Modeling the impacts of urbanization and open water surface on heavy convective rainfall: a case study over the emerging Xiong'an city, China

In this study, we examine the impacts of urbanization and open water surface on heavy convective rainfall based on numerical modeling experiments using the Weather Research and Forecasting (WRF) model. We focus on a severe storm event over the emerging Xiong'an city in northern China. The storm event consists of two episodes, and features intense moisture transport and strong large‐scale forcing. A set of WRF simulations were implemented to examine the sensitivity of spatiotemporal rainfall variability in and around the urban area to different land use scenarios. Modeling results highlight contrasting roles of open water and urban surface in dictating space‐time organizations of convective rainfall under strong large‐scale forcing. Dynamic perturbation to atmospheric forcing dominates the impacts of open water and urban surface on spatial rainfall distribution during the second storm episode, while urban surface promotes early initiation of convection during the first storm episode through enhanced buoyant energy. Open water surface contributes to convective inhibition through evaporative cooling but can enhance moist convection when the impact of urban surface is also considered. The synergistic effect of open water and urban surface leads to rainfall enhancement both over and in the downwind urban area. Changes in rainfall accumulation with different spatial extents of urban coverage highlight strong dependence of urban‐induced rainfall anomalies on urbanization stages. Our results provide improved understandings on hydrometeorological impacts due to emerging cities in complex physiographic settings, and emphasize the importance of atmospheric forcing in urban rainfall modification studies

Landslide riskscapes in the Colorado Front Range: a quantitative geospatial approach for modeling human-environment interactions

2021 Spring.Includes bibliographical references.This research investigated the application of riskscapes to landslides in the context of geospatial inquiry. Riskscapes are framed as a landscape of risk to represent risk spatially. Geospatial models for landslide riskscapes were developed to improve our understanding of the spatial context for landslides and their risks as part of the system of human-environment interactions. Spatial analysis using Geographic Information Systems (GIS) leveraged modeling methods and the distributed properties of riskscapes to identify and preserve these spatial relationships. This dissertation is comprised of four separate manuscripts. These projects defined riskscapes in the context of landslides, applied geospatial analyses to create a novel riskscape model to introduce spatial autocorrelation methods to the riskscape framework, compared geostatistical analysis methods in these landslide riskscape assessments, and described limitations of spatial science identified in the riskscape development process. The first project addressed the current literature for riskscapes and introduced landslides as a measurable feature for riskscapes. Riskscapes are founded in social constructivist theory and landslide studies are frequently based on quantitative risk assessment practices. The uniqueness of a riskscape is the inclusion of human geography and environmental factors, which are not consistently incorporated in geologic or natural hazard studies. I proposed the addition of spatial theory constructs and methods to create spatially measurable products. I developed a conceptual framework for a landslide riskscape by describing the current riskscape applications as compared to existing landslide and GIS risk model processes. A spatial modeling formula to create a weighted sum landslide riskscape was presented as a modification to a natural hazard risk equation to incorporate the spatial dimension of risk factors. The second project created a novel method for three geospatial riskscapes as an approach to model landslide susceptibility areas in Boulder and Larimer Counties, Colorado. This study synthesized physical and human geography to create multiple landslide riskscape models using GIS methods. These analysis methods used a process model interface in GIS. Binary, ranked, and human factor weighted sum riskscapes were created, using frequency ratio as the basis for developing a weighting scheme. Further, spatial autocorrelation was introduced as a recommended practice to quantify the spatial relationships in landslide riskscape development. Results demonstrated that riskscapes, particularly those for ranked and human factor riskscapes, were highly autocorrelated, non-random, and exhibited clustering. These findings indicated that a riskscape model can support improvements to response modeling, based on the identification of spatially significant clustering of hazardous areas. The third project extended landslide riskscapes to measurable geostatistical comparisons using geostatistical tools within a GIS platform. Logistic regression, weights of evidence, and probabilistic neural networks methods were used to analyze the weighted sum landslide riskscape models using ArcGIS and Spatial Data Modeler (ArcSDM). Results showed weights of evidence models performed better than both logistic regression and neural networks methods. Receiver Operator Characteristic (ROC) curves and Area Under the Curve validation tests were performed and found the weights of evidence model performed best in both posterior probability prediction and AUC validation. A fourth project was developed based on the limitations discovered during the analytical process evaluations from the riskscape model development and geostatistical analysis. This project reviewed the issues with data quality, the variations in results predicated on the input parameters within the analytical toolsets, and the issues surrounding open-source application tools. These limitations stress the importance of parameter selection in a geospatial analytical environment. These projects collectively determined methods for riskscape development related to landslide features. The models presented demonstrate the importance and influence of spatial distributions on landslide riskscapes. Based on the proposed conceptual framework of a spatial riskscape for landslides, weighted sum riskscapes can provide a basis for prioritization of resources for landslides. Ranked and human factor riskscapes indicate the need to provide planning and protection for areas at increased risk for landslides. These studies provide a context for riskscapes to further our understanding of the benefits and limitations of a quantitative riskscape approach. The development of a methodological framework for quantitative riskscape models provides an approach that can be applied to other hazards or study areas to identify areas of increased human-environment interaction. Riskscape models can then be evaluated to inform mitigation and land-use planning activities to reduce impacts of natural hazards in the anthropogenic environment

