scholarly journals Machine Learning for Conservation Planning in a Changing Climate

2020 ◽  
Vol 12 (18) ◽  
pp. 7657
Author(s):  
Ana Cristina Mosebo Fernandes ◽  
Rebeca Quintero Gonzalez ◽  
Marie Ann Lenihan-Clarke ◽  
Ezra Francis Leslie Trotter ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Climate Projections ◽  
Sage Grouse ◽  
Climate Conditions ◽  
Fragmented Habitat ◽  
Related Data ◽  
Temperature And Precipitation ◽  
Methodical Approach

Wildlife species’ habitats throughout North America are subject to direct and indirect consequences of climate change. Vulnerability assessments for the Intermountain West regard wildlife and vegetation and their disturbance as two key resource areas in terms of ecosystems when considering climate change issues. Despite the adaptability potential of certain wildlife, increased temperature estimates of 1.67–2 °C by 2050 increase the likelihood and severity of droughts, floods, heatwaves and wildfires in Utah. As a consequence, resilient flora and fauna could be displaced. The aim of this study was to locate areas of habitat for an exemplary species, i.e., sage-grouse, based on current climate conditions and pinpoint areas of future habitat based on climate projections. The locations of wildlife were collected from Volunteered Geographic Information (VGI) observations in addition to normal temperature and precipitation, vegetation cover and other ecosystem-related data. Four machine learning algorithms were then used to locate the current sites of wildlife habitats and predict suitable future sites where wildlife would likely relocate to, dependent on the effects of climate change and based on a timeframe of scientifically backed temperature-increase estimates. Our findings show that Random Forest outperforms other competing models, with an accuracy of 0.897, and a sensitivity and specificity of 0.917 and 0.885, respectively, and has great potential in Species Distribution Modeling (SDM), which can provide useful insights into habitat predictions. Based on this model, our predictions show that sage-grouse habitats in Utah will continue to decrease over the coming years due to climate change, producing a highly fragmented habitat and causing a loss of close to 70% of their current habitat. Priority Areas of Conservation (PACs) and protected areas might be deemed insufficient to halt this habitat loss, and more effort should be put into maintaining connectivity between patches to ensure the movement and genetic diversity within the sage-grouse population. The underlying data-driven methodical approach of this study could be useful for environmentalists, researchers, decision-makers, and policymakers, among others.

scholarly journals Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

2020 ◽  
Vol 13 (1) ◽  
pp. 10
Author(s):  
Andrea Sulova ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
Google Earth ◽  
Summer Season ◽  
Driving Factors ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Training Dataset ◽  
Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

scholarly journals Projections for Mexico’s Tropical Rainforests Considering Ecological Niche and Climate Change

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 119
Author(s):  
Antonio Fidel Santos-Hernández ◽  
Alejandro Ismael Monterroso-Rivas ◽  
Diódoro Granados-Sánchez ◽  
Antonio Villanueva-Morales ◽  
Malinali Santacruz-Carrillo
Keyword(s):  
Climate Change ◽  
Ecological Niche ◽  
Potential Distribution ◽  
Tropical Rainforest ◽  
Statistical Significance ◽  
Climate Change Scenarios ◽  
Climate Conditions ◽  
Tropical Regions ◽  
Temperature And Precipitation ◽  
Ecological Importance

The tropical rainforest is one of the lushest and most important plant communities in Mexico’s tropical regions, yet its potential distribution has not been studied in current and future climate conditions. The aim of this paper was to propose priority areas for conservation based on ecological niche and species distribution modeling of 22 species with the greatest ecological importance at the climax stage. Geographic records were correlated with bioclimatic temperature and precipitation variables using Maxent and Kuenm software for each species. The best Maxent models were chosen based on statistical significance, complexity and predictive power, and current potential distributions were obtained from these models. Future potential distributions were projected with two climate change scenarios: HADGEM2_ES and GFDL_CM3 models and RCP 8.5 W/m2 by 2075–2099. All potential distributions for each scenario were then assembled for further analysis. We found that 14 tropical rainforest species have the potential for distribution in 97.4% of the landscape currently occupied by climax vegetation (0.6% of the country). Both climate change scenarios showed a 3.5% reduction in their potential distribution and possible displacement to higher elevation regions. Areas are proposed for tropical rainforest conservation where suitable bioclimatic conditions are expected to prevail.

