scholarly journals Investigating uncertainty of climate change effect on entering runoff to Urmia Lake Iran

2013 ◽  
Vol 10 (2) ◽  
pp. 2183-2214 ◽  
Author(s):  
P. Razmara ◽  
A. R. Massah Bavani ◽  
H. Motiee ◽  
S. Torabi ◽  
S. Lotfi
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Water Surface ◽  
Urmia Lake ◽  
Future Time ◽  
Risk Levels ◽  
Temperature And Precipitation ◽  
Time Period ◽  
Future Time Period ◽  
The Future

Abstract. The largest lake in Iran, Urmia Lake, has been faced with a sharp decline in water surface in recent years. This decline is putting the survival of Urmia Lake at risk. Due to the fact that the water surface of lakes is affected directly by the entering runoff, herein we study the effect of climate change on the runoff entering Urmia Lake. Ten climate models among AOGCM-AR4 models in the future time period 2013–2040 will be used, under the emission scenarios A2 and B1. The downscaling method used in this research is the change factor-LARS method, while for simulating the runoff, the artificial neural network was applied. First, both the 30-yr and monthly scenarios of climate change, temperature, and precipitation of the region were generated and weighted by the Beta function (β). Then, the cumulative density function (cdf) for each month was computed. Calculating the scenarios of climate change and precipitation at levels of 25, 50, and 75% of cdf functions, and introducing them into LARS-wg model, the time series of temperature and precipitation in the region in the future time period were computed considering the uncertainty of climate variability. Then, introducing the time series of temperature and precipitation at different risk levels into the artificial neural network, the future runoff was generated. The findings illustrate a decrease of streamflow into Urmia Lake in scenario A2 at the three risk levels 25, 50, and 75% by, respectively, −21, −13, and −0.3%, and an increase by, respectively, 4.7, 13.8, and 18.9% in scenario B1. Also, scenario A2 with its prediction of a warm and dry climate suggests more critical conditions for the future compared to scenario B1 and its cool, humid climate.

scholarly journals ESTIMATION OF HYDRO-METEOROLOGICAL EXTREMES IN BEAS BASIN OVER HISTORIC, PRESENT AND FUTURE SCENARIO

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 139-147
Author(s):  
V. Sharma ◽  
B. R. Nikam ◽  
P. K. Thakur ◽  
V. Garg ◽  
S. P. Aggarwal ◽  
...  
Keyword(s):  
Coefficient Of Determination ◽  
Future Time ◽  
Future Period ◽  
Flood Peak ◽  
Time Period ◽  
Future Time Period ◽  
The Future ◽  
Model Efficiency ◽  
Near Future ◽  
Far Future

Abstract. The North West Himalayan basins have always been prone to hydro-meteorological disasters. Among them Beas Basin is one of the highly affected basins. Beas basin is prone to cloudburst which causes huge loss to life and property every year. Increase in these devastating events have been noticed in the recent years. Climatic change is considered as the major driver for this increased occurrence of these events in the recent past. The analysis of long-term hydrological extremes over the basin will help in understanding the pattern of the hydro-meteorological extremes and also predicting its nature in near and far future. The Variable Infiltration Capacity (VIC) model at the grid size of 0.025° × 0.025° has been used in the present study, for simulating the hydrological behaviour of the Beas Basin. The parameterization of the model inputs is derived from Remote Sensing based and field observed datasets. The model was forced with meteorological dataset of ERA-Interim for the past and present time period and CORDEX dataset for the future time period. The model was calibrated using observed discharge data of Nadaun and Sujanpur stations. The Nash-Sutcliffe model efficiency of calibrated model was achieved to be 0.77 and 0.72 and coefficient of determination (R2) was 0.80 and 0.72, respectively. The validation results of the model for the same stations shows the model efficiency to be 0.73 and 0.74 with coefficient of determination (R2) as 0.67 and 0.82, respectively. The well calibrated model was used to simulate the hydrological behaviour of historic period (1979–2000), present period (2001–2017), near future period (2018–2050) and far future period (2051–2099). The exceedance probability curve method has been utilized in estimating the flood peak value for the future time period. The flood peak discharge value for the future time period comes out to be 1050 m3/s. The hydro-meteorological extremes rate per year in each period was found to be 9, 9, 12 and 14, respectively. The hydro-meteorological extremes rate is showing increasing trend in near future and very high increase in far future. The study highlights the probability of occurrence of catastrophic events in coming future. The methodology and results of the present study can be beneficial for sustainable development of the basin to counter the effect of probable hydro-meteorological extremes in coming future.

