scholarly journals Estimation of future changes in photovoltaic potential in Australia due to climate change

2021 ◽  
Vol 16 (11) ◽  
pp. 114034
Author(s):  
Shukla Poddar ◽  
Jason P Evans ◽  
Merlinde Kay ◽  
Abhnil Prasad ◽  
Stephen Bremner
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Emission Scenario ◽  
Radiation Temperature ◽  
Pv Systems ◽  
The Future ◽  
High Emission ◽  
High Emission Scenario ◽  
Far Future

Abstract Solar photovoltaic (PV) energy is one of the fastest growing renewable energy sources globally. However, the dependency of PV generation on climatological factors such as the intensity of radiation, temperature, wind speed, cloud cover, etc can impact future power generation capacity. Considering the future large-scale deployment of PV systems, accurate climate information is essential for PV site selection, stable grid regulation, planning and energy output projections. In this study, the long-term changes in the future PV potential are estimated over Australia using regional climate projections for the near-future (2020–2039) and far-future (2060–2079) periods under a high emission scenario that projects 3.4 °C warming by 2100. The effects of projected changes in shortwave downwelling radiation, temperature and wind speed on the future performance of PV systems over Australia is also examined. Results indicate decline in the future PV potential over most of the continent due to reduced insolation and increased temperature. Northern coastal Australia experiences negligible increase in PV potential during the far future period due to increase in radiation and wind speed in that region. On further investigation, we find that the cell temperatures are projected to increase in the future under a high emission scenario (2.5 °C by 2079), resulting in increased degradation and risks of failure. The elevated cell temperatures significantly contribute to cell efficiency losses, that are expected to increase in the future (6–13 d yr−1 for multi-crystalline silicon cells) mostly around Western and central Australia indicating further reductions in PV power generation. Therefore, long-term PV power projections can help understand the variations in future power generation and identify regions where PV systems will be highly susceptible to losses in Australia.

Sewer storage tank performance under climate change

2007 ◽  
Vol 56 (12) ◽  
pp. 29-35 ◽  
Author(s):  
D. Butler ◽  
B. McEntee ◽  
C. Onof ◽  
A. Hagger
Keyword(s):  
Climate Change ◽  
Emission Scenario ◽  
Rainfall Time Series ◽  
Storm Events ◽  
Average Volume ◽  
High Emission ◽  
High Emission Scenario ◽  
And Performance

This paper describes a study of the potential effects of climate change on the design and performance of sewer storage tanks. A long-term synthetic rainfall time-series has been derived based on the IPPC medium-high emission scenario for a case study in London. Results indicate a 35% increase in the number of storm events that cause filling of the tank and a 57% increase in the average volume of storage required. A method to estimate the required future storage volume for any given return period has been developed and described. Indications are that significantly larger storage volumes will be required to maintain the same level of flood protection.

scholarly journals Statistical Modeling to Predict Climate Change Effects on Watershed Scale Evapotranspiration

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1565
Author(s):  
Rajendra Khanal ◽  
Sulochan Dhungel ◽  
Simon C. Brewer ◽  
Michael E. Barber
Keyword(s):  
Emission Scenario ◽  
Climatic Conditions ◽  
Watershed Scale ◽  
Climate Change Effects ◽  
Machine Learning Model ◽  
The Future ◽  
Sensing Model ◽  
High Emission ◽  
High Emission Scenario ◽  
Agricultural Areas

Estimation of satellite-based remotely sensed evapotranspiration (ET) as consumptive use has been an integral part of agricultural water management. However, less attention has been given to future predictions of ET at watershed-scales especially since with a changing climate, there are additional challenges to planning and management of water resources. In this paper, we used nine years of total seasonal ET derived using a satellite-based remote sensing model, Mapping Evapotranspiration at Internalized Calibration (METRIC), to develop a Random Forest machine learning model to predict watershed-scale ET into the future. This statistical model used topographic and climate variables in agricultural areas of Lower Yakima, Washington and had a prediction accuracy of 88% for the region. This model was then used to predict ET into the future with changed climatic conditions under RCP4.5 and RCP8.5 emission scenarios expected by 2050s. The model result shows increases in seasonal ET across some areas of the watershed while decreases in other areas. On average, growing seasonal ET across the watershed was estimated to increase by +5.69% under the low emission scenario (RCP4.5) and +6.95% under the high emission scenario (RCP8.5).

scholarly journals Susceptibility of Hydropower Generation to Climate Change: Karun III Dam Case Study

