoretical framework that characterizes the dispersion of individuals through a generalized Double Poisson distribution and estimates the landscape wide statistics using a soil moisture model accounting for tree canopies and root systems overlapping. Considering both the shading effect (light interception) of the canopies and the partitioning of water fluxes due to the presence of multiple individual root systems in one point, the optimum spacing between individuals at a given stand density is determined. This framework allows identifying the climatic boundaries for different landscape patterns in terms of optimal water use and stress. This simple scheme explains well the observed patterns of vegetation in arid and semiarid ecosystems
Modeling Vegetation Patterns in Semiarid Environment
No full textThe aim of this work is to deepen our understanding on the mutual relationship between climate, vegetation and soil water budget within an ecohydrological framework. To this end a coupled hydrological/ecological model is adopted to describe simultaneously vegetation pattern evolution and soil water budget in a semiarid river basin in New Mexico. This represents an ideal area to study the properties of water-controlled ecosystems. Analysis have been carried out using a recently formulated framework for the water balance at the daily level linked with a vegetation model for the description of the spatial organization of vegetation. Using this approach, we identified the dynamic water stress of vegetation during the growing season paying particular attention to the spatial distribution of solar radiation and the initial soil moisture condition at the beginning of the growing season. Several different variants of the vegetation model have been tested with the aim to identify the main drivers for the spatial organization of the vegetation. Results clearly show that vegetation patterns emerge from minimization of water stress and the maximization of water use
An Evaluation and Redesign of Philip Johnson¿s Tent of Tomorrow Through the Theory of Preservation Potential
No full textAt the heart of this thesis lies architect Philip Johnson¿s modern day ruin, the Tent
of Tomorrow, an iconic, abandoned pavilion from New York¿s 1964-1965 World¿s Fair.
Utilizing the Tent of Tomorrow as a case study, this thesis seeks to address both the
immediate need for a redesign of the structure and the long-term need for an integrated
method of analyzing preservation projects. A method for analyzing the structure
spanning multiple disciplines that acknowledges engineering, social, and artistic aspects
in the preservation and redesign process was developed. This multi-disciplinary
assessment, termed the theory of preservation potential, draws from engineering
Professor David Billington¿s theory of structural art. Utilizing the theory of preservation
potential, an analysis of the Tent of Tomorrow was carried out which produced findings
aimed at assisting in the structure¿s redesign. These findings were then used to redesign
the structure¿s roof. It is the hope of the author that future development of the theory of
preservation potential leads to a methodical and integrated system that informs how to
move forward with any preservation project
An Analysis of Conglomerate CLIGEN Climate Files in WEPP for Climate Stations of Chile
No full textSoil erosion rates have risen worldwide due to human activities including but not limited
mining and agriculture. Nowhere is this truer or more pressing of an issue than in
developing countries. However, these also tend to be the countries where data necessary
for accurate soil erosion modeling is unavailable. The USDA-developed Water Erosion
Prediction Project has been shown capable of accurately modeling soil erosion for sites
around the world, but requires at least hourly rainfall data. Using five climate stations from three different locations in Chile where hourly data is available, this study compares the accuracy of erosion modeling results based on measured hourly rainfall values and those based on borrowed hourly rainfall data from U.S. climate stations. Inputs for WEPP were created both from real data and from a combination of real and substitute data. The model was run based on all inputs and the results were statistically compared.
It was found that use of borrowed rainfall data yields unreliable results. For some
locations and some borrowed data, modeled erosion rates are comparable to those based
exclusively on measured data. However, this is not universally true, and no simple
method was found to relate daily data with hourly data so as to know which borrowed
data sets may yield statistically significant results and which may not. These
considerations may assist soil erosion modelers and researchers outside the U.S. to more accurately model soil erosion in WEPP and aid policy makers to decide whether to invest in data-gathering infrastructure
An Investigation of Groundwater Potential in the Timau River Basin, Kenya
No full textWater scarcity is Kenya is becoming a greater concern as a result of climate
change and population increase. In the Timau River Basin, a small catchment within the
Upper Ewaso Ng¿iro North River Basin, northwest of Mt. Kenya, substantial agricultural
water use is attributed to both small-scale and commercial farms. With a relatively recent
influx of immigrants to the lush mountain slopes, a highly productive region for
agriculture, increased upstream water abstraction from the Timau River and others has
left riverbeds to run dry in the more arid downstream areas. Commercial flower farms
also use groundwater, but the resource is not significantly exploited among small-scale
water users.