Tree growth response to climate change at the deciduous–boreal forest ecotone, Ontario, Canada

2005 ◽  
Vol 35 (11) ◽  
pp. 2709-2718 ◽  
Author(s):  
D Goldblum ◽  
L S Rigg
Keyword(s):  
Climate Change ◽  
Boreal Forest ◽  
Sugar Maple ◽  
White Spruce ◽  
Balsam Fir ◽  
Northern Limit ◽  
Monthly Temperature ◽  
Temperature And Precipitation ◽  
The Future ◽  
Forest Ecotone

We consider the implications of climate change on the future of the three dominant forest species, sugar maple (Acer saccharum Marsh.), white spruce (Picea glauca (Moench) Voss), and balsam fir (Abies balsamea (L.) Mill.), at the deciduous–boreal forest ecotone, Ontario, Canada. Our analysis is based on individual species responses to past monthly temperature and precipitation conditions in light of modeled (general circulation model) monthly temperature and precipitation conditions in the study area for the 2080s. We then consider the tree species sensitivity to past climate with predicted conditions for the 2080 period. Sugar maple, located at its northern limit in the study area, shows the greatest potential for increased growth rates under the predicted warming and altered precipitation regime. White spruce is likely to benefit less, while the understory dominant balsam fir is likely to experience a decrease in growth potential. These projected changes would enhance the future status of sugar maple at its northern limit and facilitate range expansion northward in response to global warming.

scholarly journals THE IMPACTS OF CLIMATE CHANGE ON ENERGY-WATER SYSTEMS AND ECONOMIC ANALYSIS

2020 ◽  
Vol 53 (2F) ◽  
pp. 1-17
Author(s):  
Safieh Javadinejad
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Electricity Consumption ◽  
Coupled Model ◽  
Planning System ◽  
Temperature And Precipitation ◽  
Future Time Period ◽  
History Of ◽  
Climate Change Model ◽  
Policy And Planning

In order to develop a valued decision-support system for climate alteration policy and planning, recognizing the regionally-specific features of the climate change, energy-water nexus, and the history of the current and possible future climate, water and energy supply systems is necessary. This paper presents an integrated climate change, water/energy modeling platform which allows tailored climate alteration and water-energy assessments. This modeling platform is established and described in details based on particular regional circumstances. The modeling platform involves linking three different models, including the climate change model from Coupled Model Intercomparison Project Phase 5 under the most severe scenario (Representative Concentration Pathways, Water Evaluation, and Planning system and the Long-range Energy Alternatives Planning system). This is to understand the impacts of climate variability (changes in temperature and precipitation) on water and electricity consumption in Zayandeh Rud River Basin (Central Iran) for the current (1971–2005) and future time period (2006–2040). Climate models have projected that the temperature will increase by 7 °C and precipitation will decrease by 44%, it is also proposed that electricity imports will rise during a severe dry scenario in the basin, while power generation will decrease around 8%.

scholarly journals Tax Consequences of Compensation Policies

1969 ◽  
pp. 111
Author(s):  
Vern Krishna
Keyword(s):  
Direct Relationship ◽  
Income Taxation ◽  
Tax Rates ◽  
Future Time ◽  
Time Period ◽  
Future Time Period ◽  
Wage Earners

The importance of compensation policies and reward structures bears direct relationship to the burden imposed by the incidence of income taxation. When, as at the present time, inflation and substantial tax rates erode an in dividual's earning, indirect compensation and devices to reduce income taxa tion assume greater significance to all wage earners. In this paper Mr. Krishna examines several alternative schemes to direct remuneration which have the effect of minimizing current taxation and deferring the incidence of taxes to some future time period, thereby mitigating against the ultimate erosion of ear nings. The emphasis of the paper is on the deferral of tax and indirect compen sation schemes, and is premised on the principle that deferral is tantamount to tax saving.

scholarly journals Future Climate Change Renders Unsuitable Conditions for Paramo Ecosystems in Colombia

