https : / / agu . confex . com / agu / fm 19 / meetingapp . cgi / Index / EPoster ~ 1 AbstractThe world has faced climate change and can no longer ignore it . Among the effects of climate change , heatwaves occur in many parts of the world and the damage is serious . High temperatures can be a burden on the body and can lead to death in severe cases . High temperature is not the only factor that affects people . The humidity in the air is related to the ability to regulate body temperature , so if the humidity increases , the evaporation of sweat will be suppressed and the body have a worse effect . In this study , the apparent temperature using temperature and humidity was used as an index to define the heatwave event , and since the adaptation degree of people against temperature and humidity differs from region to region , the onset threshold of heatwave event is set differently for each region . Because heatwaves are more dangerous to the human body when various heat sources such as depth , timing , six types of heatwave events such as frequency , duration , severity , season length of heatwave event , the first occurrence date of the heatwave event thought of the year , and the last occurrence date of the heatwave event thought of the year were investigated . Of the six characteristics , a more in - depth analysis was conducted , focusing on the frequency , duration , severity , and season length of the heatwave event . In an analysis using data from the past 46 years for 60 sites in Korea , 56 % of the sites showed an increasing tendency in two or more heatwaves characteristics . Sites with statistical significance in three or more heatwave event characteristics were identified as concern areas and severe areas . These sites were not a big city except for three sites . Compared with large cities , rural areas are more vulnerable to heatwaves due to high levels of outdoor activity and high elderly rates . Therefore , it is considered that countermeasures appropriate to the region are needed instead of nationwide heat wave countermeasures . AcknowledgementThis work was supported by Korea Environment Industry & Technology Institute ( KEITI ) through Intelligent Management Program for Urban Water Resources Project , funded by Korea Ministry of Environment ( MOE ) ( 2019002950003 ) . The authors also acknowledge that this work was supported by the National Research Foundation of Korea ( NRF ) grant funded by the Korea government ( MSIT ) ( No . NRF - 2019 R 1 A 2 C 1003114 ) . AbstractLarge - scale Urban Building Energy Modeling ( UBEM ) is a nascent field . Local governments are increasingly interested in these models in order to develop strategies for energy efficiency and district - level intervention . The potential of UBEM lies in the temporally dense estimation of energy and water demand for each building in an urban area and the possibility to evaluate different scenarios . A major obstacle in the widespread use of UBEM is the difficulty to obtain the necessary building information , such as three - dimensional , watertight building envelops and building systems . Previous UBEM efforts have resulted in the utilization of a low Level of Detail ( LoD ) , as well as the application of labor - intensive and propitiatory methods / data , respectively . In this study , we propose a workflow to automatically generate a city model with LoD 3 . The workflow employs publicly accessible tools and data . Public Light Detection and Ranging ( LiDAR ) point clouds are combined with building footprints from OpenStreetMaps ( OSM ) to derive a detailed geometry of the buildings . A watertight mesh is created by the Screened Poisson surface reconstruction algorithm from an oriented and unstructured point cloud , which had initially been denoised . Convolutional Neural Networks ( CNN ) are used to identify the building archetypes according to the EU TABULA project based on aerial urban photographs . The EU TABULA project assessed the European building stock and created national residential building typologies with information on materials , construction , windows , thermal transmittance , heating , ventilation , and air conditioning ( HVAC ) , etc . This workflow provides the necessary input data for individual simulations with the US - DOE EnergyPlus . The simultaneous energy simulations for each building in an urban area form the Urban Building Energy Model . Its functionality is extended by using real - time weather data and the potential usage of real - time building occupancy data . In addition , the data is visualized by the NASA WorldWind framework . The German city of Aachen with its approximate 40 . 