Herbaceous production in South India-limiting factors and implications for large herbivores

This study's goal was to better understand the growth pattern and limitations of the herbaceous production that supports South India's rich large herbivore grazer assemblage. We conducted a fully factorial nitrogen and water (three levels each) treatment field experiment in the herbivore rich South Indian Western Ghats region to determine the seasonal pattern and the extent to which nitrogen and water availability limit herbaceous production. Graminoid production was found to be nitrogen limited. Despite low rainfall, additional water did not significantly increase overall biomass production nor extend growth in the dry season. Accumulated standing biomass was highest in the late wet season (November) and lowest in the dry season (May). Leaf nitrogen was highest in the early wet season (June) and lowest in the late dry season (March). Grazing had a positive effect on grass production by extending the growing season. Biomass production and graminoid leaf nitrogen concentration levels in the study area were similar to other tropical areas in the world. Also similar to other tropical large herbivore areas, the dry season poses an annual challenge for large herbivores in the study area -particularly the smaller bodied species-to satisfy their nutrient requirements

Learning-Based Modeling of Weather and Climate Events Related To El Niño Phenomenon via Differentiable Programming and Empirical Decompositions

This dissertation is the accumulation of the application of adaptive, empirical learning-based methods in the study and characterization of the El Niño Southern Oscillation. In specific, it focuses on ENSO’s effects on rainfall and drought conditions in two major regions shown to be linked through the strength of the dependence of their climate on ENSO: 1) the southern Pacific Coast of the United States and 2) the Nile River Basin. In these cases, drought and rainfall are tied to deep economic and social factors within the region. The principal aim of this dissertation is to establish, with scientific rigor, an epistemological and foundational justification of adaptive learning models and their utility in the both the modeling and understanding of a wide-reaching climate phenomenon such as ENSO. This dissertation explores a scientific justification for their proven accuracy in prediction and utility as an aide in deriving a deeper understanding of climate phenomenon. In the application of drought forecasting for Southern California, adaptive learning methods were able to forecast the drought severity of the 2015-2016 winter with greater accuracy than established models. Expanding this analysis yields novel ways to analyze and understand the underlying processes driving California drought. The pursuit of adaptive learning as a guiding tool would also lead to the discovery of a significant extractable components of ENSO strength variation, which are used with in the analysis of Nile River Basin precipitation and flow of the Nile River, and in the prediction of Nile River yield to p=0.038. In this dissertation, the duality of modeling and understanding is explored, as well as a discussion on why adaptive learning methods are uniquely suited to the study of climate phenomenon like ENSO in the way that traditional methods lack. The main methods explored are 1) differentiable Programming, as a means of construction of novel self-learning models through which the meaningfulness of parameters arises from emergent phenomenon and 2) empirical decompositions, which are driven by an adaptive rather than rigid component extraction principle, are explored further as both a predictive tool and as a tool for gaining insight and the construction of models

Signal Optimal Smoothing by Means of Spectral Analysis

This chapter introduces two new empirical methods for obtaining optimal smoothing of noise‐ridden stationary and nonstationary, linear and nonlinear signals. Both methods utilize an application of the spectral representation theorem (SRT) for signal decomposition that exploits the dynamic properties of optimal control. The methods, named as SRT1 and SRT2, produce a low‐resolution and a high‐resolution filter, which may be utilized for optimal long‐ and short‐run tracking as well as forecasting devices. Monte Carlo simulation applied to three broad classes of signals enables comparing the dual SRT methods with a similarly optimized version of the well‐known and reputed empirical Hilbert‐Huang transform (HHT). The results point to a more satisfactory performance of the SRT methods and especially the second, in terms of low and high resolution as compared to the HHT for any of the three signal classes, in many cases also for nonlinear and stationary/nonstationary signals. Finally, all of the three methods undergo statistical experimenting on eight select real‐time data sets, which include climatic, seismological, economic and solar time series

Multi-scale controls on spatial patterns of soil water storage in the hummocky regions of North America

