scholarly journals Estimation of the Future Probable Precipitation based on the Assumption of Non-Stationarity in Seoul

2020 ◽  
Vol 20 (1) ◽  
pp. 19-29
Author(s):  
Minsu Jeong ◽  
Taesam Lee ◽  
JooHeon Lee ◽  
Hyeonseok Choi ◽  
Sunkwon Yoon

In this study, an estimation of the future probable rainfall in Seoul, Korea, was performed, using non-stationary frequency analysis according to climate change and it was compared with the current probable rainfall. Hourly rainfall data provided by the Korea Meteorological Administration with durations of 1, 2, 3, 6, 12, 24, and 48-h were used as input. For the future projection of precipitation, the RCP 8.5 scenario was selected with the same durations. Moreover, the future hourly rainfall was extracted from using the daily precipitation from 29 Global Climate Models (GCMs), based on the statistical temporal down-scaling method and their corresponding bias corrections. Subsequently, the annual maximum precipitation was extracted for each year. In this study, both stationary and non-stationary frequency analysis was applied based on the observed and predicted time series data sets. In particular, for the non-stationary frequency analysis, the Differential Evolution Markov Chain technique, which combines the Bayesian-based Differential Evolution and Markov chain Monte Carlo methods, was adopted. Finally, the current and future intensity-duration-frequency curves were derived from the optimal probability distribution, and each probable rainfall was estimated. The results of the 29-scenario are presented with quantile estimations. The non-stationary frequency analysis results for Seoul revealed rainfalls of 94.4 mm/h for 30 y, 101.7 mm/h for 50 y, and 111.5 mm/h for 100 y return periods. The average value of the 29-GCM model ensemble was estimated to be approximately 5 mm/h higher than that obtained from the stationary frequency analysis. Considering the changes in hydrological characteristics due to climate change in Seoul, the results of this study could be utilized to pro-actively respond to natural disasters due to such phenomena.

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1548
Author(s):  
Suresh Marahatta ◽  
Deepak Aryal ◽  
Laxmi Prasad Devkota ◽  
Utsav Bhattarai ◽  
Dibesh Shrestha

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.


2014 ◽  
Vol 687-691 ◽  
pp. 4946-4949
Author(s):  
Ying Cheng ◽  
Kai Tan ◽  
Qiao Wang ◽  
Dong Fang Zhao

This paper focuses on the time series data about the closing price of international gold for 84 trading days between December 2013 and March 2014. The related information is from Shanghai Gold Exchange. Firstly, the analysis concerning fluctuations of return rate about the international gold market is conducted through various descriptive statistics such as skewness, kurtosis and etc. Then, the Markov Chain is applied in distinguishing the five states in international gold market, as well as the shifts and patterns among them. And these states are reasonably subdivided by the return rate of international market. The analysis implies that during the stable period of the international financial environment, the shock market will take the lead, with the international gold market continue to fluctuate. To be specific, 38% of the future will be featured by a fluctuation rate between-0.5% and 0.5%; 28% of the future time the gold price will go down, and 34% of the time it will go up.


2019 ◽  
Vol 11 (1) ◽  
pp. 1035-1045
Author(s):  
Farzad Parandin ◽  
Asadollah Khoorani ◽  
Ommolbanin Bazrafshan

Abstract One of the most crucial consequences of climate change involves the alteration of the hydrologic cycle and river flow regime of watersheds. This study was an endeavor to investigate the contributions of climate change to maximum daily discharge (MDD). To this end, the MDD simulation was carried out through implementing the IHACRES precipitation-runoff model in the Payyab Jamash watershed for the 21st century (2016-2100). Subsequently, the observed precipitation and temperature data of the weather stations (1980-2011) as well as 4 multi-model outputs of Global Climate Models (GCMs) under the maximum and minimum Representative Concentration Pathways (RCPs) (2016-2100) were utilized. In order to downscale the output of GCMs, Bias Correction (BC) statistical method was applied. The projections for the 21st century indicated a reduction in Maximum Daily Precipitation (MDP) in comparison with the historic period in the study area. The average projected MDP for the future period was 9 mm/day and 5 mm/ day under 2.6 and 8.5 RCPs (4.6% and 2.6% decrease compared with the historical period), respectively. Moreover, the temperature increased in Jamash Watershed based on 2.6 and 8.5 RCPs by 1∘C and 2∘C(3.7% and 7.4% increase compared with the historical period), respectively. The findings of flow simulation for the future period indicated a decrease in MDD due to the diminished MDP in the study area. The amount of this decrease under RCP8.5 was not remarkable (0.75 m3/s), whereas its value for RCP2.6 was calculated as 40m3/s (respectively, 0.11% and 5.88% decrease compared with the historical period).