Structural safety and design under climate change

2019 ◽  
Author(s):  
Pietro Croce ◽  
Paolo Formichi ◽  
Filippo Landi
Keyword(s):  
Climate Change ◽  
Greenhouse Gas ◽  
Structural Reliability ◽  
Greenhouse Gas Emission ◽  
Extreme Value ◽  
Structural Safety ◽  
Climate Projections ◽  
Climate Conditions ◽  
Impact Of Climate Change ◽  
The Impact

<p>The impact of climate change on climatic actions could significantly affect, in the mid-term future, the design of new structures as well as the reliability of existing ones designed in accordance to the provisions of present and past codes. Indeed, current climatic loads are defined under the assumption of stationary climate conditions but climate is not stationary and the current accelerated rate of changes imposes to consider its effects.</p><p>Increase of greenhouse gas emissions generally induces a global increase of the average temperature, but at local scale, the consequences of this phenomenon could be much more complex and even apparently not coherent with the global trend of main climatic parameters, like for example, temperature, rainfalls, snowfalls and wind velocity.</p><p>In the paper, a general methodology is presented, aiming to evaluate the impact of climate change on structural design, as the result of variations of characteristic values of the most relevant climatic actions over time. The proposed procedure is based on the analysis of an ensemble of climate projections provided according a medium and a high greenhouse gas emission scenario. Factor of change for extreme value distribution’s parameters and return values are thus estimated in subsequent time windows providing guidance for adaptation of the current definition of structural loads.</p><p>The methodology is illustrated together with the outcomes obtained for snow, wind and thermal actions in Italy. Finally, starting from the estimated changes in extreme value parameters, the influence on the long-term structural reliability can be investigated comparing the resulting time dependent reliability with the reference reliability levels adopted in modern Structural codes.</p>

scholarly journals A high-resolution downscaled CMIP6 projections dataset of essential surface climate variables over the globe coherent with the ERA5-Land reanalysis for climate change impact assessments

2021 ◽  
Author(s):  
Thomas Noël ◽  
Harilaos Loukos ◽  
Dimitri Defrance
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Climate Models ◽  
Mapping Method ◽  
Cumulative Distribution ◽  
Climate Projections ◽  
Climate Modelling ◽  
Temperature And Precipitation ◽  
Surface Climate ◽  
Emissions Scenarios

A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP6 experiment using the ERA5-Land reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.1°x 0.1°, comprises 5 climate models and includes two surface daily variables at monthly resolution: air temperature and precipitation. Two greenhouse gas emissions scenarios are available: one with mitigation policy (SSP126) and one without mitigation (SSP585). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modelling community standards and value checking for outlier detection.

scholarly journals Impact of Climate Change on Livestock Returns and Rangeland Ecosystem Sustainability in the Southwest

2018 ◽  
Vol 47 (2) ◽  
pp. 336-356 ◽  
Author(s):  
Gregory L. Torell ◽  
Katherine D. Lee
Keyword(s):  
Climate Change ◽  
Current Method ◽  
Adaptive Methods ◽  
Livestock Grazing ◽  
Climate Projections ◽  
Stocking Rate ◽  
Forage Production ◽  
Temperature And Precipitation ◽  
Stocking Rates

Climate change will increase variability in temperature and precipitation on rangelands, impacting ecosystem services including livestock grazing. Facing uncertainty about future climate, managers must know if current practices will maintain rangeland sustainability. Herein, the future density of an invasive species, broom snakeweed, is estimated using a long-term ecological dataset and climate projections. We find that livestock stocking rates determined using a current method result in lower forage production, allowable stocking rate, and grazing value than an economically efficient stocking rate. Results indicate that using ecology and adaptive methods in management are critical to the sustainability of rangelands.

