There is a lack of measured, long-term, reliable, and well-distributed hydrological variables in Canada. These hydrological variables include, but are not limited to: temperature, precipitation, ground runoff, evapotranspiration, soil moisture, and snow water equivalent. The objective of this thesis was to establish the best possible distributed estimates of these hydrological variables for Canada over the period of 1961-2000. The first step was to interpolate measured temperature and precipitation across the country. These interpolated values were then used to calculate the other hydrological variables using the Waterloo Flood Forecasting Model (WATFLOOD). The Waterloo Mapping technique (WATMAP) was developed to use topographic and land cover databases to automatically and systematically derive the information needed to create the drainage database. WATFLOOD was calibrated with the Dynamically Dimensioned Search (DDS) algorithm using the difference between the measured and simulated streamflow as the objective function. After a final calibration of 100 separate DDS runs, distributed time series for the hydrological variables were created. A simple assessment was made for the predictive uncertainty in the simulated streamflow results based on the results of the final calibration. As well, the implications of various climate change scenarios were examined in the context of how they would change the hydrological variables. The major recommendations for future study included: finding other gridded datasets that could be used to verify the ones that were created in this study and examining further the magnitudes of the different kinds of predictive uncertainty (data, model, and parameter). The results of this thesis fit in well with the goals of the study on Predictions in Ungauged Basins. This thesis was organized along the principle of “design the process, not the product”. As such, although a set of final products are presented at the end, the most important part of the thesis was the process that achieved these products. Thus it is not assumed that every technique designed in this thesis will be applicable to every other researcher, but it hoped that most researchers in the field will be able to use at least some parts of the techniques developed here.

There is a lack of measured, long-term, reliable, and well-distributed hydrological variables in Canada. These hydrological variables include, but are not limited to: temperature, precipitation, ground runoff, evapotranspiration, soil moisture, and snow water equivalent. The objective of this thesis was to establish the best possible distributed estimates of these hydrological variables for Canada over the period of 1961-2000. The first step was to interpolate measured temperature and precipitation across the country. These interpolated values were then used to calculate the other hydrological variables using the Waterloo Flood Forecasting Model (WATFLOOD). The Waterloo Mapping technique (WATMAP) was developed to use topographic and land cover databases to automatically and systematically derive the information needed to create the drainage database. WATFLOOD was calibrated with the Dynamically Dimensioned Search (DDS) algorithm using the difference between the measured and simulated streamflow as the objective function. After a final calibration of 100 separate DDS runs, distributed time series for the hydrological variables were created. A simple assessment was made for the predictive uncertainty in the simulated streamflow results based on the results of the final calibration. As well, the implications of various climate change scenarios were examined in the context of how they would change the hydrological variables. The major recommendations for future study included: finding other gridded datasets that could be used to verify the ones that were created in this study and examining further the magnitudes of the different kinds of predictive uncertainty (data, model, and parameter). The results of this thesis fit in well with the goals of the study on Predictions in Ungauged Basins. This thesis was organized along the principle of “design the process, not the product”. As such, although a set of final products are presented at the end, the most important part of the thesis was the process that achieved these products. Thus it is not assumed that every technique designed in this thesis will be applicable to every other researcher, but it hoped that most researchers in the field will be able to use at least some parts of the techniques developed here.

No description available.

In regions characterized by semi-arid climates where households’ welfare primarily relies on rainfed agricultural activities, extreme weather events such as droughts can present existential challenges to their livelihoods. To mitigate these risks, numerous social protection programs have been established to assist vulnerable households affected by weather events. Despite efforts to monitor environmental changes through remotely sensed technology, estimating the impact of weather variability on livelihoods remains challenging. This is compounded by the need to select appropriate statistical distribution for weather anomaly measures and household characteristics. We address these challenges by analyzing household consumption data from the Living Standards Measurement Study survey in Niger and systematically evaluating how each input factor affects vulnerability estimates. Our findings show that the choice of statistical distribution can significantly alter outcomes. For instance, using alternative statistical distribution for vegetation index readings could lead to differences of up to 0.7%, which means around 150,000 more households might be misclassified as not vulnerable. Similarly, variations in household characteristics could result in differences of up to 10 percentage points, equivalent to approximately 2 million households. Understanding these sensitivities helps policymakers refine targeting and intervention strategies effectively. By tailoring assistance programs more precisely to the needs of vulnerable households, policymakers ensure that resources are directed where they can make the most impact in lessening the adverse effects of extreme weather events. This enhances the resilience of communities in semi-arid regions. / Master of Science / In drought-prone regions where many families rely on rainfed farming, extreme weather can devastate livelihoods. Governments have created aid programs to assist the most vulnerable households during these climate crises, but identifying who needs help is extremely challenging. Part of this difficulty lies in selecting the right statistical methods for analyzing weather data and household information. In this paper, we focus on Niger, a country that experiences frequent droughts and where over 80% of the population depends on rainfed agriculture. By evaluating household consumption data, we aim to assist in identifying the households who has high probability of becoming poor as a result of unfavorable weather events and thus needs support from social protection programs. In our analysis, we systematically evaluate how each input factor (including household characteristics and statistical distributions) affects households likelihood of becoming poor in the event of weather crises. We find that compared to alternative statistical distributions, using a conventional normal distribution could lead to misclassifying around 150,000 households as non-vulnerable, leaving them without vital assistance. Similarly, using different sets of household characteristics can result in up to 10 percentage points which equivalents to 2 million households that would miss out on much-needed support. Understanding these sensitivities is crucial for policymakers in refining how aid programs identify the vulnerable populations and include them into the protection programs. The improved targeting approach will enhance the resilience of communities in semi-arid regions facing increasing weather variability.