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1025 ◽  
Author(s):  
Maryam Beheshti ◽  
Ali Heidari ◽  
Bahram Saghafian
Keyword(s):  
Climate Change ◽  
Power Generation ◽  
General Circulation ◽  
Average Power ◽  
Circulation Model ◽  
Precipitation Trend ◽  
Hydropower Generation ◽  
Near Future ◽  
Far Future

Climate change can cause serious problems for future hydropower plant projects and make them less economically justified. Changing precipitation patterns, rising temperatures, and abrupt snow melting affect river stream patterns and hydropower generation. Thus, study of climate change impacts during the useful life of a hydropower dam is essential and its outcome should be considered in assessing long-term dam feasibility. The aim of this research is to evaluate the impacts of climate change on future hydropower generation in the Karun-III dam located in the southwest region of Iran in two future tri-decadal periods: near (2020–2049) and far (2070–2099). Had-CM3 general circulation model predictions under A2 and B2 SRES scenarios were applied, and downscaled by a statistical downscaling model (SDSM). An artificial neural network (ANN) and HEC-ResSim reservoir model respectively simulated the rainfall–runoff process and hydropower generation. The projections showed that the Karun-III dam catchment under the two scenarios will generally become warmer and wetter with a slightly larger increase in annual precipitation in the near than the far future. Runoff followed the precipitation trend by increasing in both periods. The runoff peak also switched from April to March in both scenarios, due to higher winter precipitation, and earlier snowmelt, which was caused by temperature rise. According to both scenarios, hydropower generation increased more in the near future than in the far future. Annual average power generation increased gradually by 26.7–40.5% under A2 and by 17.4–29.3% under B2 in 2020–2049. In the far period, average power generation increased by 1.8–8.7% in A2 and by 10.5–22% under B2. In the near future, A2 showed energy deduction in the months of June and July, while B2 revealed a decrease in the months of April and June. Additionally, projections in the 2070–2099 under A2 exhibited energy reduction in the months of March through July, while B2 revealed a decrease in April through July. The framework utilized in this study can be exploited to analyze the susceptibility of hydropower production in the long term.

Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

2020 ◽  
Author(s):  
Do-Eun Choe ◽  
Gary Talor ◽  
Changkyu Kim
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Weather Data ◽  
Local Wind ◽  
Offshore Wind Power ◽  
The Future

Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.

scholarly journals Loss of connectivity among island-dwelling Peary caribou following sea ice decline

2016 ◽  
Vol 12 (9) ◽  
pp. 20160235 ◽  
Author(s):  
Deborah A. Jenkins ◽  
Nicolas Lecomte ◽  
James A. Schaefer ◽  
Steffen M. Olsen ◽  
Didier Swingedouw ◽  
...  
Keyword(s):  
Sea Ice ◽  
Population Connectivity ◽  
Future Climate Change ◽  
Landscape Resistance ◽  
Population Mixing ◽  
Sea Ice Decline ◽  
Rapid Changes ◽  
High Emission ◽  
High Emission Scenario

Global warming threatens to reduce population connectivity for terrestrial wildlife through significant and rapid changes to sea ice. Using genetic fingerprinting, we contrasted extant connectivity in island-dwelling Peary caribou in northern Canada with continental-migratory caribou. We next examined if sea-ice contractions in the last decades modulated population connectivity and explored the possible impact of future climate change on long-term connectivity among island caribou. We found a strong correlation between genetic and geodesic distances for both continental and Peary caribou, even after accounting for the possible effect of sea surface. Sea ice has thus been an effective corridor for Peary caribou, promoting inter-island connectivity and population mixing. Using a time series of remote sensing sea-ice data, we show that landscape resistance in the Canadian Arctic Archipelago has increased by approximately 15% since 1979 and may further increase by 20–77% by 2086 under a high-emission scenario (RCP8.5). Under the persistent increase in greenhouse gas concentrations, reduced connectivity may isolate island-dwelling caribou with potentially significant consequences for population viability.

Planning to 'remember to forget'?

