Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin

Groundwater depletion has been one of the major challenges in recent years. Analysis of groundwater levels can be beneficial for groundwater management. The National Aeronautics and Space Administration’s twin satellite, Gravity Recovery and Climate Experiment (GRACE), serves in monitoring terrestrial water storage. Increasing freshwater demand amidst recent drought (2000–2014) posed a significant groundwater level decline within the Colorado River Basin (CRB). In the current study, a non-parametric technique was utilized to analyze historical groundwater variability. Additionally, a stochastic Autoregressive Integrated Moving Average (ARIMA) model was developed and tested to forecast the GRACE-derived groundwater anomalies within the CRB. The ARIMA model was trained with the GRACE data from January 2003 to December of 2013 and validated with GRACE data from January 2014 to December of 2016. Groundwater anomaly from January 2017 to December of 2019 was forecasted with the tested model. Autocorrelation and partial autocorrelation plots were drawn to identify and construct the seasonal ARIMA models. ARIMA order for each grid was evaluated based on Akaike’s and Bayesian information criterion. The error analysis showed the reasonable numerical accuracy of selected seasonal ARIMA models. The proposed models can be used to forecast groundwater variability for sustainable groundwater planning and management

Visualisation of Origins, Destinations and Flows with OD Maps

We present a new technique for the visual exploration of origins (O) and destinations (D) arranged in geographic space. Previous attempts to map the flows between origins and destinations have suffered from problems of occlusion usually requiring some form of generalisation, such as aggregation or flow density estimation before they can be visualized. This can lead to loss of detail or the introduction of arbitrary artefacts in the visual representation. Here, we propose mapping OD vectors as cells rather than lines, comparable with the process of constructing OD matrices, but unlike the OD matrix, we preserve the spatial layout of all origin and destination locations by constructing a gridded two‐level spatial treemap. The result is a set of spatially ordered small multiples upon which any arbitrary geographic data may be projected. Using a hash grid spatial data structure, we explore the characteristics of the technique through a software prototype that allows interactive query and visualisation of 105‐106 simulated and recorded OD vectors. The technique is illustrated using US county to county migration and commuting statistics

Analyzing tree distribution and abundance in Yukon-Charley Rivers National Preserve: developing geostatistical Bayesian models with count data

Master's Project (M.S.) University of Alaska Fairbanks, 2018Species distribution models (SDMs) describe the relationship between where a species occurs and underlying environmental conditions. For this project, I created SDMs for the five tree species that occur in Yukon-Charley Rivers National Preserve (YUCH) in order to gain insight into which environmental covariates are important for each species, and what effect each environmental condition has on that species' expected occurrence or abundance. I discuss some of the issues involved in creating SDMs, including whether or not to incorporate spatially explicit error terms, and if so, how to do so with generalized linear models (GLMs, which have discrete responses). I ran a total of 10 distinct geostatistical SDMs using Markov Chain Monte Carlo (Bayesian methods), and discuss the results here. I also compare these results from YUCH with results from a similar analysis conducted in Denali National Park and Preserve (DNPP)