2020 ◽  
Vol 12 (20) ◽  
pp. 8373
Author(s):  
Matilda Cresso ◽  
Nicola Clerici ◽  
Adriana Sanchez ◽  
Fernando Jaramillo
Keyword(s):  
Climate Change ◽  
Greenhouse Gas Emission ◽  
Dry Season ◽  
Capital City ◽  
Maximum Temperature ◽  
Future Climate Change ◽  
Mountain Range ◽  
Wet Season ◽  
Temperature And Precipitation ◽  
The Future

Paramo ecosystems are tropical alpine grasslands, located above 3000 m.a.s.l. in the Andean mountain range. Their unique vegetation and soil characteristics, in combination with low temperature and abundant precipitation, create the most advantageous conditions for regulating and storing surface and groundwater. However, increasing temperatures and changing patterns of precipitation due to greenhouse-gas-emission climate change are threatening these fragile environments. In this study, we used regional observations and downscaled data for precipitation and minimum and maximum temperature during the reference period 1960–1990 and simulations for the future period 2041–2060 to study the present and future extents of paramo ecosystems in the Chingaza National Park (CNP), nearby Colombia’s capital city, Bogotá. The historical data were used for establishing upper and lower precipitation and temperature boundaries to determine the locations where paramo ecosystems currently thrive. Our results found that increasing mean monthly temperatures and changing precipitation will render 39 to 52% of the current paramo extent in CNP unsuitable for these ecosystems during the dry season, and 13 to 34% during the wet season. The greatest loss of paramo area will occur during the dry season and for the representative concentration pathway (RCP) scenario 8.5, when both temperature and precipitation boundaries are more prone to be exceeded. Although our initial estimates show the future impact on paramos and the water security of Bogotá due to climate change, complex internal and external interactions in paramo ecosystems make it essential to study other influencing climatic parameters (e.g., soil, topography, wind, etc.) apart from temperature and precipitation.

ASSESSMENT OF FLUCTUATIONS IN THE CASPIAN SEA LEVEL UNDER THE INFLUENCE OF CLIMATE CHANGE FOR THE FUTURE UNTIL 2050

2021 ◽  
Vol 100 (1) ◽  
pp. 70-77
Author(s):  
N.I. Ivkina ◽  
◽  
A.V. Galayeva ◽  
◽  
Keyword(s):  
Climate Change ◽  
Sea Level ◽  
Air Temperature ◽  
Caspian Sea ◽  
Water Surface ◽  
Climate Changes ◽  
Meteorological Parameters ◽  
Climate Scenarios ◽  
The Caspian Sea ◽  
The Future

The article considers the possible fluctuation of the Caspian Sea level in the future until 2050, taking into an account the climate changes. For this purpose, possible changes in the river inflow to the sea and meteorological parameters (precipitation, air temperature and evaporation from the water surface) were predicted. Changes in the meteorological parameters were estimated according to two climate scenarios RCP4. 5 and RCP8.5.

scholarly journals Assessing the Impact of Climate Change on Temperature and Precipitation Over India

2021 ◽  
pp. 121-142
Author(s):  
Sridhara Nayak ◽  
Tetsuya Takemi
Keyword(s):  
Climate Change ◽  
Distribution Patterns ◽  
Future Climate ◽  
Future Climate Change ◽  
Humid Climate ◽  
Frequency Distributions ◽  
Annual Cycles ◽  
The North ◽  
Temperature And Precipitation ◽  
The Future

AbstractThis study explores a comprehensive assessment of future climate change in terms of the climatologies, distribution patterns, annual cycles, and frequency distributions of temperature and precipitation over India by analyzing 190 mega-ensemble experimental results. The results indicate that the annual mean surface temperatures over Indian regions are typically 25 ℃ or higher in the present climate (1951–2010) and are expected to increase by 3–5 ℃ in the future climate (2051–2110). Some desert regions in the west and tropical humid climate types in the central and south regions of the country show possible temperature increases of 4–5 ℃, while the temperatures over the subtropical humid climates in the north and east regions of the country show increases of 3–4 ℃. The precipitation amounts over the arid and semiarid climate types in the western region and over some tropical rainforest climate zones in the southwest region show increases of 0.5 mm d−1 in the future climate, and the precipitation amounts over the temperate, rainy climate types in the northeast region show increases of more than 1 mm d−1. This study also discusses future changes in various climatic variables, including vertical velocity, air temperature, specific humidity, cloud cover, and relative humidity.