2012 ◽  
Vol 76 (8) ◽  
pp. 3491-3496 ◽  
Author(s):  
S. Mobbs
Keyword(s):  
Radioactive Waste ◽  
Early Stage ◽  
Management Control ◽  
Regulatory Control ◽  
The Future ◽  
Passive Management ◽  
Operational Phase ◽  
Geological Formations ◽  
Far Future

AbstractDisposal in deep geological formations aims to provide isolation of long-lived radioactive waste for hundreds of thousands of years. This raises the question of the long-term governance of the repository throughout its lifetime. In the operational phase the repository is under active regulatory control. Once closed, there will be a phase of passive management control or indirect oversight. This will be followed, at some time in the future, by a period in which there is no oversight. This may be a result of a decision to cease management control or it may occur through loss of records or a change in priorities. The importance of the main exposure scenarios (exposure as a result of the gradual transport of radionuclides in groundwater, transport of radionuclides in gas, and exposure arising from inadvertent intrusion into the repository) are discussed with reference to these different phases. An interesting question is 'How do we minimize the risk of inadvertent intrusion in the far future?' Perhaps it is better to ensure that the repository is forgotten and should we try to plan for this? The different approaches are discussed and the importance of deciding on a strategy at an early stage is emphasized.

Memory of Futures

KronoScope ◽  
2004 ◽  
Vol 4 (2) ◽  
pp. 297-315 ◽  
Author(s):  
Barbara Adam
Keyword(s):  
Genetic Modification ◽  
Nuclear Power ◽  
The Other ◽  
Way Of Life ◽  
Memory Aids ◽  
The Past ◽  
The Future ◽  
The One ◽  
Far Future

AbstractWe think of memories as being focused on the past. However, our ability to move freely in the temporal realm of past, present and future is far more complex and sophisticated than commonsense would suggest. In this paper I am concerned with our capacity to produce and extend ourselves into the far future, for example through nuclear power or the genetic modification of food, on the one hand, and our inability to know the potential, diverse and multiple outcomes of this technologically constituted futurity, on the other. I focus on this discrepancy in order to explore what conceptual tools are available to us to take account of long-term futures produced by the industrial way of life. And I identify some historical approaches to the future on the assumption that the past may well hold vital clues for today's dilemma, hence my proposal to engage in 'memory of futures'. I conclude by considering the potential of 'memory aids for the future' as a means to better encompass in contemporary concerns the long-term futures of our making.

scholarly journals Simulation of the Potential Distribution of the Glacier Based on Maximum Entropy Model in the Tianshan Mountains, China

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1541
Author(s):  
Tongxia Wang ◽  
Zhengyong Zhang ◽  
Lin Liu ◽  
Zhongqin Li ◽  
Puyu Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Distribution Pattern ◽  
Potential Distribution ◽  
Emission Scenario ◽  
Tianshan Mountains ◽  
Glacier Area ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
High Emission ◽  
High Emission Scenario

Under the background of global climate change, the variation in the spatial distribution and ice volume of mountain glaciers have a profound influence on regional economic development and ecological security. The development of glaciers is like biological succession; when climate change approaches or exceeds the threshold of suitable conditions for glacier development, it will lead to changes in potential distribution pattern. Therefore, from the perspective of the "biological" characteristics of glaciers, it is a beneficial exploration and attempt in the field of glaciology to explore its potential distribution law with the help of the niche model. The maximum entropy model (MaxEnt) can explain the environmental conditions suitable for the survival of things by analyzing the mathematical characteristics and distribution laws of samples in space. According to glacier samples and the geographical environment data screened by correlation analysis and iterative calculation, the potential distribution pattern of Tianshan glaciers in China in reference years (1970–2000) was simulated by MaxEnt. This paper describes the contribution of geographical environmental factors to distribution of glaciers in Tianshan Mountains, quantifies the threshold range of factors affecting the suitable habitat of glaciers, and predicts the area variation and distribution pattern of glaciers under different climate scenarios (SSP1-2.6, SSP5-8.5) in the future (2040–2060, 2080–2100). The results show that the MaxEnt model has good adaptability to simulate the distribution of glaciers. The spatial heterogeneity of potential distribution of glaciers is caused by the spatio-temporal differences of hydrothermal combination and topographic conditions. Among the environmental variables, precipitation during the wettest month, altitude, annual mean temperature, and temperature seasonality have more significant effects on the potential distribution of glaciers. There is significant spatial heterogeneity in the potential distribution of glaciers in different watersheds, altitudes, and aspects. From the forecast results of glacier in various climatic scenarios in the future, about 18.16–27.62% of the total reference year glacier area are in an alternating change of melting and accumulation, among which few glaciers are increasing, but this has not changed the overall retreat trend of glaciers in the study area. Under the low emission scenario, the glacier area of the Tianshan Mountains in China decreased by 18.18% and 23.73% respectively in the middle and end of the 21st century compared with the reference years and decreased by 20.04% and 27.63%, respectively, under the high emission scenario, which showed that the extent of glacier retreat is more intense under the high emission scenario. Our study offers momentous theoretical value and practical significance for enriching and expanding the theories and analytical methods of the glacier change.

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