Chapter 1 examines alternative specifications of futures-based forecasting models to improve upon existing approaches constrained by restrictive assumptions and limited information sets. We replace historical averages with rolling regressions and incorporate current market information through the deviation of the current basis from its historical average. To address potential non-stationarity and structural changes in the cash-futures price relationship, we employ a five-year rolling estimation window. Our findings indicate that the rolling regression approach yields significant improvements in both accuracy and information content of cotton season-average price forecasts, primarily at short forecast horizons. Chapter 2 addresses challenges in vulnerability assessment for semi-arid regions dependent on rainfed agriculture, where extreme weather events pose significant risks to household livelihoods. Despite advancements in remotely sensed technology, accurately estimating weather variability's impact on household livelihoods remains challenging. This study evaluates the effects of weather anomaly measures, spatial resolutions (i.e., geographic level at which the weather anomaly measures are evaluated), and household characteristics on household likelihood of falling into poverty (i.e., vulnerability) estimates. Combining household consumption data for Niger with remotely sensed agro-environmental measures, we find significant variations in vulnerability estimates based on the use of various weather condition measures (3 percentage points, equivalent to 600,000 households), spatial resolutions (8 percentage points, totalling 1.6 million households), and household characteristics (10 percentage points, equivalent to approximately 2 million households). Chapter 3 evaluates student learning outcomes from student involvement in hands-on learning settings, specifically focusing on student-managed investment funds. To assess the changes in the obtained technical and practical skills, we combine knowledge tests with grading rubrics. As part of practical skills, we consider commodity market analysis, critical thinking, informed decision-making, and insightful interpretation of market analysis results. We evaluate our students' understanding of commodity markets and their practical trading skills before and after joining the student-managed investment fund program. We find significant improvements in student learning outcomes, with students showing an average increase of 28% in disciplinary or technical knowledge and 38% in practical skills. Our findings highlight the importance of hands-on learning experiences to bridge the gap between theoretical knowledge and real-world application and in developing the well-rounded skill set demanded by the job market. / Doctor of Philosophy / Chapter 1 explores several alternative specifications of futures-based forecasting models to improve existing approaches constrained by restrictive assumptions and limited information sets. Accurate prediction of cotton prices is vital for the agricultural sector, significantly impacting decisions made by farmers, traders, and policymakers. Reliable forecasts enable farmers to optimize their planting and harvesting strategies, allow traders to manage risk more effectively, and guide policymakers in developing informed agricultural policies. However, the inherent volatility of commodity markets, particularly cotton, presents substantial challenges to price forecasting. Traditional forecasting methods often struggle to capture rapid market changes, resulting in less reliable predictions. Our proposed more responsive forecasting approaches lead to a significant gain in accuracy and information content of cotton price projection and provide valuable insights that can enhance decision-making processes throughout the cotton industry. Chapter 2 explores how extreme weather events, like droughts, affect households in semi-arid regions where people's livelihood largely depends on rain-fed farming. While satellite technology helps monitor environmental changes, it is still challenging to accurately measure how weather changes impact people's lives. Our study focuses on Niger and uses household survey data to assess how various factors influence our understanding of the risk of falling into poverty (i.e., household vulnerability) due to adverse weather events. We found that the methods we use to measure weather conditions, the geographic scale at which we measure them, and the household information we include can all significantly alter our estimates of how many households are at risk of becoming poor. For example, different methods for measuring weather impacts can change estimates of household vulnerability by about 3 percentage points, affecting around 600,000 households. The geographic level (administrative unit level or within a 20 km buffer around an enumeration area) at which we assess weather conditions can shift our estimates by 8 percentage points, which is equivalent to 1.6 million households. Additionally, considering different household characteristics can change our estimates by 10 percentage points, impacting around 2 million households. Our findings are crucial for policymakers who aim to better understand and address the effects of weather on vulnerable communities. Chapter 3 evaluates student learning outcomes from participation in the Commodity Investing by Students program, a student-managed investment fund within the Department of Agricultural and Applied Economics at Virginia Tech. Our study focuses on students from the 2022/23 and 2023/24 academic years, assessing both their technical knowledge and practical skills gained during a year-long involvement in the program. To measure changes in technical skills, we administered knowledge-testing quizzes before and after the training class. Practical skills, such as commodity market analysis, critical thinking, informed decision-making, and insightful interpretation of market analysis results, we evaluated through trading projects submitted during and at the end of the training class. We grade these student submissions using a specific practical skill evaluation rubric. We find significant improvements in student learning outcomes. On average, students demonstrated a 28% increase in disciplinary knowledge and a 38% improvement in practical skills. Our findings highlight the effectiveness of hands-on learning in improving both technical knowledge and practical skills that are highly valued in today's job market.