2019 ◽  
Vol 271 ◽  
pp. 01006
Author(s):  
Omid khandel ◽  
Mohamed Soliman

Climate change has recently been recognized as a significant factor that can drive changes to current design and life-cycle assessment practices of infrastructure systems. The instability in temperature profiles and precipitation patterns in recent decades indicate that the future flood hazard occurrence rate may not necessarily follow historical trends. In addition to the impact of climate change on flood hazard occurrence rate and the associated scour progression, it could also affect the corrosion propagation in structural components. This paper presents a probabilistic framework for quantifying the multi-hazard failure risk of bridges under gradual and sudden deterioration considering climate change. Downscaled climate data adopted from the global climate models are employed to predict the future streamflow and temperature profiles at a given location. These profiles are subsequently used to quantify future failure probability and risk under corrosion and flood hazard. The proposed framework is illustrated on an existing bridge located in Oklahoma.


2019 ◽  
Vol 11 (2) ◽  
pp. 341-366 ◽  
Author(s):  
Hashim Isam Jameel Al-Safi ◽  
Hamideh Kazemi ◽  
P. Ranjan Sarukkalige

Abstract The application of two distinctively different hydrologic models, (conceptual-HBV) and (distributed-BTOPMC), was compared to simulate the future runoff across three unregulated catchments of the Australian Hydrologic Reference Stations (HRSs), namely Harvey catchment in WA, and Beardy and Goulburn catchments in NSW. These catchments have experienced significant runoff reduction during the last decades due to climate change and human activities. The Budyko-elasticity method was employed to assign the influences of human activities and climate change on runoff variations. After estimating the contribution of climate change in runoff reduction from the past runoff regime, the downscaled future climate signals from a multi-model ensemble of eight global climate models (GCMs) of the Coupled Model Inter-comparison Project phase-5 (CMIP5) under the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios were used to simulate the future daily runoff at the three HRSs for the mid-(2046–2065) and late-(2080–2099) 21st-century. Results show that the conceptual model performs better than the distributed model in capturing the observed streamflow across the three contributing catchments. The performance of the models was relatively compatible in the overall direction of future streamflow change, regardless of the magnitude, and incompatible regarding the change in the direction of high and low flows for both future climate scenarios. Both models predicted a decline in wet and dry season's streamflow across the three catchments.


Author(s):  
Ole Bøssing Christensen ◽  
Erik Kjellström

The ecosystems and the societies of the Baltic Sea region are quite sensitive to fluctuations in climate, and therefore it is expected that anthropogenic climate change will affect the region considerably. With numerical climate models, a large amount of projections of meteorological variables affected by anthropogenic climate change have been performed in the Baltic Sea region for periods reaching the end of this century.Existing global and regional climate model studies suggest that:• The future Baltic climate will get warmer, mostly so in winter. Changes increase with time or increasing emissions of greenhouse gases. There is a large spread between different models, but they all project warming. In the northern part of the region, temperature change will be higher than the global average warming.• Daily minimum temperatures will increase more than average temperature, particularly in winter.• Future average precipitation amounts will be larger than today. The relative increase is largest in winter. In summer, increases in the far north and decreases in the south are seen in most simulations. In the intermediate region, the sign of change is uncertain.• Precipitation extremes are expected to increase, though with a higher degree of uncertainty in magnitude compared to projected changes in temperature extremes.• Future changes in wind speed are highly dependent on changes in the large-scale circulation simulated by global climate models (GCMs). The results do not all agree, and it is not possible to assess whether there will be a general increase or decrease in wind speed in the future.• Only very small high-altitude mountain areas in a few simulations are projected to experience a reduction in winter snow amount of less than 50%. The southern half of the Baltic Sea region is projected to experience significant reductions in snow amount, with median reductions of around 75%.


