Linking Climate Change and Socio-economic Impact for Long-term Urban Growth in Three Mega-cities

Urbanization has become a global trend under the impact of population growth, socio-economic development, and globalization. However, the interactions between climate change and urban growth in the context of economic geography are unclear due to missing links in between the recent planning megacities. This study aims to conduct a multi-temporal change analysis of land use and land cover in New York City, City of London, and Beijing using a cellular automata-based Markov chain model collaborating with fuzzy set theory and multi-criteria evaluation to predict the city\u27s future land use changes for 2030 and 2050 under the background of climate change. To determine future natural forcing impacts on land use in these megacities, the study highlighted the need for integrating spatiotemporal modeling analyses, such as Statistical Downscale Modeling (SDSM) driven by climate change, and geospatial intelligence techniques, such as remote sensing and geographical information system, in support of urban growth assessment. These SDSM findings along with current land use policies and socio-economic impact were included as either factors or constraints in a cellular automata-based Markov Chain model to simulate and predict land use changes in megacities for 2030 and 2050. Urban expansion is expected in these megacities given the assumption of stationarity in urban growth process, although climate change impacts the land use changes and management. More land use protection should be addressed in order to alleviate the impact of climate change

Decision-centric adaptation appraisal for water management across Colorado's Continental Divide

A multi-step decision support process was developed and applied to the physically and legally complex case of water diversions from the Upper Colorado River across the Continental Divide to serve cities and farms along Colorado's Front Range. We illustrate our approach by simulating the performance of an existing drought-response measure, the Shoshone Call Relaxation Agreement (SCRA) [the adaptation measure], using the Water Evaluation and Planning (WEAP) tool [the hydrologic cycle and water systems model]; and the Statistical DownScaling Model (SDSM-DC) [the stochastic climate scenario generator]. Scenarios relevant to the decision community were analyzed and results indicate that this drought management measure would provide only a small storage benefit in offsetting the impacts of a shift to a warmer and drier future climate coupled with related environmental changes. The analysis demonstrates the importance of engaging water managers in the development of credible and computationally efficient decision support tools that accurately capture the physical, legal and contractual dimensions of their climate risk management problems

Information sources to support ADB climate risk assessments and management

This technical note provides information that supports climate risk assessment experts undertaking early stages of project development in Asia and the Pacific region. The Asia and Pacific region is vulnerable to extreme temperatures, flooding by heavy rainfall, sea level rise, coastal erosion, and damage by tropical cyclones. This technical note provides information that supports climate risk assessment experts undertaking early stages of project development in the region. The information is grouped into four major categories: inventories of national emissions, climate risks, vulnerability, and impacts; historic weather, climate, and environmental change; regional climate change projections; and climate change impacts and adaptation. The note also identifies opportunities for capacity development in key skills such as geospatial analysis, data testing and post-processing, regional climate downscaling, and impact assessment

Hydrological investigation of climate change impact on water balance components in the agricultural terraced watersheds of Yemeni highland

Hydrological models serve as valuable instruments for assessing the impact of climate change on water resources and agriculture as well as for developing adaptation measures. In Yemen, climate change and variability are imposing a significant impact on the most important sectors such as agriculture and economy. The current study evaluates the influence of future climate on hydrology and water balance components in Yemen’s highlands using a semi-distributed physical-based hydrologic model Soil Water Assessment Tool (SWAT) and employing high-resolution climate projections. The SWAT was calibrated and verified using observed streamflow data from 1982 to 2000 in three large catchments. Ground data from 24 stations and statistically downscaled future climate data for the period 2010–2100 under RCP2.6 and RCP8.5 are used. SWAT performance was assessed using multiple statistical methods, which revealed the commendable performance of SWAT during the calibration (average NSE = 0.80) and validation (NSE = 0.72) periods. The outcome indicates an increase in future seasonal and annual rainfall, maximum temperature, and minimum temperature in the 2020s and the 2080s under both RCP2.6 and RCP8.5 scenarios. This projected increase in the rainfall and the local temperature will result in increased averages of surface runoff, evapotranspiration, soil water, and groundwater recharge in the representative three catchments up to 6.5%, 21.1%, 7.6%, and 6.4%, respectively. Although, the projected increase in the water balance components will benefit the agriculture and water sector, specific adaptation measures will be crucial to mitigate potential flood impacts arising from the increased precipitations as well as to minimize the consequences of the increased temperature. Likewise, demand for supplementary irrigation is expected to increase to offset the higher evapotranspiration rates in the future

Local climate change projections and impact on the surface hydrology in the Vea catchment, West Africa

Water security has been a major challenge in the semi-arid area of West Africa including Northern Ghana, where climate change is projected to increase if appropriate measures are not taken. This study assessed rainfall and temperature projections and its impact on the water resources in the Vea catchment using an ensemble mean of four bias-corrected Regional Climate Models and Statistical Downscaling Model-Decision Centric (SDSM-DC) simulations. The ensemble mean of the bias-corrected climate simulations was used as input to an already calibrated and validated Soil and Water Assessment Tool (SWAT) model, to assess the impact of climate change on actual evapotranspiration (ET), surface runoff and water yield, relative to the baseline (1990–2017) period. The results showed that the mean annual temperature and actual ET would increase by 1.3 °C and 8.3%, respectively, for the period 2020–2049 under the medium CO$_{2}$ emission (RCP4.5) scenario, indicating a trend towards a drier climate. The surface runoff and water yield are projected to decrease by 42.7 and 38.7%, respectively. The projected decrease in water yield requires better planning and management of the water resources in the catchment