On the assessment of surface urban heat island: size, urban form, and seasonality

To what extent cities can be made sustainable under the mega-trends of urbanization and climate change remains a matter of unresolved scientific debate. Our inability in answering this question lies partly in the deficient knowledge regarding pivotal humanenvironment interactions. Regarded as the most well documented anthropogenic climate modification, the urban heat island (UHI) effect – the warmth of urban areas relative to the rural hinterland – has raised great public health concerns globally. Worse still, heat waves are being observed and are projected to increase in both frequency and intensity, which further impairs the well-being of urban dwellers. Albeit with a substantial increase in the number of publications on UHI in the recent decades, the diverse urban-rural definitions applied in previous studies have remarkably hampered the general comparability of results achieved. In addition, few studies have attempted to synergize the land use data and thermal remote sensing to systematically assess UHI and its contributing factors. Given these research gaps, this work presents a general framework to systematically quantify the UHI effect based on an automated algorithm, whereby cities are defined as clusters of maximum spatial continuity on the basis of land use data, with their rural hinterland being defined analogously. By combining land use data with spatially explicit surface skin temperatures from satellites, the surface UHI intensity can be calculated in a consistent and robust manner. This facilitates monitoring, benchmarking, and categorizing UHI intensities for cities across scales. In light of this innovation, the relationship between city size and UHI intensity has been investigated, as well as the contributions of urban form indicators to the UHI intensity. This work delivers manifold contributions to the understanding of the UHI, which have complemented and advanced a number of previous studies. Firstly, a log-linear relationship between surface UHI intensity and city size has been confirmed among the 5,000 European cities. The relationship can be extended to a log-logistic one, when taking a wider range of small-sized cities into account. Secondly, this work reveals a complex interplay between UHI intensity and urban form. City size is found to have the strongest influence on the UHI intensity, followed by the fractality and the anisometry. However, their relative contributions to the surface UHI intensity depict a pronounced regional heterogeneity, indicating the importance of considering spatial patterns of UHI while implementing UHI adaptation measures. Lastly, this work presents a novel seasonality of the UHI intensity for individual clusters in the form of hysteresis-like curves, implying a phase shift between the time series of UHI intensity and background temperatures. Combining satellite observation and urban boundary layer simulation, the seasonal variations of UHI are assessed from both screen and skin levels. Taking London as an example, this work ascribes the discrepancies between the seasonality observed at different levels mainly to the peculiarities of surface skin temperatures associated with the incoming solar radiation. In addition, the efforts in classifying cities according to their UHI characteristics highlight the important role of regional climates in determining the UHI. This work serves as one of the first studies conducted to systematically and statistically scrutinize the UHI. The outcomes of this work are of particular relevance for the overall spatial planning and regulation at meso- and macro levels in order to harness the benefits of rapid urbanization, while proactively minimizing its ensuing thermal stress

