scholarly journals Sustainable City Planning: A Data-Driven Approach for Mitigating Urban Heat

2021 ◽  
Vol 6 ◽  
Author(s):  
Andrew MacLachlan ◽  
Eloise Biggs ◽  
Gareth Roberts ◽  
Bryan Boruff
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
Climate Models ◽  
Global Climate ◽  
Three Dimensional ◽  
Global Climate Models ◽  
Data Driven ◽  
Climate Data ◽  
Data Driven Approach ◽  
Urban Heat

Urban areas are expected to triple by 2030 in order to accommodate 60% of the global population. Anthropogenic landscape modifications expand coverage of impervious surfaces inducing the urban heat island (UHI) effect, a critical twenty first century challenge associated with increased economic expenditure, energy consumption, and adverse health impacts. Yet, omission of UHI measures from global climate models and metropolitan planning methodologies precludes effective sustainable development governance. We present an approach that integrates Earth observation and climate data with three-dimensional urban models to determine optimal tree placement (per square meter) within proposed urban developments to enable more effective localized UHI mitigation. Such data-driven planning decisions will enhance the future sustainability of our cities to align with current global urban development agendas.

scholarly journals An Assessment of Environmental Impacts on the Ecosystem Services: Study on the Bagmati Basin of Nepal

2020 ◽  
Vol 12 (19) ◽  
pp. 8186
Author(s):  
Shiksha Bastola ◽  
Sanghyup Lee ◽  
Yongchul Shin ◽  
Younghun Jung
Keyword(s):  
Ecosystem Services ◽  
Urban Areas ◽  
Climate Models ◽  
Global Climate ◽  
Low Impact Development ◽  
Global Climate Models ◽  
Water Yield ◽  
Climate Data ◽  
Integrated Valuation ◽  
Precipitation And Temperature

The upsurges in population, internal migration, and various development works have caused significant land use and land cover (LULC) changes in the Bagmati Basin of Nepal. The effects of climate change such as increased precipitation and temperature are affecting the provision of ecosystem services (ES). In this regard, this study particularly treated water yield (WY), soil loss, nitrogen export, and carbon fluctuation in the basin. Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) tools were used to carry out a comparative analysis of ES based on LULC data for 2000 and 2010 and corresponding climate data. To analyze the future period (2010–2099), we have used climate data from the multi-model ensemble (MME) of statistically downscaled and bias-corrected 12 best global climate models (GCMs). A raw GCM analysis (based on historical observational data) from 29 GCMs was done first. The results shows with a subsequent degradation of ES providers like forests and an increment in agricultural and urban areas, ES are on a verge of degradation. Furthermore, a projection of future climate patterns depicts increased precipitation and temperature. Thus, urgent measures are required for the sustainable provision of ES. Outcomes of the study are expected to help in the incorporation of ES in development policies promoting low-impact development along with maintaining ecological and economic goals. The study closes by presenting a recommendation for model application and future study needs.

scholarly journals Prediction of the Long-Term Potential Distribution of Cryptorhynchus lapathi (L.) under Climate Change

Forests ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 5 ◽  
Author(s):  
Ya Zou ◽  
Linjing Zhang ◽  
Xuezhen Ge ◽  
Siwei Guo ◽  
Xue Li ◽  
...  
Keyword(s):  
Climate Change ◽  
North America ◽  
Potential Distribution ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Prevention Measures ◽  
Climate Data ◽  
Effective Prevention ◽  
Future Global Warming