Climate Change and Atlantic salmon (Salmo salar): Changes in Flow and Freshwater Habitat in the Burrishoole Catchment

Climate change is anticipated to impact the flow regime of riverine systems with resultant consequences for the freshwater habitat of Atlantic salmon (Salmo salar) and the long-term sustainability of their population numbers. The Burrishoole catchment, a relatively small but productive salmon catchment (~90 km2) located on Ireland’s west coast, is used as a case study to investigate this. A series of high resolution climate scenarios were employed to examine potential changes in the climate and hydrology of this catchment. The climate scenarios used represent different combinations of greenhouse gas emission scenarios, driving GCMs and statistical/dynamical downscaling models; in addition, three different rainfall-runoff models (HBV, HYSIM and TOPMODEL) were employed – integrating across both structural and parameter uncertainty. By considering multiple model pathways this study attempts to sample across the uncertainties encountered at each stage in the process of translating prescribed anthropogenic forcings into local scale responses in the catchment system. The hydrological projections were examined in the context of the habitat and flow requirements of Atlantic salmon at key stages in their life-cycle (e.g. spawning, migration). Model projections suggest that the catchment is likely to become warmer, with wetter winters and drier summers occurring. The results of the hydrological modelling suggest that this will be accompanied by an increase in the seasonality of its flow regime - manifest in an increase in low (Q95) summer and high (Q05) winter flows. If realised, these changes are likely to impact salmon through a reduction in the availability of preferred habitat, a loss in connectivity across the catchment system and a disruption to the evolved synchrony between the occurrence of optimal in-stream conditions and the time at which certain life history events occur. Each of these factors is likely to impact the processes of migration, reproduction and recruitment - each of which is critical for the long-term viability of healthy, self-sustaining wild stocks in the catchment. Based on the projected flow data it is likely that the carrying capacity and productivity of the catchment may be reduced. In addition, by affecting those life stages which are already subject to significant mortality losses (e.g. fry emergence, smolt migration), changes in climate may result in population collapse - particularly if successive year-classes are affected. The results of the hydrological modelling highlight the sensitivity of smaller spatey catchments to changes in climate. Given that the Burrishoole system is typical of many catchment systems found along Ireland’s western seaboard, the results highlight a vulnerability to climate change which is present more generally across the region

Climate Change and Atlantic salmon (Salmo salar): Changes in Flow and Freshwater Habitat in the Burrishoole Catchment

Climate change is anticipated to impact the flow regime of riverine systems with resultant consequences for the freshwater habitat of Atlantic salmon (Salmo salar) and the long-term sustainability of their population numbers. The Burrishoole catchment, a relatively small but productive salmon catchment (~90 km2) located on Ireland’s west coast, is used as a case study to investigate this. A series of high resolution climate scenarios were employed to examine potential changes in the climate and hydrology of this catchment. The climate scenarios used represent different combinations of greenhouse gas emission scenarios, driving GCMs and statistical/dynamical downscaling models; in addition, three different rainfall-runoff models (HBV, HYSIM and TOPMODEL) were employed – integrating across both structural and parameter uncertainty. By considering multiple model pathways this study attempts to sample across the uncertainties encountered at each stage in the process of translating prescribed anthropogenic forcings into local scale responses in the catchment system. The hydrological projections were examined in the context of the habitat and flow requirements of Atlantic salmon at key stages in their life-cycle (e.g. spawning, migration). Model projections suggest that the catchment is likely to become warmer, with wetter winters and drier summers occurring. The results of the hydrological modelling suggest that this will be accompanied by an increase in the seasonality of its flow regime - manifest in an increase in low (Q95) summer and high (Q05) winter flows. If realised, these changes are likely to impact salmon through a reduction in the availability of preferred habitat, a loss in connectivity across the catchment system and a disruption to the evolved synchrony between the occurrence of optimal in-stream conditions and the time at which certain life history events occur. Each of these factors is likely to impact the processes of migration, reproduction and recruitment - each of which is critical for the long-term viability of healthy, self-sustaining wild stocks in the catchment. Based on the projected flow data it is likely that the carrying capacity and productivity of the catchment may be reduced. In addition, by affecting those life stages which are already subject to significant mortality losses (e.g. fry emergence, smolt migration), changes in climate may result in population collapse - particularly if successive year-classes are affected. The results of the hydrological modelling highlight the sensitivity of smaller spatey catchments to changes in climate. Given that the Burrishoole system is typical of many catchment systems found along Ireland’s western seaboard, the results highlight a vulnerability to climate change which is present more generally across the region

On network backbone extraction for modeling online collective behavior

Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information. Several studies have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. However, only a fraction of edges contribute to the actual investigation. Even worse, the often large number of non-relevant edges may obfuscate the salient interactions, blurring the underlying structures and user communities that capture the collective behavior patterns driving the target phenomenon. To solve this issue, researchers have proposed several network backbone extraction techniques to obtain a reduced and representative version of the network that better explains the phenomenon of interest. Each technique has its specific assumptions and procedure to extract the backbone. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios where no ground truth exists. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given a phenomenon of interest. We characterize ten state-of-the-art techniques in terms of their assumptions, requirements, and other aspects that one must consider to apply them in practice. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We show that each method can produce very different backbones, underlying that the choice of an adequate method is of utmost importance to reveal valuable knowledge about the particular phenomenon under investigation