Case Study of Seoul, Korea

학위논문(석사)--서울대학교 대학원 :환경대학원 환경계획학과,2019. 8. Jige Quan.With the focus on energy efficient development, the role of urban form as influencer of building energy efficiency has been an area of interest in recent researches. However, the research literature lack common methodology and definition of urban form to measure its efficiency, has been diverse and subjective at best. Parallelly, Local Climate Zone (LCZ) framework, primarily developed in the urban climatology field to study urban heat island and microclimate, has been gaining traction, both in classification methodology advancements and its applications. The framework defines 17 classes based on urban and natural features with existing research showing that each class has a unique air and surface temperature profile. Based on the hypothesis that microclimate is indeed the major cause of urban forms impact on building energy consumption, this researcher investigates the relationship between LCZ classes and building electricity and gas consumption. Firstly, the appropriate unit to determine urban form is established with care (to minimize modifiable area unit problem) using LCZ parameters spatial autocorrelation and distribution curve. At this point, as has been described by past literature, due to heterogeneous nature, parts of urban area do not fall within any LCZ class. To address it, this research explores the use of machine learning algorithms to identify the closest resembling LCZ class for unidentified areas. Results from algorithms are compared using surface temperature to determine the most suitable classification. Finally, using this LCZ classification of Seoul and building electricity and gas consumption data (2015-18), the research investigates how energy consumption varies for each LCZ class as compared to sparsely built area which lacks any urban context. For this, two way fixed effect panel data analysis is used so that the effect of both LCZ classes and time can be modeled simultaneously, since the influence of urban form can vary depending upon broader seasonal conditions. The research indicates that overall, open low rise and compact low rise are the only urban LCZ classes which are energy efficient. Rest of classes appears to have higher energy consumption (per sq. m of floor space) as compared to sparsely built areas. Lastly, we compare residential zoning classes with LCZ classes to determine what zoning parameters results in a specific LCZ class. Thus, this research helps urban planners to determine urban form which is energy efficient. So far, the focus on achieving energy efficiency has been limited to improvement in building technology or transportation energy demand, but it is hoped that using this research, it would be possible to identify urban form which is most energy efficient when all the energy consumption aspects are assimilated.에너지 효율적인 개발에 중점을 두면서 건축물 에너지 효율성에 영향을 미치는 도시형태의 역할이 최근 많은 연구자들의 관심 영역이 되었다. 하지만 도시형태의 에너지 효율성에 관한 연구는 명확하게 구분되지 않고 주관적인 정의와 공통적인 방법론에 국한되여 있다. 이와 동시에, 도시 열섬현상과 미기후를 연구에서 도시 기후학 분야에서 개발된 지역 기후 구역(LCZ) 프레임워크는 도시형태의 분류 방법론과 적용 모두에서 주목을 받고 있다. LCZ프레임워크는 도시와 자연적 특징을 바탕으로 도시형태를17개의 클래스로 분류하며, 기존 연구에서는 각 클래스에서 특정한 공기온도와 표면온도를 나타내고 있음을 보여준다. 본 연구에서는 미기후가 건축 에너지 효율성에 미치는 도시형태의 주요 원인이라는 가설을 바탕으로 LCZ클래스와 건물 전기 및 가스 소비 사이의 관계를 연구한다. 첫째, 도시형태를 결정하는 적절한 단위는 LCZ 매개변수의 공간적 자기 상관관계 및 분포 곡선을 이용하여 (수정 가능한 면적 단위 문제를 최소화하기 위해) 주의하여 설정한다. 다음, 선행연구에서 설명한 바와 같이, 일부 도시형태의 이질적인 특성 때문에 어떤 LCZ 클래스에도 속하지 않는다. 이를 해결하기 위해, 본 연구는 명확하지 않은 도시형태 영역에 대해 가장 유사한 LCZ 클래스를 식별하기 위한 인공지능 기계 학습 알고리즘의 사용하였다. 알고리즘 적용 결과의 적합성을 위하여 표면 온도데이터와 비교한다. 마지막으로, 서울의 이러한 LCZ 클래스와 건물 전기 및 가스 소비 데이터(2015-18)를 사용하여, 도시적 환경이 전혀 없는 낮은 밀도 건축 면적에 비해 LCZ 클래스별로 에너지 소비량이 어떻게 달라지는지 분석한다. 이를 위해 도시형태의 영향은 광범위한 계절 조건에 따라 달라질 수 있으므로 LCZ 클래스와 시간의 효과를 동시에 모델링할 수 있도록 양방향 고정 효과 패널 데이터 분석 기법을 사용한다. 본 연구는 전반적으로, '넓은 부지의 고층건물 지역'과 '넓은 부지의 저층건물 지역'이 에너지 효율이 높은 유일한 도시 LCZ 클래스라는 것을 보여준다. 나머지 등급은 낮은 건설밀도 부지에 비해 에너지 소비량이 높은 것으로 보인다(건축부지 1㎡ 당). 마지막으로, 용도지구와 LCZ 클래스과 비교하여 어떤 구역제 매개 변수가 특정 LCZ 클래스를 초래하는지 결정한다. 하여 본 연구는 도시 계획가들이 에너지 효율성이 높은 도시 형태를 결정하는데 도움을 준다. 지금까지 에너지 효율성 달성에 초점을 맞춘 것은 건축 기술이나 교통 에너지 수요의 향상에 국한되어 왔지만, 본 연구를 통해 가장 에너지 효율성이 높은 도시형태를 파악하는 것이 가능하기를 희망한다.I. Introduction 1 1.1 Research Question 2 1.2 Research Range 2 II. Theoretical Background and Literature Review 3 2.1 Urban Heat Island (UHI) 3 2.1.1 Local Climate Zones (LCZs) 4 2.1.2 LCZ parameters 8 2.1.3 Critic of LCZ framework 11 2.1.4 LCZ classification 11 2.2 Classification Algorithms 13 2.2.1 Random Forest 13 2.2.2 K-Means 14 2.3 Boundary Issues 15 2.3.1 Grid size 15 2.3.2 Grid location 16 2.4 Urban Form and Building Energy Consumption (BEC) 17 2.4.1 LCZ as measure of urban form for BEC studies 18 2.5 Statistical Analysis 20 2.5.1 Time series 20 2.5.2 Panel data analysis 20 III. Research Methodology and Case Study 23 3.1 Methodology 23 3.1.1 LCZ classification 23 3.1.2 Linking urban form with building energy consumption 23 3.2 Issues with Methodology 24 3.3 Case Study 25 IV. Analysis and Result 26 4.1 LCZ Classification of Seoul 26 4.1.1 Calculations for LCZ parameters 26 4.1.2 Grid size and location 27 4.1.3 Classic classification 28 4.1.4 Algorithm based Classification 31 4.1.5 Surface temperature profile of LCZs 33 4.1.6 LCZ parameters for Seoul 35 4.2 LCZ and Building Energy Consumption 37 4.2.1 Model Results 38 4.3 Alternative Model 41 4.3.1 Landscape in LCZ 41 4.3.2 Mountainous landscape, LCZ and BEC 42 V. Conclusion, Policy Suggestions and Limitations 45 5.1 Conclusion 46 5.2 Policy Suggestions 47 5.2.1 LCZ and zoning regulations 47 5.3 Limitations 50 Bibliography 52 Trends in Electricity and Gas Consumption (2016-18) 59 Coefficients for Models Dummy Variables 60 Distribution of BEC Sample w.r.t Floor Space 62 Spatial Autocorrelation (SA) 64 Abstract in Korean 66Maste

Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment

Land use regression (LUR) modelling is increasingly used in epidemiological studies to predict air pollution exposure. The use of stationary measurements at a limited number of locations to build a LUR model, however, can lead to an overestimation of its predictive abilities. We use opportunistic mobile monitoring to gather data at a high spatial resolution to build LUR models to predict annual average concentrations of black carbon (BC). The models explain a significant part of the variance in BC concentrations. However, the overall predictive performance remains low, due to input uncertainty and lack of predictive variables that can properly capture the complex characteristics of local concentrations. We stress the importance of using an appropriate cross-validation scheme to estimate the predictive performance of the model. By using independent data for the validation and excluding those data also during variable selection in the model building procedure, overly optimistic performance estimates are avoided. (C) 2017 Elsevier Ltd. All rights reserved

Spatially explicit species distribution models: A missed opportunity in conservation planning?

Aim: Systematic conservation planning is vital for allocating protected areas given the spatial distribution of conservation features, such as species. Due to incomplete species inventories, species distribution models (SDMs) are often used for predicting species habitat suitability and species probability of occurrence. Currently, SDMs mostly ignore spatial dependencies in species and predictor data. Here, we provide a comparative evaluation of how accounting for spatial dependencies, that is, autocorrelation, affects the delineation of optimized protected areas. Location: Southeast Australia, Southeast U.S. Continental Shelf, Danube River Basin. Methods: We employ Bayesian spatially explicit and non-spatial SDMs for terrestrial, marine and freshwater species, using realm-specific planning unit shapes (grid, hexagon and subcatchment, respectively). We then apply the software gurobi to optimize conservation plans based on species targets derived from spatial and non-spatial SDMs (10% 50% each to analyse sensitivity), and compare the delineation of the plans. Results: Across realms and irrespective of the planning unit shape, spatially explicit SDMs (a) produce on average more accurate predictions in terms of AUC, TSS, sensitivity and specificity, along with a higher species detection probability. All spatial optimizations meet the species conservation targets. Spatial conservation plans that use predictions from spatially explicit SDMs (b) are spatially substantially different compared to those that use non-spatial SDM predictions, but (c) encompass a similar amount of planning units. The overlap in the selection of planning units is smallest for conservation plans based on the lowest targets and vice versa. Main conclusions: Species distribution models are core tools in conservation planning. Not surprisingly, accounting for the spatial characteristics in SDMs has drastic impacts on the delineation of optimized conservation plans. We therefore encourage practitioners to consider spatial dependencies in conservation features to improve the spatial representation of future protected areas. © 2019 The Authors. Diversity and Distributions Published by John Wiley and Sons LtdThis study was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 642317. SDL has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska‐Curie grant agreement No. 748625, and SCJ from the German Federal Ministry of Education and Research (BMBF) for the “GLANCE” project (Global Change Effects in River Ecosystems; 01 LN1320A). We wish to thank Gwen Iacona and two anonymous referees for their constructive comments on an earlier version of the manuscript