Call number: LD2668 .R4 1967 L46

Mankind's ability to predict weather on earth has been greatly enhanced by new instrumentation technology. Similarly, mankind's ability to predict space weather benefits from new technologies. Just as increasing the amount of atmospheric measurements on earth heightened mankind's ability to predict Earth weather, many scientists expect that expanding the amount of plasma measurements in space could be key to enhancing mankind's ability to predict space weather. Small satellites are one of these new technologies that have the potential to greatly enhance our ability to predict space weather. Utilizing many low-cost small satellites allows scientists to take data from more locations than possible with a few high-cost large satellites. Two instruments that have historic use measuring plasma are the Retarding Potential Analyzer and the Ion Drift Meter. Previous work has been done to combine the functionality from both of these instruments into one unit suitable for a small satellite in terms of size, power, and mass. An electrical and mechanical design has been completed to this end and this new instrument is called the Gridded Retarding Ion Distribution Sensor. This thesis describes the design and testing of the FPGA code that runs this new instrument.

Since its inception, the Pasture, Rangeland, and Forage Rainfall Index (PRF-RI) insurance program has issued a total of $8.8 billion in payouts. Given the program's significance, this thesis investigates methodologies to help improve it. For the first part, we evaluated the impact of finer-scale precipitation data on insurance payouts by comparing how the payout distribution differs between the program's current dataset and the finer-scale precipitation dataset by creating a simulated scenario where all parameters are constant except the rainfall index computed by the respective dataset. The analysis for Texas in 2021 revealed that using the finer-scale dataset to compute the rainfall index would result in payouts worth $27 million less than the current dataset. The second part of the research involved the development of two interactive decision-support tools: the "Next-Gen PRF" web tool and the "AgInsurance LLM" chatbot. These tools were designed to help users understand complex insurance parameters and make informed decisions regarding their insurance policies. User studies for the "Next-Gen PRF" tool measured usability, comprehension decision-making efficiency, and user experience, showing that it outperforms traditional methods by providing insightful visualizations and detailed descriptions. The findings suggest that using fine-scale precipitation data and advanced decision-support technologies can substantially benefit the PRF-RI program by reducing spatial basis risk and promoting user education, thus leading to higher user engagement and enrollment. / Master of Science / The Pasture, Rangeland, and Forage Rainfall Index (PRF-RI) program helps farmers manage drought risk. Since it started, it has paid farmers about $8.8 billion. This study looks into ways to improve the program. We first examined whether using rain data at a more finer spatial resolution could affect how much money is paid out. In Texas in 2021, we found that using this finer spatial resolution data could have reduced payouts by $27 million, underscoring the importance of evaluating our proposed change. Additionally, we created two new tools to help farmers understand and choose their insurance options more easily: the "Next-Gen PRF" web tool and the "AgInsurance LLM" chatbot. These tools seek to provide clear visuals and explanations. User studies with these tools show they help users learn more effectively and make more informed decisions compared to existing tools. Overall, our research suggests that using finer spatial resolution precipitation data as well as these interactive tools can enhance the insurance program, including by making it easier to engage with, and enabling farmers to evaluate if and how this program can help them resolve their weather risk management problems.