The poplar and willow borer, Cryptorhynchus lapathi (L.), is a severe worldwide quarantine pest that causes great economic, social, and ecological damage in Europe, North America, and Asia. CLIMEX4.0.0 was used to study the likely impact of climate change on the potential global distribution of C. lapathi based on existing (1987–2016) and predicted (2021–2040, 2041–2080, and 2081–2100) climate data. Future climate data were simulated based on global climate models from Coupled Model Inter-comparison Project Phase 5 (CMIP5) under the RCP4.5 projection. The potential distribution of C. lapathi under historical climate conditions mainly includes North America, Africa, Europe, and Asia. Future global warming may cause a northward shift in the northern boundary of potential distribution. The total suitable area would increase by 2080–2100. Additionally, climatic suitability would change in large regions of the northern hemisphere and decrease in a small region of the southern hemisphere. The projected potential distribution will help determine the impacts of climate change and identify areas at risk of pest invasion in the future. In turn, this will help design and implement effective prevention measures for expanding pest populations, using natural enemies, microorganisms, and physical barriers in very favorable regions to impede the movement and oviposition of C. lapathi.

scholarly journals An Analysis of Convectively Coupled Kelvin Waves in 20 WCRP CMIP3 Global Coupled Climate Models

2010 ◽  
Vol 23 (11) ◽  
pp. 3031-3056 ◽  
Author(s):  
Katherine H. Straub ◽  
Patrick T. Haertel ◽  
George N. Kiladis
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Three Dimensional ◽  
Kelvin Waves ◽  
Global Climate Models ◽  
Specific Humidity ◽  
Tropospheric Temperature ◽  
Wave Band ◽  
Kelvin Wave ◽  
Shallow Convection

Abstract Output from 20 coupled global climate models is analyzed to determine whether convectively coupled Kelvin waves exist in the models, and, if so, how their horizontal and vertical structures compare to observations. Model data are obtained from the World Climate Research Program’s (WCRP’s) Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset. Ten of the 20 models contain spectral peaks in precipitation in the Kelvin wave band, and, of these 10, only 5 contain wave activity distributions and three-dimensional wave structures that resemble the observations. Thus, the majority (75%) of the global climate models surveyed do not accurately represent convectively coupled Kelvin waves, one of the primary sources of submonthly zonally propagating variability in the tropics. The primary feature common to the five successful models is the convective parameterization. Three of the five models use the Tiedtke–Nordeng convective scheme, while the other two utilize the Pan and Randall scheme. The 15 models with less success at generating Kelvin waves predominantly contain convective schemes that are based on the concept of convective adjustment, although it appears that those schemes can be improved by the addition of convective “trigger” functions. Three-dimensional Kelvin wave structures in the five successful models resemble observations to a large degree, with vertically tilted temperature, specific humidity, and zonal wind anomalies. However, no model completely captures the observed signal, with most of the models being deficient in lower-tropospheric temperature and humidity signals near the location of maximum precipitation. These results suggest the need for improvements in the representations of shallow convection and convective downdrafts in global models.

scholarly journals Impacts of future climate change on potential yields of major crops in China

2014 ◽  
Vol 5 (1) ◽  
pp. 617-647
Author(s):  
Y. Yin ◽  
Q. Tang ◽  
X. Liu
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Future Climate ◽  
Future Climate Change ◽  
Yield Reduction ◽  
Climate Data ◽  
Potential Yield ◽  
Crop Models

Abstract. Climate change may affect crop development and yield, and consequently cast a shadow of doubt over China's food self-sufficiency efforts. In this study we used the model projections of a couple of global gridded crop models (GGCMs) to assess the effects of future climate change on the potential yields of the major crops (i.e. wheat, rice, maize and soybean) over China. The GGCMs were forced with the bias-corrected climate data from 5 global climate models (GCMs) under the Representative Concentration Pathways (RCP) 8.5 which were made available by the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP). The results show that the potential yields of rice may increase over a large portion of China. Climate change may benefit food productions over the high-altitude and cold regions where are outside current main agricultural area. However, the potential yield of maize, soybean and wheat may decrease in a large portion of the current main crop planting areas such as North China Plain. Development of new agronomic management strategy may be useful for coping with climate change in the areas with high risk of yield reduction.