scholarly journals Modeling the Impacts of Climate Change on Crop Yield and Irrigation in the Monocacy River Watershed, USA

Climate ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 139
Author(s):  
Manashi Paul ◽  
Sijal Dangol ◽  
Vitaly Kholodovsky ◽  
Amy R. Sapkota ◽  
Masoud Negahban-Azar ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Swat Model ◽  
Global Climate Models ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
River Watershed ◽  
Temperature And Precipitation ◽  
The Impact

Crop yield depends on multiple factors, including climate conditions, soil characteristics, and available water. The objective of this study was to evaluate the impact of projected temperature and precipitation changes on crop yields in the Monocacy River Watershed in the Mid-Atlantic United States based on climate change scenarios. The Soil and Water Assessment Tool (SWAT) was applied to simulate watershed hydrology and crop yield. To evaluate the effect of future climate projections, four global climate models (GCMs) and three representative concentration pathways (RCP 4.5, 6, and 8.5) were used in the SWAT model. According to all GCMs and RCPs, a warmer climate with a wetter Autumn and Spring and a drier late Summer season is anticipated by mid and late century in this region. To evaluate future management strategies, water budget and crop yields were assessed for two scenarios: current rainfed and adaptive irrigated conditions. Irrigation would improve corn yields during mid-century across all scenarios. However, prolonged irrigation would have a negative impact due to nutrients runoff on both corn and soybean yields compared to rainfed condition. Decision tree analysis indicated that corn and soybean yields are most influenced by soil moisture, temperature, and precipitation as well as the water management practice used (i.e., rainfed or irrigated). The computed values from the SWAT modeling can be used as guidelines for water resource managers in this watershed to plan for projected water shortages and manage crop yields based on projected climate change conditions.

scholarly journals A comparison between direct and indirect frameworks to evaluate impacts of climate change on streamflows: case study of Karkheh River basin in Iran

2017 ◽  
Vol 8 (4) ◽  
pp. 652-674 ◽  
Author(s):  
Mohsen Nasseri ◽  
Banafsheh Zahraie ◽  
Leila Forouhar
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Hydrological Modeling ◽  
Future Climate ◽  
Multivariate Adaptive Regression Splines ◽  
Group Method ◽  
Climate Projections ◽  
Climate Conditions ◽  
Karkheh River Basin ◽  
Impacts Of Climate Change

Abstract In this paper, two approaches to assess the impacts of climate change on streamflows have been used. In the first approach (direct), a statistical downscaling technique was utilized to predict future streamflows based on large-scale data of general circulation models (GCMs). In the second approach (indirect), GCM outputs were downscaled to produce local climate conditions which were then used as inputs to a hydrological simulation model. In this article, some data-mining methods such as model-tree, multivariate adaptive regression splines and group method of data handling were utilized for direct downscaling of streamflows. Projections of HadCM3 model for A2 and B2 SRES scenarios were also used to simulate future climate conditions. These evaluations were done over three sub-basins of Karkheh River basin in southwest Iran. To achieve a comprehensive assessment, a global uncertainty assessment method was used to evaluate the results of the models. The results indicated that despite simplifications included in the direct downscaling, this approach is accurate enough to be used for assessing climate change impacts on streamflows without computational efforts of hydrological modeling. Furthermore, comparing future climate projections, the uncertainty associated with elimination of hydrological modeling is estimated to be high.

