scholarly journals Improving semi-automated glacial mapping with a multi-method approach: areal changes in Central Asia

2014 ◽  
Vol 8 (5) ◽  
pp. 5433-5483
Author(s):  
T. Smith ◽  
B. Bookhagen ◽  
F. Cannon

Abstract. Central Asia has been strongly impacted by climate change, and will continue to be impacted by diverse climate stressors in the coming decades. This study aims to decipher the impact of climate change on glaciers in the central Tien Shan Mountain Range, a large and understudied region located northeast from the Pamir Knot. To address glacier characteristics over a wide swath of Central Asia, the authors designed and implemented a glacial mapping algorithm which delineates both clean glacial ice – methods which are well documented – and glacial debris tongues, which often require extensive manual digitization. This research improves upon methods developed to automatically delineate glacial areas using spectral, topographic, velocity, and spatial relationships. The authors found that the algorithm misclassifies between 2 and 10% of glacial areas, as compared to a ~750 glacier control dataset. After validating the algorithm against multiple manually digitized control datasets, the authors applied it to a study area encompassing eight Landsat scene footprints stretching from the central Pamir through the central Tien Shan. A statistically significant, though minor, gradient in glacier area loss was found, where glaciers in the west of the study area have shrunk less than those glaciers in the east. This gradient is explained by differences in regional climate, where extratropical cyclones propagating from the west weaken and disband under continued topographic influence, as well as differences in topography, where high-elevation glaciers are thermally insulated from some of the impacts of changing temperatures in the region.

2021 ◽  
Author(s):  
Yuan Qiu ◽  
Jinming Feng ◽  
Zhongwei Yan ◽  
Jun Wang

Abstract Central Asia (CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need to achieve robust projection of regional climate change. In this study, we applied three bias-corrected global climate models (GCMs) to conduct 9km-resolution regional climate simulations in CA for the present (1986–2005) and future (2031–2050) periods. Dynamical downscaling based on multiple bias-corrected GCM outputs obtains numerous added values not only in reproducing the historical climate but also in projecting the climate changes in CA, in comparison to the original GCMs. The regional climate model (RCM) simulations indicate significant warming over CA in the near-term future, with the regional mean increase of annual daily mean temperature (Tmean) in a range of 1.63–2.01℃, relative to the present period. This increase is expected to be higher north of ~ 45°N in each season except summer and the high-elevation areas have a weaker warming signal than the plains through the year. The season with the largest warming rate is not consistent among the RCM simulations, highlighting the necessity of using multiple GCMs as the boundary conditions to give a range of the projected climate changes. A slight increase in annual precipitation is consistently projected in most plain areas, although the changes over few areas are statistically significant. The climate projections presented here serve as a robust scientific basis for assessment of future risk from climate change in CA.


2021 ◽  
Author(s):  
Moshe Gophen

AbstractPart of the Kinneret watershed, the Hula Valley, was modified from wetlands – shallow lake for agricultural cultivation. Enhancement of nutrient fluxes into Lake Kinneret was predicted. Therefore, a reclamation project was implemented and eco-tourism partly replaced agriculture. Since the mid-1980s, regional climate change has been documented. Statistical evaluation of long-term records of TP (Total Phosphorus) concentrations in headwaters and potential resources in the Hula Valley was carried out to identify efficient management design targets. Significant correlation between major headwater river discharge and TP concentration was indicated, whilst the impact of external fertilizer loads and 50,000 winter migratory cranes was probably negligible. Nevertheless, confirmed severe bdamage to agricultural crops carried out by cranes led to their maximal deportation and optimization of their feeding policy. Consequently, the continuation of the present management is recommended.


Author(s):  
Pietro Croce ◽  
Paolo Formichi ◽  
Filippo Landi ◽  
Francesca Marsili

<p>As consequence of global warming extreme weather events might become more frequent and severe across the globe. The evaluation of the impact of climate change on extremes is then a crucial issue for the resilience of infrastructures and buildings and is a key challenge for adaptation planning. In this paper, a suitable procedure for the estimation of future trends of climatic actions is presented starting from the output of regional climate models and taking into account the uncertainty in the model itself. In particular, the influence of climate change on ground snow loads is discussed in detail and the typical uncertainty range is determined applying an innovative algorithm for weather generation. Considering different greenhouse gasses emission scenarios, some results are presented for the Italian Mediterranean region proving the ability of the method to define factors of change for climate extremes also allowing a sound estimate of the uncertainty range associated with different models.</p>