The intensification of land-water management due to agriculture, forestry, and urbanization is a global phenomenon increasing the pressure on world’s water resources and threatening water security in North America. The Prairie Pothole Region of North America covers approximately 775,000 km2 and contains millions of wetlands that serve important hydrological and ecological functions. The unique hummocky topography and the variable effect of different processes contribute to high spatio-temporal variability in soil water, posing major challenges in hydrological studies. The objectives of this study were to a) examine the spatial pattern of soil water storage and its scale and location characteristics; and b) to identify its controls at multiple scales. Soil water content at 20 cm intervals down to 140 cm was measured along a transect extending over several knoll–depression cycles in a hummocky landscape. High water storage in depressions and low water storage on the knolls created a spatial pattern that was inversely related to elevation. Spatial patterns were strongly similar within any given season (intra-season rank correlation coefficient as high as 0.99), moreso than between the same season over different years (inter-annual rank correlation coefficient as high as 0.97). Less similar spatial patterns were observed between different seasons (inter-season rank correlation coefficients as high as 0.90). While the intra-season and inter-annual spatial patterns were similar at scales >18 m, the inter-season spatial patterns were similar at much large scales (>72 m). This may be due to the variations in landform elements and micro-topography. The similarity at scales >72 m were present at any time and depth. However, small- and medium-scale spatial patterns changed with depth and with season due to a change in the hydrological processes. The relative dominance of a given set of processes operating both within a season and for the same season over different years yielded strong intra-season and inter-annual similarity at scales >18 m. Moreover, similarity was stronger with increasing depth, and was thought to be due to the dampening effect of overlying soil layers that are more dynamic. Similarity of spatial patterns over time helps to identify the location that best represents the field averaged soil water and improves sampling efficiency. Change in the similarity of scales of spatial pattern helps identify the change in sampling domain as controlled by hydrological processes. The scale information can be used to improve prediction for use in environmental management and modeling of different surface and subsurface hydrological processes. The similarity of spatial pattern between the surface and subsurface layers help make inferences on deep layer hydrological processes as well as groundwater dynamics from surface water measurements

Frequent burning promotes invasions of alien plants into a mesic African savanna

Fire is both inevitable and necessary for maintaining the structure and functioning of mesic savannas. Without disturbances such as fire and herbivory, tree cover can increase at the expense of grass cover and over time dominate mesic savannas. Consequently, repeated burning is widely used to suppress tree recruitment and control bush encroachment. However, the effect of regular burning on invasion by alien plant species is little understood. Here, vegetation data from a long-term fire experiment, which began in 1953 in a mesic Zimbabwean savanna, were used to test whether the frequency of burning promoted alien plant invasion. The fire treatments consisted of late season fires, lit at 1-, 2-, 3-, and 4-year intervals, and these regularly burnt plots were compared with unburnt plots. Results show that over half a century of frequent burning promoted the invasion by alien plants relative to areas where fire was excluded. More alien plant species became established in plots that had a higher frequency of burning. The proportion of alien species in the species assemblage was highest in the annually burnt plots followed by plots burnt biennially. Alien plant invasion was lowest in plots protected from fire but did not differ significantly between plots burnt triennially and quadrennially. Further, the abundance of five alien forbs increased significantly as the interval (in years) between fires became shorter. On average, the density of these alien forbs in annually burnt plots was at least ten times as high as the density of unburnt plots. Plant diversity was also altered by long-term burning. Total plant species richness was significantly lower in the unburnt plots compared to regularly burnt plots. These findings suggest that frequent burning of mesic savannas enhances invasion by alien plants, with short intervals between fires favouring alien forbs. Therefore, reducing the frequency of burning may be a key to minimising the risk of alien plant spread into mesic savannas, which is important because invasive plants pose a threat to native biodiversity and may alter savanna functioning

CODAR\u27s Surface Flow at the Mouth of the Chesapeake Bay: Relation to Bay\u27s and Atlantic\u27s Forcing

Surface currents in the lower Chesapeake Bay (CB) observed with land-based high-frequency radar antennas, or Coastal Ocean Dynamics Application Radar (CODAR), produce hourly 2D maps of current velocities used for search and rescue, pollution tracking, and fishing operations. This study analyzes the correlations between a 9-year record of surface currents measured by CODAR to coastal sea level, local wind forcing, river discharge into CB, and water transport through the Florida Straits, representing the Gulf Stream’s control on sea level along the U.S. mid-Atlantic coast. The goal of this study is to find ways to use CODAR data to detect and monitor long-term sea level changes in CB, which may aide numerical modeling of the lower Bay for long-term forecasting and trend analysis. Linear regression, spectral and wavelet analyses, and Empirical Mode Decomposition (EMD) are applied to the datasets. Linear regression and spectral analysis show high frequencies of CODAR surface currents driven primarily by winds and link to variations in water levels, while low frequencies explained by river discharge and Gulf Stream. Both spectral and wavelet capture the annual cycle, wavelet suggesting anti-correlation between CODAR outflow and water level at this period. Because these methods only capture signals up to about two years, EMD, which separates lower frequency oscillating modes, is also used. EMD trendlines are qualitatively consistent with known dynamics or may be part of larger decadal oscillations longer than this 9-year dataset. Spectral and EMD agree at high frequencies, but also suggests river and Gulf Stream flow may be linked with CODAR currents on longer time scales. EMD achieves realistic long-term trends and correlations for CODAR, but a longer time series is necessary to produce significant results that could use this data to truly monitor long-term sea level changes for the CB in this manner. The study demonstrated the complex nature and interconnections between the different factors and different time scales affecting the currents at the mouth of the CB. This analysis may be the first of its kind in the attempt at combining all these different observations in a single study

Drought variation of western Chinese Loess Plateau since 1568 and its linkages with droughts in western North America

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Adaptations to climate-mediated selective pressures in sheep

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