Future change in renewable energy availability in West Africa: a time of emergence approach

2020 ◽  
Author(s):  
Marco Gaetani ◽  
Benjamin Sultan ◽  
Serge Janicot Serge Janicot ◽  
Mathieu Vrac ◽  
Robert Vautard ◽  
...  
Keyword(s):  
Climate Change ◽  
Renewable Energy ◽  
Solar Radiation ◽  
West Africa ◽  
Energy Production ◽  
Renewable Energy Sources ◽  
Climate Projections ◽  
Climate Conditions ◽  
Projected Change ◽  
Wind Potential

&lt;p&gt;Independence in energy production is a key aspect of development in West African countries, which are facing fast population growth and climate change. Sustainable development is based on the availability of renewable energy sources, which are tightly tied to climate variability and change. In the context of current and projected climate change, development plans need reliable assessment of future availability of renewable resources.&lt;/p&gt;&lt;p&gt;In this study, the change in the availability of photovoltaic (PV) and wind energy in West Africa in the next decades is assessed. Specifically, the time of emergence (TOE) of climate change in PV and wind potential is estimated in 29 CMIP5 climate projections.&lt;/p&gt;&lt;p&gt;The ensemble robustly simulates a shift into a warmer climate in West Africa, which already occurred, and projects a decrease in solar radiation at the surface to occur by the 70s. The reduction in solar radiation is associated with a projected increase in the monsoonal precipitation in the 21st century. It results a likely change into climate conditions less favourable for PV energy production by the 40s. On the other hand, the projected change in the monsoonal dynamics will drive the increase in low level winds over the coast, which in turn will result in a robustly simulated shift into climate conditions favourable to wind power production by mid-century. Results show that climate model projections are skilful at providing usable information for adaptation measures to be taken in the energy sector.&lt;/p&gt;

Projecting future vegetation change for northeast China using CMIP6

2020 ◽  
Author(s):  
Wei Yuan ◽  
Shuang-ye Wu ◽  
Shugui Hou
Keyword(s):  
Climate Change ◽  
Vegetation Change ◽  
Management Strategies ◽  
Future Climate ◽  
Future Climate Change ◽  
Vegetation Changes ◽  
Emission Scenarios ◽  
Climate Data ◽  
Climate Conditions ◽  
Temperature And Precipitation

&lt;p&gt;This study aims to establish future vegetation changes in the east and central of northern China (ECNC), an ecologically sensitive region in the transition zonal from humid monsoonal to arid continental climate. The region has experienced significant greening in the past several decades. However, few studies exist on how vegetation will change with future climate change, and great uncertainties exist due to complex, and often spatially non-stationary, relationships between vegetation and climate. In this study, we first used historical NDVI and climate data to model this spatially variable relationship with Geographically Weighted Logit Regression. We found that temperature and precipitation could explain, on average, 43% of NDVI variance, and they could be used to&amp;#160;model NDVI fairly well. We then establish future climate change using the output of 11 CMIP6 models for the medium (SSP245) and high (SSP585) emission scenarios for the mid-century (2041-2070) and late-century (2071-2100). The results show that for this region, both temperature and precipitation will increase under both scenarios. By late-century under SSP585, precipitation is projected to increase by 25.12% and temperature is projected to increase 5.87&lt;sup&gt;o&lt;/sup&gt;C in ECNC. Finally, we used future climate conditions as input for the regression models to project future vegetation (indicated by NDVI). We found that NDVI will increase under climate change. By mid-century, the average NDVI in ECNC will increase by 0.024 and 0.021 under SSP245 and SSP585. By late-century, it will increase by 0.016 and 0.006 under SSP245 and SSP585&amp;#160;respectively. Although NDVI is projected to increase, the magnitude of increase is likely to diminish with higher emission scenarios, possibly due to the benefit of precipitation increase being gradually encroached by the detrimental effects of temperature increase. Moreover, despite the overall NDVI increase, the area likely to suffer vegetation degradation will also expands, particularly in the western part of ECNC. With higher emissions and later into the century, region with low NDVI is likely to shift and/or expand north-forward. Our results could provide important information on possible&amp;#160;vegetation changes, which could help to develop effective management strategies to ensure ecological and economic sustainability&amp;#160;in the future.&lt;/p&gt;

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