Persistence of in-stream waterholes in ephemeral rivers of tropical northern Australia and potential impacts of climate change

2014 ◽  
Vol 65 (12) ◽  
pp. 1131 ◽  
Author(s):  
David McJannet ◽  
Steve Marvanek ◽  
Anne Kinsey-Henderson ◽  
Cuan Petheram ◽  
Jim Wallace
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Landsat Imagery ◽  
Global Climate Models ◽  
Aquatic Biota ◽  
Water Resource Development ◽  
Northern Australia ◽  
Climate Data ◽  
Ephemeral Rivers ◽  
Impacts Of Climate Change

Many northern Australian rivers have limited or non-existent dry season flow and rivers tend to dry to a series of pools, or waterholes, which become particularly important refugial habitat for aquatic biota during the periods between streamflow events. The present study developed techniques to identify in-stream waterholes across large and inaccessible areas of the Flinders and Gilbert catchments using Landsat imagery. Application of this technique to 400 scenes between 2003 and 2010 facilitated the identification of key waterhole refugia that are likely to persist during all years. Relationships for predicting total waterhole area from streamflow characteristics were produced for four river reaches. Using these relationships and streamflow predictions based upon climate data scaled using 15 global climate models, the potential impacts of future climate on waterhole persistence was assessed. Reductions in waterhole area of more than 60% were modelled in some years under drier scenarios and this represents a large reduction in available habitat for areas that already have limited in-stream refugia. Conversely, under wetter future climates the total area of waterholes increased. The approach developed here has applicability in other catchments, both in Australia and globally, and for assessing the impacts of changed flow resulting from water resource development.

scholarly journals Evaluation of data-driven models to downscale rainfall parameters from global climate models outputs: the case study of Latyan watershed

2018 ◽  
Vol 11 (1) ◽  
pp. 200-216 ◽  
Author(s):  
Reza Haji Hosseini ◽  
Saeed Golian ◽  
Jafar Yazdi
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Climate Models ◽  
Fuzzy Inference ◽  
Global Climate ◽  
General Circulation Models ◽  
Global Climate Models ◽  
Data Driven ◽  
Observation Data ◽  
Inference System

Abstract Assessment of climate change in future periods is considered necessary, especially with regard to probable changes to water resources. One of the methods for estimating climate change is the use of the simulation outputs of general circulation models (GCMs). However, due to the low resolution of these models, they are not applicable to regional and local studies and downscaling methods should be applied. The purpose of the present study was to use GCM models' outputs for downscaling precipitation measurements at Amameh station in Latyan dam basin. For this purpose, the observation data from the Amameh station during the 1980–2005 period, 26 output variables from two GCM models, namely, HadCM3 and CanESM2 were used. Downscaling was performed by three data-driven methods, namely, artificial neural network (ANN), nonparametric K-nearest neighborhood (KNN) method, and adaptive network-based fuzzy inference system method (ANFIS). Comparison of the monthly results showed the superiority of KNN compared to the other two methods in simulating precipitation. However, all three, ANN, KNN, and ANFIS methods, showed satisfactory results for both HadDCM3 and CanESM2 GCM models in downscaling precipitation in the study area.

scholarly journals A Comprehensive Intermediate-Term Drought Evaluation System and Evaluation of Climate Data Products over the Conterminous United States

2021 ◽  
Author(s):  
Zeyu Xue ◽  
Paul Ullrich
Keyword(s):  
Evaluation System ◽  
Climate Models ◽  
Global Climate ◽  
Statistical Significance ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
Statistical Hypothesis ◽  
Climate Data ◽  
Statistical Hypothesis Testing