Vuong and Wald tests. Simplicity vs. Complexity

The specification of cross-sectional models is usually solved following a traditional procedure, highly supported by practitioners. In the first step, a simple model is proposed that will be subsequently improved with different elements if the evidence so advises. This procedure expedites the econometric solution and fits well into the Lagrange Multiplier approach, which contributes to explain its current popularity. However, there are other methods that could also be used, and some of them are considered in this paper. Specifically, we turn our attention to the Vuong test, developed in the context of the Kullback-Leibler information measure. This test represents an intermediate solution between the complexity inherent in the Wald test and the simplicity of the Lagrange Multiplier principle.

Holistic Measures for Evaluating Prediction Models in Smart Grids

The performance of prediction models is often based on "abstract metrics" that estimate the model's ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures. Inspired by energy consumption prediction models used in the emerging "big data" domain of Smart Power Grids, we propose a suite of performance measures to rationally compare models along the dimensions of scale independence, reliability, volatility and cost. We include both application independent and dependent measures, the latter parameterized to allow customization by domain experts to fit their scenario. While our measures are generalizable to other domains, we offer an empirical analysis using real energy use data for three Smart Grid applications: planning, customer education and demand response, which are relevant for energy sustainability. Our results underscore the value of the proposed measures to offer a deeper insight into models' behavior and their impact on real applications, which benefit both data mining researchers and practitioners.Comment: 14 Pages, 8 figures, Accepted and to appear in IEEE Transactions on Knowledge and Data Engineering, 2014. Authors' final version. Copyright transferred to IEE

Are all patches worth exploring? Foraging desert birds do not rely on environmental indicators of seed abundance at small scales

Background: Consumers should show strong spatial preferences when foraging in environments where food availability is highly heterogeneous and predictable. Postdispersal granivores face this scenario in most arid areas, where soil seed bank abundance and composition associates persistently with vegetation structure at small scales (decimetres to metres). Those environmental features should be exploited as useful pre-harvest information, at least to avoid patches predicted to be poor. However, we did not find the expected spatial association in the algarrobal of the central Monte desert by observing foraging seed-eating birds, a field technique influenced by how much they exploit visited patches. In this work we tested if the first stage of foraging by granivorous birds (patch visit, encounter or exploration) is positively associated with environmental indicators of patch quality by recording the removal of single seeds from 300 scattered experimental devices during seasonal trials. Spatial selectivity was analysed by comparing the structural characteristics of used vs. available microhabitats, and evaluated against bottom-up and top-down hypotheses based on our previous knowledge on local seed bank abundance, composition and dynamics. Their foraging activity was also explored for spatial autocorrelation and environmental correlates at bigger scales. Results: Postdispersal granivorous birds were less selective in their use of foraging space than expected if microhabitat appearance were providing them relevant information to guide their search for profitable foraging patches. No microhabitat type, as defined by their vegetation structure and soil cover, remained safe from bird exploration. Analyses at bigger temporal and spatial scales proved more important to describe heterogeneity in seed removal. Conclusions: Closeness to tall trees, probably related to bird territoriality and reproduction or to their perception of predation risk, seemed to determine a first level of habitat selection, constraining explorable space. Then, microhabitat openness (rather than seed abundance) exerted some positive influence on which patches were more frequently visited among those accessible. Selective patterns by birds at small scales were closer to our predictions of a top-down spatial effect, with seed consumption creating or strengthening (and not responding to) the spatial pattern and dynamics of the seed bank.Fil: Milesi, Fernando Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ecología, Genética y Evolución. Grupo de Investigación en Ecología de Comunidades del Desierto; ArgentinaFil: Lopez de Casenave, Javier Nestor. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ecología, Genética y Evolución. Grupo de Investigación en Ecología de Comunidades del Desierto; ArgentinaFil: Cueto, Víctor. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ecología, Genética y Evolución. Grupo de Investigación en Ecología de Comunidades del Desierto; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Centro de Investigación Esquel de Montaña y Estepa Patagónica. Universidad Nacional de la Patagonia San Juan Bosco. Centro de Investigación Esquel de Montaña y Estepa Patagónica; Argentin