Empirical estimations of CO₂ flux component over different ecosystems

Vertical CO2 fluxes are the result of the net exchange between an ecosystem and the overlying atmosphere. The complex balance among biogenic and anthropogenic components varies from site to site and can be observed by using the micrometeorological Eddy Covariance (EC) technique. Different approaches can be used then to partition the EC flux measurements in order to understand the influence of each contribution to the flux. The general aim of this PhD thesis is to relate carbon balance of different ecosystems to the main controlling factors and to look for general and empirical relations that allow to estimate CO2 flux components by using environmental variables and the percentage of vegetation cover. The work is organized into three main parts: the first is about CO2 flux partitioning over three Mediterranean sites by using literature-based empirical relations and a new combined approach. The second part is about estimating anthropogenic components of the CO2 flux. A review of empirical methods is presented, as well as the recent improvements of the processbased model ACASA and its evaluation over a suburban site. Finally, the third part combines the results of previous chapters to present a new developed empirical simple model, based on land cover fraction and environmental variables. It can simulates the daily mean trend of CO2 flux over different ecosystems, and a first validation of the biogenic module, both over a natural and a suburban site, is presented

Land, Water, Infrastructure And People: Considerations Of Planning For Distributed Stormwater Management Systems

When urbanization occurs, the removal of vegetation, compaction of soil and construction of impervious surfaces—roofs, asphalt, and concrete—and drainage infrastructure result in drastic changes to the natural hydrological cycle. Stormwater runoff occurs when rain does not infiltrate into soil. Instead it ponds at the surface and forms shallow channels of overland flow. The result is increased peak flows and pollutant loads, eroded streambanks, and decreased biodiversity in aquatic habitat. In urban areas, runoff is typically directed into catch basins and underground pipe systems to prevent flooding, however such systems are also failing to meet modern environmental goals. Green infrastructure is the widely evocative idea that development practices and stormwater management infrastructure can do better to mimic the natural hydrological conditions through distributed vegetation and source control measures that prevent runoff from being produced in the first place. This dissertation uses statistics and high-resolution, coupled surface-subsurface hydrologic simulation (ParFlow.CLM) to examine three understudied aspects of green infrastructure planning. First, I examine how development characteristics affect the runoff response in urban catchments. I find that instead of focusing on site imperviousness, planners should aim to preserve the ecosystem functions of infiltration and evapotranspiration that are lost even with low density development. Second, I look at how the spatial configuration of green infrastructure at the neighborhood scale affects runoff generation. While spatial configuration of green infrastructure does result in statistically significant differences in performance, such differences are not likely to be detectable above noise levels present in empirical monitoring data. In this study, there was no evidence of reduced hydrological effectiveness for green infrastructure located at sag points in the topography. Lastly, using six years of empirical data from a voluntary residential green infrastructure program, I show how the spread of green infrastructure depends on the demographic and physical characteristics of neighborhoods as well as spatially-dependent social processes (such as the spread of information). This dissertation advances the science of green infrastructure planning at multiple scales and in multiple sectors to improve the practice of urban water resource management and sustainable development

Introduction to Environmental Science: 2nd Edition

2nd Edition: Revised by Kalina Manoylov, Allison Rick VandeVoort, Christine Mutiti, Samuel Mutiti and Donna Bennett in 2017. Authors\u27 Description: This course uses the basic principles of biology and earth science as a context for understanding environmental policies and resource management practices. Our planet is facing unprecedented environmental challenges, from oil spills to global climate change. In ENSC 1000, you will learn about the science behind these problems; preparing you to make an informed, invaluable contribution to Earth’s future. I hope that each of you is engaged by the material presented and participates fully in the search for, acquisition of, and sharing of information within our class. Environmental Science Laboratory (ENSC 1000L) is a separate class and you will receive a separate grade for that course. Course Objectives Upon completion of this course, you will be able to: Evaluate the diverse responses of peoples, groups, and cultures to environmental issues, themes and topics. Use critical observation and analysis to predict outcomes associated with environmental modifications. Demonstrate knowledge of the causes & consequences of climate change. Apply quantitative skills to solve environmental science problems. Demonstrate knowledge of environmental law and policy. Design and critically evaluate experiments. Interpret data in figures and graphs. This open textbook for Introduction to Environmental Science was created under a Round Two ALG Textbook Transformation Grant. Accessible files with optical character recognition (OCR) and auto-tagging provided by the Center for Inclusive Design and Innovation.https://oer.galileo.usg.edu/biology-textbooks/1003/thumbnail.jp