Data from boreholes in and around the Timau River Basin were investigated and
analyzed in order to assess the hydrological characteristics of the catchment. Static water
levels were used to establish a potentiometric water surface, which generally follows the
contours of the land surface. Pumping logs were utilized to calculate the hydraulic
properties of the aquifers. These properties, along with other characteristics of the
aquifer and the regional climate, were used to create a simple groundwater flow model
using MODFLOW-2005 and Groundwater Vistas.
Results from the model indicate that the region has significant potential for
increased groundwater use, with maximum pumping potential nearing six times current
rates. However, the model depicts some negative effects on streamflow due to greater
groundwater abstraction. Thus, this study demonstrates that development of groundwater
should by highly considered by water governance institutions in Kenya, though should
undoubtedly involve further research
Dynamics of herbivory in an altered post-war ecosystem at Gorongosa National Park, Mozambique
No full textFollowing a violent civil war that resulted in the loss of 90-100% of the individuals of all large mammal species in Gorongosa National Park in Mozambique, scientists observed a shift from grass dominance to forb dominance in the floodplain vegetation community. Understanding the vegetation shift is essential for restoration efforts because forbs generally provide less palatable plant biomass for herbivores, and we do not know if a forb-dominated floodplain will be able to support an abundant and diverse large herbivore population. To better understand the drivers of the novel floodplain dynamics, I explored the question: were the abundant herbivores present on the floodplain before the war responsible for maintaining the grass dominance? To address this question, I conducted clipping experiments to examine the effect of heavy grazing on the floodplain plant community. If the high level of pre-war herbivory was responsible for the grass dominance, then I would expect grasses to benefit from clipping and forbs to be negatively affected. I found no significant differences between the responses of grasses and forbs to clipping. However, the relationship between grazing and plant response is complex and further study is required to fully determine the potential differences between grass and forb responses to herbivory.
Although most large mammal species are recovering slowly following the war, waterbuck have staged a spectacular recovery and are dominating the recovering park’s mammal community. In light of this, I asked the question: what effect is this dominance having on the park’s vegetation community and the recovery of other animals? To address this, I conducted observations of waterbuck to understand factors that affect their behavior. If waterbuck feed preferentially in grass-dominated areas, for example, they may be playing a role in preventing grasses from re-establishing dominance. I found no effect of vegetation type on feeding, though waterbuck spent more time moving in grass-dominated areas relative to forb-dominated areas. Several other factors, including group size and time of day, also affected time spent on different behaviors. Understanding the factors that affect waterbuck behavior will lead to better understanding of their influence on the habitat, which will in turn help restoration efforts
TYPHOID, PAST AND PRESENT: Rain-Driven Seasonality and the Influence of Municipal Water Systems on Disease Dynamics
No full textDespite successful control of the disease in the United States and the developed world
almost a hundred years ago, typhoid fever causes over 21.7 million cases of illness and
217,000 deaths each year (Crump et al. 2004). It is important to continue research
into the disease dynamics and factors which led to the success of typhoid control in
the developed world if we hope to lessen and eliminate this burden of disease. This
thesis seeks to better quantify the influence of environmental drivers, particularly
rainfall, on typhoid dynamics as well as explore the impact of the development of
various municipal water systems on typhoid prevalence and patterns. To do this, typhoid
data is analyzed from six American cities, including New York, NY, Pittsburgh,
PA, Philadelphia, PA, Washington, DC, Chicago, IL and Baltimore, MD, between
1888 and 1932 as well as Kathmandu, Nepal, between 1993 and 2011. Seasonal and
multi-year cycles as well as correlation with rainfall are analyzed using power spectral
density analysis, magnitude squared coherence estimates, and wavelet analysis. Typhoid
dynamics are also explored through the adaptation of a doubly stochastic TSIR
(time series susceptible-infected-recovered) model. Through these tests, it was discovered
that the strength of annual cycles of typhoid is correlated with water source,
as cities with remote, pristine water sources brought to the city by way of aqueduct
experienced strong, clear annual cycles while cities with water sources more vulnerable
