published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

Weather and climate events are costly to society both financially and in terms of human health and well being. The costs associated with extreme climate events have motivated governments, NGOs, private investors, and insurance companies to use the data and tools at their disposal to estimate the past, present, and future hazards associated with a wide range of natural phenomena in an effort to develop mitigation and/or adaptation strategies. The nonstationary nature of climate risks requires the use of numerical climate models, often general circulation models (GCMs), to project future risk. The climate risk field, however, currently finds itself in a predicament because GCMs can be biased and do not provide a clear way to credibly estimate their uncertainty with respect to simulations of future surface climate conditions. In response to this predicament, I lay the groundwork for a set of GCM credibility assessments by identifying the large-scale drivers of surface climate events that evolve over a range of timescales ranging from daily to multi-decadal. I specifically focus on three types of climate events relevant to the water and energy sectors: 1) seasonal precipitation, which impacts drinking water supplies and agricultural productivity; 2) extreme precipitation and the costly associated riverine flooding; and 3) temperature, wind, and solar radiation fields that modulate both electricity demand and potential renewable electricity supply. In chapter I, I derive a set of atmospheric indices and investigate their efficacy to predict distributed seasonal precipitation throughout the conterminous United States. These indices can also be used to diagnose the impact of tropical sea surface temperature heating patterns on conterminous United States precipitation. This is particularly of interest in the aftermath of the unexpected precipitation patterns in the conterminous United States during the 2015-2016 El Niño event. I show that the set of atmospheric indices, which I derive from zonal winds over the conterminous United States and portions of the North Atlantic and Pacific oceans, can skillfully predict precipitation over most regions of the conterminous United States better than previously recognized mid-latitude atmospheric and tropical oceanic indices. This work contributes a set of intermediate atmospheric indices that can be used to assess the efficacy of forecasting and simulation climate models to capture signal that exists between tropical heating, mid-latitude circulation, and mid-latitude precipitation. In chapter II, I first show that the frequency of regional extreme precipitation events, which are predictive of riverine flooding, in the Ohio River Basin are poorly simulated by a GCM relative to historical precipitation observations. I then illustrate that the same GCM is much better able to simulate the statistical characteristics of a set of atmospheric field-derived indices that I show to be strongly related to the precipitation events of interest. Thus, I develop a statistical model that allows for the simulation of the precipitation events based on the GCM's atmospheric fields, which allows me to estimate future hazard based on credibly simulated GCM fields. Lastly, I validate the fully Bayesian statistical model against historical observations and use the statistical model to project the future frequency of the regional extreme precipitation events. I conclude that there is evidence of increasing regional riverine flood hazard in the Central US river basin out to the year 2100, but that there is high uncertainty regarding the magnitude of the trend. This work suggests that the identification of atmospheric circulation patterns that modulate the probability of extreme precipitation and riverine flood risk may improve flood hazard projections by allowing risk analysts to assess GCMs with respect to their ability to simulate relevant atmospheric patterns. In chapter III, I present the first comprehensive assessment of quasi-periodic decadal variations in wind and solar electricity potential and of covariability between heating and cooling electricity demand and potential wind and solar electricity production. I focus on six locations/regions in the conterminous United States that represent different climate zones and contain major load centers. The decadal variations are linked to quasi-oscillatory variations of the global climate system and lead to time-varying risks of meeting heating + cooling demand using wind/solar power. The quasi-cyclical patterns in renewable energy availability have significant ramifications for energy systems planning as we continue to increase our reliance on renewable, weather- and climate-dependent energy generation. This work suggests that certain modes of low frequency climate variability influence potential wind and solar energy supplies and are thus especially important for GCMs to credibly simulate. All of the investigations are designed to be broadly applicable throughout the mid-latitudes and are demonstrated with specific case studies in the conterminous United States. The dissertation sections represent three cases where statistical techniques can be used to understand surface climate and climate hazards. This understanding can ultimately help to mitigate and adapt to climate variabilities and secular changes, which impact society, by assisting in the development, improvement, and credibility assessment of GCMs capable of reliably projecting future climate hazards.