000 residential buildings has been selected for a case study . Lack of smart metered residential buildings remain a considerable challenge for validation . AbstractGroundwater is a finite natural resource , and is a vital resource of drinking water , irrigated agriculture , and many ecosystems . It is essential to establish systematic plans for using groundwater . Groundwater potential map ( GPM ) , designed to evaluate groundwater potential , is an efficient tool to maintain and manage groundwater . The goal of this study is mapping GPM in Busan , South Korea using geographic information system ( GIS ) - based datasets and two machine learning methods : random forest ( RF ) and support vector machine ( SVM ) . The datasets consist of two parts ; first datasets were constructed for groundwater well datasets as dependent variable , second datasets were constructed for groundwater influence factors as independent variable . Groundwater well datasets were collected 308 springs from extensive field surveys and governmental reports , 214 springs ( 70 %) were used for model training , 94 springs ( 30 %) were used for model validation . Groundwater influence factors consisted of 14 factors ; 4 topographical factors ( altitude , slope degree , slope aspect , and curvature ) , 4 hydrological factors ( topographic wetness index , stream power index , distance from drainage , and drainage density ) , 5 geological factors ( lithology , distance from fault , fault density , distance from lineament , and lineament density ) , and land cover factor . GPMs were constructed using RF and SVM models . The predicted results from RF and SVM models were validated using the receiver operating characteristics ( ROC ) curve . The accuracy assessment of GPMs for RF and SVM models was calculated using the area under the curve ( AUC ) and the AUC for RF and SVM models were calculated as 0 . 7441 and 0 . 7138 . Therefore , RF model presents the higher prediction result compared to SVM model . AbstractThe development of urban systems is full of complex problems ; water - food - energy nexus security is one of them . Solving these problems requires deeper understanding of the underlying system structures that drive dynamic system behaviors . Water , food , and energy are the basis for urban metabolism ; they are also closely interrelated and require an analysis of the system as a whole . Trends in urban systems – such as population growth , progressive urbanization , and changing lifestyles – are increasing pressures on already limited natural resources . A sustainable urban system requires the achievement of mitigating human impact on ecosystems while fulfilling our need for development . Understanding of the dynamic urban metabolism , or other complex systems , would benefit from concepts of system thinking . Although indexes and checklists are a helpful tool to audit our world , they do not include the effect of complex structures or non - linear behaviors . Causal modeling methods , such as System Dynamics , improve understanding of feedback loops and dynamic behavior . These models are built from causal relations and usually developed manually based on human - agent judgments . With the rapid advancement in text - mining techniques , text data is becoming an important resource that holds great potential for automated causal discovery and model building . This study is an effort to develop procedures for causal extraction from natural text . To improve understanding and better utilize causal extraction , a relationship spectrum was created . These processes identify relationships within the defined relationship spectrum . While the process is independent of any particular domain , the topic of water - food - energy nexus was used as a case study for this study . While not conclusive , it is a substantial effort to expand relationship extraction from natural text for use in causal modeling . The study provides a useful procedures and workflow for efficient search of causal statements and model building . AbstractThe increasing demand for water , energy and food in response to the rapid growth of population and economic development has become a serious concern for many countries and regions in recent decades . The effectual management of water , energy and food nexus ( WEF Nexus ) would be a holistic measure taken to promote food security whilst conserving natural resources for achieving the Sustainable Development Goals ( SDGs ) set by the United Nations General Assembly in 2015 for the year 2030 . However , poor cross - sectoral coordination and institutional fragmentation usually result in an unsustainable use of water , energy and food resources and therefore pose dangers to water , energy and food security . This study explores a nexus approach to optimizing water , energy and food resources utilization in a perspective of food self - sufficiency through artificial intelligence techniques . We investigate the food self - sufficiency in Taiwan based on a large number of datasets of water , energy and food resources as well as rainfall , agricultural land area and population collected during 2008 and 2017 . Promotion of food production demands for more water and energy resources . It is also noted that the ratio of irrigation areas to agricultural areas is 45 %, which allows for more crop production activities . Therefore , a joint operation of reservoirs and irrigation ponds is proposed to offer more water and hydropower / small hydropower for irrigation purpose . Taking 2016 as an example , the results of our proposed nexus approach show that an increase of 10 % irrigation water would lead to a 5 % increase of energy supply ( mainly from hydropower / small hydropower ) , and therefore drives a promotion of the food self - sufficiency rate from 30 . 