to pollution had more variability and longer trends exhibited by the typhoid
cycles. Rainfall and typhoid cycles are also correlated. This correlation did not seem
to be related to water source, but did seem to be associated with strength of rainfall
cycle. Finally, using a TSIR model to predict disease dynamics of historical typhoid
data in New York and Philadelphia revealed that the model was better equipped to
predict the regular and strong seasonality of New York rather than the variability of
typhoid in Philadelphia. These results show the impact of water sources and water
treatment on typhoid dynamics, as well as the influence of environmental drivers on
typhoid patterns, and the importance of considering these factors when designing
typhoid models or public health interventions
Farmer forecasts: Impacts of seasonal rainfall expectations on agricultural decision-making in Sub-Saharan Africa
No full textSeasonal climate variability frequently undermines farm yields, reduces food availability, and lowers income. This is particularly evident among small-scale agricultural producers in both irrigated and non-irrigated agroecosystems in the Global South where maize cultivars constitute a critical component of food production. In these systems, farmers make climate-sensitive decisions that include the selection of late- and/or early-maturing seed varieties, the diversity of seed varieties sown, and when to plant. Farmers’ expectations of future rainfall would therefore seem to be critical determinants of agricultural outcomes and foreshadow climate impacts. However, few studies have quantified the linkages between on-farm decisions and farmer seasonal predictions. We report on detailed household and phone surveys of 501 smallholder farmers in central Kenya based on the 2018 growing seasons and expectations for the 2019 March-April-May growing season. We show that farmers’ expectations of the upcoming seasonal rainfall have important associations with selections of seed maturity varieties and the number of maturing varieties farmers expect to plant and less important associations with the seeds’ planting dates. Furthermore, we show that 79% of the farmers form an expectation of the future seasonal climate and about two-thirds of them formed expectations based on a heuristic that connects the past climate to future seasonal conditions. More problematically, one-third of the farmers formed their rainfall expectation based on the prior season, and we show that no such correlation exists in observational data nor is correlation of seasonal rainfall supported by current understanding of climate variability. These results highlight the challenges farmers face in anticipating seasonal rainfall, which has implications for crop diversification and choices to adopt drought tolerant cultivars. The results suggest that farmers’ expectations of upcoming seasonal climate are important measures of farm decision-making.</p
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Convolutional Neural Network Based Approaches for Instance Segmentation of Irrigated Agriculture in Satellite Imagery
Full text linkIrrigated agriculture makes up the large majority of consumptive water use, and demand for water has greatly increased over the 21st century. To date, fine scale information about where irrigated agriculture is expanding is difficult to acquire due to: 1) a lack of manually delineated field boundaries due to: 1) a lack of centralized planning and management of agricultural development and 2) the limited ability of shallow machine learning models based on these limited data to generalize beyond small geographies or accurately map instances using remotely sensed imagery. Convolutional neural networks (CNNs) have been demonstrated to outperform shallower models with less learned non-linear feature transformations, such as decision tree based ensembles or SVMs, in image classification and segmentation of true color photography. To date there has been little research on the performance of CNN-based models for segmentation in the field of remote sensing. This thesis examines the performance of Mask R-CNN and Fully Convolutional Instance Aware Segmentation, two CNN-based methods for segmenting objects in images. There is a need to evaluate how these new methods perform in the remote sensing domain, given that images in these datasets tend to have a higher variance of objects and the geospatial dataset labels are more limited in number compared to large image corpuses, like ImageNet and COCO, which are used to test the performance of deep learning image recognition algorithms. Results show that true color Landsat 5 scenes can be used to produce sufficiently accurate instance detections of center pivot fields, even with a coarser resolution relative to newer sensors such as Sentinel-2. This opens the possibility of using Landsat’s long historical record for longitudinal studies of irrigation and cropping dynamics of center pivot agriculture
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