The main objective of the study was to assess the extent to which resource-poor households in selected villages of Nkonkobe Local Municipality in the Eastern Cape Province of South Africa are vulnerable to drought by using an improvised remote sensing and Geographic Information System (GIS)-based mapping approach. The research methodology was comprised of 1) assessment of vulnerability levels and 2) the calculation of established drought assessment indices comprising the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) from wet-season Landsat images covering a period of 29 years from 1985 to 2014 in order to objectively determine the temporal recurrence of drought in Nkonkobe Local Municipality. Vulnerability of households to drought was determined by using a multi-step GIS-based mapping approach in which 3 components comprising exposure, sensitivity and adaptive capacity were simultaneously analysed and averaged to determine the magnitude of vulnerability. Thereafter, the Analytical Hierarchy Process (AHP) was used to establish weighted contributions of these components to vulnerability. The weights applied to the AHP were obtained from the 2012 - 2017 Nkonkobe Integrated Development Plan (IDP) and perceptions that were solicited from key informants who were judged to be knowledgeable about the subject. A Kruskal-Wallis H test on demographic data for water access revealed that the demographic results are independent of choice of data acquired from different data providers (χ2(2) = 1.26, p = 0.533, with a mean ranked population scores of 7.4 for ECSECC, 6.8 for Quantec and 9.8 for StatsSA). Simple linear regression analysis revealed strong positive correlations between NDWI and NDVI ((r = 0.99609375, R2 = 1, for 1985), 1995 (r = 0.99609375, R2 = 1 for 1995), (r = 0.99609375, R2 = 1 for 2005) and (r = 0.99609375, R2 = 1 for 2014). The regression analysis proved that vegetation condition depends on surface water arising from rainfall. The results indicate that the whole of Nkonkobe Local Municipality is susceptible to drought with villages in south eastern part being most vulnerable to droughts due to high sensitivity and low adaptive capacity.

近幾十年來，全球氣候變化──特別是城市氣候變化──影響城市環境及居民生活質素的程度已引起公眾廣泛的討論。然而，過去研究一般採用之低空間解析度並不足夠為城市規劃及設計提供完善的資訊，引致對於氣候變化缺乏充分的考慮。高密度的城市環境（如香港）需要高時間解析度的氣候數據以制定有效的適應和減緩策略來應對未來氣候的變化。 / 本研究採用線性迴歸技術，以預測未來香港市區和郊區的氣溫。本研究利用氣象站和統計延伸得出之基線氣溫數據來建立統計降尺度模型，以預測未來香港市區和郊區之平均氣溫、最高氣溫和最低氣溫。 / 根據結果顯示，統計降尺度模型能夠有效建立大氣氣象參數和本港氣溫兩者之間的關係，尤其春季、秋季和冬季之氣溫預測表現理想。另外，冬季氣溫的上升趨勢則出現較大的升幅。研究結果亦顯示夜間氣溫的上升趨勢一般比日間為高。在未來的日子，郊區的溫度上升亦將會比市區為高。隨著城市化的影響納入預測溫度因素之中，預計郊區的氣溫將超過城市核心（天文台總部之氣象站），而郊區暖化的速度亦比市區和近郊為高。 / 本研究發現統計降尺度方法能有助利用全球氣候模型（GCM）提供之數據，以預測未來氣候之變化。城市規劃與設計過程是需要大量的數據進行評估氣候變化對城市環境的影響之研究，儘管統計降尺度方法有一定程度的局限性，它仍然是一個低成本而有效的方法。根據未來預測之氣溫，本研究具體指出未來之氣候變化對於城市規劃和設計過程的影響，亦提出了一系列於不同規劃層面適用之適應和減緩措施的建議。 / The effects of global climate change on urban environment have been widely discussed in recent decades. In particular, changes in urban climate have received much attention as they affect the living quality of urban dwellers. However, the coarse spatial scales employed in recent climate change studies were found to be insufficient in the context of urban planning and design. It leads to the lack of information on the changing urban climate and insufficient consideration of climate change in urban planning and design processes. In high-density cities like Hong Kong, the complex urban environment requires climatic data at very fine temporal resolution in order to formulate effective adaptation and mitigation strategies for future climate change. / The present study employed regression techniques to establish empirical relationship between large-scale predictor variables and local predictands in order to obtain future air temperature of urban and rural areas of Hong Kong. 40-year baseline conditions of local air temperature were obtained from both the observational and statistically extended temperature record. Monthly