Characterizing the Spatial Patterns and Spatially Explicit Probabilities of Post-Fire Vegetation residual patches in Boreal Wildfire Scars

Wildfire is one of the main natural disturbances that consume a substantial amount of forest cover, influencing and reshaping the landscape mosaic of boreal forests. Wildfires do not burn the entire landscape; they rather create a complex mosaic of post-fire landscape structure with different degrees of burn severity. The resulting spatial mosaic includes fully burned, partially burned, and unburned areas. Even though the most visible components of a fire disturbed landscape are the completely burned areas, a considerable number of residual patches of various size, shape, and composition are retained following a fire. The residual patches refer to remnants of the pre-fire forest ecosystem that left completely unaltered within the fire footprint. Improved understanding of the patterns and characteristics of wildfire residuals provides insights for investigating the effects of fire disturbances, emulating forest disturbances in harvesting operations, and improving forest management planning. Knowledge about the post-fire residuals relies on how well we measure the patterns and characteristics of post-fire residuals, determine the factors that explain their occurrence and patterns, and what consistent measurement framework we use to understand the patterns and predict their likely occurrence. In this study, the patterns and characteristics of post-fire residuals was initially examined based on eleven boreal wildfire events within northwestern Ontario; each ignited by lightning and never suppressed. The wildfire events were occurred in ecoregion 2W during the fire seasons of 2002 and 2003. In order to design a consistent and repeatable method for measuring the patterns of residuals, an integrate approach has been designed. This involves assessing the spatial patterns where the composition, configuration, and fragmentation of residual patches were assessed based on selected spatial metrics; examining the importance of predictor variables that explain residuals and their marginal effects on residual patch occurrence using Random Forest (RF) ensemble method; and developing a spatially explicit predictive model using the RF method where the combined effects of the variables were examined. Finally, the three approaches are applied and evaluated using a recent and independent data from the extensive RED084 wildfire event that occurred in 2011 within the adjacent ecoregion (3S). The effects of analytical scale (i.e., spatial resolution) on characterizing the spatial patterns, determining the relative variable importance, and predicted probabilities of residual patches are assessed. The results show that the composition and configuration of wildfire residuals vary as a function of measurement, spatial resolutions, and fire event sizes, suggesting the variation in fire intensity and severity across the fire events. The patterns of wildfire residuals are also sensitive to changing scale, but the responses of the spatial metrics to changing spatial resolutions are grouped into three categories: monotonic change and predictable response in which three shape related metrics (LSI, MSI, and FRAC) show a predictable responsible; monotonic change with no simple scaling rule; and non-monotonic change with erratic response. The results also reveal that the factors that are incorporated in this study interactively affect the occurrence and distribution of residual patches, but natural firebreak features (e.g., wetlands and surface water) were among the most important predictors to explain wildfire residuals. Furthermore, the model implemented to predict residual patches has a reasonable or high predictive performance (‘marginal’ to ‘strong’ model performance) when it was applied in wildfire events that occurred in the same ecoregion. However, the predictive power of the model is low for the independent fire event (RED084). The overall findings of this dissertation reveal that the 1) predictive model based on RF is robust enough to determine the relative importance of the predictors and their marginal effect; 2) the model was flexible enough to identify areas where wildfire residuals are likely to occur; and 3) there is a repeatable, robust measurement framework for characterizing residual patches and understanding their variability across different wildfire events