2021 ◽  
Author(s):  
Blanka Bartok

&lt;p&gt;As solar energy share is showing a significant growth in the European electricity generation system, assessments regarding long-term variation of this variable related to climate change are becoming more and more relevant for this sector. Several studies analysed the impact of climate change on the solar energy sector in Europe (Jerez et al, 2015) finding light impact (-14%; +2%) in terms of mean surface solar radiation. The present study focuses on extreme values, namely on the distribution of low surface solar radiation (overcast situation) and high surface solar radiation (clear sky situation), since the frequencies of these situations have high impact on electricity generation.&lt;/p&gt;&lt;p&gt;The study considers 11 high-resolution (0.11 deg) bias-corrected climate projections from the EURO-CORDEX ensemble with 5 Global Climate Models (GCMs) downscaled by 6 Regional Climate Models (RCMs).&lt;/p&gt;&lt;p&gt;Changes in extreme surface solar radiation frequencies show different regional patterns over Europe.&lt;/p&gt;&lt;p&gt;The study also includes a case study determining the changes in solar power generation induced by the extreme situations.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Jerez et al (2015): The impact of climate change on photovoltaic power generation in Europe, Nature Communications 6(1):10014, 10.1038/ncomms10014&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;


2020 ◽  
Vol 12 (8) ◽  
pp. 3223 ◽  
Author(s):  
Soheil Fathi ◽  
Ravi S. Srinivasan ◽  
Charles J. Kibert ◽  
Ruth L. Steiner ◽  
Emre Demirezen

In developed countries, buildings are involved in almost 50% of total energy use and 30% of global annual greenhouse gas emissions. The operational energy needs of buildings are highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based energy modeling of buildings, Artificial Intelligence (AI) has the capability to provide faster and higher accuracy estimates, given buildings’ historic energy consumption data. Looking beyond individual building levels, forecasting building energy performance can help city and community managers have a better understanding of their future energy needs, and to plan for satisfying them more efficiently. Focusing at an urban scale, this research develops a campus energy use prediction tool for predicting the effects of long-term climate change on the energy performance of buildings using AI techniques. The tool comprises four steps: Data Collection, AI Development, Model Validation, and Model Implementation, and can predict the energy use of campus buildings with 90% accuracy. We have relied on energy use data of buildings situated in the University of Florida, Gainesville, Florida (FL). To study the impact of climate change, we have used climate properties of three future weather files of Gainesville, FL, developed by the North American Regional Climate Change Assessment Program (NARCCAP), represented based on their impact: median (year 2063), hottest (2057), and coldest (2041).


Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 959
Author(s):  
Ana María Durán-Quesada ◽  
Rogert Sorí ◽  
Paulina Ordoñez ◽  
Luis Gimeno

The Intra–Americas Seas region is known for its relevance to air–sea interaction processes, the contrast between large water masses and a relatively small continental area, and the occurrence of extreme events. The differing weather systems and the influence of variability at different spatio–temporal scales is a characteristic feature of the region. The impact of hydro–meteorological extreme events has played a huge importance for regional livelihood, having a mostly negative impact on socioeconomics. The frequency and intensity of heavy rainfall events and droughts are often discussed in terms of their impact on economic activities and access to water. Furthermore, future climate projections suggest that warming scenarios are likely to increase the frequency and intensity of extreme events, which poses a major threat to vulnerable communities. In a region where the economy is largely dependent on agriculture and the population is exposed to the impact of extremes, understanding the climate system is key to informed policymaking and management plans. A wealth of knowledge has been published on regional weather and climate, with a majority of studies focusing on specific components of the system. This study aims to provide an integral overview of regional weather and climate suitable for a wider community. Following the presentation of the general features of the region, a large scale is introduced outlining the main structures that affect regional climate. The most relevant climate features are briefly described, focusing on sea surface temperature, low–level circulation, and rainfall patterns. The impact of climate variability at the intra–seasonal, inter–annual, decadal, and multi–decadal scales is discussed. Climate change is considered in the regional context, based on current knowledge for natural and anthropogenic climate change. The present challenges in regional weather and climate studies have also been included in the concluding sections of this review. The overarching aim of this work is to leverage information that may be transferred efficiently to support decision–making processes and provide a solid foundation on regional weather and climate for professionals from different backgrounds.


Zootaxa ◽  
2020 ◽  
Vol 4790 (1) ◽  
pp. 198-200
Author(s):  
VITALY M. SPITSYN ◽  
GRIGORY S. POTAPOV

Seven Arctiine genera have recently been synonymized with the genus Chelis Rambur, 1866 using a comprehensive multi-locus phylogeny (Rönkä et al. 2016). The genus Chelis s. str. contains nine species, the ranges of which cover temperate and subtropical areas of Eurasia from the Iberian Peninsula to the Pacific Ocean coast (Dubatolov & de Vos 2010, Ortiz et al. 2016). Two species, i.e. Chelis ferghana Dubatolov, 1988 and C. strigulosa (Böttcher, 1905), are endemic to the Tien Shan Mountain Range. These taxa can be distinguished by morphological differences in the apical part of the valva. 


2014 ◽  
Vol 140 (5) ◽  
pp. 714-723 ◽  
Author(s):  
David E. Rheinheimer ◽  
Joshua H. Viers ◽  
Jack Sieber ◽  
Michael Kiparsky ◽  
Vishal K. Mehta ◽  
...  

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