The Resiliency of High-Altitude Watershed to Dry Condition Under the Changing Climate

2021 ◽  
Author(s):  
Eshrat Fatima ◽  
Akif Rahim ◽  
Shabeh ul Hasson ◽  
Mujtaba Hassan ◽  
Farhan Aziz ◽  
...  
Keyword(s):  
Climate Change ◽  
Hydrological Cycle ◽  
Daily Temperature ◽  
Climate Projections ◽  
Temperature And Precipitation ◽  
Monthly Streamflow ◽  
The Future ◽  
Dry Conditions ◽  
The Impact ◽  
Far Future

<p>The hydrological cycle is generally known as a recurring result of various forms of water movement and changes in its physical state in nature over a specific area of ​​the earth (river or Lake Basin, a continent, or the whole earth). It is most likely that the increase in global warming will intensively affect the hydrological cycle regionally and globally which will ultimately affect the ecosystem, public health, and municipal water demand. Therefore, the resiliency of watershed to extreme events play a vital role to understand the health of the watershed. This study aims to quantify the resiliency of the Hunza watershed, which lies in the Western Karakoram, to dry conditions under the climate change projections i.e. RCPs 2.6, 4.5, and 8.5. We used a fully distributed hydrological model SPHY to simulate the impact of climate change on future water availability. The SPHY Model was calibration and validation for the time periods (1994-2000) and (2001-2006) respectively. The performance of the model was tested through statistical analysis such as Nash-Sutcliffe efficiency (NSE), coefficient of determination (R<sup>2</sup>), percentage of bias (PBIAS), and root mean square error (RMSE).To develop future water scenarios, the daily temperature, and precipitation data were obtained from the CORDEX South Asia domain under three Representative Concentration Pathways (RCPs). The empirical quantile mapping method was used for the correction of the daily temperature and precipitation biases under the regional scale. The model was run for near (2007-2036), mid (2037-2066), and far-future (2067-2096) climate projections i.e. RCP2.6, RCP4.5, and RCP8.5. The resilience of watershed defined as the speed of recovery from dry conditions. The monthly Streamflow Drought Index (SDI) was used as an indicator of the dry condition. The resiliency of the watershed determines with the threshold levels of -0.5 and -1.0. The analysis indicates that the resiliency of the watershed has increased from 0.3 to 0.5 in the future under the RCP of 2.6. The value of resilience under the RCP of 4.5 is 0.29, 0.45, and 0.52 for near, mid, and far futures respectively. Under extreme climate conditions RCP 8.5, the watershed resilience is 0.2 in the near future and 0.3 in the mid-future, and 0.6 for the far future. Therefore, it can be concluded that the health of the reservoirs will be very good in the future to stabilize the drought.  </p>

scholarly journals The Future of Drought in the Southeastern U.S.: Projections from Downscaled CMIP5 Models

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 259 ◽  
Author(s):  
David Keellings ◽  
Johanna Engström
Keyword(s):  
Climate Models ◽  
Warm Season ◽  
Cold Season ◽  
Cmip5 Models ◽  
Time Period ◽  
Dry Spells ◽  
Future Time Period ◽  
Dry Spell ◽  
The Future ◽  
Southern Half

After being repeatedly struck by droughts in the last few decades, water managers and stakeholders in the Southeast U.S. dread the future extremes that climate change might cause. In this study, the length of future dry periods is assessed using a sub-ensemble of downscaled CMIP5 climate models, which are proven to perform well in precipitation estimations. The length of a dry spell with a twenty-year return period is estimated for the cold and warm seasons for two time periods; 2020–2059 and 2060–2099, and considering two emission scenarios: RCP 4.5 and 8.5. The estimates are then compared with historical dry spells and differences in length and geospatial distribution analyzed. Based on the findings of this paper, little change can be expected in dry spell length during the warm season. Greater changes are to be expected in the cold season in the southern half of Florida, where dry spells are expected to be up to twenty days shorter, while dry spells in Alabama, Mississippi and Tennessee are predicted to be up to twenty days longer. The changes predicted by the models are positively associated with emission trajectory and future time period.

scholarly journals Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Vinushi Amaratunga ◽  
Lasini Wickramasinghe ◽  
Anushka Perera ◽  
Jeevani Jayasinghe ◽  
Upaka Rathnayake
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Climatic Factors ◽  
Computational Time ◽  
Training Algorithm ◽  
Climate Data ◽  
Nonlinear Relationships ◽  
The Future ◽  
Artificial Neural ◽  
Paddy Yield

Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models.

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