La question des liens entre forme urbaine et transport se trouve depuis une vingtaine d'années au cœur des réflexions sur la mise en place de politiques d'aménagement durable. L'essor de la diffusion de données sur grille régulière constitue dans ce cadre une nouvelle perspective pour la modélisation de structures urbaines à partir de mesures de densités affranchies de toutes les contraintes des maillages administratifs. A partir de données de densité de population et de surface bâtie disponibles à l'échelle de la France sur des grilles à mailles de 200 mètres de côté, nous proposons deux types de classifications adaptées à l'étude des pratiques de déplacement et du développement urbain : des classifications des tissus urbains et des classifications des morphotypes de développement urbain. La construction de telles images classées se base sur une démarche de modélisation théorique et expérimentale soulevant de forts enjeux méthodologiques quant à la classification d'espaces urbains statistiquement variés. Pour nous adapter au traitement exhaustif de ces espaces, nous avons proposé une méthode de classification des tissus urbains par transfert d'apprentissage supervisé. Cette méthode utilise le formalisme des champs de Markov cachés pour prendre en compte les dépendances présentes dans ces données spatialisées. Les classifications en morphotypes sont ensuite obtenus par un enrichissement de ces premières images classées, formalisé à partir de modèles chorématiques et mis à œuvre par raisonnement spatial qualitatif. L'analyse de ces images classées par des méthodes de raisonnement spatial quantitatif et d'analyses factorielles nous a permis de révéler la diversité morphologique de 50 aires urbaines françaises. Elle nous a permis de mettre en avant la pertinence de ces classifications pour caractériser les espaces urbains en accord avec différents enjeux d'aménagement relatifs à la densité ou à la multipolarité / Since a couple of decades the relationships between urban form and travel patterns are central to reflection on sustainable urban planning and transport policy. The increasing distribution of regular grid data is in this context a new perspective for modeling urban structures from measurements of density freed from the constraints of administrative division. Population density data are now available on 200 meters grids covering France. We complete these data with built area densities in order to propose two types of classified images adapted to the study of travel patterns and urban development: classifications of urban fabrics and classifications of morphotypes of urban development. The construction of such classified images is based on theoretical and experimental which raise methodological issues regarding the classification of a statistically various urban spaces. To proceed exhaustively those spaces, we proposed a per-pixel classification method of urban fabrics by supervised transfer learning. Hidden Markov random fields are used to take into account the dependencies in the spatial data. The classifications of morphotypes are then obtained by broadening the knowledge of urban fabrics. These classifications are formalized from chorematique theoretical models and implemented by qualitative spatial reasoning. The analysis of these classifications by methods of quantitative spatial reasoning and factor analysis allowed us to reveal the morphological diversity of 50 metropolitan areas. It highlights the relevance of these classifications to characterize urban areas in accordance with various development issues related to the density or multipolar development

La question des liens entre forme urbaine et transport se trouve depuis une vingtaine d'années au cœur des réflexions sur la mise en place de politiques d'aménagement durable. L'essor de la diffusion de données sur grille régulière constitue dans ce cadre une nouvelle perspective pour la modélisation de structures urbaines à partir de mesures de densités affranchies de toutes les contraintes des maillages administratifs. A partir de données de densité de population et de surface bâtie disponibles à l'échelle de la France sur des grilles à mailles de 200 mètres de côté, nous proposons deux types de classifications adaptées à l'étude des pratiques de déplacement et du développement urbain : des classifications des tissus urbains et des classifications des morphotypes de développement urbain. La construction de telles images classées se base sur une démarche de modélisation théorique et expérimentale soulevant de forts enjeux méthodologiques quant à la classification d'espaces urbains statistiquement variés. Pour nous adapter au traitement exhaustif de ces espaces, nous avons proposé une méthode de classification des tissus urbains par transfert d'apprentissage supervisé. Cette méthode utilise le formalisme des champs de Markov cachés pour prendre en compte les dépendances présentes dans ces données spatialisées. Les classifications en morphotypes sont ensuite obtenus par un enrichissement de ces premières images classées, formalisé à partir de modèles chorématiques et mis à œuvre par raisonnement spatial qualitatif. L'analyse de ces images classées par des méthodes de raisonnement spatial quantitatif et d'analyses factorielles nous a permis de révéler la diversité morphologique de 50 aires urbaines françaises. Elle nous a permis de mettre en avant la pertinence de ces classifications pour caractériser les espaces urbains en accord avec différents enjeux d'aménagement relatifs à la densité ou à la multipolarité / Since a couple of decades the relationships between urban form and travel patterns are central to reflection on sustainable urban planning and transport policy. The increasing distribution of regular grid data is in this context a new perspective for modeling urban structures from measurements of density freed from the constraints of administrative division. Population density data are now available on 200 meters grids covering France. We complete these data with built area densities in order to propose two types of classified images adapted to the study of travel patterns and urban development: classifications of urban fabrics and classifications of morphotypes of urban development. The construction of such classified images is based on theoretical and experimental which raise methodological issues regarding the classification of a statistically various urban spaces. To proceed exhaustively those spaces, we proposed a per-pixel classification method of urban fabrics by supervised transfer learning. Hidden Markov random fields are used to take into account the dependencies in the spatial data. The classifications of morphotypes are then obtained by broadening the knowledge of urban fabrics. These classifications are formalized from chorematique theoretical models and implemented by qualitative spatial reasoning. The analysis of these classifications by methods of quantitative spatial reasoning and factor analysis allowed us to reveal the morphological diversity of 50 metropolitan areas. It highlights the relevance of these classifications to characterize urban areas in accordance with various development issues related to the density or multipolar development