83 % to 33 . 43 %. This study suggests that it is of high potential to promote food self - sufficiency through adequate management of the WFE Nexus , without affecting the current utilization of WFE resourcesAbstractWater , energy , and food are fundamental resources for human beings and sustainable society , and these resources are interlinked each other . Therefore , water - energy - food ( WEF ) nexus is important for integrated management and governance by increasing synergies and reducing trade - offs among the three resources . In addition to these , three resources are connected in multi - spatial scale , such as local , national , regional and global , through food and energy trades , therefore we need multiscale analyses for sustainable resources management . Water footprint and energy footprints though food trade are one of the tool for understanding how each area is inter - and intra - connected on WEF nexus . Belmont Forum SUGI project “ METABOLIC ” are going to establish three databases of resources , interlinkage , and scenario in multi - spatial scale including Kyoto city , Kyoto prefecture , Kansai area , Japan , and Asia . A nexus model to analyze the change of the nexus structure has been made , and assessment of the changes in three resources , carbon emission , environmental and economic impacts are analyzed . AbstractThis study aims to develop multi - local system dynamic modeling for regional Water - Energy - Food ( WEF ) nexus linking prefectures at Kansai region in Japan . This WEF nexus was based on physical and virtual trans - boundary of resource management such as integrated water management , virtual water trade through food trade , and distribution network of electricity . Therefore , resource - shed concept was adapted such as water - shed , food - shed , and energy - shed . For example , Yodo river and Biwa lake were the main water resource for Kyoto , Osaka , and Shiga prefectures , thus water - shed management considered water allocation to these prefectures but also to multi - stakeholders such farmer , industry , and people . Food - shed management was related to food security in consumption area and resource supply for producing food in production area . Energy - shed management mainly considered electricity generation and its distribution to prefectures . After then , we developed system dynamics modeling of each prefecture including multi - stakeholder , inter - linkage analysis , and integrated assessment . Finally , the local system dynamics were combined into regional WEF nexus in order to assess the impacts of local policies of resource security , economic growth , and environment management in each prefecture to Kansai region . AbstractDuring the last decade , wildfires in the Aburrá Valley watershed , located in northwestern Colombia , have caused significant forest and ecosystem losses , health issues in nearby communities associated with aerosols from biomass burning , and increases in the CO 2 emissions . Human activities , along with weather variability , modulate the occurrence of forest fires during the dry seasons , and the efforts to reduce them have shown limited success , highlighting the need for the development of holistic prevention strategies . We implemented a general strategy involving real - time monitoring , modeling , and warning based on a distributed Bayesian model coupled with a distributed hydrological model and a regional weather model ( WRF ) to estimate wildfire susceptibility in the basin . The model operates with a spatial resolution of 60 m and an hourly temporal resolution . The model uses static and time - dependent ( dynamic ) information . Static variables include land use , urban - rural fringe area , historical fire occurrence , and are updated occasionally . The dynamic variables change at each time step , and they depend on meteorological conditions and include soil moisture , cumulative rainfall during the last ten days , and an estimation of the surface temperature . These variables are obtained from in - situ rain gauges and quantitative precipitation estimation ( QPE ) techniques using C - band weather radar reflectivity , in - situ pyranometers and automatic weather stations , and output from a distributed hydrological model and WRF - based weather forecasts . The Bayesian model allows the generation of fire susceptibility predictions that help optimize prevention strategies implemented by the fire departments in the region . The model has been evaluated using the location of historical wildfires showing high skill . Along with the model , there are efforts in the region implemented for early - detection , and quantification of forest fires using in - situ and drone - borne thermal and high - definition cameras , a continuous monitoring strategy is established .