AbstractClimate models are frequently-used tools for adaptation planning in light of future uncertainty. However, not all climate models are equally trustworthy, and so model biases must be assessed to select models suitable for producing credible projections. Drought is a well-known and high-impact form of extreme weather, and knowledge of its frequency, intensity, and duration key for regional water management plans. Droughts are also difficult to assess in climate datasets, due to the long duration per event, relative to the length of a typical simulation. Therefore, there is a growing need for a standardized suite of metrics addressing how well models capture this phenomenon. In this study, we present a widely applicable set of metrics for evaluating agreement between climate datasets and observations in the context of drought. Two notable advances are made in our evaluation system: First, statistical hypothesis testing is employed for normalization of individual scores against the threshold for statistical significance. And second, within each evaluation region and dataset, principal feature analysis is used to select the most descriptive metrics among 11 metrics that capture essential features of drought. Our metrics package is applied to three characteristically distinct regions in the conterminous US and across several commonly employed climate datasets (CMIP5/6, LOCA and CORDEX). As a result, insights emerge into the underlying drivers of model bias in global climate models, regional climate models, and statistically downscaled models.

scholarly journals Detecting change in precipitation indices using observed (1977–2016) and modeled future climate data in Portland, Oregon, USA

2020 ◽  
Author(s):  
Alexis Kirsten Cooley ◽  
Heejun Chang
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Historical Record ◽  
Hydrologic Cycle ◽  
Global Climate Models ◽  
Future Climate ◽  
Trend Detection ◽  
Climate Indices ◽  
Climate Data ◽  
Expert Team

Abstract This study addresses how regional changes to precipitation may be identified by exploring the effect of temporal resolution on trend detection. Climate indices that summarize precipitation characteristics are used with Mann–Kendall monotonic testing to investigate precipitation trends in Portland, Oregon (OR) from 1977 to 2016. Observational records from rain gages are compared with downscaled global climate models to determine trends for the historic (1977–2005) and future (2006–2100) periods. Standard indices created by the Expert Team on Climate Change Detection and Indices (ETCCDI) are deployed. ETCCDI indices that summarize conditions at the annual level are generated alongside a limited number of ETCCDI indices summarized at the monthly level. For the future climate, the indices summarized at the annual level demonstrate trends indicative of an intensifying hydrologic cycle. The historical record depicted by annual indices does not show trends. The historical record is viewed differently by changing the indices to monthly summaries, which causes trend detection to increase and hallmark indicators of an intensifying hydrologic cycle to become apparent.

The Global Character of the Flux of Downward Longwave Radiation

2012 ◽  
Vol 25 (7) ◽  
pp. 2329-2340 ◽  
Author(s):  
Graeme L. Stephens ◽  
Martin Wild ◽  
Paul W. Stackhouse ◽  
Tristan L’Ecuyer ◽  
Seiji Kato ◽  
...  
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Cloud Base ◽  
Climate Data ◽  
Clear Sky ◽  
Downward Longwave Radiation

Abstract Four different types of estimates of the surface downwelling longwave radiative flux (DLR) are reviewed. One group of estimates synthesizes global cloud, aerosol, and other information in a radiation model that is used to calculate fluxes. Because these synthesis fluxes have been assessed against observations, the global-mean values of these fluxes are deemed to be the most credible of the four different categories reviewed. The global, annual mean DLR lies between approximately 344 and 350 W m−2 with an error of approximately ±10 W m−2 that arises mostly from the uncertainty in atmospheric state that governs the estimation of the clear-sky emission. The authors conclude that the DLR derived from global climate models are biased low by approximately 10 W m−2 and even larger differences are found with respect to reanalysis climate data. The DLR inferred from a surface energy balance closure is also substantially smaller that the range found from synthesis products suggesting that current depictions of surface energy balance also require revision. The effect of clouds on the DLR, largely facilitated by the new cloud base information from the CloudSat radar, is estimated to lie in the range from 24 to 34 W m−2 for the global cloud radiative effect (all-sky minus clear-sky DLR). This effect is strongly modulated by the underlying water vapor that gives rise to a maximum sensitivity of the DLR to cloud occurring in the colder drier regions of the planet. The bottom of atmosphere (BOA) cloud effect directly contrast the effect of clouds on the top of atmosphere (TOA) fluxes that is maximum in regions of deepest and coldest clouds in the moist tropics.

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