means of daily mean, maximum, and minimum air temperatures for both daytime and night-time were calculated for establishing statistical downscaling (SD) models to project future air temperature of urban and rural areas of Hong Kong. / The results suggest that regression-based downscaling techniques are able to capture the relationship between large-scale atmospheric conditions and station-scale meteorological parameters. The SD models performed particularly well in winter and considerably satisfactory results were obtained in spring and autumn. Night-time temperature trends generally exhibited greater increases than daytime trends. Seasonal variations were present with greatest increases observed in winter. Rural areas would likely experience greater warming than the urban areas in the future. With urbanization effect incorporated into the projected temperature series, it was found that air temperature projected for suburban stations would exceed that for the urban core. Rural warming also exhibited a higher rate than those observed in suburban and urban stations. / The present study shows that statistical downscaling approach provides a method to obtain information about future climatic conditions at local scale by using GCM outputs which are widely accepted to be useful tools to assist climate change studies. Despite of the limitations that historical climate would persist in projected climatic series, it allows a low-cost but effective measure for climate impact assessments, particularly in the context of urban planning and design, which requires extensive data for a wide range of studies. Based on the projected air temperature, implications of future climate change on urban planning and design of potential development were discussed and recommendations on potential adaptation and mitigation measures at different planning levels were also presented. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Lau, Ka Lun. / Thesis (Ph.D.) Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 159-173). / Abstracts also in Chinese.

The phenomenon of climate change exists in a liminal state between denial and acceptance, past and future, theory and reality, problem and catastrophe, unfolding in the spaces between apparently stable forms. This thesis considers different temporalities emerging within this transition through a creative exploration of extreme weather and climatic events that seeks to foreground the idea of change itself. Research centers around the Florida Keys, a low lying archipelago that is widely expected to become uninhabitable in the next half century due to sea level rise, but only if the islands do not suffer a similar fate much sooner with the sudden arrival of a catastrophic hurricane. While most Keys residents are unconcerned about the growing reality of sea level rise, hurricanes are a constant threat generating a palpable atmosphere of anticipation and corresponding precaution. In resonance with this regular storm activity in the Florida Keys, the project also reflects on the coincidental occurrence of Hurricanes Irene (2011) and Sandy (2012), two errant and devastating storms that visited the northeastern United States over the course of this project and personally affected the author. Thus, extreme weather provides a material entry point into the complex and far-reaching event of climate change, offering an opportunity to theorize transition and to reflect on what might be creatively recuperated from cross currents of climate and catastrophe. In conclusion, the thesis proposes an ontology inspired by the unique reproductive strategy of the mangrove plant that has thickly and extensively colonized the coastline of southern Florida and through which events are understood to possess qualities of latency, accrual and distribution and to give rise to a future that is germinal, a present that is continuously resignified and a past that remains profoundly creative.