2019 ◽  
Vol 271 ◽  
pp. 04002 ◽  
Author(s):  
Cesar Do Lago ◽  
Eduardo Mendiondo ◽  
Francisco Olivera ◽  
Marcio Giocomoni

Potential consequences of climate change are the increase in the magnitude and frequency of extreme rainfall storm events. In order to assess what are the potential impacts of climate change in the transportation infrastructure, new intensity-duration-frequency curves are needed. In this study, projected IDF curves were created based on three Global Climate Models (GCM) for the representative concentration pathways (RCP) 4.5 and 8.5. The selected GCMs are: ACCESS1-0, CSIRO-MK3-0-6 and GFDL-ESM2M. Projected IDFs for the near (2025-2049), mid (2050-2074) and far future (2075-2099) were created after disaggregating the project rainfall time series using the Bartlett-Lewis Rectangular Pulses Stochastic Model. The projected IDFs were compared with the IDF currently used and generated based on historical data. The results indicate that climate change is likely to decrease rainfall intensities in all the future horizons in the tested area of San Antonio, Texas. Further analysis is recommended, including the use of bias correction of those GCM models and use of a broader range of models that can better quantify uncertainty of the future rainfall regime.


2015 ◽  
Vol 154 (2) ◽  
pp. 175-185 ◽  
Author(s):  
F. SHABANI ◽  
B. KOTEY

SUMMARYThe present study applies refined and improved scenarios for climate change to quantify the effects of potential alterations in climatic factors on localities for wheat and cotton production, which are two crops important to Australia's economy. The future distributions of Gossypium (cotton) and Triticum aestivum L. (wheat) were modelled using CLIMEX software with the A2 emission scenario generated by CSIRO-Mk3·0 and MIROC-H global climate models. The results were correlated to identify areas suitable for these economically important crops for the years 2030, 2050, 2070 and 2100 in Australia. The analysis shows that the areas where wheat and cotton can be grown in Australia will diminish from 2030 to 2050 and 2070 through to 2100. While cotton can be grown over extensive areas of the country until 2070, the area grown to wheat will decrease significantly over the period.


2014 ◽  
Vol 51 (2) ◽  
pp. 244-263 ◽  
Author(s):  
FARZIN SHABANI ◽  
LALIT KUMAR ◽  
SUBHASHNI TAYLOR

SUMMARYOne consequence of climate change is change in the phenology and distribution of plants, including the date palm (Phoenix dactyliferaL.). Date palm, as a crop specifically adapted to arid conditions in desert oases and to very high temperatures, may be dramatically affected by climate changes. Some areas that are climatically suitable for date palm growth at the present time will become climatically unsuitable in the future, while other areas that are unsuitable under current climate will become suitable in the future. This study used CLIMEX to estimate potential date palm distribution under current and future climate scenarios using one emission scenario (A2) with two different global climate models (GCMs), CSIRO-Mk3.0 (CS) and MIROC-H (MR). The results of this study indicated that Saudi Arabia, Iraq and Iran are most affected countries as a result of climate change. In Saudi Arabia, 129 million ha (68%) of currently suitable area is projected to become unsuitable by 2100. However, this is based on climate modelling alone. The actual decrease in area may be much smaller when abiotic and other factors are taken into account. On the other hand, 13 million ha (33%) of currently unsuitable area is projected to become suitable by 2100 in Iran. Additionally, by 2050, Israel, Jordan and western Syria will become climatically more suitable. Cold and heat stresses will play a significant role in date palm distribution in the future. These results can inform strategic planning by government and agricultural organizations to identify areas for cultivation of this profitable crop in the future, and to address those areas that will need greater attention because they are becoming marginal regions for date palm cultivation.


Author(s):  
Omid Khandel ◽  
Mohamed Soliman

<p>Hydraulic-related hazards (e.g., flood and scour) are recognized as the leading stressors that threat the safety of bridges during their service life. In addition, climate change has been recently recognized as a significant factor that can drive changes to the frequency and intensity of hydraulic-related hazards. Consequently, current design, management, and decision-making methodologies should adapt to these changes to ensure the satisfactory performance of bridges under these hazards. This paper presents a multi-hazard probabilistic framework that can help bridge officials and decision makers to establish flood fragility curves with respect to service life and variability of the future floods. In this paper, downscaled climate data, adopted from the global climate models, are employed to predict future flood hazards at a given location. Time-variant scour depth profiles based on the predicted streamflow data are then estimated and used to predict the future condition of the bridge. Deep learning networks and finite element modeling are then employed to quantify the structural performance of the investigated bridge under applicable hazards. The proposed framework is illustrated on an existing bridge in Oklahoma.</p>


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