Thesis (M. Sc. (Statistics)) -- University of Limpopo, 2021 / The effects of ozone depletion on climate change has, in recent years, become a reality, impacting on changes in rainfall patterns and severity of extreme floods or extreme droughts. The majority of people across the entire African continent live in semi-arid and drought-prone areas. Extreme droughts are prevalent in Somalia and eastern Africa, while life-threatening floods are common in Mozambique and some parts of other SADC (Southern African Development Community) countries. Research has cautioned that climate change in South Africa might lead to increased temperatures and reduced amounts of rainfall, thereby altering their timing and putting more pressure on the country’s scarce water resources, with implications for agriculture, employment and food security. The average annual rainfall for South Africa is about 464mm, falling far below the average annual global rainfall of 860mm. The Limpopo Province, which is one of the nine provinces in South Africa, and of interest to this study, is predominantly agrarian, basically relying on availability of water, with rainfall being the major source for water supply. It is, therefore, pertinent that the rainfall pattern in the province be monitored effectively to ascertain the rainy period for farming activities and other uses. Modelling and forecasting rainfall have been studied for a long time worldwide. However, from time to time, researchers are always looking for new models that can predict rainfall more accurately in the midst of climate change and capture the underlying dynamics such as seasonality and the trend, attributed to rainfall. This study employed Exponetial Smoothing (ETS) State Space and Seasonal Autoregressive Integrated Moving Average (SARIMA) models and compared their forecasting ability using root mean square error (RMSE). Both models were used to capture the sporadic behaviour of rainfall. These two models have been widely applied to climatic data by many scholars and adjudged to perform creditably well. In an attempt to find a suitable prediction model for monthly rainfall patterns in Limpopo Province, data ranging from January 1900 to December 2015, for seven weather stations: Macuville Agriculture, Mara Agriculture, Marnits, Groendraal, Letaba, Pietersburg Hospital and Nebo, were analysed. The results showed that the two models were adequate in predicting rainfall patterns for the different stations in the Limpopo Province. / National Research Foundation (NRF)

With warming temperatures, Arctic ecosystems are changing from a net sink to a net sourceof carbon to the atmosphere, but the Arctic’s carbon balance remains highly uncertain. Landscapes are often assumed to be homogeneous when interpreting eddy covariance carbon fluxes, which can lead to biases when gap-filling and scaling-up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales. Plant functional types, soil moisture, thaw depth, and microtopography, for example, vary across the landscape and influence carbon dioxide (CO₂) and methane (CH4) fluxes. In Chapter 2, I reported results from growing season CO₂ and CH₄ fluxes from an eddy covariance tower in the Yukon-Kuskokwim (YK) Delta in Alaska. I used flux footprint models and Bayesian Markov Chain Monte Carlo (MCMC) methods to unmix eddy covariance observations into constituent landcover fluxes based on high resolution landcover maps of the region. I compared three types of footprint models and used two landcover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. I used artificially created gaps of withheld observations to compare gap-filling performance using our derived landcover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. I also compared regional carbon budgets scaled up from observations using heterogeneous and homogeneous approaches. Gap-filling methods that accounted for heterogeneous landscapes were better at predicting artificially withheld gaps in CO₂ fluxes than traditional approaches, and there were only slight differences performance between footprint models and landcover maps. I identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). I resolved distinct seasonality in tundra growing season CO₂ fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO₂ sink by a factor of two and underestimated CH₄ emissions by a factor of two when compared to scaling with any method that accounts for landscape heterogeneity. I showed how Bayesian MCMC, analytical footprint models, and high resolution landcover maps can be leveraged to derive detailed landcover carbon fluxes from eddy covariance timeseries. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic. Climate change is causing an intensification in tundra fires across the Arctic, including the unprecedented 2015 fires in the YK Delta. The YK Delta contains extensive surface waters (approximately 33% cover) and significant quantities of organic carbon, much of which is stored in vulnerable permafrost. Inland aquatic ecosystems act as hot-spots for landscape CO₂ and CH₄ emissions and likely represent a significant component of the Arctic carbon balance, yet aquatic fluxes of CO₂ and CH₄ are also some of the most uncertain. In Chapter 3, I measured dissolved CO₂ and CH₄ concentrations (n = 364), in surface waters from different types of waterbodies during summers from 2016 to 2019. I used Sentinel-2 multispectral imagery to classify landcover types and area burned in contributing watersheds. I developed a model using machine learning to assess how waterbody properties (size, shape, and landscape properties), environmental conditions (O₂ concentration, temperature), and surface water chemistry (dissolved organic carbon composition, nutrient concentrations) help predict in situ observations of CO₂ and CH₄ concentrations across deltaic waterbodies. CO₂ concentrations were negatively related to waterbody size and positively related to waterbody edge effects. CH₄ concentrations were primarily related to organic matter quantity and composition. Waterbodies in burned watersheds appeared to be less carbon limited and had longer soil water residence times than in unburned watersheds. My results illustrated the importance of small lakes for regional carbon emissions and demonstrate the need for a mechanistic understanding of the drivers of greenhouse gasses in small waterbodies. In the Arctic waterbodies are abundant and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. In Chapter 4, I demonstrated how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO₂ and CH₄ fluxes across the YK Delta of south-west AK. I found that waterbody size and contour were strong predictors for aquatic CO₂ emissions, attributing greater than two-thirds of the influence to the scaling model. Small ponds (<0.001 km²) were hotspots of emissions, contributing fluxes several times their relative area, but were less than 5% of the total carbon budget. Small to medium lakes (0.001–0.1 km²) contributed the majority of carbon emissions from waterbodies. Waterbody CH₄ emissions were predicted by a combination of wetland landcover and related drivers, as well as watershed hydrology, and waterbody surface reflectance related to chromophoric dissolved organic matter. When compared to my machine learning approach, traditional scaling methods that did not account for relevant landscape characteristics overestimated waterbody CO₂ and CH₄ emissions by 26%–79% and 8%–53% respectively. This chapter demonstrated the importance of an integrated terrestrial-aquatic approach to improving estimates and uncertainty when scaling carbon emissions in the arctic. In order to understand carbon feedbacks with the atmosphere and predict climate change, we need to develop methods to model and scale up carbon emissions. Gridded datasets of carbon fluxes are used to benchmark Earth system models, attribute changes in rates of atmospheric concentrations of greenhouse gases, and project future climate change. There are two main approaches to deriving gridded datasets of carbon fluxes and global or regional carbon budgets: bottom-up scaling, and top-down atmospheric inversions. There is often divergence between approaches, with carbon budgets calculated from bottom-up and top-down studies rarely overlapping. The resulting uncertainty in carbon budgets calculated from either approach is more pronounced in high-latitudes. One of the challenges with combining bottom-up models and comparing top-down models is the variable spatial resolutions used in each approach. In Chapter 5, I applied flux scaling models from earlier chapters to create bottom-up carbon budgets at very high resolution (10 m) for the entire YK Delta domain. I used ERA5 land reanalysis data to extend the flux models to 2012-2015 and 2017 growing seasons to coincide with airborne observations of atmospheric CO₂ and CH₄ concentrations from NASA CARVE and Arctic-CAP campaigns. I progressively coarsened remote sensing imagery for the region to 30 m, 90 m, 250 m, and 1 km to create coarser landcover maps and corresponding bottom-up carbon budgets. The high resolution bottom-up models, when convolved with concentration footprints, produced simulated atmospheric enhancements that were similar to observed atmospheric enhancements. There was little change coarsening to 30 m and 90 m, but simulated atmospheric enhancements and especially carbon budgets were quite different at 250 m and 1 km spatial resolution. The changes with resolution were largely the result of an increase in area mapped as wetlands and shrub tundra, and less area mapped as small waterbodies and lichen tundra. Coarser resolution bottom-up scaling consistently overestimated CH4 budgets. By evaluating flux models against atmospheric observations, I was able to diagnose missing components such as inland water carbon emissions and times when the scaling models overestimated emissions to missing seasonal dynamics. This dissertation combined novel uses of statistical techniques with a high density of field observations to yield process-level understanding of carbon cycling that could be applied to scaling-up carbon emissions. By merging terrestrial and aquatic perspectives and concurrently mapping ecosystem landcovers and disturbances at high spatial resolution, I avoided common sources of uncertainty in carbon budgets such as double-counting of areas. I investigated how we represent the landscape in terms of both spatial resolution and the level of landscape heterogeneity, and determined the effects of these choices on carbon fluxes and budget estimates. By comparing to the atmosphere, I evaluated the validity of different approaches to modeling carbon fluxes in the Arctic. Together, the chapters in this dissertation provided a holistic study of carbon cycling in the Arctic.